Quantifying Temporal and Spatial Variability in NO3-N Leaching Across Iowa

The work conducted during this final quarter of the project focused on using the cropping system model (APSIM) to simulate the effect of simultaneously implementing multiple practices on yield-scaled NO3-N losses in maize and soybean cropping systems at three long-term experimental sites. Every site-specific model was used to conduct a full simulation with combinations of management practices, initial soil conditions, and weather-years, which produced more than 3 million simulated scenarios. Analysis of this simulated dataset, which is ongoing, will help characterize the main sources of environmental variability, plus identify combinations of management practices that consistently produce effective NO3-N reductions.

The focus this quarter was to conduct the second year of in-season sampling activities at the field sites. High-resolution data was collected on soil water, temperature, drainage and crop growth from the three field sites. Partners at the Iowa Soybean Association also collected critical field measurements across another 15 monitoring sites. The field data helped validate the simulated estimates provided by the APSIM model. Preliminary analysis of the field and simulated datasets has provided insight on the most important factors that explain the temporal and spatial variation in N leaching responses, and how this relates to N use efficiency. The next step is building a simulated database, which will be the backbone of the decision support tool. This will include simulated data across levels of management (N time and rate, planting date), genetics, environment (weather type, soil type), and locations. 

Using the soil water, temperature, drainage and crop growth data collected during the 2016 growing season in the long-term experimental sites, researchers concluded parameterization and testing of the APSIM model at those sites. In general, the APSIM model is able to adequately reproduce the long-term and the 2016 seasonal experimental datasets. The fit is good to observed corn and soybean yields and to the long-term trends for the tile drainage variables measured. Researchers concluded the APSIM was able to satisfactorily capture the behavior of the cropping system during the season as well as the long-term trends, and the model could be used to explore short- and long-term trends and variation in NO3-N leaching and to extrapolate findings at these sites. 

The focus this quarter was to execute in-season sampling activities at the field sites. High-resolution data on soil water, temperature, drainage and crop growth from the three field sites was collected, and used to test the model performance at those sites. Work continued to organize the historical data, and conduct scenario analyzes at two sites. These were directed towards testing the effect of N fertilizer application rates on N leaching across a wide-range in variation in weather. Some interesting findings related to crop rotation and management will be useful in designing the decision support tool.

During this quarter, the Iowa State University team made important progress in improving the model to better capture the effect of cover crops on NO3-N leaching and yield. The model now is ready to conduct “what if” scenarios. Research partners at the Iowa Soybean Association narrowed their statewide tile-drainage database to about 30 sites from which soil and drainage samples will be collected. In late March, ISU team members collected soil samples and also installed soil temperature and moisture sensors at the Nashua research farm site. A meeting with key personnel from the ISU research farms was held to plan summer soil and crop samplings at all ISU sites.