Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. 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. 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. 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. 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. 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. 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. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

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农业系统建模简史

农业系统科学产生的知识使研究人员能够考虑复杂的问题或做出明智的农业决策。这门科学的丰富历史证明了它们运行和研究的系统和规模的多样性。建模是农业系统科学中的一个重要工具，由来自广泛学科的科学家完成，他们贡献了超过 60 年的概念和工具。随着农业科学家现在考虑解决社会面临的日益复杂的系统问题所需的“下一代”模型、数据和知识产品，重要的是要评估这段历史及其教训，以确保我们避免重新发明和努力考虑相关挑战的所有方面。为此，我们在这里总结了农业系统建模的历史，并确定了可以帮助指导下一代农业系统工具和方法的设计和开发的经验教训。一些过去的事件与其他领域的整体技术进步相结合，对农业系统建模的发展做出了重大贡献，包括基于过程的作物和牲畜生物物理模型的开发、基于历史观察的统计模型以及经济优化和家庭和区域到全球范围内的模拟模型。农业系统模型的特征因所涉及的系统、规模以及促使不同学科研究人员开发和使用这些模型的广泛目的而有很大差异。跨机构、跨学科以及公共和私营部门之间更广泛合作的最新趋势表明，下一代模型、数据库、知识产品和决策支持系统所需的农业系统科学的重大进展已经准备就绪. 在社区开发下一代农业系统模型时，应考虑历史教训以帮助避免障碍和陷阱。