Simulation of Crop Growth: CROPGRO Model

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Abstract
Mathematical simulation of crop growth and yield processes was initiated about 30 years ago (de Wit, 1965; de Wit et al., 1978; Duncan et al., 1967; Keulen, 1975). These pioneering modelers provided a systems framework for modeling carbon balance, water balance, and energy balance in a complex crop production system. This is basically the approach that has been followed by most crop modelers since that time. Early crop modeling efforts were initially limited by the availability and capacity of computers, by inadequate understanding of physiological growth processes, and by lack of input/output standards and access to common file access and graphics tools. Early crop models also focused primarily on potential production, often not considering water, nutrient, and pest limitations. As a result, there was not much realism in the simulated outcomes. In the intervening years, computer technologies have advanced manyfold and considerable 652physiological information has been obtained to better describe crop growth mechanisms, to parameterize them, and to test crop models. There are now many examples of successful crop model use in research, and there is increased optimism that crop simulation models will have good on-farm applications to address the many interactions between the crop and its physical and biological environment, to predict the consequences on seed yield or total dry matter production (Boote et al., 1996).

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