Evaluating and improving corn nitrogen fertilizer recommendation tools across the U.S. Midwest

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.