Traditional sampling methods are inadequate for assessing the interrelated physical, chemical, and biological soil properties responsible for variations in agronomic yield and ecological potentials across a landscape. Recent advances in computers, global positioning systems, and large‐scale sensors offer new opportunities for mapping heterogeneous patterns in soil condition. We evaluated field‐scale apparent electrical conductivity (ECa) mapping for delineating soil properties correlated with productivity and ecological properties. A contiguous section of farmland (250 ha), managed as eight fields in a no‐till winter wheat (Triticum aestivum L.)–corn (Zea mays L.)–millet (Panicum miliaceum L.)–fallow rotation, was ECa mapped (≈0‐ to 30‐cm depth). A geo‐referenced soil‐sampling scheme separated each field into four ECa classes that were sampled (0‐ to 7.5‐ and 7.5‐ to 30‐cm depths) in triplicate. Soil physical parameters (bulk density, moisture content, and percentage clay), chemical parameters (total and particulate organic matter [POM], total C and N, extractable P, laboratory‐measured electrical conductivity [EC1:1], and pH), biological parameters (microbial biomass C [MBC] and N [MBN], and potentially mineralizable N), and surface residue mass were significantly different among ECa classes (P ≤ 0.06) at one or both depths (0–7.5 and 0–30 cm). Bulk density, percentage clay, EC1:1, and pH were positively correlated with ECa; all other soil parameters and surface residue mass were negatively correlated. Field‐scale ECa classification delimits distinct zones of soil condition, providing an effective basis for soil sampling. Potential uses include assessing temporal impacts of management on soil condition and managing spatial variation in soil‐condition and yield‐potential through precision agriculture and site‐specific management.

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...

., "Apparent soil electrical conductivity: applications for designing and evaluating field-scale experiments" (2005 AbstractOn-farm field-scale research has become increasingly common with the advent of new technologies. While promoting a realistic systems perspective, field-scale experiments do not lend themselves to the traditional design concepts of replication and blocking. Previously, a farm-scale dryland experiment in northeastern Colorado was conducted to evaluate apparent electrical conductivity (EC a ) classification (within-field blocking) as a basis for estimating plot-scale experimental error. Comparison of meansquare (MS) errors for several soil properties and surface residue mass measured at this site, with those from a nearby plot-scale experiment, revealed that EC a -classified within-field variance approximates plot-scale experimental error. In the present study, we tested these findings at a second and disparate experimental site, Westlake Farms (WLF) in central California. This 32 ha site was EC a mapped and partitioned into four and five classes using a response-surface model. Classification based on EC a significantly delineated most soil properties evaluated (0-0.3 and/or 0-1.2 m) and effectively reduced MS error (P ≤ 0.10). The MS's for several soil properties evaluated at the site were then compared with those of an associated plot-scale experiment; most MS's were not significantly different between the two levels of scale (P ≤ 0.05), corroborating results from the Colorado experiment. These findings support the use of within-field EC a -classified variance as a surrogate for plot-scale experimental error and a basis for roughly evaluating treatment differences in non-replicated field-scale experiments. This alternative statistical design may promote field-scale research and encourage a reversal in research direction wherein research questions identified in field-scale studies are pursued at the plot-scale.

Site‐specific management (SSM) can potentially improve both economic and ecological outcomes in agriculture. Effective SSM requires strong and temporally consistent relationships among identified management zones; underlying soil physical, chemical, and biological parameters; and crop yields. In the central Great Plains, a 250‐ha dryland experiment was mapped for apparent electrical conductivity (ECa). Eight fields were individually partitioned into four management zones based on equal ranges of deep (ECDP) and shallow (ECSH) ECa (approximately 0–30 and 0–90 cm depths, respectively). Previous experiments documented negative correlations between ECSH and soil properties indicative of productivity. The objectives of this study were to examine ECSH and ECDP relationships with 2 yr of winter wheat (Triticum aestivum L.) and corn (Zea mays L.) yields and to consider the potential applications of ECa–based management zones for SSM in a semiarid cropping system. Within‐zone wheat yield means were negatively correlated with ECSH (r = −0.97 to −0.99) and positively correlated with ECDP (r = 0.79–0.97). Within‐zone corn yield means showed no consistent relationship with ECSH but positive correlation with ECDP (r = 0.81–0.97). Equal‐range and unsupervised classification methods were compared for ECSH; within‐zone yield variances declined slightly (0–5%) with the unsupervised approach. Yield response curves relating maximum wheat yields and ECSH revealed a boundary line of maximum yield that decreased with increasing ECSH. In this semiarid system, ECSH–based management zones can be used in SSM of wheat for: (i) soil sampling to assess residual nutrients and soil attributes affecting herbicide efficacy, (ii) yield goal determination, and (iii) prescription maps for metering inputs.

Agronomic researchers are increasingly accountable for research groups have implemented this research philosophy in programs and outcomes relevant to producers. Participatory research-where farmers assume leadership roles in identifying, design-the form of farmer-comprised focus groups that function ing, and managing on-farm field-scale research-addresses this direc-in an advisory capacity. These associations have resulted tive. However, replication is often unfeasible at this level of scale, in increased on-farm experimentation. underscoring a need for alternative methods to estimate experimental Some types of research, or research goals, require the error. We compared mean square errors to evaluate: (i) within-field precision achievable only through controlled plot-scale variability for estimating experimental error (in lieu of replication) experimentation; other research goals are well suited to and (ii) classified within-field variability, using apparent electrical on-farm investigations. Experiments best conducted on conductivity (EC a), for estimating plot-scale experimental error. Eight farm include those (i) requiring specific soil types or 31-ha fields, within a contiguous section of farmland (250 ha), were environmental conditions not found on an experiment managed as two replicates of each phase of a no-till winter wheat station, (ii) involving the study of farm management, (Triticum aestivum L.)-corn (Zea mays L.)-millet (Panicum miliaceum L.)-fallow rotation. The section was EC a-mapped (approxi-(iii) analyzing integrated systems such as crop and livemately 0-to 30-cm depth) and separated into four classes (ranges stock production, (iv) evaluating performance of a manof EC a). Georeferenced sites (n ϭ 96) were selected within classes, agement system under real farm conditions, (v) examinsampled, and assayed for multiple soil parameters (0-to 7.5-and ing occurrences requiring large land areas (e.g., runoff, 0-to 30-cm depths) and residue mass. Within-field variance effectively erosion, and pest infestation), (vi) studying the longestimated experimental error variance for several evaluated parameterm effects of specific management practices, and (vii) ters, supporting its potential application as a surrogate for replication. evaluating farmer innovations (Lockeretz, 1987). Many Comparison of data from the field-scale experimental site to that from of these examples require field-scale analyses. New techa nearby plot-scale experiment revealed that EC a-classified withinnologies used in site-specific management and other field variance approximates plot-scale experimental error. We prosustainable management practices, including global popose using this relationship for a systems approach to research wherein treatment differences and their standard errors, derived from EC a-sitioning systems, geographic information systems, and classified field-scale experiments, are used to roughly evaluate treat-field-scale sensors, are also best evaluated at the field ments and identify research questions for further s...