Brief history of agricultural systems modeling.
Brief history of agricultural systems modeling.
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DOI：
10.1016/j.agsy.2016.05.014
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发表时间：
2017-07
影响因子：
6.6
通讯作者：
Wheeler TR
中科院分区：
文献类型：
--
作者：
Jones JW;Antle JM;Basso B;Boote KJ;Conant RT;Foster I;Godfray HCJ;Herrero M;Howitt RE;Janssen S;Keating BA;Munoz-Carpena R;Porter CH;Rosenzweig C;Wheeler TR
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作和研究的系统和规模的多样性。建模是农业系统科学的一项重要工具，已由来自广泛学科的科学家完成，他们在60多年中贡献了概念和工具。随着农业科学家现在考虑“下一代”模型、数据和知识产品，以满足社会面临的日益复杂的系统问题，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，并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。许多过去的事件与其他领域的整体技术进步相结合，有力地促进了农业系统建模的发展，包括基于过程的作物和牲畜生物物理模型的发展，基于历史观测的统计模型，以及家庭和区域到全球尺度的经济优化和模拟模型。农业系统模型的特征根据所涉及的系统、它们的规模以及促使不同学科的研究人员开发和使用它们的广泛目的而有很大的不同。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要的农业系统科学重大进展的阶段已经确定。应该考虑历史的教训，以帮助社区在开发下一代农业系统模式时避免障碍和陷阱。在引起经济或环境问题的事件发生后，技术进步对农业系统建模产生了重大影响。通过开放、协调的数据，实现了向稳健模型发展的进程。下一代模型需要具备模块化和互操作性的特征，以推进农业模型，学科和数据之间需要更多的整合
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. Advances were fastest after events that caused economic or environmental concerns Technological advances have had major benefits on agricultural system modeling Progress toward robust models has been enabled through open, harmonized data Modularity and interoperability are features needed for next generation models More integration among disciplines and data are needed to advance agricultural models