Diversification and Horticultural Crops: A Case of Himachal Pradesh

DIVERSIFICATION AND
HORTICULTURAL CROPS: A
CASE OF HIMACHAL PRADESH
THESIS SUBMITTED TO THE UNIVERSITY OF MYSORE
THROUGH THE DEPARTMENT OF ECONOMICS,
UNIVERSITY OF MYSORE, MYSORE
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY IN ECONOMICS
By:
PRADEEP KUMAR MEHTA
UNDER THE SUPERVISION OF
DR M. MAHADEV A,
ASSOCIATE PROFESSOR,
ADRTC, ISEC, BANGALORE- 560078
MAY 2009

DECLARATION
I declare that this thesis titled "Diversification and Horticultural Crops: A Case of
Himachal Pradesh" is the result of my own work and that it has not been submitted
previously, either wholly or in part, to this University or any other University for any
Degree or Diploma. Due acknowledgements have been made wherever anything has been
borrowed from other sources.
r
Date: tv~ \ ) -' 2 (l (1 ~
adeep Kumar Mehta)
Doctoral Fellow
Agricultural Development and Rural Transformation Centre
INSTITUTE FOR SOCIAL AND ECONOMIC CHANGE
Prof. V.K.R.V. Rao Avenue, Nagarbhavi, Bangalore. 560072, India
.0

CERTIFICATE
I hereby certifY that the present thesis titled "Diversification and Horticultural Crops:
A Case of Himacbal Pradesb" incorporates the result of the independent research of
Pradeep Kumar Mehta, Doctoral Fellow, Agricultural Development and Rural
Transformation Centre, Institute for Social and Economic Change, Bangalore, conceived,
designed and carried out under my guidance and supervision.
I also certify that it has not previously formed the basis for the award of any Degree,
Diploma or Associate Fellowship of the Mysore University or any other University.
Date:
~
(M. Mabadeva)'
Associate Professor
Agricultural Development and Rural Transformation Centre
INSTITUTE FOR SOCIAL AND ECONOMIC CHANGE
Prof. V.K.R.V. Rao Avenue, Nagarbhavi, Bangalore.560072, India

Dedicated to,
My Dearest Father Late Shri. Ram Pal Mehta

ACKNOWLEDGEMENT
PhD. is a long way to look back. In the process, one find many people, who have helped
to come out of slack phases, dilemma, and confusion and have shown the right path. I
owe greatly to Prof. R.S. Deshpande, who put me into research track and built the
foundation of my Ph.D work. lowe to Dr. M. Mahadeva, more than my supervisor to
guide me through out my research.
There are many people who helped me out when I was struggling at different phases of
my PhD work. My special thanks to them, including Prof. S. Bisalliah, Dr. Lalith Achoth,
Dr. N. Jayaram, Dr. M.G Chandrakant, Dr. Gopal Kadekodi, Dr. M.R. Narayana, Dr. S.
Errapa, Prof. S.Madeshwaran, Dr. Hemlata Rao, Dr. Ranvir Singh, Dr. P.K Joshi, Dr.
Ramesh Chand, Dr. V.S Vyas and Prof. K.V. Raju.
I thank all the government departments including, National Horticultural Board, New
Delhi, Directorate of Agriculture, Horticulture, Economics and Statistics and Land
Records, Shimla, HP, who provided logistic and data support and helped me carrying out
my fieldwork.
This research could not have been carried out without the fellowship from ICSSR and
administrative support of ISEC, Bangalore. I, therefore, take this opportunity to thank
ICSSR and ISEC administration, for providing me with the fellowship and necessary
logistic support that enabled me to carry out this study. I sincerely acknowledge the help
and assistance I received from Krishna Chandran, Satish Kamath and members of Library
Staff, especially Kalyanappa, Venkatesh, and those from administration, especially
Margaret, K S Narayana and Govinda Rao.
Further, I express my gratitude to the Mysore University, Staff and Faculty for allowing
me to register for Ph. 0 and for extending all possible support and cooperation in
carrying out this study.
However, I somewhere feci that to acknowledge is to deny, even if they are my own, like,
my mother, brothers, wife, and sister-in-Iaws, Mrs. Deshpande and Munna and friends
like Khalil Shah, Yogeshwari, Prasana, Amalendu, Srikant, Prashob, Badri, Krushna,
Sara la, Sashi, Mohan, Ananda Vadivelu, .... and many more with whom I shared my joy
and low times.
PRADEEP KUMAR MEHTA

CONTENTS
Declaration
Certificate
Acknowledgement
Table of Contents
List of Figures
List of Tables
Appendices
List of Abbreviations
Abstract
III
III
V
VI
VII
CHAPTER I
INTRODUCTION
1.1 Background
1.2 Link Between Components of Diversification and Output Growth
1.2.1 Diversity in the Cropping Pattern and Growth of Output
1.2.2 Land Allocation in Favour of High Value Crops and Growth
of Output
1.2.3 Impact of Changing Cropping Pattern on Growth of Output
1.3 Historical Context of Diversification and its Need in Indian Agricultural
Sector .
1.4 Horticultural Crops as a Case for Diversification in India
1.5 Research Issues
1.6 Research Questions
I.7 Objectives of the Study_
1.8 Sampling and Methodology
1.9 Outline of the Study
CHAPTER II
REVIEW OF LITERATURE
2.1 Factors Affcctin&.Diversification- Drivers and Constraints
2.1.1 Diversification as a Changing Allocation of Area under Crops
2.1.2 Diversification in terms of Diversity in the Cropping Pattern
2.1.3 Diversification represented as Share of High Value Crops
2.2 Risk, Uncertainty and Diversification Decisi()lls
2.3 Role of Changing Typology of Crop Diversification on Agricultural
Growth
CHAPTER III
DIVERSIFICATION AND CROP OUTPUT GROWTH IN INDIA: A
STATE LEVEL ANALYSIS
3.1 Introduction
3.2 Crop Diversification in India
3.2. I Croppin.z Pattern Change in India
1-31
1
6
6
7
8
9
14
16
19
20
20
26
32-61
33
33
40
45
49
56
62-93
62
64
64

3.2.2 Diversity and Spread in the Cropping Pattern 66
3.2.3 Land Allocation in Favour of High Value Crops
..._.
69
3.3 Crop Diversification and Income-Risk Pattern 70
3.3.1 Trend in Income and Risk in Ag.riculture 70
3.3.2 Link of Pattern ofCro!, Diversification with Income and Risk 72
3.4 Relationship between Changing Cropping Pattern Mix and Growth of Crop 75
Output
3.4.1 Trends in Growth of Crop Output in States of India 75
3.4.2 Model for Identification of Role of Diversification in Growth of 77
Crop Output
3.4.3 Components of Growth of CroP Output in India 80
3.5 Summary 87
CHAPTER IV
DIVERSIFICATION AND HORTICULTURAL CROPS: A CASE OF 94-128
HIMACHAL PRADESH
4.1 Introduction 94
4.2 Horticultural Sector in India 95
4.2.1 State-Wise Pattern of Development of Horticultural Sector 96
4.3 Diversification and Horticultural Crops: A Case of Himachal Pradesh \01
4.4 Significance of Horticultural Cro!,s in Himachal Pradesh \02
4.5 An Overview of Selected Villages and Socio-Economic Characteristics of \08
Fanners
4.6 Income and Risk from Horticultural Crops Production 1I2
4.6.1 Income and Risk from Cauliflower 1I3
4.6.2 Labour Demand and Output Supply Estimates: A Profit Function 1I8
Approach
4.6.3 Income and Risk from Apple 124
6.7 Summary 127
CHAPTER V
ROLE OF ECONOMIC AND NON-ECONOMIC FACTORS IN 129-156
DIVERSIFICATION FROM FOOD CROPS TO HORTICULTURAL
CROPS
5.1 Introduction 129
5.2 Behaviour of Price and Area Cycles of Fruit and Vegetable crops 132
5.3 Diversification from Food Crops towards Horticultural Crops 138
5.3.1 Food Security Concerns in Diversification 141
5.3.2 Relative Significance of Economic and Non-Economic Factors in 144
Diversification
5.4 Source of Diversity in Area Response to Cauliflower Prices 145
5.5 Magnitude of Diversification from Food Crops to Horticultural Crops 150
5.5.1 Socio Economic Features and Diversification 150
5.5.2 Factors Influencing Decision of Diversification towards 151
Horticultural C,ops
11

5.6 I Summary 154
CHAPTER VI
EXPECTATIONS, RISK ATTITUDE AND LAND ALLOCATION 157-187
DECISIONS
6.1 Introduction 157
6.2 Price Expectations at Farm Level 160
6.2.1 Dynamics of Price Expectations 160
6.2.2 Relation of Price Expectation with Other Economic Factors 163
6.3 Relative Role of Price and Yield Risk in Fruits and Vegetables 166
6.4 Typology and Determinants of Risk Attitudes of Fanners 169
6.5 Role of Economic Factors in Land allocation in Favour of Horticultural 176
Crops
6.5.1 Typology of Land Allocation 176
6.5.2 Socio Economic Characteristics and Land Allocation 177
6.5.3 Land Allocation at Different Levels of Expectations and Risk 180
Attitudes
6.5.4 Determinants of Land Allocation in Favour of Horticultural Crops 182
6.6 Summary 184
CHAPTER VII
CONCLUSIONS AND POLICY IMPLICATIONS 188-204
REFERENCES AND SELECTED BIBLIOGRAPHY 205-220
LIST OF FIGURES
1.1 Diversification by Substitution and Expansion 4
1.2 Map of Study Area 22
1.3 Percentage of Gross Cropped Area under Fruits and Vegetables in Blocks of 24
Shimla, 2006
4.1 Compound Growth Rate in Area under Horticultural Crops in India 97
4.2 Location Quotient of Horticultural Crops in India 100
4.3 Agro-Climatic Zones in Himachal Pradesh 103
4.4 Economics of Cauliflower Crop 115
4.5 Economics of Cauliflower by Farm Size for Different Level of Land 115
Allocation
4.6 Sensitivity Analysis of Income from Cauliflower 117
4.7 Sensitivity Analysis of Income from Cauliflower by Farm Size 117
5.1 Types of Area and Price Cycles 133
5.2 Trend in Area and Price of Apple in Shimla 137
LIST OF TABLES
1.1 Demand for Various Foods in India- 2015 and 2030 16
1.2 Compound Growth Rates in Area of Major Crop Groups m Blocks of 23
111

Shimla, 1997-2006
1.3 Absolute Change in Area under Horticultural Crops in Blocks of Shimla, 24
1997-2006
1.4 Diversification Indexes of Blocks, 2006 25
3.1 Contribution of Crops to Gross Cropped Area and Value of Output Produced 65
in Agricultural Sector in India
3.2 Number of Crops Produced by States in India 67
3.3 Trends in Spread or Concentration in Cropping Pattern by Indian States 68
3.4 Share of Non-Food Crops in Gross Cropped Area in India 70
3.5 Ranking of States According to Gross Value of Output per ha @ 1993-94 71
pnces
3.6 Coefficient of Variation in Gross Value of Output @ 1993-94 prices 72
3.7 Growth and Variability of Income According to Spread and Concentration of 74
Cropping Pattern
3.8 Growth and Variability of Income According to Diversification towards 75
Non-Food Crops
3.9 Trend in the Growth Rates of Crop Output in States 77
3.10 Contribution of Different Components in Growth of Gross Value of Output 81
by Regions in India
3.11 Area Expansion and Substitution Effect in India 85
3.12 Factors Influencing Nature and Direction of Diversification in India 87
4.1 Share of Horticultural Crops in the Total Value of Agricultural Output in 98
India
4.2 Net Returns from Various Crops in Himachal Pradesh, 2001 104
4.3 Productivity of Major Crops in Himachal Pradesh and India, 2001 (Rs.lha) 104
4.4 Growth Rate in Area and Value of Output of Major Crop Groups in HP 105
4.5 Share of Crop Groups in Gross Value of Output in Agriculture in HP \05
4.6 Compound Growth Rates in Area of Major Crops in Districts of HP 106
4.7 Value of Agricultural Output and Productivity in Districts ofHP \07
4.8 Details of the Selected Villages 109
4.9 Farm Size and Sampling from the Selected Villages 1\0
4.10 Demographic Features of the Selected Farmers III
4.11 Input Use Behaviour and Other Farm-related Characteristics of the Selected 112
Farmers
4.12 Farm Size and Resource Allocation by Cauliflower Growers 114
4.13 Trend in Price and Yield of Cauliflower Crop 116
4.14 Joint Estimation of Profit Function and Labour Demand Function with 121
Current Price
4.15 Joint Estimation of Profit Function and Labour Demand Function with 121
Expected Price
4.16 Indirect Estimates of Production Function Coefficients 122
4.17 Labour Demand and Reduced Form Output Elasticities 123
4.18 Farm Size and Resource Allocation by Apple Growers 125
4.19 Economics of Apple Production by Farm Size 126
IV

4.20 Simulation Analysis of Apple Production by Farm Size 127
5.1 Area, Production and Yield of Cauliflower in Shimla, HP, 1998-2006 135
5.2 Seasonal Price of Cauliflower in Shimla, HP, 2004-06 135
5.3 Area, Production and Price of Apple in Shimla, HP, 1993-2002 137
5.4 Typology and Extent of Diversification towards Horticultural Crops 140
5.5 Typology of Diversification towards Horticultural Crops by Farm Size 141
5.6 Cropping Pattern Shift and Food Self-Sufficiency among Horticultural Crop 143
Growers
5.7 Level of Subsistence among the Horticultural Crop Growers by Farm Size 143
5.8 Relative Importance of Factors for Diversification towards Horticultural 145
Crops
5.9 Average Values of Descriptive Variables among Cauliflower Growers 148
5.10 Socio-Economic Characteristics at Different Levels of Shift in Cropping 151
Pattern
5.11 Factors Affecting Decision for Diversification towards Horticultural Crops 153
6.1 Farmers Experience with Price of Cauliflower and Apple (Rs./kg) 162
6.2 Comparison of Actual Results and Farmers' Expected Values of Selected 163
Distributions
6.3 Input Use Propensities of Horticultural Crop Growers According to Different 164
Price Expectations
6.4 Factors Explaining Price Expectations of Horticultural Crop Growers 166
6.5 Decomposition of Income Risk from Apple and Cauliflower 168
6.6 Correlation between Production and Price of Apple and Cauliflower, 2004- 169
06
6.7 Frequency Distribution of Farmers on the Basis of Risk Attitudes 173
6.8 Factors Influencing Risk Attitudes of Farmers 175
6.9 Extent of Land allocation in Favour of Horticultural Crops 177
6.10 Extent of Land Allocation in Favour of Horticultural Crops by Farm Size 177
6.11 Socio-Economic Characteristics at Different Levels of Land Allocation 178
6.12 Income-Consumption Gap at Different Levels of Land Allocation 179
6.13 Role of Expectations and Risk on Extent of Land Allocation in Favour of 181
Horticultural Crops
6.14 Factors Affecting Land Allocation Decisions of Farmers Producing 184
Horticultural Crops
APPENDICES
Al.1
Al.2
A3.1
A3.2
A 3.3
A 3.4
Facets of Diversification
Indicators of Diversification
List of Crops
Share of Rice and Wheat in Gross Cropped Area in India
Compound Growth Rates of Area under Rice
Compound Growth Rates of Area under Wheat
28
29
90
90
91
91
v

A3.5
A3.6
A3.7
Compound Growth Rates of Area under Non-Food Crops 92
Gross Value of Output of States in India @ 1993-94 prices 92
Distribution of States by Trend towards Spread or Concentration in Cropping 93
Pattern
A 3.8 Distribution of States by Trend in Shift of Cropping Pattern towards Non- 93
Food Crops
A 6.1
A 6.2
Fanners Experience with Price of Cauliflower (Rs./kg) 187
Fanners Experience with Price of Apple (Rs./kg) 187
LIST OF ABBREVIATIONS
ANOVA: Analysis of Variance
CPI-AL: Consumer Price Index- Agricultural Labourer
CV: Coefficient of Variation
DARA: Decreasing Absolute Risk Aversion
EU: Expected Utility
F&V: Fruits and Vegetables
GCA: Gross Cropped Area
HP: Himachal Pradesh
HVCs: High Value Crops
HYV: High Yielding Varieties
IRR: Internal Rate of RetUrit
J&K: Jammu & Kashmir
L&S: Lahaul & Spiti
MCN: Minimum Consumption Needs
MT : Metric Tones
NHB: National Horticultural Board
NPV: Net Present Value
OFI: Off-farm Income
RC: Risk Coefficient
SUR: Seemingly Unrelated Regression
TE: Triennium Ending
TMO: Technology Mission on Oilseeds
TVP: Total Value Productivity
UOP: Unit Output Price
VaR: Value at Risk
wrO: World Trade Organization
VI

ABSTRACT
Diversification is one of the components of growth in addition to area expansion and
technology development. Recently, diversification assumed high importance in the recent
past due to two reasons; fatigue in other components of growth in Indian agriculture and
new opportunities for diversification towards high value crops due to both the demand
and supply side factors. In this context, we investigate the relative role of diversification
on output growth, and growth inducive or depressive impact of changing process of crop
diversification in India. Studies dealing with diversification mainly concentrated on price
of the crop and considered price as one of the major guiding factors in the decisionmaking.
However, price may not be the only factor in decision-making; heterogeneity in
resource and capital endowments of farmers and difference in their access to input and
output markets also playa role in decision-making. It could be hypothesized that farmers
with relatively higher level of productivity may diversify more with lower price
expectations. Thus, we analysed the nature of price expectations of the diversified
farmers, examine its relationship with other economic factors and identify the role of
different expectations of price, yield and income of farmers on their diversification
decisions. Horticultural crops are also considered as risky crops due to high fluctuations
in the price and production, thus, finally, we estimated the role of overall risk of
production and consumption on the extent of land allocation in favour of horticultural
crops?
Undoubtedly, at the macro or state level, diversification has become a major
component for stepping up growth. The picture of the last few decades points that the
gain from diversification is contingent upon the technology development of high value
crops in India. Within the horticultural sector, there are two different categories of crops
i.e fruits and vegetables and thus there is a need for different policies for these categories.
Irrigation is vital for diversification towards vegetable crops, whereas availability of
labour is crucial for fruit crops. Development and integration of markets are vital to
increase farmer's welfare; high price expectations are positively linked with the prices
received by farmers over a period of time and this, in tum, influence their input use
hehaviour and productivity. While, taking diversification decisions, farmers attach more
Vll

importance to expected total net income from the crop and hence consider both the price
and yield. Relative incomes from the crops explain the crop substitution decisions of
farmers i.e., farmers calculate the aggregate gain from the crop than considering only the
price of the crop. The capacity to generate higher productivity and availability of better
marketing prospects together explains farmers' decision. Thus, intervention in production
technology and streamlining markets are concomitantly required; harping on market
improvement for increasing land in favour of horticultural crops will not suffice. In terms
of risk, farmers are concerned not only about crop-specific risk but also their aggregate
production and consumption risk. So improving farmers' orientation towards high value
crops require intervention in food grain markets by improving the technology. Better
technology in food crop can lead to increase in the productivity of food crops which
would help in reducing consumption constraints for higher allocation of land to high
value horticultural crops. In addition, development of labour market can be critical for
land allocation decisions by farmers as it would help farmers to earn more income and
alleviate income constraints for diversification.
Vlll

CHAPTER I
INTRODUCTION
1.1 Background
The decision of diversification by a farmer is considered to be one of the major
economic decisions that has strong bearing on his welfare in terms of income level and
variability in returns (Heady, 1952, Johnson and Brester, 2001, Pope and Prescott, 1980).
In general, regular high income with less variability is considered welfare augmenting.
Understanding the decision-making process of farmers in the context of poor growth
either due to low income or high variability of returns is important especially, when the
policy seeks to understand the factors affecting shifting to high value crops like
horticultural crops, in the event of stagnant yields of food-grains, poor agricultural
growth and increased concern of farmers' welfare in terms of low and variable income
(Joshi, 2005, Chand, 2006, Evenson et al. 1999).
In farm planning, farmer as a decision maker takes three decisions - what to
produce, how to produce and how much to produce (Van and Keller, 2006). The farmer
has to decide between alternative uses of resources at hislher disposal in order to address
these three different but inter-related questions. While deliberating on these aspects, a
farmer has to choose which crops to produce and to what extent to specialize, or
alternatively diversify the area. This directly or indirectly affects the aggregate output at
the farm. In general, there are three major components of aggregate output - area, crop
yields and level of diversification. The growth of output could be improved by increasing
the area under cultivation, either by extension or intensification of area or reducing the
cost of production, either by decreasing the prices of inputs or cost of obtaining inputs, by
introducing new technology that improves productivity of crops. In addition to these
components or policy options, diversification is another major component of growth that
influences output through its impact on cost, income and risk (Grimes, 1929).

Crop diversification is one of the sub-sets of a larger matrix of production
alternatives in the cropping sector. From an economic point of view, diversification is
treated from two analytical perspectives: as a problem of determining, given prices, the
optimal crop mix on a production possibility frontier; and second as a mechanism for
incorporating risk aversion into a fanner's decision making process in which crop
specialisation may lead to highly unstable income due to variance in yield, production, or
price for the particular crop (World Bank, 1988). In a broad manner, diversification is
seen as having two main properties; it expands the production possibility set or area
allocation frontier, thereby increasing opportunities for income generation and
employment creation. It also reduces the risk of having all of one's eggs in a basket with
a few crops with potentially high covariance risk (Samuelson, 1967).
There are several sub-components of diversification that include both the spatial
and temporal aspects. There is a continuum of the decision making process in
diversification, whereby farmers need to take four critical decisions that together, and not
in isolation, affect their welfare in terms of income and risk outcomes. Farmers make
choices in the context of their production possibility frontier, their expectations of
relative prices and their sense of risk from both an agronomic and market perspective for
various alternatives (World Bank, 1990). The first decision is about the choice of number
of crops. Within the area allocation or on the production possibility frontier, a farmer
decides about the number of crops that could be produced. Some farmers prefer to
produce more number of crops than others within a homogenous climatic and land
quality. This system of diversification is related purely to the diversity aspect, where
diversification is only related to the number of crops produced.
When a fanner decides about the number of crops, it influences the aggregate level
of spread or concentration in the cropping pattern; this determines the ex tent of
specialisation at the farm level. In this context, diversification is seen not only in terms of
the number of crops but also in terms of the balance among different crops. The concept
can be explained by considering minimum diversification as the practice of having a
single crop and maximum diversification as equal distribution of land to all crops
(Grosskopf, et al. 1992). In previous cases, it could be seen that a household producing
2

two crops would be considered more diversified than a household producing a single
crop. But in the latter case, a household producing two crops, each contributing equally to
the total, would be more diversified than a household with two crops, of which one crop
covers 90 percent of the total area under cultivation (Minot et aI., 2006). Here, higher
spread is synonymous to higher level of diversification. Higher spread in cropping pattern
is expected to help farmers for in a fuller or better utilization of resources, in realizing
regular and quick results and reducing risks out of crop or market failures.
While taking decision on number of crops and level of spread in the cropping
pattern, farmers also take another critical decision as to which crops to produce and how
much land to be allocated to each crop. There can be different combinations of low
versus high value crops or mix of subsistence and commercial crops. The value of crops
is determined on the basis of their capacity to generate economic returns per unit of land
or labour (Minot et aI., 2006). It is important to note that diversification from staple crop
production into high-value crops need not imply greater diversity in crops. For example,
if a mixed grain and cotton grower decides to specialize in cotton production, this would
represent diversification into a high-value crop, but not diversification in the sense of
multiple cropping. Here, diversification is seen also from the angle of commercialization.
Three components pertaining to spatial dimension of diversification as discussed
above could be classified as:
I. Diversity in the cropping pattern
2. Spread of the cropping pattern
3. Land allocation in favour of high value crops
Pingali and Rosegrant (1995) have viewed all these components of diversification
in terms of three stages - subsistence, semi-commercial and commercial. According to
them, in the first stage, when farming is characterized by higher level of subsistence, the
level of diversity tends to be higher. As there is an increased tendency towards higher
extent of commercialization or higher allocation of land in favour of high value crops,
,uch farming may lead to high concentration. It conjectures a positive relation between
3

levels of commercialization and increasing level of concentration at the farm level. It
includes the process of switching from low value crops to high value crops.
in addition to the spatial picture of diversification, there is also a temporal
dimension. Over a period of time, due to changing market and technological conditions,
etc., the cropping pattern undergoes change and thus alters the pattern of diversification.
It also influences the extent of diversity, level of spread in cropping pattern and land
allocation across low and high value crops. TItis is especially because change in cropping
pattern can take place either by substitution of crops, extensification of area or
intensification of area. The difference in the pattern followed for crop shift results in
difference in the spatial dimensions of diversification, which could be as shown in figure
1.1:
Figure 1.1: Diversification by Substitution and Expansion
Crop A
CropB
A. Substitution B. Expansion or intensification
Figure 1.1 depicts two distinct ways in which farmer may add area under any
particular crop. When a farmer needs to increase the area under crop B, it can be done
either by reducing the same extent of area under crop A or by increasing! adding area
under cultivation, while keeping the area under crop A constant. An increase in area of a
particular crop (crop B) by reducing the same amount of area of other crop (crop A)
leaves the level of diversity unchanged. However, it may change the crop mix pattern due
to cifferences in the values of the crops that alters the allocation of land among low and
high value crops. For the purpose of producing more crops or adding new crops to the
prevailing crops, if farmer purchases new land or else expands the production to the
4

marginal land previously uncultivated (adding area under cultivation) than substituting
the crops, it would increase the level of diversity at the farm level. In such cases, the
farmer may not require to forgo the prevailing cropping pattern; it leads only to increase
in the level of diversity and spread in the cropping pattern.
Increasing the total number of crops or having more diversity III the cropping
pattern may not by itself ensure more income or less risk or both (Heady, 1952). This is
also conditioned upon other typologies of diversification. All these typologies or
components of diversification are inter-linked and often one typology of diversification
precludes another typology of diversification '. The mutual exclusiveness or trade-off can
take place in several ways. As noted, there is a possibility that an increase in the
allocation of area to high value crops in the portfolio could lead to a decline in the
diversity of cropping pattern. It would represent diversification into high-value crops, but
not diversification in the sense of multiple cropping. Hence one needs to be cautious in
the distinction between "diversification into" and "diversification in". Also, much caution
is needed in defining diversification on the basis of area and value. If a farmer grows peas
in 2 hectare of land and rice in 10 hectare, with each crop yielding Rs. 1000 net income,
then clearly, acreage proportions may be a deficient measure of diversification. For
example, a farmer who grows peas in 6 hectare of land and rice in 6 hectare, with income
on peas at Rs. 1000 and rice at Rs. 500. In this case, income proportions may be a poor
measure of diversification (Pope and Prescott, 1980). Additionally, a conflict in
diversification at macro and micro levels is possible. The extent of diversification at the
macro (state or district) level does not always leads to same level of diversification at
micro (farm) level. In the scenario of heterogeneous climatic zones, possibility exists that
diversity of cropping pattern at macro level would result in a higher level of
specialisation at the micro level. There is also a distinction between diversification
defined in a temporal and spatial manner. In the fonner case, it is about cropping pattern
shift and in the latter case it is about number of crops, spread in the cropping pattern and
land allocation across low and high value crops. In addition, each component of
divcrsi fication influences growth in a different way.
I The detailed infonnation about facets and indicators of diversification are provided in appendix 1.1 and
1.2
5

1.2 Link between Components of Diversification and Output
Growth
Diversification is one among the several components of growth, in addition to
area under cultivation and crop productivity. As there are several sub-components of
diversification, there is a need to identify ways in which these sub-components influence
growth of output.
1.2.1 Diversity in the Cropping Pattern and Growth of Output
Diversity or spread of cropping pattern influences the growth of output through its
impact on both income and risk in one of the following ways: (i) by increasing the
production of those crops that are relatively high in price and offer satisfactory margins
above the cost of production, (ii) by reducing the cost of producing all crops by
distributing fixed costs over the larger number of crops without materially increasing
variable costs and (iii) by securing more of the subsistence of the farmer and his family
from the farm by producing for home use such products that are not of major importance
in the agriculture of the region but can be satisfactorily produced in limited quantities
(Grimes, 1929). Also, diversifying cropping pattern is one of the measures that farmers
can take to cope with an uncertain future situation. There are two aspects of
diversification which deal with maximizing profits and minimizing variance of returns.
The first one is about planning diversification under perfect knowledge, as in the case of
individual farmer, wishing to maximize profits, will equalize the marginal rate of crop
substitution with the price ratio of the crops. The task before the farmer is that of
maximising profit or optimize income. The other aspect of diversification is that of
minimising the variance of returns, i.e., putting a floor under the income level or
preventing the occurrence of undesirable outcomes. The farmer, naturally, unable to
predict price and yield outcomes, may wish to select a combination of crops or exhibit
higher level of diversity in the cropping pattern which gives a steady year to year flow of
in,;ol1\e (Heady, 1952). But, the impact of diversity in the cropping pattern on risk is
6

conditioned by the levels of correlation (positive, negative and zero) between the price
and yields of the crops {lroduced (Quiroz and Valdes, 1995).
1.2.2 Land Allocation in Favour of High Value Crops and
Growth of Output
Land allocation in favour of high value crops is closely linked to output growth
through its impact on land productivity. Land productivity represents the average value of
output per acre and is not quite synonymous with per acre crop yield rates. Changes in
land productivity can be decomposed into three elements, i.e., change in yield of the
crops produced, change in the cropping pattern and locational shift of crops. There are
possibilities when land productivity declines despite an increase in the yield rates of all
crops, if the cropping pattern shifts from high yield crops to low yield crops. Shifting the
location of the crops from one state or region to another also influences land productivity
in the absence of any change in the yield of all crops. If there is a relative shift in area
under the i lb crop from states where its per hectare yield is lower to states where it is
higher, it would impart a rising trend to its overall yield even if the per hectare yield in
the individual states remained unchanged over time. Similarly, a shift in the total cropped
area away from the crops with lower value of output per hectare towards crops with
higher value of output per hectare would produce a rising trend in the index of
productivity, even if the yield of the individual crops remained unchanged over time.
Thus, the growth of productivity is made up of three components respectively, reflecting
the contribution of; a. cropping pattern changes, b. locational shifts of area under
individual crops, and c. pure increase in the yields of individual crops in different regions
(Narain, 1976). It is important to note that changes in cropping pattern from low value to
high value crops and Iocational shift of crops from low productivity to high productivity
regions always result in increase in income other things remaining constant.
7

1.2.3 Impact of Changing Cropping Pattern on Growth of
Output
Change in cropping pattern and resulting change in typologies of diversification can
have either income inducing or depressing effects, which is not necessarily always linked
positively with growth. Over-time, farmers or regions reallocate their land among
alternative crops that have different values. Since, the relative values of crops do not
remain same, the changes in the crop mix vis a vis the values of different crops affect the
income or output of farmers. The change in cropping pattern adversely affects income
when farmers increase acreage under a particular crop whose relative yield or value as
compared to other crops declines over a period of time. This is likely in the case of
several food crops. The objective of ensuring food security of the family may motivate
the farmers to take such decisions. Also, additional factors like access to market and
resource availability influence these decisions of farmers. Increase in the relative weight
of the crops in the gross cropped area, whose yield or value decline is indicative of
income-depressive effect of change in cropping pattern. It denotes that the crop structure
has shifted in favour of those crops which have lower or poor growth of yield or value.
The effects of change in cropping pattern become positive on growth of output, when the
crop structure shifts in favour of those crops which exhibit higher growth of yield and
value. This denotes income-inducive effect of change in cropping pattern. In sum, the
diversification strategy by the farmers can affect their income in either direction- positive
or negative.
The differences in dimensions of diversification and its relationship with growth
raise some important questions: first, why do farmers need diversification and under what
context? Diversification has been a common phenomenon and Indian agriculture and
farmers in India are no exceptions. Secondly, as there could be either growth inducing or
depressing effects of diversification, under which circumstances diversification assume
imROrtance? Which is better process of diversification and in which direction it must
move? What are the areas of diversification and finally, diversification at what level?
These are discussed in the ensuing sections of this chapter.
8

1.3 Historical Context of Diversification and its Need in Indian
Agricultural Sector
After independence, Indian economy was confronting with the problem of food
insecurity. Both the market and technology were under developed and these were
responsible for low income and higher variability in the returns of food and non-food
crops (Rudra, 1982). At that time, diversification was looked primarily from the angle of
risk and food security. Diversification, in terms of diversity of cropping pattern, was one
of the means to minimise risk and overcome food insecurity. It was primarily the stateled
diversification, where state sought to propagate diversification primarily to meet the
food-security goals of the economy. The introduction of Green Revolution was one of the
steps towards changing the orientation of Indian farmers towards adoption of new
technology. This technology was limited to the introduction of High Yielding Varieties
(HYV) of selected crops. It was expected to help farmers in terms of higher income or
profitability and less variability of returns. Undoubtedly, this process led to many
benefits, particularly in improving the growth of agricultural sector (Hazra, 2(03). The
Technology Mission on Oilseeds (TMO) in late 19808 was an additional effort to
improve technology in another vital sector of agriculture in India. Both of these efforts
laid emphasize on technology. But, mechanism of price support and subsidies were
operationaiized to improve such technology-led diversifICation. This type of
diversification could also be termed as resource-led, as resources like irrigation, and other
inputs including high Yielding Varieties (HYVs) and fertilizer were provided to farmers
and regions to push diversification. This process resulted in reduction in the level of
diversity among crops and instead increased concentration in cropping pattern in many
states as large acreage was diverted towards few prominent crops like, rice and wheat.
This development initiative or policy came critical lens, particularly after the emergence
of World Trade Organization (WTO) in 1995 (Chand, 2004). Liberalization of
agricultural sector and emphasis on international trade with reduction in support and
subsidies was the major agenda of this policy. At the same time, there were
apprehensions that the impact of Green Revolution was fading away (Joshi et a!. 2(06). It
was felt that in order to increase the growth of agricultural sector, it was critical to divert
9

attention to other components of growth. This favoured the price or market-led
diversification, where diversification is provoked by shifts in the sentiments of the market
which is reflected in the demand of the crops.
Higher economic growth and increased per capita income during 1990s favoured
the process of price-led diversification as it was based on shifting of area towards crops
whose dem'lmd and consequently price, was increasing at a faster rate. In other words,
after 1990, liberalization of the economy has created an opportunity by increasing the
benefits from changing cropping pattern towards high value crops including non-food
crops. High economic growth, rising per capita income and growing urbanization after
1990 have been causing a shift in the consumption patterns towards high value crops like
fruits and vegetables and away from staple food such as rice, wheat and coarse cereals
(Joshi, 2005).
There are several additional merits and justifications for favouring diversification
towards high value crops in India. The need for such diversification can be explained on
the basis of supply and demand side conditions in the agricultural sector. On the basis of
supply side conditions, the major factor that raises the need for diversification is the poor
perfonnance of agricultural sector in the recent past. With different patterns of change in
cropping, a distinct growth pattern in the agricultural sector is observed in India. Prior to
the introduction of Green Revolution, the growth in agricultural sector was poor (RudrB,
1982). Slow growth in technology, poor input and output markets as well as diversity in
the cropping sector were responsible for slow growth during this period. The post Green
Revolution period saw some improvement in the growth of the agricultural secto,-2. This
trend continued until mid 1990s, after which the growth of agricultural sector in India
plummeted (Chand, 2006). Structural changes brought about during the 1990s and
economic reforms in agriculture could not improve the situation significantly.
Surprisingly, not only has the agricultural growth registered an abysmally poor growth
after mid 1990, the lowest decadal growth in the post-independence history but
, However, several studies also argue that growth during 1970s and 1980. was reslric1ed only to particular
crops and that too in particular regions only (Rao, 1971).
10

agricultural production during this period also remained highly volatile compared to the
19808.
Several studies blame the policy initiatives of the past for the present picture of
gloom in the agricultural sector (Hazra, 2003, and Rudra, 1982). It is argued that Green
Revolution was a successful experiment in increasing the level of food production, but
despite achieving self-sufficiency in production of food grains, the emphasis on cereal
production over the last three decades has also resulted in low output prices and
profitability for cereals, which led to dampened agricultural growth (Barghouti et aI.
2004). The recent trend of stagnation in productivity of these crops with increase in cost
of production is making these crops un-remunerative and hence affecting the income of
farmers adversely (Evenson et al. 1999). This is primarily because the post Green
Revolution period saw a shift in the cropping pattern in agricultural sector towards crops
that have registered higher growth in the yield. This process is characterized as
technology-led diversification 3 • As significant acreage was shifted towards selected foodgrain
crops like rice, wheat and maize, it led to the emergence of specialisation in many
states. The recent years have witnessed stagnancy in the yields of many of these food
crops that adversely affected the growth rate of output of specialized states. Not only that
the cost of production of many of the food crops have also been increasing over a period
of time, affecting remuneration of these crops (Singh and Sidhu, 2004, and Sidhu, 2(02).
The increased tendency towards specialisation has further resulted in the emergence
of environmental concerns and sustainability issues, which also hinges on the need for
diversifYing cropping pattern. Specialisation has led to over-exploitation and
unsustainable use of natural resources, due to which many regions are now grappling
with the problems of environmental degradation (Joshi, 2001). Land degradation due to
factors like soil erosion, water logging and application of chemicals, several outcomes
like depletion of underground water, outbreak of new diseases and pests and water
contamination have become widespread. These developments have depleted the natural
) Green Revolution has helped Indian farmers to increase the output through better use of inputs and High
yielding varieties (HYV) that helped increasing productivities of crops. In other words, technological
devefopment was the: major source of growth in output.
II

esource base for agricultural production and future growth. It is certain that any future
strategy of agricultural development has to strike a balance with environmental and
ecological concerns in the interest of long-term well being of the people. As rising
population pressure has been squeezing agricultural land for cultivation (Joshi et al..
2006) and several states in India are now hitting the upper limit in use of fertilizers and
irrigation (Chand, 2005). there is a need to look for diversification towards high value
crops as a policy option. In other words. diversification with its emphasis on increased
food grain production has led to environmental problems, and making farmers more
vulnerable to weather and market risks.
Additionally. there are concerns was related to equity in the agricultural sector of
Indian economy. as there has been duality in growth across regions 4 • The present
scenario is that while the net income of farmers in the developed regions is declining
continuously, farmer's income in developing regions is either stagnant or declining
(Chand 1996). In the recent past, many of the developed regions that benefited from the
Green Revolution have experienced poor growth. The major challenge before policy
makers is that now both developed and underdeveloped regions are facing the problem of
low growth. Therefore, there is a need to fmd a conunon model for both the regions to
accelerate growth. It is argued that most of the regions that attained high growth in the
past were beneficiaries of high subsidization of inputs, which the government is
increasingly fmding difficult to provide. besides being unjustifiable in the present era of
globalization (Chand, 2004). The need of the hour is to provide support to developed as
well as underdeveloped regions in order to achieve short and long-term goals like higher
income. growth, employment and environmental conservation.
Another factor that promotes diversification is the changing economic and trade
scenario. Both, annual real rates of gross capital formation and public investment in the
agriculture sector witnessed a sharp declining trend during the 1990s (Chand. 2004 and
2006). The policy shifts in terms of liberalizing agricultural sector further brought about
.. Only some developed regions (in terms of infrastructure like irrigation, proximity to market and roads)
were able to grow faster as compared to other regions. The tcchnological change experienced in Indian
agriculture during the last three decades has produced two distinct patterns of growth and development,
which resulted in duality in tenns of developed and underdeveloped regions.
12

additional challenges to the sector like price volatility, price decline, increased
competition of most food-grains, and declining investment in agriculture sector. The
growth rate of agriculture has become stagnant and more volatile in the postliberalization
era. The diversification away from the food-grain towards high value crops
is also supported by fast improving technology. New production technologies and
management techniques, such as improved agricultural machinery, biotechnology, new
pest and disease contml products, etc. have enabled better use of the sources of
competitive advantage of several regions. Technological advances in communication,
logistics, and marketing systems have induced supply-side growth of non-food crops
(Joshi, 20051. In sum up, the decline in agricultural growth during 19905 is attributed to
increased pattern of specialisation and higher orientation towards a few food grains. The
recent stagnancy in food grains productivity, incomplete agricultural transformation and
fatigue in major components of growth including area and productivity highlights the
need for diversification towards high value crops.
Diversification in favour of high value crops is also desirable due to several
demand-side factors. The present stage of poor performance in agriculture along with
structural changes taking place provided a new opportunity for diversification. In other
words, stimulus for diversification came not only from the problems created by overemphasis
on food-grain economy in the past but also because of the fast increasing
demand for high value crops including horticultural crops and increasing export
opportunities abroad.
Sustained economic growth, rising per capita income and growing urbanization
have resulted in increased demand for high-value food commodities like fruits,
vegetables, dairy, poultry, meat and fish products from staple food such as rice, wheat
and coarse cereals. This is evident from the share of these commodities in the total
expenditure on food which increased from 34 percent in 1983 to 44 percent in 1999-2000
in rural areas, and from 55 to 63 percent in the urban areas (Kumar and Mruthyunjaya,
2(02). In addition, trade liberalization and overall higher growth in the economy have
resulted in higher remuneration from many non-food-grain crops due to changes in the
trade scenario and changing consumption pattern towards high value crops. Increased
13

liberalization, declining public support and poor relative growth in the agriculture sector
have thrown up new concerns. Policy makers are seeking ways to change the way
agriculture sector operates in India works in order to remove inconsistencies and achieve
better levels of food security for poor and malnourished people. This calls for alternative
production systems or strategies to generate new employment, growth and higher
incomes. On such a backdrop, diversification of agriculture towards high value crops like
fruits and vegetables is suggested as a viable solution to stabilize and raise farm income,
enhance agricultural growth, increase employment opportunities and conserve natural
resources (Vyas, 1996 and Joshi, 2(05).
It is noted that though diversification per se is not a new phenomenon. At present,
diversification is considered as policy initiative to tackle or improve the situation in the
agricultural sector. It is now emerging as an important option because of several key
developments in the economy such as booming economy, changing consumption pattern
towards non-staple crops both in the rural and urban areas, trade liberalization and
declining public support. In this context, diversification is seen mainly as a shift in
cropping pattern from low value crops towards high value crops and higher allocation of
land to high value crops at any given point of time. Among the high value crops in India,
horticultural crops command high value not only in terms of their potential in generating
income and employment, but also on the basis of export-earning opportunities. The future
prospects of the horticultural sector are also bright thanks to the changing consumption
pattern towards horticultural crops. Additionally, rising per capita income, growing
urbanization and liberalization of the economy have led to increase in internal demand
and export opportunities for horticultural crops.
1.4 Horticultural Crops as a Case for Diversification in India
In India, demand of horticultural crops5 and their export opportunities have been
continuously increasing. It is felt that the relative significance of the crops which are
becoming highly remunerative due to price factor, especially horticultural crops, should
be increased in the cropping pattern mix, as there is a positive nexus between an increase
, Fruits and vegetables forms the largest sub-sector of horticultural crops
14

in the relevance of such crops and growth in overall output (Joshi et a1., 2006 and Birthal
et a1., 2(07). The demand of fruits and vegetables are increasing at a greater .pace than
other crops. The data on consumption pattern of people in India show that there has been
a constant increase in demand for fruits and vegetables (F& V) as compared to other
crops. The per capita consumption of fruits was estimated at 25 kg which increased to
approximately to 40 kg in 200 I, an increase of 60% in the last two decades. The annual
per capita consumption of vegetables increased from 47 kg in 1983 to 76 kg in 1999
(Singh et a1., 20(4). The overall increase in vegetable consumption was about 53%
during this period. Given the population base and the fact that real per capita income
levels have increased at the rate of 3.4% per year in between 1981 and 1999, the
household expenditure on F&V increased at 5% per annum. Not only this, it is also
expected that the consumption of fruits and vegetables will continue to increase in future
as well; demand for F& V is expected to increase from 98 Million Tonnes (MT) to 220
MT by 2020, assuming a population growth of 1.7% per annum and commitment for
exports (Singh et aI., 20(4). Among the many high value products like egg, milk, fish,
rice, wheat, it is the fruit and vegetable crops whose demand is expected to increase the
most until 2030 in India (table 1.1).
In addition, since horticultural crops are labour-intensive and generate high and
quick returns, these provide farmers a chance to utilize their surplus labour and augment
their incomes (Birthal et a1., 2(07). Horticultural crops have been acclaimed as a special
case for diversification on several other counts as well. The increased trend towards
specialisation in cropping pattern in few states coupled with stagnancy in their yields and
increasing cost of production is bringing down the real income of the farmers. This
highlights the need to look for alternative cropping pattern with a view to remedy the
situation. Due to higher returns and productivity of horticultural crops, this group
emerged as an important area for diversification and as an alternative cropping pattern.
Available evidence shows that one hectare under horticultural crops can generate an
annual income up to Rs. 20,000, compared to hardly Rs. 10000 and Rs. 4000 by rice and
ragi respectively (Singh et a1., 20(4). Cultivation of fruits can generate employment to
the tune of 860 man-days as against 143 man-days for cereals. Diversification towards
15

