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Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectare...

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Autores principales: Biswas, Anju, Andrade, Mario Henrique Murad Leite, Acharya, Janam P., de Souza, Cleber Lopes, Lopez, Yolanda, de Assis, Giselle, Shirbhate, Shubham, Singh, Aditya, Munoz, Patricio, Rios, Esteban F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8689394/
https://www.ncbi.nlm.nih.gov/pubmed/34950163
http://dx.doi.org/10.3389/fpls.2021.756768
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author Biswas, Anju
Andrade, Mario Henrique Murad Leite
Acharya, Janam P.
de Souza, Cleber Lopes
Lopez, Yolanda
de Assis, Giselle
Shirbhate, Shubham
Singh, Aditya
Munoz, Patricio
Rios, Esteban F.
author_facet Biswas, Anju
Andrade, Mario Henrique Murad Leite
Acharya, Janam P.
de Souza, Cleber Lopes
Lopez, Yolanda
de Assis, Giselle
Shirbhate, Shubham
Singh, Aditya
Munoz, Patricio
Rios, Esteban F.
author_sort Biswas, Anju
collection PubMed
description The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.
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spelling pubmed-86893942021-12-22 Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.) Biswas, Anju Andrade, Mario Henrique Murad Leite Acharya, Janam P. de Souza, Cleber Lopes Lopez, Yolanda de Assis, Giselle Shirbhate, Shubham Singh, Aditya Munoz, Patricio Rios, Esteban F. Front Plant Sci Plant Science The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials. Frontiers Media S.A. 2021-12-07 /pmc/articles/PMC8689394/ /pubmed/34950163 http://dx.doi.org/10.3389/fpls.2021.756768 Text en Copyright © 2021 Biswas, Andrade, Acharya, de Souza, Lopez, de Assis, Shirbhate, Singh, Munoz and Rios. https://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
Biswas, Anju
Andrade, Mario Henrique Murad Leite
Acharya, Janam P.
de Souza, Cleber Lopes
Lopez, Yolanda
de Assis, Giselle
Shirbhate, Shubham
Singh, Aditya
Munoz, Patricio
Rios, Esteban F.
Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)
title Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)
title_full Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)
title_fullStr Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)
title_full_unstemmed Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)
title_short Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)
title_sort phenomics-assisted selection for herbage accumulation in alfalfa (medicago sativa l.)
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8689394/
https://www.ncbi.nlm.nih.gov/pubmed/34950163
http://dx.doi.org/10.3389/fpls.2021.756768
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