An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize (Zea mays) and Common Bean (Phaseolus vulgaris)
A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model was used to perform soil coring at six locations on thre...
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| Published in | Plant phenomics Vol. 2020; p. 3252703 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Association for the Advancement of Science (AAAS)
01.01.2020
AAAS |
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| Online Access | Get full text |
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| Abstract | A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model
was used to perform
soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided
-test (TOST) analysis of
data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species.
and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere. |
|---|---|
| AbstractList | A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model
OpenSimRoot
was used to perform
in silico
soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided
T
-test (TOST) analysis of
in silico
data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species.
In silico
and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere. A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere. A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model was used to perform soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided -test (TOST) analysis of data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere. A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere. |
| ArticleNumber | 3252703 |
| Author | Nord, Eric A. Postma, Johannes A. York, Larry M. Black, Christopher K. Sidhu, Jagdeep S. Burridge, James D. Lynch, Jonathan P. |
| AuthorAffiliation | 2 Department of Biology, Greenville University, 315 E. College Ave, Greenville, IL 62246, USA 1 The Pennsylvania State University, Department of Plant Science, Tyson Building, University Park, PA 16802, USA 3 Forschungszentrum Jülich GmbH, Institute of Bio-and Geosciences-Plant Sciences (IBG-2), 52425 Jülich, Germany 4 Noble Research Institute, LLC, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA |
| AuthorAffiliation_xml | – name: 2 Department of Biology, Greenville University, 315 E. College Ave, Greenville, IL 62246, USA – name: 4 Noble Research Institute, LLC, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA – name: 1 The Pennsylvania State University, Department of Plant Science, Tyson Building, University Park, PA 16802, USA – name: 3 Forschungszentrum Jülich GmbH, Institute of Bio-and Geosciences-Plant Sciences (IBG-2), 52425 Jülich, Germany |
| Author_xml | – sequence: 1 givenname: James D. surname: Burridge fullname: Burridge, James D. – sequence: 2 givenname: Christopher K. orcidid: 0000-0001-8382-298X surname: Black fullname: Black, Christopher K. – sequence: 3 givenname: Eric A. surname: Nord fullname: Nord, Eric A. – sequence: 4 givenname: Johannes A. orcidid: 0000-0002-5222-6648 surname: Postma fullname: Postma, Johannes A. – sequence: 5 givenname: Jagdeep S. orcidid: 0000-0002-4672-3701 surname: Sidhu fullname: Sidhu, Jagdeep S. – sequence: 6 givenname: Larry M. orcidid: 0000-0002-1995-9479 surname: York fullname: York, Larry M. – sequence: 7 givenname: Jonathan P. orcidid: 0000-0002-7265-9790 surname: Lynch fullname: Lynch, Jonathan P. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33313549$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1708913$$D View this record in Osti.gov |
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| Copyright | Copyright © 2020 James D. Burridge et al. Copyright © 2020 James D. Burridge et al. 2020 |
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| SubjectTerms | BASIC BIOLOGICAL SCIENCES beans canopy computer simulation corn discriminant analysis Phaseolus vulgaris phenomics root systems soil t-test Zea mays |
| Title | An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize (Zea mays) and Common Bean (Phaseolus vulgaris) |
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