horticulturaI crops is expounded as the strength of Indian agriculture, due to its potential
to produce a wide variety ofhorticulturaI crops under varied. agro-climatic conditions.
Table 1 . l' . Demand for Various Foods in India- 2015 and 2030
Foods Year 2015 Y •• r 2030
LIG I-llG LIG I-llG
Rite 101886 101441 114499 113893
Wheat 74607 72411 83045 80087
Pulses 21303 22578 24515 26312
Foodgrains 235399 232547 263752 259993
Edible Oils 10355 10863 11870 12581
Vegetables 123824 151861 150823 193562
Fruits 69678 84099 84336 106126
Milk 109092 127805 130502 158325
Meat 8196 10396 11181 13534
Eggs 2889 3664 3566 4770
Fish 8460 10731 10444 13971
Note:
(i). LIG is Low Income Group (3.5 Per cent pc:r capita GDP) and
(ii) HIG is High Income Group (5.5 per cent pc:r capita GDP)
Source: Rao, 2003
Currently, India produces about 70 varieties of vegetables and equally diverse
variety of fruits, which consists of 70% tropical and sub-tropical type and the remaining
30010 of temperate type. This large and varied production base offers an opportunity to
expand domestic and overseas markets for horticultural produce. A trend in the increase
volume of international trade of horticultural crops is also realiZed. World production of
fiuit and vegetables grew by 30 % between 1980 and 1990 and 56 % between 1990 and
2003. World trade in fiuits and vegetables, both fresh and processed, increased by 30 %
since 1990, and reached $71.6 billion in 2001. Taking all fiuit and vegetable products
together, the value of world trade grew at 2-3 percent a year during the 1990s (Diop and
Jaffee, 2005). There is an opportunity for India to participate and join hands with many
countries that have benefited enormously from increasing the share of horticultural
production.
1.5 Research Issues
The studies dealing with diversification cover a wide range of aspects and there are
m!-nad ways to interpret the significance of changing trends in diversification. At the
16

macro level, the major debate is about the quantification and significance of crop
diversification in growth of output. As noted earlier, there are several sub-components of
diversification and each component affects growth in different ways. In terms of
diversification towards high value crops, many researchers have argued that a silent
revolution of shift in cropping pattern towards high value crops is already under way
(Joshi, 2006, Vyas, 1996, Chand, 2005, Rao et al, 2004, Birthal et al,. 2(07). But, how
the process of diversification has affected the growth of output in India. While dealing
with this issue, many studies have dealt with the static aspects of diversification, without
emphasising the dynamic aspect. Actually, diversification affects growth through both
static and dynamic effects. A positive static effect of diversification would show a shift in
crop pattern in favour of high initial productivity crops. This does not capture
development in the technology of crops that changes over time and may alter relative
values of crops. In other words, over-time, the profitability or relative values of crops
does not remain constant and change in the productivity of crops may change the
comparative advantage of the crops. Hence, it is also important to consider the dynamic
effects of diversification that capture the concomitant movements of yield and cropping
pattern change. The dynamic aspect of diversification indicates the impact of the
combined movements of diversification of crop pattern and technological change on
overall output change. A positive signs of the dynamic effect of diversification is a shift
in crop structure in favour of those crops which exhibit relatively higher growth in yield.
Similarly, a negative dynamic effect implies, over-time, a shift in cropping pattern in
favour of crops which could not experience higher growth in productivity. The question
here is about the role of diversification in the growth of output in Indian agricultural
sector i.e., whether the changing cropping pattern has been growth inducive or depressive
and what are the factors that have contributed to the nature and direction of
diversification in India.
Any decision-making involved in the process of diversification towards high value
crops could be analysed at both macro (state or district) and micro (farm) levels. At the
macro level, such decisions are examined on the basis of price response models,
introduced by Nerlove (N erlove, 1958), who devised a model relating expected "normal"
I
17

price to past-observed prices. Many studies came later that used Nerlovian model with
some modifications th~t investigated the importance of price of crop in shaping farmers'
supply response behaviour 6 (Krishna, 1963, Behrman, 1968, De, 2005, Askari and
Cummings, 1976, Sawant, 1978, Mythili, 2006). However, there are several limitations to
this method of analysing changing land allocation decision using macro level data.
Measurement of supply response usually requires time series data for quantities, cost
and prices. Such data are generally not reliable, available for short duration and provide
only partial coverage in terms of crops and area (Medellin, et al. 1994). A large number
of crops especially high value horticultural crops are therefore excluded from the analysis
due to lack of reliable time series data in India. Another limitation of these models is
regarding identification of the competing crop. At any given time, not only two crops
compete with each other for land but there are possibilities of many crops competing for
land. In addition, the macro level studies mainly concentrated on price of the crop as a
major economic factor in shaping farmers' changing land allocation decision. However,
at the farm level, there exists a difference in endowments and access to markets. Price
alone may not be the factor in decision-making; heterogeneity in the resource and capital
endowments of farmers and difference in their access to input and output markets also
play a role in decision-making. It could be hypothesized that farmers with relatively
higher level of productivity may allocate more land to the crop with lower price
expectations. Hence, it is important to examine the link between price and income with
the shifting cropping pattern decisions of farmers. The question arises that to what extent
price and in\:ome from the crop influence.diversification towards high value crops.
Fruits and vegetables crops are highly remunerative but are also considered risky
crops. A major feature of horticultural crops is that prices of these crops fluctuate widely
and even within a single season. Lack of any support price and high perishability of the
crops make horticultural growers even more vuInerable to risk and uncertainty than
growers of other high value crops like rice or sugarcane. In the event of greater extent of
risk and uncertainty, the importance of the same is high for farmers while taking land
6 S~ppl)l response behaviour is the changing land allocation among different crops due to cbange in
economic values of crops.
18

allocation decisions. At the fann level, the concept of expectation is generally used in
terms of a response to uncertainty involved in the production process; fanners' speculate
on different economic outcomes including price, yield and income. It is therefore crucial
to examine the link between different expectations including price, yield and income in
the land allocation decisions by the fanners.
Risk also could be considered as a strong behavioural force affecting decisionmaking
in land allocation to food crops and commercial crops (Blank, 1990, Braun, 1995
and Dercon, 1996). A vital perspective of risk is how far and how often returns unable to
reacb a below the mean level of return. Risk is also taken as a cost of decision in fanners'
decision pertaining to land allocation to high value crops (Roumasset, 1976). The Safety­
First principle (Roy, 1952) accounts for such costs in analyzing fanners' behaviour
towards risk. Due to fluctuation in the components of revenue from the crop, one can find
two kinds of fanners: the first group would prefer to be on safe side and hence their
allocation decisions would be explained by income and yield variance of the crop, as
relative to the perceived disaster level of income of the fann household. The second
group constitutes fanners who prefer to take risk in their land allocation decisions, and
undertake risk as their disaster level of income remains higher than the mean income
from the crop produced for commercial purposes. The fanners take risk of meeting
sufficient food for consumption by increasing the allocation of land more in favour of
high value crop against the subsistence crop. But, not all fanners prefer to take such risk
and hence keep their land allocation low in favour of commercial or high value crops.
They follow the principle of 'Safety-First' as they realize low utility from the production
of horticultural crops. This often results in selection of low-risk crops that deters higher
land allocation to horticultural crops.
1.6 Research Questions
l. What is the nature and extent of crop diversification across states in India and its
role in the growth of output in the agricultural sector?
2. Whether the process of crop diversification in India has been growth inducive or
lIepressive?
19

3. What is the relative significance of economic and non-economic factors m
diversification from food crops to horticultural crops?
4. Whether it is the relative price or relative income that matters more m when
farmers decides to diversify towards horticultural crops?
5. What is the nature of price expectations of farmers and its relationship with other
economic factors such as crop yield, cost of production and input-use
propensities?
6. What is the relative importance of different expectations I.e. price, yield and
income expectations of farmers on their land allocation decisions?
7. Are the farmers growing horticultural crops averse to risk or not, and what are the
determinants of their risk-taking behaviour?
8. Does the higher risk of production. and consumption hinder land allocation in
favour of horticultural crop by the farmers?
1.7 Objectives ofthe Study
I. To investigate the relative role of diversification on output growth, and growth
inducive or depressive impact of changing process of crop diversification in India
2. To analyse the nature of price expectations of the diversified farmers, examine its
relationship with other economic factors and identify the role of different
expectations of price, yield and income of farmers on their land allocation
decisions
3. To estimate the role of overall risk of production and consumption on the extent
of land allocation in favour of horticultural crop?
1.8 Sampling and Methodology
Among the several facets of diversification, we have selected diversification
towards horticultural crops as its major proxy. The measures of diversification include
both the spatial and temporal aspects. On the basis of spatial aspect, diversification is
calculated by land allocation in favour of horticultural crops. Measures based on temporal
aspect of diversification include growth rate in area under horticultural crops and
20

magnitude of crop substitution towards horticultural crops. The criterion of selecting field
area is guided by these measures of diversification.
A multi-stage purposive sampling procedure was followed in order to select the
state, district, block and villages (figure 1.2). The selection was based on them being
representative in diversification towards horticultural crops. Himachal Pradesh was
chosen as a study area. In Himachal Pradesh, the importance of horticultural sector in its
agricultural sector is very high (representative in terms of value of the location quotient
exceeding four) and the state experienced moderate to higher growth in area under
horticultural crops over the past three decades. We then selected Shimla district, which
was representative in diversification towards horticultural crops as it experienced highest
substitution of area towards horticultural crops and maximum allocation of land to
horticultural crops 7 •
We then collected the last ten year block level data from three major sources­
Directorate of Horticulture, Directorate of Agriculture and Directorate of Land Records,
to identify the temporal and spatial pattern diversification in blocks of Shimla district. We
calculated the compound growth rate for important crops from agriculture and
horticulture sector and also measured the magnitude of crop substitution from food grains
to horticultural crops, as a mean to identify the degree and scale of diversification. This
was followed by calculation of the proportion of horticultural crops in the gross cropped
area and diversification indexes of blocks.
7 The state and district level data analysis is provided in Chapter fouT.
21

Figure 1.2: Map of Study Area
......, ..
LOCA nON OF HIMACHAL
PRADESH IN INDIA
_...
•
_1001 ____ "'"
SHIMLA DISTRICT
THEOGBLOCK
!
Source: www.images.google.co.in
22

One block, Theog, was chosen as a study area. The temporal picture of
diversification and horticultural development shows that agricultural crops experienced
negative growth in area in all the blocks of ShirnIa in the last 10 years and vegetable
sector grew faster than fruit sector (table 1.2). The trend of growth of vegetable and fruit
sector varies by blocks with Chiragaon turning out as representative in the growth of
fruits and Theog representing the vegetable sector. Theog has been able to reallocate
most of the area from food grains or non-horticultural crops towards horticultural crops
(table 1.3). From the spatial picture, it emerges that the most diverse region is Theog,
which has substantial area under vegetables (40%), followed by fruits, especially apple
(2oolo) (figure 1.3). lubbal and Narkanda blocks specialize in apple with a share of 62%
and 58% respectively. In Rohru, apple covers 50% of area and a sizeable area (20%) is
covered by potatoes (20%). Basantpur, Chopal and Mashobra blocks have more area
under food grains like maize and wheat and are least diversified (table 1.4). We
amalgamated gross cropped area under horticultural crops with the diversification index
(Herfindahl) to fmd the representative block in both diversification and horticultural
deVelopment. Theog turns out to be representative and was chosen as the area of study.
Table 1.2: Compound Growth Rates in Area of Major Crop Groups in Blocks of
Shimla 1997-2006 ,
Blocks J\ericulturalcrops* Veeetables Fruits Horticultural crops--
Basantpur ·0.72 0.73 1.29 1.23
Chopal ·0.65 ·2.62 0.99 ·0.20
Mashobra ·0.73 3.52 1.39 2.18
Chira2aon -0.63 3.67 1.58 2.63
Rampur ·0.59 -0.73 1.76 1.25
NarkaDda ·2.02 2.81 1.16 1.38
Jubbal -0.57 ·3.15 0.85 0.34
Rohru -1.81 1.32 1.31 1.31
Theog -2.71 3.87 1.26 2.69
Shimla District -1.00 2.00 1.24 1.45
Note: Wheat and m3.1ze are the major agncultural crops JO the region
• Agricultural crops constitutes of all food and non-food grain crops, excluding fruits and vegetables
•• Horticultural crops constitutes of all fruit and vegetable crops.
Source: Directorate of J\griculture and Horticulture, Shimla, lIP
23

Table 1.3: Absolute Change in Area under Horticultural Crops in Blocks of Shimla,
1997-2006
Blocks Vejle."bles Fruits HorticullUral crops
Basantpur 25 176 201
Chopal ·193 427 234
Masbobra 416 338 754
Chiragaon 442 594 1036
Ram pur -18 682 664
Narkanda 181 691 872
JubbaJ -254 611 357
Robru 383 674 1057
Theog 1953 588 2541
Shim" District 2935 4781 7716
Note:
i Area in Hectare
Source: Directomte of Agriculture and Horticulture, Shiml .. HP
Figure 1_3: Pen:entage of Gross Cropped Area under FNits and
Vegetables in Blocks of Shim", 2006
~00r-------------------------------------------------'
j rooof------­
I 8)00 j--------------------------­
j 5000 t------------------------.
0>.00 j--------------------------------
I.,oot-----
1
l>Joot---
I ~oo
1000
000
Basanps 0I0pII MaIkJtn R-mp.w Ct\ngIorI N ..,.. Rc:IYu Theog JudIeI HP
......
Source: Directorate of Agriculture and Horticulture, Shimla, HP
24

T a bl e 1.4: Diversification Indexes of Blocks, 2006
Blod •• nerfindahllodex of Diversification GCA under horticultural Crops D1*PAFV
Ba.an!J>ur 0.784 22.08 0.173
. Chop.1 0.850 32.09 0.273
. Ma.bobra 0.831 37.57 0.312
Cbira2aoR 0.805 40.49 0.326
Rampur 0.840 40.30 0.339
Narkanda 0.704 71.24 0.502
JubbaJ 0.676 81.50 0.551
Robru 0.734 79.68 0.585
Th_ 0.819 80.76 0.661
Note.
i. The Value of Herfindahllndex range from 0-1, 0 being full concentration and I means spread in cropping pattern
ii. GCA is the Gross Cropped Area
iii. DloP AFV is multiplication of diversification index with percent of area under fruits and vegetables
Source: Authors calculation on the basis of the data obtained from Directorate of Agriculture and
Horticulture, Sbiml .. HP
Due to the difference in the nature of the crops within horticultural sector, four
villages were selected (two villages each for fiuits and vegetables) from Theog block, as
these villages (Govai, Sainj, Shilaru and Sandhu) are representatives in diversification
towards fiuits and vegetables respectively. The choice of villages was based on the
discussion from agricultural and horticultural development officers of Theog block. In the
ftrst two villages namely Govai and Sainj, vegetables cover 72% and 84% of the total
gross cropped area respectively. Among vegetables, most of the diversification has been
towards cauliflower crop. In villages, Shilaru and Sandhu, fruits are grown at a higher
scale. Apple is the major crop in these villages that covers 85% and 89% respectively of
total cultivated area. Both, cauliflower and apple crops were chosen for this study in
examining diversification of land in favour of these horticultural crops. Sample of 30
farm bouseholds (120 farmers in total) was drawn from each of the four villages
following a stratified and proportional random sample approach 8 •
To address the major objectives of the study, primary and secondary data are used.
The secondary data covers 30 major crops for the past three decades across 17 states in
India. The decomposition model provided by Minhas and Vaidyanathan (1965) is
employed to explain agricultural growth in terms of both static and dynamic manner. For
II Sirlce, interviews with fanners include recall method, many fanners were found to have given inadequate
linformation. ~ence, re-sampling was done after the completion of fanners from the first Jist of 120. The
'm, -del of stratified and proportional random sample approach was kept intact while designing re-sampling.
~n 10131, 167 farmers were interviewed to cover the complete infonnation from 120 farmers
25

measuring the trend in area expansion and substitution, we adopted the method by
Venkataramanan and Prahladachar (1980) in measuring gross cropped area elasticities.
To work out the economics of vegetable and fruit crop, different concepts are used. Farm
Management Studies concepts of cost and returns are applied for examining economics of
vegetable crop (cauliflower). We measured the output supply and factor demand
equations from farm level data of cauliflower using the profit function provided by Lau
and Y otopoulos (1972). Only one variable, i.e., labour is treated as a variable factor, as
prices of other factors of production i.e., fertilizer, chemicals and irrigation does not vary
across farmers within the region under study. The current year price and expected price
are used in measuring the profit due to marked difference in prices received by farmers
over any single season. Net Present Value (NPV), Benefit-cost Ratio, Internal Rate of
Return (IRR) and payback period are measured to work out the economics of a fruit crop
(apple). For the purpose of identifying the relative importance of price and production
risk, method by Barah and Binswanger (1982) is employed where the gross revenue
variability is decomposed into price, yield and price-yield interaction components. The
method of weighted mean is employed to examine the significance of economic and noneconomic
factors in diversification decisions of farmers. Source of diversity in farmers'
response to change in price of cauliflower is examined by analysis of variance
(ANOVA). Elicitation method is employed to obtain the expectations of farmers of
various economic outcomes from the production of the selected horticultural crops.
Farmers' expectations are linked with their socio-ecooomic and other characteristics
along with their resource allocation including land and labour. To account for the role of
overall risk, including production and consumption risk, we measured risk attitudes of
farmers under a Safety-First framework provided by Roy (1952). Regression models are
used to examine the role of risk and uncertainty 00 land allocation in favour of
horticultura1 crops by farmers.
1.9 Outline of the Study
In addition to the first chapter I.e., Introduction, which covers concepts and
indicatoIi of diversification, gaps, research questions, objectives, sampling details and
26

methodology, the thesis is organized into seven chapters. Chapter 2 consists of review of
literature. Chapter 3 provides a picture of nature and extent of crop diversification in
India, and examines the link between several typologies of diversification and
agricultural growth over the past few decades in India. This chapter also analyzes the
relative importance of diversification process in affecting the growth of output in India,
and seeks to test whether crop diversification has been growth inducive or depressive.
Also, outlined in this chapter are the factors responsible for typology of diversification in
India. Chapter 4 provides an overview of the horticultural sector in India, analyses the
role of horticulture crops in the growth of agricultural sector in Himachal Pradesh and
presents the economics of selected horticultural crops i.e. apple and cauliflower. In
Chapter 5, the focus is on examining the process of reallocation of land from food crops
to the selected horticultural crops. This chapter also explores the factors responsible for
diversity in response to change in prices of vegetable crop along with analysing the role
of price and income on the reallocation ofland from food crops to selected horticultural
crops. Chapter 6 deals with land allocation in favour of the selected horticultural crops by
farmers. The role of risk and uncertainty (expectation) in land allocation in favour of
horticultural crops are analyzed in this chapter. The concluding chapter (chapter 7) details
the conclusion and policy implications.
27

Appendix 1.1: Facets of Diversification
The term 'diversification' is used in Several ways and in different contexts. It is not
limited to the cropping sector, but used generally in the context of overall activities. The
basic theorem of diversification is that if returns from two activities are independently
and identically distributed, then diversification is said to be optimal with equal
distributions to each of the activities. Diversification is likely to be optimal for riskaverter,
when returns have equal means but covariance are negative. However, large
positive covariance, large disparities in mean returns (e.g. Scale economies), or resource
constraints may provide incentives for specialisation (Pope and Prescott, 1980). Since it
has two different dimensions, there are distinct aspects- "diversification into" and
"diversification in". There are different levels at which diversification can take place:
1. At sector level (between agriculture and other non-agricultural sectors),
2. At sub-sectoral level (between or within sub-sectors like livestock, horticulturaI
crops, fisheries),
3. At producers level (small, medium and large farmers),
4. At crop level (annual, perennial or their several combinations),
5. At consumer level (rural and urban),
6. At intensification or extensification level (double or multiple cropping) etc (Eisa,
1987).
From an analytical viewpoint, two facets of diversification are discemable, i.e.,

the degree of development in allied activities including food grain crops, horticultural
crops, livestock sector,_ fisheries sector, and forestry sector. Activities outside agricultural
sector, like mining, tourism etc. are included in non-agricultural diversification.
AgriculturaI, non-agricultural and crop diversification activities are inter-related and form
part of a process which needs to be viewed in/our stages. Initially, diversification comes
at the cropping level where it is about a shift away from monoculture. At the second
stage, the farm has more than one ente!prise/crop and may produce and sell crops at
different times of year. At the subsequent stage, diversification is understood as being
mixed farming. Finally, activities beyond agriculture are incorporated into the meaning of
diversification. Such activities could include on-farm processing, the provision of nonagricultural
products and services on-farm. In addition, it envisages the use of farm
resources for non-agricultural activities. At this fmal stage, diversification may be
considered as unconventionality with respect to traditional farm family type agricultural
activities (Chaplin, 2000). In other words, the concept of diversification is often taken to
mean as a shift away from the production of surplus commodities to those which may be
expanded. This is consistent with non-agricultural literature, and explains diversification
as a strategy of utilizing excess capacity of production factors.
Finally, diversification gets a different meaning at farm level, agricultural sector
and at regional or country level and therefore the process of diversification needs to be
analysed at various levels: individual fields within a single farm, single farm within a
region, region to region, and country to country. It is possible that although the country
may be well diversified, individual farmers and regions may not be. In other words, even
if the farms in different agro-climatic regions specialize in certain products in which
these regions have a comparative advantage, the aggregate picture may still show higher
level of diversification as may be revealed by the food basket of the region as a whole.
Appendix 1.2: Indicators of Diversification
There are different ways in which diversification can be measured. Agricultural
diversification can be examined in terms of diversity of crop and non-crop enterprises.
When the emphasis is especially on crop diversification, it ought to be related to diversity
29

in crops including cereals, pulses, fruits, vegetables etc. At the macro level, the important
indi~ators of crop diversity can be found out from the share or contribution of crops in the
cropping pattern. If we consider diversification as a temporal phenomenon, then the
annual and compound growth rates in different crops can be used as an indicator of
diversification. Crop diversification can also be measured by the changes in cropping
pattern in terms of substitution and expansion effects on crop area. Substitution effect is
the relative decline in area under some crops and the corresponding increase in area under
other alternative crops for a given gross cropped area. Expansion effect is the effect of
increase in gross cropped area (Venkataramanan and Prahladachar, 1980).
At the micro level, the extent of diversity can be viewed in terms of the number of
component crops within a farming system and the relative shares of the component crops
in the total farm income and activity. First, crop diversity increases with increase in the
number of crops grown. The other notion relates to the relative importance of each crop
in the cropping system. From this point of view, a more diversified farm is one which
does not depend too heavily on any single crop (Rahman and Talukder, 200 I). It is
argued that, perhaps, no farmer in India specializes in a single crop. If a farm generates a
large proportion of its total income from a single crop, it cannot be called a diversified
farm. It is in this context that farm management textbooks distinguish between a
specialized and diversified farm by using a benchmark of 50%. A farm is treated as a
diversified farm if no single product accounts for 50"10 or more of the total income
(Acharya, 2003). In addition, different strategies, such as multi-cropping and
intercropping signify diversification of agricultural production at the farm level. Farmers
diversify by growing a variety of crops at one time, by growing different crops in
different locations at the same time, or by growing different crops over successive
periods of time. It is vital to delineate between the diversification of crop area as against
diversification in terms of value or income. For the cross-sectional analysis, both net
income and crop acreages are potential variables over which to defme diversification.
Apart from the above indicators, there are some indices that are commonly used to
mea5ure diversification of the acreage and income sources. These include Number of
30

enterprises, Index of maximwn portfolio, Simpson index, Entropy index, Ogive index,
and Herfmdahl index, the details of which are given in the table below.
Indicators of Diversification
Measure Formula Explanation
Index of maximum Max Pi It is a measure of concentration of most
proportion
dominant activity and has a range from 0 to I
where 0 means perfect diversification.
Number of N Where I is zero-one indicator, i.e., when Pi
enterprises
exists I takes a value of one, if Pi does not
D= L I (P;) exists, I takes a zero value. For increasing
diversification, D increases. D may take a
i=I
value of more than one; it takes a value one
when there is complete specialisation.
Herfindahl Index N Pi is the proportion area (or value) of the ith
crop in the Gross Cropped Area (GCA). The
HI= 1- L Pi 2 index approaches zero for perfect
specialisation and has upper limit of one,
i=1 which signifies diversification.
Entropy Index N This index is a weighted swn of proportions
[weights being log (llPi)]. It attains 0 with
EI= L Pi . [log (11Pi) complete specialisation and log (N) with
perfect diversification. This IS good for
i=1 capturing the 'diversity' aspect of
diversification as N varies, Thus, it shows
how diversified is a distribution.
Ogivelndex N This index is a measure of deviation of a
given distribution from an equal distribution
01= L[ {Pi-(IIN)}2/(IIN}] benchmark. It attains 0 with complete
diversification and a maximum value (as set
i=1 by N) for complete specialisation. This is
good for reflecting the 'deviation' aspect
when N IS fixed. Thus, it shows how
unbalanced is the distribution.
31

CHAPTER II
REVIEW OF LITERATURE
It is well recognized in the literature that there are several dimensions to
diversification. Therefore, any attempt to review requires systematic arrangement of
issues. This has to be achieved on the basis of its rationale and impact on income and
risk. It can either be a strategy to optimize income or an alternative strategy to deal with
risk or both. These two aspects deal with the farmer's decision making process under the
assumption of perfect knowledge. However, it is highly unlikely that such ideal situation
exists and hence diversification also has to be a strategy choice to deal with a situation of
imperfect knowledge or uncertain situation. In addition to separating the positive and
normative issues of diversification, it is necessary to contextualize the view-points
according to various definitions of diversification used in various studies. For the purpose
of convenience, we have grouped the review under the following broad themes. These
themes are designed keeping in view the need for the present research.
l. Studies dealing with positive issues, i.e., factors affecting diversification that
include drivers of and constraints on diversification. This section is divided into
three sub-sections according to the definition of diversification:
a. Diversification as changing land allocation under crops
b. Diversification in terms of diversity of cropping pattern
c. Diversification represented as proportion of High Value Crops
2. Studies dealing with normative issues that investigated the link between the risk,
uncertainty and land allocation decisions
3. Studies examining the role of changing typology of crop diversification on growth
of agriculture
32

2.1. Factors Affecting Diversification- Drivers and Constraints
2.1.1 Diversification as a Changing Allocation of Area under
Crops
The factors affecting decision-making in the allocation of area under the crops can
be broadly grouped into the price and non-price factors. According to economic theory,
prices influence relative profitability and play an important role in decision-making. At a
macro level, this is generally articulated with the belp of the concept of expectations.
Nerlove (1958) initiated study on this aspect to frod the role of farmer's expectation of
future prices in shaping their decisions as to how much land they should devote to crops.
He devised a model relating expected "normal" price to the past-observed prices. The
basic Nerlove model is a simple three-equation model focused on price expectation:
A*t = a. + alP*t + Ut ---------------------------------------------------1
P*t -p* '·1= b (Pt. I - P*t.l) -----------------·--------------------2
A.- At.l = c (A*,- At.l) ----------------------------------------------------3
The fmal equation was
Where At is actual area under cultivation, A *, is desired area to be put under
cultivation, Pt is actual price of output, P*t is expected price, Z. is other exogenous factors
affecting supply and Ut is the random disturbance or error. The subtext 't' denotes the
time period t.
The first equation relates to expected future price to the desired planted area. The
second equation indicates that the expected price is the previous year price plus 'b' times
the differellce between the expected and realized price. The third equation indicates that
in each period actual planted area is adjusted in proportion to the difference between the
33

planted area and desired area. The major problem identified by Ner\ove in his model was
its inability to capture the role of technology and infrastructure through prices. Following
this. Askari and Cummings (1976) attempted to explore the role of expected yield in the
supply response model. Their model indicates that the present year crop yield is first the
function of past yield. Second, since land quality is not homogenous. use of additional
land or reduction in planted acreage could impact average yield. For instance, if inferior
land is bought under cultivation then the average yield is likely to decrease. Thirdly.
shifts in the use of non-land inputs such as fertilizer. machinery and irrigation facilities as
well as changes in rainfall affect yield. They criticized the original Nerlovian equation on
the count that in a desired acreage equation, the appropriate variable is the expected yield
rather than the actual yield. It is primarily because the farmer does not have priori
knowledge about yield from the crop at the time of planting. He rather makes some
independent estimates of what the yield is likely to be on the basis of change in input use
and weather conditions. Significant results were observed by this study after including
the expected yield in the Nerlovian model.
Most of the initial analysis in the price response models was restricted to the
developed countries. Behrman (1968) was one among the firsts to analyze the supply
response in the context of developing countries. He examined the Nerlovian dynamic
aggregate supply response model using the area planted in a crop as the dependent
'!ariable. The result of the study supported the hypothesis that farmers in underdeveloped
countries respond significantly to economic incentives. Institutional constraints do not
seem to have precluded significant response to economic incentives. Later, many studies
also came up in India.
In the case of Indian economy, initially, a commonly held view was that Indian
farmers are not price responsive and they generally produce for subsistence. This
paradigm was disproved with empirical evidence by Krishna (1963) and Narain (I 965).
-They indicated that Indian farmers do respond to prices. Krishna (1963) fitted the acreage
respom,e 1fnction to 11 crops for Punjab. By correctly specifying the relevant non·price
variables like irrigation, yield and rainfall, he obtained the elasticities with respect to
price variables. In other words. he included only those variables in the estimating
34

equation of a given crop which were found to be important in detennining the acreage of
that particular crop. Krishna (1963) introduced the concept of shifter variable and
concluded that by correctly specifYing the relevant non-price variables, one can obtain
significant net regression co-efficient and elasticities of price variable.
Narain (1965) analysed the producer's behaviour in response to price changes,
whereby a graphic comparison of the year to year variations in acreage of crops with
variation in prices and yields was done. The prices of competing crops and data on
rainfall have also been juxtaposed. He examined farmer responsiveness to prices and
noted a striking difference in behaviour of farmers growing cash crops and food crops.
The study concluded that the Indian farmer is responsive to price, but there is a striking
difference of behaviour between cash crops and food crops. In the case of food grains,
rainfall assumes that status which price does in the case of cash crops. Cummings (I 975)
analysis of the supply response behaviour of Indian farmers by using nine crops data for
the period of 1949 to 1969 also supported the hypothesis that Indian farmers were aware
of market and its potential rewards for cash and food crops. Sawant (I 972) analysed two
different types of supply response of paddy crop. The first represents productivity of
paddy and the other relates to acreage under paddy. The results show that the estimates of
price elasticity for yield per acre were higher than those for the acreage. Also, dominance
of small fanners and tenant holdings restrain supply response.
The dynamic aspect of the decision making process of the farmers was studied by
Maji et aI. (\971). They incorporated the risk factor into the Nerlovian model of supply
response. The ratio of actual standard deviation of the price of crop relatively to the
standard deviation of the price of alternative crop was used as a proxy for relative price
risk. This study was followed by the incorporation of relative profitability and income in
the price responsiveness of farmers by Deshpande and Chandrashekar (1980). In their
study on growth and supply response of pulses, they noted the perverse supply response
of area under pulses to prices. This motivated them to incorporate the concept of income
in the supply response of farmers growing pulses and other slow growth crops. After
trying several equations to study the supply response behaviour of the farmers in the high
growth and low growth regions, they found out the positive response of these crops to the
IS F. C U:' Rf. "'':' n,v~:: ALORE
Ace. Nu •.• =t1:t J ...... t(i
.~ .~ ,..... ~-
35

eal price and income of the crop. They suggested that it was mainly the lack of
technology than the prices that was responsible for the slow growth of pulses.
These macro models are based on the assumption that farm decision making is
solely dependent on the parametric prices. This drew criticism as these models
underestimated the role of net income. In other words, these models over-emphasized the
role of relative prices and undennined the role of income in the decision-making process.
Second, these models were based on the assumption of perfect functioning of inputs and
output markets, which is erroneous to assume, especially in the developing countries.
Third, there is always a problem of identifying competing crops. Many a time more than
two crops compete for a land and hence render the supply responsive model less
significant. In addition, analysis of perennial crops was also not touched by these studies
due to lack of time series data on these crops in India.
Though, macro models are important for policy fonnulation, understanding of
responsiveness to various policy stimuli at the farm level are also of great importance.
But, it was expected that the results of farm level studies would differ across regions due
to changes in cropping pattern and through various time periods. In the context of farm
level studies, Johnson (1967) built a theoretical model in which he introduced separation
theorem in order to explain the dynamics of decision-making by farmers on combining
risky options with risk-less options. On the basis of the assumption that there exists a
risk-free option in terms of crops or land leasing, it was demonstrated that this theorem
could be extended to study the impact on land leasing and long-run diversification of crop
activities. By making use of expected net returns and variability, one can determine the
optimal enterprises mixes or crops. This model was one of the few that planned to
e){amine diversification decision by farmers through objective analysis. Prior to this
study, most of the studies, explaining such diversification decisions, were based on
analysis using subjective judgment of farmers. However, there is a lack of studies on the
empirical examination of separation theorem. It is more of a theoretical model and the
theorem hifS not been verified empirically either by him or others.
36

As noted, in the case of price responsiveness of fanners, most of the studies at the
macro level dealt with supply response in the context of developed countries. The major
concern is that models and findings in the context of developed countries may not be
applicable to developing countries. This is especially so because in developed countries,
almost all crops are produced primarily for commercial purposes. Nowshirvani (1971)
argued against the relevance of these models in the context of developing countries where
considerable number of crops is produced for self-consumption. This study showed that
element of risk and uncertainty caused by fluctuations result in diversification of
cropping pattern. In other words, diversity in cropping pattern cannot be explained
entirely by the heterogeneity of the land or the need to spread labour inputs over the
entire year. Nowshirvani (1971) developed a theoretical model of allocation of land
among different crops when both yield and prices are subject to uncertainty. This was
done primarily in order to explore the implications of the government policy of price
stabilization and impact of change in expected yields and prices on farmers'
diversification decision. The study indicates that price stabilization has bad effect on
farmers' welfare if crop is produced for self consumption. Direction of supply response to
change in expected prices may not be positive. There is dramatic difference between cash
and food crops and inclusion of uncertainty makes the direction of area-price response
ambiguous in case of food crops. This study is significant due to its focus on developing
countries. However, the study remains to be tested empirically.
Until early 1960s, studies on supply response by farmers largely emphasized price
as the major driving force in explaining their behaviour. Later other economic
components viz, price stabilization, income and cost factors drew the attention of studies
related to diversification. While examining the role of price stabilization policy by the
government, Martin et ai, (I %5) evaluated the effect of alternative cotton acreage
allotment on price situations. They noted that cotton would be more profitable even if the
prices were to be below the support price and any increase in the allotment acreage level
would result in higher production of cotton. But, for some high value crops like cotton,
price polic~ was found to be ineffective in impacting the acreage decisions of the
farmers. Therefore, in order to maintain potential income level, cotton production needs
37

igller subsidy payments unless there is a considerable increase m the free market
equilibrium price.
Since prices partially affect the relative profitability of crops, use of net income for
decision making in the choice between crops becomes an important issue for discussion.
Imminck and Alarcon (1993) examined the role of economic and non-economic factors
on crop diversification decisions of farmers. Use of income as an economic factor and
food availability as non-economic factor, they explored its relation with different crop
mixes including food and commercial crops like wheat, potato and vegetable. The study
inferred that net returns play an important role in diversification towards commercial
crops. Net income effect of crop substitution depends on relative net returns of different
crops and on the degree of crop substitution. Lack of market access and high fluctuation
in farm output and input prices could reduce gross margins substantially and lead to
income loss. Therefore, access to credit and market playa critically important role in
promoting diversification towards higll value commercial crops.
In addition to the relevance of prices and income, another aspect i.e. cost of
obtaining food crop when a food crop is substituted for cash crop, becomes an important
field of discussion where studies by Fafchamps (1992), Jayne (I994) and Key, et al.
(2000) drew a lot of significance. Fafchamps (1992) examined the role of food market
integration in reducing the need of food-self-sufficiency and decision about cash crop
production. He presents a theoretical model of crop portfolio choice wherein the revenue
of individual crops is correlated with consumption prices. Also, the effects of
consumption preferences on output choices were examined and the possible impact of
market integration on optimal crop choices simulated. The study found that as
agricultural productivity increased, per unit transport cost goes would go down and socioeconomic
factors would have less affect on crop choice decisions. The logic of this
explanation is that improvement in market and reduction in transport cost equalizes price
movements across a larger regional or intemational market. In other words, food market
integrallo\l would reduce price variance and the correlation between individual output
and aggregate supply. Thus, is likely to increase the market price elasticity of food
demand and decrease the correlation between crops -:evenue. These effects combine
38

together to decrease small farmers' need to rely on their own food production which
weakens the individual aspiration for food self- sufficiency. Jayne (1994) demonstrated
that high food marketing costs encourage household self-sufficiency and limited cash
crop production in Zimbabwe. The study brought out that not only returns from the high
value crops matter for diversification decision by forgoing food crop but also the cost of
obtaining the food crop drive the decision of farmers to choose high value crops. The
study grouped farmers on the basis of grain deficit and grain surplus, to analyse the role
of food self-sufficiency on diversification towards commercial crops. It is argued that
cash crop production might be economically unviable for diversification decision of
farmers despite higher returns of commercial crops to land and labour. It is the level of
food self-sufficiency that matters more for increasing land allocation to commercial crop.
Key et al. (2000) used household data to explain how fixed and variable transaction costs
that includes the time to sell the crop in the market and distance to market is affecting the
maize supply response of rural households.
There was also emphasis on non-price factors in the diversification decisions.
Lichtenberg (1989) examined the interaction between land quality, crop choice and
technological change. The study indicated that acreage allocated to different crops vary
significantly over land quality. Crops tend to be grown on specific types of land quality
and the introduction of pivot technology induced significant changes in cropping patterns.
In another interesting study, Kurosaki (2005) investigated the impact of agricultural
policies on fann production decisions and rural incomes in rural Myanmar. In this
analysis, paddy was considered as a lucrative or diversified crop and other crops are
considered as non-lucrative crops. The study showed that the acreage share of nonlucrative
paddy is higher for farmers who were under tighter control of the local
authorities. The government policies that put unwarranted or disproportionate emphasis
on paddy crop with low income per acre end up in sub-optimal use of agricultural
resources in Myanmar and act as a constraint to diversification towards other crops.
In stjrn, studies that concentrated on crop reallocation decisions and prices of crops
are at the core of analyses. The short-term and long-term elasticities of supply response to
prices are computed to gauge the relevance of price in changing land allocation decisions
39

y farmers. However, several lacunae associated with the models drew criticisms against
using these models f~r supply response, especially in the context of developing countries.
A large number of crops, particularly high value crops including horticultural crops have
been excluded from the studies. Furthermore, only price is considered in the response of
farmers and income potential of the crop is mainly ignored in analysing such decision
making. Price alone may not be the only factor in decision-making. Heterogeneity in the
resources, capital endowments of the farmers, difference in access to input and output
market by the farmers are other important factors. Such difference influences prices and
productivity of the crop and hence income attained at the farm level.
2.1.2. Diversification in terms of Diversity in the Cropping
Pattern
Over a period of time, resource allocation to crops can change in rwo different
ways: 1) the amount of resources to crops can be increased and 2) the amount of
resources can be held constant while shifting a part of them to other crops. The first
system has ramifications closely related to the capital and increasing risk considerations,
whereas the second system has more widespread application since most farmers have
limited land and capital (Heady, 1952). Most studies concentrate on the second system of
diversification, where diversity and spread in the cropping pattern are used as proxy for
diversification.
IS
The expected trade-off berween specialisation and diversity in the cropping pattern
the major area of debate in literature. The relation berween farm size and
diversification is an indicator of trade-offs berween risk reduction and possible
economies of scale due to specialisation in a particular crop. That is, if there are not
substantial economies of scale in a particular crop if one specializes in that crop, one
clearly gives up a large expected return in order to insure against risk through
diversification (Pope and Prescott, 1980). In other words, increase in the level of diversity
of the cropping pattern is contingent upon the condition that gain from diversification
will offset gain from specialisation. By diversifying, the farm sacrifices economies of
40

scale or income, but tends to stabilize returns at a lower level with the benefits of risk
reduction (White and Irwin, 1972). Thus, if risk management motivates diversification,
the average income comes down. Diversification can reduce risk and smoothen the
consumption in the face of weather and market shocks (Reardon, et a1. \988). Thus,
diversification is expected to occur when income sources are highly variable and
households are particularly risk averse. This may be one of the reasons why poor rural
households practicing rain-fed agriculture in low-potential areas are more likely to have
diverse cropping pattern than richer households in areas with greater agro-ecological
potential (Minot et al. 2006). According to the study by Koukebene et a1. (1996), at low
level of risk aversion, farmers increases investment in off-farm activities and prefer to
specialize in few crops like soyabean or com crops. The study fmds the policy of
government support responsible to higher levels of specialisation.
Empirically examining the hypothesis of the income-risk trade-off, Johnson and
Brester (200 I) argues that the decision to diversify by farmer can be explained by
effective economic diversification. According to them, three primary economic
considerations are important when a farm manager contemplates increasing diversity in
the cropping pattern through the introduction of alternative crop. These are; I) Will the
alternative crop be a profitable crop?, 2) Will the introduction of alternative crop result in
some positive impact in terms of increase in net farm income?, and 3) Will the
introduction of alternative crop result in economic diversification? The decision of
diversification is economic in two conditions i.e. when by diversification, farmer gets the
same net fann income with less year-to-year variability in income or when it increases
the farm income with same or an acceptable level of increase in income variability. The
study supported the hypothesis that farmers' decision of diversification depends primarily
on the condition of effective economic diversification.
The comparison of risk with farm income was one aspect of examining factors
responsible for diversification. However, these studies ignored the relationship between
risk and major costs in production of crops that also may influence diversification
decisions. In this line of thought, Nartea and Barry (1994) examined the effects of
diversification on the variance of farm rates of return, and on transportation, monitoring
41

and other operating costs. Their results indicate that risk reduction from diversification in
Illinois is relatively small and is more than offset by increased operating costs. Rather
demand for land is the major motivation for observed levels of diversification of farmers.
But, they warned that their conclusion only applies to Illinois and one may get different
results in other geographical areas.
As farm size influence income, risk and cost in the production of crops, it was
expected that farm size greatly influence capacity to diversify by farmers. Pope and
Prescott (1980) revealed a negative relation between farm size and specialisation.
According to them, experience, price risks, wealth and education are few of the
constraints for diversification. It was noted that an individual's likelihood of participating
in a risky project increases with wealth and thus, large & wealthier farms tend to exhibit a
propensity to specialize. This was attributed to the fact that wealthier farmers were less
risk-averse. This argument challenged the fmdings of White and Irwin (1972), who got a
positive relation between farm size and specialisation. The difference in results of these
studies was attributed to different definitions of diversification used by them. However,
later some researcher also challenged the relation between farm size and diversification
on the basis in which farm size is defmed. By changing the way in which farm size is
defined Pagoulatos et al (1987) noted an inverse relation between farm size and level of
diversification. They used farm acreage as an indicator of farm size. According to their
study, as diversification increases, farm income per acre increase substantially and small
farmers need not necessarily be more diversified than large fanners.
Singh (1996) differentiated between diversity in crop and plot diversity in order to
examine the link of different types of diversity with economic and non-economic factors.
According to him, larger land holdings are associated only with plot diversification and
not with crop diversification. However, plot diversification does not offer significant
protection against instability in gross crop returns. Both crop and plot diversifications are
not strongly correlated with mean levels of household net crop income. Interestingly, he
illustrated that the impact of irrigation on crop diversification and income stability is
dependant upon cropping conditions and the level of irrigation. When expansion of
irrigation takes place in an assured rainfall region, it leads to more crop specialisation in
42

paddy production. In contrast, when an investment in well irrigation occurs in a dry-land
region, it enhances opportunities for further crop diversification. Hence, the role of
irrigation on crop specialisation or diversification depends on regional factors. In
conclusion, it was noted that crop and plot diversification fluctuation was more in
drought prone regions and crop diversification was more sensitive to agro-climatic
determinants than plot diversification.
There was a well-established belief that commercialization fostered specialisation.
This was first challenged by Gregson (\994). He hypothesized that diversification was a
function of labour availability, risk due to unstable prices and soil diversitY. In other
words, the crop mix decision is a function of farm gate prices and farmer's agronomic
endowment lO and overtime, farmers' decision to plant a new crop is influenced by
increasing demand, falling transport cost and the suitability of soil for the crop. The study
found that other than distance from market, the geography of the farm had little effect on
crop choice when transport cost was high. But, when transport costs falls, the
geographical locations of farms becomes a factor in crop choice. He concludes that
commercialization need not necessarily lead to higher specialisation and it can lead to
higher level of diversification. In another study, Gregson (1996) notes that change in the
crop mix is a function of relative profitability of crops due to less cost or high revenue.
Higher specialisation takes place by more fertilizer use as it allows farmers to get higher
and stable income. In other words, more use of fertilizer lead farmers away from
diversified farming to a more specialized crop mix. This happens because the application
of chemical fertilizer permits farmer to alter the relationship between soil and crop
suitability.
In examining the role of socio-economic factors on the levels of diversification,
Minot et al. (2006) indicated that increase in diversity is positively related with age,
education, and larger household. But, no significant relationship was noted between farm
size and diversity in the cropping pattern. Both, farm size and irrigation were negatively

