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Integration of biophysical andsocioeconomic constraintsin rainfed lowland rice farmcharacterization: techniques,issues, and ongoing IRRI researchC.M. Edmonds and S.P. KamThis chapter reviews research incorporating socioeconomic and biophysicalvariables into analysis to characterize rainfed rice environments. The use ofgeographic information systems (GIS) as a tool for integrating these two typesof data is highlighted. GIS starts as a useful tool for organizing and displayinggeo-referenced social and economic data. The increasing ease of use ofGIS software and improvements in geographic positioning systems and remote-sensingtechnology facilitate the generation of more accurate spatiallyreferenced data that can be integrated into analyses of agricultural practicesand outcomes. GIS software, combined with other software, provides analyticaltechniques for converting distinct types of biophysical and socioeconomicdata to a common scale amenable to analysis as an integrated database.These techniques are outlined. A final area of GIS application in socioeconomicanalysis involves linking GIS analysis with other modeling techniques.Econometric modeling and linear programming models using spatially referencedbiophysical and socioeconomic data are examples of this type of research.In the second part of the chapter, we review research that the InternationalRice Research Institute is carrying out in the Mekong River Delta ofVietnam in collaboration with the Institute of Agricultural Sciences in Ho ChiMinh City. The research applies geo-informational techniques and methodologies,and review of it enables consideration of the material covered inpart one. Here, we develop a spatial economic model of crop choice andland-use intensity to provide an analytical framework for empirical examination.Empirical estimates provide insight into the roles that biophysical andsocioeconomic constraints play in explaining changes in land use and productivity,and enable exploration of the interrelation between biophysical andsocioeconomic production constraints. Random effects probit estimates showwhich factors influence farm land use. This estimator uses the panel structureof the data, and provides robust estimates. Ordered probit and multinomiallogit estimates of cropping intensity and cropping pattern adopted werecarried out using single years of the survey to enable estimation of the effectof time invariant characteristics on these outcomes. Findings from theseestimates are reviewed and extensions and future applications of GIS econometricintegration are considered.Integration of biophysical and socioeconomic constraints . . . 441
Both biophysical and socioeconomic constraints influence land-use decisions andlimit the production and income of rice-farming families in Asia. Accounting for thebiophysical environment in agricultural research is essential in understanding productionoutcomes and the decisions of farm operators. Without proper characterizationof the constraints imposed on farms by climate, topography, soils, and similarfactors, efforts to understand outcomes in order to design and disseminate new technologiesto bring about outcomes are likely to fail. An understanding of the socioeconomicenvironment in which farms operate is equally important in agricultural research,technology development, and agricultural extension. Socioeconomic constraintssuch as the availability of agricultural inputs and presence of buyers for agriculturaloutput, or noneconomic constraints such as cultural sanctions against certain castesor against women engaging in particular production activities, exert significant influenceover farm households. Characterizing both types of constraints and understandingtheir relation to each other is essential to the development of technologies andpolicies to increase rice production and the incomes of rice farms. Integration of traditionaleconometric techniques with data organized in a geographic information system(GIS) offers a promising method for modeling both types of constraints.This paper is divided into two parts. The first part provides an overview of thegeneral theme of geo-informational techniques combining biophysical and socioeconomicvariables in the characterization of rainfed areas. We describe some of thetechniques applied in carrying out such integration, and discuss some of the problemsfaced in such research. To make these points more concrete, in part two we reviewresearch applying these methodologies that the International Rice Research Institute(IRRI) and the Institute of Agricultural Sciences (IAS) of Vietnam are carrying out inthe Mekong River Delta.Geo-informational techniques in the characterization of rainfed environmentsGeo-informational techniques, which include remote sensing, GIS, and related technologies,offer a useful framework for integrating spatially referenced socioeconomicdata and biophysical information. GIS facilitates capturing the spatial dimension andspatial analysis of the effects of these factors and their interactions.Characterizing rainfed lowland ecosystems:Which characteristics are important?In work characterizing rainfed areas, we view the objective of research to be to identifybiophysical constraints that limit agricultural production rather than to provide aholistic description of the environment. Agricultural scientists are accustomed to thinkingabout biophysical constraints to agricultural production and to using maps ofbiophysical characteristics in designing in-field experiments or explaining variationin farm outcomes across seasons. Because the biophysical environment provides manyof the essential inputs to plant growth, the link between biophysical conditions andagricultural outcomes is relatively clear. Relative to biophysical characterization, socioeconomiccharacterization is less developed, and features a much greater range of442 Edmonds and Kam
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The International Rice Research Ins
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Monitoring rainfed and irrigated ri
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ForewordRainfed rice areas are asso
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AcknowledgmentsIRRI is most gratefu
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2 Singh et al
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development, for example, provision
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subecosystems were recognized withi
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Table 3. Methods commonly used in e
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Table 4 continued.Scale of analysis
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Table 4 continued.Scale of analysis
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This included standard countrywide
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deepwater rice ecosystems. The rese
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Table 5 continued.Main MethodologyC
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Table 5 continued.Main MethodologyC
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Table 5 continued.Main Scale presen
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levels of involvement. For example,
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●●●ecosystem classifications
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Conclusionsclearly that they are di
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IRRI (International Rice Research I
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Widawsky DA, O’Toole JC. 1990. Pr
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Rice is the staple food crop for mo
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first working group to identify the
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TropicsSubtropicsTemperateSummer/wi
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ferred it for wide adoption by farm
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ing, remote sensing, and computing
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ice environments will be needed to
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Insects and diseasesInsects and dis
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The completeness of characterizatio
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Table 7. Levels and scales for agro
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Jamin JY, Andriesse W. 1993. Discus
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Tools and methodologiesfor biophysi
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Effect of climate, agrohydrology,an
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to water deficit because the crop
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for different periods of the year:
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depth scenarios (shallow, medium, a
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Water table depth (cm)500Shallow wa
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Simulated yield (kg ha -1 )6,0005,0
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Perception, understanding,and mappi
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and rice yield at regional or more
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SubareaSampling sites:GridTransectR
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was approximately 2,000 m with a to
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kg -1 in 50% of the topsoil and sub
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Table 3. Class medians were calcula
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Hard point dataClay, silt, sandHard
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A. Silt (%) B. 0.5 ccdfC. 0.2 ccdf
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In summary, the chosen geostatistic
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elated over slightly longer distanc
