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High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield

BACKGROUND: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars...

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Autores principales: Santana, Dthenifer Cordeiro, de Oliveira Cunha, Marcos Paulo, dos Santos, Regimar Garcia, Cotrim, Mayara Fávero, Teodoro, Larissa Pereira Ribeiro, da Silva Junior, Carlos Antonio, Baio, Fabio Henrique Rojo, Teodoro, Paulo Eduardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812231/
https://www.ncbi.nlm.nih.gov/pubmed/35109882
http://dx.doi.org/10.1186/s13007-022-00848-4
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author Santana, Dthenifer Cordeiro
de Oliveira Cunha, Marcos Paulo
dos Santos, Regimar Garcia
Cotrim, Mayara Fávero
Teodoro, Larissa Pereira Ribeiro
da Silva Junior, Carlos Antonio
Baio, Fabio Henrique Rojo
Teodoro, Paulo Eduardo
author_facet Santana, Dthenifer Cordeiro
de Oliveira Cunha, Marcos Paulo
dos Santos, Regimar Garcia
Cotrim, Mayara Fávero
Teodoro, Larissa Pereira Ribeiro
da Silva Junior, Carlos Antonio
Baio, Fabio Henrique Rojo
Teodoro, Paulo Eduardo
author_sort Santana, Dthenifer Cordeiro
collection PubMed
description BACKGROUND: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. RESULTS: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. CONCLUSIONS: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs.
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spelling pubmed-88122312022-02-07 High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield Santana, Dthenifer Cordeiro de Oliveira Cunha, Marcos Paulo dos Santos, Regimar Garcia Cotrim, Mayara Fávero Teodoro, Larissa Pereira Ribeiro da Silva Junior, Carlos Antonio Baio, Fabio Henrique Rojo Teodoro, Paulo Eduardo Plant Methods Research BACKGROUND: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. RESULTS: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. CONCLUSIONS: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs. BioMed Central 2022-02-02 /pmc/articles/PMC8812231/ /pubmed/35109882 http://dx.doi.org/10.1186/s13007-022-00848-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Santana, Dthenifer Cordeiro
de Oliveira Cunha, Marcos Paulo
dos Santos, Regimar Garcia
Cotrim, Mayara Fávero
Teodoro, Larissa Pereira Ribeiro
da Silva Junior, Carlos Antonio
Baio, Fabio Henrique Rojo
Teodoro, Paulo Eduardo
High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
title High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
title_full High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
title_fullStr High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
title_full_unstemmed High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
title_short High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
title_sort high-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812231/
https://www.ncbi.nlm.nih.gov/pubmed/35109882
http://dx.doi.org/10.1186/s13007-022-00848-4
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