Information on how soil texture and related soil properties affect corn (Zea mays L.) nitrogen (N) response is needed to improve N management in corn production. We conducted a study at 12-site yr in Quebec to assess the effect of N rate (0–250 kg N ha⁻¹) and soil surface textural groups [clay, loam, sandy belonging to the gleysolic soil order (Sg), and sandy belonging to the podzolic soil order (Sp)] on corn grain yield, stover yield, total N uptake (TNU), nitrogen uptake efficiency (NUE), thousand kernel weight (TKW), test weight, and chlorophyll meter readings (CMR). Corn was more responsive to N rate in the clay soil textural group for most of the parameters due to lower soil N supply, and least responsive in the Sp group, except for test weight and CMR, due to possibly greater leaching in this group. The CMR at flowering accounted for 87%, 87%, 82%, and 25% of the variation in grain yield, TNU, TKW, and test weight, respectively. This study suggests that soil surface texture has a major influence on corn N response, but other soil properties such as drainage may also be important.

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (EC_a) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between EC_a and soil properties were obtained (0.22 < r < 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.