UC Davis

This dissertation assesses the impact of rice-based systems on productivity and environmental sustainability in Uruguay using data from two sources. The first two chapters use data from a long-term experiment conducted in the major rice-growing region of the country, while the third chapter utilizes data from 2042 Uruguayan farmers across four seasons, accounting for 220,000 hectares. The first chapter examines the effects of different rice rotations on soil organic carbon and nitrogen as well as rice yield. The second chapter evaluates the stability of a multi-criteria performance index that considers productivity, environmental, and economic indicators. The third chapter employs geospatial machine learning models to evaluate the impact of management practices and soil features on yield and its spatial variations. The findings of this study highlight the importance of integrating rice with pastures and livestock for a balanced combination of profitability and environmental performance. Additionally, the research provides a successful example of data sharing among farmers and researchers and highlights the important role of farmers’ data in guiding agronomic decisions for sustainable and yield-increasing practices, with potential implications for extension and investment in agricultural research programs.