Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making. (July 2017)

• Record Type: Journal Article
• Authors: Capalbo, Susan M.; Antle, John M.; Seavert, Clark
• Is Part Of: Agricultural systems. Volume 155(2017)
• Page Start: 191
• Page End: 199
• Publication Date: 2017-07
• Subjects: Data systems -- Knowledge products -- AgBizLogic -- TOA-MD -- Next generation; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2016.10.009
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: 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, Camelina sativa, 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. Highlights: Better data are needed to support the development of improved knowledge products for policy decision making. Farm-level decision models can be used to support farm decision making and to acquire data for landscape-scale policy analysis. A farm-level model ( Ag Biz Logic ) and an economic impact assessment model (TOA-MD) illustrate possible model linkages. A case study of biofuel adoption illustrates how these models could be used to improve data acquisition and analysis linked to knowledge products.