This study was conducted in Paris, IL, from 2001 to 2003 involving three corn (Zea mays L.) hybrids, five N rates (0, 112, 168, 224, and 336 kg ha−1), and six site‐year comparisons to determine the significance of within‐field variation in corn yield and quality responses to N fertilization, differences between hybrids in yield and quality, and the feasibility of within‐field variable hybrid selection. On average, N fertilization significantly increased corn yield, protein content, and test weight, but decreased corn oil and starch content. The overall economically optimum nitrogen rate (EONR) was 125 kg ha−1, but EONR varied from 93 to 195 kg ha−1 in different environments. The N rates that would maximize protein content and test weight (MAXN) varied from 143 to 303 kg ha−1 and 0 to 235 kg ha−1 in different environments, respectively. Significant within‐field variability in N response was detected in five of six environments for yield, but not in more than two environments for any quality parameter. Hybrid differences were significant in all six environments for test weight, followed by oil content (five), protein and starch content (four), and yield (three). Hybrid differences between 33G26 and 33J24 in test weight response to N were consistent across environments, showing the potential of hybrid‐specific N management for this quality parameter. However, hybrid differences in yield and quality did not vary significantly over space in most environments, showing limited potential of within‐field variable hybrid selection. Further studies involving more diverse within‐field soil–landscape conditions and hybrids are needed.

Soil, landscape and hybrid factors are known to influence yield and quality of corn (Zea mays L.). This study employed artificial neural network (ANN) analysis to evaluate the relative importance of selected soil, landscape and seed hybrid factors on yield and grain quality in two Illinois, USA fields. About 7 to 13 important factors were identified that could explain from 61% to 99% of the observed yield or quality variability in the study site-years. Hybrid was found to be the most important factor overall for quality in both fields, and for yield as well in Field 1. The relative importance of soil and landscape factors for corn yield and quality and their relationships differed by hybrid and field. Cation exchange capacity (CEC) and relative elevation were consistently identified as among the top four most important soil and landscape factors for both corn yield and quality in both fields in 2000. Aspect and Zn were among the top five most important factors in Fields 1 and 2, respectively. Compound topographic index (CTI), profile curvature and tangential curvature were, in general, not important in the study site-years. The response curves generated by the ANN models were more informative than simple correlation coefficients or coefficients in multiple regression equations. We conclude that hybrid was more important than soil and landscape factors for consideration in precision crop management, especially when grain quality was a management objective.

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There is a growing interest in real-time estimation of soil moisture for site-specific crop management. Non-contacting electromagnetic inductive (EMI) methods have potentials to provide real-time estimate of soil profile water contents. Soil profile water contents were monitored with a neutron probe at selected sites. A Geonics LTD EM-38 terrain meter was used to record bulk soil electrical conductivity (EC A ) readings across a soil-landscape in West central Minnesota with variable moisture regimes. The relationships among EC A , selected soil and landscape properties were examined. Bulk soil electrical conductivity (0-1.0 and 0-0.5 m) was negatively correlated with relative elevation. It was positively correlated with soil profile (1.0 m) clay content and negatively correlated with soil profile coarse fragments (>2 mm) and sand content. There was significant linear relationship between ECA (0-1.0 and 0-0.5) and soil profile water storage. Soil water storage estimated from ECA reflected changes in landscape and soil charactenstics.

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)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(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 . Applying average hybrid-and MZ-specific EONRs across years did not consistently improve economic returns over URN application; however, applying the hybrid-and MZ-specific N rates that maximized long-term net returns would improve economic return by an

Precision crop management for optimizing yield and quality is important for developing a consistent product for different end uses of grain. This study was conducted to evaluate the potential impact of variable‐rate N (VRN) application, hybrid selection, and hybrid‐specific N management on corn (Zea mays L.) yield, protein content, and test weight. On‐farm experiments were conducted during three site‐years in eastern Illinois using a split‐plot design, with the main plots consisting of five N rates and the subplots two corn hybrids (Pioneer 33G26 and 33J24). Nitrogen response curves of corn yield and quality were fitted at 19 and 16 within‐field locations in Fields 1 and 2, respectively, and the potential impacts of different N management strategies were evaluated. Results indicated that within‐field economically optimum N rates (EONR) ranged from 82 to 336 kg N ha−1, while N rates that would maximize grain quality ranged from 0 to 336 kg N ha−1 Compared with a uniform‐rate N (URN) application of 168 kg N ha−1, the VRN application at EONR would increase corn yield for hybrid 33J24 while having an inconsistent impact on yield of 33G26, without significantly improving grain quality of either hybrid. Hybrid 33J24 would have higher yield, quality, and economic returns than 33G26 under either URN or VRN application. Hybrid‐specific N applications could have either negative or positive impacts on corn yield and protein content, without significantly affecting test weight. These results suggest that selecting the right hybrid(s) was more important and practical than the evaluated precision N management practices for optimizing both corn yield and grain quality during the study site‐years.

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and costeffective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%-9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%-38.1%), and soil nutrient and pH variability (9.4%-14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%-48.9%, 16.1%-17.3% and 13.2%-18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.