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.