Maize‐N: A Decision Tool for Nitrogen Management in Maize

Abstract

Optimizing N fertilizer use in maize production is critical for maximizing profi t and reducing N losses and associated negative environmental impacts. Th at an optimal solution is possible can be inferred from studies that have evaluated crop yield response and N losses across a wide range of N application rates. For example, Broadbent and Carlton (1978) found that NO 3 leaching from irrigated maize was small when the rate of applied N fertilizer did not exceed requirements for 90% of maximum grain yield. Similarly, in a meta-analysis of N 2 O emissions from arable crops, van Groenigen et al. (2010) concluded that yield-scaled emissions were constant until N fertilizer inputs exceeded N uptake by the aboveground biomass. Th e EONR is the N rate at which no further increase in net return occurs, and this point on the response curve occurs well below maximum yield levels at grain and N fertilizer prices typical of the past 40 yr (Dobermann et al., 2011).In practice, the EONR is diffi cult to predict before planting because the actual shape of the yield response to applied N varies fi eld to fi eld, and year to year due to in-season weather and crop management operations that infl uence the N supply-crop N demand balance. Th e EONR can be estimated by (i) the amount of N the crop obtains from the indigenous N supply (including N mineralization from organic matter, wet-dry deposition, and in irrigated systems, the NO 3 -N applied with irrigation), (ii) the shape of the N response function relating yield to the rate of N application, and (iii) prices for N fertilizer and maize grain. Th e shape of the yield response is determined by the yield potential when the crop is no longer limited by N (which defi nes the maximum attainable yield level), the agronomic fertilizer effi ciency (AE, Δyield/Δapplied N), which in turn is determined by the effi ciency of N uptake from the applied N (the recovery effi ciency, RE) and the effi ciency with which the acquired N is converted to grain yield (the physiological effi ciency, PE) (Novoa and Loomis, 1981).Despite the dynamic nature of the crop N response, extension programs in most U.S. Corn Belt states have established N fertilizer recommendations based on algorithms derived from regional fi eld tests that do not directly account for fertilizer N use effi ciency (Dobermann et al., 2006a). While such approaches can perform well in the region where they were developed, they may not be robust in other regions with diff erent soils, climate, and crop rotations. Given the limitations of regional calibration and the high degree of temporal and spatial variability in factors aff ecting crop response to applied N, new approaches that are responsive to this variability are under development.One approach is to apply N in response to conditions during the growing season, such as in-season adjustment of the N application rate in relation to leaf or canopy N status using sensor technologies (Kitchen et al., 2010;Olfs et al., 2005) or a chlorophyll meter (Scharf et al., 2006). In-season adj...

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

“…Testing of the Hybrid-Maize tool is reported in Yang et al (2004), Raymond et al (2009), andGrassini et al (2009), which showed good agreement with field-measured crop variables in validations using known yield and weather conditions. The Maize-N tool demonstrated good agreement between estimated and measured EONRs based on trials in western Corn Belt states under both dryland and irrigated conditions (Setiyono et al, 2011, Thompson et al, 2015.…”

“…Maize-N mechanistically estimates indigenous N supply and relates it to yield through an N uptake requirement to yield response function based on a database from research and farmer field trials. It combines crop response with physiological and N use efficiency factors as affected by soil and fertilizer management to formulate a recommendation (Setiyono et al, 2011). Testing of the Hybrid-Maize tool is reported in Yang et al (2004), Raymond et al (2009), andGrassini et al (2009), which showed good agreement with field-measured crop variables in validations using known yield and weather conditions.…”

“…There was one post-hoc comparison of several yield-based recommendation systems and N needs predicted by the Maize-N model (Setiyono et al, 2011). The root mean square error for observed and simulated EONRs for 11 experiments conducted in Nebraska and South Dakota were 41, 48, 33, and 61 kg N ha -1 using recommendations from Nebraska, Kansas, South Dakota, and Missouri, respectively.…”

“…For corn N recommendations in North America, two computer simulation model-database systems have been developed by universities: Adapt-N (Melkonian et al (2008), now commercialized) and Maize-N (Setiyono et al, 2011). Private companies also have developed computer simulation model-database systems to aid decisions about N rates for corn: Monsanto's FieldView Pro and DuPont Pioneer's Encirca.…”

“…The N recommendation is based on the derivative of a spherical-plateau function that describes the relationship of N uptake requirement to corn yield (Setiyono et al, 2011). This derivative calculates the EONR.…”