Apparent soil electrical conductivity: applications for designing and evaluating field-scale experiments

On-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 (ECa) classification (within-field blocking) as a basis for estimating plot-scale experimental error. Comparison of mean-square (MS) errors for several soil properties and surface residue mass measured at this site, with those from a nearby plot-scale experiment, revealed that ECa-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 ECa mapped and partitioned into four and five classes using a response-surface model. Classification based on ECa 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 ECa-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.