Along with yield mapping, producers have expressed increased interest in characterizing soil and topographic Many producers who map yield want to know how soil and landvariability (Wiebold et al., 1998). Numerous properties scape information can be used to help account for yield variability influence the suitability of soil as a medium for crop and provide insight into improving production. This study was conducted to investigate the relationship of profile apparent soil electrical root growth and yield. These include soil water-holding conductivity (EC a) and topographic measures to grain yield for three capacity, water infiltration rate, texture, structure, bulk contrasting soil-crop systems. Yield data were collected with combine density, organic matter, pH, fertility, soil depth, topograyield-monitoring systems on three fields [Colorado (Ustic Haplarphy features (i.e., slope, aspect, etc.), the presence of gids), Kansas (Cumuic Haplustoll), and Missouri (Aeric Vertic Epiarestrictive soil layers, and the quantity and distribution qualfs)] during 1997-1999. Crops included four site-years of corn (Zea of crop residues. These properties are complex and vary mays L.), three site-years of soybean (Glycine max L.), and one sitespatially (and with some, temporally) within fields. No year each of grain sorghum [Sorghum bicolor (L.) Moench] and winter single measurement adequately describes the influence wheat (Triticum aestivum L.). Apparent soil electrical conductivity of the soil environment on rooting and crop growth and was obtained using a Veris model 3100 sensor cart system. Elevation, obtained by either conventional surveying techniques or real-time yield. Georeferenced soil sampling for fertility status, kinematic global positioning system, was used to determine slope, typically from the surface layer from 0 to 20 cm, is often curvature, and aspect. Four analysis procedures were employed to used by producers in developing recommendation maps investigate the relationship of these variables to yield: correlation, for variable-rate fertilizer application. Information obforward stepwise regression, nonlinear neural networks (NNs), and tained from these samples [including fertility, organic boundary-line analysis. Correlation results, while often statistically matter, cation exchange capacity (CEC), and texture] significant, were generally not very useful in explaining yield. Using has also been used in some research to evaluate yield either regression or NN analysis, EC a alone explained yield variability variation (Kravchenko and Bullock, 2000; Nolin et al., (averaged over sites and years R 2 ϭ 0.21) better than topographic 2001; Ward and Cox, 2001), but usually little or no variables (averaged over sites and years R 2 ϭ 0.17). In six of the nine site-years, the model R 2 was better with EC a than with topography. significance has been found. Combining EC a and topography measures together usually improved Inexpensive and accurate methods for measuring model R 2 values (averaged over sites and years R 2 ϭ 0.32). ...
