Groundwater nitrate contamination is a significant concern in agricultural watersheds worldwide with it becoming a more pervasive problem in the last three decades. Models are great tools that are used to identify the sources and spatial patterns of nitrate contamination of groundwater due to agricultural activities. This Systematic Review (SR) seeks to provide a comprehensive overview of different models used to estimate nitrate contamination of groundwater due to agricultural activities. We described different types of models available in the field of modeling groundwater nitrate contamination, the models used, the input requirements of different models, and the evaluation metrics used. Out of all the models reviewed, stand-alone process-based models are predominantly used for modeling nitrate contamination, followed by integrated models, with HYDRUS and LEACHM models being the two most commonly used process-based models worldwide. Most models are evaluated using the statistical metric Root Mean Square Error (RMSE) followed by the correlation coefficient (r). This study provides the current basis for model selection in modeling nitrate contamination of groundwater due to agricultural activities. In addition, it also provides a clear and concise picture of the state of the art and implications to the scientific community doing groundwater quality modeling studies.

As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region.

Rising irrigation demand and declining groundwater resources threaten the sustainability of irrigated maize production in Nebraska. Benchmarking farmers’ applied irrigation against optimal amounts offers insight into management performance and highlights opportunities for improvement. However, the few benchmarking studies conducted in Nebraska have often relied on simplified modeling assumptions to estimate optimal irrigation, potentially overestimating the magnitude of differences from actual practices and overlooking factors that influence farmers’ decisions. This study applied the DSSAT CERES-Maize model to determine optimal irrigation using multi-year data from the University of Nebraska-Lincoln’s Testing Ag Performance Solutions (UNL-TAPS) program. The model, calibrated for cultivar traits and site-specific conditions, demonstrated strong agreement between simulated and observed phenology and yield during validation (NRMSE < 5%, d > 0.80). The validated model was used to benchmark farmer teams’ irrigation decisions and quantify deviations from optimal irrigation. Seasonal differences between applied and optimal irrigation ranged from − 175 to 306 mm, with a mean of 4 mm. Fifty-four percent of the teams applied more water than optimal, with a mean over-irrigation of 58 mm, less than the extent and magnitude of over-irrigation reported in previous studies. The variability in optimal irrigation (CV = 21%) was lower than actual irrigation (CV = 44%), indicating that behavioral factors played an important role in irrigation decisions made by farmer teams. These findings provide evidence to support improved on-farm water management and the development of policies that encourage sustainable irrigation practices.