;()rreiated with diversification. In terms of labour use and its availability, it was expected
rhat substitution of capital for labour would promote specialisation and result in scale
tconomies. Conversely, when production is labour intensive, farm tends to be smaller
and m()re diversified. In this context, Zimmerer (1991) argues that shortage of labour in
ihe labour-intensive crops results in low profit by increasing diversification and hence it
restricts diversification towards such crops. Additionally, seasonality in labour
requirement results in looking for other jobs and it increases dependence on hired labour
for crop production. In such conditions, farmers prefer to specialize in one crop.
The role of market size on explaining specialisation or diversification was first
investigated by Emran and Shilpi (2007). They provided an empirical evidence of nonlinearity
in the relationship between crop specialisation and the extent of market size. The
e~dence
indicates existence of a monotonically increasing relationship between the
degree of crop diversification among the households in a village and the extent of the
relevant urban market. They showed evidence of a 'U' shaped pattern in the stages of
agricultural specialisation as it related to the extent of the market. The semi-parametric
instrumental variable estimates explain that the villages initially diversify the crop
p

2.1.3 Diversification represented as Share of High Value Crops
In literature, the term diversification is also connoted with higher allocation of land
in favour of high value crops. The major issue here is the lack of any definite definition
of the index of proportion of high value crops. Different measures have been used by
many studies, but interestingly some of them have identical views on the certain factors
in driving diversification towards high value crops in India.
The role of farm size in diversification towards high value crops has received
widespread attention. It is because in many developing countries, including India, small
farmers dominate the landholdings. In the context of India, as the size of landholding and
farm size has been shrinking over a period of time, it is argued that it may bring natural
constraints for diversification. Lack of scale economies and poor resource base may
retard diversification. Besides, small farmers lack bargaining power and face high risk.
They are over-dependent on their land for their subsistence and that could playa critical
role in diversifYing towards high value crops. In addition to farm size, market
unavailability or small size of markets, lack of proper land rights, lack of irrigation
infrastructure and lack of investment and institutions could be additional constraints in
diversification towards high value crops (Pingali, 2004, Joshi, 2005, Barghouti et aI.,
2004, Mruthyunjaya and Chauhan, 2003, Chand, 1996, Pingali, 1997; Alagh and Alagh,
2003 and Agarwal, 2004).
Non-availability of inputs including labour and seeds has been indicated as strong
emerging constraint in diversification. Some studies have located an inverse relation
between farm size and diversification towards high value crops (Chand, 1996, Chand and
Chauhan, 2(02). This was mainly because of the per unit decline in profit with increase
in farm size. The profit declines due to application of limited resources including labour
(Saleth, 1996, Chand, 1996 and Haque, 1996). The role of farm size as a constraint on
diversification towards high value crops was highlighted by Chand and Chauhan (2002).
They used a dynamic measure of diversification in production choices. In their analysis,
they have showed that in India, availability of irrigation, network of roads and markets
45

promote diversification in a very significant way whereas, small size of landholding act
as a strong constraint.
Several other studies on this issue have not found any bias against pro-small farmer
in diversification towards high value crops. This could be attributed to difference in the
definition or index of diversification used. Joshi (2005) used an index of gross value of
horticultural crops as a proxy to diversification towards high value crops in order to
analyse the factors determining diversification in four broad regions in India. . The study
noted a negative and significant relation between farm size, irrigation and diversification.
This means that fann size and irrigation do not act as a constraint to diversification
towards high value crops in India. This is in contrast to the argument put forth by Pingali
(2004), who argued that lack of rainfall and irrigation facilities act as constraint for
diversification.
Chand (I996) noted an inverse relation between farm size and diversification,
where he defmed diversification as an index of high proportion of vegetables in area
under cultivation. According to the study, it is irrigation and market access in terms of
distance that increase fanner's diversification towards vegetable crops. Agarwal (2004)
illustrated that diversification in favour of high paid crops can be enhanced by increasing
profit and reducing risk. But the effect is more visible in highly favourable regions as
compared to unfavourable regions.
Almost similar results were established by Rao et aL (2004), who used different
indicators to analyse diversification towards high value crops (HVCs). Their prime aim
was to examine the role of urbanization in diversification in India. Separating districts on
the basis of their share of HVCs, the study established that absence of access to
technology, inadequate infrastructure and lack of policy support are responsible for low
diversification. Urban districts have a higher share of HVCs as compared to the urban
areas surrounded and other districts. This is because urban surrounded districts with
better road network have been able to diversify faster due to the demand for HVCs in
these centers. More importantly, the study also confirmed the results of other studies that
small fanns were able to diversify more towards HVCs. Technological, agro-climatic,
46

agrarian structure, and infrastructure variables like roads, markets and veterinary facilities
are additional factors that have significantly influenced diversification towards HVCs in
India.
Birthal et aI, (2007), using proportion of area under fruits and vegetable as an index
of diversification, established that diversification exhibits a pro smallholder rather than a
pro large-holder bias. The smallholders, however, playa proportionally larger role in the
production of vegetables than in the cultivation of fruits. The explanation for this
outcome is that small farmers in India are endowed with high amount of labour which is
reflected in greater family size. As vegetable crops are more labour intensive, labour
endowment of small farmers induce them to diversify towards vegetables. Cultivation of
fruits is more capital intensive; this could potentially explain the choice of production of
the small-holders in India, who are generally poor. They do not find evidence that a
greater share of land is allocated by either the small or the medium holders to fruits or
vegetables. Indeed, the share allocated to vegetables is significantly higher only if the
family size is bigger.
Diversification could be an accumulative or survival strategy. While treating
diversification as a desperate strategy, risk is taken as a significant factor. The riskaversion
is expected to have an important influence on farmer behaviour, e.g. least riskaverse
farmers would hold portfolios with high-risk investment, which generate higher
average profits per unit of wealth as risky assets generate higher returns (Rosenzweig and
Binswanger, 1993). Such behaviour may be revealed by higher level of diversification
towards high value crops. Economic theory suggests that a risk neutral producer allocates
labour such that expected marginal returns are equalized (Heady, 1952). If one crop
carries a greater marginal return, more labour will be supplied to that opportunity. A riskaverse
farmer perceives a greater variance in earnings in one crop compared to another
and will devote more time to less risky alternative and accept lower earnings (Mishra and
Goodwin, 1997). In other words, risk can be an important constraint for increasing
diversification towards high value crops.
47

Risk is important because as farmers allocate a large share of labour and land to
production of commercial crops, they become vulnerable to the fluctuations in the price
of both food and commercial crops. In other words, by diversification, household
dependency on market for food increases, which may be risky, if the prices of the
commercial and food crops are highly variable. Based on this logic, it is expected that
households with large family size will not diversify much towards high value commercial
crops. Another explanation for subsistence production is the desire to avoid high
transaction costs in selling crops and buying food. Transaction costs reduce the selling
price of food crops and raise the buying price of food crops. The greater the transaction
costs, including the cost of transportation to or from the market, it is more likely that
those rura1 households will be subsistence farmers. Diversification towards high value
commercial crops is also likely to be greater for rura1 households with good market
access, as against remote and sparsely populated areas (Minot et al. 20(6).
Until early 1990s, one of the most common critiques on diversification was that
switching from food production to cash crop production may adversely affect food
security and nutrition. This view was disputed by Braun (1995), who cited a series of
studies based on household surveys that compared income, food intake, and nutritional
status of farm households. The study i11ustrated that farmers involved in cash crop
production were generally better off on various dimensions than similar households that
were more subsistence oriented. Another major detenninant diversification is the
opportunity cost of family labour and level of market demand for food and other high
value crops (Pingali and Rosegrant, 1995). In addition, Reardon et al. (1992) showed that
households' capacity to cope with the drought shocks were strongly associated with the
extent of their diversification patterns.
Diversification towards high value crops is also explained on the basis of the
capacity offarmers to involve in non-farm income activity. It can affect diversification in
both positive and negative manner. It is argued that risk management helps in explaining
diversification away from crop production toward non-farm activities such as wage
labour and non-farm enterprises (Evans and Nau, 1991). Following conventional
argument, increased demand associated with rising non-farm income leads to
I
48

diversification, and the growth of jobs in non-fann sector (Mellor, 1976). As the rural
households are increasingly engaged in the non-farm activities, the proportion of nonfarm
income in total income increases and may result in the poor productivity of the
crops. Hence, there may be a trade off between the non-farm income activity
participation and better technology adoption and diversification towards high value crop.
In one of the studies Evans and Ngau (1991), explained a positive correlation between
income diversification, and crop production decisions of farmers that resulted in
increased diversification towards high value crops and commercialization. Barrett and
Reardon et al (2000) examined the role of heterogeneity and incentives due to several
factors in the income strategies by the farmers and its impact on their income. The study
assessed the role of livelihood diversification on the pattern of asset allocation and
income sourcing among rural population by using data at farm level from three countries.
The study found that the households having no access to non-fann activities or sufficient
productive non-labour assets i.e., land and livestock devoted themselves entirely to onfarm
agricultural production. They rely on a low return strategy by complete dependence
on agriculture and often [md themselves caught in a dynamic poverty trap. This also
explains their apathy for diversification towards high value crops.
In sum, majority of the studies emphasized primarily on non-economic factors.
Additionally, while dealing with economic factors, most of them concentrated on ex post
outcomes as a guiding principle of diversification decisions by farmers. However, it may
be argued that while taking diversification decision, farmers remain ignorant about the
actual economic outcome of their behaviour. But, their economic reasoning is influenced
by their past experience in the production of crop, which is considered ex ante. Also, as
diversification decision changes over a period of time from low value to high value crop
and vice versa, there arises a need to consider the relative role of economic and noneconomic
factors in diversification decisions of farmers.
2.2 Risk, Uncertainty and Diversification Decisions
Risk and uncertainty are vital aspects in the decision making of farmers with regard
1 0 diversification. There can be three definitions of risk, namely, risk as the chance of a
49

ad outcome, risk as the variability of outcomes and risk as uncertainty of outcomes
(Hardaker, 2000). In classical literature, the basic difference between risk and uncertainty
is that risk can be quantified, whereas uncertainty cannot be (Knight, 1921). Analysis of
risk is objective in nature whereas analysis of uncertainty is subjective in nature.
The studies on risk and uncertainty commenced with the emergence of Expected
Utility (EU) approach. Von Neumann and Morgenstem (1944) were the major
contributors to a larger body of work that provided normative justification for the use of
EU by rational decision makers. The EU model is based on a set of axioms, which
impose intuitively possible restrictions on preferences. It states that a decision maker
chooses between risky or uncertain prospects by comparing their expected utility values.
The shape of the utility function characterizes a decision maker's risk attitude. A riskaverse
individual or fanner will prefer certainty in income, a risk-neutral individual will
be indifferent between the two and a risk-taker will prefer to gamble. The intuitive way of
understanding risk aversion comes from the behaviour that a possible loss of a given size
is more important than a gain of the same size. In the study of economic choices in risky
situations, it is often convenient to have a quantitative measure of risk aversion. The most
commonly used measure of risk aversion was originally developed by Pratt (1964), but
cften called as the Arrow-Pratt measure of 'absolute risk aversion'. Later, Arrow (1971)
provided another measure of risk-aversion, which is called as 'relative risk aversion'.
Arrow assumed that a person would be more willing to accept risk as his income
increased with increasing wealth.
However, in spite of its wider use, EU model was criticized as a growing number of
empirical observations reported violation of some of its axiomatic foundations and a
divergence of observed decisions from what was predicted by the EU models. EU models
were found inadequate to account for the impacts of anxieties and worry associated with
random outcomes or choices, or the effort and experience needed for optimal selection
(Buschena and Zilberman, 1994). Additionally, EU models could not include elements
()ther than income in the utility function. Roumasset (1976), criticized the EU models and
mean variance analysis for studying the decision making of farmers, especially in the
50

context of developing countries as it paid no attention to the cost of gathering and
processing information. _
The studies pertaining to downside risks also criticised EU based models. Downside
risk measures are based on the intuition that most decision makers including fanners give
more weight to negative deviations. There was growing empirical evidence suggesting
that most farmers exhibited decreasing absolute risk aversion (decreasing absolute risk
aversion (DARA» (Binswanger, 1981), which implied that fanners were averse to
"downside risk". Roy (1952) was the pioneer in putting downside risks in perspective.
The concept was further developed by Markowitz (\959), who developed the concept for
portfolio management. He developed his argument on the premise that investors were
interested in minimizing downside risk for two reasons: (I) only down side risk or
Safety-First is relevant to them and (2) security distributions may not be nonnally
distributed. The most commonly used downside risk measures are semi-variance, lower
partial moments and Value at Risk (VaR) (Yilma, 2(05).
The Expected utility maximization models were described as a "full optimality
model" that excluded the costs of making decision. The full optimality models do not
take into account the limited capacities of individual decision makers for imagination and
computation. Roumasset (\ 976) argues that when costs of obtaining and processing
information are substantial, it is not necessarily rational for an individual to act
consistently with his underlying preferences. He defines risk as the probability that the
stochastic variable, often net income, will take on a value less than some critical
minimum or disaster level. He explained three Safety-First models:
I. Safety principle involves minimization of the probability of some objective
function, usually profits, falling below a specified disaster level. This can be
expressed as: min a=. Pr (x

exogenously specified crucial probability. This can be expressed as: max E(I[)
. subject to a ~Pr(Jt < d) where expected profit E(Jt) is the objective function, d is the
exogenously determined disaster level and a is the exogenously specified
probability limit.
3. Safety-Fixed principle maximizes the minimum return, which can be attained with a
fIXed confidence level. That is: max d subject to a=. Pr(Jt

and figure out what will be the possible outcomes. It is the enjoyment or satisfaction
provided by these outcomes that guide the choice of the decision-maker. Shackle (1958)
brought the concept of potential surprise here. According to him, between a feeling of
certainty that a given event will happen and a feeling of certainty that it will not, there
seems to be a continuous range of different levels at which degree of belief can stand.
The higher the degree of belief, higher is the potential surprise. Surprise means that the
individual's structure of expectations either contains a misjudgement or has been
incomplete. The real incentive for choosing an action is the enjoyment of anticipating a
high level of success. The intensity of enjoyment is a decreasing function of the degree of
potential surprise and increasing function of the outcome. The combination of potential
surprise and the attention-arresting power produces what Shackle calls focus-values.
These will be the best (focus-gain) and the worst (focus-loss) outcomes that concern the
decision maker. Comparing these focus-values, farmer would assess the attractiveness of
his course of action in a comparison with others and take the decision. But, Shackle
(1949) restricts from carrying out any empirical exercise on the concepts introduced by
him. The question is how these expectations are formed? In this line of thought, Williams
(1951) examines the nature of price expectations as a response to uncertainty. The moot
question addressed was that whether farmers formed expectations of all or some crops
together or they formed price expectation of each crop separately. William (I9S1)
obtained prices that farmers expected from the production of the selected crops and
grouped the farmers on the basis of different ranges of price expectations. The study
indicated that farmers did formulate expectations for each crop separately and for all
crops together.
There have been a few empirical attempts to examme the role of risk and
uncertainty on diversification decision of farmers. Prominent among these are
Shahabuddin et al. (1986), who tried to fmd the extent to which diversification by farmers
were caused by risk attitude. They incorporated Roy's Safety-First principle into the
resource allocation models where the risk coefficient was 'I' = [(d-u,)/cr,J. The relative
magnitude of the variables'd' and 'Ur'determines whether the farm family is forced to
gamble (d>u,.) or allowed to trade expected return for reduced risk (d

crop portfolio. They attempted to explain the intra-household variations in risk
coefficients in terms of variations in the income earning potential and subsistence needs
of the farm families. The study found a sigoificant relation between farm size (-), family
size (+), otT-farm activities (-) and risk aversion.
In order to account for the uncertainty in representing farmer's diversification
decision, Boussard and Petit (1967) used the concept of possibility of ruin, a concept
closely related to Shackle's focus of loss. They integrated this into the linear
programming production decision model by incorporating constraint of crop choice on
the basis of working capital requirements, access to credit, minimum permitted loss and
technical constraints in four types of cropping patterns. The study illustrates that the
model with security and credit constraints is more predictive as compared to model only
with either technical constraint or security constraint. They have taken into account yield
and price uncertainties in explaining diversification decisions of the farmer but
overlooked other types of uncertainty like labour requirements and timeliness of
operations, which are equally important. Binswanger (1981) assessed the risk attitudes of
rural households in semi-arid tropics of India and asserted that higher level of risk lead to
under-investment in agriculture relative to the expected profit maximizing levels.
According to the study, it is the progressive farmers that are slightly less risk averse than
average farmers at high pay-otT levels and there is a negative relationship between risk
aversion and size of landholding. It was noted that farmers in areas which had the worst
history of droughts, were more risk averse than their counterparts in other areas.
Kunreuther and Wright (1979), showed that cash crop are in-fact the risky choice
for a subsistence farmer. A farmer who buys his food must consider the yield variance of
the cash crop as well as the price variance of both the crops. But, for the farmer who
grows or consumes his own crop, only yield variance is relevant. The study tried to build
a model based on the assumption of profit-maximizing behaviour of farmers with
constraint due to some minimum requirements (consumption) critical for survival of
farmers. They found that the farmers following Safety-First strategy might misallocate
their resources by allocating substantial portion of their land with a food crop with a
I01er expected return than the cash crop. This misallocation arises from the institutional
54

arrangements of agricultural sector like farm size, tenancy or technical inefficiency.
According to them, to achieve a more efficient allocation of resources. two choices are
open: either to reduce the Safety-First farmers to risk-takers or give them enough security
so that they will focus more on the relative expected returns from their crops and less on
their variance.
The consumption requirements depict the partial requirement of farmers and it does
not capture their overall concern. It excludes farmers' access to credit and assets at their
disposal. Since the overall resources and access to resources differs from farmer to
farmer, the level of risk taken by them also differs. Accounting for this, Ortiz (1979)
reviewed how uncertainty affects the process of decision, the ability to formulate
prospects of gain or loss, as well as value of the lowest permissible return I I. He explained
that risky incomes were responsible for the subsistence-first strategy by farmers. The
uncertainty in availability of inputs inhibits the formulation of prospects and any a priori
planning of food production. Once basic subsistence requirements are insured, peasants
plant one of the local cash crops even when the chances were that the return would barely
cover costs.
In sum, expectations and risk are the major aspects of this group of studies.
However, there are very few empirical studies on these aspects probably due to
measurement and other challenges. At the farm level, the concept of expectation is
generally used in terms of a response to uncertainty involved in the production process.
There are several ways in which the information regarding expectation is obtained, which
used probability or possibility analyses in measuring such expectations. Since, probability
is much of a utopian concept, possibility only is considered as relevant. But, there is
hardly any study on how such expectations are formed, and what factors influence
expectations of farmers. Additionally, farmers build expectations about different
economic outcomes including price, yield and income. There is a need to examine the
11 Permissible loss given by Boussard as [Lv ~ E (m~e;) X; • (em + F m)] where m; is Ihe unitary gross
receipts on the ith activity at tbe focus gain level, 'c' is the corresponding current expenses, C is the
farmer's vital consumption, F is all thp compulsory payments not included in 'e' and 'j' is the number of
;lcti~ties
55

extent to which different expectation about economic outcome helps m guiding
diversification decisions of farmers.
There are several studies dealing with risk that consider crop-specific risk in guiding
diversification decisions. But, in the event of dominance of one crop in the income
generating activity at the farm level, aggregate risk on income and production is more
vital in guiding the decision of farmers. It is vital to capture such decisions by using the
fann level data regarding price, yield and cost to compute the risk typology and its
influence on diversification decisions.
2.3. Role of Changing Typology of Crop Diversification on
Agricultural Growth
Decomposition models are pivotal for examining the relevance of changing
allocation of land on income growth. lbrough decomposition model, one can gauge the
role of various components of growth for any particular crop as well as for a region as a
whole. It is important to highlight that most work on decomposition was on improving
the methodology in explaining the growth rate in income of crop and region. There were
several attempts before 1965 to explain agricultural growth, but only area and yield were
considered as components of growth. Incorporation of another component, diversification
or changing cropping pattern, was an important development in the analyses of
agricultural growth. Minhas and Vaidyanathan (\965) were the first to add the
component of cropping pattern and a residual component representing interaction
between yields and cropping pattern, in order to explain the agricultural growth. The
observed change in aggregate output was decomposed into four-component elements i.e.,
the contribution of (i) changes in area, (ii) changes in per acre yields, (iii) changes in
cropping pattern, and (iv) the interaction of the latter two elements. Here, cropping
pattern is synonymous to reallocation of land among crops of different values. The
positive interaction between yield and cropping pattern would mean the diversion of land
towards the crops whose yields are higher. By using a district level analysis, Minhas and
Vaidyanathan (1965) found that role of cropping pattern was of less significance with the
exception of few states like Punjab, Andhra Pradesh etc. Later Minhas (1966) gave a
56

seven-component version that added new interaction terms between area, croppmg
pattern and yields. A positive area~ropping pattern interaction indicated an increase in
gross crop area in favour of high productivity crops. However, by following the new
seven~mponent analysis, the study could not find any significant change in the results
as compared to results of the four components model. It was suggested that the seven
component model should be used only if the role of interaction effect in the four
component model is significantly high.
Minhas model was criticized by Narula and VidyaSagar (1973) for having given
weights for current year and base year to the changes in yield and area, respectively.
They argued that the postulation created biases as it always gave a preferential treatment
to the changes in yield by giving a weight for current year area and to changes in area by
giving weight for base year yield specifically in a situation where both area and yield
rates varied overtime. The authors suggested that these biases of weights could be
reduced by giving average weights i.e., to take current and base years average of area and
yields as weight to the change in yield and change in area respectively.
incorporation of the price variable in the decomposition model was done for the
first time by VidyaSagar (·1977) who decomposed the change in the value of gross
agricultural output into gross components including area, productivity, price and their
interactions. The analysis proceeds mainly in terms of productivity, which refers to the
value of agricultural output per hectare at the current price structure. Price structure is
defined as "the set of current year prices such that the overall average price of
agricultural crops remains at a constant level, while at the same time it incorporates the
movements in prices relative to each other". Here, the price component measures the
effect of inflation on the growth in the value of output. His analysis in Rajasthan
illustrates that despite incorporating price variable, the change in the income was still
influenced more by productivity followed by the area expansion. The role of price was
very less and that of a cropping pattern mix was negative.
Another component of decomposition analysis was the in:roduction of crop
structural component i.e. the locational shift by Narain (1976). According to him, a
57

positive locational component implies a shift in crop locations from low productivity
regions to high productivity regions. A positive interaction will similarly show a shift in
favour oflocations with higher growth in crop yields. The study indicates that in India the
real gain in productivity resulting from locational shifts is rather small, thus reflecting on
the limited role-play of the market forces in bringing about inter-regional specialisation in
the production of crops. Narain (1976) included the cropping pattern as a component of
yield growth but he did not include the role of changing structure of prices in the crops
movements. The same method was modified and used by Ranade (1980) who
investigated the role of cropping pattern, fertilizer and irrigation by using district level
data spread over 16 states and 54 agro-c1imatic zones. The difference is that Narain
(1976) considers the base year yield and prices of different crops while Ranade (1980)
uses an index all-India average yields and prices of different crops. He used tabulation,
correlation and regression model to run yield regression on fertilizer, irrigation and
cropping pattern index. The study established significant and positive impact of cropping
pattern index on the agriculturaI productivity.
Along with additive schemes, some of the studies dealing with decomposition of
agricuituraI growth have employed multiplicative schemes. Unlike additive schemes,
which decompose absolute increase and hence give linear growth rate of output, the
multiplicative scheme decomposes compound rate of growth. Parikh (1966) expresses the
index number of output as a multiple respectively of the index numbers of area, change in
cropping pattern and change in crop yields. By fitting exponential time-trend to each
series of index numbers, he gets multiplicative scheme for growth. In order to estimate
the contribution of each component, two indices were constructed - index number of
agriCUltural production for constant crop pattern and index number of changes in crop
pattern. In the first instance, a series of area under crops keeping crop pattern constant at
base year level were evolved. This was done by reallocating the gross cropped area to
individual crops on the basis of their proportion in the base year. Secondly, the area under
individual crops was multiplied by their respective yield per acre to obtain the production
of individual crops, which when added, gave aggregate production keeping out the effects
of changes in crop pattern. This gave two series- (i) total production which includes the
58

influence of changes in crop pattern, and (ii) total production which does not include the
influence of crop pattern change. The second series is an index of production for constant
crop pattern. The growth rate of each of these components was obtained by fitting the
semi-log trend.
Later Minhas (1966) gave his multiplicative scheme as an improvement over
Parikh's version. He extends the identity given by Parilch in order to measure the
cropping pattern effect at base period yields and in the process introduces a residual
component. The residual figure is the ratio of the product of any given period
productivity and base period productivity to the product of the same period productivity
obtained by changing the crop pattern and by growth in crop yields respectively. This is
the same as the interaction between cropping pattern and yield components in the
additive models.
The major concern was that most of the studies had computed growth in output or
productivity and its components by comparing the end points of the series. The use of
point-to-point method of measuring growth may lead to biased estimates of growth unless
care is taken in selecting the points of comparison. Often three year time points are
considered to adjust for the short run fluctuations arising from the vagaries of weather.
But this may prove wrong in some cases and therefore great care is needed in selecting
both the base year and the averages of years.
Taking into account the problem related to the use of end point, Minot et al. (2006)
used total derivative, instead of two different points for decomposing growth rate. The
aim of the study was to find how much diversification was occurring in Vietnam and how
much did it contribute to income growth. The study decomposes income growth into
yield, area expansion, price changes, diversification into high value crops and
diversification into non-farm activities. It was found that poor farmers earned more by
increasing crop yields, while richer household expanded the area under cultivation. The
analysis indicated that diversification into higher value crop accounted for just 6 percent
of the crop income growth. But, they conceded that there results were regional and time
specific and the same might change across regions through different time periods.
59

The same model was used by Joshi (2005) to find study the role of cropping pattern
shift on income growth in the pre and post libernlization period across different regions in
India. The study confirmed that sources of agricultural growth had changed dramatically
over the last two decades in India. More importantly, it was found that the significance of
agricultural diversification is consistently increasing in explaining the growth in
agriculture. But, there are stark differences in the relative importance of components of
growth across regions in India. Kurosaki (2003) empirically examined the long-term
trends in agricultural production in West Punjab by using decomposition model. This
study revealed that area increase accounted for 71 percent of the growth in an index of
agricultural output over 1903-1952, but during 1952-1992 the most important
contributors were yield increases (53 percent) and diversification (7 percent). Finally, his
analyses across districts indicated that road density and irrigation were associated with
higher diversity during the first period, and these factors led to increased specialisation
during second period.
The studies dealing with diversification cover a wide range of aspects. Also there
are diverse ways to interpret the significance of changing diversification. At the macro
level, the major debate hovers around quantifying the significance of crop diversification
in growth of output. As noted, there are several sub-components of diversification and
each component affects growth in different ways. In terms of diversification towards high
value crops, many researchers argue that a silent revolution of shift in cropping pattern
towards high value crops is already under way (Joshi, 2006, Vyas, 1996, Chand., 2005,
Rao et al, 2004, Birthal et al,. 2007). But, how the process of diversification has affected
the growth of output in India. Diversification may affect the growth through static and
dynamic effects. But most of the studies dealt with the static aspect of diversification,
ignoring the dynamic aspect of the same. A positive static effect of diversification is a
shift of crop structure in favour of high initial productivity crop. This does not capture the
developments in the technology of crops that may change over time and may alter
relative values of crops. In other words, over-time, the profitability or relative values of
crops does not remain constant and change in the productivity of crops may change the
comparative advantage of the crops. Hence, it is also important to consider the dynamic
60

effects of diversification that capture the concomitant movements of yield and cropping
pattern change. The dynamic aspect of diver;;ification represents the impact of the joint
movements on the diversification of crop pattern and that of technological change on
overall output change. A positive dynamic effect of diversification will be a shift in crop
structure in favour of those crops which show relatively higher growth in yield. Similarly,
a negative dynamic effect implies over-time, cropping pattern shift in favour of those
crops which could not experience higher growth in productivity. This results in growthdepressive
impact of diversification on output generated in the agriCUltural sector. The
question here is what is the role of diversification in the growth of output in Indian
agricultural sector? Whether the process of changing cropping pattern has been growth
inducive or depressive, and what factors have contributed to the nature and direction of
diversification in India?
61

CHAPTER III
DIVERSIFICATION AND CROP OUTPUT
GROWTH IN INDIA: A STATE LEVEL ANALYSIS
3.1 Introduction
Agricultural development strategies in India have gone through four broad phases.
These are: a) intensification of efforts in identified areas using traditional technology and
expansion of area during the pre-Green Revolution period; b) embarked on Green
Revolution using modem inputs and high yielding varieties in irrigated areas during the
late sixties and the seventies; c) then a period of greater focus on management of linkages
and infrastructure, such as, marketing, trade and institution building; d) and fmally an era
ofliberalization and relaxation of controls during the nineties (Hazra, 2003).
Several commendable achievements have been attained in the process of
development Food grains production increased four-fold since independence, from 51
Million tonnes (MT) in 1950-51 to 203 MT in 2000-01. The Green, White, Yellow and
Blue Revolutions were landmarks that have been claimed and recognized the world over.
India is now the largest producer of wheat, fruits, cashew nut, milk and tea in the world
and second largest producer of vegetables. India is also the largest producer, consumer
and exporter of spices in the world and the largest exporter of cashew. More importantly,
the agricultural sector has left behind the era of food shortages and arrived at a stage of
self-sufficiency.
There is, however, another side to this argument. First, the trend of decreasing
output contribution by agriculture to Gross Domestic Product (GOP) and stagnant
employment dependence on agriculture reflect the picture of an incomplete agricultural
transformation. The higher economic and per capita growth in India over the previous
few decades accompanied with drastic decline in agriculture share to GOP. Besides it, the
,
62

economy did not witness any perceptible movement of labour from agriculture to nonagricultural
sector. Second, the perfonnance of agriculture sector in the recent years has
been tardy as evidenced by the shatp deceleration in growth rate of agricultural output.
The changing scenario of agriculture has forced the fanning community and policy
makers to search for a more remunerative and viable production portfolios (Joshi, 2(05).
Diversification has turned out to be one such viable policy option to promote overall
agricultural growth due to its potential to stabilize and raise farm income, decrease risk,
increase employment opportunities and conserve natural resources in India (V yas 1996,
Joshi 2005 and Chand 2006).
Diversification has several components that include diversity in the cropping
pattern, land allocation to high value crops and change in cropping pattern among crops
of different values. These components influence growth in different ways12. In the
context of India, over a period of time, difference in the policies and external
environment has led to a varied pattern of diversification and related typology across
states. Broadly, states can be distinguished on the basis of three patterns. In the first
pattern, we have states, which followed the Green Revolution and adopted High Yield
Varieties of few selected crops i.e. rice and wheat. These states shifted much of their area
towards rice and wheat that led to specialisation. In the second pattern, we have states,
which did not gain much from Green Revolution as they could not diversify much
towards rice and wheat crops. However, these groups of states increased land allocation
towards other high value crops including non-food crops. In the third category, there are
states, which neither diversified meaningfully towards rice and wheat nor towards nonfood
crops.
Investigating into the process of diversification requires initially an analysis of the
typology which throws light on the number of crops, spread and concentration and
proportions of various crop groups. These have to be linked with the pattern of income
and risk across states in India to view the macro-level picture. Besides this, the
investigation into trends in growth of output and mapping the relative importance of
various components will help to understand the pattern of growth in output and the role
I~ These relationships are discussed in detail in Chapter one
63

of diversification in the process. For the purpose, initially, vanous indices of
diversification were computed for the past three decades. Then, we linked these
components of diversification with the pattern of income and risk. The subsequent
section deals with the investigation of the trends in growth of output and gauges the
relative importance of various components, including diversification in the pattern of
growth of output. The last section examines the factors influencing the nature and
direction of diversification in India.
We confine our analysis to 30 crops (appendix 3.1) owing to non-availability of
data for a few minor crops for all states and for all the decades. These 30 crops account
for an overwhelmingly large proportion of total cropped area and can be taken to be a fair
approximation to overall picture. We have split the data into three decades 1970s, 1980s
and 1990s. The reason for this split is that different policies marked these decades which
influenced the typology of crop diversification in distinct ways. The period of I 970s was
characterised by intensification of the use of inputs and adoption of high yielding
varieties of few selected crops. In the decade of 1980s, the emphasis of technology led
development (Green Revolution and introduction of Technology Mission on Oilseeds)
was furthered with increased ernphasis on infrastructure and markets. It was in the 1990s
that liberalization of economy along with relaxation of controls took place. All the 17
states have been categorised broadly into four distinct regions IJ. Each region is relatively
homogenous in agro-ecological characteristics, and compositions of crop, although they
differ in infrastructural and other socio-economic characteristics (Joshi et al. 2006)
3.2 Crop Diversification in India
3.2.1 Cropping Pattern Change in India
The change in the typologies of diversification across states gets reflected at the
economy level through different composition of crop contribution to gross cropped area
Il Northern region includes Haryana, Himachal Pradesh, Punjab, J&K and Uttar Pradesh. Eastern region
comprises of Bihar, Assam, West Bengal and Orrisa. Western region consists of Rajasthan, Gujarat,
idhya Pradesh and Maharashtra and Southern regioo includes AP, Kamataka, Kernla and Tamil Nadu
64

and output generated in the agricultural sector. Despite existence of diverse a agroclimatic
scenario, food grains dominate the crop sector, amongst which rice and wheat
are the prominent crops (table 3.1). Over a period of time, the area under these crops has
been steadily increasing and at present, these crops cover more than one-third of the gross
cropped area (GCA) in India However, there has been a declining trend in the share of
area under other food grain crops. This is primarily due to reduction in area under coarse
cereals and pulses, though, one can observe different patterns across states in India.
Table 3.1: Contribution of Crops to Gross Cropped Area and Value of Output Produced in
A \2ncu . It ura 1St ee or ID ' I n d' la
TE TE TE TE TE TE TE
1950-51 1960-61 1970-71 1980-81 1990-91 1999-00 2005-06
Rice 23.20 22.80 22.72 23.28 23.76 24.03 22.11
(17.1) (20.5) • (19·31 (19.9) (20.3) (18.2)
Wheat 7.50 8.75 11.56 13.21 13.04 13.84 13.88
(4.0) (4.7) (7.9) • (9.6) . (10.8) (11.6)
Total 15.63 15.05 13.88 13.62 13.29 11.40 12.43
Pulses (12.3) (12.4) (9.1) (6.9) (6.7) (4.10)
Food grains 77.01 75.66 74.99 73.38 68.83 65.83 63.76
(49.23) (49.15) (48.21) , (48.12) (46.2a (38.53)
Sugarcane 1.40 1.58 1.58 1.55 1.99 2.46 2.78
(4.7) (5.9) (5.6) I (8.0) (6.7) (6.8)
Total 8.3 9.01 10.04 10.20 13.0 13.56 15.85
Oilseeds (10.0) (9.6) (10.1) I (7.9) (11.0) (9.9)
Horticulture 1.6 1.77 2.22 2.89 3.7 5.38
(14.8) (11.3) .. (17.3) (18.4) (17.7) (22.7)
-
Note:
i. Figures are the percentage of area under the crop (group) to total gross cropped area
ii. Figures in Parenthesis are the Share of value of crops to total Value of Agricultural Output in India@ 1993-94 prices
Source: India stat, National Account Statistics, Various issues, and Mythili G, 2006
The pace of increase in the share of oil seeds and sugarcane in the GCA has been
slow, whereas the proportion of horticultural crops in the GCA has been increasing
rapidly. The area under oilseeds and sugarcane has increased from 8.3% and 1-40%
respectively in triennium ending (TE) 1950-51 to 15.85 % and 2.78 % in TE 2005-06.
Substantial growth in area under oilseeds was observed in the post 1980s as a result of
Technology Mission on Oilseeds (TMO). The share offruits and vegetable sector in total
area increased from 1.60 % in TE 1950-51 to 5.38% in TE 1999-00. There picture is
different in regard to the extent of dominance of a few sectors in Indian agriculture when
contribution of crops value in the agricultural output is considered. The contribution of
ffd grains declined drastically from 49% in TE 1950-51 to 38% in TE 2000-01,
65

whereas, the contribution of fiuits and vegetables increased significantly fiuro 14.8 % to
22.7 % during the same period. Higher output contribution of horticultural crops, despite
less area coverage reflects the high potential of this sector for improving agricultural
growth in India.
3.2.2 Diversity and Spread in the Cropping Pattern
The biological conditions of any region dictate, to a great extent, the initial pattern
of crop mix. However, other economic, technological and marketing factors bring in the
change in the pattern of diversification. VariOllS indices of diversification are computed
for the past three decades and trends in different pattern of the same are presented.
The extent of diversity is reflected in the number of crops produced in a state as
well as by the aggregate level of spread in cropping pattern. The results show that most 0 f
the states in the northern region have less diverse agriculture (table 3.2). These states
produce mainly food grain crops. Southern region is the most diverse as most of the states
produces large number of crops with Kamataka producing the highest number of crops.
Due to different climatic conditions, many plantation crops are produced in this region
which cannot be produced in other regions. Strikingly, the eastern region, which is
characterised as having low level of irrigation and humid climate, is relatively more
diverse in their cropping pattern and produces large number of foodgrain crops and
vegetable crops. In the western region, excepting Maharashtra, states are less diverse.
Diversity in cropping pattern reflects only a partial picture of diversification. ]t is
necessary to look at the distribution pattern in terms of spread and concentration of
cropping sector.
66

Table 3.2: N urn b ero fC rops Produced by States in In dia
St.te
Number of Crops
Northern ROEion
Jammu & Kashmir 13
Puniab 14
Harvana 15
Himachal Pradesh 16
Uttar Pradesh 19
E.stern ROEion
Bihar 20
West Bengal 23
Assam 24
Orrisa 24
Western Region
Rajasthan 18
Gujarat 19
Madhya Pradesh 19
Maharashtra 23
Southern Region
Kerala 18
Andhra Pradesh
2S
Karnataka 27
Tamil Nadu 24
..
Source: CMIE, and indIan Agncultural StatIstICS (Vanous Issues)
Diversification is also reflected in the spread of cropping pattern. Herfindahl Index
is chosen as an index of spread or concentration in the cropping pattern. The range of
Herfmdahl index is from zero to one, where zero means full concentration i.e., entire area
under one crop, and the value of Herfindahl index at one signifies full spread or that area
is evenly distributed across crops. In terms ofHerfmdahl index, states in the northern and
the eastern regions exhibit higher level of concentration (table 3.3). In the northern
regions, only two crops i.e., rice and wheat dominate the cropping pattern, whereas in the
eastern region, most of the cropped area is under rice. Both southern and western regions
exhibit higher level of spread in cropping pattern with few exceptions. This is primarily
because these regions have also allocated considerable area to pulses, oilseeds and
horticultural crops. No single crop dominates the cropping pattern in these regions. It is
not strictly the case that the states with large number of crops also have higher index of
spread.
67

Tabe I 33 . : T ren d· 1m S ~rea d or C oncentration ID Croppin2 Patt em by Indian States
States I TE 1972 ~ TE 1982 TE 1992 TE 2002
Nortbern Region
Jammu & Kashmir 0.703 0.721 0.717 0.718
Haryana 0.825 0.800 0.791 0.757
Himachal Pradesb 0.711 0.664 0.646 0.645
Punjab 0.749 0.660 0.634 0.612
Uttar Pradesb 0.833 0.779 0.760 0.733
Eastern Region
Qrrisa 0.332 0.499 0.317 0.280
Assam 0.425 0.489 0.458 0.460
Bihar 0.517 0.468 0.450 0.427
West Bengal 0.385 0.434 0.417 0.476
Western R"1tion
Madhya Pradesh 0.855 0.836 0.823 0.817
Gujarat 0.834 0.863 0.875 0.863
Maharashtra 0.795 0.797 0.819 0.838
Rajasthan 0.796 0.805 0.816 0.786
Soutbern R"1tion
Andhra Pradesh 0.801 0.802 0.798 0.838
Karnataka 0.874 0.873 0.888 0.904
Tamil Nadu 0.768 0.767 0.785 0.819
Kerala 0.780 0.798 0.792 0.782
Note:
i. The levels of spread are measured by Herfindahllodex tbat range from 0-I, 0 being full concentration
and 1 means spread in cropping pattern
Source: CMIE, and lodian Agricultural Statistics (Various Issues)
On the basis of the changing pattern of spread or concentration over a period of
time, three broad patterns can be noticed. The first pattern includes states that have shown
almost no significant change in their index. Three states, Jammu and Kashmir, Kerala and
Rajasthan have been exhibiting stagnancy in their index of spread or concentration.
Among these states, though Kerala experienced considerable change in the cropping
pattern mix over the given time period, it did not resulted in changing the concentration
index of the state. In the second pattern, states are moving towards higher level of
concentration in the cropping pattern. These include states from northern regions like
Punjab, Haryana and Uttar Pradesh. They increased considerable area under rice and
wheat crops after the introduction of Green Revolution. The trend of shift in the cropping
pattern towards these specialized crops continued even during 1990s. The third category
consists of the regions that are increasing their spread among the crops. Most of the
68

southern states come under this category. This is specifically because these states are able
to diversifY towards non-traditional crops including oilseeds and horticultural crops.
3.2.3 Land Allocation in Favour of Higb Value Crops
Land allocation in favour of non-food crops is used as a proxy to measure
diversification. Greater the share of non-food crops in the gross cropped area, higher is
the diversification. The temporal picture of change in absolute area under these crop
groups shows an interesting picture. Haryana and Uttar Pradesh (so-called specialized
states in India), along with Rajasthan, Gujarat and Bihar, have increased the allocation of
area under both the crop groups i.e., rice/wheat and non-food crops (see table 3.4 and
appendix 3.2). They have replaced other inferior food crops like pulses in order to
increase the overall allocation of non-food crops in their cropping pattern. In contrast
Punjab increased the allocation of rice and wheat by reducing area under other high value
non-food crops. West Bengal, Tamil Nadu, Maharashtra, Jammu and Kashmir, Assam
and Andhra Pradesh reduced the proportion of area under rice or wheat in order to
increase area under non-food crops. Both the northern and the eastern regions could
increase the share of non-food crops only marginally. It is mostly the southern and
western regions that increased the share of non-food crops in the GCA to a great extent.
We also measured the growth rate of area under these crops in order to find the relative
role of growth in area under crops and growth of area under cultivation in changing
relative weights of crops (appendix 3.3. 3.4 and 35). More interestingly, four states
(Kerala, Assam, Tamil Nadu and J&K) experienced negative growth in the area under
non-food crops during 1990s but, the relative share of these crops increased in the gross
cropped area. In addition, two states (UP and Maharashtra) experienced lower rate of
growth in area under non-food crops during 1990s as compared to 1980s but the relative
weight of non-food crops in the gross cropped area increased during 1990s.
69

