| Agriculture is vulnerable to unexpected or extreme climate. Factors seen as increasing agriculture's vulnerability include increasing human population, growing competition for fresh water resources, and heightened exposure of agricultural producers to unregulated markets. Climate prediction can reduce uncertainty, enabling adaptation to anticipated climate. The purpose of this research was to develop a methodology for linking climate forecasts to optimization of field-scale crop management; to demonstrate the potential of the methodology through application to diverse farming systems; and, compare the results. The research linked the widely used DSSAT family of crop models to a simulated annealing algorithm and a partial budget calculator.{09}Expected (average) profit was optimized using historical weather records, grouped as analog years according to ENSO phase, to produce crop management strategies for El Niño, La Niña and neutral years. To demonstrate the methodology, case study sites were selected in Argentina, Costa Rica and Mexico. Scientists in each country provided environmental and genotype inputs needed by crop simulation models. Primary data, including details of production practices and costs, were gathered from end users via surveys and interviews. Interpretation of these data provided the technical and economic boundaries within which management was optimized by varying all or some of plant variety, plant density, planting date, nitrogen fertilizer rate and timing, and/or irrigation amount and timing. Average 2–10% profit increase is modest, but can increase dramatically during the least favorable phase. Expected risks are also low. Observed attitudes and practices point to a direct relationship between larger farm size and technology adoption, but farmer organization can improve adoption among small farmers. This research demonstrates the theoretical feasibility of a simulation model-based, optimization approach to the integration of climate forecasts into agricultural management in a wide variety of farming systems. Through careful targeting, small farmers can be included among the beneficiaries of this new management technology. Anticipated improvement in crop simulation models and climate prediction methods will increase the value of this methodology. |
