Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Berry, Jeffrey C, Qi, Mingsheng, Sonawane, Balasaheb V, Sheflin, Amy, Cousins, Asaph, Prenni, Jessica, Schachtman, Daniel P, Liu, Peng, Bart, Rebecca S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
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
_version_ 1784745569732788224
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
work_keys_str_mv AT berryjeffreyc increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT qimingsheng increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT sonawanebalasahebv increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT sheflinamy increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT cousinsasaph increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT prennijessica increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT schachtmandanielp increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT liupeng increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents
AT bartrebeccas increasedsignaltonoiseratioswithinexperimentalfieldtrialsbyregressingspatiallydistributedsoilpropertiesasprincipalcomponents