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

Abstract

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). ...

Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat

“…In fields with a uniform N application rate, the relation between yield and sensor measurements is often linear. However, nonlinear responses have also been observed for example NDVI (Lukina et al, 2001), soil EC a (Kitchen et al, 2003) and slope (Yang, Peterson, Shropshire, & Otawa, 1998). To facilitate both situations, a second order polynomial was selected to describe the relation between yield and sensor measurement.…”

Identifying important factors influencing corn yield and grain quality variability using artificial neural networks

“…However, multiple linear regression analysis is limited to describing linear relationships between crop parameters and site variables, and the results may be misleading when these relationships are not linear (Kitchen, Drummond, Lund, Sudduth, & Buchleiter, 2003;Liu, Goering, & Tian, 2001). Several non-linear, nonparametric techniques have been applied in such situations, including project pursuit regression (PPR) (Drummond, Sudduth, Joshi, Birrell, & Kitchen, 2003), state-space analysis (Wendroth, Kuhn, Jurschik, & Nielsen, 1997), classification and regression tree (CART) (Stewart et al 2002), boundary line analysis (Kitchen et al 1999) and artificial neural network (ANN) analysis Kitchen et al 2003).…”

“…Relating soil EC and topography to spatial crop yield patterns for contrasting soil-crop systems in three states, Kitchen et al (2003) found that neural networks outperformed MLR in all but one case, stepwise multiple quadratic regression (MQR) in all but two cases and MQR with two-way linear interactions (MQR +Int ) in the majority of cases. In a study to compare techniques that can reliably quantify relationships between 10 soil and topographic variables and crop yield within 10 individual site-years, Drummond et al (2003) found that ''the neural techniques consistently outperformed both SMLR and PPR and provided minimal prediction errors in every site-year''.…”

Two classification methods for developing and interpreting productivity zones using site properties

“…Grain data was recorded on 1-second intervals and corrected to 13 and 15.5% moisture, in October and September, for soybean and maize, respectively. Unreliable yield observations including positioning errors, abrupt changes in combine speed, and grain flow were removed following the guidelines of Kitchen et al (2003). Yields were transformed into standard deviation units.…”