Attempts to relate agricultural production to weather go back to the start of domestication of plants and are still evolving. These were initially qualitative studies which were later followed by statistical analyses. The use of growth chambers and the development of precision instruments and their use in field observations provided quantitative estimates of how plant processes responded to variations in temperature, available water, and other environmental conditions. The quantitative measurements also provided an understanding of the microclimatic characteristics of biological systems. By the late 1960s and early 1970s extensive literature documented the response of plant growth and development to environmental conditions (Decker, 1994). These developments paved the way in the 1980s and 1990s for work on mathematical models of plant response and yields to varying environmental conditions.
The comprehensive development and use of plant and animal dynamic simulation models started with the availability of the computer in the early 1970s. By the end of the twentieth century several thousand computer-based plant and animal dynamic simulation models were developed to expand scientific insight into complex biological and environmental systems. Both simple and complex models are now available. In some cases, simple models are not appropriate because they are not programmed to address a particular phenomenon. In other cases, complex models are not appropriate because they may require inputs that are not practical to obtain in a field situation (Boote, Jones, and Pickering, 1996; Jorgensen, 1999). In a review of agrometeorology over the last two centuries, Decker (1994) described a progression from a descriptive science to a modeling approach based on analytical procedures using biological and physical processes.
Crop growth models have many current and potential uses for answering questions in research, crop management, and policy. Models can assist in synthesis of research understanding about the interactions of genetics, physiology, and the environment, integration across disciplines, and organization of data. They can assist in preseason and in-season management decisions on cultural practices, fertilization, irrigation, and pesticide use. Crop models can assist policymakers by predicting soil erosion, leaching of agro-chemicals, effects of climatic change, and large-area yield forecasts. Their use has resulted in huge economic benefits.
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