Network for Evaluating and Improving Soybean Crop Models and their Role in Environmental Impact and Sustainability of Agronomic Systems

Grants and Contracts Details


Legumes play a critical role in agroecosystems by improving nitrogen balances, but there are challenges to maintaining sustainable productivity of our major legume crop, soybean: i) productivity trends show large deficits relative to the future demand due to increased temperatures and water limitations, (ii) decreasing trends in seed protein may increase the use of N fertilizer, and iii) annual C inputs from soybean residue to the soil are often inadequate to maintain soil C stocks and may decrease soil quality over the long-term. Finding solutions to these challenges requires a holistic approach to analyze the role of soybean in rotation with other crops that considers how climate, genetics, resource availability, and management factors interact. Process-based simulation models are powerful tools to study these complex interactions. There is a need and an opportunity to accelerate the improvement and applicability of these models by increasing the availability of existing experimental datasets to model developers, and providing opportunities for collaboration. This project will provide a networking platform within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to share experimental data for testing and improving soybean models. We will address four key bottleneck areas that cause high uncertainty in model predictions: response to increased CO2, prediction of water use, atmospheric N2 fixation, and predicting sowing date and cultivar maturity management adaptations. This project will be the first attempt to conduct a major international multi-model comparison and improvement for a legume crop and will quantify the uncertainties, challenges and opportunities for sustainable soybean production.
Effective start/end date4/15/194/14/24


  • National Institute of Food and Agriculture: $470,000.00


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.