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Miura K. 1990. Genetic features of
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Rainfed lowland rice, growing with
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tively. In 1993, following dry till
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Rainfall (mm)Water table depth (cm)
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Saturated hydraulic conductivity (c
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in the uncompacted C0 plots (6.7 wk
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Clay classified≤ 2% 50 ha (2% but
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NotesAuthors’ addresses: D. Harnp
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target area of a breeding program.
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which is calculated from the estima
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hence grain yield. The three major
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ainfall data for each location. Mea
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such as 1980 and 1996, yield was hi
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Table 1 shows the results of the se
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Grain yield (t ha -1 )3UBN 19942103
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decreased with the use of old seedl
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The use of five genotypes with diff
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Mackill DJ, Coffman WR, Garrity DP.
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In rainfed agriculture, crop perfor
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GrainSite Year Code Group yield San
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Criteria considered for selecting r
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Table 1. Characteristics of the ref
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equired as the quality of the repro
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Cooper M, Rajatasereekul S, Immark
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144 Amien and Las
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As Indonesia’s staple food, rice
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Soil and climate of rainfed lowland
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isky when the crop is planted late.
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The decrease in harvested area only
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than in tidal swamp rice, for which
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Monitoring rainfed and irrigated ri
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and, indirectly, methane emission f
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Radar backscatter (dB)-7ERS windsca
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ERS SAR imageseries2. RSpreprocessi
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Fig. 6. Supervised classification o
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ConclusionsMonitoring of rice crops
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Characterizing soil phosphorusand p
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Table 1. Area and distribution of s
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Table 2. Lowland area (ha) classifi
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Fig. 1. Extractable soil P map (25%
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Characterizing soil phosphorus and
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Table 4. Recommendations for P appl
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Fig. 4. Extractable soil K map of t
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Fig. 5. Extractable soil K map of t
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● acidic pH (< 6)● low organic
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trials, K recommendations for uplan
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O’Brien DT, Blair G, Lefroy R. 19
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Planning and managing rice farmingt
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lowlands is submergence-prone. Ther
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situations and the allocation of re
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estimation of drought were determin
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Table 2. Effect of date of sowing,
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from October to February and a summ
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Table 3. Promising areas for ground
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1. There are possibilities of growi
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mmmm450350250150ASoil moisture useS
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Table 9. Yield a of preflood ahu (a
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Grain yield (t ha -1 )6Farmers’ l
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ReferencesIRRI (International Rice
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network (Sastry 1976). Temperature
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Temperature (°C) and solar radiati
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Table 1. Characterization of rainfa
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in eastern Uttar Pradesh under norm
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fluctuations/variations in rainfall
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the desirable rainfall period at 50
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The radiation values range from les
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not affect the growth and developme
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Agrohydrologic and drought riskanal
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Analytical procedureThe nature and
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late because of low rainfall at the
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Probability (%)10080604020Vegetativ
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15 August), which may happen twice
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texture), then once in two years (5
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Characterizing biotic stresses
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Characterizing biotic constraintsto
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included in these studies. Later st
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Average number of insects per sweep
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Table 2. Categorization of weed and
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Table 5. Properties of individual c
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ResultsRelations among pests and cr
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Start hereInterview farmers todocum
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Table 10. Information used to prior
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Interpreting the results of indepen
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level. Again, we augment our databa
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NotesAuthors’ address: Cambodia-I
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in soil nutritional status, especia
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flora. The range in elevation acros
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Table 1. Soil characteristics of th
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Table 3. Weed species recorded and
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Proportional abundance (log scale)%
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1.0Echi_colCype_iriMoll_penLind_spp
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+3.0Echi_cruMars_creEcli_albLept_ch
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indicates the need to deploy broad-
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ReferencesBangun P, Pane H, Jatmiko
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Socioeconomic characterization
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The role of characterizationin ex a
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ApproachThe following steps were ta
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PriceDS 0S 1P 0P 1dS 0S 1acbD0 Q 0Q
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Table 1. Ex ante assessment of rese
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Table 2 continued.Ranked byResearch
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Table 3. Research resource allocati
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NotesAuthors’ address: National C
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In addition to the unavailability o
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Socioeconomic constraintsFarm sizeF
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found that a higher rural labor sup
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Table 3. Rice area under HYVs from
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Rice varieties in farmers’ fields
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Table 9. Constraints for low adopti
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vehicle for realizing the untapped
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Parthasarathy G, Prasad DS. 1978. R
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Appendix. Agroclimatic zones of Bih
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such that losses are reduced during
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P = 0.5 R [a 2 C r2+ 2 a (1 - a) g
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and Itgaon, however, the plot-level
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espectively, to farmers’ income.
