# Agronomy Journal - Article

1. Vol. 103 No. 4, p. 1276-1283
OPEN ACCESS

Published: July, 2011

* Corresponding author(s):
View
Permissions
Share

doi:10.2134/agronj2011.0053

# Maize-N: A Decision Tool for Nitrogen Management in Maize

1. T. D. Setiyonoa,
2. H. Yangb,
3. D. T. Waltersa,
4. A. Dobermannc,
5. R. B. Fergusona,
6. D. F. Robertsd,
7. D. J. Lyone,
8. D. E. Clayf and
9. K. G. Cassman *a
1. a Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE 68583
b Monsanto Company, 800 N. Lindbergh Blvd., St. Louis, MO 63167
c IRRI, DAPO Box 7777, Metro Manila, Philippines 1301
d Dep. of Plant and Soil Sciences, Mississippi State Univ., Starkville, MS 39762
e Panhandle Research and Extension Center, Univ. of Nebraska-Lincoln, Scottsbluff, NE 69361
f Drought Center, South Dakota State Univ., Brookings, SD 57007

## Abstract

Nitrogen fertilizer efficiency has a large influence on profit, energy efficiency, N losses to the environment, and greenhouse gas emissions in maize (Zea mays L.) production. Our purpose was to develop a robust decision-support tool to help inform N fertilizer recommendations and to compare performance of this tool relative to existing recommendation approaches. Maize-N is a simulation model for estimating economically optimum N fertilizer rates (EONR) for maize. The model estimates the EONR based on uptake efficiency of the applied N, expected yield response, market prices of grain and N fertilizer, and mechanistic components of soil N mineralization. Uptake efficiency and expected yield response are derived from a database of yield response to applied N from field experiments in the United States, Asia, and South America. The model is responsive to: (i) soil properties and indigenous soil N supply capacity, (ii) local climatic conditions and yield potential, (iii) crop rotation (including type and yield of previous crop), (iv) tillage method and timing of tillage operations, and (v) fertilizer formulation, application method, and timing. Validation of Maize-N across N management regimes and environments in the western U.S. Corn Belt indicated reasonable agreement between observed and measured values of EONR (RMSE of 21 kg N ha−1), which compares favorably with RMSE values of 33 to 61 kg N ha−1 for other methods based on empirical relationships derived from regional field tests in Kansas, Missouri, Nebraska, South Dakota, and Iowa.

### Abbreviations

AE, agronomic efficiency; EONR, economically optimum nitrogen rate; ME, mean error; PE, physiological efficiency; RE, recovery efficiency; SOM soil organic matter

Optimizing N fertilizer use in maize production is critical for maximizing profit and reducing N losses and associated negative environmental impacts. That 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 NO3 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 N2O 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. The 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 difficult to predict before planting because the actual shape of the yield response to applied N varies field to field, and year to year due to in-season weather and crop management operations that influence the N supply–crop N demand balance. The 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 NO3–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. The shape of the yield response is determined by the yield potential when the crop is no longer limited by N (which defines the maximum attainable yield level), the agronomic fertilizer efficiency (AE, Δyield/Δapplied N), which in turn is determined by the efficiency of N uptake from the applied N (the recovery efficiency, RE) and the efficiency with which the acquired N is converted to grain yield (the physiological efficiency, 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 field tests that do not directly account for fertilizer N use efficiency (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 different soils, climate, and crop rotations. Given the limitations of regional calibration and the high degree of temporal and spatial variability in factors affecting 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 adjustments can also be responsive to actual weather conditions that affect the N response (Moebius-Clune et al., 2009). In all of these approaches, a portion of the total N requirement is applied preplant and the rest in response to conditions during the growing season. While promising, each of these methods requires further development and validation to support widespread adoption.

Another approach is to use a simulation model that accounts for the dynamic interactions between management and environmental conditions to estimate N fertilizer requirements. Although some existing crop simulation models such as WOFOST (Supit and van der Goot, 2003) and Ceres-Maize (Jones and Kiniry, 1986) can be used for post-season evaluation of nutrient limitations in a maize crop, they were not designed to support preplant or in-season decisions about fertilizer N management. Given this situation, our objective was to develop and evaluate a simulation model for estimating maize N fertilizer requirements that is sensitive to the key factors governing the maize response to applied N. The new model, called Maize-N, builds on the Hybrid-Maize model (Yang et al., 2004), which simulates maize growth and yield in response to climate and water supply. Maize-N extends to include sensitivity to factors governing soil N mineralization and the recovery of N fertilizer, while also accommodating differences in crop rotation, tillage practices, form of N fertilizer, method of application, and prices for grain and N fertilizer.

## MATERIALS AND METHODS

### Model Development

The Maize-N model consists of four components that estimate (i) maize yield potential, (ii) soil C and N mineralization, (iii) N use efficiencies, and (iv) yield vs. N response (Fig. 1). Inputs for the model consist of weather variables, management practices in the coming season for which the N rate is to be estimated (crop maturity, planting date, population, grain price, and yield history), previous season management (method of crop and residue management), N fertilizer practices including timing of application, and soil edaphic inputs. Optional inputs include residual soil NO3 before planting (if measured) and manure application (if applied). In addition to the EONR, Maize-N provides collateral outputs including estimated attainable yield, N uptake from indigenous soil sources, and the daily rate of C and N mineralization. All grain yields are based on standard grain moisture content (0.155 kg H2O kg−1 grain).

Fig. 1.

Inputs, processes, and outputs of the Maize-N model to determine the economically optimum N rate (EONR).

In Maize-N, attainable yield (Ya) is assumed to be a known fraction of the yield potential (Yp) or can be supplied based on the yield history of a given site. For a given field, Yp was estimated using the Hybrid-Maize model and long-term weather data from a nearby weather station (Yang et al., 2004). The weather data required to run Hybrid-Maize include daily values for maximum and minimum temperatures, solar radiation, and rainfall. The fraction Ya/Yp is treated as an internal model parameter (user modifiable) with a default value of 0.85. Because it is neither economical nor environmentally acceptable to provide the input levels required to achieve 100% of Yp, evidence from studies using on-farm data suggest that yield levels of 80 to 90% of Yp can be attained in well-managed maize fields (Grassini et al., 2011). Thus, in Maize-N, Ya defines the upper yield limit in the response to the rate of applied N (Fig. 2). A spherical function (Dobermann et al., 2006b) is used to relate yield to N rate:where Y is the predicted maize yield (Mg ha−1), Y0 is the yield without applied fertilizer N (Mg ha−1), N is the N rate (kg ha−1), b is the difference between Ya and Y0 (Mg ha−1), and c is the N rate as the yield approaches Ya (kg ha−1). All yield terms are expressed as grain mass with 0.155 kg kg−1 moisture content.

In addition to Ya, the yield without applied fertilizer N (Y0) and AE also govern the shape of the spherical function of yield vs. N rate (Eq. [1]). The EONR is calculated by using the first derivative of the function relating net return to N and the N rate:where R is price ratio of maize to N fertilizer (US$kg−1 grain/US$ kg−1 N).

The spherical yield response model of N rate provides a good fit to the actual N response, as shown in the example from Clay Center, NE, in 2002 (Fig. 2). In this well-managed site, attainable yield reached 90% of Yp and the estimated EONR was 153 kg N ha−1 (actual EONR was 161 kg ha−1). The observed and estimated EONR were based on a maize grain price of US$0.141 kg−1 (October 2009 price) (Economic Research Service, 2010b) and N price of$0.831 kg−1 (adjusted to an elemental N price from the actual price of NH4NO3 for March 2009) (Economic Research Service, 2010a).

Fig. 2.