UC Davis

Nitrate (NO3) leaching from agriculture represents the primary source of groundwater contamination and freshwater ecosystem degradation. At the field level, NO3 leaching is highly variable due to interactions among soil, weather and crop management factors, but the relative effects of these drivers have not been quantified on a global scale. Using a global database of 82 field studies in temperate rainfed cereal crops with 961 observations, our objectives were to (a) quantify the relative importance of environmental and management variables to identify key leverage points for NO3 mitigation and (b) determine associated changes in crop productivity and potential tradeoffs for high and low NO3 loss scenarios. Machine learning algorithms (XGboost) and feature importance analysis showed that the amount and intensity of rainfall explained the most variability in NO3 leaching (up to 24 kg N ha-1), followed by nitrogen (N) fertilizer rate and crop N removal. In contrast, other soil and management variables such as soil texture, crop type, tillage and N source, timing and placement had less importance. To reduce N losses from global agriculture under changing weather and climatic conditions, these results highlight the need for better targeting and increased adoption of science-based, locally adapted management practices for improving N use efficiency. Future policy discussions should support this transition through different instruments while also promoting more advanced weather prediction analytics, especially in areas susceptible to extreme climatic variation.