T a bl e 34' , , Sh a r eo iN 00-F 00 de rops 'G ID ross C ropped Area io India
Regions TE 1972 TE 1982 I TE 1992 I TE2002
Northern Rtl!ion
-Harvana 13.35 15.90 24.34
24.27
Himachal Pradesh 4.53 4.11 4.09
5.&4
Punjab 19.88 15.24 12.84
10.49
Jammu and Kashmir 5.28 7.22 7.80
7.90
Utlar Pradesh 13.26 13.66 15.07
15.65
Eastern Ret!ion
Assam 2\.94 24.94 24.19
24.65
Bihar 9.02 9.54 10.51
9.95
Orrisa 10.66 17.27 10.45
9.59
West Ben2a1 12.81 17.32 19.03
22.18
Western Ret!ion
Gujara( 48.25 49.43 53.62
59.19
Madhya Pradesh 18.13 13.92 15.71
14.35
Maharashtra 25.25 27.05 26.64
28.83
Rajasthan 12.81 15.86 25.62
22.36
Southern Region
Andhra Pradesh 27.43 32.01 43.87
42.81
Kamataka 32.75 31.24 33.10
3\.66
Kerala 66.10 69.06 79.90
87.34
Tamil Nadu 3\.81 33.04 42.36
42.59
Note.
i. Non-food crops include crops other than all cereals and pulses
Source: CMIE, and ' lndian Agricultural Statistics (Various Issues)
3.3 Crop Diversification and Income-Risk Pattern
3.3.1. Trend in Income and Risk in Agriculture
The gross value of output per hectare of the selected thirty crops is used as a proxy
to income from agriculture, and standard deviation in the gross value of output as a proxy
for risk. Since, the initial differences in the gross value of output across states is expected
to differ according to the initial conditions and other bio-physical factors, for the purpose
of comparison, we have used the changing relative ranks of the states by their gross value
of output per hectare over the last three decades (the levels of gross value of output are
given in appendix 3.6). The regions exhibiting higher extent of concentration (Punjab and
Haryana) indeed have improved their relative ranking. More importantly, another highly
specialized state i.e. Uttar Pradesh retained the first position throughout the decades
(table 3.5). The eastem and the southern regions, like Orrisa, Bihar, Kerala and Tamil
Nadu have shown decline their ranking. The latter two states have substituted huge
70

amount of area from food crops to non-food crops. This shows that merely increasing the
share of area under non-food crops is not sufficient to increase income and that there are
other important factors involved. Among the western region, Gujarat and MP have
slipped in their ranking while both Maharashtra and Rajasthan could improve their
ranking.
T abe I 35 . : Ra n ki ~o fS tates A ccor d' 102 to Gross Value ofOu put per ba @ 1993-94 prices
Siaies Rank in TE Rank in TE RankinTE Rankin TE
1972 1982 1992 2002
Northern Region
Uttar Pradesh I 1 1 1
Haryana 13 13 12 \I
Punjab 11 5 3 4
Himacbal Pradesh 17 17 17 17
Jammu & Kashmir 16 16 16 16
Eastern Region
Bihar 7 \0 11 12
Orrisa 14 14 14 IS
Assam 15 15 15 14
West Bengal 8 9 7 6
Western Region
Gujarat 5 4 \0 10
Madhya Pradesh 3 6 5 8
Maharashtra 9 3 4 3
Rajasthan 12 11 9 9
Southern R

including Jammu and Kashmir, Kerala and Himachal Pradesh are the oncs with lesser
variability in output. It is important to note that the highest growth in the gross value of
output during 1980s was accompanied by higher variability of the same. How different
pattern of diversification resulted in different income and risk scenario is the subject of
investigation in the next section.
Table 3.6: Coefficient of Variation in Gross Value of Output (jiJ 1993-94 pric es
States I 1970. 1980. I 19900 ABDecades
Northern Rq;on
Puniab 16.63 14.40 7.18 31.10
Harvana 19.34 17.31 7.81 34.25
Uttar Pradesh 10.05 9.05 7.05 26.24
Jammu & Kashmir 10.39 9.26 8.00 15.04
Himachal Pradesh 7.76 13.14 8.08 1750
Eastern R ... ion
Bihar 9.37 14.04 12.04 18.99
Assam 6.38 6.46 6.36 20.64
Orrisa 13.45 16.73 15.66 21.10
West Ben2a1 8.93 19.86 11.20 32.79
Western Rq;on
Madhva Pradesh 15.89 10.78 16.68 26.32
Maharashtra 22.83 14.20 13.40 29.19
Guiarat 24.76 30.62 24.97 31.79
Raiasthan 17.43 19.63 16.35 34.67
Southern ROI!ion
Tamil Nadu 10.92 16.03 10.57 26.91
Kerala 5.15 7.64 8.10 14.63
Andhra Pradesh 12.94 14.85 10.98 28.26
Kamataka 10.19 11.01 13.90 29.84
India 8.62 10.87 7.69 25.78
Source: Central Stallsbcal OrganIzatIon (Vanous Issues)
3.3.2. Link of Pattern of Crop Diversification with Income and
Risk
The pattern of diversification influences income and risk, but the impact of the
same may differ according to the components of diversification. It is important to note
states differ in terms of their magnitude of shift in their concentration index or changing
share of area under non·food crops. Therefore, their link with income and risk needs
\
further exploration.
72

We have grouped the states on the basis of their pattern of diversification. When
diversification pertain to _spread in the cropping pattern, we have three broad groups;
states- increasing spread, increasing concentration and showing no significant change in
either spread or concentration in their cropping pattern (appendix 3.7). When
diversification is measured as land allocation in favour of non-food crops, we again have
three broad groups; states- increasing share of non-food crops, declining share of food
crops and showing no significant change in share of food crops in their GCA (appendix
3.8).
The pattern of the rate of growth in income and its variability illustrates that the
group of states, which did not change significantly the index of concentration, performed
better during 1970s and 1980s, but experienced negative growth in their income during
the 1990s (table 3.7). More importantly, they also faced highest variability in their returns
through all the decades. The states, which increased their concentration on few crops
during the decade of 1980s experienced high growth in income. Most of these states
followed the Green Revolution and thereby experienced high growth in productivity of
crops. Interestingly, these states experienced decline in their growth of income during
1990s, indicating the declining impact of Green Revolution. Increase in the spread of
cropping pattern was not much beneficial until 1990s. However, it is found that these
groups of states achieved relatively higher growth in income during 1990s. Increase in
spread of crops was mainly due to increasing share of non-food crops in the GCA. More
interestingly, increasing spread was linked with low variability in return during all the
three decades. This means that risk arising due to market and weather conditions could be
neutralised by increasing diversity in the cropping pattern.
73

Table 3.7: Growth and Variability of Income According to Spread and
Concentration
-
0 fC roppmg P attern
Number of Crops Group of States Group of States showing Group of States
showing no change increase in Coucentration showing increase in
in Concentration of cropping pattern spread of cropping
Index
pattern
CGR in GrOll Value of Output per ha at 1993-94 prices
1970. 3.03 1.36 1.24
1980. 2.77 3.59 1.80
1990. -0.17 1.47 2.53
VariabiliQ in Income (Coefficient of Variation (CV)of Gross Value of Output at 1993-94 prices)
1970. 11.31 8.63 10.30
1980. 13.54 13.94 12.37
1990. 11.39 8.92 7.35
..
Source: CMIE, and Indian Agncultural Stallstlcs (Vanous Issues)
The results of interplay between changing share of non-food crops income and risk
are presented in table 3.8. The results indicate that greater thrust towards non-food grains
is beneficial for the agricultural economy of the regions in India. The regions which
shifted their cropping pattern towards non-food crops have been consistently performing
well through all the decades. They gained in terms of achieving higher growth in their
income. But, this gain becomes more perceptible only in the post liberalization era in
India as the growth achieved was highest among all groups during this period. It was in
the 1980s that states which increased allocation of food crops in their cropping pattern
could achieve higher growth. But, that process did not continue to benefit these groups of
states as they experienced poor growth in income during 1990s. The regions that have not
experienced any significant change in area under non-food crops registered poor growth
in output and hence lagged behind in the process of agricultural development. But, in
terms of the variability of returns, it is important to note that regions shifted their
cropping pattern towards non-food crops experienced highest variability of returns,
especially during 1980s and 1990s. The variability ofreturns declined in all the states
during 19908; however, those states, which reduced the share of area under non-food
crops experienced low variability of returns as compared to other states, during the
period.
74

Table 3.8: Growth and Variability of Income According to Diversification towards
N 00-F 00 -de
rops
Number of Crops Group of State. Group of States showing no Group of States
reducing
Share of change in Share of area increasing Share of area
area under non-food under non-food crops under non-food crops
crops
CGR in Gross Value of Output per ba at 1993-94 prices
1970. 2.03 2.08 1.34
1980s 3.11 1.37 2.05
1990s 1.35 1.71 2.44
Variability in Income (Coefficient of Variation (CYlof Gross Value of Output at 1993-94 prices)
1970. 10.03 10.23 7.87
1980s 11.33 10.70 13.63
1990s 6.34 6.73 11.21
..
Source: CMIE, and IndIan Agncultural StatistIcs (Vanous Issues)
3.4. Relationship between Changing Cropping Pattern Mix and
Growth of Crop Output
Growth in aggregate output of the agricultural sector is influenced by the level of
technology, infrastructure, government policies and crop pattern, among others. Changes in
cropping pattern help to accommodate risks originating from various factors as well as to
optimize income either by reducing cost or increasing efficiency. In an aggregate picture,
any change in cropping pattern also causes a shift in the aggregate production function of
the sector and the process of diversification is responsible for this. We initially examined
the trends in the growth of output across states in India, followed by an investigation of the
relative significance of various components of growth of output, including diversification
from low value crops to high value crops. The decomposition model is used to examine the
contribution of each component relative to each other. Finally, factors that influenced the
nature and direction of diversification in India are examined.
3.4.1. Trends in Growth of Crop Output in States of India
The trends in growth of output across states in the past two decades reveal that few
states have been able to experience higher output growth during the 1980s and 1990s
~able
3.9). West Bengal, Himachal Pradesh and Maharashtra were able to achieve
75

growth rate above the India's average growth of output during both the decades. It is
important to note that considerable area in these states is under high value horticultural
crops. These states also experienced high growth in area under these crops during both
the decades l4 • Some states i.e., Bihar, Jammu & Kashmir and Karnataka were able to
improve their growth during 1990s over that of the 1980s. These states however did not
benefit from the technological initiatives taken by the government as part of Green
Revolution and TMO but have shown signs of development after the liberalization of the
economy. Strangely, the major agricultural states, Punjab, Haryana and Uttar Pradesh,
which gained enormously from Green Revolution experienced decline in growth rates
during 1990s. These regions had shifted significant amount of area towards few selected
crops like rice and wheat after the introduction of High Yielding Varieties. Consequently,
they showed significant improvement in the growth of output. But, there were signs of
fatigue in the technological development of these crops that resulted in dampened growth
during 199Os. Some states continued to exhibit poor growth during both the decades and
have not been able to benefit either from either Green Revolution or liberalization regime
such as Assam, Kerala and Andhra Pradesh. At the same time, there are a few regions
which did not gain from Green Revolution, but have picked up growth in the recent past.
Such a distinct pattern of growth across region and time makes it imperative to quantify
the major components of growth in the regions across time.
14 This is evident from the growth rate in area under horticultural crops and their proportion in the area
under cultivation Ihe details of which are presented in the next chapter of the thesis
76

Tb39T a Ie . : ren d' ID t h e Growth Rates ofCropOutputin States *
High Agriculture growth States (in %)
StatesIY ear 1980s StatesIYear 19905
West Bengal 5.76 Bihar 4.27
Punjab 5.16 West Bengal 3.85
Haryana 4.44 Himachal Pradesh 3.69
Rajasthan 4.31 Maharashtra 3.65
Maharashtra 3.85 Jammu & Kashmir 3.45
Madhya Pradesh 3.65 Kamataka 3.26
Tamil Nadu 3.49
Himachal Pradesh 3.46
Low Agriculture growth States (in %)
StatesIY ear 1980s StatesIY ear 1990s
Uttar Pradesh 2.93 Assam 2.75
Kerala 2.05 Rajasthan 2.7
Bihar 2.60 Haryana 2.67
Karnataka 2.5 Kerala 2.62
Assam 2.39 Andhra Pradesh 2.34
AndhraPradesh 2.3 Uttar Pradesh 2.21
Orrisa 1.46 Tamil Nadu 2.11
Jammu & Kashmir 1.2 Guiarat 1.81
Gujarat -0.81 Punjab 1.27
Orrisa 0.46
Madhya Pradesh -0.03
• Compound growth rate
Source: Central Statistical Organization (Various Issues)
3.4.2. Model for Identification of Role of Diversification in
Growth of Crop Output
In order to understand the full impact of diversification, it is necessary to approach
through decomposition of the rates of growth that not only provides the contributions of
the components of growth but also helps to indicate their relative positions.
Decomposition model helps in identifying the factors responsible for the observed growth
of output in different regions. Such analyses of past experience and comparative studies
of regions with different rates of growth, in particular, could provide valuable insights
into factors, other than purely technological growth, which help or impede exploitation of
known possibilities. This insight, in turn, might indicate changes in programmes and
77

policies which could make agricultural planning more effective (Minhas and
Vaidyanathan, 1965).
The decomposition model introduced by Minhas and Vaidyanathan (1965) was
used to explain the components of output growth across regions in India. The observed
change in aggregate output was decomposed into four component elements- i.e.,
contribution of (i) changes in area (ii) changes in per acre yields (iii) changes in cropping
pattern and (iv) the interaction of the latter two elements. The scheme of decomposition
initially looked at the absolute changes in the value of gross agricultural output,
QrQo~ At~lIctY ctPe - A;i:.1Ico Y coPe ----------------------------------------------------1
which can be further extended as
Where
-------------------------------------------------------------2
Qt and Q. ~ Value of gross agricultural output at constant prices (P) during period-t and
during base period-O respectively
At and Ao = Gross cropped area during period-t and during base period-O respectively
IIct and 8co= (Acl At) = proportion of area under crop-c (Act) to the gross cropped area (At)
during period-t and during base period-O respectively
Y ct and Y e.~ physical output per acre of crop-c during period-t and during base period-O
respectively
P, = price of the crop--c.
The first three components of the model represent the cOlitribution of change in
area, yield and cropping pattern or diversification in absolute change in the value of gross
akcultural output. The first element on the right side of the equation is the area effect.
78

That is, an increase in output of this magnitude could have taken place in the absence of
any changes in per acre crop yields and the crop pattern. The second elernent in the
equation is the effect of yield changes for a constant crop pattern. The third element
portrays the effects of change in crop pattern in the absence of any change in per acre
crop yields. The fourth or last element shows interaction effect of changes in yield and
cropping pattern in the growth of output. It reflects whether or not the crops, whose
relative weight in the cropping pattern is increasing has also been experiencing positive
growth in productivity or yield. ]t is important to note that the components (area, yield,
cropping pattern & interactions) do not convey a misleading impression of causation in
any way but explains only a particular form in which the growth of output has taken place
in the past. The interaction terms are obtained because of the multiplicative nature of the
identity.
There are two components related to diversification or shift in cropping pattern. The
first term (third element) is called "static" diversification effect and second term (fourth
element) is called "dynamic" diversification effect. The frrst term shows static effects lS
since it becomes more positive when the area under crops whose yields were initially
(during the base period) high, increases relatively as compared to other crops. In contrast,
the second term shows dynamic effects 16 because it turns out to be more positive when
the area under the crops whose yields are improving, increases relative to the area under
other crops, whose yields are decreasing or stagnant. The maximum potential for the
static diversification effects depends on the initial level of cropping pattern and the range
of feasible crop shares that can be grown under agronomic constraints. The maximum
potential for the dynamic shift-effects depends on the same factors as for the static
effects, and the response of cropping pattern to changes in productivity of the crop
(Kurosaki, 2(03).
Most of the studies that employed decomposition models for explaining the
components of growth did concentrate mainly on the static effects of diversification and
" Nole that in

productivity growth (Joshi, 2005, Bhatia and Sinha, 1975, Kalamkar, 2005). The dynamic
effect, which is captured through the interaction effect, is ignored. It denote the
concomitant changes in the components of growth of output, i.e., change in cropping
pattern and productivity of the crops. As noted, diversification may affect the growth
through static and dynamic effects. A positive static effect of diversification would show
a shift of crop structure in favour of high initial productivity crops. This does not capture
development in technology of crops which may change over time and may alter relative
values of crops. In other words, over-time, the profitability or relative values of crops
does not remain constant. Also change in the productivity of crops may change the
comparative advantage of crops. Hence, it is important to consider the dynamic effects of
diversification i.e., the concomitant movements of yield and cropping pattern change.
The dynamic aspect of diversification indicates the impact of the joint movements in the
diversification of crop pattern and technological change on overall output change. A
positive dynamic effect of diversification would imply that the crop structure has shifted
in favour of those crops which show relatively higher growth in yield. In a similar way,
negative dynamic effect implies over-time, cropping pattern shifts in favour of those
crops which could not experience higher growth in productivity. This is the growthdepressive
impact of diversification on output in the agricultural sector.
3.4.3. Components of Growth of Crop Output in India
Respective contribution of area, yield and changes in cropping pattern to the gross
crop output across regions are computed for four broad regions. The relative significance
of each component in the growth of output has implications for future agricultural
policies. If the growth stems from technological change (yield improvements),
investments in research and extension need to be accorded priority. The area-driven
growth implies need for greater extension efforts to make agriculture broad based. If the
growth occurs due to crop diversification. there is a need for increasing investments in
development of market and infrastructure (Joshi, 2005).
80

Table 3.10: Contribution of Different Components in Growth of Gross Value of OUtpllt
by Rfltions in India
Region Period Area Diversification Aggregate Diversification
Effect effect (Static) Yield effect effect (Dynamic)
North 1970s 28.45 08.65 54.34 08.56
I 980s 10.35 11.54 72.54 05.57
1990s 22.54 18.54 53.25 05.67
East
All decades 23.45 12.65 60.12 03.78
1970s 43.65 30.15 30.78 -04.58
1980s 24.56 08.36 73.80 -06.72
1990s 45.25 07.10 45.35 02.30
West
All decades 23.15 10.05 64.00 02.80
I 970s 18.33 23.85 58.64 -00.82
1980s 15.48 31.07 51.54 01.91
1990s -15.85 77.65 38.45 -00.25
South
All decades 19.95 48.25 30.54 01.26
1970s -20.85 30.04 87.05 03.76
1980s 04.34 38.65 52.15 04.86
1990s -11.00 76.54 35.26 -00.80
India
All decades -04.15 48.56 58.45 -02.86
1970s 19.78 14.66 62.12 03.44
1980s 14.33 19.39 69.17 -02.89
1990s 11.29 48.65 37.66 02.40
Note.
All decades 17.33 24.66 61.52 -03.51
i. Figures arc in percentage
Source: Computations based on the data obtained from Central Statistical Organization (Various Issues),
CMIE. and Indian Agricultural Statistics (Various Issues)
The results of the sources of growth suggests that, at the economy level, the
significance of diversification or crop-pattern mix in the growth of output has been
increasing over a period of time. The effect of diversification was low during the decades
of I 970s and 1980s but increased significantly during the post-liberalization period (table
3.\0). When the economy was closed, it was technological development that made the
largest contribution to the output growth. The area and technology effect has slowed
down; the contribution of technology effect to output growth declined dramatically from
69 to 37 percent from 1980s to I 990s. The declining contribution of technology to output
growth was compensated by improvements in the diversification effect in the Indian
agricultural sector which increased from around 20 % during I 980s to 48% during 1990s.
The region-wise picture exhibits a distinct assortment of components of growth
acrors them. For the eastern region, the contribution of area stood at around 45 % to the
81

change m growth of output during 1990s. It indicates that area expansion through
extensification or intensification is still the major contribu~ing factor to output growth.
This is a cause of concern as area expansion is the major source of growth of output; this
process is less viable and unsustainable in the long run. The trend in the other
components of growth reflects the role of technology, which accounted for more than 70
% change in the output growth during 1980s. It came down drastically during 1990s and
accounted for only 45% of variation in growth. Crop pattern shifts continue to play a
minimal role in the growth of output, as this region continues to diversify towards highly
specialized crop like rice. The agricultural sector of this region is dominated by rice with
less diversification towards other high value non-food crops.
In the northern zone, which benefited enormously from Green revolution and
devoted high amount of area towards high value food crops, technology contributed to
major change in the growth of output. Although, crop pattern shift has been increasing
over time, the contribution of the same is still low at around 18 %. Low diversification
effect is attributed to the fact that the change in cropping pattern has resulted in increased
specialisation towards crops like rice and wheat. At the same time, the yield of these
crops in this region increased many-fold, which resulted in increased role ofyield17. The
area effect contributed around 22 % towards change in the output growth during 1990s.
The percentage contribution was 10 % in the previous decade. Major concern is that like
eastern region, the significance of area effect increased during the 1990s, while that of
technology declined drastically. This is also probably due to decline or stagnation in the
yields of majority of the crops during 1990s as compared to 1980s.
There is a huge contrast in terms of the relative role of components in southern and
western regions in comparison other regions. The contribution of crop diversification in
the growth of output has been quite high and over time and the same is increasing at a
fast pace. The share of diversification in the growth of output jumped from 31 % and 38
" Yield effect is given by do'y, which signifies that yield effect increases as the yield of the specialized
crop~ increases faster. This is because the change in per unit yield of crop is weighted by the relative
weight of that crop in the total cropping pattern. Accordingly, diversification effect which is given by 'dyo
Will bF higher if cropping pattern shifts more towards the crops which commands relatively high per unit
Yldd luring the base period.
82

% respectively in western and southern region during 1980s to more than 70010 during
1990s. On the other hand, the share of technology in growth declined from around 50%
to nearly 35% in both the regions during the same period. Interestingly, both these
regions are experiencing declining contribution of area in their output growth and its
contribution became negative during 199Os. Decline in the area under cultivation was
compensated by the increased shift in cropping pattern towards high value crops rather
than by productivity growth, which added to the high effect of diversification.
In a nutshell, technological development continues to be the major component of
growth in the northern region, though its effect is declining. This is primarily due to
decline in the yield of a few specialized crops. Hence, other components of growth
including diversification need attention. In the eastern region, area expansion is the major
source of growth in the absence of any significant technological development and crop
shifts. As avenues to increase area under cultivation are rather bleak, crop substitution
towards high value crops and development in technology will have to be the potential
source of growth in future in this region. In both the southern and western regions,
diversification has become an important component for growth primarily due to the
decline in area under cultivation and poor technological development.
Despite variations in the relative contributions of components, the common feature
that emerges from the decomposition analysis is that with notable exceptions, a
significant portion of output growth in these regions is explained by change in crop
pattern in tenos of relative weight of crops. And, its significance has been increasing over
time. It is vital to note that change in the relative weights of crop is a function of the
growth rate of area under the crop vis a vis growth in the cropped area. As noted, the
pattern of changing relative weight and growth rate of area under crops indicates that four
states (Kerala, Assam, Tamil Nadu and J&K) experienced negative growth of area under
non-food crops during 19905, but the relative weight of non-food crops increased in the
1990s. This raises the need to examine the trend in the growth of area under cultivation.
The aggregate change in the cultivated area of a region can be segregated on the
basis10f 'substitution' and 'expansion' effects by comparing the area growth rates of
83

individual crops with the corresponding growth rate in gross cropped area. In other
words, the aggregate change in the cropping pattern in each region can be due to either
substitution of area among crops or by expansion of area under cultivation. For
measuring the trend in area expansion and substitution, we used the method by
Venkataramanan and Prahladachar (1980). According to this method, an unchanged
cropping pattern (no change in crop mix) can be a situation where the respective areas
under aU crops bear the same proportion to the gross cropped area (GCA) over the years.
It implies that the rate of growth in area under individual crops must equal the rate of
growth in the gross cropped area over the same time period. Such a change can be
expressed in the form of a linear homogenous gross cropped area function, where given
proportionate changes in area under individual crops are related to equal proportionate
change in gross cropped area. The differences in the rates of growth in the area of
individual crops from the rate of growth of GCA, therefore provide evidence of change in
the cropping pattern. The total change in the cropping pattern over time is the sum total
of the substitution effect (the relative decline in area under some crops and corresponding
equivalent increase in area under other substitutable crops for a given gross cropped area)
and the expansion effect (effect of increase in the gross cropped area).
An analysis of the relative role of area expansion and area substitution in change in
cropping pattern across four broad regions in India provides interesting insights (table
3.11). The net change in area was negative during 1990s signifying that either there was
decline in the area available for cultivation or that the level of cropping intensity
decreased during this period. The decade of the 1980s saw increase in area expansion
effect which declined during the 19905. The expansion of area faced decline during this
period together with increased substitution of crops in the cropping pattern. It is
important to note that the role of substitution effect in change in cropping pattern has
been continuously increasing over a period of time. This indicates that the avenues for
increasing growth through area expansion are rather dim and there is a need to look for
other factors for stepping up growth. The region wise picture indicates that it is the
northern region which has demonstrated strong area expansion, whereas the eastern
regi,?n has shown poor performance in terms of area expansion as well as crop
84

substitution. Interestingly, crop substitution is becoming more important in the western
and s;outhem states. These states experienced negative net area change during the 199Os.
The increased role of crop substitution along with decline in area for cultivation actually
resulted in magnifying role of crop diversification in the growth of output in these
regions.
Tbl311A
a e . : rea E xpanslOn an d S u b stitution . Efli eel ' ID I n d' la
India Northern Eastern Western Southern
Region Region Region Region
Area E~.D5ion
1970. 11683 5152 1957 3653 921
1980. 13741 3528 2087 4850 3276
1990s 12957 4028 1566 4506 2857
Area Subslitulion
1970. 10041 2595 1045 3487 2914
1980. 11366 2483 886 4071 3926
1990s 11632 1767 1234 46\0 4021
Net Area Change
1970. 1642 2557 912 166 -1993
1980. 2375 1045 1201 779 -650
1990s 1325 2261 332 -104 -1164
..
Note: I. Area If 10 ooO'ha
Source: Computations based on the data obtained from CM1£, and Indian Agricultural
Statistics (Various Issues)
Importance of any of the individual components points to the static effect of it on
the growth of output. The dynamic effect, i.e. interaction term indicates shifts in cropping
pattern towards the crops which also experienced higher technological development. The
results show that for the economy as a whole, the increased role of diversification was
complemented by positive dynamic effects during the 19908. This indicates that the
typology of the shift in the cropping pattern during the same period did not result in
growth-depressing effect; it was other components of growth that adversely affected the
growth of output. Among the regions, northern region alone has the distinction of being
able to improve the crop pattern towards such crops that experienced better technological
growth, through all the decades under review. In other words, most of the crops, which
registered high growth in their yield relative to other crops, have improved their relative
weight in the cropping pattern. The crops whose yield rates have gone down exhibit
dedire in the proportion of gross cropped area under them.
85

All other regions have shown mixed trends over a period of time. The eastern
region has been able to achieve positive growth in productivity of crops towards which
they have diversified during the 19908. This trend was absent during the previous two
decades, when the dynamic effect was negative. Western and southern regions
experienced high and negative dynamic effect during 19908, raising concerns about lack
of technological improvement in the crops that contributed high to the cropping pattern.
Ioterestingly, both these regions experienced positive dynamic effect during 1980s which
turned negative subsequently. Though, there has been increased growth in the crop
pattern shift towards high value crops during 1990s, the growth rate of these crops yield
saw a dramatic decline during the same period, which resulted in the growth-depressing
effect of diversification. This points out that diversification towards high value per se is
not sufficient for increasing growth; it is also important that these crops remain
remunerative over a period of time, through proper technological development, otherwise
the gains from diversification will be meagre.
To understand the underlying factors that influenced the nature (static effect) and
direction (dynamic effect) of diversification in India, coefficient of correlation between
several factors such as inputs development, market development, and socio-economic
development have been computed. Correlation coefficients are measured separately for
the static coefficient of diversification of rice and wheat crop as one group, for non-food
crops as another, and separately for dynamic coefficient ofrespective crop groups (table
3.12).
The coefficient correlation between the static coefficient of diversification and input
deVelopment shows that use of fertilizer and irrigation development are important for
diversification towards rice and wheat. However, for non-food crops, it is irrigation that
is important as many of these crops are highly water-intensive. Interestingly, the regions
that have experienced high variability in rainfall over the last three decades could not
diversifY towards non-food crops and this has adversely affected the growth of output in
these regions. This illustrates that the variability in weather has influenced the process of
crop diversification and hence the growth of output in India. The availability of number
of "

food crops but not on tbe diversification towards rice and wheat crops. This is expected
as non-food crops are highly labour intensive compared to rice and wheat. The
improvement in rural literacy is found important for tbe spread of non-food crops but not
for food crops like rice and wheat. Development of markets exerted a positive influence
on the diversification towards eitber of tbe crop groups (rice and wheat and non food
crop) and making diversification growth inducive.
Table 3.12: F actors In o uencinll N ature and Direction of Diversification III India
Correlation coefficient Rice & Wheat Non-food crops Total
Static (Area) Dynamic Static (Area) Dynamic Dynamic
Fertilizer kglha 0.409 0.493 -0.158 0.251 00407
Gross Irrigated Area 0.290 0.173 0.443 0.532 0.482
Coefficient of variation 0334 0.351 -0.045 -0.128 -0.008
of rainfall
Total workers -0.127 -0.001 00471 0.287 0.216
(Agricultural labourers
and cultivators)
Rural literacy rate 0.000 -0.124 0.445 -0.125 -0.033
No. of regulated markets 0286 0.493 0.226 0.313 0.201
Roads (in KIns.) -0.103 -0.266 0.319 0.125 00418
Tractor (in Numbers) 0.609 0.609 0.120 -0.073 0.117
Source: ComputatIOns based on the data obtamed from Central StatIstIcal Orgamzallon (Vanous Issues),
CMIE, and Indian Agricultural Statistics (Various Issues), Fertilizer Statistics, Statistical Abstract India,
Ghosh and Prabir, 2005, and Indian Labour Statistics (Various Issues)
3.5. Summary
Diversification is one of components of output-growth besides area and crop
productivity. There are several sub-components of diversification that influence growth
in different ways. The components including diversity and spread in cropping pattern and
share of high value crops in GCA influence growth through their impact on income and
risk. Also, over-time, tbe relative role of all components of growth is expected to change
due to changing policy, technology and external factors like economic liberalization and
international trade. Decline in agricultural growth during 1990s raises tbe need to identil'y
the relative significance of components of growth in order to design appropriate
agricultural policy to catalyse future growth in the sector.
87

Initially, states were classified on the basis of different sub-components of
diversification. These different dimensions of diversification are analysed in the context
of growth of output for the last three decades. The results indicate that at the state or
regional level, there is a mixed picture regarding the typology of diversification. Some
states exhibit high diversity in the cropping pattern but at the same time have less
proportionate area under high value crops. There is also no direct link between diversity
in cropping pattern with spread of crops. In regard to the relationship of different
components of diversification with income and risk, income growth is higher in the states
that have increased spread in the cropping pattern. These states have also shifted higher
amount of area to non-food grains. However, this effect was perceptible only during the
post-liberalization period in India. Until early I 990s, only the states which followed
Green Revolution strategy were the major beneficiaries, but the effect has reduced during
liberalization era as those regions performed relatively poor as compared to other states.
Among the relative components of output growth in India, diversification has
become an important source of growth of output. This is again apparent in the postliberalization
period. However, in the previous two decades, technological development
was mainly responsible for output growth. Crop diversification has turned out to be a
important component of growth in the southern and western regions, but this is attributed
primarily to the negative net aggregate area change which in turn has increased the
importance of crop substitution.
The results of the dynamic effect indicate that northern region is the only one which
through all the decades has been able to shift the crop pattern towards the crops that also
experienced technological growth. Western and southern regions experienced high and
negative dynamic effect during the 19908, raising the concerns of the lack of
technological development in the crops that contributes significantly to the cropping
pattern and growth of output of the region. It is clear now that diversification towards
high value per se is not sufficient for increasing growth. It is important that these crops
remain to be remunerative over a period of time through proper technological
development; otherwise the gains from diversification will be meagre. For northern and
easter{! regions, it is more important to emphasis on increasing the crop diversification
88

towards high value crops that include several non-food crops. These regions are highly
vulnerable to external shocks due to specialisation in rice and wheat. For southern and
western regions, it is more important to improve the technology of many of the high
value crops which are becoming crucial for the growth of the region.
For improving the nature and direction of diversification in India, fertilizer and
irrigation development are important for catalysing shift of cropping pattern towards rice
and wheat crops. However, for non-food crops, irrigation is important as many of these
crops are highly water-intensive. High-labour intensity of these crops demands high
density of workers. Finally, development of markets is vital in order to drive
diversification towards either of the crop group (rice, wheat or non-food crops) and to
improve the aggregate growth of agriculture sector in India.
The results of this chapter indicate that the significance of diversification towards
high value crops has increased over-time and it has turned out to be an important
component of growth of agricultural sector. In India, horticultural crops are high value
crops not only in tenns of returns to land and labour but also for their potential to provide
higher level of employment in the sector. This calls for investigation of the trend in the
growth of area and value of horticultural crops, the significance of horticultural crops in
the study area i.e. Himachal Pradesh. Also, required is an examination of the economics
of selected horticultural crops in the study area.
89

Appen d" IX 31 L" CC
" "
1St 0 rops
S.No Crop Name SNo Crop Name SNo Crop Name
I Rice II Potato 21 Tea
2 Wheat 12 Jute 22 Coffee
3 Jowar 13 Ragi 23 Tobacco
4 Bajra 14 Tur 24 Rubber
5 Maize 15 Barley 25 Dry chillies
6 Gram 16 Linseed 26 Black pepper
7 Groundnut 17 Sesamum 27 Arecanut
8 Rapseed & Mustard 18 Castor 28 Banana
9 Sugar 19 Cocunut 29 Cashewnut
10 Cotton 20 Mesta 30 Tapioca
A ~ppen d" IX 32 Sh
" : are 0 CRi cean dWh eat "G ID ross c roppe dA rea " ID I n dia
Regions I TE 1972 I TE 1982 TE 1992 I TE 2002
Nortbern Reejon
Haryana 34.21 48.48 52.58 59.88
Hi machal Pradesh 51.27 55.18 54.57 55.34
Punjab 58.55 75.58 83.05 86.62
Jammu and Kashmir 56.94 57.93 57.72 55.21
Uttar Pradesh 53.14 61.76 64.97 68.49
Eastern Region
Assam 77.27 74.20 75.11 74.58
Bihar 68.86 70.11 71.16 74.43
Orrisa 81.34 71.48 82.87 84.83
West Bengal 83.86 80.02 79.76 76.58
Western Ree:ion
Gujarat 12.84 14.05 14.83 15.18
Madhya Pradesh 38.77 40.53 43.79 45.58
Maharashtra 15.54 15.24 14.01 14.87
Rajasthan 16.06 16.71 16.73 16.77
Sonthern region
Andhra Pradesh 35.25 36.19 35.92 32.43
Kamataka 17.69 16.66 17.47 15.89
Kerala 33.78 30.90 20.06 12.62
Tamil Nadu 41.94 42.34 40.03 36.52
Note .. The data consists 000 crops
Source: CMIE, and . Indian Agricultural Statistics (Various Issues)
90

Appen d' II 3.3 : C om pouo dG rowtb Rates of Area under Rice
All
Stales 1970s 1980s 1990s Decades
Andhra Pradesh 0.96 1.18 1.58 0.38
Assam 0.95 0.73 0.44 0.72
Haryana 8.16 2.50 5.09 4.59
Kamataka -0.13 0.33 1.36 0.93
Maharashtra 1.41 0.43 -0.42 0.29
Rajasthan 3.16 -1.66 1.96 0.07
West Bengal -0.55 1.62 0.21 0.58
Bihar 1.48 1.42 2.20 -0.14
Himachal Pradesh 0.40 -0.86 -0.16 -0.66
Kerala -1.32 -4.14 -5.90 -3.52
Orrisa -1.56 0.63 0.02 0.04
Tamil Nadu -0.05 -1.79 -0.54 -0.84
Gujarat 4Jl6 0.04 1.14 1.32
J&K 1.56 -0.23 -1.24 0.20
Madhya Pradesh 1.34 -0.15 2.03 0.51
Punjab 12.50 4.37 2.49 5.67
Uttar Pradesh 2.26 0.46 1.85 1.00
Source: CMIE, and Indian Agncu1tuTaI StatIStICS (Vanous Issues)
Appendii 3. 4: Compound Growtb Rates of Area uode rWbeat
All
States 1970s 1980s 1990. Decades
Andhra Pradesh -3.92 -8.40 3.71 -2.79
Assam 10.08 -3.68 -0.63 1.09
Haryana 3.75 0.80 2.28 2.32
Kamataka ·0.58 -6.22 1.95 -1.86
Maharashtra 2.39 -4.35 2.17 -1.34
Rajasthan 1.58 -1.77 1.55 1.43
West Bengal -2.61 -1.38 4.58 -0.55
Bihar 0.78 1.79 0.82 1.00
Himachal Pradesh 1.79 0.25 -0.04 0.70
Kerala NA NA NA NA
Orrisa ·1.56 0.63 0.02 0.04
Tamil Nodu NA NA NA NA
Gujarat 3.34 -2.31 -4.40 -0.59
J&K 0.91 1.60 0.83 1.28
Madhya Pradesh 0.41 0.33 0.37 1.08
Puniab 2.99 0.68 0.43 1.29
Uttar Pradesh 3.98 0.45 1.02 1.65
Note. (I). NA IS not apphcable.
Source: CMIE, and Indian Agricultural Statistics (Various Issues)
91

Appen dix3.5 : C ompound Growth Rates of Area under Non-Food Crops
All
States I 970s 1980s 1990s Decades
Andhra Pradesh -0.74 4.43 -0.34 2.28
Assam 1.77 1.36 1.28 1.59
Haryana 3.96 4.27 0.08 3.17
Kamatak. 0.72 3.30 -0.96 1.86
Maharashtra 2.19 0.98 0.59 1.13
Raiasthan 2.24 3.91 1.38 3.18
West Bengal 4.31 3.91 5.29 3.27
Bihar 0.16 3.08 -2.36 1.90
Himachal Pradesh 2.14 7.47 3.98 6.95
Kerala -0.26 1.87 -0.57 0.65
Orrisa 7.74 2.41 -1.39 1.15
TamilNadu -1.17 4.09 -2.54 0.62
Gujarat 2.69 -1.29 1.55 0.81
J&K 4.29 7.43 -1.65 5.32
Madhya Pradesh -0.62 7.69 2.13 4.60
Punjab -0.86 1.09 -2.41 -0.30
Uttar Pradesh 1.43 1.30 0.53 1.64
Source. CMIE, and indIan Agncultural StatistICS (Vanous Issues)
Appendix 3_6: Gross Value of Output of States in India @J993-94 prices
TE 1972 TE 1982 TE 1992 TE2002
Andhra Pradesh 596344 854018 1151967 1380645
Assam 232988 291762 368852 432583
Bibar 485038 487862 610484 701505
Gujarat 547764 771741 753674 759615
Haryana 288122 374300 606125 730574
Himachal Pradesh 41808 44192 59196 61288
Jammu & Kashmir 52613 74273 76686 70728
Karnataka 491105 590095 827505 1139481
Kerala 448102 398119 485199 583693
Madhya Pradesh 590515 672096 930210 883386
Maharashlra 462352 837740 1015724 1328372
Orrisa 265296 308074 422560 356616
Punjab 426688 679359 1018558 1190417
Rajasthan 401725 469204 766035 791150
Tamil Nadu 585907 567287 897779 1069361
Vttar Pradesh 1314870 1712200 2292292 2687352
West Belll1.al 467249 495091 871970 1130809
India 7739386 9675553 13302690 15770206
lfOlc:
j. Figures are in Rs. Lakhs
Source: Central Statistical Organization, Various Issues.
92

Appendix 3.7: Distribution of States by Trend towards Spread or Concentration in
C .
nlppinl Pattern
1'170. 1980s . 1990s All Decad ..
Group of States TamilNadu, Haryana, Kerala, Madhya Pradesh, Rajasthan. Kerala,
showing no chauge Kamataka, Andhra Jammu & Himachal Pradesh, Jammu & Kashmir
in Conceutration Pradesh, Kashmir, Andhra Jammu & Kashmir,
ludex
Mabarashtra, Pradesh Assam
Raiasthan
Group of States Kerala, Jammu & Rajasthan, Kamataka, Tamil Clujarat, Karnataka,
showing increase Kashmir, Gujarat, Gujarat, Nadu, Maharashtra Assam, Andhra
in spread of West Bengal, Karnataka, Tamil Andhra Pradesh, Pradesh,
cropping pattern
Assam, Orrisa Nadu, West Bengal Maharashtra, Tamil
Himachal Pradesh Mabarashtra N adu, West Bengal
Group of States Punjab, Uttar Punjab, Uttar Orrisa, Haryana, Punjab, Uttar
sbowing increase Pradesh, Bihar, Pradesh, Bihar, Rajasthan, Punjab, Pradesh, Bihar"
in Concentration Haryana, Madhya Orrisa, Assam, Uttar Pradesh, Bihar, Haryana, Himachal
Pradesh
of cropping
Himachal Clujamt, Kerala Pradesh, Orrisa,
Pradesh, West
Madhya Pradesh
pattern
Bengal, Madhya
Pradesh
Source. CMIE, and Indian Agncultural StattslIcs (Vanous Issues)
Appendix 3.8: Distribution of States by Trend in Shift of Cropping Pattern towards
N on-F 00 de rill's
1'170. 19800 1990s All Decad ..
Group of States Punjab, Madhya Orrisa, Punjab, Rajasthan, Punjab, Punjab, Madhya
reducing Sbare Pradesh, Assam, Karnataka, Madhya Pradesh, Kamataka,
of area under Kamataka, Maharashtra, Pradesh, Andhra Orrisa, Himachal
Himachal Pradesh Himachal Pradesh, Orrisa, Pradesh
non-food crops
Pradesh
Bihar, Himachal
Pradesh, Haryana
Group of States Uttar Pradesh, Jammu & Jammu & Kashmir, Bihar, Uttar Pradesh,
sbowing no Bihar, Jammu & Kashmir, Bihar, Tamil Nadu, Assam, Jammu & Kashmir,
change in Sbare Kashmir, Gujamt, Uttar Pradesh, Uttar Pradesh Assam
Tamil Nadu, West Bengal,
of area uuder
Mabarashtra, Madhya Pradesh,
non-food crops
Kamataka
Group of States Haryana, Kerala, Gujarat, Maharashtra, West Mabarashtra, West
increaSing Sbare Assam, Rajasthan, Haryana, Tamil Bengal, Gujarat, Bengal, Rajasthan,
of area under
nOD-food crops
West Bengal, Nadu, Rajasthan, Kerala Tamil Nadu,
Andhra Pradesh, Kerala, Andhra Haryana, Gujarat,
Orrisa Pradesh Andhra Pradesh,
Kerala
Source. CMIE, and indIan Agncultural StatIstIcs (Vanous Issues)
93

CHAPTERN
DNERSIFICATION AND HORTICULTURAL
CROPS: A CASE OF HIMACHAL PRADESH
4.1 Introduction
There are many facets of diversification but it is necessary to emphasise on
diversification towards high value crops as its major proxy. Among the high value crops
grown in India, horticultura1 crops emerge as the representative and also an interesting
case-study of diversification in Indian agriculture. Therefore, an overview of horticultura1
sector and its growth in India over a period of time is mapped. Not only the past
performance, but also the future prospects of the horticultural sector are bright. This is
mainly because consumption pattern of people has been shifting towards horticultura1
crops. Rjsing per capita income, growing urbanization and liberalization of the economy
have led to increase in demand and exports of these crops. It is here one need to find the
significance of horticultural crops in the agricultural sector. Himachal Pradesh (HP)
presents an interesting case in diversification towards horticultural crops in India. In this
study, the significance of horticultural crops in the agricultural sector of HP and income
and risk patterns of the selected horticultural crops are analysed. To begin with, the trend
in the growth of horticultural sector and its relevance in the agriCUltural sector are
examined. We then provide details of the selected villages and the socio-economic
features of the selected farmers. Income and risk patterns of the farmers producing
selected horticultural crops have been worked out. This is complemented by estimation of
labour demand and output supply elasticities of the cauliflower producers by using a
profit function approach. Impact of extent of land allocation to horticultural crops across
different farm sizes on income and risk is also assessed.
94

4.2 Horticultural Sector in India
Fruits and vegetables form the single largest sub-sector of horticultural crops in
India, accounting for 63.8% of the area and more than 80% of the total production.
(National Horticultura\ Board, 2009). India is the world's second largest producer of
fruits & vegetables contributing to \0% and 14.4% respectively of the total world
production. India is the largest producer of banana, mango, sapotas and acid limes and
enjoys reputation for highest productivity in grapes, sapota and banana. In vegetables,
India occupies prime position in the production of cauliflower and pea; second in onion;
cabbage, tomato and brinjal, and third in cabbage in the world (Singh et a1. 2004).
Over the last five decades, there has been a three-fold increase in area under fruits
and four-fold increase in its production. It is estimated that since 1961, area, production
and productivity of fruits increased by 3, 6.2, and 2 times respectively. Vegetable
production has tripled in the last 50 years. The total production of vegetables has
increased from 40.08 MT in 1980 to 90.35 MT 2000 registering a growth rate of 4.92%
per year. The value output from fruits and vegetables demonstrated a growth rate of
5.76% per annum between 1960-61 and 1992-93 in real terms. Historically, there was
virtually no export of fruits and vegetables. The exports of these crops have picked up
remarkably .after 1980s. Exports of fruits and vegetables more than doubled during the
last two decades. The value of horticultura\ exports from India increased from 487 crores
in 1991-92 to 2394 in 2002-03. During the same period, the annual growth rate in
horticultural export is measured at 12% in quantity and 34% in value. It is important to
note that although India is ranked second in the world production of fruits and vegetables,
per capita availability of fruits and vegetables in India still continues to be much below
dietary requirements (Singh et al. 2(04).
95

4.2.1. State-wise Pattern of Development of Horticultural
Sector
There are three prime indices by which the importance of horticultural sector and its
trend can be quantified. One measure is the trend in the compound growth rate in area
under the horticultural crops. The contribution of the horticulture to the total output
produced in the agricultural sector and location quotients provide the details about
relative significance of the sector in agriculture at any given point of time.
Growth rates of area under horticultural crops in the regions where horticultural
crops are of high significance indicate that only Himachal Pradesh (HP) and West Bengal
could demonstrate higher growth (figure 4.1). Bihar, Jammu & Kashmir (J&K), Tamil
Nadu, Assam and Orrisa have registered either negative or stagnant growth in area under
horticultural crops. These states have experienced positive growth in area under fruits but
they have shown dramatic decline in the area under vegetable crops. This may be because
of lack of adequate irrigation facilities in these states as vegetables are relatively more
water-intensive crops and fruits are capital intensive. There are several states, where
horticultural crops cover insignificant amount of area in the gross cropped area. Some of
these states Le., Maharashtra, Karnataka, and Andhra Pradesh are now experiencing
higher growth in area under horticultural crops enhancing the significance of horticultural
crops in the GCA. States like Gujarat, Rajasthan and Haryana have demonstrated higher
growth rates in area under horticultural crops, but still horticultural crops cover
insignificant area of the gross cropped area in these states.
96

Figure 4.1 :Compound Growth Rate In Area under Horticultural Crops In India
14.00,--------------------------------------------------------------------
12.00 +1------------------------------------
10.00 +-1 --------------------------------
-
~
8.00 I ----I
~
i
t
6.00 f-------
4.00 +-
~ I ~ I L
I ::: ·llJlll· ... ~.
.l
··.-1970S
~
.1980.
[] 19905
--------
I 400 II
, .~..

