Towards a new generation of agricultural system data, models and knowledge products: Design and improvement. (July 2017)

• Record Type: Journal Article
• Authors: Antle, John M; Basso, Bruno; Conant, Richard T; Godfray, H Charles J; Jones, James W; Herrero, Mario; Howitt, Richard E; Keating, Brian A; Munoz-Carpena, Rafael; Rosenzweig, Cynthia; Tittonell, Pablo; Wheeler, Tim R
• Is Part Of: Agricultural systems. Volume 155(2017)
• Page Start: 255
• Page End: 268
• Publication Date: 2017-07
• Subjects: Agriculture -- Systems -- Models -- Data -- Knowledge products -- Next generation; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2016.10.002
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools. Highlights: NextGen data and models could accelerate innovation needed to achieve sustainable systems and meet global food security objectives. We envision new data and models compatible with new knowledge products needed to support user-defined information needs. Our design strategy involves "pre-competitive" and "competitive" spaces for data, model and knowledge product developments. We identify potential advances for disciplinary model components and their integration to address key Use Cases.