Success of hybrid performance trials has always been susceptible to the efficiency at which the experimental design can remove any effect of spatial autocorrelation associated with environmental factors. Blocking in a randomized design is one way of accounting for this. Another method is to have a model of the environmental variability. Measures of soil variability could be useful to represent spatial structure in a trial. Soil apparent electrical conductivity (ECa) measurements can be collected rapidly and non-invasively and have been well documented to be able to map soil variability at the meter scale. We present a statistical evaluation that compares the effectiveness of the traditional replicated block designs with spatially explicit soil ECa measurements. Soil ECa, sorghum (Sorghum bicolor (L.) Moench) grain yield, and plant height were measured from six sorghum hybrid evaluation trials across Texas in 2017. Three linear models were tested to determine the presence or absence of spatial autocorrelation of model residuals within each performance trial. Moran’s I tests on model residuals showed that neither method was consistently effective in accounting for spatial variability. Blocking was more effective at one site for both plant height and grain yield, whereas ECa data were more effective at two sites for grain yield only. Based on these results, and the relatively low cost of using both methods together, we propose that plant breeders interested in addressing spatial autocorrelation in models from trial results may consider using both methods and select the best model, post hoc.
Supplementary notes can be added here, including code and math .