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

Authors

Jeffrey C. Berry, Donald Danforth Plant Science Center
Mingsheng Qi, Donald Danforth Plant Science Center
Balasaheb V. Sonawane, Washington State University Pullman
Amy Sheflin, Colorado State University
Asaph Cousins, Washington State University Pullman
Jessica Prenni, Colorado State University
Daniel P. Schachtman, University of Nebraska - LincolnFollow
Peng Liu, Iowa State University
Rebecca S. Bart, Donald Danforth Plant Science CenterFollow

Date of this Version

7-1-2022

Citation

Berry et al. eLife 2022;11:e70056.

DOI:10.7554/eLife.70056

Comments

Copyright Berry et al. This article is distributed under the terms of the Creative Commons Attribution License

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 deter-mined 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 microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly 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 method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.