Regression-based evaluation of ecophysiological models

Ecophysiological models are increasingly used as research and decision support tools in agriculture, but it is often difficult to assess how suitable a model is for a particular application. Model evaluations usually involve bivariate linear regression between observed and simulated values, which assumes statistical independence among observed values. However, observed data often have dependencies if they originate from series of experiments or involve experiments using nested designs (e.g., with split plots). By representing experiments, cultivars, or other variables as factors, linear regression models can specify expected dependencies, permitting analyses that are statistically more rigorous and provide more insights into model performance. This study evaluated the Cropping System Model (CSM)-CROPGRO-Soybean model using regressions that included environment and cultivars as factors as well as continuous variables such as temperature or daylength. When applied to 28 data sets for soybean [Glycine max (L.) Merr.], representing 113 treatment combinations, the regressions showed that the model simulated days to anthesis and grain yield well for a wide range of environments. Differences among environments represented a larger portion of unexplained variation than did differences among cultivars. Further improvements thus might be sought in modeling crop response to environment rather than in representing cultivar differences, or alternatively, in characterizing soil profiles or daily weather rather than cultivars. A submodel for photosynthesis that scaled leaf-level values to canopy simulated grain yield more accurately than a simpler submodel. Multiple regressions provided much more information on model performance than simple bivariate comparisons.