Models for predicting nitrate leaching and assessing cover crop mitigation: A systematic review

Nitrate (NO₃-N) leaching from agricultural systems threatens the environment and human health. Cover crops are effective for mitigating nitrate leaching by scavenging residual nitrogen. While field studies offer valuable insights, they are expensive, time-consuming, and limited in spatial and temporal scope. Model simulations provide a cost-effective alternative for investigating nitrate leaching across scales, diverse conditions, and scenarios involving varied management practices. However, selecting the most appropriate model is challenging due to differences in intended purpose, accuracy, data requirements, scalability, and user-friendliness. The ability of existing models to simulate mitigation measures differs substantially. This study systematically reviewed, using the PRISMA framework, models suitable for predicting nitrate leaching and simulating cover crop mitigation. We identified and analyzed the ten most used nitrate leaching models, assessing their usability, efficiency, simulation capacity for mitigation treatments, and strengths and weaknesses. The review revealed the dominance of models like SWAT, DNDC, HYDRUS, and APSIM, primarily due to their flexibility and accessibility. These models have been widely applied in Europe, North America, and China. Although NSE, RMSE, and R² are commonly used for performance evaluation, there is absence of standardized methodologies making direct comparisons challenging. Our findings highlight that no single model excels across all criteria. The choice of suitable models depends on specific research objectives, available resources, and user expertise. We therefore propose a selection protocol: first define the primary objective (e.g., policy assessment vs. process understanding), then prioritize models based on their mechanistic representation of cover crops, scalability, and alignment with available data and expertise.