Evaluating and improving corn nitrogen fertilizer recommendation tools across the U.S. Midwest
No Thumbnail Available
Authors
Meeting name
Sponsors
Date
Journal Title
Format
Thesis
Subject
Abstract
Determining which corn (Zea mays L.) nitrogen (N) recommendation tools best predict the economically optimal N rate (EONR) would be valuable for maximizing profits and minimizing environmental consequences. The objectives of this research were to evaluate the performance of publicly-available N fertilizer recommendation tools across a wide range of soil and weather environments for 1) prescribing EONR for planting and split N fertilizer applications, 2) improve understanding of the economic and environmental impact of these tools, 3) improve N recommendation tools by integrating soil and weather information, and 4) improve N recommendation tools by combing multiple tools. The evaluation was conducted on 49 N response trials that spanned eight states and three growing seasons. Soil and plant samples, weather, and management information were collected using standardized procedures to allow for a side-by-side comparison of tools. Tool N recommendations were for fertilizer applications either atplanting or an inseason applied at V9 corn development stage. Only 11of 31 tool recommendations were weakly related to EONR (P [less than or equal to] 0.10 and r[2] [less than or equal to] 0.24). These tools related to EONR resulted in only 21-47% of sites within [plus or minus]30 kg N ha-1 of EONR. When considering partial profit for these 11 tools the average profitability relative to EONR range from -$56 to -155 ha-1. An environmental assessment of these 11 tools found there was no difference found between tools, with environmental costs ranging from -$49 to 55 ha-1 relative to EONR. Using an elastic net regression model to incorporate soil and weather information helped to improve six N recommendation tools. This improvement resulted in a stronger linear relationship with EONR (r[2] [less than or equal to] 0.20 but [less than or equal to] 0.39; P < 0.01) and resulted in [greater than or equal to] 35% but [less than or equal to] 55 % of the sites within [plus or minus] 30 kg N ha-1 of EONR. Using other ways to improve tools included combing two or three unique tools. The best results for an at-planting N fertilizer recommendation occurred when three at-planting N recommendation tools were combined with all interactions included in the elastic net regression model. This combined recommendation tool had an improved significant linear relationship with EONR (r[2] = 0.46; P <0.001) compared with the best tool evaluated alone (an increase in r2 of 0.27). The best combination of N recommendation tools for a split N fertilizer application occurred when using three tools with a decision tree (r[2] = 0.45; P <0.001) over the best tool evaluated alone (an increase in r[2] of 0.18). However, while improvements to these publicly-available tools were noteworthy, over half of the variation in EONR was still unexplained. This was not surprising since many other factors that impact soil-crop N dynamics are unconsidered, including factors that occur after a sidedress N application.
Table of Contents
DOI
PubMed ID
Degree
Ph. D.
Thesis Department
Rights
OpenAccess.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.