![]() ![]() The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.Ĭurrent practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Full descriptions of commodity specific implementations will be given elsewhere.Īgricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The features of the CALEX package are illustrated with brief descriptions of some of the commodity specific implementations. ![]() This paper describes the architecture of the basic CALEX program. Initial development of the program has focused on the development of a package of modules for California cotton and another package for peaches. The CALEX package is domain independent and can be used with any commodity. The expert system makes the actual management decisions. The scheduler generates a sequence of management activities by repeatedly activating the expert system. The executive serves as the primary interface to the user, to models, and to the disk. ![]() The program, which is implemented in conjunction with domain specific modules, consists of three separate subprograms: an executive, a scheduler, and an expert system shell. This paper describes the CALEX system, a microcomputer based integrated expert decision support system for agricultural management. ![]()
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