Capalbo Susan M, Antle John M, Seavert Clark
Department of Applied Economics, Oregon State University, United States.
Agric Syst. 2017 Jul;155:191-199. doi: 10.1016/j.agsy.2016.10.009.
Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, , which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.
对下一代农业系统模型的研究表明,当前最重要的限制因素是数据,无论是用于农场决策支持、研究投资还是政策决策。最大的数据挑战之一是获取关于农场管理决策的可靠数据,包括当前状况以及生物物理和社会经济条件发生变化的情景下的数据。本文提出了一个使用农场层面和景观尺度模型及数据的框架,以提供可用于下一代知识产品(如移动应用程序或个人计算机数据分析与可视化软件)的分析。我们描述了两种分析工具——AgBiz Logic和TOA-MD,它们展示了农场层面和景观尺度模型的当前能力。通过对一种可用于生产喷气式航空燃料的油料作物的案例研究,探讨了这些工具的使用。最后,我们讨论了为促进使用农场和政策层面模型以生成数据和分析从而改进知识产品所需的创新。