Abstract
Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30–40years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a “NextGen” study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation.
Highlights
Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decisionmakers in the private and public sectors
Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30–40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the generation of agricultural systems data, models and knowledge products
The Special Issue is based on a “NextGen” study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation
Summary
Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decisionmakers in the private and public sectors. Significantly improved data and models can contribute to development of advanced farm-management systems, and by making better information available about new systems, could accelerate the adoption and efficient use of more productive and more sustainable technologies Such data and models are essential tools for assessing the landscape scale impacts of technologies, evaluating policies to improve resource management, and projecting the performance of technologies under changing climatic and other environmental conditions. These processes are slow to improve, even with advances in genetic techniques and information technology, and become less effective in environments of increasing uncertainty due to climatic and other changes Taken together, these conditions suggest that, with appropriate investments, it may be possible to use simulation experiments carried out with models to greatly reduce the need for trial-and-error learning, focusing field experiment to develop capabilities and increase the robustness of simulation tools. These advances could increase the rate of agricultural innovation, and increase the rate at which these innovations are successfully adopted and implemented on farms
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