Since value of crops is an important indicator for understanding the relevance of
crops in the agricultural sector, the contribution of horticultural crops to the total value of
agricultural output is computed for all states for the last two decades. The results are
presented in table 4.1. Some of the highly specialized zones or marginal regions in India
have highest dependence on horticultural sector for their agricultural output and growth.
Many eastern states fall under this category viz; Assam, Orissa, and West Bengal among
others. For HP and J&K, the contribution of horticultural sector to the gross output
generated in the cropping sector (minus livestock sector) is around 47% and it ranges
between 30-32% if livestock sector is included. However, with or without taking
livestock sector into account, these states continue to have the highest contributions from
horticultural crops to total value of gross output in the agricultural sector. Among other
regions, Kamataka, Maharashtra and Tamil Nadu show moderate level of significance for
these crops in the total value of their agricultural output and this trend has been
increasing significantly over a period of time.
Table 4.1: Share of Horticultural Crops in the Total Value of Agricultural Ou~ut in India
States TE 1982 TE 1992 TE 2002
I Rajasthan 1.52 2.89 4.12
Punjab 4.52 5.21 7.08
Haryana 5.15 4.49 9.95
: Madh~a Pradesb 6.45 6.69 12.11
Uttar Pradesh 11.56 11.11 15.05
Andbra Pradesh 8.83 11.39 15.76
Gujarat 8.27 10.55 18.58
Kerala 21.33 32.35 27.00
~i1Nadu 9.83 19.82 27.53
Karnataka 5.34 25.14 28.68
Mabarasbtra 7.36 24.32 30.61
i Assam 17.51 26.95 31.40
, West Bengal 20.54 28.00 38.99
Orrisa 18.21 28.01 42.50
l.!Jibar 29.84 31.92 45.15
I Himacbal Pradesh 17.89 32.52 47.43
Jammu & Kashmir 17.86 34.04 48.31
Note.
i. Figures are in percentage
Source: National Account Statistics (Various Issues)
98

Location quotient is used as a measure to find the relative significance of
horticultural crops across states. The higher the value of location quotient, the greater is
the concentration of the selected sector in that state. The results show that these crops are
highly concentrated in J&K, Himachal Pradesh, West Bengal, Assam, Orrisa and Bihar
(figure 42). These are the states which are relatively less developed in terms of irrigation
and modern technology. KeraIa, where horticultural sector was of great significant in the
agricultural sector (indicated by the location quotient coefficient for the Triennium
Ending (TE) 1972 period), experienced a dramatic fall in its relevance over a period of
time. Some states like Karnataka and Haryana have been showing higher growth in
horticultural crops. However, they are among the states where this sector is yet not a
major contributor to the agricultural sector. The value of location quotient is less than one
in their case. Himachal Pradesh is one of the few states, which has demonstrated high
value of location quotient, huge contribution from horticultural sector to the aggregate
output of the agricultural sector and that experienced moderate to high growth in area
under horticultural crops in the last three decades.
99

---~ -
figure 4.2: Location Quotient of Horticultural Crops In India
10
9
8
-
I e
5
j
!

4.3. Diversification and Horticultural Crops: A Case of
Himachal Pradesh
Diversification towards horticultural crops is acknowledged as an alluring option,
but where such diversification be more induced in the country like India, which is highly
diverse in its climatic features, is a question of great interest among the policy makers.
Should all states move towards diversifying their production pattern? This may have
serious consequences for food security in India, as it is found that there has been an
increase in substitution effects besides technological fatigue in major food grains. It is
vital to follow an agro-c1imatic approach by identifying the relative comparative
advantages of states in specific crops in order to decide the direction and regions for
diversification. The second concern is about the regional dimension. Diversification
towards horticultural crops requires better market and infrastructure facilities and at the
same time, government expenditure on infrastructure and public investment has been
declining continuously over the years (Chand, 2005). The moot question is whether
backward regions with relative comparative advantages in some horticultural crops can
realize any growth in diversification? What follows is an appraisal of this complex issue.
Diversification means to induce spreading of crops given the diversity in the agroecological
zones. In other words, cropping pattern is dictated more by comparative
advantage. India presents a picture of great diversity in geography and agro-climatic
zones, some of which are less conducive to cereals and more conducive to non-cereals.
Yet, the over-emphasis on promoting food-grains through support, subsidy and research
inputs have resulted in the domination of cereals at the cost of other crops 18. There are
instances of states like Himachal Pradesh (HP), where agriculture is still dominated by
food grains despite distinct comparative advantage for horticultural crops, in terms of its
agro-climatic conditions. Such non agro-ecological approach at the national and regional
level have spawned many problems, including creating dualism in agriculture in terms of
backward and progressive regions.
" PresenIl y 66% of GCA under food grains
101

The unresolved question is whether diversification can be a panacea; if so will it
solve the problem of regional disparity. Given this challenge, can India hope to address
the problem of regional disparity through diversification, which, by definition, requires
better infrastructure, better access to markets etc., all of which calls for enormous state
funding. There is a fear that marginal regions will further get marginalized and may not
participate, despite having better potential for diversification. Indian government is
increasingly providing higher subsidies to food grains while its expenditure on
infrastructure has shown dramatic decline, which contradicts the government claims of
funding infrastructure to improve diversification and achieve better growth and regional
development. The issue is whether the low profile states with higher potential for
producing horticultural crops can participate in the quest for earning better income and
growth. It is feared that the regions with high growth in the past might surpass the weaker
states in diversification and further accentuate the problem of regional disparities and
growth in India.
The major concern is about emphasising diversification in states, which have
inherent advantages in production of non-cereal crops, but are precluded to produce so
mainly due to irrational policies of the government. Himachal Pradesh is one of the best
example, as agro-dimatic wise it is endowed with potential of growing wide range of
fruits and vegetables, but is still dominated by food crops. This region has enormous
potential and comparative advantage in production of fruits and vegetables that have high
domestic and international demand. There is need to encourage diversification in HP.
4.4. Significance of Horticultural Crops in Himachal Pradesh
Himachal Pradesh is a mountainous state with very wide climate diversity ranging
from sub-tropical to temperate regions. In total, there are four different agro-c1imatic
zones in the state (figure 4.3). Himachal Pradesh has all the climatic attributes to produce
wide range of crops, ranging from food grains, horticulture to medicinal, herbs and
flrcu.lture. Himachal Pradesh is called the horticultural state of.lnd~ due to its
potenllal for producing most of the horticultural crops grown In India. Wide range of
102

fruits and vegetables are grown in Himachal Pradesh that includes around 25 types of
frui ts and roughly all vegetables that are produced in other parts of the country.
Figure 4.3: Agro-Climatic Zones in Himachal Pradesh
.--._-
--- _
--
......... -,--
Zone 1 Zone 2 Zone 3 Zone 4
Low-hill
EcololZV ubtrooical Mid hill sub-humid Hildt hill temoerate wet Hildt hill tempeJllte dry
Geographic area (in %) 35 32 25 8
rroooed area (in %) 33 53 11 3
Irrigated area (in %) 17 18 8 5
~itude (MASL*) Uoto 914 915-4523 1524-2472 2476-7000
Rainfall (cm) 100-150 \50-300 100-200 20-50
Note.
i. MASL is mean above Sea Level
Source: Directorate of Agriculture, Shiml&, lIP
Diversification of agriculture towards selective horticultural crops is compatible
with the comparative advantage of the region (Sharma, 2005). Table 4.2 indicates that net
returns from production of different fruits and vegetables are relatively higher than
returns from other crops produced in the state. The state has seasonal, quality and cost
advantage in production of a variety of fruits and vegetables and adoption of horticulture
crpps is advantageous to Himachal Pradesh economy in several ways. First, it promotes
the productive use of abundant marginal lands available in the region. Second, these
crops help in maintaining and improving the ecology and environment by promoting soil
103

conservation and improving soil fertility. In economic terms, it can lead to significant
improvement in the income and employment and quality of Iiftl of the people (Sharma et
aI, 2003). A comparison of productivity of major crops in Himachal Pradesh with that of
all India shows that the productivity of many of the horticultural crops in Himachal
Pradesh is higher than all-India average (table 4.3).
Table 4.2: N et R etums f rom V' anous C rops In 'Hi machal Pradesb, 2001
Cate20rv Crops I Net returns (Rslha)
Cereals
Wheal 2259
Maize 1715
Barley 901
Paddy 1120
V02etables
Tomato 32989
Capsicum 18493
Peas 10706
Cauliflower 33691
Cabbage 10811
Beans 3136
OtbercroDS
I Potato I 5058
I Ginger I 31939
Plantation Cro,,"
Apple 26900
Citrus 9400
Plum 6570
Peach 6340
Apricot 12650
Pear 3810
Source: Sharma, et aI. 2003
Table 4.3: Productivity of Major Crops in Himacbal Pradesb and India, 2001
(Rs.lba)
CroDs Himachal Prodesb India
Rice 1423 2914
!wheat 1266 2756
Maize 2272 1769
~omato 34645 15068
Beans (ereen) 9921 9600
Pea (2reen) 9574 10000
Cabbage 28663 18085
k:auliOower 18164 15000
~icum
9355 9074
tato 23890 18657
Source. Shanna, et 01. 2003
104

The relevance of any crop or crop-group in the agricultural sector of the economy
can be gauged from the relative growth in th~ir area and value of output. The comparison
of horticultura1 sector crops with non-horticultural crops in Himachal Pradesh shows that
horticultura1 crops are an important source for the growth of agriculture. The growth of
area and value of output of horticultural crops is relatively high as compared to nonhorticultura1
crops (table 4.4). This signifies the growth in the technology (productivity)
and market (prices) of crops within horticultura1 sector. Since there is enonnous
difference in the rate of growth in the value of output of horticultural crops as compared
to other crops, this can result in significant increase in the contribution of horticultural
sector to the overall growth in the agricultural sector over a period of time as is evidenced
by the fact that the share of horticultural crops in the total value of output generated in
agriCUlture increased significantly from 24 % in TE 1982 to 47% in TE 2002 (table 4.5).
Ta ble 4.4: Growth Rate in Area and Value of Output of Major Crop Groups in UP
19705 19805 19905
Area Value Area Value Area Value
Non-horticultural Crops 0.88 0.96 0.23 1.47 -0.41 0.75
Horticultural Crops 4.12 7.25 4.72 5.47 3.60 5.90
Total 1.03 1.74 0.51 2.20 -0.55 3.33
Source: NatIOnal Account Statistics, Annual Season and Crop report and Directorate of HortIculture
(Various Issues)
Table 4.5: Sh fC rop roups ID ross VI a ueo f 0 urpu t t' ID A 19ncu • ltu re ID • HP
TE 1982 TE 2002
Total Cereals 60 40
Horticultural Crops (F&V) 24 47
Others Crop Groups 16 13
Note:
i. Figures are in Percentage
Source: National Account Statistics (Various Issues)
are 0 G 'G
Broadly, there are four different agro-c1imatic zones in Himachal Pradesh. The
geographical area and agro-climatic features of the districts in Himachal Pradesh are
quite diverse which explains, partially the difference in the pattern of crop mix across
districts. The regional pattern of rate of growth in area under different crops indicates that
arta under fruits and vegetables has grown at a faster rate in all the districts of Himachal
Pradesh, with few exceptions (table 4.6). Wheat and maize, which dominated the
\05

cropping pattern in early 19708, did not experience significant growth in area. Further,
area under few other hi&!! value crops, like rice and oil seeds declined during the same
period. Apple is the most prominent horticultural crop in Himachal Pradesh. The district
level pattern demonstrates that major growth of area under apple has taken place in
Shimla, Chamba, Kinnaur and Lahaul & Spiti (L&S). Though, all the districts have
shown higher rate of growth in the area under fruits and vegetables than any other crop,
only few districts have significant proportion of area in their gross cropped area under
these crop groups.
a e . . ompoun d G ro wth Rat
esm . A rea 0 fM' alor C rops In . D' Ishicts 0 fHP
T bl 46 C
Districts Rice Maize Wheat Barley Millets Pulses Oilseeds Potato Voget Apple
I
abies
. Bilaspur -3.\3 0.83 1.08 -0.53 NI ·9.95 ·1.98 -0.28 3.44 NI
: Chamba -0.43 0.21 0.78 -2.12 -2.55 ·0.03 0.25 1.28 1.35 13.75
Hamirpur -3.11 0.84 0.66 -0.60 NI -13.10 -3.00 0.27 -0.64 NI
Kangra 0.04 0.82 0.60 -2.26 NI -3.50 -1.76 2.81 2.20 NI
, Kinnaur NI 0.37 -5.51 -2.35 -3.39 6.64 NI -2.92 1.60 8.29
i KulJu -2.49 1.07 1.17 -2.42 -6.04 0.21 1.75 0.38 2.64 5.36
, L&S NI 3.76 4.46 -2.70 -3.53 9.42 0.69 0.35 0.34 NI
Mandi -0.90 2.27 0.61 -1.17 -0.98 -2.14 0.98 0.61 3.41 -2.65
Shimla -2.84 -1.10 -2.12 -2.39 ·2.61 0.90 4.46 -1.45 2.02 5.54
, Sirrnour 0.28 0.04 0.13 -1.25 -0.88 -0.59 0.36 2.60 5.12 -0.27
. Solan -1.03 0.01 0.58 0.20 NI 4.80 -1.78 -2.40 4.80 NI
Una 0.22 2.00 1.10 NI NI ·9.79 2.14 6.87 3.56 NI
Nt crop IS not lmportant for the dlstnct
Source: Annual Season and Crop Report, various issues
Fruits
6.90
5.67
6.43
7.40
6.50
5.75
NI
1.87
5.66
1.18
0.45
6.65
The distribution of value of output, productivity and its growth over a period shows
that the districts, that experienced higher rate of growth in area under horticultural crops
have achieved relatively high growth in the value of aggregate output and productivity
(table 4.7). Shirnla, KuJlu and Labaul and Spiti experienced a compound growth of 7.76
%,9.54 % and 9.89 % respectively in value of output and compound growth of 8.85 %,
7.17 % and 8.65 % respectively in productivity levels. In districts, where horticultural
production is low due to them having lower acreage under the crops, the rate of growth in
output and productivity is either stagnant or negative. Bilaspur and Una are the two
districts, where the allocation of area under the horticultural crops is minimal and the
~wth rate of output in these regions stands at -0.02 and 0.15 respectively. The growth
rale in aggregate land productivity ill these regions has been only -0.23 and -0.06
respectively from 1972 to 200 I.
106

Table 4.7: Value of Agricultural Output and Productivity in Districts ofHP
i Districts Value of Value of Growth rate in Total Value of Total Value of Growth rate in
-
! output (IE output (IE value of output Productivity Productivity productivity
1974) 2001) 1972-2001 . (TE 1974) (TE2001) 1972-2001
I
,
4869 4846
, Bilasl)llr
-0.02 8852
8502 -0.23
I Chamba
5030 8554 2.69 8\13 13160
2.39
• Harnirpur
4929 5328 0.31 6846 76\1
0.43
Kangra
19204 27079 1.58 %50
12365
1.08
,
Kinnaur
896 2764 8.01 8150 307\1
10.56
, Kullu 5800 20192 9.54 \1373 32568
7.17
i Lahaul & Spiti
583 2084 9.89 19444 63152
8.65
, Mandi
12851 18045 1.55 8802
11421
1.14
. Shiml.
12449 37583 7.76 \1857 39149
8.85
,
~Sinnour
6889 12959 3.39 9064
17051
3.39
lSolan
6629 12154 3.21 9470
18991
3.87
I Una
5813 6041 0.15 8676
8630
-0.06
I Himachal
lPradesh
85944 157629 3.21 9372
16605
2.97
Note.
I, The value of output and productivity are @ 1993-94 prices (in Rs.)
Source: Annual Season and Crop report (Various Issues)
To examine the effect of fluctuating proportionate area under fruits and vegetables
and consequent change in level of productivity, regression method is used. It is
hypothesized that productivity levels (value of output per ha) in a region is a function of
the proportionate area under fruits and vegetables. It is important to note that change in
crop pattern in terms of the proportion of area under crops represent the mechanism of
adjustment to adapt to or to take advantage of opportunities emerging out of changes in
economic environment, accessibility to market, labour market, infrastructure and
technology. Changes in crop pattern could be an important source of output and
productivity growth even in the absence of technological advancement (Chand, 1996)_
Separate regressions is done for the periods, 1972-75 and 1998-2001
"f"rp 1972-75 = 7927.84 + 549.17* PAFV
-v~luc (3.761)
R2~O.756, N=12
107

TVP 1998-2001 = 1187.80 + 1089.33* PAFV
t-value (3.\54)
R2 = 0.706, N=12
* Significant at I % level
TVP: Total Value Productivity
PAFV= Proportionate area under fruits and vegetables
The relationship between productivity levels and land allocation in favour of fruits
and vegetable is high and significant. In other words, the change in productivity levels
during early 1970s and late 1990s were significantly influenced by the diversification of
cropping pattern towards high value horticultural crops. The picture of different time
period indicates that significance of allocation in favour of horticulturaJ crops has
increased substantially.
4.5. An Overview of Selected ViUages and Socio-Economic
Characteristics of Farmers
A multi-stage purposive sampling procedure was followed in order to select the
state, district, block and villages. A district was chosen on the basis of how representative
it is in diversification towards horticultural crops. On the basis of the temporal and spatial
pattern of diversification and horticultural development across blocks, Theog was chosen
as the block of study. The sample was drawn from four villages, two villages each for
fruits and vegetables from this block as being representatives of diversification towards
fruits and vegetables respectively. Sample of 30 farm households was drawn from each of
the four villages with the aid of a stratified and proportional random sample approach '9 .
Cauliflower and apple were chosen as the respective vegetable and fruits crop due to the
higher diversification towards these crops in the selected villages.
Table 4.8 provide the details of selected villages. In Village I and II, vegetables are
grown extensively. These villages have respectively 592 and 398 hectare of total
cultivated area out of which around 50% and 60% respectively is irrigated. Vegetables
c'.v~r 72% and 84% respectively of the to~al gross area. Among vegetables, cauliflower, a
19
The details of sampling are provided in Chapter one
108

water-intensive crop, is the favourite and covers most of the area in these villages,
followed by bean and peas. Maize is one of the most prominent subsistence crops of the
regions; all other food crops are produced at lower scale. The two villages (Village III
and N), where fruits are grown at a higher scale, have 532 and 586 hectare of total
cultivated area, of which respectively 30"10 and 12 % of area is irrigated. Apple is the
major crop in these villages and covers 85% and 89% respectively of total cultivated
area. Bearing apple trees cover only about 50"10 area in village III, pointing to the late
onset of diversification. In village IV, the process of diversification commenced in the
early part of 1900 when apple was introduced in Himachal Pradesh by Mr. Stocks who
came down from Holland. In village III, considerable area is also under vegetables.
Relatively higher level of irrigation in this village had made it possible to grow waterintensive
vegetable crops, but, most of the area is now non-bearing apple trees and hence
this region, like Village IV would reach a high level of specialisation in the near future.
High amount of area being under bearing apple trees resulted in the low level of cropping
intensity in these two villages.
Table 4.8: Details of the Selected Villaaes
-
Crops Cauliflower
VIII.ges
Uait
Number offana Housebokl Numbers 146
Total tultivated land Net Cropped Area (Ha) 592
T otallrrlg.ted land Net Irrigated Area (Ha) 338
Area Under crops
Apple-Bearing Trees (Ha) 0
Apple- Non-Bearing Tress (Ha) 0
Other fruits (Ha) 15
Vegetables (Ha) 945
Food grains (Ha) 153
Gross Cropped Area I (Ha) 1113
Cropping Intensity I (Percentage) 188.01
Note: I. Ha- Hectare
ii. Village I,II,III,IV are Govai, Sainj, Sandhu and Shilaru respectively
Source: Primary Dala
dOBlloated Apple dominated
VOla ... Vill ....
Village I vW"I_n ViDag.1II ViII·C_ IV
174 148
398 532
198 192
35 266
52 186
7 16
508 118
97 106
699 692
175.63 130.08
The distribution of farm size differs in villages of both crop groups (table 4.9). In
the base of fruits , these villages are dominated , by the semi-medium and medium farmers
135
586
76
410
117
29
47
99
702
119.80
109

(own land between 2-10 hectare), whereas, in the case ofvegetables, small and marginal
fanners (owns between 0-2 hectare) are in majority. Semi-Medium farmers are
considerable in numbers in these villages, while medium and large farmers are small in
numbers.
Cauliflower
Table 4.9: Farm
-
s· Izean dS amDllD1!. r f rom the Selected ViUal!.es
Crops
Villages Village I Villagen
Farm size
Unit
Marginal Farmon Less than I Ha 35 (7) 43 (8)
Small Farmon 1·2 Ha 49(11) 67 (12)
dom.inated Villages
Semi-Medium Farmen 24Ha 37 (7) 48 (8)
Medium Farmen 4-10 Ha 13 (2) 16 (2)
Large F Brmen More than 10 Ha 14 (3) 0(0)
*Note:
i. Figures in parenthesis are the sample collected from each village
Sou,ce: Primary Data
Apple dominated ViDages
Village III ViDageIV
17 (3) II (2)
47 (9) 29 (6)
63 (14) 51 (12)
IS (3) 30(7)
6 (1) 14 (3)
The demographic features of selected fanners show that there is not much
difference in the age of the household head across villages and most of the households
are headed by old persons (table 4.10). In many developing countries, it is the household
head that generally takes the decision of resource allocations (Yilma, 2(05). It is
expected that young farmers tend to take more risk and hence could make higher level of
land allocation towards high value commercial and risky crops. The incidence of
illiteracy among the household head is quite high though fanners have many years of
experience in fanning of the selected crops. The households are mostly headed by male
members and there is not much difference in the family size across villages. Interestingly,
the dependency ratio is higher for the vegetable growers which mean that it could be a
forced commercialization in absence of income from other sources. However, fruit
growers are experiencing higher level of shortage of labour as indicated by higher landlabour
ratio, which could act as a constraint in higher level of land allocation towards the
selected labour-intensive crops.
IIO

Table410 , : D em02rapJ h' Ie F eatures of tbe Selected Farmers
Variables Indi.ators Cauliflower dominated Villages Apple domiDated Villl£es
Age Mean age of the
Village I Village II Total Villae" III Villae" IV
60.43 58.63 59.53 61.13 61.50
household head
Education Number
household
of 13 13 13 9 13
headed by
illiterate
Experience Average yerus of 17.30 11.36 14.33 20.40 23.46
experience in
farming (in
numbe;)
Gender Number of 3 9 6 7 7
households
headed by
females
Family SIze Average number 5.9 7.4 6.65 6.2 6.3
of household
members
Dependency Average number 2.0
ratio of household
2.5 2.25 1.6 1.9
member not
involved in any
earning activity
RHource Land/Labour 2.37 3.90 3.13 4.35 7.56
Constraint
Source. Primary Data
It is clear from the table 4.I I that farmers involved in fruit fanning are using higher
amount of fertilizer per hectare than vegetable growers. Also, for vegetable growers, the
intensity of irrigation is significantly high, which shows that irrigation plays vital role in
vegetable fanning and that not all horticultural crops are suited to rain-fed regions, where
irrigation facilities are not developed. Fruit crops are more labour-intensive in
comparison to vegetable crops. Due to shortage of home labour, most of the apple
growers are dependant on hired labour for crop production and marketing operations. It is
also noticed that sizeable number of apple growers are also involved in non-farm income
activities which provides additional money to the farmer for investment in crop
production and marketing operations. Non-farm income also could hedge against farmrelated
risk and may act as a factor in improving farmers' propensity towards allocating
land to high value commercial crops.
Total
61.31
II
21.93
7
6.25
1.75
5.95
III

Table 4.11: Input Use Behaviour and Other Farm-related Characteristics of the
Selected Farmers
Variables Indieaton Cauliflower dominated Villa!!es Aj>ple dominated Villa!!es
Fertilizer Use Fertilizer use kg
pcrha
Irrigation Share of net
intensity irrigated area to
net cropped area
Labour hiring Percentage of
propeosities households
hiring labour for
fann purposes
Non-Farm Percentage of
Income
households
earning non-farm
income
Assets VaIue Value of farm
assets (in
Rupees)
Access to credit Percentage of
households
obtaining credit
from formal
a£eDCV (Bank)
Note.
i. N=30 for each village
Source: Primary Data
Villa2e I ViUa\lell Total ViUagem Village IV Total
1.28 1.52 1.4 1.66 2.12 1.89
57.04 49.86 53.45 11.47 10.29
60.00 46.66 53.33 76.66 83.33
20.00 73.33 46.66 46.66 66.66
22740 36933 29836 35510 45150
30.00 20.00 25 40.00 30.00
10.88
79.995
56.66
40330
35
4.6. Income and Risk from Horticultural Crops Production
There is a basic difference between the selected fruit and vegetable crops in terms
of the gestation period involved in production and marketing options. Cauliflower is an
annual crop, and the returns from the crop are related with corresponding year of cost.
Apple is a perennial crop and there is a gestation period in production of 5-7 years after
which farmers start getting production. The production cycle for apple also varies; after
12-15 years of plantation only farmers start getting higher level of production from the
crop. Additionally, apple is relatively less perishable as compared to cauliflower. More
marketing opportunities exist for selling apple. These differences determine the returns
and risk from either of the crops. Thus, the economics of the selected crops are worked
out separately.
112

4.6.1. Income and Risk from Cauliflower
The aggregate cost and returns determine the viability of production of a crop.
Difference in the farm size and its relation with costs and returns from the crop is of high
importance. It is generally argued that due to the presence of scale economies, many
small farmers prefer not to indulge in the production of high value crops (White and
Irwin, 1972, Pope and Prescott, 1980). Only farmers having larger holdings gain more
economically from the high value crops, but it can be argued that larger resources are
required as the farm size increases. Also, high managerial requirements and other
resources might increases the transaction costs in producing the crop and make the high
value crop production relatively less remunerative in large holdings. Additional factors
like resource availability including irrigation and lahour, technology, capital
intensiveness etc also may influence the overall returns from the crop. The decision of
allocation of different resources (land, labour and capital) also affects the viability of
crop. In this context, costs and returns are computed and resource allocation examined
across different farm size for cauliflower crop. In order to work out the cost and returns
of cauliflower cultivation, cost and return concept propounded in Farm Management
Studies in India are used.
Resource allocation, especially use of fertilizer, chemical spray, and irrigation is
important from the point of view of its impact on the viability and returns from the crop.
Interestingly, the intensity of all vital resources including fertilizer, irrigation and
chemical spray declined with increase in farm size (table 4.12). In other words, there is a
negative relation between the allocation of resources and farm size. This indicates that as
the farm size increased, farmers probably faced more constraints of resources20. Decline
in the use of intportant resources could affect the viability and profitability of the crop;
this can be tested through the calculation of costs and returns. The following paragraph
attempts an analysis of cost and returns of farm operations.
" It'is found previously that as farm size incr",*", the availability of home labour decreases that increases
dependency of farmers to increase costs
113

Tbl412F
tlCa on BY a I ower G rowers
Farm Size Fertilizer Spray per Irrigation Share of Income from
use per ba (in Rs) per ba (in Cauliflower to aggregate
ba (in Rs)
Rs) income of housebold*
a e . : arm s· Ize an dReso uree All ti b C uro
-
Maf'ldnaI Farme .. 1816.41 2211.28 310.96
Small Farme .. 1474.59 1679.73 220.74
Medium Farme .. 876.35 1267.73 168.46
Large Farme .. 729.94 901.30 109.16
Note:
i. Aggregate income includes both farm and non·farm income
Source: Primary Data
56.57
55.87
50.90
41.00
The results demonstrate that as farm size increases both aggregate cost (C2) as well
as paid-out cost of crop production declines (figure 4.4). This was expected as there was
a decline in allocation of resources with the increase in farm size. Consequent, we noticed
decline in the productivity, gross and net returns as the farm size increases. It is found
that small farmers are able to obtain the highest productivity and returns from the
production of cauliflower. In order to find the link of land allocation with farm size, all
fann size are divided on the basis of proportion of area under cauliflower crop to the total
net area. There are two categories; lower «.50) and higher (>.50) land allocation to
cauliflower (figure 4.5). The comparison of farmers with identical farm size illustrates
that farmers with higher level of land allocation gains more by decline in cost per ha and
get increased net returns from the crop. This demonstrates that increase in land allocation
did prove remunerative and more profitable for the farmer, irrespective of the farm size.
Higher extent of land allocation towards high value crop has a favourable effect on crop
economy brought about hy a decline in per unit cost with increased returns from the crop.
114

Figure 4.4: Economics of Cauliflower Crop
150000 t--------------j
I_ ".rpinal Farrnera
I .Smell FarmefW
,CSerTII·MedlUfTl Farmel"l
1 C Medium and L.arge F~
Cost per He Cost (Pliid out) YIeld Per Ha Gross rftims Net returns over Nat returns o-..r
(C2) per He (KQ) p., Ha Cost 2 Pw He PIIi::t-oul cost
"'Ho
Source: Primary Data
Figure 4.6: Economics of Cauliflower by Farm Size for Different Level
of Land Allocation
150000
f-
~ 100000
o
t '.
M_ ...., I
I
low (0- i High low(Q- High
0.50) I (>0.51) 0.50) (>0.51)
-
~--
l_(O- High '-(0- High
050) (>0.51) 0.50) (>0,51)
-., I S-Medi'n and IA ...
.Coat per Ha (C2)
i. PilICkIut Cost pel' Ha (Puchlsed)
IOGrou rl'turl1ll Per HIlI
10 Net returns over C2 ~ Ha
8Net returna aver Pald-out 00II: Per ....
Source: Primary Data
liS

Further, mean price, yield and income received by the cauliflower growers are
calculated on the basis of three years data (table 4.13). The results demonstrate that
medium and large fanners received relatively low price for their produce but faced less
variability in the price. On the contrary, the same group of farmers experienced low yield
along with high variability in crop productivity. Small and marginal farmers' gets better
price for their produce and their productivity of the crop is also higher.
Tbl a e 413 T d" Pri
"
: ren ID cean d Y" Ie Id so fC au rfl lower c rop
Farm Size Mean leyels of Mean levels of Yield
Price and its and its variability
variability
Martrlnal Fannen 10.49 (1.62) 19447 (2835)
Small Farmers 10.81 (1.58) 19359 (3005)
Semi-Medium 10.78 (1.80) 17697 (2271)
Farmers
Medium and Large 8.44 (1.55) 13925 (3057)
-.
Farmers
Note:
i_ Data is calculated on the basis of fann level data of three years, 2004, 2005 and 2006
ii. Price is in Rslkg, and Yield is in Production per hectare
iii. Figures in the parenthesis are the Standard Deviation.
Source: Primary Data
Information on different prices and production realized by farmers in the last ten
years was collected. Using different prices and production figures of cauliflower, the
maximum, expected and minimum net income over the paid-out cost by the farmers are
computed (figure 4.6). The result illustrated that the farmers who experienced high
income in the event of favourable market and weather condition also lost the most when
both price and production crashed. The picture of gain and loss is different, when one
considers the same across different farm sizes (figure 4.7). Small and marginal farmers
lost the most when both the price and production crashed. But, medium and large farmers
gained relatively more in the situation of favourable price and production conditions. It is
the small and marginal farmers who bear the brunt of price and production crashes. This
demonstrates difference in the results, when one compares the three year average data
with the expectation or experiences of the farmers over a period of time.
116

Figure 4.8: Sensitivity Analysis of Income from Caulltlo_r
o
·'oaooo
~
I
• ;\ ! I t
~[) ,. \'~\}'f d f\
., II ft I .', I '• •
~\0U ~~ l~j~/~,:fi.·~~vy.:.~~,Jo
...
, 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 3 7 50
F ....
-+-- Mnimtm Incon'I/t ~
C2
----- Elq:)ecttd I~ 0-
C2
---.- Maximum Ir.::onB OWl
C2
Source: Primary Data
Figure 4.7: Sensitivity Analysis of Income from Caullftower by Fann
Size
,«OOOr--------------------------------------------
.MBrga"*1 Fal'n'len
.Small Firmer.
D5eml-Medlum Fa",*"
DMectum and large F~
Expected
income- peilbut
'""'
Ma;drT'lum
neon.- C2
Source: Primary Da13
117

4.6.2. Labour Demand and Output Supply Estimates: A Profit
Function Approach
Efficiency of agricultura1 production is an important aspect from the point of view
of agricultural development in developing countries. Assessment of economic efficiency
has implications for policy issues regarding price policy and input supply among others.
Economic efficiency in this study is defmed in the context of a profit function as
developed and applied by Lau and Yotopoulos (\972) in analyzing Indian agriculture.
Profit function shows profit as a function of prices only. Here, it is assumed that
prices are determined exogenously. There is an advantage in estimating an indirect profit
function i.e., a function of prices and quantities of fixed factors of production as
compared to estimating a production or revenue function that expresses quantity of output
as a function of quantities of all inputs. The advantage is that no endogenous variable
(output or input level) is included in the indirect profit function as an explanatory variable
and therefore simultaneous equation problems or bias are avoided in the estimates. The
reduced form elasticities obtained from an indirect profit function reflect the output
supply response of a profit maximizing price-taking firm assuming constant prices of
variable factors. However, unlike the production functions elasticities, they do allow for
the adjustment of the quantities of variable factors to an increase in the fixed factors. As
the quantity of a fixed factor such as capital increases, the marginal productivity of all
variable factors is expected to rise. This increase in the marginal productivity result in
employing of more of variable factors. Under these conditions, the mutalis mutandis
elasticities obtained from profit function is found to be more appropriate for policy
analysis than the ceteris paribus elasticities obtained from a direct production function. It
is known that the Unit Output Price (UOP) profit function is decreasing and convex in the
normalized prices of variable inputs and increasing in quantities of fIXed inputs and in the
prices of the output. The major property of the UOP profit function model is that it
provides input (like labour) demand as a function of normalized wage rate and quantities
of ~ther inputs like land, fertilizer, irrigation and capital.
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We have measured the output supply and factor demand equations by using the
profit function as provided by Lau and Yotopoulos (1972). Labour is treated as the only
variable factor as only the wages paid to the labour vary across farmers. This is because
prices of other factors of production i.e., fertilizer, chemicals and irrigation do not vary
across fanners. Profit is calculated by using the price received by farmer in the current
year and the price expected by them. The rationale for running two profit functions is that
the price of vegetable crop fluctuates too often and price in any given year may be of less
significance in the decision of farmers regarding the employment of the variable factors.
The expected price also could influence the decision-making of the farmers. Currently,
there is no study that tries to fmd the relative significance of one-year price and expected
prices on the economic efficiency of the farmers. The model of profit function is given
below.
Consider a firm's production function as
Y = i{XI ..... X.; Zl ..... z,.) --------------------1
Where Y= Quantity of output; x= quantity of variable inputs and Z are quantity of fixed
inputs
The profit function from a single product is given as
n = P. Y - Ifi Xi -----------------------------2
where P and Y are the price and output respectively and ri is the unit price of the Xi
variable input.
Substituting 1 into 2 we get
n=P. f(xl .... .x.; Zl ..... z,.) - Irj Xj ---------3
Where 11 is profit defmed as current revenues less current variable costs, P= unit price of
outpu~, and fj = unit price of the ith variable input and Xj= quantity of ith input
119

Dividing both sides of equation 3 by P, we get
It' = P. f(Xl ••..• x,,; Zl •••.. z..) - Lr;' X; ----------4
where It' is defmed as the unit output price (UOP) and r;' is the nonnalized price of the
ith input
The equation 4 may be solved for the optimal quantities of variable inputs X; s, as
functions of the nonnalized prices of the variable inputs and of the quantities of the fixed
inputs. By converting the profit function (3) to the UOP profit function (4), we will have
all functions used in the analysis in terms of relative prices.
Labour is treated as the only variable factor of production and capital and land as
fixed factors. Using Shepard lemma, both the output supply and labour demand functions
are estimated below:
Ln It' = Ln A + al Ln w + ~l Ln K + Il2 Ln T -----------------5
and
- w L Ill' = a I ---------------------------------------------6
Where It' is UOP profit (total revenue less total variable cost, divided by the price of
output), w is the normalized wage rate, K is fixed capital, T is cultivable land under the
crop, L is the quantity of labour employed, and Ln is the natural logarithm.
On the basis of the assumption that a farm maximizes profit subject to unknown
exogenous disturbance, the additive error in the second equation may arise from the
differential abilities to maximize profits or divergences between expected and realized
prices. Given this specification of errors, it is apparent that Zellner's method, imposing
known constraints on the coefficients in the equations, provides an asymptotically
efficient method of estimation. Equations 5 and 6 were first estimated jointly imposing
thi constraint that a 1 * is identical from both equations. Next, we proceeded to the
additional hypothesis of constant returns 10 scale (~1 + p2= 1) in production by imposing
120

the second restriction using Zellner Seemingly Unrelated Regression (SUR) method. The
results of the jointly estimated equations, imposing the condition that «I is identical in
both the equations by using the current year price and expected price are presented in
tables 4.14 and 4.15 respectively.
·able 414· . Joint Estimation of Profit Function and Labour Demand Function with Current Price
FllBction Parameter Single Zellner's method with restriction
equation Unrestricted One Two restriction
OLS restrietion al*= al* &
(al*= al*) 111+ 112=1
UOP Profit 110 ·9.385 ..(J.017 0.009 ..(J.910
fundion aI-labour 0.190 0.188 -0.083 ..(J.083
III-Capital 2.080 0.931 .133 1.010
112-Area ·1.144 ·0.055 -0.121 ..(J.022
Labour al ..(J.690 -0.084 -0.083 ..(J.083
demand
function
Source: Pnmary Data
Table 4.15: Joint Estimation of Profit Function and Labour Demand Function with Expected
Price
FUDttion Parameter Single ZeHner'. method "ilb reslrittion
equation Unrestricted One Two restriction
OLS restriction gl*= ai'll &
(al*=al*) 81+ 82=1
UOPProfit 80 -1.442 ..(J.027 ·0.010 ..(J.731
function ai-labour -0.713 -0.785 ·0.156 -0.159
III-Capital 2.880 1.130 0.172 0.790
112-Area ·1.855 0.188 0.135 0.195
Labour al -0.630 ·0.297 -0.156 -0.159
demand
function
Source: Primary Data
It is clear from tables 4.14 and 4.15 that the UOP profit function is not only
decreasing in wages but also convex. However, a difference is noted in terms of using
current year price and expected price. The coefficient of land is positive in the case of
expected price and not so when current year price is used. This might be so because
prices of cauliflower fluctuate widely and due to the absence of any minimum price,
fanner considers the most expected price in their decision-making. The coefficient of
capital is found high and positive in both the cases.
Indirect estimates of production function coefficients can be obtained from the joint
estimates of profit and labour demand function. These estimates are consistent; a property
121

that is absent in the estimates derived directly from production functions as estimated by
ordinary least squares. The formula derived from Lau and Yotopoulos (1972) show the
correspondences between the estimated coefficients of profit function and indirect
estimates.
Labour= all (ai-I)
Capital= fll / (1- al)
Land= 1321 (1- al)
- -
Table 4 16- Indirect Estimates of Production Function Coefficients
Coefficient
RestrictioDs
None
One
Current price
Labour -0.232 0.076
Land -.0684 -0.112
Cagital 1.\30 1.040
Sum of elasticities 0.829 1.004
Expected price
Labour 0.439 0.135
Land .1050 0.117
Capital 0.532 1.140
Sum of elasticities 0.967 1.150
Source: Computed usmg pnmary data
Two
0.079
-0.021
0.940
1.000
0.137
0.168
0.693
1.000
The elasticities estimates for fanns, on the basis of expected pnce, have the
expected signs and have statistical significance (table 4.16). In terms of current year
prices, land turns out to be negative. Capital in both the cases has high elasticity.
Labour demand and reduced form output elasticities can be calculated m the
following manner.
From 6
-wU7t= al
Taking logs on both side, one get
\n~=ln(-al)+In7t -lnw .................................................... 7
Substituting this into the 5 one get,
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InL=In(-aI)+InA+(al-I)Inw+JlI InK+Jl2InT .............. 8
The labour demand function here is the function of nonnalized wage rate, land and
capital. The labour demand elasticities with respect to the wage rate, land and capital are
given byoInLlaInw
=aInltlaInw - 1
and for land it is
oInL I a tnT =aInlt laInT
and similarly for capital it is
oInL I a InK =alnlt loInK
The labour demand elasticity with respect to the price of output is given by
olnL I a Inp =oInL I 0 Inw
olnw/oInp
Table 4.17: Labour Demand and Reduced Form Output Elastici ties
I Current vear price E,pected price
L.bour demand elasticities
WaKe rate ·1.083 -1.159
Land ·0.022 0.195
Capital 1.0lO 0.790
Price of output 1.083 1.159
Reduced form output elasticities
WaKe rate -0.083 -0.159
Land -0.022 0.195
Capital 1.0lO 0.790
Price of output 0.083 0.159
Source. Primary Data
The labour demand elasticities with respect to wage rate, land, capital and price of
output, respectively, are -1.083, -0.022, 1.010 and 1.083 for present year price, whereas it
:s -\.159,0.195, 0.790 and 1.159 on the basis of expected price (table 4.17). The reduced
fonn output elasticities with respect to wage rate, land, capital and price of output,
123

espectively, are -0.0835, -0.0223, 1.010 and 0.0835 for present year production, whereas
it is -0.159, 0.195, 0.79 and 0.159 on the basis of expected price. The own price elasticity
of labour is greater than one in absolute value indicating an elastic response of labour
utilization to wage rate. On the other hand, labour demand also responds positively to
increase in the endowments in land, capital and increase in the expected price of the
output. The high coefficient of capital indicates its importance on the greater use of
labour with increase in capital intensiveness. The positive coefficient for land is
consistent with a priori notions when price expectation is used and not on the basis of
current year price. Furthermore, the output response elasticities show that output
decreases with an increase in the normalized wage rate. The output supply responds
positively to change in the price of the crop though the coefficient is not high. One may
also note that with the expected year price, the response of output to price tends to
improve.
As there is difference in the nature of cauliflower and apple crop, there is a need to
use different concepts for working out the economics of both the crops. The production
pattern of a fruit crop is different from that of vegetable crop. In general, there is a
gestation period of around 5-7 years after the plantation of the crop to start getting any
production. Therefore, there is a need to have different criteria to measure the economic
viability of the fruit crop. This is done in the subsequent section.
4.6.3. Income and Risk from Apple
Use of fertilizer and chemical spray are important activities for viability and returns
from the crop. Though, it is important to note that the results cannot be easily generalized
in the case of apple crop due to the disparity in the years of planting trees with each farm.
Interestingly, the results show that, like cauliflower, the use of fertilizer and chemical
spray is found decreasing with increase in the farm size of apple growers (table 4.18). In
other words, there is a negative relation between resource allocation and farm size.
124

