Estimating parameters for soil water balance models using adaptive simulated annealing

A desirable feature of crop simulation models is for the same model to be a pplicable in any region if one provides required soil, weather and crop inp ut information. However, this is not an easy task. Each model requires a nu mber of parameters that have to be measured or estimated. Moreover; many ex isting crop models have empirical relationships that represent important so il and crop processes, such as root growth and soil water extraction. Thus, objective methods are needed for estimating parameters for such crop model s because they may not be known or easily estimated front readily available field or laboratory data. An objective procedure based on the Adaptive Sim ulated Annealing (ASA) optimization technique has been developed to estimat e soil and root growth parameters for both the "current" (CROPGRO-Soybean v 3.5) and "revised" models. We used this procedure to Estimate a soil impeda nce factor (SIF) and a root hospitality factor (RHFAC) in the "revised" mod el; and the root weighting function (WR) in the "current" model. This proce dure uses field measurements of volumetric soil water content at different depth increments over time during a growing season. It estimates parameters that minimize the error sum of squares between observed and simulated valu es. Data sets from East Campus, Nebraska (Specht et al., 1986), Gainesville , Florida (Hammond et al., 1978, unpublished), and Castana, Iowa (Mason et al., 1980), were used to evaluate the ASA optimization program and to compa re the performance of the two models. The results demonstrated that the ASA program was able to fit the predicted soil water in the "revised" model. E xcept for the Nebraska experiment, the "current" model with the optimized W R also fit soil water content over time. Although the two models predicted reasonably well final grain field for the Florida and lower experiments, th e "current" model under predicted grain yield by more than 40% for the Nebr aska location. The absolute percent errors in soil water contents estimated for the Nebraska experiment rising the "revised" and "current" models were less than 5% and 8%, respectively The global Adaptive Simulated Annealing (ASA) optimization method can be used successfully for estimating root grow th and soil water extraction parameters for dynamic crop models.