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Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits
In this study, the differential rankings of 36 groundnut genotypes under varying environmental conditions were studied at various levels of phenotype. Locations that are generally accepted by the crop- and soil-based research community to represent the entire Guinea and Sudan Savanna agro-ecological...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006269/ https://www.ncbi.nlm.nih.gov/pubmed/33790928 http://dx.doi.org/10.3389/fpls.2021.637860 |
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author | Oteng-Frimpong, Richard Kassim, Yussif Baba Puozaa, Doris Kanvenaa Nboyine, Jerry Asalma Issah, Abdul-Rashid Rasheed, Masawudu Abdul Adjebeng-Danquah, Joseph Kusi, Francis |
author_facet | Oteng-Frimpong, Richard Kassim, Yussif Baba Puozaa, Doris Kanvenaa Nboyine, Jerry Asalma Issah, Abdul-Rashid Rasheed, Masawudu Abdul Adjebeng-Danquah, Joseph Kusi, Francis |
author_sort | Oteng-Frimpong, Richard |
collection | PubMed |
description | In this study, the differential rankings of 36 groundnut genotypes under varying environmental conditions were studied at various levels of phenotype. Locations that are generally accepted by the crop- and soil-based research community to represent the entire Guinea and Sudan Savanna agro-ecological zones in Ghana were characterized, this time using a crop. The characterization was done based on four farmer-preferred traits (early and late leaf spot disease ratings, and haulm and pod yields) using three models (i.e., AMMI, GGE, and Finlay–Wilkinson regression). These models were used to capture specific levels of phenotype, namely, genotype-by-environment interaction (GE), genotype main effect plus GE (G+GE), and environment and genotype main effects plus GE (E+G+GE), respectively. The effect of three major environmental covariables was also determined using factorial regression. Location main effect was found to be highly significant (p < 0.001), confirming its importance in cultivar placement. However, unlike genotypes where the best is usually adjudged through statistical ranking, locations are judged against a benchmark, particularly when phenotyping for disease severity. It was also found that the locations represent one complex mega-environment, justifying the need to test new technologies, including genotypes in all of them before they can be approved for adoption nationally. Again, depending on the phenotypic level considered, genotypic rankings may change, causing environmental groupings to change. For instance, all locations clustered to form one group in 2017 for early and late leaf spot diseases and pod yield when GE was considered, but the groupings changed when G+GE was considered for the same traits in the same year. As a result, assessing genotypic performance at the various levels to arrive at a consensus decision is suggested. Genotypes ICGV-IS 141120 and ICGV-IS 13937 were found to be the best performing. |
format | Online Article Text |
id | pubmed-8006269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80062692021-03-30 Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits Oteng-Frimpong, Richard Kassim, Yussif Baba Puozaa, Doris Kanvenaa Nboyine, Jerry Asalma Issah, Abdul-Rashid Rasheed, Masawudu Abdul Adjebeng-Danquah, Joseph Kusi, Francis Front Plant Sci Plant Science In this study, the differential rankings of 36 groundnut genotypes under varying environmental conditions were studied at various levels of phenotype. Locations that are generally accepted by the crop- and soil-based research community to represent the entire Guinea and Sudan Savanna agro-ecological zones in Ghana were characterized, this time using a crop. The characterization was done based on four farmer-preferred traits (early and late leaf spot disease ratings, and haulm and pod yields) using three models (i.e., AMMI, GGE, and Finlay–Wilkinson regression). These models were used to capture specific levels of phenotype, namely, genotype-by-environment interaction (GE), genotype main effect plus GE (G+GE), and environment and genotype main effects plus GE (E+G+GE), respectively. The effect of three major environmental covariables was also determined using factorial regression. Location main effect was found to be highly significant (p < 0.001), confirming its importance in cultivar placement. However, unlike genotypes where the best is usually adjudged through statistical ranking, locations are judged against a benchmark, particularly when phenotyping for disease severity. It was also found that the locations represent one complex mega-environment, justifying the need to test new technologies, including genotypes in all of them before they can be approved for adoption nationally. Again, depending on the phenotypic level considered, genotypic rankings may change, causing environmental groupings to change. For instance, all locations clustered to form one group in 2017 for early and late leaf spot diseases and pod yield when GE was considered, but the groupings changed when G+GE was considered for the same traits in the same year. As a result, assessing genotypic performance at the various levels to arrive at a consensus decision is suggested. Genotypes ICGV-IS 141120 and ICGV-IS 13937 were found to be the best performing. Frontiers Media S.A. 2021-03-15 /pmc/articles/PMC8006269/ /pubmed/33790928 http://dx.doi.org/10.3389/fpls.2021.637860 Text en Copyright © 2021 Oteng-Frimpong, Kassim, Puozaa, Nboyine, Issah, Rasheed, Adjebeng-Danquah and Kusi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Oteng-Frimpong, Richard Kassim, Yussif Baba Puozaa, Doris Kanvenaa Nboyine, Jerry Asalma Issah, Abdul-Rashid Rasheed, Masawudu Abdul Adjebeng-Danquah, Joseph Kusi, Francis Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits |
title | Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits |
title_full | Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits |
title_fullStr | Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits |
title_full_unstemmed | Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits |
title_short | Characterization of Groundnut (Arachis hypogaea L.) Test Locations Using Representative Testing Environments With Farmer-Preferred Traits |
title_sort | characterization of groundnut (arachis hypogaea l.) test locations using representative testing environments with farmer-preferred traits |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006269/ https://www.ncbi.nlm.nih.gov/pubmed/33790928 http://dx.doi.org/10.3389/fpls.2021.637860 |
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