Assessing the Effectiveness of Individual Versus Multiple Nutrient Reduction Practices on Water Quality and Economic Viability

Date: 
Aug 2019

Issue

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.

Objective

 effect of individual versus multiple/stacked nutrient loss reduction practices
What if scenario: effect of individual versus multiple/stacked nutrient loss reduction practices

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.

Approach

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.

Project Updates

December 2020

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.

Outreach included 1 presentation.