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eturns to purchased inputs due to unstable yields (Wade 1998). In such environments,farmers’ behavior and use of land are guided very much by their perception ofrisk (Roetter and Van Keulen 1997, Singh HN et al, this volume). This is especiallyimportant in subsistence agriculture, where rural livelihood often depends on theseason’s harvest. Any land-use analysis should therefore explicitly recognize and addressthese critical biophysical and human characteristics of rainfed lowlands. So far,neither the temporal variability of weather and economic parameters nor the decisionbehavior of farmers under risk has been taken into account in SOLUS or LUPAS.Spatial and temporal variabilityIn SOLUS and LUPAS, physical production of a land-use system is a key technicalcoefficient in the optimization model. Production is calculated or predetermined inthe target-oriented approach from, among others, biophysical properties of the variousland units identified. In rainfed environments, where water availability largelydetermines production, the amount and distribution of rainfall, the terrain (slope, positionwithin the landscape), and hydrological soil properties are key properties bywhich to distinguish land units. Novel ways for mapping and delineating such propertiesand their spatial variability are presented by Oberthür et al (this volume, 1999).Other approaches combine biophysical land characteristics with hydrological modelingto generate yield surfaces. In SOLUS and LUPAS, land units are considered homogeneousin biophysical and socioeconomic conditions and technical coefficientsare determined for “average” conditions. Simulation models can help translate variability(or uncertainty) in biophysical parameters into variability in crop yield. Forinstance, Bouman (1994) used Monte Carlo techniques and an ecophysiological ricegrowth model to generate probability distributions of rice yield from variability insoil properties and management parameters. The main challenge is to translate theresulting maps into several manageable land units that can be handled in the optimizationmodel, while retaining information on parameter variability. Simulation modelsare also suitable for calculating temporal variability in yield caused by variation inweather (Hammer and Muchow 1991). GIS linking soil and climatic data surfaceswith yield probability distributions generated by crop simulation models in combinationwith agroeconomic data has been applied to calculate spatio-temporal variabilityof yield, production, and economic risk for well-defined production systems (Roetterand Dreiser 1994). Besides simulation modeling and GIS techniques, expert knowledge,field inquiries, and experimental data can all help quantify variability in yield(or other technical coefficients) of land-use systems. Once the variability is quantified,several methods can be used to address such variability in optimization models(Hazell and Norton 1986). A main research question, however, is how to handle thespatial representation of variable model input and output parameters.RiskVariability in yield turns into risk when it affects farmers’ livelihoods and influencestheir decisions on land use. Besides the biophysical variability (or, derived from it,the economic variability in financial returns), farmers’ behavior toward risk is impor-484 Bouman et al
tant in risk analysis (Roetter and Van Keulen 1997). Several techniques handle risk inoptimization modeling, most of them based on expressions of variability and farmers’risk perceptions (Hazell and Norton 1986, Selvarajan et al 1997). In the simplesttechniques, mean yields in an objective function are modified by an expression ofvariability (such as standard deviation) times a farmer’s risk aversion factor, whichshould be derived from interviews. Another approach is to quantify farmers’ utilityfunctions—that include their risk perception—and to make these the objective functionof the optimization model (Kruseman et al 1995). In regional land-use studies,farmers could be categorized according to their utility functions. Farm categoriescould then be optimized individually or together in an iterative manner taking intoaccount feedback at higher levels of spatial aggregation (e.g., Roebeling et al 2000).Conclusions and recommendationsCharacterization of rainfed environments is not a goal in itself, but depends on thetype of information to be generated. It needs to be based on a sound understanding ofthe prevailing biophysical and socioeconomic processes, be it at the field, farm, orregional level. Both exploratory and predictive land-use studies have in common thatthey synthesize fragmented agricultural knowledge and integrate data on resourcesover time and space. In rainfed rice areas, high temporal and spatial variability ofproduction resources complicates the analysis. Farmers’ diverse responses to climaticand economic risks must be taken into account, which eventually demands strongerlinks between on-farm research and operational research for meaningful policy formulationand implementation.ReferencesBouman BAM. 1994. A framework to deal with uncertainty in soil and management parametersin crop yield simulation: a case study for rice. Agric. Syst. 46:1-17.Bouman BAM, Jansen HGP, Schipper RA, Nieuwenhuyse A, Hengsdijk H, Bouma J. 1999a. Aframework for integrated biophysical and economic land use analysis at different scales.Agric. Ecosyst. Environ. 75:55-73.Bouman BAM, Nieuwenhuyse A, Ibrahim M. 1999b. Pasture degradation and restoration bylegumes in humid tropical Costa Rica. Trop. Grassl. 33:98-110.Bouman BAM, Jansen HGP, Schipper RA, Bouma J, Kuyvenhoven A, Van Ittersum MK. 2000.A toolbox for land use analysis. In: Bouman BAM, Jansen HGP, Schipper RA, HengsdijkH, Nieuwenhuyse A, editors. Tools for land use analysis on different scales, with casestudies for Costa Rica. Dordrecht (Netherlands): Kluwer Academic Publishers. p 213-232.Crissman CC, Antle JM, Capaldo SM. 1997. Economic, environmental, and health trade-offsin agriculture: pesticides and the sustainability of <strong>And</strong>ean potato production. Dordrecht(Netherlands): Kluwer Academic Publishers.Regional land-use analysis to support agricultural and environmental . . . 485
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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
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Boserup E. 1981. Population and tec
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Economics of intensive rainfed lowl
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Area (ha)50,00040,000Irrigated30,00
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The economic characterization and i
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Table 1. Rice varieties planted (19
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Table 4. Yield and input use of maj
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TFP, output and input indices200160
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Lynam JK, Herdt RW. 1989. Sense and
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406 Garcia et al
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Increased rice production had alway
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Table 1. Total rice area, average y
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use of water by avoiding seedbed pr
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water often brought with it a varie
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Table 4. Distribution of farm house
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Soil types found in the villages we
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Table 10. Ownership of agricultural
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in a group) at the rate of $60 per
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Table 13. Pattern of loan use (% of
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Labor and employmentTable 16 presen
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Table 17. Incidence of child labor
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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
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Page 432 and 433: Integration of biophysical andsocio
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Page 436 and 437: 1995-96 Rice production (t)TvRd6Rd1
Page 438 and 439: Farm-gate price of agricultural goo
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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 476 and 477: Francisco SR, Navarrete Jr. RL, Dim
Page 478: Wade L. 1998. Nutrient research on
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