A qualitative simulation model for cotton growth and development
Qualitative simulation models use variables that take on categorical rather than continuous values. Qualitative models for crop growth may serve as useful alternatives or complements to traditional crop growth models. The objective of this study was to demonstrate the development and use of a methodology for constructing qualitative crop models. We developed and tested a qualitative simulation model for the growth and development of Acala cotton in the San Joaquin Valley, California. The model is based on a logical description of cause and effect, and on the comparison of input data with projected solutions. The simulation contains eight time stages, ranging from crop emergence to late-season production of fruiting structures. Implementation of the model requires that it be calibrated, verified, and validated. Procedures for carrying out these operations are described. The model is tested by comparison with field data from soils at different salinity levels, and it provides reasonable predictions of the observed data. Thus, the development of qualitative simulation models for crop production appears feasible. These models may be useful as components of crop management decision support systems.
crop production expert systems agroecological models