Rural Development Policies and Sustainable Land Use in the ...

26 CHAPTER 3
decide the number of clusters to consider.
However, hierarchical clustering can give
rise to misclassification of observations at
the boundaries between clusters (Wishart
1999). Using k-means analysis corrects for
this problem. The k-means cluster analysis
is an iterative process that allows for starting
points and their means to be set at the
beginning of the process. We used the number
of clusters and the means of each factor
in these clusters as starting centers for the
k-means analysis. Observations were then
assigned to groups that they are “closest” to.
Based on the addition of each subsequent
observation, cluster centers were recalculated
and progressively calibrated through
successive iterations. This process was repeated
until all observations were assigned
across groups.
Empirical Model
Rural people and policymakers are most
interested in what drives outcome variables
such as agricultural production, household
income, and resource conditions. Once we
have clustered the household sample into
livelihood strategy groups, the household’s
livelihood choice can be explained based on
a set of predetermined asset-based variables
that include natural and human capital and
geographic determinants of comparative
advantage. Livelihood strategies are an important
part of a wider set of explanatory
asset-based variables that determines household
income and besides exogenous assetbased
variables also include physical, financial,
and social capital. In this way a
household’s asset holdings has both a direct
and indirect (via their impact on the livelihood
strategy choice) influence on income.
Of the wider set of asset-based variables, we
consider social capital assets (measured by
the household’s participation in programs
and organizations) as endogenous and influenced
by the same factors determining the
household’s livelihood strategy. Resource
conditions are linked to land management
decisions which are influenced by the same
set of variables as household income plus
other variables that reflect field-specific
characteristics. Finally, agricultural production
can be explained by the same set of
variables as land management decisions,
the use of labor and external inputs, and land
management decisions themselves. Labor
and external input use, in turn, are determined
by a set of factors similar to that for
land management decisions.
Based on the preceding discussion, the
variables of interest for our econometric
model are agricultural production; use of
labor, external inputs, and land management
practices; choice of livelihood strategy and
participation in programs and organizations;
and household income per capita. In the
following subsections we summarize the empirical
model for each of these variables.
Value of Crop Production
For agricultural production, we focus on the
value of crop production, in order to avoid
estimation of large numbers of individual
production functions for single crops in different
seasons. We assume that the value of
production of crop type i (i indexes annuals
or perennials) by household h on plot p in
season t (y i
hpt
) is determined by the labor
inputs applied to the plot (family labor,
hired wage labor, piece labor) (L hpt
); the
land management practices used (no burning,
minimum or zero tillage, incorporation of
crop residues, use of mulch, use of manure)
(LM hpt
); the external inputs applied to the
plot (inorganic fertilizer, herbicide, insecticide,
other purchased inputs) (IN hpt
); the
“natural capital” of the plot (NC hpt
) (biophysical
characteristics such as size, altitude,
slope, position on the slope, and inherent soil
fertility, and presence of land investments
such as stone walls, live barriers, and planted
trees at the beginning of the period); 26 the
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NC hpt
includes rainfall since we used a GIS to “assign” rainfall data to each plot.