Quantifying Temporal and Spatial Variability in NO3-N Leaching Across Iowa
The Iowa Nutrient Reduction Strategy summarized a number of practices that can reduce nitrogen leaching from corn and soybean fields, yet a high variability exists for the majority of recommended practices. This reflects the differences between soils, climate and management practices, and shows the need for analysis and development of site-specific relationships that account for temporal and spatial aspects.
This study will help prioritize the best performing nutrient management practices, and shed light into the mechanisms responsible for the variation in nitrogen load between sites and cropping systems. The results of this project will be used to develop an interactive decision support web tool that can greatly assist the decision- making process.
The Iowa Soybean Association’s statewide tile-drainage database of 150 sites, which capture spatial variability, will be merged with the Iowa State University long term tile-drainage database, which captures temporal variability. Then modeling will be applied to develop relationships to assist the site-specific decision-making process.
This project evaluated practices to help determine the most effective ways to reduce tile drainage nitrate losses from Iowa fields. Some results:
- Rye cover crop is effective at reducing tile water nitrate concentration and loads only if the cover crop is successfully established and produces good growth.
- Winter rye cover crop can slightly reduce corn yield in some years, if biomass is too high.
- Sites with cover crops had lower nitrate concentrations than those without cover crop. Fields planted under continuous corn had slightly higher nitrate concentration than those in rotation with soybean.
- Simulation studies using 30 years of weather data indicated environmental factors such as seasonal precipitation, carry-over nitrate from the previous season and water table effects accounted for 75-90% of the variation in N loads, while management accounted for 5-20%.
- Cover crops are more effective in reducing nitrate losses in soybean when paired with timely planting of long-season, high-yield varieties. Improving nitrogen fertilizer management by reducing rates and improving application timing was the most effective way to reduce soil nitrate left over after corn harvest.
- Only 45% of the nitrate losses from well-managed corn and soybean Iowa cropland can be attributed to the inefficient use of nitrogen fertilizers. The rest originates from the native soil fertility, plus failure of current cropping practices, which often leave the soil bare and unproductive for long periods of time.
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.
Quality control is being done on the field data collected during the second year of in-season sampling activities at the field sites. The team collected high-resolution data on soil water, temperature, drainage and crop growth from the field sites (COBS, Nashua and Crawfordsville). Partners at the Iowa Soybean Association revised the two-year dataset collected from the monitoring sites, which will be used to test the extrapolated predictions provided by the APSIM model. Preliminary analysis of the field and simulated datasets has yielded 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. These findings have been included in a manuscript accepted for publication in the Agriculture Ecosystems and the Environment Journal. Work continues on developing sound calibration protocols to be used in the large-scale regional simulations, which are necessary to build the simulated database — the backbone of the decision support tool.
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.
The calibrated version of the APSIM model was used to estimate the long-term NO3-N leaching potential and N-use efficiency of these systems, and extrapolate findings to other levels of N fertilizer and crop rotations (e.g. continuous corn, corn-soybean with rye cover crop). In-season data collection for 2017 at the long-term ISU research sites began. These include high-resolution measurements of many hydrological, soil, root and crop growth variables, which will enable researchers to continue to test and improve of the performance the APSIM model. Collaborative work with the Iowa Soybean Association continues on analysis of the geographic dataset, which now includes two years of data collected at 15 sites.
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.
This quarter’s focus was to revise, format, and summarize high-resolution data collected from the three field sites during the 2016 season on soil water, temperature, drainage and crop growth. These data helped with the continued testing of the APSIM model at the three sites. With the new 2016 data available, empirical relationships at the sites were reanalyzed, including cumulative drainage flow vs. precipitation, and cumulative Nitrate-N load. Data from two scenario analyses were generated, including eight rates of nitrogen at Nashua in a corn-soybean rotation across 35 sequential years of weather records, and factorial arrangements of N rate and timing, genetics, planting date, residual N, initial water table depth, previous crop, and corn or soybeans, with 36 years of seasonal weather records. During a progress meeting with Iowa Soybean Association collaborators, it was reported the following samples were collected in 2016 at 15 sites — drainage water NO3 concentration, soil profile samples, and management information and drainage characteristics.
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.
ISU and Iowa Soybean Association research partners met April 8 to discuss preliminary data analysis and agree on a sampling and analysis protocol. It was decided the analysis of the statewide tile-drainage and historical ISU-research stations databases should continue with small modifications. A priority is to collect information on drainage characteristics (drain depth and spacing) from field sites, as well as management information (dates and rates for planting, N fertilization, tillage). In-season data collection at the long-term ISU research sites began. These include high-resolution measurements of many soil, root and crop growth variables, and will be used in the model to estimate the effect of cover crops on NO3-N leaching and yield under various “what if” scenarios.
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.
An initial meeting with Iowa State University and Iowa Soybean Association (ISA) employees was held, with discussion centered on responsibilities, data analysis, work sites and sampling plans. Requirements were set for selection of ISA on-farm study sites. Statistical analysis got underway to determine which ISA fields would yield the most explanatory results, such as fields that had good management practices, but still had high N leaching. Sites were selected and ongoing soil sampling has been performed at two sites to determine soil characteristics required for modeling. Historical data from ISU research farms involved in the study is being organized into a database for analysis, and to be used for model set-up and calibration. The model is ready for one site, with monitored tile drainage and model improvement for winter rye characteristics and N loss ongoing. Other work has included modeled scenario analysis of four corn-based systems in central and northwest Iowa with a focus on nitrogen dynamics.