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). ...

Responsive in-season nitrogen management for cereals

“…2. Soil map units (Wibawa et al, 1993), topography (Kravchenko et al, 2000), remote sensing (Schepers et al, 2004), electrical conductivity sensors (Kitchen et al, 2003;Heiniger et al, 2003;Johnson et al, 2003), crop yield (Flowers et al, 2005;Kitchen et al, 2005) and producer experience (Fleming et al, 2004) have all been used with varying success to delineate MZ. While these data sources for MZ delineation can be used to consistently characterize spatial variation in soil physical and chemical properties that partially affect crop yield potential, they are less consistent in characterizing spatial variation in crop N requirements because of the apparent effect of temporal variation on expression of yield potential (Schepers et al, 2004;Lambert et al, 2006).…”

Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

“…Measurements of apparent soil electrical conductivity (ECa) can easily provide spatial data for characterizing variations in soil and yield (Kitchen et al, 2003;Serrano et al, 2010). ECa can be related to clay, water, soil nutrients, organic matter, cation exchange capacity and exchangeable Ca and Mg (Machado et al, 2006(Machado et al, , 2015.…”

A survey of the combined effects of waterlogging and salinity on fruit yield in the date palm groves of the Wargla basin, Algeria

“…Nevertheless, these approaches assume that the factors are independent of one another. In the cloud of data, correlations indicating a decrease in yield with increasing salinity are often significant, but the models generally do not explain yields satisfactorily [11].…”

“…The principle of the boundary line approach was described by Webb [12], and subsequently applied to describe the effect of environmental variables such as soil nutrients [13], salinity [11,14] or a combination of factors [15]. This approach assumes that the boundary line at the outer rim of the data body depicts the functional dependence between a dependent variable (e.g., crop yield) and an independent variable (e.g., soil salinity), and may be of greater interest than the line of best fit through the cloud of data [11].…”

“…This approach assumes that the boundary line at the outer rim of the data body depicts the functional dependence between a dependent variable (e.g., crop yield) and an independent variable (e.g., soil salinity), and may be of greater interest than the line of best fit through the cloud of data [11]. At the boundary limit, the independent variable is assumed to be the main factor limiting the dependent variable, while the effects of other factors are insignificant.…”