Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions

High-throughput field phenotyping offers an efficient solution for identifying and selecting genotypes of interest in plant breeding. This study aimed to develop multivariate models using spectral reflectance data to estimate physiological and yield traits in spring wheat genotypes exposed to differ...

Descripción completa

Detalles Bibliográficos
Autores: Gámez, Angie L., Chocarro, Alvaro, Garriga, Miguel, Romero-Bravo, Sebastián, Aranjuelo, Iker, Lobos, Gustavo A., Pozo, Alejandro del
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/406001
Acceso en línea:http://hdl.handle.net/10261/406001
https://api.elsevier.com/content/abstract/scopus_id/105012431991
Access Level:acceso abierto
Palabra clave:Carbon isotope composition
Chlorophyll fluorescence
High-throughput phenotyping
Hyperspectral reflectance
Leaf gas exchange
Wheat
id ES_93bdfd1c851beb7dcee07609b71a60c1
oai_identifier_str oai:digital.csic.es:10261/406001
network_acronym_str ES
network_name_str España
repository_id_str
spelling Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditionsGámez, Angie L.Chocarro, AlvaroGarriga, MiguelRomero-Bravo, SebastiánAranjuelo, IkerLobos, Gustavo A.Pozo, Alejandro delCarbon isotope compositionChlorophyll fluorescenceHigh-throughput phenotypingHyperspectral reflectanceLeaf gas exchangeWheatHigh-throughput field phenotyping offers an efficient solution for identifying and selecting genotypes of interest in plant breeding. This study aimed to develop multivariate models using spectral reflectance data to estimate physiological and yield traits in spring wheat genotypes exposed to different water regimes. Fifteen spring wheat varieties and one triticale genotype were evaluated in sixteen environments, which were generated by combining data from over four seasons in two Mediterranean locations in Chile, along with two water regimes (irrigated and water deficit). Measured traits were leaf pigments, leaf area index (LAI), leaf water potential (Ψ<inf>leaf</inf>), gas exchange, chlorophyll fluorescence, grain yield, and carbon isotope composition (δ<sup>13</sup>C). Hyperspectral reflectance was recorded at the leaf level and canopy level (45° and 90°) at anthesis and grain filling and used to generate predictive models using partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), and elastic net (E.net) regression. Models explained over 60% of the trait variation (R<sup>2</sup>) for 70% of traits analysed. Fluorescence parameters (R<sup>2</sup> ​= ​0.78–0.88), δ<sup>13</sup>C (R<sup>2</sup> ​= ​0.80), leaf pigments (R<sup>2</sup> ​= ​0.50–0.74), Ψ<inf>leaf</inf> (R<sup>2</sup> ​= ​0.72), and LAI (R<sup>2</sup> ​= ​0.68) had the most robust predictions. LASSO regression showed the highest R<sup>2</sup> and accuracy, while canopy-level spectra at 90° excelled in predicting grain yield and LAI, and leaf-level spectra were best for fluorescence traits. These methods facilitated the identification of genotypes with superior water-deficit adaptation and yield potential, accelerating breeding, enhancing crop resilience to climate change, and improving food security.The authors would like to express their gratitude to the National Agency for Research and Development (ANID), Chile, grants FONDEF Idea 14I10106 and ANILLO ATE220001, and the Marie Skłodowska-Curie Actions (MSCA), Research and Innovation Staff Exchange (RISE), H2020-MSCA-RISE-2019.Peer reviewedElsevier BVAgencia Nacional de Investigación y Desarrollo (Chile)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/406001https://api.elsevier.com/content/abstract/scopus_id/105012431991reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésThe underlying dataset has been published as supplementary material of the article in the publisher platform at https://doi.org/10.1016/j.crope.2025.04.004https://doi.org/10.1016/j.crope.2025.04.004Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4060012026-05-22T06:33:51Z
dc.title.none.fl_str_mv Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
title Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
spellingShingle Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
Gámez, Angie L.
Carbon isotope composition
Chlorophyll fluorescence
High-throughput phenotyping
Hyperspectral reflectance
Leaf gas exchange
Wheat
title_short Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
title_full Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
title_fullStr Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
title_full_unstemmed Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
title_sort Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditions
dc.creator.none.fl_str_mv Gámez, Angie L.
Chocarro, Alvaro
Garriga, Miguel
Romero-Bravo, Sebastián
Aranjuelo, Iker
Lobos, Gustavo A.
Pozo, Alejandro del
author Gámez, Angie L.
author_facet Gámez, Angie L.
Chocarro, Alvaro
Garriga, Miguel
Romero-Bravo, Sebastián
Aranjuelo, Iker
Lobos, Gustavo A.
Pozo, Alejandro del
author_role author
author2 Chocarro, Alvaro
Garriga, Miguel
Romero-Bravo, Sebastián
Aranjuelo, Iker
Lobos, Gustavo A.
Pozo, Alejandro del
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Nacional de Investigación y Desarrollo (Chile)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Carbon isotope composition
Chlorophyll fluorescence
High-throughput phenotyping
Hyperspectral reflectance
Leaf gas exchange
Wheat
topic Carbon isotope composition
Chlorophyll fluorescence
High-throughput phenotyping
Hyperspectral reflectance
Leaf gas exchange
Wheat
description High-throughput field phenotyping offers an efficient solution for identifying and selecting genotypes of interest in plant breeding. This study aimed to develop multivariate models using spectral reflectance data to estimate physiological and yield traits in spring wheat genotypes exposed to different water regimes. Fifteen spring wheat varieties and one triticale genotype were evaluated in sixteen environments, which were generated by combining data from over four seasons in two Mediterranean locations in Chile, along with two water regimes (irrigated and water deficit). Measured traits were leaf pigments, leaf area index (LAI), leaf water potential (Ψ<inf>leaf</inf>), gas exchange, chlorophyll fluorescence, grain yield, and carbon isotope composition (δ<sup>13</sup>C). Hyperspectral reflectance was recorded at the leaf level and canopy level (45° and 90°) at anthesis and grain filling and used to generate predictive models using partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), and elastic net (E.net) regression. Models explained over 60% of the trait variation (R<sup>2</sup>) for 70% of traits analysed. Fluorescence parameters (R<sup>2</sup> ​= ​0.78–0.88), δ<sup>13</sup>C (R<sup>2</sup> ​= ​0.80), leaf pigments (R<sup>2</sup> ​= ​0.50–0.74), Ψ<inf>leaf</inf> (R<sup>2</sup> ​= ​0.72), and LAI (R<sup>2</sup> ​= ​0.68) had the most robust predictions. LASSO regression showed the highest R<sup>2</sup> and accuracy, while canopy-level spectra at 90° excelled in predicting grain yield and LAI, and leaf-level spectra were best for fluorescence traits. These methods facilitated the identification of genotypes with superior water-deficit adaptation and yield potential, accelerating breeding, enhancing crop resilience to climate change, and improving food security.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/406001
https://api.elsevier.com/content/abstract/scopus_id/105012431991
url http://hdl.handle.net/10261/406001
https://api.elsevier.com/content/abstract/scopus_id/105012431991
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The underlying dataset has been published as supplementary material of the article in the publisher platform at https://doi.org/10.1016/j.crope.2025.04.004
https://doi.org/10.1016/j.crope.2025.04.004
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869413622854189056
score 15.81155

Similares en LA Referencia