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a USDA-ARS, Cropping Syst. and Water Qual. Res. Unit, Columbia, MO 65211
b Veris Technol., 601 N. Broadway, Salina, KS 67401
c USDA-ARS, Water Manage. Unit, Ft. Collins, CO 80523
* Corresponding author (kitchenn{at}missouri.edu)
Received for publication June 1, 2001. Many producers who map yield want to know how soil and landscape information can be used to help account for yield variability and provide insight into improving production. This study was conducted to investigate the relationship of profile apparent soil electrical conductivity (ECa) and topographic measures to grain yield for three contrasting soil–crop systems. Yield data were collected with combine yield-monitoring systems on three fields [Colorado (Ustic Haplargids), Kansas (Cumuic Haplustoll), and Missouri (Aeric Vertic Epiaqualfs)] during 1997–1999. Crops included four site-years of corn (Zea mays L.), three site-years of soybean (Glycine max L.), and one site-year each of grain sorghum [Sorghum bicolor (L.) Moench] and winter wheat (Triticum aestivum L.). Apparent soil electrical conductivity was obtained using a Veris model 3100 sensor cart system. Elevation, obtained by either conventional surveying techniques or real-time kinematic global positioning system, was used to determine slope, curvature, and aspect. Four analysis procedures were employed to investigate the relationship of these variables to yield: correlation, forward stepwise regression, nonlinear neural networks (NNs), and boundary-line analysis. Correlation results, while often statistically significant, were generally not very useful in explaining yield. Using either regression or NN analysis, ECa alone explained yield variability (averaged over sites and years R2 = 0.21) better than topographic variables (averaged over sites and years R2 = 0.17). In six of the nine site-years, the model R2 was better with ECa than with topography. Combining ECa and topography measures together usually improved model R2 values (averaged over sites and years R2 = 0.32). Boundary lines generally showed yield decreasing with increasing ECa for Kansas and Missouri fields. Results of this study can benefit farmers and consultants by helping them understand the degree to which sensor-based soil and topography information can be related to yield variation for planning site-specific management.
Abbreviations: CEC, cation exchange capacity • DEM, digital elevation model • ECa, apparent soil electrical conductivity • ECa-dp, deep (100 cm) apparent soil electrical conductivity • ECa-sh, shallow (30 cm) apparent soil electrical conductivity • GPS, global positioning system • MLR, multiple linear regression • MQR, multiple quadratic regression • MQR+Int, multiple quadratic regression including two-way linear interactions • NN, neural network
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