Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components.

Abstract

Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create micro-treatments throughout the field. To make matters worse, the variation within different soil properties is often non-randomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a tool with which sources of environmental variation in field data can be identified and removed, improving our ability to resolve effects of interest and to quantify subtle phenotypes. IMPORTANCEData from field experiments are notoriously noisy. Proper field designs with high replication aid in mitigating this challenge, yet true biological correlations are still often masked by environmental variability. This work identifies soil property composition as a spatially distributed source of variance to three types of characteristics: plant phenotype, microbiome composition, and leaf traits. We show that once identified, spatial principal component regression was able to account for these effects so that more precise estimates of experimental factors were obtained. This generalizable method is applicable to diverse field experiments.