T a bl e 418 . : F arm S' lZean dR esource AU ocation b I>y ApI Ie G rowers
Farm Size Fertilizer use Spray per Share of Income from
per ha (in Rs) ha (in Rs) apple to aggregate income
of household*
Small and Marginal
•
Farmers 3177.78 3788.89 87.39
Medium Farmers 2340.06 3167.87 80.47
Large Farmers 1535.55 2388.73 79.50
Note:
i. Aggregate income includes both farm and non·fann income
Source: Primary Data
i
I
Four measures are used to estimate the economic viability of apple as given below:
S.No Measure Description
1 Net Present The NPV is the sum of discounted net cash·flows over a period. It is a
Value (NPV) relatively objective method of determining the improvement in output
resulting from production. In the case of annual cash-flows, and
assuming the current year is 'year 0', the formula for NPV is:
NPV ~ CF(O) + CF(I) + CF(2) + ..... + CF(n)
l+r (1+r)2 (I+r)n
where CF(n) is the net cash-flow for NPV purposes in period n and r
is the discount rate. Note that the initial cash-flow is not discounted as
it is the initial period of the beginning of the crop plantation (in period
0).
2 Benefit-cost The BC ratio is given by: BC - Discounted Sum of present values of
ratio (Be ratio) benefits (cash inflows) divided by the Discounted Sum of present
values of costs (cash outflows). A BC-ratio above one implies an NPV
greater than zero. In the case of single cash outflow (occurring in the
first period), the benefit-cost ratio is also called the Profitability Index.
The BC ratio is a useful measure because when there are a large
number of crops, there may not be enough resources available to
undertake them all, even if they all have high net present values. As a
rule of thumb, picking the crop with the highest Be ratios can ensure
maximum value for money in terms of contributin.o: to outcomes.
3 Payback Period This method determines the point in time at which cumulative net
cash-flows exceed zero. For a crop, large output at the beginning of
the project is followed by smaller net inflows for several periods, with
the cumulative inflows eventually covering the initial outlay and
providing some net benefit. The point at which the initial outlay is
covered is the payback period. The payback method has several major
weaknesses. Firstly, it does not discount cash-flows (although
discounting could be added). Secondly, it does not take account of
cash-flows beyond the payback period, which could be large and
affect the desirability of undertaking the project. Thirdly, it is a
-
measure of time, not a measure of value
I 4 The Internal This method can be useful for crops for which it is very difficult to
Rate of Return determine a suitable discount rate. The IRR is the discount rate which
L (lRR) method would give an NPV of zero, .o:iven expected cash-flows.
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IRR-r,+ NDl_ (r,-r,)
ND,+ND,
Where r, is the last discount rate which makes NPV positive
r 2 is the first discount rate which makes NPV negative
NO, is the last positive NPV
NO, is the flTSt negative absolute value of NPV
For measuring the economics of apple crop, all the above mentioned measure are
computed across farm size using the present year price which is Rs 25. The production
cycle is worked out by using the last 30 years data of apple production in Shimla district.
Hence, NPV has been calculated for the period of 30 years. Initially 10 % discount rate
was used to measure the indicators and later the simulations have been done to find the
impact of different discount rates, prices and costs on the economic viability of apple
crop.
The results are presented in table 4.19. Small and marginal farmers have higher
NPV as well as higher benefit-cost ratio of apple crop production as compared to the
large farmers. However, large farmers generally have a lesser pay back period for getting
returns from the apple cultivation. Large farmers also have lesser internal rate of return,
which points to the better position of large farmers in comparison to smaller farmers in
terms of getting the total invested money back earlier. They face low discount rate, which
is crucial in calculating the benefits from apple production.
T able 4.19: Economics of Apple Production by Farm Size
Farm Size NPV(in Benefit- Payback IRR(%)
Rs.) Cost Ratio period (in
(D/C) Years)
Small and marginal
Farmers 220892 1.54 14.17 14.44
Medium Farmers 223873 1.55 14.12 14.71
Larl!e Farmers 213901 1.49 13.65 13.84
Source. Pnmary Data
The simulation analysis is conducted in order to find the impact of difference in the
expected price by the farmers, maximum price received by the farmers, as also by
,acloring in an increase in cost by 1 Q% and increase in discount rate to 15%. The
126

simulation analysis presents some interesting findings (table 420). The change in price
from current year price to the price expected by the farmer tends to improve the position
of small and marginal farmers, who perform better in all four measures of cash flow
analysis as compared to large farmers. In other words, small and marginal fanner have
higher NPV and high benefit cost ratio along with lower pay back period and lower
discount rate. But, in terms of the most favourable price received by the farmers, large
farmers performed better in all counts. This is due to the advantage of large fanners in
terms of better bargaining power in the market to fetch higher prices. When there is an
increase in discount rate from 10 to 15%, small and marginal farmer tend to perform
better in terms of NPV and BIC but not in terms of IRR and payback period.
changing the cost of production, no change in the relative position of farmers is observed
in all the four measures. This shows that the impact of change in the tax and other
policies may not have any affect on the redistribution of benefits and costs across
different farm sizes.
T a bl e 4 . 20 : S· Imu I· atioD An all'Sls I . 0 f A \pple I P r od UCtiOD b II}' F arm S· lze
Simulations F ..... Size & Small and marginal Medium Fal'JDen Larp
distribwtioa Farmers FarmUi
NPV 130221.63 110142.89 124247.99
ElJIOded pri .. @ Ble 1.08 1.02 1.06
distonnt rate I O~. Payba

in India. It can promise higher output, productivity, employment and ensure sustainability
of natural resources. Shift in .consumption pattern from staple to high value horticultural
crops has been increasing the demand for these crops. Higher allocation of area to these
crops is bound to result in higher growth of the agricultural sector, as the relative prices
of these crops are increasing fast. In Himachal Pradesh, higher allocation of area to
horticultural crops has had beneficial results in terms of higher area productivity and
growth of value of output.
The evaluation of economics of the selected horticultural crops shows that small and
marginal farms are more remunerative, especially because of application of more
resources like fertilizer, use of chemical spray and irrigation. In addition, small and
marginal farmers get higher yield with less variabilitf. This probably helps them to
exhibit higher level of allocation of area to horticultural crops. Higher level of land
allocation to the selected horticultural crop pays equally to small and large farmers as no
such bias is found in the study. However, in a situation of unfavourable market and
weather conditions (crash of both price and production), small farmers suffer more. In the
event of most favourable market and weather conditions (highest price and production),
large farmers gains the most. This might be due to the advantage of large farmers in
terms of better bargaining power in the market compared to small and marginal farmer.
The price expectation also plays an important in the land allocation decisions of farmers.
Horticultural crops are high value crops and in the study area, the selected
horticultural crops are found to compete with food crops for land. Though, small and
marginal farmers are able to obtain higher returns from these crops, scarcity of land and
poor income base are likely to playa critical role in their decision-making regarding shift
from food crops to commercial crops. They face the choice of trade-off between their
goal of food security and income maximization. At the same time, medium and large
fanners have to face the constrains of resources including labour which may influence
their decision regarding increasing allocation of land to horticultural crops that are highly
labour and capital intensive crops. This calls for examining the role of economic and non­
(cohomic factors in fanners' decision of diversifying their land from low value food
crops to high value horticultural crops.
128

CHAPTER V
ROLE OF ECONOMIC AND NON-ECONOMIC
FACTORS IN DIVERSIFICATION FROM FOOD
CROPS TO HORTICULTURAL CROPS
5.1 Introduction
Over a period of time, allocation of land changes across crops of different values.
Due to changing land allocations, cropping pattern shifts from low value to high value
crops or vice versa. The issues relating to shifting land allocation include typology,
direction and magnitude of changing land allocation across low and high value crops.
The decision-making process relating to shifting area towards crops of different
values is analysed at both macro (state or district) and micro (farm) levels. At the macro
level, such decisions are examined on the basis of area supply response models initially
introduced by Nerlove in 1958 and later on modified by many. The emphasis was on
rmding the role of farmer's expectation of future prices in shaping their decisions in
regard to the extentofland they devote to crops. Nerlove (1958) devised a model relating
expected "normal" price to past-observed prices. Many studies came later that used
Nerlovian model with some modifications to investigate the importance of price of crop
in shaping farmers' supply response behaviou1' (Krishna, 1963, Behrman, 1968, De,
2005, Askari and Cummings, 1976, Sawant, 1978, Mythili, 2(06). However, there are
several limitations that exist in analysing changing land allocation decision, using macro
level data.
Measurement of supply response usually requires time series data for quantities, cost
and prices. Such data are seldom reliable, available for short duration and provide partial
coverage in terms of crops and area. As a result, a large number of crops especially high
value horticultural crops are excluded from the analysis due to lack of reliable time series
; Lpp\y response behaviour is 'he changing land allocation among different crops due to change in
economic values of crops_ The same is a proxy for diversification from food to horticultural crops in this
chapter
129

data. In addition, the time series data under conditions of tecbnological change and
variable weather constitute a weak basis for estimating supply response and hence fann
level studies are more important in analysing such decisions (Medellin, et aI., 1994).
Another limitation of these models is regarding identification of the competing crop.
At any given time, not only two crops compete with each other for land but there are
possibilities of many crops competing for land. In addition, the crops that compete for
land at a particular point of time vary across regions and farmers, due to heterogeneity in
agro-climatic factors, among others. A macro picture cannot capture the difference in the
choice of competing crops. Additionally, it assumes that only one crop competes with
another crop. The supply response also varies across the groups of fanners who shift
from food crops to commercial crops vis a vis the farmers who shift from one commercial
crop to another. Price can be a vital component for the second group of farmers, whereas
for the fIrst group, the concern for food security can be an important factor while making
land allocation decisions. Hence, response elasticity of area to change in price is expected
to differ for both groups of farmers.
The macro level studies mainly concentrate on price of the crop as a major economic
factor in shaping farmers' changing land allocation decision. District or state consists of
disaggregated units, i.e. farmers and at farm level, there exists disparity in endowments
and access to markets. Price alone may not be the factor in decision-making due to
heterogeneity in the resource and capital endowments of farmers and difference in their
access to input and output market. Varied influences are visible on prices and
productivity of the crop attained at farm level. It could be hypothesized that farmers with
relatively higher level of productivity will allocate more land to the crop even at lower
expected price. Hence, it is important to examine the link between the price and income
in the context of shifting cropping pattern decisions of farmers. Although, Deshpande and
Chandrasekhar (1980) attempted to study the role of net income in the farmers' decisions
at the district level, heterogeneity in cost that varies across farms makes it more robust to
Hudy such decisions at the micro level.
I
130

At the micro level, most studies contain analyses that are based on probability and
possibility like pay-off, expected utility, betting, subjective probability distribution of
economic returns (price, yield and income) and possibility of returns (Arrow 1951,
Shackle, 1949, Binswanger, 1981). The analyses based on the probabilities are criticized
on the ground that farmers are seldom aware of the probability of economic outcomes but
they know the possibility of different outcomes related to price, yield and income
(Shackle, 1949) where possibility is a credible event. Shackle (1949) introduced the
concept of expectations, focused 'gain and loss' in dealing with decision-making of the
fanners 22 • But, there is hardly any empirical analysis based on his proposition. In other
words, there are very few studies that have attempted to link the area reallocation
decisions of farmers with their price and income possibilities or expectations. As the
process of shift in cropping pattern can take place in two different directions, i.e., from
low value to high value crops and vice versa, it is also important to examine the factors
related to diversity in the· supply response to change in price of crops2J.
In this context, micro-level decision making pertaining to area shift towards selected
horticultural crops becomes important. Initially, it will be necessary to understand price
and area cycles of horticultural crops. It is also vital to identify the role of economic and
non-economic factors, including food security concerns in diversification decision as
fanners reallocate their cropping pattern from food crops to commercial crops.
Variability in area responsiveness of farmers growing cauliflower to change in its price is
another aspect in analyzing their response to economic stimuli. The method involves
observing the change in price and area allocation behaviour of sample farmers and
grouping them on the basis of supply response behaviour. Then, the factors which explain
difference in response of area under cauliflower to change in its prices are identified. We
then examined the relative role of price and income in the decision-making of farmers
regarding reallocation from low value food crops to high value horticultural crops.
2~ I. a decision is a process of commitment by the decision maker with an action scheme. whose outcome is
unk?own. the question that should be raised is about the process in which such a decision is made.
t.cc~rding to Shackle, the decision maker is concerned with the consequence of his choice in the future. As
the outcome is not known before hand, he has to res·)rt to imagining the figure (expectation) of what will be
~he possible outcome. This guides choices of decision maker (Crocco, 1976)
.3 This can be done only in the case of annual crop like cauliflower and not for perennial crop like apple.
131

Response to economic stimuli is, however one aspect of the impact of market forces on
the cropping pattern change. Equally important are market infrastructure and institutional
arrangements. The delivery system of inputs and credit are other important factors in
determining cropping pattern decisions of the farmers (De, 2005). Thus, other noneconomic
factors are also examined in analyzing farmers' decision regarding shifting
from food to horticultural crops.
5.2. Behaviour of Price and Area Cycles of Fruit and Vegetable
Crops
Fruits and vegetables are two distinct categories in horticultural crops in terms of
difference in their cyclic periods of production. The cycles tend to be longer for fruits
because of the longer gestation period involved. Figure 5.1 depicts typical cyclical
behaviour that could exist, i.e., divergent, convergent and continuous fluctuations. The
first situation deals with divergent fluctuation, where the elasticity of supply is greater
than the elasticity of demand. In this case, starting with a moderately large supply of
commodity in period 1 (Ql) and a corresponding price PI, the series of reactions is traced
by the line drawn. In the second period, there is a moderately reduced supply (Q2) with a
corresponding higher price P3. This high price calls for a considerable increase in supply,
Q3 in the third period, with the consequent result of decline in price to P3, which further
leads to decline in the quantity to Q4 in the next period. Under these conditions, the
situation continue to grow more and more unstable, price falls to absolute zero or a limit
is reached so that production could no longer expand. The reverse situation, with supply
less elastic than demand in the next part of the diagram is the instance of convergent
fluctuation. Here, starting with the large supply and low price in the first period, PI there
would be a very short supply and high price, Q2 and P2 in the second period. Production
would expand again in the third period to Q3 but to a smaller production than that in the
first period. This would set the low price, P3 in the third period with a moderate reduction
in quantity to Q4 in the fourth period and a moderately high price P4. Overtime, the
pr

Figure 5.1: Types of Area and Price Cycles
Price
P2
Q3
P2
Q3
Q2 P3 PI
Q2
QI
Quantity
QI
Divereent Cobweb
Convergent Cobweb
Quantity
Price
Q3
PI.P3
QI
Continuous Cobweb
In the case of continuous fluctuation or cobweb, the quantity in the initial period
Illo/ge, attracting a relatively low price where it intersects the demand curve at P I. This
low price, intersecting the supply curve, calls forth in the next period a relatively short
133

supply Q2. This short supply gives a high price P2 where it intersects the supply curve.
This high price calls forth a corresponding increased production, Q3 in the third period
with a corresponding low price P3. Since, this low price in the third period is identical
with that in the first, the production and price in the fourth, fifth and subsequent periods
will continue to rotate around the path Q2, P2, Q3, P3 etc. As long as price is completely
determined by the current supply, and supply is completely determined by the preceding
price, fluctuation in price and production will continue in this unchanging pattern
indefinitely without an equilibrium being reached. This is true in this particular situation
because the demand curve is exactly reverse of supply curve so that at their overlap each
has the same elasticity (Ezekiel, 1938).
The cycles for vegetable crops like cauliflower are expected to be smaller. A
grower in a given season may receive high price relative to other years for the produce.
Therefore, in the following season, farmer is expected to increase area under cauliflower
as a response to high price. Other cauliflower growers receiving similar stimulus may
also increase their area. If the season is good for the crop and the yields are high, the
aggregate production of all growers will be in excess of the previous year and as a result
prices will fall. Growers thus will be dissatisfied with the price and transfer some area to
other crops, reducing the total crop marketed next year. The price may improve and the
cycle starts again. This is primarily because there is generally no price support for this
group of crops. This effect is particularly common in all vegetable crops where cycles are
expected to be short.
In order to analyze the pattern of area and price fluctuations, the data on the trend
in area, production and productivity of cauliflower is collected for the last ten years from
the Directorate of Agriculture, Himachal Pradesh. Last three years' (2004, 2005, and
2006) data on prices of cauliflower could be obtained from the main market (Shimla)24.
The seasonal prices of cauliflower are collected and grouped into three categories, i.e.,
seasonal high, average and low. The trend in area under cauliflower shows increase over
, i
-. There is no fonnal agency that collects data on prices of vegetable crops_ Only the market committee in
the district (capital of the state) collects data on prices and keeps these records on the monthly basis_ They
provided only three years price data of Cauliflower crop.
134

a period of time (table 5.1). However, it stagnated in the last year only. interestingly, the
preceding year experienced the lowest price (table 5.2), but the total area under this crop
did not decline, though remained stagnated. This may be because many farmers also
responded perversely to prices due to their expectation of increase in the price ahead in
the yeal'. The trend in production and yield has always been fluctuating without trend
towards any specific direction.
Tbl a e SIAr . : rea. P ro d uction an dY' Ie I d 0 fC ault 'n ower in Shimla. HP, 1998-2006
Years Area (ha) Production (Tonnes) Yield (ToDBeslha)
1998-99 325 6535 20.11
1999-00 345 6755 19.58
2000-01 355 6600 18.59
2001-02 340 6560 19.29
2002-03 360 7015 19.49
2003-04 365 8325 22.81
2004-05 400 8854 22.14
2005-06 420 9240 22.00
20()6.07 420 9106 21.68
Source: Directorate of Agnculture, Shiml., lIP
-
Table S 2' Seasonal Price of Cauliflower in Shimla HP 200406
, ,
~. ' .
Years
Seasonal high
Seasonal Low
Seasonal Average
2004-05 240
2005-06 115
20()6.07 435
Note:
i. Cauliflower Prices are in Rs per 20 kg
Source: Shimla Market, Dhalli, Shimla
180 150
25 50
110 225
For apple, data for last ten years is obtained from the Directorate of Horticulture on
its area under bearing trees and non-bearing trees, production, and productivity (table
5.3). The data on wholesale price of apple is obtained from the National Horticultural
Board (NHB). Four months average of the prices is taken (August-November), which
coincide with the harvesting season as farmer gets prices for their produce only during
these months and they do not have access to storage facility. Companies like Reliance
" This behaviour was noticed in case of majority of the farmers interviewed
135

and Adani procure and store apple and sell it in different part of the countIy at different
times 26 •
Adjustments are expected to be completely discontinuous in the case of apple. A
large swing in area change under non-bearing apple trees is indicative of the response of
farmers by adjusting planned area in the light of their expectation of prices. However,
there is some gestation period and plantation varies over years, and nothing concrete can
be said about their production response to prices. Farmers generally could respond to any
single year price by deciding whether to plant more trees next year or not. In the data,
there is no clue of any immediate response from farmers that links any single year price
to next years' decision on the area to be brought under non-bearing apple trees as peaks
and troughs in prices does not follow peaks and trough in area under non-bearing apple
trees (figure 5.2). In many developing countries, the markets are generally small which
either do not have the capacity to store or capacity to ensure some minimum price to the
producers (Singh et ai, 2004). Thus, farmer may not influence the market prices through
storage. The only option available to them is either to sell through different markets or
sell for processing. The support price for apple is quite low Rs. 4.25 per Kg. Farmers give
only low grade apples for processing which may not fetch good price in the market. In
such a situation, they save on the expenditures on boxes and avoid transport cost as they
sell the produce in the local market.
U Reliance and Adani group initiated their operations in the Shimla district in 2006.
136

5.3. Diversification from Food Crops towards Horticultural
Crops
Shift in cropping pattern towards the selected horticultural crops is measured on the
basis of reference to the major changes made in terms of reallocating land from food crop
to chosen horticultural crop in the past seven years27. The economic variables which link
successive time periods are the attitudes and the expectations of farmers and the
entrepreneurial decisions or acts which are motivated by thern. These attitudes, decisions
and acts influence the position attained by the farm in later period of time (Williams,
1951). Thus, in order to assess decision making process of farmers, the time of major
change in cropping pattern made by them is used as a proxy for shift in area which also
has influence on the prevailing pattern of crop mix or land allocation to crops.
The questionnaire is designed to include various aspects relating to area reallocation
by farmers. Initially, a seven year view (1999 - 2006)28 was taken and questions were
asked like,
1. When the farmer has made any change in the cropping pattern from food crop to
the selected horticultural crop;
2. When did they experience the major change towards horticultural crop;
3. How much area was reallocated from a food crop to horticultural crop;
4. Whether the addition of area to horticultural crop was done by substitution of a
food or other commercial crop or is it done by extensification of area under
cultivation;
27 We exercised caution regarding the decision of fe-plantation and new plantation of apple crop while
conducting the interview, as it otherwise would not have let capture the process of diversification by the
fanners. Last three years data on fanner's area allocation among crops is also taken, the same is not used
for proxy for shift in cropping pattern towards horticultural crops. This is especially so as majority of the
cauliflower growers have not changed any area under the crop. In addition, in case of fruits, farmers 9
iecision of allocating area is generally inflexible and unidirectional in ~he short run and it is not based ~n
th~ response to year to year price. Few years' data on area and pnce may not capture the area shl'fi
de~isions of the farmers growing fruits especially apple. .
" This period was selected on the basis of the discussion with Revenue Officer (Patwan) and head of the
village in terms of diversification pattern in the selected villages
138

5. What were the previous years' price and yield, and what were price and yield
thresholdsllevels ~t influenced their decision to reallocate area in favour of
horticultural crops?
In the case of sampled fanners, it is noticed that shift of cropping pattern towards
apple was made from two crops i.e., wheat and maize, whereas, cauliflower growers had
shifted only from one crop i.e., wheat. The major change in the cropping pattern in favour
of the selected horticultural crop happened in the year 2002 and 2003 and majority of the
farmers shifted from food crops to the horticultural crops only once in the past seven
years 29 • Such shifts were by substitution of food crop with few exceptions 3o • Both the
absolute and relative measure for shifting area in favour of horticultural crops are
measured; these inc1ude- amount of area changed from food crop to selected horticultural
crop; area changed from food crop to selected horticultural crop with respect to the initial
area under selected horticultural crop and with respect to net cultivated area of the fann.
The typology of reallocation of cropping pattern indicates that shift with respect to
initial area is high by the fanners growing cauliflower, whereas shift with respect to net
cultivated area is higher for apple growers (table 5.4). More than 100% change with
respect to initial area in total signifies the importance of diversification towards high
value crops in these villages. Cauliflower is of more significance in Govai (village I) in
comparison to Sainj (village II) even though Sainj has higher intensity of irrigation. This
reflects the role of other economic, non-economic and financial factors that influence
area under the crop. In the case of apple, the crop contributed more to the aggregate value
of output obtained from the farm in Shilaru (village IV) as compared to Sandhu (village
1lI). This is primarily due to more area under bearing apple trees than non-bearing apple
trees in village IV, as the crop was adopted much earlier in this village. However, shift of
29 Although, in case of cauliflower crop. some farmers have reversed the decision of shifting area to food
crop, the same data is not used as a proxy for diversification towards horticultural crops. This data is
utilized in the fourth section of this chapter in order to examine diversity in response of farmers to change
In crop prices.
JOlIn case of cauliflower, only one fanner has extended the area, whereas for apple crop, there are f?",
fanners who have extended the area for increasing the importance of the gIven crop 10 theor cropplOg
pattern mix.
139

area towards apple in both villages is leading to higher level of specialisation as indicated
by the number of crop produced in a year and the Herfmdahl index.
Table 5.4: TypolO2Y and Extent of Diversification towards Horticultural Crops
Variable 1D1cator Cauliflower dominated Villages Apple dominated VUlages
Villa2e I Vma.e" Total Village UI Vma.elV
loitial Area (hal A 658 7.70 14.28 29.52 44.00
Shift in area UDder !Au·~l
diversified crop A,.
w.r.t. InItial Area
('!o) 108.93 179.39 144.16 65.54 62.57
Shift in area uDder !Au. ~.l
divenified crop NCA
w.r.t.
Net
Cultivated Area
('!o) 24.50 31.33 27.91 33.71 35.94
Sbare of diversified (a,Il:A)
crop area to total
area after shift iB
crooom2 .attern 49.95 54.21 46.47 70.05 67.18
Share of diversified (v,ll:v)
crop value to total
value of output
after shift ia
croopm2 .attern 65.12 74.41 68.45 7\.63 8 \.66
Diversity in
Average
Cropping Pattern Number of
crops
produced in
a vear 4.1 4.3 4.2 3.10 3.57
Index of Herl"mdahl
concentration Index 0.69 0.70 0.689 0.38 0.43
Note:
i. (A H
• A.o F Difference in the area at the time of changing the area under the particular crop
ii. (a;LAF proponion of area (a) under particular crop (i) in the total cropped area (A)
iii. (V;fLV}= proponionate value (v) of a particular crop (i) in the total value of the farm output (V)
Source: Primary Data
To",1
73.52
64.05
34.82
68.85
73.08
3.33
0.41
All
VillA2es
87.80
104.10
31.37
5425
70.39
3.76
0.557
The results of distribution of shift towards selected horticultural crops across
different farm sizes illustrate that in the case of cauliflower. large farmers have shifted
the highest extent of area followed by the category of marginal farmers (table 5.5). This
shows that marginal farmers have been able to diversify to a great extent while
maintaining relatively higher level of subsistence. Not much difference in extent of shift
is noticed across farm sizes for apple growers. Even after reallocation of area, cauliflower
growers have a higher level of spread as against the apple growers who are getting highly
specialized due to shift in cropping pattern. It is interesting to note that land-labour ratio
riles with the increase in the farm size for both the crops pointing to increased scarcity of
140

labour with increase in area under horticulturaJ crops. Big fanners are hence expected to
be more dependant on the hired labour for higher allocation of land to horticultural crops.
Ta bl e SST •• l'VP OI02V I 0 fD' Iversification towards Horticultural Crops by Farm Size
Arta under Area under Shift in area ProportioD of Levdof Number of L .. dlL ........
diversified crop diversified crop IlDder divtrSifitd diversified crop Conteatratiou rops produce( Ratio··
before mitt ill after crop"'.r .t.laitiaI .rea to total .rea ill. Year
cropping p.ttent sbift ill ':O~":l
....
-
(ba)
(ii.i ..... m ba 1'1.'
l.dicMor A" A.,-A" {AtI::~) (a,Il:A) HI'
~A..
Call1iflower domillated Villactl
Marginal 2.50 3.44 137.50 58.33 0.71 3.10
1.39
Sman 6.69 725 108.41 52.67 0.73 4.04
Semi-mcdiom 3.31 3.81 115.09 51.78 0.77 4.37
Medium 1.38 1.63 118.18 35.20 0.65 5.00
l.arge 1.50 6.25 416.67 62.61 0.78 5.00
Apple do ..... ' ... Villages
Marginal 2.00 2.88 65.71 71.62 0.43 2.00
SmaD 2.32 8.25 55.93 72.98 0.37 2.54
Semi-medium 3.76 22.00 74.26 69.06 0.49 3.30
Medium 5.51 11.06 57.84 51.86 0.45 3.70
Large 10.25 5.63 54.88 66.90 0.56 3.89
• Herfindahllndex (Hl)
•• Land is the net cultivated area and labour is number of agricultural labour at borne
Source: Primary Data
5.3.1 Food Security Concerns in Diversification
2.00
2.32
3.76
5.51
2.08
2.74
4.32
6.16
6.32
One of the major features of farming sector in the developing countries is the coexistence
of subsistence and commercial crop production by large number of farmers
(Narain, 1965)_ Concerns of food security for the family could hinder higher shift in
cropping pattern (crop substitution) from food crop to commercial crop. However, for
many of the commercial crops, especially horticultural crops, the earnings are high,
which tend to improve the food security position of the family through higher net income
from fanning. This might lead to increase in the consumption level and their standard of
living. However, high fluctuation in the prices of horticultural crops could potentially
prove detrimental to the food security of the farm household. High variability in returns,
higher cost of obtaining food from the market due to lack of infrastructure and overall
lor income of fanners might force them to engage in more of subsistence crop
production (Jayne, 1994; Nowshirvani, 1971).
141

include:
For the details on food security several questions were asked to the farmers, which
I. Were they food self-sufficient or not at the time of shifting area from food crop
to the selected horticultural crop?
2. Whether at present their food grain consumption is influenced by the changes in
prices of food grains in the market.
The results indicate that majority of the farmers who had diversified towards
cauliflower were food-self-sufficient before they undertook shift in cropping pattern,
whereas this is not the case for farmers who diversified towards apple (table 5.6). This
could be mainly because of relatively high returns from apple crop as compared to
cauliflower. Also, the family size as. well as level of dependency ratio is larger for
farmers growing cauliflower, which probably resulted in difference found in the extent of
shift in cropping pattern decisions of farmers 31 . However, both cauliflower and apple
growers are less responsive to the change in the prices of food grains in tenns of
changing their consumption. Apple growing farmers are less responsive to price change
due to gross margin from the crop which is so high that it is sufficient to cover more than
four to five years of farm and non-farm expenditure of fann families, in general. For
cauliflower, prices does go very low sometimes but according to farmers this trend does
not last for more than one year due to which they are confident of getting good returns
over a period of time. Easy access to credit allows them to hedge against the loss created
either by production or price decline in any given year. Farmers mentioned another
reason for this behaviour in the interview. They believe that there is abundance of food
crops in the state and the prices of food crops are very low due to the higher level of
production of these crops in adjoining states (Punjab and Haryana). They have never
faced any problem in obtaining food crops from the nearby market and that too at low
price.
)] Details of the socio-cconomic characteristics or the selected farmers is given in Chapter four
142

TGable 5.6: Cropping Pattern Shift and Food Self-Sufficiency among Horticultural Crop
rowers
Cauliflower dominated Villages
Apple dominated Villages
Village I Village II Village Village IV
III
Whether farmer shifted from Yes 63.33 66.66 13.33
being food .elf-sufficient (0,,) No 36.67 33.34 86.67
Whether increase in prices of Yes 19.33 23.33 13.33
food grains reduces its
No
Consumption (%)
80.67 76.67 86.67
Level of sUbsistence before
Shift in CrOnnillf1. Pattern' (%)
Level of subsistence after
42.54 38.33 22.84
Shift in CrOnnillf1. Pattern' (~o)
Note:
24.26 29.49 5.33
~: F~gures in terms. of percmtage,. where for each village N is 30
II. Level of SUbsIstence: Calculated as proportiooate of area under subsist ..... crops to total cropped area
Source: Primary Data
30.00
70.00
16.67
83.33
35.47
6.22
The significance of farm size on the shift in cropping pattern through its impact on
food crop production show that the marginal fanners have maintained relatively higher
level of subsistence after shift in their cropping pattern from food crop to commercial
crops (table 5.7). Extent of subsistence is very low for apple growers and the same has
shrunk to a great extent after shift in cropping pattern. This is because introduction of
new varieties of vegetable crops enabled them to produce vegetable crops in the area
which was under non-bearing apple trees. In the long run, they are unwilling to be called
as vegetable farmers due to two reasons; lack of relatively better and stable gross margin
in vegetables and higher social value associated with apple growers and not with the
vegetable growers.
T able 5.7: Level of Subsistence among the Horticultural Crop Growers bv Fa rm Size
Cauliflower dominated
ViIll2es
Farm size Unit
Marginal Less than I Ha 34.04
Farmers
SmaUFarmen \-2 Ha 22.92
Semi-Medium 2-4Ha 23.18
Farmers
Medium Farmers 4-\0 Ha 23.68
Large Farme" More than 10 Ha 26.04
Note:
i. The Fi gures arc in percentage, where N for each category is 60
Source: Primary Data
Apple dominated
VilIaRes
13.48
2.55
5.31
6.27
7.37
143

5.3.2. Relative Significance of Economic and Non-Economic
Factors in Diversification
Horticultural crops are commercial crops with high productivity and income levels.
In addition to this, these crops have several other advantages that are expected to sway
the fanner while taking diversification decisions. Especially, horticultural crops are
characterized as highly resource intensive crops which require huge amount of labour,
investment etc. Not only the production but also the marketing of these crops demand
huge investment; marketing expenditure constitute around one-third of the aggregate
expenditure. In addition, since these crops are competing with food crops, it is vital for
farmers to identify its relative importance. In order to understand this, farmers were asked
about their relative preferences to several economic and non-economic factors while
taking decisions of shift from food crop to horticultural crops. Six key variables were
selected and fanners were asked to rank these from one to six in descending order. Then,
the weighted mean for all the six categories was calculated by using weights to ascertain
the value of the relative importance of the variables in influencing farmers' decision to
shift from food crops to horticultural crops. The formulae for weighted mean is
6
Weighted mean = L WjXj
i=1 n
Where Wi are the non-negative weights for all six categories (for instance, the
variable ranked one is the most important and gets a weight of six; ranked two gets a
weight of five; ........ ranked six gets a weight of one so that the most important factor
will get the highest weight), Xi are the number of responses for the i lb category/variable
and n is the total number of farmers who ranked the variables which is 60.
The results show that for both the crops, price is the most important factor in the
1versification decision offarmers (table 5.8). But, ~agnitude-wise, other fac~ors are al.so
quite crucial. Labour availability and food producuon from land are more cnllcal, while
144

diversifying from food crop to apple; the significance shifts to inigation and productivity
of the crop in the case of cauliflower, which is primarily because cauliflower is a waterintensive
crop. Food availability is not a real problem for cauliflower growers especially
because of the availability of one more season in a year for growing food crops. It is also
due to the scope for flexibility in decision involved in cauliflower cultivation. In other
words, while taking a diversification decision farmers are conscious of the flexibility in
the decision in terms of shifting from one crop to another at any point of time. Such
flexibility is absent when farmers plan to diversify from a food crop to a fruit crop ( apple)
as apple trees deprive land for alternative crops and make land less fertile for other crops,
when they are produced in the same area.
Table 5.8: Relative Importance of Facton for Divenification towards Horticultural
Crops
Weighted MeaD CauliOower Apple
Wei2bted Mean RIlIIk Wei2bted MeaD Rank
Price 5.05 I 5.50 I
Yield 4.87 2 3.40 4
Irri2atioD availability 4.55 3 1.00 6
Labour availability 3.53 4 4.95 2
Food availability for
consumption 1.70 5 4.05 3
Credit availability 1.30 6 2.08 5
Source: Pnmary data
5.4. Source of Diversity in Area Response to Cauliflower Prices
Diversification of cropping pattern can take any direction i.e., from low value to
high value crops and vice versa. In tenos of high value horticultura1 crops, price change
is dramatic and that too within a single season. Consequently, the overall supply elasticity
of these crops could be generally low as compared to other crops. Due to lack of time
series data under conditions of technological change and variable weather constitute a
weak basis for estimating response behaviour and hence an alternative observation
method is required (Medellin et aI., 1994), which is attempted here. Using three years
data, farmen were classified on the basis of their response to price. There are three
different group of farmers, i.e., farmers that responded positively to the change in prices
(they increased/decreased area under the crop in the event of increase/decrease in the
145

price of the crop), the group of fanners that responded perversely to the change in prices
(they increased/decreased area under the crop in the event of decrease! increase in the
price of the crop) and the group that did not responded to the change in prices (no change
in area under the crop in the event of change in price).
Several economic and non-economic factors ranging from farm, household to crop
specific can influence such difference in response. The household, whose aggregate
income is more dependent on one crop, is expected to respond readily to change in price
as compared to the household to which the crop do not contribute much to the total
household income. Since high value crops like cauliflower are characterized as highly
resource and capital intensive, difference in supply response also could differ due to the
typology of household in terms of human capital, resowce availability and access to
credit etc. Prices of crops change every year and when the price of the crop increases,
farmers' response to price becomes dependant on the availability of resources and access
to credit. It is especially so because, in order to increase area under the high value crops,
more labour and capital are needed. Increased area under the crop with insufficient labour
can adversely affect the returns from the crop as these crops are highly perishable and
non-availability of sufficient labour at the time of harvesting and marketing can lead to
enormous loss to the growers. In other words, farmers lacking resources may not able to
respond to change in prices. There is a possibility that when the resource becomes scarce,
farmers are forced to decrease the area under the crop even when price increase is
anticipated. As the price and the production of high value crops fluctuate widely, some
farmers often gains enormously in the event of getting most favourable market (price)
and technology (yield) conditions. This results in their not readily responding to change
in price. The whole set of linkages between economic and non-economic factors and
supply response shows that there is need to consider farm, household and crop specific
factors that tend to collectively influence farmers' response pattern.
The response of each participant is analyzed on the basis of an analysis of variance
(ANOVA). Pair-wise comparison among the three groups of farmers is subsequently
mfde using Tukey's test as the variables are continuous and not discrete in nature. The
factors chosen include socio-economic characteristics like age and family size, farm-
146

specific factors like farm size, annual income, disaster level of income 32 of the household,
etc, resource-specific like irrigation, land to labour ratio and crop-specific factors like
productivity of the crop and the focused income 33 from cauliflower crop.
The results indicate that the farmers who have responded positively to change in
prices of cauliflower are young with an average age of 46 years, whereas those who
responded perversely to the crop prices consist of old farmers who are generally more
conservative (table 5.9). The disaster level of income of the farmers who responded
positively to price is high despite having relatively less family members at home. But
interestingly they also earn higher annual income from their farm and non-farm activities
which helps them to respond positively to change in prices. High income enables them to
take higher risk in the production decision and hire more resource at the time of
requirement. Additionally, the relatively proximity of their farm to road probably reduces
the cost of selling the crop with less wastage during transportation. They also face lower
resource constraints. Relatively low ratio of land to agriCUltural labour indicates the
availability of family labour; it helps them to be more flexible in responding to change in
price, as they need not be dependent on hired labour for diverting the area under
cauliflower, which is highly labour intensive crop. But, labour intensity in terms of man
days per ha of these farmers is the lowest. High value of farm assets testify that farmers
invest more on capital (capital-intensive) than on labour in the production of the crop.
The farmers of this group are found to have achieved high productivity. Interestingly,
they have higher average farm size which points to the link between farm size,
productivity and supply response behaviour of farmers. Since farmers have to decide on
the area allocation between a food or commercial crop, a guaranteed high income from
commercial crop would indicate their choice in addition to liberating them consumption
constraints and enable them to positively respond to higher price expectation from
commercial crop.
32 The disaster level of income is computed as d = MCN - OFI, where MCN is the minimum consumption
requirements of the farm family plus other critical expenditures by the household during a year and OFI is
the annual off·farm income offarm household
lJ Focused income is the mean value of the focused gain and focused loss. The focused gain is the net
i~come of the farmers (income over paid-out cost) in a situation .when f~er gets the maximum p~ce and
maximum production together. Similarly, the net focused loss IS the net Income of the farmers (,"come
OVer paid-out cost) in a situation when fanner gets the minimum income and mmlmum YIeld together.
147

Ta bI e 59 . : A verage Val ueso fD escriptive Variables amon!! Cauliflower Growers
No Response Positive Response Perverse Response
Group (35) Group (16) . Group (9)
Socio-EcODOmiC Factors
Age (Years)a, < * 62.80 46.31
Family Size (Numbers)
6.66 6.44
Farm-Specific Factors
70A4
7.00
Farm Size (Ha) 2.37 4.18
Percentage of area under
cauliflower to GCA 50.87 52.10
Number of Crops Produced
in a Year 3.94 4.75
Annual Income (Rs.) < * 144745 165792
Farm Assets (Rs.) 19117.14 20959.38
Disastrous Income (Rs.) • * 55893 78915
Labour Intensity (Man days
I per hal 324.87 296.78
Distance of farm to nearest
road (Meters) • ** 308.14 163.75
Resource-Specific Factors
. Land to Labour Ratio' ** 0.3394 0.2294
Irrigation Intensity (Net
irrigated area/Net Cropped
Area) 65.95 64.66
Crop-Specific Factors
Yield of the crop
(ProductionlHa) a,b,< * 21254.02 26065.42
! Focused Income from the
! Crop (Rs.) 49340.40 86365.56
• . .
Indicates difference of no response group vIs positive response group
b Indicates difference of no response group vis perverse response group
C Indicates difference of positive response group vIs perverse no response group
.. 1 % indicates significant level and .* indicates 5 % significant level
Source: Primary data
2.86
56.00
4.22
84896
18055.56
102405
308.15
312.77
0.4719
73.88
13314.23
76224.53
The farmers who did not respond to the change in prices (no response group) have
the lowest average size of farm and they produced relatively less number of crops in a
year. Since, they have low diversity in their cropping pattern; the aggregate risk in the
farm production is higher for them. This substantiates their non-responsive behaviour.
Ihterestingiy, these farmers do not have really high expectations about income from the
148

crop, over a period of time, which is reflected in the lowest focused income from
cauliflower crop. Since, productivity of the crop of these farmers is high, it could be
possible that they received relatively low price from their produce that in turn reduced
overall income from the crop. Their farms are relatively far away from the road and
market that reduces their market access advantage; it results in higher amount of wastage
of the produce during transportation and marketing. In other words, lack of access to road
inhibits their area supply response. They are risk-averse farmers as they have allocated
lower share ofland to cauliflower.
The farmers' perverse response to price is indicative of high food requirements at
home coupled with low annual income. Both characteristics point to the high risk
situation of these farmers in the event of unfavourable market and weather conditions.
Higher food and other critical expenditure at home can be a reflection of the relatively
bigger size of family. Low aggregate annual income 34 is either due to lack of non-farm
income source or due to low land productivity. It could be that these farmers realize the
lowest productivity from the crop despite having the highest proportionate of area under
irrigation. More importantly, their high land-labour ratio indicates constraint of own
resources for the production of cauliflower, which is a high labour intensive crop that
require huge amount of labour not only for harvesting purposes but also for marketing the
crop. As farmers mainly sell their produce in far-off markets; therefore transport
bottlenecks due to distance from road-head, lack of sufficient labour at the time of
marketing etc, be important factors for their perverse supply response. Since, this group is
relatively poor due to low annual income and high consumption needs; they probably
could not hire labour when increase in price of cauliflower is anticipated. This group of
farmers is usually headed by the old people. They experience the lowest crop productivity
and in addition low resource base for investment and labour hiring propensity evidently
affects the supply response behaviour of these farmers.
34 Farm and non~fann activities
149

