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Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components
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 experimen...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275819/ https://www.ncbi.nlm.nih.gov/pubmed/35819140 http://dx.doi.org/10.7554/eLife.70056 |
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author | Berry, Jeffrey C Qi, Mingsheng Sonawane, Balasaheb V Sheflin, Amy Cousins, Asaph Prenni, Jessica Schachtman, Daniel P Liu, Peng Bart, Rebecca S |
author_facet | Berry, Jeffrey C Qi, Mingsheng Sonawane, Balasaheb V Sheflin, Amy Cousins, Asaph Prenni, Jessica Schachtman, Daniel P Liu, Peng Bart, Rebecca S |
author_sort | Berry, Jeffrey C |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9275819 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-92758192022-07-13 Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components Berry, Jeffrey C Qi, Mingsheng Sonawane, Balasaheb V Sheflin, Amy Cousins, Asaph Prenni, Jessica Schachtman, Daniel P Liu, Peng Bart, Rebecca S eLife Plant Biology 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. eLife Sciences Publications, Ltd 2022-07-12 /pmc/articles/PMC9275819/ /pubmed/35819140 http://dx.doi.org/10.7554/eLife.70056 Text en © 2022, Berry et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Plant Biology Berry, Jeffrey C Qi, Mingsheng Sonawane, Balasaheb V Sheflin, Amy Cousins, Asaph Prenni, Jessica Schachtman, Daniel P Liu, Peng Bart, Rebecca S Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
title | Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
title_full | Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
title_fullStr | Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
title_full_unstemmed | Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
title_short | Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
title_sort | increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components |
topic | Plant Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275819/ https://www.ncbi.nlm.nih.gov/pubmed/35819140 http://dx.doi.org/10.7554/eLife.70056 |
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