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...
| Autores: | , , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universidad Pública de Navarra |
| Repositório: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/55408 |
| Acesso em linha: | https://hdl.handle.net/2454/55408 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Carbon isotope composition Chlorophyll fluorescence High-throughput phenotyping Hyperspectral reflectance Leaf gas exchange Wheat |
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Hyperspectral reflectance at canopy and leaf levels for predicting yield and physiological traits in spring wheat under Mediterranean conditionsGámez Guzmán, Angie LorenaChocarro, AlvaroGarriga, MiguelRomero-Bravo, SebastiánAranjuelo Michelena, 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 (Ψleaf), gas exchange, chlorophyll fluorescence, grain yield, and carbon isotope composition (δ13C). 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 (R2) for 70% of traits analysed. Fluorescence parameters (R2 = 0.78–0.88), δ13C (R2 = 0.80), leaf pigments (R2 = 0.50–0.74), Ψleaf (R2 = 0.72), and LAI (R2 = 0.68) had the most robust predictions. LASSO regression showed the highest R2 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 Sklodowska-Curie Actions (MSCA), Research and Innovation Staff Exchange (RISE), H2020-MSCA-RISE-2019.ElsevierAgronomía, Biotecnología y AlimentaciónAgronomia, Bioteknologia eta Elikadura2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/55408reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2025 The Authors. Published by Elsevier Ltd on behalf of Huazhong Agricultural University. This is an open access article under the CC BY license.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/554082026-06-17T12:41:47Z |
| 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 Guzmán, Angie Lorena 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 Guzmán, Angie Lorena Chocarro, Alvaro Garriga, Miguel Romero-Bravo, Sebastián Aranjuelo Michelena, Iker Lobos, Gustavo A. Pozo, Alejandro del |
| author |
Gámez Guzmán, Angie Lorena |
| author_facet |
Gámez Guzmán, Angie Lorena Chocarro, Alvaro Garriga, Miguel Romero-Bravo, Sebastián Aranjuelo Michelena, Iker Lobos, Gustavo A. Pozo, Alejandro del |
| author_role |
author |
| author2 |
Chocarro, Alvaro Garriga, Miguel Romero-Bravo, Sebastián Aranjuelo Michelena, Iker Lobos, Gustavo A. Pozo, Alejandro del |
| author2_role |
author author author author author author |
| dc.contributor.none.fl_str_mv |
Agronomía, Biotecnología y Alimentación Agronomia, Bioteknologia eta Elikadura |
| 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 (Ψleaf), gas exchange, chlorophyll fluorescence, grain yield, and carbon isotope composition (δ13C). 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 (R2) for 70% of traits analysed. Fluorescence parameters (R2 = 0.78–0.88), δ13C (R2 = 0.80), leaf pigments (R2 = 0.50–0.74), Ψleaf (R2 = 0.72), and LAI (R2 = 0.68) had the most robust predictions. LASSO regression showed the highest R2 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 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2454/55408 |
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https://hdl.handle.net/2454/55408 |
| dc.language.none.fl_str_mv |
Inglés |
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Inglés |
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https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier |
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Elsevier |
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reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname:Universidad Pública de Navarra |
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Universidad Pública de Navarra |
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Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
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Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
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