5.5. Magnitude of Diversification from Food Crops to
Horticultural Crops
The farmers do not differ only in terms of the nature and direction of shift in
cropping pattern towards horticultural crops but also in terms of the magnitude of shift in
area from food crops to horticultural crops. In this section, an overview of the socioeconomic
characteristics of fanners at different levels of shift towards horticultural crops
is outlined. In addition, the role of economic and non-economic factors is also examined.
5.5.1. Socio Economic Features and Diversification
Socia-economic factors can exert significant influence on the typologies of
reallocation in cropping pattern through their influence on resource availability and risk
management abilities at fann level. The results show that farmers who have shifted large
share of area in favour of cauliflower have larger family size and more dependants (table
5.1 0). In the case of apple, the magnitude of shift in area is inversely related to family
size and dependants. Irrigation is found vital for reallocation decision towards
cauliflower, but not for apple crop. However, there is similarity in the role of home in
shifting area towards both the crops. Availability of more home labour, including wife
and children significantly influences the extent of shift in the cropping pattern. Both
crops are highly labour intensive and availability oflabour has helped farmers to take the
decisions of reallocating higher extent of area towards these crops.
150

Table S.lO: Socio-Economic Characteristics at Different Levels of Shift in Cropping
Pattern
.
Family Number of Farm Irrigation LandILabour Annual
Extent of Cropping size dependants size intensity** Non-farm
I Pattern Shift" (No.) (No.) (ha) income
CRs.)
I C.nIIfIower Low 5.88 2.17 2.34 61.27 0.20 90673
Medium 6.82 2.10 3.16 61.50 0.23 85484
IIi2h 7.20 2.80 3.14 82.94 0.18 83127
Apple Low 7.12 2.25 5.56 22.29 0.39 96300
Medium 6.28 1.78 4.92 9.35 0.47 63875
Hith 6.05 1.70 4.11 8.45 0.38 91100
Note.
i. • Extent of shift: For Low ·Iess than 10% shift in area under diversified crop ofw.r.1. Net Cropped Area),
Medium (\ 0-20% shift in area under diversified crop of w.r.1. Net Cropped Area) and High (More than
30"10 shift in area under diversified crop ofw.r.1. of Net Cropped Area)
ii. *. Percentage of net irrigated area to net cropped area
Source: Primary Data
5.5.2. Factors Influencing Decision of Diversification towards
Horticultural Crops
The micro level decision in tenns of shift of area in favour of high value crop is
analyzed in tenns of level of substitution of food crop by high value horticultural crop.
The farmers were asked as to when they initiated the major change in the cropping
pattern in favour of high value crops, and what the extent of shift or substitution was in
terms of area and from which crops. They were further asked to give details of the price
and yield of both the crops that were interchanged or substituted at the time of switch and
what influenced their decision of shift35. The rationale for using the price and yield as
expected by farmers 36 is that prospective yield and price has a strong motivating force
which in the long run is likely to affect their acreage planning (De, 2005). Since farmers
have experienced major changes in their cropping pattern in different periods, it is
important to do price adjustment and it is done by deflating the price of the crops.
Consumer Price Index- Agricultura1 labourer (CPI-AL) measure is used for price
3S In the short run, changes in price of the crops may not bring about a significant change in acreage under
crops due to the particular nature of agricultural production and land allocation of high value crops is
generally a long tenn decision by the farmers, especially in the case of a fruit crop.
"According to Shackle (1949), fanners, while deciding about changing land allocation among crops aTe
c~ncerned with the consequence of the decision in. the ~utu~e. Since, the outcome I~ not known at the l~me
ot taking the decision, farmer has to restore to ImagmatlOn of figure (expectatIOn) about the pOSSIble
outcome.
151

adjustment (the price of the crops is deflated by CPI-AL in order to arrive at the real
price). Information regarding their socio-economic status and others factors are such as
their education, farm size, irrigation, and food requirements are obtained. Regression
analysis is used to gauge the factors affecting the extent of substitution by farmers while
considering economic and non-economic factors as explanatory factors. The relative
price and relative income are used as explanatory variables in order to test whether
farmers care fOT only price or also the income in their crop substitution decisions. The
results are outlined separately for apple and cauliflower in view of the difference in the
nature of the crops. The specification of the equation is as follows
Where,
A,,2 -Ac.; is the shift in area from low value crop (C2) (Wheat or Maize) to high value
crop (C.) (apple or cauliflower)
PcllPc2 ; is the ratio of the real prices of the crops C. and C 2 (at which farmer made the
area shift decision)
I.:dIc2 ; is the ratio of the real income (gross returns per ha) of the crops C. and C2 (at
which farmer made the area shift decision)
lNF ; is the annual non-farm income (Rs.)
IE ; is the education level of farmers
Res (FS.IRRI); denotes farm size (in hal and irrigation (net irrigated area)
Cons; is the level of annual food requirement of substituted crop at home (in Rs)
152

Table S.I1: Factors Affecting Decision for Diversification towards Horticultural
C rops
Area Shift as a Dependut In Favour of Cauliflower In Favour of ADDie
Variable Coefficient Coefficient
Constant -0.1 I I (.770 0.624 (1.589)
Relative Price.JBs.JKg) -0.620 (.518 0.034 (.288)
Relative Income (OutputIHa) 0.481 (7.098 • 0.579 2.058)··
Education (Years) -0.037 (.980) -0.233 (2.138)··
Farm Size (Ha) 0.610 (8.675)· 0.204 (1.379)
Irrigation Intensity (Net 0.107 (2.926)·. 0.031 (0269)
irrigated Area/Net Cropped
Area)
Non-Farm Income (Rs.) 0.028 (0.771) 0226 (2.022)··
Food crop (Wbeat/Maize) -0.123 (3.071).· -0.011 (.095)
requirements at bome iRs.)
Note.
i. Competing Crops for Cauliflower: Wheat; For Apple: Wheat & Maize
ii. Figures in the Parenthesis are t-values
iii. Cauliflower R2: 0.944 Adjusted R2 :0.933, N~60
Apple R2: 0.448, Adjusted R2 :0.361 ,N~60
iv. • and •• signifies level of significance at I % and 5 % respectively
The results show that the relative income from the crop is positive and statistically
significant in explaining the crop substitution decisions of farmers (table 5.11). The
relative price variable has come out to be insignificant and even negative in the case of
cauliflower_ This illustrates that farmers generally calculate the aggregate gain from crop
in their decision than referring only to the price of the crop. Their capacity to generate
higher productivity along with the better market prospects together explains farmers'
decision. In terms of importance of resources, irrigation and farm size are positive and
statistically significant for cauliflower growing farmers, whereas for apple growers, nonfarm
income is found to be positive and statistically significant. It might be because
cauliflower is primarily a water-intensive crop and it is critical to have more irrigation to
increase the area under this crop. Additionally, both the resources i.e., irrigation and farm
size affect productivity of cauliflower. Since, there is a gestation period in the cultivation
of apple, farmers are more concerned about the availability of non-farm source of income
during the gestation period, while taking decision of area shift in favour of the crop.
Education has negative and significant impact on such decision-making by apple
growers. As higher shift in favour of apple is directly linked with increasing level of
specialisation. Apple being a perennial and plantation crop; it deprives land for
cultivation of other crops. Educated farmers are concerned also about risk from the
153

production of crop, and hence prefer to have higher level of diversity in their cropping
pattern than being fully specialized in one crop. They were found to have more awareness
about the trade-off between risk and income, whereas uneducated farmers concentrate
more on income optimization than worry about the risk situation. The food crop
requirements impact the decision of substitution; it is negative in both the cases and also
significant for the farmers growing cauliflower. It means that higher food requirements at
home inhibited the extent of crop substitution decision of farmers. The income from
cauliflower is low as compared to apple and once apple growers get a bumper crop and
good price, it would cover many years of food and farm expenditure at home for many
years to come. Thus, cauliflower growers are relatively more cautious in substituting food
crop to high value crop.
5.6 Summary
Diversification towards horticultura1 crops is one of the vital components of growth
of output in addition to area, productivity and price change. In the case of diversification
of area among crops, the concerns pertain to shift in the area, direction of changing land
allocation among crops, and magnitude of shift in area from one crop to another. The
study captures all these dimensions of diversification.
The typology of shift from low to high value horticultural crops shows that shift
with respect to initial area was high by cauliflower growers, whereas shift with respect to
net cultivated area was higher by apple growers. In the case of cauliflower, large farmers
dominate the extent of shift followed by the category of marginal farmers. This shows
that marginal farmers have been able to diversify to a great extent while maintaining
relatively higher level of subsistence. In terms of food security concerns, majority of the
farmers who diversified towards cauliflower were food-self-sufficient before change in
cropping pattern. This is not the case for farmers who have diversified towards apple.
This could be mainly because of relatively high returns from apple crop. Cauliflower and
apple growers are less responsive to the change in the prices of food grains in terms of
ranging their consumption level.
154

Undoubtedly, price is the utmost important factor that governs decision making as it
directly influences the income potential of the crop. But other non-economic factors are
also significant in such decision-making and significance varies between vegetable crops
and fruit crops. Labour availability and food production from the land are more critical
while diversifying from food to apple. Irrigation and productivity of the crop are more
significant for farmers growing cauliflower primarily because of the water-intensive
nature of cauliflower crop.
Both fruits and vegetables are high value crops that promise huge gains in terms of
output per ha, higher employment and farm income. However, there is a difference in the
decision-making process of diversification towards these crops by farmers; it pertains to
degree of flexibility in crop, relative returns of the crops and consequence of
diversification on the allocation profile of the farmer 17 • Relative incomes of the crop
determine the crop substitution decisions of fanners. This means that farmers calculate
the aggregate gain from the crop than considering only the price of the crop. Their
capacity to generate higher productivity along with better market prospects together
explains farmers' decision. Thus, upgradation of technology and marketing facilities are
concomitantly required; only harping on market improvement for increasing land in
favour of horticultural crops is not adequate. In the case of cauliflower, resource
availability at farm level is more important for diversification decision, while availability
of additional income source is found to be vital for apple growers. Interestingly,
education is inversely related to the diversification decision of apple growers as such
decisions are determined by the level of specialisation required due to it being a perennial
or plantation crop. Educated farmers are concerned also about the risk from production of
crop and hence prefer to have higher level of diversity in their cropping pattern than
being fully specialized in one crop.
Land is more of an inelastic factor of production as compared to other resources
including labour and capital. Additionally, land allocation to fruit crop is inflexible as
decision of land allocation to apple cannot be easily reversed due its being a plantation
j h 'aI" I n1'k' f
Higher exlenl of shift by apple growers are expected 10 make I em specl lze In app e, U 1 e In case 0
cauliflower crop
155

crop. Consequently, over a period of time, farmers face both risk and uncertainty due to
their decision of land allocation to high value crops. Thus, their aversion to both risk and
uncertainty are expected to influence their decision to stick to the prevailing pattern of
laod allocation to horticultural crops. The next chapter deals with examining the role of
both risk aod uncertainty in farmers' decision ofland allocation to horticultural crops.
156

CHAPTER VI
EXPECTATIONS, RISK ATTITUDES AND LAND
ALLOCATION DECISIONS
6.1. Introduction
The decision ofland allocation among food and commercial crops is also one of the
precautions which resource administrators or managers can use in adjusting to risky and
uncertainty situations (Heady, 1952). There are two aspects of planning pertaining to land
allocation decisions that deal with risk and uncertainty independently. One is the aspect
of problem of planning under perfect knowledge, where the farmer is knowledgeable
about the outcome of the decisions made; the other aspect deals with the planning under
imperfect knowledge, wherein the aim of the decision-maker is to minimize the variance
of outcome, or prevent the occurrences of undesirable outcomes. In the situation of
imperfect knowledge, farmer may attempt to combine food and commercial crops in such
a manner as to minimize the probability of income dropping below levels required to
meet family living expenses, farm costs and principal payments (Boussard and Petit,
I 967).
Farmers take production decisions in a largely uncertain environment, where the
outcomes of the decisions could have large subjective probabilities. The motivation for
higher allocation of land in favour of high value crops is driven due to desire of
generating high income, productivity and employment. Fruits and vegetables are highly
remunerative crops but are also considered as risky crops. The major feature of
horticultural crops is that the prices of crops fluctuate widely even within a single season.
Lack of any support price and high perishability of the crops make the horticultural
growers even more vulnerable to risk and uncertainty than growers of other high value
crops like rice or sugarcane. In the event of greater extent of risk and uncertainty, the
kportance of the same is high for the farmers while taking land allocation decisions.
157

At the farm level, the concept of expectation is generally used in terms of a response
to uncertainty involved in the production process. There are several ways in whi~h the
information regarding expectation is obtained; such information pertains to the
probability and possibility of different prices, incomes or events at the fann level.
Specific questions here include: which price level is considered as most profitable by the
farmer; what are the possibilities of different range of prices, what is the probability of
occurrences of different levels of prices; the price level that might surprise fanners, etc
(Shackle, 1949; Williams, 1951; Binswanger; 1981 and Hardaker, 2(00). Expectation
signifies the preferences and confidence levels of fanners in production of a particular
crop and hence the resource allocation, including land.
Farmers tend to have expectations about different economic outcomes including
price and yield, though available literature on the topic seems to lay more emphasis on
the price expectations only. Price may not be the only factor in decision making;
heterogeneity in resources and capital endowments of the fanners and access to input and
output market, all together affect the output and its variability. Difference in the level of
crop yields influences revenue directly and hence both price and output are important. It
could be hypothesized that fanners with relatively higher level of crop productivity may
allocate more land to high value crop even at a low expected price. In this context, it is
vital to examine the link between the price and income expectation in the land allocation
decisions by the fanners.
Risk is also generally considered a strong behavioural force affecting decision
making. There are several ways in which risk is measured. One aspect of risk relates to
the variability in returns across time, which is a function of yearly changes in yield, crop
prices and input costs 38 • The risk from the production of the crop can be decomposed into
price and yield risk. Since, the nature of the fruit and vegetable crops is different, it is
vital to evaluate the relative role of price and yield risk in the aggregate income risk
independently. Another vital perspective of risk is how far and how often returns unable
3, Since variance is taken as an indicator of variability. risk aversion is assumed to stand against departure
rIbm the mean, even if income is rising. The est:mation of ri~k aversion is pure1y a subjective task and any
chosen value is an extremely difficult task (Boussard and Petll, 1967).
158

to reach a below mean level of return. Here, risk is considered as a cost in fanners'
decision pertaining to land allocation to high value crops (Rourn~t, 1976). The Safety­
First principle (Roy, 1952) accounts for such costs in analyzing fanners' behaviour
towards risk. Here, fanners are preoccupied not with the objective of maximizing income
but with maximizing their chances of survival. Decisions of fanners do not wholly
depend on their attitudes towards risk, but also on the differences in their subjective
needs, and resource endowments (Shahaduddin et al. 1986).
Due to fluctuation in the components of revenue from the crop, one can visualise
two groups of fanners: the first group of fanners would prefer to be on the safe side and
hence their allocation decisions are explained by the income and yield variance of their
crop as compared to the disaster level of income of the farm household. The second
group constitutes farmers who prefer to take risk in their land allocation decisions. Here,
fanners are risk-takers as their disaster level of income remains higher than the mean
income from the crop produced for commercial purpose 39 • Risk taking behaviour in the
production of high value crop like horticultural crops could be generic due to two
reasons; the first reason is that the decision of allocation is generally based on
expectations about the future outcomes and hence farmers tend to operate under
imperfect knowledge. Here, they take their production decision in an uncertain
environment (William, 1952). When the actual results deviate from the anticipated
harvest outcomes, fanners tend to bear the risk of both income and consumption, as they
have allocated land to a commercial crop against food crop. Second, due to the existence
of huge band of price and yield, there could be high fluctuations in the revenue. Not only
rich fanners but also the poor farmers take risk to reduce poverty (Kunreuther and
Wright, 1979). The farmers take risk by increasing the allocation of land in favour of
high value crop against the subsistence crop and take the risk about their food
consumption and the ability of meeting other needs at home like education of children.
But, not all farmers prefer to take such risk; instead they keep their land allocation low in
favour of commercial or high value crops. They follow the principle of Safety-First as
~!} Such cases are prominent when one crop is the major source of farm income.
159

they realize low utility from the production of horticultural crops. This often results in
selection of low-risk crops rather than making jUgher allocation to horticultural crops.
In this context, it is important to analyse the dynamics of price expectation at the
farm level, its relation with crops' productivity, income and cost. For this purpose, we
obtained the price expectations of fanners and the same are compared with the empirical
observations on horticultural crops. Then, the price expectations by the farmers are linked
with their input-use propensities and income, productivity and cost outcomes. In the next
section, the relative role of price and yield risk in the aggregate income risk faced by the
farmers producing fruit and vegetable crops are presented. We then examined the risk
attitudes of the farmers and its detenninant factors. Fanners are grouped on the basis of
their risk attitudes under the Safety-First framework, and factors behind their risk attitude
are identified. Finally, we present the typology of land allocation and analysis of the role
of expectation and risk related factors in production decisions by the farmers.
6.2. Price Expectations at Farm Level
Economic decision theory recognizes the importance of decision makers'
expectations about future events and these expectations are often expressed as subjective
probability distributions (Grisley and Kellogg, 1983). Here, fanners' expected outcomes
about price and yield are elicited. Then, a comparison is attempted between the
expectations and actual harvest results.
6.2.1. Dynamics of Price Expectations
Fanners take their decisions in an uncertain environment, therefore expected value
of returns and its variability has to be accounted for (Nowshirvani, 1971). Farmers tend to
build expectations about the outcome of income, profitability and risk pattern, which are
based on their past experiences of different ranges of outcomes in terms of price, and
yield. Therefore, before obtaining price expectations from farmers, we had to obtain their
experience of the minimum and maximum prices and production of the selected
160

horticultural crop4(). This information was collected to ensure that expectation of the price
and production by them cO!Des within the band of maximum and minimum prices and
production. Then, the question was asked about the most expected price and production
of the crop that might have influenced them to take the decision. In addition, the data on
area, price and production in the last three years was obtained. The input-wise cost data
was collected to fmd various types of costs incurred by the farmers (fixed, variable, paidout,
production, marketing cost etc.).
Data on price expectations of cau1if1ower growers illustrates that majority of the
farmers expected a price of Rs. 10 per kg, though the band varied from Rs.6 to Rs.l5 per
kg (table 6.1)41. The average value of the expected price of cauliflower is Rs. 9.89 per kg.
The price expectation by twenty-two of the farmers was less than the average value,
while price expectation of eleven of the farmers was greater than Rs 9.89 per kg. The
range of minimum price varied from Rs.2 to Rs 9.5 per kg and Rs.5.5 per kg is its mean
value. In terms of maximum price, the mean price is Rs. 14.25 per kg. But, thirty eight of
the farmers were not able to get this price for the produce, which shows only a few
fanners were able to realize a favourable price. In the case of apple, the average value of
the expected, minimum and maximum price was Rs 17.50, Rs 10.20 and Rs 25.60 per kg
respectively. Also, large number of farmers growing apple never realized high price for
their produce. At the same time, many of them received price that was far below the
average value of the minimum price of the crop. This means that majority of the apple
growers experienced low or unfavourable prices. Relatively, the maximum price of apple
is far higher than the cauliflower price. In terms of prices of cauliflower and apple, only a
few farmers were able to realize the most favourable price of the crop, pointing to
variance in returns accrued in marketing.
" 'Good and bad years' in the produclion and price is a general feature of agricultural sector, especially in
1eVeioping countries and this also influences the revenue from the crop. '"
The detailed picture of price expectation! by cauliflower and apple growers are proVJded 10 appendIX 6.1
and 6.2
161

Table 61 . . F armers Experience with Price of Cauliflower and Apple (Rs.lk2)
Range of Frequency* Range of Frequency Range of FrequeD

gestation period in the production of apple crop and difference in the age of trees across
different farmers.
Table 6.2: Comparison of Actual Results and Farmers' Expected Values of Selected
Distributions
Below·20% ·20 to ·10% ·10% to 0% o to 10% 10-20% Above 20%
Cauliflower 6 2 8 21 13 10
Price
Cauliflower 4 0 5 6 11 34
Yield
Apple Price 0 3 8 20 16 13
Apple Yield 21 4 7 9 13 6
Note.
i. The units are the number of sampled farmers and the figures show the number of farmers who received
actual values of economic outcome (price and yields) that were within each percent interval above or below
their expected value of the same variable. For each category there are 60 farmers
Source: Primary Data
6.2.2. Relation of Price Expectations with Other Economic
Factors
According to economic theory, price expectation is likely to exert a strong influence
on the behaviour of farmers regarding resource allocation, including inputs like land,
labour and capital (Williams, 1951, Roy, 1952, and Medellin et aI. 1994). Generally,
farmers who expect a better price are likely to engage in more resource intensiveness as
compared to others. Here, the influence of price expectation on the input-use propensities
of the farmers which affects the economic outcomes in terms of productivity and income
was analysed. Then, the factors that influenced the price expectations by the farmers were
identified.
The input propensities of farmers growing cauliflower crop show that higher price
expectations did not intensify either irrigation or labour 43 by farmers (table 6.3).
Irrigation is generally restricted by the availability of water in the farm. However, the
fanners with higher price expectations hire high amount of labour and are also more
willing to pay high wages for the same. Majority of them are willing to reinvest their
profits on land and other crop related activities. When the total cost of production and
paid out cost by the farmers were compared, it turned out that for the farmers with higher
I
" This includes total home and hired lahour used for the selected crop
163

price expectations, the total cost of production was less than those fanners whose price
expectations was lower. But, the paid out cost which includes the cost of fertilizer,
irrigation, labour hiring and use of chemical spray etc. of the fanners of this group was
highest among the three groups. The lower total cost of production is probably due to
factors like higher depreciation of farm assets, greater value of land, etc. In the case of
apple crop, the fanners with higher price expectations have higher extent of labour
intensity and they hire more labour for carrying out production and marketing activities.
Again, they are more willing to pay for labour and are more willing to reinvest their
profit in the crop-related activities. The results for both cauliflower and apple crop
illustrates that price expectations are vital for the input-use propensities of fanners. In
other words, better expectations about the price influences the propensity to involve in
more and better use of inputs.
Table 6.3: Input Use Propensities of Horticultural crop Growen According to Different
p' nce E xpecta ti ons
Cauliflower
Apple
Price expect.tioD Below Me.n Around Mean Above Mean Below Meln Around
(N=22) (N=27) (N=1l) (N=28) MUD
~lS)
Irrigation IntemilI" 71.47 64.67 62.65 13.05 12.46
Labour Intensity (Man- 302.21 . 339.38 280.05 192.41 197.37
days per bal
Percentage of 23.18 24.07 2955 49.21 40.67
housebolds
hiring
labour for farm
I purposes
Percentage of farmen 45.45 40.74 5454 64.28 60.00
I willing to pay higb for
, labour hirinl!
Paid out cost (lb. Per b. 73895 85512 80396 392.00 374.00
or Rs. per plant)
i Total cost (Rs. Per ha or 115074 128832 119634 480.00 472.00
Rs. per plant)
I Mean Crop Output 43823 49176 58603 268252 178051
(2004-06) (lb.)
I Percentage of farmen 50.00 62.22 63.60 53.55 66.66
I willingness to reinvest
i profit
! Share of are. under tbe 53.20 50.70 52.63 71.24 71.87
: selected crop to GCA
1. The cost for cauliflower IS III Rs. Per hectare basts, whereas the cost for apple crop IS gJven In Rs. per plant
ii .• Percentage of net irrigated area to net cropped area
~i .. N for each category is 60
Source: Primary data
Above
Mean
~=!7l
5.55
235.61
75.00
88.23
483.00
558.00
345451
70.55
69.23
164

Both economic and non-economic factors are considered in identifying variables
that influence price expectations of fanners. The results are presented in table 6.4. It is
clear from the results that price expectation by fanners is greatly influenced by their past
experience with the range of prices. Fanners, within the group in which the expected
price is higher than the mean expected price, experienced high price as compared to
farmers of other groups. The average maximum price realized by these fanners is Rs
17.50 and Rs. 28.47 per kg for cauliflower and apple respectively. At the same time,
farmers with higher price expectation did not experience very low price for the crop,
which was Rs. 10.65 and Rs. 5.36 per kg for apple and cauliflower respectively in
comparison to Rs. 8.75 and Rs. 5.01 per kg for farmers with low price expectation.
Interestingly, the farmers with higher price expectation have lower yield expectation in
the case of cauliflower, but not for apple. This presents a paradox in the decision-making
of fanners regarding fruits and vegetable crops. In terms of socio-economic
characteristics, fanners having high expectations are young and more educated in case of
both apple and cauliflower. Their household is mainly headed by male members and they
have higher fann size. This confirms that higher level of production helps in getting good
price on account of the better bargaining power in the market.
165

T a bl e 64 . . F actors E ~) PlalDlDl! I" P rice Expectations of Horticultural crop Growers
CaoliRo"er
Apple
Priceexpectatioa Below meaD ArouadMean Above mean Below mean Around Above
(N=22) (N=27) (N=Il) (N=28) Mean mean
(IS) (N~ 17)
Minimum Priee 5.01 6.14 5.36 8.75 12.41 10.65
Maximum price 13.17 13.77 17.50 24.46 24.81 28.47
Expected Yield 14650 15689 14256 37.27 34.36 43.20
(production per ha or
Jll!r plant)
Average value of 54904 61520 84165 122514 115908 252817
maximum and
minimum income over
paid out cost (RI.)
Distance of farm from 371.59 182.04 277.27 448.21 328.00 417.06
main road (m)
Age of the Household 60.50 61.56 52.64 61.64 62.53 59.71
Head (years)
Education of tb. 50.00 51.85 81.82 53.57 66.67 70.59
Household Head
(Number of bousebold
beaded by illiterate)
Sex of tbe Household 86.36 70.37 81.82 71.43 86.67 70.59
Head (penentage of
Households headed by
Male)
Firm Size (ba) 2.90 2.80 3.28 5.06 3.72 5.10
.. . .
Note. The YIeld for caulIflower IS m per hectare basIs, whereas the YIeld for apple crop IS gIVen m ProductIOn per plant
Source: Primary data
6.3. Relative Role of Price and Yield Risk in Fruits and
Vegetables
Fanner faces two types of risk in his revenue from the crop, i.e., pnce and
production. The variability in both together explains the crop revenue risk. The revenue
risk for apple and cauliflower is decomposed separately mainly because the difference in
the nature of the crops lies broadly in terms of the gestation period of production and
marketing options which in tum influences the significance of prices and yield risk.
Cauliflower is an annual crop and the decision of area allocation is very flexible in the
sense that every year farmer can think of changing the area under the crop. In apple, there
is a gestation period in production of 5·7 years initially, only after which fanners start
getting returns and only after 12·15 years of planting, farmers get higher level of yield
\from the crop. The decision here is inflexible, unlike vegetable crops, and it is not easy to
reallocate land to other crops in the same land, where apple plantation exists. Fruit crop
166

(apple) is relatively less perishable as compared to vegetable crop (cauliflower) and there
are more marketing opportunities for the fruit crop. Due to high perishability of vegetable
crop, fanners in Himachal Pradesh are not able to sell their crop beyond Delhi market,
whereas apple growers are able to sell in far away markets like Calcutta and Bangalore.
Apple growers are able to hold their crop in the farm and also in the markets in order to
wait for a better price. This improves their bargaining power in selling the crop at higher
prices. Vegetable growing farmers on the other hand cannot hold their crop in the field
and once they take their produce to the major market like Delhi, they cannot either hold
back the produce or move to any other market. This bestows to poor bargaining power to
the vegetable crop growers in comparison to the higher bargaining power of fruit crop
growers. These differences in the production and marketing of the crops highlight the
disparity associated with the risk of price and production as also the correlation between
price and production of the crop.
In order to identify the relative importance of price and production risk, the gross
revenue variability is decomposed into price, yield and price-yield interaction
components as provided by Barab and Binswanger (1982)44. If P is price, y is the yield
and R is the gross revenue, the R =
approximated as
py and the variance of gross revenue can be
Var (R) = y2 Var (P) + p2 Var(y) + 2y.p Cov (p,q)
Where Var is the variance operator, p and y are the mean value of price and yield
respectively and Cov is the covariance operator. Thus, the above identity splits variance
of gross revenue into a price component (the fITSt term), and yield component (the second
term) and a price-yield interaction component (the third term). The above identity can be
used to compute the proportion of variability in gross revenue that is due to its individual
components by rewriting it as
i" The discussion of B",",,!, and Binswanger's work is substantially drawn from Walker and Ryan (1990) as
\heJr paper is cited as a diSCUSSion paper CIrculated m international Crops Research InstJtute for Semi-Arid
Tropics (ICRlSAT) and it is therefore unpublished.
167

1= Y 2 Var (p) + p2 Var (y) + 2y.p Cov (p.g)
Var(R) Var(R) Var(R)
where the fIrst term is the contribution of price, the second tenn the contribution of
yield and the third term the contribution of the interaction term to revenue variability. By
multiplying both sides of the above equation by 100, the contribution of the price, yield
and interaction terms can be expressed in tenns of percentages. If the sum of the price
and yield terms exceeds 100"10, it means that the price-yield interaction is negative
because of negative correlation.
By using the data 45 on prices and production levels of the selected horticultural
crops, the revenue from the crop is decomposed into the price, yield and their interaction.
The results of risk decomposition are summari2ed in table 6.5.
T bl 65 -D
ecomposl ·ti ono fI ncome Ri s kf rom A lPI e an de au lifl owe r
Number of times Price risk less Price risk greater Negative
than yield risk than )'ield risk interaction
Apple 52 8 40
Cauliflower 28 32 18
Note:
i. The units are the number of sampled farmers. The first two columns would add to 60.
Source: Primary Data
a e . .
A stark difference in found between apple and cauliflower. 52 of the 60 apple
growing farmers experiences high variability in yield of the crop as compared to
variability in price, whereas majority of the fanners growing cauliflower experiences
high price variability than yield (32 out of 60). The negative interactions indicate that
prices and yields are negatively covariate. The negative correlation between prices and
yields reduces crop revenue fluctuations and provides a natural hedge to farmers. This
suggests the possibility that perfect price stabilization could destabilize income for some
farmers (Ramaswami et ai, 2004). This would happen if the 'yield' component is greater
than the sum of 'price' and the price·yield interaction components. Higher chance of this
means larger is the negative correlation between price and yield. Indeed, when the price
lerm and the price-yield inleraction term is set 10 zero (as would be the case with perfect
1------
" Three years (2004, 2005 and 2006) fann level data is obtained from the farmers on the price and
production levels.
168

price stabilization), the variability of crop revenues increases in the case of 40 apple
growing farmers as against 18 cauliflower growers. Thus, the major beneficiaries of
reduced price variability are the cauliflower growers and not apple growers. Stabilizing
yield of the crop would be much more effective in stabilizing revenues of apple whereas
stabilizing price, on the other hand, would be a more effective strategy to reduce revenue
risk of cauliflower.
The correlation between production and price provides a picture of difference in
the nature of marketing of these crops. Table 6.6 indicates a positive and significant
correlation between price and production for apple, whereas same is negative for
cauliflower. It means apple growers who received higher level of production have been
able to receive higher price of the crop. This is mainly because they are able to sell the
produce in different forms and at different locations including Delhi, Calcutta and
Bangalore. Lack of such opportunity in vegetable market results in negative correlation
between production and price. Farmers with higher produce of cauliflower also did not
receive higher price. The correlation between the variability in price and production
illustrates that cauliflower growers experienced a positive correlation between the
variability of production and price, whereas this is not the case for apple. Vegetable is a
more perishable crop with fewer opportunities in terms of scope of marketing.
T able 6.6: Correlation between Production and Pnce 0 fA
Correlation Mean Price and
CV in price and
Coefficient • production
production
Apple
0.317'
-0.287'
Cauliflower
• I % level of slgmficance
** 5% level of significance
Source: Primary Data
·0.111
0.462""
~pple an
d Cauliflower, 2004-06
6.4. Typology and Determinants of Risk Attitudes of Farmers
Risk is one of the major characteristics in the farming sector. Primarily, there are
three types of risk, i.e., risk as variable outcome, risk as unexpected, and risk as uncertain
¥Iardaker, 2000). In the first definition, risk is generally calculated by measures such as
standard deviation or coefficient of variation of outcome including price, yield or income.
169

In the second definition of risk, risk is considered on the basis of chance of a bad
outcome. Here, risk attitudes and behaviour of the farmelS are e~amined through different
methods 46 • Earlier studies were based on eliciting risk attitudes of the farmers and
initially most of the research depended on subjective probabilities to explain farmers'
behaviour. Shackle (1949) criticized the probability analysis on the basis that farmers are
seldom aware of the probability of outcome. He put forth a system of potential surprise
and focused on a loss function that indexes the extent to which a given gain/loss and
surprise combination "stimulates the mind". In addition to Shackle's contribution, the
downside risk and the Safety-First principle drove attention of studies dealing with land
allocation decisions by farmelS.
All for the third deftnition of risk, where it is dermed as uncertainty of outcome,
there are studies that used relative preferences of the outcome 47. In some situations,
farmers were quizzed about the premium that measures how much a farmer is ready to
forgo for some speciftc level of price and so on. Here, the degree of risk avelSion is the
amount an individual is willing to pay to avoid participation in a fair bet, or the risk
premium (Young, 1979). Many of these analyses were based on the skills of the
interviewer in obtaining the information and hence possible interviewer-biases made the
findings less signiftcant. In addition, it is argued that the above studies were based on
concepts like probability, expected value and binary comparison of lottery that might not
be meaningful to the average farmer, and hence questions depending on these concepts
hardly reveal the actual behaviour. Risk attitudes may not be unravelled by asking the
farmers to reveal their risk attitudes or by an approach that attributes deviation from risk
neutral optimism to risk preferences. Rather, the attitudes towards risk must be obtained
from the institutional and social environment that farmer lives in, his wealth position, the
credit available to him, his investment possibilities and so on (Roumasset, 1976). Such
environment is important for analysing the land allocation decisions by the farmers as
" The major difference in the role of different types of risk in decision-making of farmers is that in the first
case (risk as variability) farmers faces trade-off between profit and vanance, whereas In case of latter two
~efinitions (risk as a chance of bad o~tcome. ~r uncertainty of outcome). fanners faces trade-off between
profit and security in their land allocation decISIons.
47 Includes expected utility models, pay-ffs, be .. etc
170

these decisions involve costs, especially when both prices and yield are subject to huge
fluctuations.
The Safety-First principal propounded by Roy in 1952 was one of the methods that
factored in such costs in farmers' decision. The Safety-First principle lays emphasis on
the role of downside risk in farmers' attitude towards risk. Here, decision makers are
preoccupied not with maximising income, but with maximising their chances of survival.
Different choices by farmers do not depend on differences in their attitudes towards risks,
but on the differences in their subsistence needs, resource endowments and perceptions of
the riskiness among competing crops (Shahabuddin et al. 1986). Farmers face downside
risk 48 in their income process and their aggregate income risk is important if it results in
consumption fluctuations, especially when credit and insurance markets are absent. In
such a situation, a possible strategy for households is to take low value crops even if it
implies lower returns (Dercon, 1996). Differences in resource endowments, socioeconomic
characteristics and access to market influence the overall risk attitudes of the
farmers which in tum affect their gain and loss from the land allocation decisions and
influence their allocation towards horticultural crops.
In order to fmd the role of risk on production decisions, Roy's Safety-First
coefficient is used. Roy's Safety-First principle incorporates into a resource allocation
model yields the "efficiency" condition
MFCi = p{ (oQt 10 Xi) + [(d-u,)1 cr,)] { Ocri I Xi)
for the farm household, where pt and Qt are the expected price and output of crop
i, Xi is the quantity of input i used, d is the farm's household disaster income, u, is the
household's expected income and cr, is the variance of the household income. MFC is the
marginal factor cost of input i. (Shahbuddin et aI. 1986).
Under Roy's Safety Principle, the impact of risk on the decision-maker is given by
the risk coefficient (RC) = (d-u,)/ cr,). The relative magnitude of the variables d and u
ttermines whether the farm family is "forced to gamble" (d>u) or "allowed to trade
48 An estimate of income to suffer a decline if the market and weather conditions tum bad
171

expected return for reduced risk" (d

level data and not using district level data as the price differs dramatically across farmers.
The inc,?me is defmed as the revenue over the paid out cost only, and is computed as
3
u= LPiQi-LW;Xi
i =1
where u is the mean income from the crop, Pi is the price of the crop in the i lb year (3
years data is used i.e., 2004, 2005 and 2006), Qi is the quantity of the crop produced by
the farmer in the i Ib year, Wi is the price of the input purchased by the farmer in the i lb
year and Xi is the quantity of the input purchased by the fanner in the i lb year 50 • The
variance of income is calculated by standard deviation of income or returns from the crop
by using last three years.
T a bl e 67 . . F requene D' Istn 'b ution . 0 fF armers on the Basis of Risk Attitudes
Risk Coeffident Caulill.wer Apple
Below-2 10.00 8.33
- 210-1 8.33 28.33
-1 to 0 20.00 30.00
o to 1
15.00 20.00
1 to 2 25.00 8.33
Above 2 21.67 5.00
Note:
i. Figures are in percmtage of farmers, where the number of farmers are 60 for each category
Sou"",: Primary Data
Based on the risk coefficient measure, results show that most of the cauliflower
growing farmers are risk-takers than averse to risk (table 6.7). More than 61 percent of
the fanners growing cauliflower have a positive risk coefficient. This indicates that risktaking
behaviour is displayed by many farm households in the production of cauliflower.
However, for apple, most of farmers take a safety-fITSt position on the basis of their land
allocation decision. Around 66 percent of the apple growing farmers have negative risk
coefficient points their safe position. The difference in the disaster level of income and
crop income explains the risk behaviour of farmers. Hence, it is either higher food
consumption requirements at home or low income from the crop that influence the value
\0 Only the paid out cost for both the production and marketing of crop is accounted for as farmers need to
pay money/cash for it.
173

of the risk coefficient. The socio-economic conditions including access to non-farm
income source, farm size etc may also affect the risk behaviour of the farmers.
In order to examine the role of socio-economic factors that influence risk behaviour
of the farmers, a regression model is used with risk coefficient as the dependant variable.
Independent factors include household-specific factors like age, family size, gender,
access to credit, non-farm income and farm-specific factors including farm size, farm
assets etc. The results are presented for cauliflower and apple growing farmers separately
and for all farmers together. Specification of the equation is as follows
RC = f (Age, credit, Gender, Farm Size, Family Size, Non-farm income source and Farm
Assets)
Where,
RC: Risk Coefficient
Age: Age in number of years
Credit: Access to formal credit agency (O=No Access, I = Access to Formal Agency)
Gender: Sex of the household head (O=Female, I=Male)
Farm Size: The size of the farm (in Hectare)
Family Size: Number of household members
Non-Farm Income source: Having non-farm income source (O=No, I=Yes)
Farm Assets: Value of farm assets (in Rs.)
The results are presented in table 6.8. Among the income related factors, farm size
and access to non-farm income source played a significant role in explaining risk
behaviour of the farmers. Farm size is negatively, and non-farm income source positively
and significantly related with the risk coefficient. An inverse relation between risk
coefficient and farm size signifies that farmers who attained a Safety-First position in
terms of their risk attitudes also have bigger farm size. Farmers with higher farm size can
eam more income from the crop (due to higher level of production) and can have higher
income from crop in relation to their disaster level of income. Access to non-farm income
lis proved to be significant factor in explaining the risk attitude of fanners growing
cauliflower. Farmers, whose disaster level of income is higher than the expected income
174

from the crop, can afford to be risk takers as non-farm income source provided them
safeguard, in terms of available money for meeting the minimum consumption needs of
the households. Family size is positively and significantly correlated with the risk
attitude. As the family size increases, the disaster level of income is expected to increase
as family members require more food for consumption which can influence the risk
coefficient adverselyl. These high value crops are capital-intensive; huge expenditure is
required for not only production of the crop, but also for its marketing 52 • The fanners,
whose risk coefficient is positive, are able to allocate higher amount of area to these crops
due to their access to credit.
Tbl68Ft If}
Roy's Risk
a e . . ac ors n uencmg Ri k Attitud
s eso f F armers
Cauliflower Apple Growing
Coefficient as a Growing Farmers Farmers
Dependent Variable
Constant -2.157 (-1.967) -0.502 (-0.384)
Age 0.004 (0.327) -0.008 (-0.419)
Credit 1.124 (3.011)* 0.150 (0.384)
Gender 0.086 (0.207) 0.860 (1.856)
Farm Size -0.173 (-3.128)* -0.027 (-0.571)
Family size 0.160 (2.455)** 0.125 (1.170)
Non-farm income 1.233 (3.492)* 1.117 (2.939)*
Assets 3.11E-05 (1.128 -3.6E-05 (-1.545)
All Farmers
-0.706 (-0.856)
-0.013 (-1.183)
0.614 (2.269)**
0.494 (1.613)
-0.087 (-2.577)**
0.175 (3.160)*
1.075 (4.146)*
-1.23 E-05 (-0.767)
FIgures In the Parenthesis are t-values
All Farmers: R': 0.277 Adjusted R':O.249, N~120
Cauliflower R': 0.444 Adjusted R':0.369, N~O
Apple R': 0.239, Adjusted R':0.136,N~
• and •• signifies level of significance at I % and S % respectively
Source: Primary Data
III Make risk coefficient positive
" Capital is required for grading, packaging and transport purpose.
175

6.5. Role of Economic Factors in Land Allocation in Favour of
Horticultural Crops
Land allocation in favour of high value crops like horticultural crops is considered
as one of the vital components of growth in agriculturaI sector. Such higher allocation
can be influenced by both economic and socio-economic considerations at the fann level.
Since higher allocation to these commercial crops tend to reduce the amount of land
available for the production of food crop, the disparity between food requirements at
home and the actual food production may influence the extent of land allocation to
commercial crops. In addition, since, horticultural crops are highly labour and capital
intensive, labour availability at home and access to credit and non-fann income sources
together influence such decisions. Among economic factors, variables relating to price
and income from the crop are considered as factors that may influence land allocation
decisions of farmers.
6.5.1. Typology of Land Allocation
The typology of land allocation in favour of horticulturaI crops is measured by the
extent of land allocated towards the selected crops. Land allocation towards these crops
would be identical to the area under the selected crops in the total net cropped area. The
results of the extent of land allocation towards apple and cauliflower in the villages show
that these crops are of high significance for the farmers in terms of their livelihood; the
crops cover over 50% of their net cropped area in the villages (table 6.9). The typology of
land allocation across different farm sizes shows that in the case of cauliflower, large
farmers scores over others in the extent of land allocation made towards the crop. Below
them comes the category of marginal farmers (table 6.10). This illustrates that marginal
farmers also have been able to allocate a considerable amount of area. Coming to apple
crops, we find that small and marginal farmers made the highest allocation of land in
favour of the crop. It is important to mention that small and marginal farmers together
own less area than farmers of other groups; their decision of allocating more area to high
I value crop can be either an accuml!lative or survival strategy (Chaplin, 2000). In several
176

circumstances, small and marginal farmers allocate large area to high value but risky
crops in order to fight against poverty, which confirms the risk-taking capacity of the
small and marginal farmers. Hence, it is not right to view on high allocation towards high
value crop as a high growth strategy; additional infonnation about the effect of land
allocation decisions on farmers' welfare in tenns of its effect on income and risk patterns
of the farmers need to be collected to make the analysis meaningful.
. . x en 0 an • F
Table 6 9· E t t f L d All ocation ID avour 0 fD ortieu . I tu ral C roDS
Variable
Inlcator Cauliflower Apple
ViIIa.oel Villal!. n Total Villan m V_.IV
Proporti .... of (all:A)
Selected crop area
10 Net Cropped
Ar .. 49.95 54.21 46.47 70.05 67.18
Note.
i. (a;I1:A)= proportion of area (a) under particular crop (i) in the Net Cropped Area (A)
Source: Primary Data
. .
To ... 1
.:
68.85 5425
Table 6 10· Extent of Land Allocation in Favour of Horticultural Crops by Farm Size
Farm Size Share of area under apple or cauliflower to GCA
CauIillower
Marginal 58.33
Small 52.67
Semi-medium 51.78
Medium 35.20
Large 62.61
Apple
Marginal 71.62
Small 72.98
Semi-medium 69.06
Medium 51.86
Large: 66.90
Source. Primary Data
6.5.2. Socio-Economic Characteristics and Land AUocation
ale
Socio-economic factors can exert significant influence on the extent of land
allocation towards horticultural crops through their affect on resource availability and
risk management abilities of farmers. The results indicate that family size and number of
dependents tend to decrease the level of land allocation in favour of apple and cauliflower
increases (table 6.11). This shows that more dependants and higher food requirements at
~ome act as a constraint to increasing allocation. to high value commercial. crops. The
land allocation in favour of horticultural crops IS higher among the fanners WIth low farm
177

size. In tenns of irrigation, which is important for the cauliflower crop for its production
and profitability, there is a positive relation between level of allocation and irrigation
intensity. As the land allocation to cauliflower increases, the net irrigated area also
increases. But, the same is not the case with apple, which does not require irrigation for
production purpose. Higher ratio of land to labour indicates the availability of home
labour, which in tum influences the decision of land allocation towards horticultural crop.
In both the cases of apple and cauliflower, there is a negative relation between the level
ofland allocation to high value crop and land to labour ratio. This indicates that more the
quantum of home labour, for which the farmers are not supposed to incur any additional
cost, more the land allocation to high value crops. Reluctance to hire more labour and
disinclination to bear more input costs results in lower level of land allocation towards
high value crops.
Table 6 . 11· . Socio-Economic Characteristics at Different Levels of Land Allocation
I, Share of ,elected crop .... Family Number of Farm Irrigation LaDdlLabour Ann~
toGCA size dependants size inttDsity*
,
N.On-far".
i
(No.) (No.) (ha)
IlICOlne
(~.)
Cauliflower Low «0.33) 7.83 2.92 4.56 55.07 0.44 IOI95s-
Medium 6.17 2.03 2.72 63.60 0.33 87331'--
(0.33-0.66)
Higb (>0.. 66) 6.92 2.50 1.93 88.13 0.23 50419--
: Apple Low «0.33) 7.57 2.86 6.05 14.92 0.77 18 1403-
Medium 7.33 2.25 4.23 18.55 0.50 171584--
(0.33-0.66)
High (>0.66) 5.78 1.51 4.67 7.80 0.69 93958-
* Percenta g e of Net Irri ga ted Area to Net C rapped Area
Source: Primary Data
Information on the farmer family's per capita food expenditure and income, their
level of dependency on market for food and total income including farm and non-farm
were obtained (table 6.12). This information would help us in finding the relevance of the
socio-economic factors pertaining to food security of farmers to their land allocation
decisions. It is possible that higher farm income of the farmers tends to improve the
overall consumption of the farmers. Such farmers would be less concerned about the
production of food crops. For a comparison, food-security indicators pertaining to other
measures of diversification including index of concentration and number of crops
lproduced in a year are also presented.
~
178