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Factors determining the adoption of
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mining whether or not modern variet
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irrigation schemes in the study are
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Using gender analysis in characteri
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lytical tool used to identify or di
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However, household members have cer
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Table 1. Village characteristics of
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taking care of dairy cattle. On the
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The biophysical environmentThe perf
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Table 3. Seasonal calendar and gend
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Table 4. Crops grown during the kha
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Table 7. Labor input (person-days h
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Table 8. Labor input (person-days h
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Table 10. Cost and returns of rice
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Gender roles in animal husbandryWhe
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farm activities, 25% in nonfarm act
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for rice but also for cash crops su
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Table 15. Access to agricultural in
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Hossain M. 1995. Recent development
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Agricultural commercializationand l
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Conceptual frameworkPopulation pres
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tional average of 18% 1 , and most
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The Dzao live in medium-altitude ar
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Table 1. Selected districts and com
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Table 2. General characteristics of
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markets for their food needs. With
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ther “extensification” in the u
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the productivity of lowland rice ca
Page 382 and 383: Boserup E. 1981. Population and tec
Page 384 and 385: Economics of intensive rainfed lowl
Page 386 and 387: Area (ha)50,00040,000Irrigated30,00
Page 388 and 389: The economic characterization and i
Page 390 and 391: Table 1. Rice varieties planted (19
Page 392 and 393: Table 4. Yield and input use of maj
Page 394 and 395: TFP, output and input indices200160
Page 396 and 397: Lynam JK, Herdt RW. 1989. Sense and
Page 398 and 399: 406 Garcia et al
Page 400 and 401: Increased rice production had alway
Page 402 and 403: Table 1. Total rice area, average y
Page 404 and 405: use of water by avoiding seedbed pr
Page 406 and 407: water often brought with it a varie
Page 408 and 409: Table 4. Distribution of farm house
Page 410 and 411: Soil types found in the villages we
Page 412 and 413: Table 10. Ownership of agricultural
Page 414 and 415: in a group) at the rate of $60 per
Page 416 and 417: Table 13. Pattern of loan use (% of
Page 418 and 419: Labor and employmentTable 16 presen
Page 420 and 421: Table 17. Incidence of child labor
Page 422 and 423: Cumulative proportion of income1.04
Page 424 and 425: Table 21. Cost and return analysis
Page 426 and 427: Table 22. Estimated rice yield and
Page 428 and 429: were often burned for disease contr
Page 430 and 431: landless population reaching 50-60%
Page 434 and 435: opinion regarding what socioeconomi
Page 436 and 437: 1995-96 Rice production (t)TvRd6Rd1
Page 438 and 439: Farm-gate price of agricultural goo
Page 440 and 441: ming the distance of all line segme
Page 442 and 443: Description of the study areaWe com
Page 444 and 445: Table 1 continued.Variable Unit1994
Page 446 and 447: CambodiaFig. 4. Main transport rout
Page 448 and 449: Table 2. Accessibility indicators f
Page 450 and 451: ice and the model is applied to exp
Page 452 and 453: Table 3. Summary of estimates of la
Page 454 and 455: estimation of a system of multinomi
Page 456 and 457: The availability of low-saline irri
Page 458 and 459: Table 5. Simulation of effects of i
Page 460 and 461: NotesAuthors’ address: Social Sci
Page 462 and 463: that they synthesize fragmented agr
Page 464 and 465: Framework for exploratory and predi
Page 466 and 467: and quantify the spatial distributi
Page 468 and 469: Seventeen different agricultural pr
Page 470 and 471: self-sufficiency in rice production
Page 472 and 473: al warming since maintaining or cre
Page 474 and 475: eturns to purchased inputs due to u
Page 476 and 477: Francisco SR, Navarrete Jr. RL, Dim
Page 478: Wade L. 1998. Nutrient research on
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