Location: Adaptive Cropping Systems Laboratory
Project Number: 8042-21600-001-009-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 15, 2026
End Date: Sep 14, 2030
Objective:
Primary objective is to test, improve, and apply crop and soil models and non-contact stress detection systems developed by USDA-ARS Adaptive Cropping Systems Laboratory for precision agriculture management systems used by farmers in the Mid-Atlantic region. Experimental data will be obtained from multiple field plot trials regarding crop and soil responses to changes in weather, management practice and cultivar. Primary focus is corn and soybean, but can include cereal rye, potato, and wheat. Field plots will integrate laboratory non-contact sensing technology and provide additional empirical data to test utility and integration with decision support tools. Models will be improved, and new components regarding soil hydrology and biogeochemistry will be added. These improvements and further evaluation of sensing systems will expand the utility and impact of laboratory tools for collaborating scientists, growers, and extension agents to identify optimum field management strategies leading to improved farm competitiveness and resilience.
Approach:
Experimental in-season crop and soil, and final yield, data from field plots will be obtained over a range of different management conditions and different crops in accordance with cooperator schedule and access to trial field locations. Laboratory non-contact sensing systems will be integrated into the data collection scheme and form part of the experimental data collection. Data will be organized into databases for archival purposes and to ease retrieval for purposes of developing crop and soil model input data. Discrepancies between simulated model outputs and empirical data will be used to identify knowledge gaps within the models that need to be addressed. Collaboration will focus on filling in these deficiencies in individual models as required to improve accuracy over a range of production conditions. Methods for improving the linkages between remotely sensed data and in-season plant status will also be tested and integrated into the crop models to improve in-season forecasting. Additional field experiments will be conducted to assess the capability of the models to provide accurate, in-season recommendations regarding fertilizer and irrigation practices with the goal of minimizing input costs while maximizing yield. Integration of these tools will also be evaluated for precision agriculture practice. These tools will be integrated into an existing computerized graphical user interface program and distributed to end-users, including farmers, extension agents, and scientists, for evaluating management options on-farm associated with nitrogen fertilizer and irrigation use and the influence of extreme weather events.