Table 6 12' In
. . come-c onsumption G ap at Different Levels of Land Allocation
Extent of Diversification
Total annuli food Totll aanual Total gap between Pertentage of food
expenditure (in income - farm per capita iDcome crop obtained from..
per capita) (RI.) and non-farm. (in. and per capita land to total food
' Der caDit. Hils.) cODSIlIDDtion oi •. ) tonsumtd (%)
Share of divenified trop area to GCA
Cauliflower Low 10644 23661 13017
Growers
Medium 10610 23451 12841
Hiltb 9853 17117 7264
Apple Low 10076 42472 32396
Growers
Medium 9700 42316 32615
High 10640 57001 46360
Index of Concentration merfmdabllndu)
Cauliflower Low 9999 16689 7645
Growers Medium 10274 23435 14332
Hil1.b 11283 26077 16108
Apple Low 10756 67781 57337
Growers Medium 10436 50625 40608
Hil(b 9655 30751 22398
Number of crops produces in a Yelr
Cauliflower Low 10016 16987 7900
Growers Medium 9944 18425 9647
High 11099 28138 18322
Apple Low 10587 77892 67503
Growers Medium 10176 41913 32137
Hil(b 10651 40068 30891
Note.
i. For the distribution of share of diversified crop area to GCA and Index of concentration, low, medium
and high signifies below 0.33, between 0.33 and 0.66 and above 0.66 of the values respectively. The range
of both the distribution is from 0 to I.
ii. For the distribution of number of crops, low, medium and high signifies crops produced below, around
and above the average number of crops produced in a year. For cauliflower and apple average number of
crops produced in a year is 4.20 and 3.33 croPs respectively.
Source: Primary Data
The results illustrate that for cauliflower as the level of land allocated to high value
crop increases, the per capita expenditure on food decreases. But, this is accompanied by
a fall in per capita income as land allocation in favour of horticultural crop increases.
However, at the higher level of land allocation to high value crop, the gap between the
per capita income and expenditure is lowest; this confirms that the farmers who have
allocated more land to high value crop face higher risk of food security. On the contrary,
though the per capital food expenditure declines with increased level of allocation for
apple plantation, the gap between the income and food expenditure widens with every
increase in the land allocation level. This means that farmers who have allocated more
land to high value crop are at lower risk of food insecurity.
I
16.48
9.16
9.78
8.14
10.67
3.95
8.04
11.20
11.91
2.04
4.66
14.57
8.40
11.l6
12.00
1.50
4.67
16.48
179

A contrasting picture is seen when other indices of diversification such as aggregate
level of spread in the cropping pattern and number of crops produced in a year are taken
into account. As both the spread and diversity increases, in general, the aggregate food
expenditure increases. However, an increase in income is noticed only for cauliflower
growers and not for apple growers. This shows that increasing the number of crops by
cauliflower growers has a favourable effect on reducing food security problems, whereas
it is not necessarily true for apple growers. However, overall, the gap between the income
and consumption is far higher for apple growers as compared to cauliflower growers due
to difference in the profitability ratios of these crops. Hence, apple growers are more food
secure in comparison to cauliflower growers.
6.5.3. Land Allocation at Different Levels of Expectations and
Risk Attitudes
Examining the role of risk and uncertainty in the land allocation decisions by
farmers, a grouping is made of the farmers on the basis of the different levels of their
expectations (price, yield and income) and risk attitude (Safety-First and risk-takers).
The extent of land allocation in favour of the selected horticultural crops is computed
across different levels of expectations and risk attitude. Again, the results are presented
separately for cauliflower and apple (table 6.13).
Price expectation is higher among farmers with larger holdings. Since, higher
expectation of price is generally influenced by the level of maximum price received by
the farmer, it can be assumed that farmers with large farm size have a better advantage in
terms of their bargaining power in the market and they tend to realize higher price for
their produce. The results also suggest that as the expectation about price increases, the
level of land allocation in favour of horticultural crop decreases. When the expected
production per ha of the farms is taken alone, there emerges a contrast between its
relationship with farm size and extent of land allocation in favour of both the crops. In
the case of apple, the expectation of production per ha increases with the increase in farm
I size, and extent ofland allocation increases as the production per ha improves. However,
180

the same is not true of cauliflower where, farmers with expectation of higher production
per ha are found to have lower fann size and relatively lower extent ofland allocation. As
far as the expected income from the crop is concerned, it was bigher for larger farms for
both crops. There is a direct and positive relationship between the total income
expectation and level of land allocation to horticultura1 crops. The farmers, who have
bigher income expectation from the crop, allocated higher extent of land allocation to
cauliflower and apple crops. This confIrms that the income expectation does play an
important role in land allocation decisions of the farmers.
Table 6.13: Role of Expectations and Risk 00 Extent of Laod Allocation in Favour of
H omcu • I tura I Crops
Cauliflower
Apple
Farm Extent of land Farm Extent of land
Size (ha) Allocation (a,lL\) Size (ba) Allocation
(.~A)
Price E!pectation
Low Expectation 2.90 53.20 5.06 91.23
Medium Expectation 2.79 50.70 3.72 91.87
High Expectation 3.28 52.62 5.10 89.23
Yield Expectation (productioDlba)
Low Expectation 3.27 58.71 3.20 87.45
Medium Expectation 3.22 50.09 5.38 90.01
High Expectation 2.04 45.81 6.22 97.71
Income Expectation (IWba)
Low Expectation 2.08 46.48 3.05 88.54
Medium Expectation 3.27 50.64 3.44 88.31
High E~ctation 3.00 57.69 5.89 92.66
Risk Coefficient
Safety·First 3.27 60.75 5.64 95.60
Risk-takers 2.63 44.29 3.84 86.05
Note.
\. In the case of expectation of yield, for cauliflower, the fanners with low, medium and high expectations are 21,
23 and 16 respectively and for apple it is 23, 23, and 14 respectively. In the case of expectations of total income
for cauliflower, the farmers with low, medium and high expectations are 13,28 and 19 respectively and for apple
it is 14, 12 and 34 respectively. In Ihe case of risk coefficient, for cauliflower, Ihe fanners with Safety-First and
risk-takers are 28 and 32 respectively and for apple it is 30 and 30 respectively
Source: Primary Data
Coming to risk attitudes, it is the small farms that take more risk in their production
decisions. In regard to apple and cauliflower, Safety-First farmers have higher farm size,
whereas farmers with risk-taking behaviour have relatively low farm size. However, the
extent of land allocation in favour of horticultural crop by risk-taking faJTIlers is lower
Ithan Safety-First farmers. Due to higher income and lower food consumption
requirements as also low variance, fanners who allocated higher amount of land in favour
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ofhorticulturaJ crops are found to have performed better in terms of Safety-Fin;t position.
It confirms that the overall risk of consumption and production did playa significant role
in explaining the land allocation decisions of fannen;. Higher risk of conswnption and
production jointly influence the extent ofland allocation.
6.5.4. Determinants of Land Allocation in Favour of
Horticultural Crops
In order to capture the role of risk and uncertainty in the land allocation decisions by
fannen;, variables which have a direct bearing on fannen;' expectations about price and
income 53 were identified along with their focused revenue from the crop, variation of
crop revenue and fannen;' disaster level of income levels. Expectations about the price of
and income 54 from the crop influence the prospective outcome that fanners expect from
the extent of land allocation to a given crop. In general, fannen; who expect high price
and income from the crop are likely to have higher allocation of area in favour of the
given crop. The variable- focused income which is the mean value of the focused gain
and focused loss by the farmer 55 is also included. Higher variability of returns from the
crop is expected to adversely affect the land allocation decision. It is important to note
that while making the land allocation decision, the fanner takes different types of risks
that include not only the risk of production but also that of consumption among others.
Farmer enters into a trade-off situation while taking decisions on allocating area to
commercial crops. Higher food consumption requirements vis a vis the total food
production capacity of the land tend to influence the extent of land allocation to the
commercial crop. In other words, greater the food requirement at home, lesser will be the
extent of land allocation to horticultural crop by the farmer. It is not only food crop
" To deal with risk and to hedge losses effectively with land allocation decisions requires understanding
the individual farms expected future variability of income by potential crop (Sumner, 2004)
54 The expected Income is obtained by multiplying the expected price and expected yield ofthe crop.
jS Favourable and unfavourable outcomes results from most actions including land allocation decisions.
The degree of concern for a particular outcome probably increases as the likelihood of its occurrences
\ decreases. Those extreme outcomes, which are sli.1I thought of as possible and which are of most interests
to the decision maker are termed as the focus gam and focus loss by the farmer (Webster and Kennedy.
1975)
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equirements or expenditures that are of vital importance to fanners, but also other home
expenditures like children' ~ education expenses etc. which are even more important.
Therefore, here the disaster level of income is used as a variable, which includes all
critical expenditures and food requirements at home. Disaster level of income is expected
to effectively higher allocation of land to high value commercial crops. The specification
of the equation is as follows
alIA = f(EP, EY, FY, N and DL Y)56
Where,
alIA is the proportion of area under the crop aj (cauliflower or apple) to the total net
cropped area (LA)
EP: Expected price is the price that fanners expects from the crop
EY: Expected Income is the income that farmers expects from the crop
FY: Focused Income is the mean value of the focused loss and gain by the farmer
N: Income variability is measured by the standard deviation of output by using 3 years
fannleveldataand
DL Y: Disaster level of income is the minimum income requirement at home
The results indicate that income expectation and not the price expectation
influences farmers' decision of land allocation to the horticultural crops (table 6.14). The
income expectation variable is positive and significant for fanners growing cauliflower
crop but, for apple growers it is only positive but not statistically significant. Expected
price variable is negative and insignificant for apple growers. Interestingly, for apple
growers, the focused income from the crop, which is the mean value of the focused gain
and loss from the crop, comes out positive and statistically significant. This is because
apple crop has a gestation period of production and as land allocation in favour of apple
is mostly inflexible in terms of reallocation to crops. Higher variation in the revenue from
crop is negative and significant for cauliflower crop. This explains how higher risk
adversely affected their land allocation decision. The disaster level of income has a
16 .
I The log values are used for regressIOn purposes
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negative influence on land allocation in favour of apple and cauliflower crops as the
coefficien.t is negative and statistically significant. This shows that higher food
consumption requirements and other critical expenditures do influence the behaviour of
farmers in their decision in regard to allocating land to high value commercial crop.
Table 6.14: Factors Affecting Land Allocation Decisions of Farmers Producing
Hrti 0 cn I tura Ie rops
Land allocation as a
In Favour of cauliflower
Dependant Variable
Coefficient
Constant 6.\30 (4.336)
Expected Price 0.089 (0.386)-
Expected Yield 0.142** (2.1521
Focused Income 0.076 (1.285)
Income Variability -0.167** (2.207)
Disaster level of Income ·0.280* (3.210\
Note.
i. Figures in the Parenthesis are t-values
ii. Cauliflower R2: 0.491 Adjusted R2 :0.444, N9;O
Apple R2: 0.366 Adjusted R2: 0.307.N~60
iii. * and ** signifies level of significance at I % and 5 % respectively
Source: Primary Data
In Favour of apple
Coeffic:ient
6.953 (7.662)
-0.218 (1.673)
0.027 (0.921)
0.103** (l27)
-0.045 (1.593)
-0.253 .. (3.543)
6.6. Summary
Higher allocation ofland in favour of high value crops like fruits and vegetables is
desirable from the point of view of not only raising the farm income and productivity of
the farmers but also to create high employment in this sector in India. Among economic
factors, both price and income are the major incentives for the farmers to decide on the
allocation of resources like elastic and inelastic factors of production, such as land. Here,
the major aim was to examine the relevance of uncertainty and risk on the land allocation
decisions by farmers which acts as a proxy for diversification towards horticultural crops.
Information about the farmers' expectations are elicited and their risk attitudes measured;
they are then linked with the land allocation decisions.
There is a huge difference in the price expectations among the horticultural
producers which is influenced by the price received by them. Improvement in market
. infrastructure that results in better price realization is likely to influence the price
lexpectations. Better expectation of prices results in use of more inputs by the farmers and
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higher price expectations improve the input-use propensity of farmers in terms of higher
intensity of labour, more willingness to pay for hired labour and more willingness to
reinvest profits in land and crop related activities. But, for the land allocation decision,
price expectation alone cannot explain the behaviour of the farmer. It is the output per ha
that is more important in explaining their decision. In other words, income expectations
by the farmers are critical in improving the land allocation decisions. Farmers, generally,
take into account the aggregate gain from the crop in their decision rather than referring
to the expected price of the crop. Their capacity to generate higher productivity along
with the better market prospects together explains farmers' decision regarding allocating
land to high value crops. It is important to build a market structure in order to influence
input-use propensities of farmers, but is not sufficient condition for improving farmers'
readiness to increase the inelastic factor of production i.e., land in favour of high value
crops.
By application of the risk coefficient measure, it is found that most of the
cauliflower growing farmers are risk-takers than risk averse. Over 61 percent farmers
growing cauliflower have a positive risk coefficient evidencing their risk-taking nature in
land allocation decision. In contrast to the cauliflower growers, most apple growers take a
safety-first position while taking the land allocation decision. The difference between
apple and cauliflower growers can be attributed mainly to the difference in the income
potential of the crop concerned; income potential is relatively higher for apple growing
farmers. In addition, most apple growers have large farm size as compared to cauliflower
growers, which again contributes to the difference in income between the two groups.
The farm size and access to non-farm income source are found to have played a
significant role in the risk behaviour of farmers. Farmers whose disaster level of income
is higher than the expected income from the crop can afford to be risk takers as non-farm
income source would hedge their secure position. Family size is positive and significantly
correlated with the risk attitude. As the family size increases, the disaster level of income
is also expected to be high as family member require more food for consumption and
higher outlays for expenditures. This can influence the risk coefficient adversely.
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Availability of credit is positively related with the risk coefficient, indicating that risktakers
could do so by having access to credit.
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AJlPendix 6.1: Farmers Experience with Price of Cauliflower (R.lI

CHAPTER VII
CONCLUSIONS AND POLICY IMPLICATIONS
Agricultural is still the vital sector of Indian economy as it contributes around one
fourth to the GDP and employs more than 58 percent of its population. Growth of
agricultura1 sector is contingent upon several instruments that include quality and
quantity of land, technological progress, market development and crop diversification.
The slow down in agricultural growth after mid 1990s was attributed to increased pattern
of specialisation, higher orientation towards a few food grains, incomplete agricultura1
transformation and fatigue in major components of growth. These reflected in the growth
rates of area and productivity. Recently, diversification has assumed high importance in
the policy pertaining to growth of the sector. Policy makers have been tracking the
factors provoking diversification towards high value crops in the face of stagnant yields
of food-grains. The impact of crop specialisation on environment has dampened the
agricultural growth, and slow down in other instruments of growth. Interestingly, this
slow down forced a change and the shift is also necessitated by the opening up of the
market to the world. Sustained economic growth, rising per capita income and growing
urbanization have been causing a shift in the consumption patterns in favour of highvalue
food commodities like fiuits, vegetables, dairy, poultry, meat and fish products
from staple food such as rice, wheat and coarse cereals. All these situations made crop
diversification as an important buzz word in the agricultural sector in India.
Crop diversification is one of the sub-sets of a larger matrix of production
alternatives in the cropping sector that has several sub-components. These components
include first the diversity in the cropping pattern or spread of crops, and land allocation to
high value crops. It also includes change in the cropping pattern among different crops.
These components together and not in isolation affect the economy of the farmer in terms
of income and risk outcomes. It can be argued that a mere increase in the total number of
crops or more diversity in the cropping pattern may not promise more income or less risk.
But, it is also specificities of diversification that decide on the income and risk effect. At
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the same time, impact of other components of diversification on growth of output is
dependent on several factors. The differences in dimensions of diversification and its
varying relationship with growth raise some important questions: Why do farmers need
diversification, and under what context? What provokes diversification? Secondly, as
there could be either growth inducing or depressing effects of diversification, under
which circumstances diversification assume more importance? Which is better process of
diversification take place, and in which direction it must move i.e. at the crop and
regional level? What are the areas of diversification and finally, diversification at what
level?
High Value Crops as a Case for Diversification in India
In India, after independence, both market and technology were under developed and
these were responsible for low income and higher variability in the returns to food and
non-food crops. It was then diversification was viewed primarily from the angle of risk
and food security. Diversification, in terms of diversity of cropping pattern, was thought
of as one of the means to minimize risk and overcome food insecurity. The introduction
of Green Revolution was one of the steps towards changing the orientation of farmers in
India towards adoption of new and better technology. The Technology Mission on
Oilseeds (TMO) in late 1980s was an additional effort to improve the technology in
another crucial sector of agriculture in India. A mechanism of price support and subsidies
was operationalized to improve such technology-led diversification. This led to reduction
in the level of diversity among crops and increased concentration of cropping pattern as
large area was diverted towards a few prominent crops i.e., rice and wheat. Such
development initiative or policy came under critical lens, particularly after the
introduction of World Trade Organization (WTO) in 1995. Higher economic growth and
increased per capita income during 1990s, favoured the process of price-led
diversification as it was based on shifting of area towards crops whose demand and
consequently price were increasing at a faster rate.
There are many additional justifications for emphasizing on diversification towards
~igh value crops in India. On the b~is of supply side conditions, the major factor that
189

aises the need for diversification is the poor perfonnance of agricultural sector in the
recent past. Policy initiatives in the past were blamed for present picture of gloom in
agricultural sector (Hazra, 2003, and Rudra, 1982). The emphasis on cereal production
over the last three decades in India has resulted in low output prices and profitability for
cereals which led to dampened agricultural growth (Barghouti et aI. 2004). As significant
amount of area was shifted towards high value food-grain crops including rice, wheat and
maize, it led to emerging scenario of specialisation in many states. The increased
specialisation patterns resulted in the emergence of environmental concerns and
sustainability issues that expounds on the need for diversification towards other high
value crops. Additionally, as rising population pressure has been squeezing agricultural
land for cultivation (Joshi et aI., 2006) and several states in India are now hitting the
upper limit in use of fertilizers and irrigation; therefore there is need to look for
diversification towards high value crops as a policy option (Chand, 2005).
Diversification in favour of high value crops is also advised on account of several
demand-side factors. The present stage of poor perfonnance in agriculture along with
structural changes taking place_ in the economy provided a new opportunity for
diversification. The consumption patterns in India has been shifting towards high-value
commodities like fiuits, vegetables, dairy, poultry, meat and fish products from staple
food such as rice, wheat and coarse cereals. This is evident from the share of these
commodities in the total expenditure on food which increased from 34 percent in 1983 to
44 percent in 1999-2000 in the rural areas, and from 55 to 63 percent in the urban areas
(Kumar and Mmthyunjaya, 2002). In addition, trade liberalization and overall higher
growth in the economy has resulted in high returns from many non-food-grain crops due
to changes in the trade scenario and changing consumption pattern. On such a backdrop,
the diversification of agriculture towards high value crops like fruits and vegetables is
suggested as a viable solution to stabilize and raise fann income, enhance agricultural
growth, increase employment opportunities and conserve natural resources (Vyas, 1996
and Joshi, 2005).
190

Move towards Horticulture
Horticultural crops emerged as a significant case representing diversification due to
several reasons. The value and productivity of horticultural crops is far higher than any
other crops. Available evidence shows that one hectare under horticultural crops (such as
fruits) can generate an annual income of up to Rs. 20,000, in contrast to hardly Rs. 10,000
and Rs. 4,000 by rice and ragi respectively (Singh et a1., 2004). Cultivation of fruits can
also generate employment to the tune of 860 man-days as against 143 man-days by
cereals In India, demand of horticultural crops and their exports have been continuously
increasing and it is expected to increase faster in the near future. The consumption of
fresh fruits was estimated at 25 kg per capita in 1981 and in 2001 approximated to 40 kg
per capita, an increase of 60010 in the last two decades. The annual per capita consumption
of vegetables increased from 47 kg in 1983 to 76 kg in 1999. The overall increase in
vegetable consumption was about 53% during this period. Given the population base and
the fact that real per capita income levels increased at the rate of 3.4% per year between
1981 and 1999, the household expenditure on F&V increased at 5% per annum during the
period. It is understood that the relative significance of horticultural crops becoming
highly remunerative due to price. There is a need to enhance area under these crops as
there is a positive relation between an increase in the relevance of such crops and growth
in overall crop output (Joshi et a1., 2006).
Research Issues and Gaps
Studies dealing with diversification cover a wide range of aspects and at the same
time there are plethora of ways to interpret the significance of changing diversification.
While dealing at macro level, the major debate is about the quantification and
significance of crop diversification in the growth of output. As noted, there are several
sui>-components of diversification and each component affects growth in a different way.
In terms of diversification towards high value crops, many researchers argue that a silent
revolution of shift in cropping pattern towards high value crops is already under way
(Joshi, 2006, Vyas, 1996, Chand, 2005, Rao et aI, 2004, Birthal et al,. 2007). But, how
the process of diversification has affected the growth of output in India. While dealing
191

with this issue, many studies have dealt with the static aspects of diversification, without
emphasising the dynamic aspect. Actually, diversification affec~ growth both through
static and dynamic effects; a positive static effect of diversification would show a shift of
crop pattern in favour of high initial productivity crop. This does not, however, capture
developments in the technology of crops that changes over time and may alter relative
values of crops. Hence, it is important to consider the dynamic effects of diversification
that capture the concomitant movements of yield and cropping pattern change.
Any decision-making in the process of diversification towards high value crops
could be analysed at both macro (state or district) and micro (farm) levels. At the macro
level, such decisions are examined on the basis of price response models. However, there
are several limitations to analysing the changing land allocation decision using macro
level data. Measurement of supply response usually requires time series data for
quantities, cost and prices. Such data are not easily available least to think of reliability.
There is a partial coverage in terms of crops and area. As a result, a large number of crops
especially high value horticulturaI crops are found excluded from the analysis due to lack
of reliable time series data on these crops in India. Another limitation of these models is
regarding identification of the competing crops. Additionally, the macro level studies
mainly concentrate on crop price as the major economic factor shaping farmers' changing
land allocation decision and seem to ignore income aspect. It could be hypothesized that
farmers with relatively higher level of productivity allocate more land to the crop with
lower price expectation. Hence, we have examined the link between price and income
with the shift in the cropping pattem decisions of farmers.
Fruits and vegetables are highly remunerative crops but these are also considered
risky crops. The major feature of the horticultural crops is that the prices of the crops
fluctuate widely even within a single season. Lack of any support price coupled with high
perishability of these crops makes the horticultural growers even more vulnerable to risk
and uncertainty than growers of other high value crops like rice or sugarcane. In the event
of greater extent of risk and uncertainty, the importance of the same is high for the
farmers while taking land allocation decisions. At the farm level, the concept of
expectation is generally used in tenns of a response to uncertainty involved in the
192

production process. Farmer tends to have expectations about different economic
outcomes including price, yield and income. Therefore, we examined the link between
different expectations including price, yield and income in the land allocation decisions
by fanners.
Risk could also be generally considered a strong behavioural force impacting
decision on land allocation between food and commercial crop. A vital perspective of risk
is how far and how often returns unable to reach a below mean level of return. In this
context, risk is considered as the cost of decision in farmers' decision pertaining to land
allocation to high value crops (Roumasset, 1976). The Safety-First principle (Roy, 1952)
accounts for such costs in analyzing farmers' behaviour towards risk. Due to fluctuation
in the components of revenue from the crop, one can find two kinds of fanners; the first
group of farmers would prefer to be on safer side and hence their allocation decisions can
be explained by the income and yield variance of their crop as relative to the perceived
disaster level income of farm household. The second group constitutes farmers who
prefer to take risk in their land allocation decisions. Here, farmers are risk-takers as their
disaster level of income would be higher than the mean income from the crop produced
for commercial purpose. The farmers take risk by increasing allocation of land more in
favour of high value crop against the subsistence crop and take the risk about their food
consumption and the credibility of meeting other needs at home like education of
children, etc. But, not all farmers prefer to take such risk and hence keep their land
allocation low in favour of commercial or high value crops. They follow the principle of
Safety-First as they tend to realize low utility from the production of horticultural crops.
This often results in selection of low-risk crops that deters higher allocation to
horticultural crops.
In consideration of the gaps in available literature on the subject, we have chosen the
following research questions; what is the nature and extent of crop diversification across
states in India and its role in the growth of output in the agricultural sector? Whether the
process of crop diversification in India has been growth inducive or depressive? What is
the relative significance of economic and non-economic factors in diversi fication from
food crops to horticultural crops? Whether it is the relative price or relative income that
193

matters more in when farmers decides to diversify towards horticultural crops? What is
the nature of price expectat!ons of farmers and its relationship with other economic
factors such as crop yield, cost of production and input-use propensities? What is the
relative importance of different expectations i.e. price, yield and income expectations of
the farmers on their land allocation decisions? Are the farmers growing horticultural
crops averse to risk or not and what are the determinants of their risk behaviour? Does the
higher risk of production and consumption hinder land allocation in favour of
horticultural crop by the farmers?
Objectives of the Study
The specific objectives of the study are
(i) To investigate the relative role of diversification on output growth, and growth
inducive or depressive impact of changing process of crop diversification in
India,
(ii) To analyse the nature of price expectations of the diversified farmers, examine
its relationship with other economic factors and identify the role of different
expectations of price, yield and income of farmers on their land allocation
decisions? and
(iii) To estimate the role of overall risk of production and consumption on the
extent of land allocation in favour of horticultural crops by farmers?
Sampling and Metbodology
Himachal Pradesh was chosen as an area of this study because of the higher
importance of horticultural sector in the agricultural sector (representative in terms of
value of the location quotient exceeding four) and moderate to high growth in
diversification toward horticultural crops over the past three decades. Shimla district and
Theog block has emerged out as the representative towards diversification towards
horticultural crops. Due to the difference in the nature of the crops within horticultural
sector, four villages were selected (two villages each for fruits and vegetables) from
194

Theog block, as these villages (Govai, Sainj, Shilaru and Sandhu) are representatives in
diversificatjon towards fruits and vegetables respectively. In the first two villages namely
Govai and Sainj, vegetables cover 72% and 84% of the total gross cropped area
respectively. Among vegetables, most of the diversification has been towards cauliflower
crop. In villages, Shilaru and Sandhu, fruits are grown at a higher scale. Apple is the
major crop in these villages that covers 85% and 89"10 respectively of total cultivated
area. Both, cauliflower and apple crops were chosen for this stody in examining
diversification of land in favour of these horticultoral crops. Sample of 30 farm
households (120 farmers in total) was drawn from each of the four villages following a
stratified and proportional random sample approach.
To address the major objectives of the stody, primary and secondary data are used.
Secondary data cover 30 major crops for the past three decades across 17 states in India.
By using this secondary data, the decomposition model propounded by Minhas and
Vaidyanathan (1965) is employed to explain agricultoral growth in tenns of both static
and dynamic manner. For measuring the trend in area expansion and substitution, we
have used the method by Venkataramanan and Prahladachar (1980) in measuring gross
cropped area elasticities. To work out the economics of vegetable and fruit crop, different
concepts are used. Farm Management Studies concepts of cost and reloms are used for
examining economics of vegetable crop (cauliflower). We measured the output supply
and factor demand equations from farm level data of cauliflower using the profit function
provided by Lau and Yotopoulos (1972). Only one variable, i.e., labour is treated as
variable factor as within the given region, the prices of other factors of production i.e.,
fertilizer, chemicals and irrigation do not vary across farmers. The expected price is used
for measuring the profit due to marked difference in prices received by farmers over any
single season. Net Present Value (NPV), Benefit-cost Ratio, Internal Rate of Return
(IRR) and payback period are used to work out the economics of a fruit crop (apple). For
the purpose of identifying the relative importance of price and production risk, the
method by Barah and Binswanger (1982) is used where the gross revenue variability is
decomposed into price, yield and price-yield interaction components. The method of
weighted mean is employed to examine the significance of economic and non-economic
195

factors in diversification decisions of farmers. Source of diversity in farmers' response to
change in price of cauliflower is examined by analysis of variance (ANOVA). Elicitation
method is employed in obtaining the expectations of farmers of various economic
outcomes from the production of the selected horticultural crops. Farmers' expectations
are linked with their socio-economic and other characteristics along with their resources
allocation behaviour including land allocation. To account for the role of overall risk
including production and consumption risk, we measured risk attitudes of farmers under a
Safety-First framework propounded by Roy (1952). Regression method is used to
examine the role of risk and uncertainty on land allocation in favour of horticultural crops
by farmers.
Major Conclusions
At the macro (state) level, land availability is reaching its limit and therefore little
land is available for cultivation. As a result, the role of substitution effect has been
continuously increasing over a period time. This has shown that avenues of increasing
growth through area increase are weak. Therefore, there is a need to look for other factors
for increasing growth including crop diversification. The trend in growth rates of overall
output illustrate that at the national level, growth of crop output has been highest during
the decade of 19805 and that growth rate of output declined during the post 1990 period.
This trend in decline of the growth rate of output during 19905 is noticed in most of the
regions in India. The northern region, which benefited enormously from Green
Revolution, followed a specialisation-led growth. This region experienced poor growth
during the 1990s. On the other hand, southern and western regions also did not
experience higher growth during 199Os.
The picture is a mixed-one regarding the typologies of diversification across states.
Some states exhibit high diversity in the cropping pattern but at the same time have less
proportionate area under high value crops. There is also no direct link between diversity
in cropping pattern and spread of crops. In terms of relationship of different components
of diversification with income and risk, income growth is higher in the states that had
witnessed increase spread in the cropping pattern. These states have also shifted higher
196

amount of area to non-food grains. However, this effect was perceptible only during the
post-liberalization period in India. Until early 199Os, states whicb followed Green
Revolution strategy were the major beneficiaries, but after the onset of liberalization,
these regions have performed relatively poor as compared to other states.
Among the components of growth, crop diversification has indeed become an
important source of growth during the post-liberalization period (1990s), and that
technological development has made the largest contribution to output growth during the
previous two decades. Region-wise, technological development continues to be the major
component of growth for northern region, but the effect of the same has declined over a
period of time. This is primarily due to decline in the yield of the specialized crops in this
region. For the eastern region, area expansion continues to be the major source of growth
in the absence of technological development and crop shifts. In the southern and western
regions, diversification has become an important component for growth primarily due to
declining area under for cultivation, non-availability of new land and stagnation in
technology development.
These regions also differ in terms of nature of shift of cropping pattern over a period
of time. Northern region is the only one, which through all the decades has been able to
shift the crop pattern towards the crops with better technological growth. Eastern region
has been able to achieve positive growth in productivity in crops towards which they had
diversified during the 1990s. Western and southern regions experienced high and
negative dynamic effect during 199Os. This raises the concerns of lack of technological
development in the crops that contributed highly towards output growth of the region.
Though, there was increased shift in the crop pattern towards high value crops during
1990s, the growth rate in those crop yields have dec! ined. This has resulted in the growthdepressing
effect of diversification. This reflects that diversification towards high value
per se is not sufficient for increasing growth but it is also important that these crops
remain remunerative over a period of time, through proper technological and market
development; otherwise the gains from diversification will be meagre. At aggregate level,
for northern and eastern regions, it is more important to emphasis on increasing crop
diversification towards high value crops that includes non-food grain crops. It is
197

imperative as these regions have higher level of specialisation in rice and wheat that
could have led to high vulnerability of these regions to external shocks. For southern and
western regions, it is more important to introduce new technology for many of the high
value crops, which are becoming important sources of growth.
For increasing diversification towards high value crops in India, it is vital to develop
irrigation facilities. Many previous studies have argued that irrigation was not necessary
for the growth of high value crops; but this can be questioned as many of the these crop
are highly water-intensive and require development of irrigation as a pre-requisite.
Interestingly, the regions, which have experienced higher variability in rainfall over the
last three decades, could not diversitY their cropping pattern towards high value crops
including non-food crops. This has affected the impact of diversification on the growth of
output in these specific regions. It is clear that variability in climate bas influenced the
process of crop diversification and thereby the growth of output in India. The availability
of number of labourers across states has significant influence on diversification towards
non-food grain crops but hardly so on diversification towards rice and wheat. Evidently,
non-food grain crops are highly labour intensive than rice and wheat crops.
The economics of vegetable (cauliflower) and fruit (apple) crop is worked out
independently keeping in view the differences in the nature of crops. In cauliflower
cultivation, use of resources including fertilizer, irrigation, chemical-spray and allocation
of land etc. to crop decreases as the farm size increases. This confirms that with the
increase in farm size, farmers face more resource constraints. As the farm size increases,
aggregate cost (C2) as well as paid-out cost of cauliflower production declines;
consequent on this, the decline in productivity and gross and net returns occur as the farm
size increases. Small farmers are able to obtain the highest productivity and returns from
the cultivation of cauliflower. In order to fmd the link of land allocation decisions with
farm size, all farm size were divided on the basis of proportion of area under cauliflower
crop in total net area. There are two categories: lower «.50) and higher (>.50) for land
allocation to cauliflower. A comparison of diversified farmers within same farm size
category showed that farmers with higher level of land allocation gained more by a
decline in cost per ha as also increased net returns from the crop. This demonstrates that
198

increase in land allocation did prove remunerative and more profitable for all farmers
irrespective of farm sizes. By using different prices and output of cauliflower, the
maximum, expected and minimum net income over the paid-out cost borne by the
farmers have been are computed. The result illustrates that farmers who received high
income in the event of favourable market and weather conditions also lost the most when
both price and production crashed. There picture of gain and loss is different, when one
considers the same across different farm sizes; small and marginal farmers lost the most
when both the price as well as production suddenly crashed. But, medium and large
farmers gained relatively more in a situation of favourable price and production
conditions. It was the small and marginal farmers who bore the brunt of price and
production crash.
Efficiency analysis of cauliflower cultivation indicates that the relation between
profit and land is consistent with a priori notion only when price expected by farmers is
used, and not on the basis of current price. This might be especially so as prices of high
value crops like cauliflower fluctuate too widely and in the absence of any floor price,
farmers consider only the expected price in their decision-making. The coefficient of
capital is high and positive. The own price elasticity of labour is greater than one in
absolute value indicating an elastic response of factor utilization. On the other hand,
labour demand also responds positively to increase in endowments like land or capital
and increase in the expected price of the output. The high coefficient of capital indicates
its significance on the greater use of labour with increase in capital intensiveness.
Regarding the economics of apple cultivation, results illustrate that small and
marginal farmers have higher Net Present Value (NPV) as well as higher benefit-cost
ratio of apple crop production than large farmers. However, large farmers generally have
a lesser pay back period in getting returns from the crop. Large farmers also have lesser
Internal Rate of Return (IRR). This points to the better position of large farmers in
comparison to smaller farmers in terms of getting the total invested money back earlier.
They have low discount rate which is crucial in calculating benefits from apple
production. The simulation analysis presents some interesting findings: the change in
price from present year price to the price expected by farmer improves the position of
199

small and marginal farmers who are found to perform better in all four measures of cash
flow analysis. Small and marginal farmer have higher NPV and Benefit-Cost ratio in
addition to lower pay back period and lower discount rate. But, when it comes to the most
favourable price received by the fanners, large farmers have better deal in all counts. This
goes to the advantage of large farmers in terms of their better bargaining power in the
market that helps them to fetch higher prices as compared to small and marginal fanner.
Undoubtedly, among the economic and non-economic factors, diversification in
favour of high value crops is better explained by price. It emerges as a most important
factor that influences decision making as it directly affects the income potential of crop.
At the same time, other non-economic factors are also significant in such decisionmaking,
but their effect on a fiuit crop is different from a vegetable crop. Labour
availability for and food production from the land are critical considerations while
diversifying from food to apple crop, and the significance shifts to both irrigation and
productivity of the crop in regard to cauliflower. This is primarily because of the critical
importance of irrigation for cauliflower and its perishability compared to apple crop as
influencing factors fanner's decision. Fruits can be grown in plenty in marginal areas
where irrigation is not assured, whereas, fanners' inclination towards vegetable crop is
received only by assured irrigation facilities, which are mostly restricted to the
developed regions in India.
The differences in the decision-making in diversification between fiuits and
vegetable crops lies in the degree of flexibility in crop, relative returns from the crops and
the consequence of shift of cropping pattern towards these crops on the allocation profile
of the farmer (higher extent of shift by apple growers make them specialize in apple,
unlike in the case of cauliflower crop). For the production of cauliflower, resource
availability at farm is more important for diversification decisions, unlike in the case of
apple, where availability of additional income source is vital. Interestingly, education is
inversely related to diversification decision by farmers growing apple as such decisions
are linked with increasing level of specialisation in apple cultivation due to it being a
perennial crop. Educated farmers are concerned with income as well as risk in the
200

Production system and hence, prefer more diversification than being fully specialized in
one crop.
There are a few similarities in the decision-making process of diversification
towards apple and cauliflower. Relative income from the crop substantiated crop
substitution decisions towards both the crops. This means that farmers take into account
the aggregate gain from the crop in their decision rather than depending solely on the
price signals. Their capacity to generate higher productivity and better market prospects
together explain fanners' decisions. Thus, intervention in the technology and markets are
concomitantly required than only harping on market improvement for increasing land in
favour ofhorticulturai crops.
Expectation about different economic outcomes reflects farmer behaviour
(pertaining to land allocation) in response to uncertainty. In regard to price, there exists a
huge difference in the expectations among the horticultural producers and the same is
found influenced by the levels of price received by them over a period of time.
Improvement in the market infrastructure enables the farmers to realize price prevailing
in the large economy. This is likely to influence the price expectations in a positive
manner. Higher price expectations improve the input-use propensity of farmers in tenns
of higher intensity of labour, more willingness to pay for hired labour and be more
willing to reinvest profits in land and crop related activities. However, for land allocation,
income expectations are vital. In other words, price expectation alone cannot explain the
land allocation decisions of the farmer, but, output per ha is more important. Price
expectations are important but it has relatively less role in allocation of inelastic factors
of production, i.e., land. It is income expectations of the farmers that are more critical in
determining the land allocation decisions. This explain that, though it is important to
build a market structure in order to influence better input-use propensities of farmers, the
same is insufficient for improving fanners' orientation to increase the inelastic factor of
production i.e., land in favour of high value crops.
The results of risk decomposition of income risk into pnce, yield and their
interactions show a stark difference in apple and cauliflower crop. 52 of the 60 apple
201

growing farmers experiences high variability in yield of the crop as compared to price
variability, whereas majority of cauliflower growers experiences high price variabi!ity
than yield (32 out of 60). In addition, the major beneficiaries of reduced price variability
are cauliflower grower and not apple growers. Stabilizing the yield of the crop would be
much more effective in stabilizing revenues of apple whereas stabilizing price, on the
other hand, is more effective strategy to reduce revenue risk of cauliflower growers. In
addition, there is a positive correlation between price and production of apple, whereas it
is negative in the case of cauliflower. This shows that apple growers who receive higher
level of production have also been able to receive higher price of crop. This is mainly
because they were able to sell their produce in different forms and at different and far off
markets like Delhi, Ahmedabad etc. Lack of such opportunity for vegetable marketing is
partially responsible for the negative correlation between production and price.
Coming to the role of overall risk including that of production and consumption in
land allocation decisions, farmers, who have allocated less land proportion as of a total
net cropped area to cauliflower or apple, seem to have lower level of disaster income.
This indicates that food consumption and other requirements like schooling of the
children, other responsibilities etc, also have a role in land allocation decisions by
farmers. Interestingly, these farmers gets lower net income from the crop as compared to
farmers who have diversified more in terms of allocating larger proportion of area to
cauliflower and apple. More interestingly, highly diversified farmers experienced less
variance in the net crop income. Due to higher income and lower food consumption
requirements and low variance, farmers with higher diversification have performed better
in terms of Safety-First position. It means that the overall risk of consumption and
production did playa significant role in explaining the land allocation decisions.
Policy ImplicatioDs
The specific policy implications of the results are as follows:
I. Diversification indeed has become an important source of agricultural growth in
India. Increased significance of diversification towards high value crops in output
202

growth is accompanied by the slow productivity growth of high value crops. There
is need to emphasize on government intervention in the techn~logy development of
high value crops in India which was otherwise being neglected by the policy
makers. Diversification towards high value crops per se, is not sufficient for
indUCing growth but it is also important that these crops remain remunerative over a
period of time. That requires proper technological and market development,
otherwise the gains from diversification will be negligible.
2. Both economic and non-economic factors influence farmers' decision of
reallocation of land from food crops to high value commercial crops. Irrigation is
vital for diversification towards vegetable crops, whereas availability of labour is
crucial for diversification towards fiuit crops. Therefore, there is a need for distinct
policies for fiuits and vegetable sectors. Policy makers need to account for
differences in the nature of the crops while designing policies for enhancing
diversification towards horticultural crops.
3. Development and integration of markets are vital to increase farmer's welfare. High
price expectations are positively linked with the prices received by fanners over a
period of time. Though it is dependant on market structure and integration. High
price expectations by farmers directly influence their input use behaviour in
addition to improving the aggregate productivity and output at fann level.
Additionally, better market integration influences the net focused gain by the
fanners which in tum affects farmers' decision of land allocation towards high
value crops.
4. While, taking the land allocation decisions, farmers attach more importance to
expected total net income from the crop and hence consider both the price and yield
in their decision-making. Relative incomes from the crops explain the crop
substitution decisions of farmers i.e., fanners calculate the aggregate gain from the
crop than consider only the price of crop. The capacity to generate higher
productivity and availability of better marketing prospects together explains
farmers' decision. Thus, intervention in production technology and streamlining
markets are concomitantly required; harping on market improvement for increasing
land in favour of horticultural crops will not suffice.
203

5. Price stabilization is more important for vegetable crops than fruit crops primarily
due to the higher level of perishability and. lack of marketing options for vegetable
crops. A policy of price stabilization will be more effective to increase farmers'
orientation towards allocating more area to vegetable crops, whereas, for fruits,
yield stabilization is more important.
6. Farmers are concerned not only about crop-specific risk but also about their
aggregate production and consumption risk. So improving farmers' orientation
towards high value crops require intervention in food grain markets by improving
the technology. Better technology in food crop can lead to increase in the
productivity of food crops which would help in reducing consumption constraints
for higher allocation ofland to high value non-food crops. In addition, development
of labour market can be critical for land allocation decisions by farmers as it would
help farmers to earn more income and alleviate income constraints for
diversification.
204

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