ESTIMATING PARAMETERS FOR SOIL WATER BALANCE MODELS USING ADAPTIVE SIMULATED ANNEALING

Abstract

A desirable feature of crop simulation models is for the same model to be applicable in any region if one provides required soil, weather, and crop input information. However, this is not an easy task. Each model requires a number of parameters that have to be measured or estimated. Moreover, many existing crop models have empirical relationships that represent important soil and crop processes, such as root growth and soil water extraction. Thus, objective methods are needed for estimating parameters for such crop models because they may not be known or easily estimated from readily available field or laboratory data. An objective procedure based on the Adaptive Simulated Annealing (ASA) optimization technique has been developed to estimate soil and root growth parameters for both the “current” (CROPGRO-Soybean v3.5) and “revised” models. We used this procedure to estimate a soil impedance factor (SIF) and a root hospitality factor (RHFAC) in the “revised” model; and the root weighting function (WR) in the “current” model. This procedure 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 values. Data sets from East Campus, Nebraska (Specht et al., 1986), Gainesville, Florida (Hammond et al., 1978, unpublished), and Castana, Iowa (Mason et al., 1980), were used to evaluate the ASA optimization program and to compare the performance of the two models.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. Except for the Nebraska experiment, the “current” model with the optimized WR also fit soil water content over time. Although the two models predicted reasonably well final grain yield for the Florida and Iowa experiments, the “current” model under predicted grain yield by more than 40% for the Nebraska location. The absolute percent errors in soil water contents estimated for the Nebraska experiment using 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 growth and soil water extraction parameters for dynamic crop models.