Evaluating Management Zone Optimal Nitrogen Rates with a Crop Growth Model Yuxin Miao,* David J. Mulla, William D. Batchelor, Joel O. Paz, Pierre C. Robert, and Matt Wiebers ABSTRACT Determining MZ (management zone)-specific optimal N rate is a challenge in precision crop management. The objective of this study was to evaluate the potential of applying a crop growth model to simulate corn (Zea mays L.) yield at various N levels in different MZs and estimate optimal N rates based on long-term weather conditions. Three years of corn yield data were used to calibrate a modified version of the CERES-Maize (Version 3.5) model for a commercial field previously divided into four MZs in eastern Illinois. The model performance in simulating corn yield for two hybrids (33G26 and 33J24) at five N levels in two independent years was evaluated. Economically optimum N rates (EONRs) were estimated based on 15 yr of simulation (1989–2003). The model explained approximately 59 and 93% of yield variability during calibration and validation, respectively. The model performed well at non-zero N rates, with most of the simulation errors being ,10%. Model-estimated EONR varied from 70 to 250 kg ha 21 . Economic analyses indicated that applying N fertilizer at year-, hybrid-, and MZ-specific EONR had the potential to increase net return by an average of US$49 (33G26) or US$52 (33J24) ha 21 over a URN (uniform rate N) application at 170 kg ha 21 . Apply- ing average hybrid- and MZ-specific EONRs across years did not consistently improve economic returns over URN application; how- ever, applying the hybrid- and MZ-specific N rates that maximized long-term net returns would improve economic return by an average of US$22 (33G26) and US$14 (33J24) ha 21 . D IVIDING a field into a few relatively uniform MZs is a practical and cost-effective approach to site- specific crop management with current technology and price relationships. Many approaches to MZ delinea- tion have been proposed and evaluated (Mulla, 1991; Fleming et al., 2000, 2004; Blackmore, 2000; Fraisse et al., 2001a; Khosla et al., 2002; Diker et al., 2004; Chang et al., 2004; Miao et al., 2005); however, the biggest challenge facing the producer is how to manage inputs to optimize profit and reduce environmental con- tamination. Management zones are generally proxies for crop response zones. For the defined zones to be use- ful and practical for site-specific management, each zone should show different crop responses to nutrient inputs, and these responses need to be reliably estimated be- fore making management decisions. This challenge has not been addressed very well in the literature due to variability in climate and the complexity of statistical methods required. With site-specific N management, several approaches can be taken to determine the optimal fertilization rates in different MZs. The first approach involves determin- ing the rate of fertilizers by applying current N rec- ommendations based on soil fertility, moisture, and crop yield potential at the MZ scale (Hornung et al., 2003; Koch et al., 2004; Inman et al., 2005); however, it has been pointed out that current N recommendations may not be suitable for site-specific N management (Pan et al., 1997; Hergert et al., 1997; Anselin et al., 2004), and information on spatial crop response variability was not sufficiently used in developing such recommenda- tions (Hurley et al., 2001; Swinton et al., 2002; Bullock et al., 2002). Current university and industry N recom- mendations are broad compromises intended for large- scale regional use (Pan et al., 1997). In addition to the difficulties in accurately estimating spatial yield goals, site-specific N recommendations based on soil organic matter have proven to be too simplistic to reflect within- field variability of N availability (Schmidt et al., 2002). An alternative approach is to use N-rate strips, in- cluding a zero-N treatment, traversing a field to quantify N responses across different soil and landscape condi- tions and determine appropriate precision N manage- ment strategies in different MZs (Pierce and Nowak, 1999; Mamo et al., 2003; Hurley et al., 2004). This ap- proach is useful in evaluating site-specific N manage- ment zones and variable N rates, but may not be practical for estimating zone-specific optimal N rates, which are affected by weather, cultivar, management, site characteristics, and their dynamic interactions. Many years of data may be required to reliably estimate the zone-specific N rates to be applied across years, even when the cultivars and management practices remain the same. A third, and promising, approach is to use crop growth models to estimate optimal N rates in different MZs within a field, based on long-term simulations using different historical weather conditions. Process-oriented crop growth models, such as CERES-Maize (Jones and Kiniry, 1986) and CROPGRO (Hoogenboom et al., 1994), can simulate the impacts of genetics, weather, soil, management practices, and their dynamic interac- tions on crop growth, development, and yield based on C, N and water balance principles (Batchelor et al., 2002). They have been extensively validated and applied under a wide range of environmental conditions (Singh, 1985; Carberry et al., 1989; Jagtap et al., 1993; Kiniry et al., 1997; Garrison et al., 1999; Gungula et al., 2003) Y. Miao, D.J. Mulla, and P.C. Robert, Dep. of Soil, Water, and Climate, Univ. of Minnesota, St. Paul, MN 55108; W.D. Batchelor, Dep. of Agricultural and Biological Engineering, Mississippi State Univ., Mississippi State, MS 39762; J.O. Paz, Dep. of Biological and Agricul- tural Engineering, Univ. of Georgia, Griffin, GA 30223; M. Wiebers, Mosaic Crop Nutrition, 616 S Jefferson Ave., Paris, IL 61944. Contri- bution of the Precision Agriculture Center, Univ. of Minnesota. Received 19 May 2005. *Corresponding author (ymiao@umn.edu). Published in Agron. J. 98:545–553 (2006). Site-Specific Analysis and Management doi:10.2134/agronj2005.0153 ª American Society of Agronomy 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: EONR, economically optimum nitrogen rate; MZ, management zone; PCM, precision crop management; URN, uniform rate N. Reproduced from Agronomy Journal. Published by American Society of Agronomy. All copyrights reserved. 545 Published online April 11, 2006 Published May, 2006