TY - JOUR TI - Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components AU - Berry, Jeffrey C AU - Qi, Mingsheng AU - Sonawane, Balasaheb V AU - Sheflin, Amy AU - Cousins, Asaph AU - Prenni, Jessica AU - Schachtman, Daniel P AU - Liu, Peng AU - Bart, Rebecca S A2 - Whiteman, Noah K A2 - Schuman, Meredith C A2 - Gloss, Andrew VL - 11 PY - 2022 DA - 2022/07/12 SP - e70056 C1 - eLife 2022;11:e70056 DO - 10.7554/eLife.70056 UR - https://doi.org/10.7554/eLife.70056 AB - 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 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. KW - sorghum KW - field research KW - microbiome KW - drought JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -