An agricultural system, or agro-ecosystem, is a collection of components that has as its overall purpose the production of crops and raising livestock to produce food, fiber, and energy from the Earth's natural resources. Although systems are abstractions of the real world defined for specific purposes, they are highly useful in science and engineering across all fields, including agriculture. These interacting components are exposed to an external environment that may influence the behavior of system components but the environment itself may not be affected by the changes that take place within the system boundary. These components interact with one another and with their environment to determine overall system behavior (e.g., see Wallach et al., 2014). Systems science is the study of real world “systems” that consist of components defined by specialists. Although there continues to be a strong emphasis on disciplinary science that leads to greater understanding of components and individual processes, there is also an increasing emphasis on systems science. These interactions transcend traditional disciplinary boundaries. We now know that interactions among components can have major influences on responses of systems, hence it is not necessarily sufficient to draw conclusions about an overall system by studying components in isolation ( Hieronymi, 2013). The information explosion is leading to greater recognition of the interconnectedness of what may have been treated earlier as independent components and processes. On the other hand, the problem of managing all of this knowledge and underpinning data becomes more difficult and risks information overload. How can science help address these complexities? On the one hand, there is a continuing explosion in the amount of published information and data contributions from every field of science. These factors are further compounded by climate change that will lead to many changes in the world as we have known it (e.g., Wheeler and von Braun, 2013). The world has become more complex in recent years due to many factors, including our growing population and its demands for more food, water, and energy, the limited arable land for expanding food production, and increasing pressures on natural resources. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models. 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. Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions.
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