Site-Specific Management Zones Based on Soil Electrical Conductivity in a Semiarid Cropping System

Abstract

geographic information systems (GIS) for spatial analysis and mapping, variable-rate applicators, and input pre-Site-specific management (SSM) can potentially improve both ecoscription maps to define management zones and direct nomic and ecological outcomes in agriculture. Effective SSM requires metering devices controlling input rates (Eliason et al., strong and temporally consistent relationships among identified man-1995). While the first three components are currently agement zones; underlying soil physical, chemical, and biological parameters; and crop yields. In the central Great Plains, a 250-ha dryland available, the last, an effective and economical basis for experiment was mapped for apparent electrical conductivity (EC a ).defining site-specific inputs, is lacking. In response to Eight fields were individually partitioned into four management zones this need, significant research effort has been directed based on equal ranges of deep (EC DP ) and shallow (EC SH ) EC a (aptoward evaluating a variety of individual and combined proximately 0-30 and 0-90 cm depths, respectively). Previous experi-GIS databases as frameworks for identifying stratified ments documented negative correlations between EC SH and soil propwithin-field management zones (regions of similar proerties indicative of productivity. The objectives of this study were to duction potential). These include kriged soil test point examine EC SH and EC DP relationships with 2 yr of winter wheat (Tritidata (Mulla, 1991); soil survey maps (Robert, 1989); cum aestivum L.) and corn (Zea mays L.) yields and to consider the topography (Kravchenko et al., 2000); remote sensing potential applications of EC a -based management zones for SSM in (McCann et al., 1996); topography and remote sensing a semiarid cropping system. Within-zone wheat yield means were (Tomer et al., 1995); topography, remote sensing, and negatively correlated with EC SH (r ϭ Ϫ0.97 to Ϫ0.99) and positively farmer experience (Fleming et al., 1999); electrical concorrelated with EC DP (r ϭ 0.79-0.97). Within-zone corn yield means ductivity sensors (Sudduth et al., 1997; Lund et al., 1999); showed no consistent relationship with EC SH but positive correlation and yield maps (Eliason et al., 1995;Stafford et al., with EC DP (r ϭ 0.81-0.97). Equal-range and unsupervised classification methods were compared for EC SH ; within-zone yield variances de-1999). These approaches to SSM have met with varying clined slightly (0-5%) with the unsupervised approach. Yield response degrees of success that are often highly soil or region curves relating maximum wheat yields and EC SH revealed a boundary specific. line of maximum yield that decreased with increasing EC SH . In thisBecause some factors affecting crop yields occur unsemiarid system, EC SH -based management zones can be used in SSM predictably, including weather, human error, and equipof wheat for: (i) soil sampling to assess residual nutrients and soil ment malfunction (operator error, plugged spray nozattributes affecting herbicide e...

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).…”

Apparent soil electrical conductivity mapping as an agricultural management tool in arid zone soils

“…Other soil properties that have also been successfully mapped using EC a data include clay content (Williams and Hoey, 1987), depth to clay layers (Doolittle et al, 1994), and moisture content (Sheets and Hendrickx, 1995;Kachanoski et al, 1988). Additionally, yield potential has been shown to be directly related to EC a data in many applications (Jaynes et al, 1993;Sudduth et al, 1995;Kitchen et al, 1999;Johnson et al, 2003).…”

“…Rhoades et al (1997) discussed how EC a survey information can be used to determine salt loading and field irrigation efficiency and Triantafilis et al (1998) described a method for estimating deep drainage from EC a data. Additionally, various authors have discussed the use and/or interpretation of conductivity survey information for precision farming applications (Plant, 2001;Corwin and Lesch, 2003;Johnson et al, 2003;Lesch and Corwin, 2003).…”

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

“…An early method consisted of visually estimating the value of parameters or using the extreme values of the data body [12]. Another method consisted of selecting a subset of data and fitting a boundary line on these data [13][14][15]. More recently, the boundary lines have been estimated using a statistical approach in which the principle is to define Y = f (X) as a function satisfying p [Y < f (X)] = α for all values of X such that f (X) represents the αth quantile regression of the variable Y [26].…”

“…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].…”