DEVELOPING GUIDELINES FOR ORGANIC GRAIN GROWERS TO MANAGE SOIL HEALTH AND MITIGATE CLIMATE CHANGE IMPACTS

In this project, we aim to leverage a comprehensive statewide soil health dataset, enhanced by new field observations, remote sensing data, and advanced machine learning models, to create a web tool offering region- and field-specific SHMPs guidelines for direct use by organic grain farmers in managing soil health and ensuring climate-resilient farming. Our specific objectives include (i) conducting state-wide sampling campaigns to assess soil health parameters, soil nitrogen mineralization rate, nitrogen use efficiency, crop climate-stress resilience, and yield under SHMPs, (ii) developing and validating farm-scale machine learning models to estimate crop yield dynamics under SHMPs, (iii) developing and validating farm-scale machine learning models to estimate changes in soil health parameters under SHMPs, and (iv) delivering SHMPs guidelines and corresponding nutrient management recommendations to organic farmers through a web tool that visualizes model outcomes and integrates existing resources. The results of this project will be disseminated to stakeholders and scientific professionals through extension activities and publications.