Calibration and use of CROPGRO-soybean model for improving soybean management under rainfed conditions

Crops such as soybean (Glycine max L.) are grown predominantly under rainfe d conditions where water is a major limiting factor and the interannual var iability in rainfall pattern is high. Crop modeling has proven a valuable t ool to evaluate the long-term consequences of weather patterns, but the can didate crop models must be tested and calibrated for new regions prior to t heir use as extrapolation tools to predict optimum cultivar choice and sowi ng dates. The objectives of this paper were to calibrate the CROPGRO-soybea n model for growth and yield under rainfed conditions in Galicia, northwest Spain, and then to use the calibrated model to establish the best sowing d ates for three cultivars at three locations in this region. The starting po int of the calibration process was the CROPGRO-soybean version previously c alibrated for non-limiting water conditions. The original model, when simul ated versus rainfed soybean field data sets, tended to simulate more severe water stress than actually occurred. In order to calibrate growth and yiel d for the actual soil we tried several ways for the modelled crop to have a ccess to more water. Modifications were made on soil depth, water holding c apacity, and root elongation rate. In addition, other changes were made to predict accurately the observed water-stress induced acceleration of maturi ty. Long-term simulations with recorded weather data showed that soybean is more sensitive to planting date under irrigated than rainfed management, i n the three studied Galician locations. (C) 2001 Elsevier Science Ltd. All rights reserved.