Data stewardship and harmonization for sustainable solutions

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. Currently, AgMIP has over 700 participants from more than 45 countries contributing their expertise to over 30 projects and activities. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. AgMIP is working to reduce the wide gap between the goal of open, accessible, and usable agricultural data and the current reality regarding data collected by researchers around the world. Vastly greater value could be obtained if data were combined across locations, time, and management conditions so that researchers could develop or evaluate models that help inform decision support systems, assess the benefits of new technologies or management, analyze impacts of changes in climate, and evaluate tradeoffs between productivity gains and environmental risks. Initiatives aiming to improve data access and use need to be harmonized so that we do not end up with multiple, different data management solutions that are difficult to interconnect. AgMIP initiatives include regional integrated assessments, global economic assessments and global crop modeling activities, data and tools to facilitate multi-model and multi-discipline assessments, and cross-cutting themes to help interpret agricultural model results for decision-making. Results from these initiatives contributed to the Intergovernmental Panel on Climate Change Fifth Assessment Report, provide important context for national and regional stakeholders interpreting climate change risks, further state-of-the-art global food security assessments and agricultural models, and deliver key inputs, such as commodity prices, into regional integrated assessments.