Contents & Foreword, Characterizing And ... - IRRI books

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