Soil Electrical Conductivity and Topography Related to Yield for Three Contrasting Soil-Crop Systems

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 (ECa) 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, ECa alone explained yield variability variation (Kravchenko and Bullock, 2000; Nolin et al., (averaged over sites and years R2 0.21) better than topographic 2001; Ward and Cox, 2001), but usually little or no 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. significance has been found. Combining ECa and topography measures together usually improved Inexpensive and accurate methods for measuring model R2 values (averaged over sites and years R2 0.32). Boundary within-field soil variation would have the potential to lines generally showed yield decreasing with increasing ECa for Kansas greatly improve site-specific crop management. Sensors and Missouri fields. Results of this study can benefit farmers and are ideal for mapping soil properties because they can consultants by helping them understand the degree to which sensorprovide data without the need to collect and analyze based soil and topography information can be related to yield variation samples and can be linked to global positioning systems for planning site-specific management. (GPS) and computers for on-the-go spatial data collection. Sensors that measure soil properties could play an important role in helping to characterize yield variation. Y monitoring and mapping have given producOne sensor-based measurement that has shown ers a direct method for measuring spatial variability promise is ECa, which is a measure of the ability to in crop yield (Lark and Stafford, 1996; Pierce and Noconduct electrical current through the soil profile. Sevwak, 1999). Yield maps have shown high-yielding areas eral authors have reported on relating ECa to variation to be as much as 150% higher than low-yielding areas in crop production caused by soil differences (Jaynes et (Kitchen et al., 1999) and have revolutionized the way al., 1995; Kitchen et al., 1999; Luchiari et al., 2001; Zhang producers view yield as they seek to learn how they and Taylor, 2001). Rapid spatial measurement of ECa might improve production. However, yield maps are can be accomplished using noncontact electromagnetic confounded by many potential causes of yield variability induction sensors (McNeil, 1992; Jaynes et al., 1993; (Pierce et al., 1997) as well as potential error sources Sudduth et al., 2001) or with direct-contact sensors such from combine yield sensors (Lamb et al., 1995; Blackas rolling coulters that measure electrical resistance dimore and Marshall, 1996). When other georeferenced rectly (Lund et al., 1999; Sudduth et al., 1999). In geninformation is available, producers naturally want to eral, ECa can be affected by a number of different soil know if and how these various layers of data can be properties, including clay content, soil water content analyzed to help explain yield variability and provide (Kachanoski et al., 1990; Morgan et al., 2001), varying insight into improving production practices. depths of conductive soil layers, temperature, salinity, N.R. Kitchen, S.T. Drummond, and K.A. Sudduth, USDA-ARS, Abbreviations: CEC, cation exchange capacity; DEM, digital elevaCropping Syst. and Water Qual. Res. Unit, Columbia, MO 65211; tion model; ECa, apparent soil electrical conductivity; ECa-dp, deep E.D Lund, Veris Technol., 601 N. Broadway, Salina, KS 67401; and (100 cm) apparent soil electrical conductivity; ECa-sh, shallow (30 cm) G.W. Buchleiter, USDA-ARS, Water Manage. Unit, Ft. Collins, apparent soil electrical conductivity; GPS, global positioning system; CO 80523. Received 1 June 2001. *Corresponding author (kitchenn@ MLR, multiple linear regression; MQR, multiple quadratic regression; missouri.edu). MQR Int, multiple quadratic regression including two-way linear interactions; NN, neural network. Published in Agron. J. 95:483–495 (2003).