Assessing the Effectiveness of Individual Versus Multiple Nutrient Reduction Practices on Water Quality and Economic Viability
The Iowa Nutrient Reduction Strategy includes approximately 20 individual nitrogen-loss reduction practices, but there is little understanding about how these practices interact to affect water quality and profitability. Do stacked practices have additive or synergistic effects on nutrient loss reduction? The answer is unknown. This knowledge gap is largely the result of cost limitations on the testing of multiple, stacked practices with conventional field experiments.
This project will quantify the effectiveness of individual versus multiple/stacked nutrient loss reduction practices to identify suites of practices that minimize trade-offs between improvements to water quality and profitability. Data will be used from eight experimental locations across Iowa to train and test the Agricultural Production Systems Simulator, or APSIM, cropping systems model.
Recent modeling work from PI Archontoulis (Fig. 1) indicates that there is no a single (best) management practice that can increase productivity in Iowa. In contrast, small changes to multiple management factors have a synergistic effect and much greater chances of increasing corn and soybean yields. The team hypothesizes the same is true for water quality, and when the objective is dual (increase both profitability and water quality), that trade-offs will arise among practices.
Data from eight experimental locations across Iowa will be used to train and test the APSIM cropping systems model. Then, the power of modeling will be used to perform scenario analysis to quantify the impact of various practices.
Note: Project reports published on the INRC website are often revised from researchers' original reports to increase consistency.
During this period, researchers noticed a consistent underestimation of the simulated optimum N rate by the APSIM model compared to the observed optimum N rate. However, the yield prediction was very good and with no apparent bias. So, the issue was from the post-simulation analysis and regression equation fit to the data. To solve the issue of having more reliable estimates of the optimum N rate from the crop model, various analyses were performed (including sensitivity and/or addition of more N rates). As a result, the team proposed the use of the quadratic-plateau models to describe the yield response to nitrogen fertilizer rate. This solution improved the APSIM model prediction of the optimum N rate by 50%. After putting all the pieces together (grain yield calibration, optimum N rate), a long-term APSIM calibration paper was submitted to the journal Agricultural Systems. Currently, the team is working on addressing reviewers’ suggestions.
Other activities during this period included one field day and one presentation.
The first area of focus during this period was incorporating more long-term nitrogen datasets into the APSIM model to increase the temporal evaluation by five more years (from 15 years to 20 years). These additional datasets contained measurements of soil organic matter, and this is a reason why researchers decided to expand the model calibration period. The model performed well in simulating both crop yields and soil organic matter by depth across seven locations.
The second area of focus was the use of the calibrated model to estimate N loss (leaching and denitrification) for all cropping years. The team took these estimates and added economic values for the N loss towards estimating an environmental optimum N rate. However, the N loss price is not yet set up in the market, so an additional sensitivity analysis was performed for this.
Other activities during this period included one field day, one presentation and one workshop.
This progress report is for the entire 2021 year. APSIM was calibrated to capture yield response to N across 14 locations within the central US Corn Belt. APSIM outputs were then used to help explain trends in the economic optimum N rate and generate predicted N leaching + N2O losses per farming system to generate an environmental optimum N rate for corn.
The model was able to simulate long-term yield response to nitrogen fertilizer application fairly well (R2 > 0.7). A sensitivity analysis was performed to better understand management x environment x genetics effects on the optimum N rate and environmental N losses.
Two papers are under preparation for submission. One is to analyze the trends of economic N rate over time, and another is focused on determining the sensitivity of yield response to N given crop-soil-management-climate interactions. This will help identify multiple practices that can improve both productivity and environmental sustainability.
Activities during this period, included two presentations.
Progress sheds light on how nitrogen leaching is influenced by nitrogen fertilizer rate, soil type, crop rotation, and environment. This was done by calibrating the APSIM process-based cropping systems model, using 56 site-years of data that monitored nitrate leaching from artificial tile drainage in continuous maize and maize following soybeans.
Other efforts during the reporting period included APSIM model simulations (calibration and testing) of long-term nitrogen trials performed at Iowa State University (source, John Sawyer) and at Illinois (source, Emerson Nafziger). In total, data from 14 locations, equalling 182-site years, were compiled and used for modeling. Overall, the effort to test APSIM simulations using N leaching datasets together with the long-term N datasets increased the confidence in using the APSIM model for water quality assessments.
in addition to field-scale modeling research, we advanced the regional scale capabilities of the APSIM model.
- 1 presentation.
Publications /Journal Articles
Martinez-Feria R, Nichols V, Basso B, Archontoulis S, 2019. Can multi-strategy management stabilize nitrate leaching under increasing rainfall? Environmental Research Letters 14, 124079.
Rotating Maize Reduces the Risk and Rate of Nitrate Leaching by Pasley, Archontoulis, Helmers, Castellano and others