Downscaling process-based soil organic matter models and nitrogen input data for use by organic grain farmers

Abstract:

Decision support tools based on data collection and process-based modeling are important to help organic grain growers in terms of managing nutrients and reducing negative environmental impacts. To work on farms, such tools need to account for land use history and changes in soil organic carbon (SOC) over time that result from crop rotations, fertilizer and manure inputs, cover cropping, and/or tillage. This work adapted a parameterized soil organic matter model (PCSOM) derived from the CENTURY/DAYCENT model previously calibrated with crop yield and publicly-available C data source to simulate SOC dynamics. Estimates of plant available-N, soil nitrous oxide (N₂O) emissions, and N leaching losses were evaluated at the county-level for several production scenarios run in Illinois and Vermont. County-level fertilizer and manure application rates were developed using data fusion by combining farm surveys, farm sales, and crop rotations rebuilt with the USDA Cropland Data Layer (CDL). To allow users to adapt the model to their farm, we designed a web survey tool to ingest location-specific cover crop sampling, soil tests, and rotation information needed for model inputs. We will share our perspectives on improving the modeling of fine-resolution SOC and N₂O emissions for organic systems using advanced datasets and data calibration techniques. The costs and benefits of the decision-making tool will be presented during the meeting.