Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?
Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully...
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doaj-1502a1d1af7045d2940d3df011fd9cc32020-11-24T21:24:30ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2017-03-01810.3389/fpls.2017.00280234256Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?Miguel Garriga0Sebastián Romero-Bravo1Félix Estrada2Alejandro Escobar3Iván A. Matus4Alejandro del Pozo5Cesar A. Astudillo6Gustavo A. Lobos7Facultad de Ciencias Agrarias, Plant Breeding and Phenomic Center, PIEI Adaptación de la Agricultura al Cambio Climático, Universidad de TalcaTalca, ChileFacultad de Ciencias Agrarias, Plant Breeding and Phenomic Center, PIEI Adaptación de la Agricultura al Cambio Climático, Universidad de TalcaTalca, ChileFacultad de Ciencias Agrarias, Plant Breeding and Phenomic Center, PIEI Adaptación de la Agricultura al Cambio Climático, Universidad de TalcaTalca, ChileFacultad de Ciencias Agrarias, Plant Breeding and Phenomic Center, PIEI Adaptación de la Agricultura al Cambio Climático, Universidad de TalcaTalca, ChileCRI-Quilamapu, Instituto de Investigaciones AgropecuariasChillán, ChileFacultad de Ciencias Agrarias, Plant Breeding and Phenomic Center, PIEI Adaptación de la Agricultura al Cambio Climático, Universidad de TalcaTalca, ChileDepartment of Computer Science, Faculty of Engineering, Universidad de TalcaCuricó, ChileFacultad de Ciencias Agrarias, Plant Breeding and Phenomic Center, PIEI Adaptación de la Agricultura al Cambio Climático, Universidad de TalcaTalca, ChilePhenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.http://journal.frontiersin.org/article/10.3389/fpls.2017.00280/fullphenomichigh-throughput phenotypingphenotypingcarbon isotope discriminationreflectance |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Miguel Garriga Sebastián Romero-Bravo Félix Estrada Alejandro Escobar Iván A. Matus Alejandro del Pozo Cesar A. Astudillo Gustavo A. Lobos |
spellingShingle |
Miguel Garriga Sebastián Romero-Bravo Félix Estrada Alejandro Escobar Iván A. Matus Alejandro del Pozo Cesar A. Astudillo Gustavo A. Lobos Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? Frontiers in Plant Science phenomic high-throughput phenotyping phenotyping carbon isotope discrimination reflectance |
author_facet |
Miguel Garriga Sebastián Romero-Bravo Félix Estrada Alejandro Escobar Iván A. Matus Alejandro del Pozo Cesar A. Astudillo Gustavo A. Lobos |
author_sort |
Miguel Garriga |
title |
Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? |
title_short |
Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? |
title_full |
Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? |
title_fullStr |
Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? |
title_full_unstemmed |
Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? |
title_sort |
assessing wheat traits by spectral reflectance: do we really need to focus on predicted trait-values or directly identify the elite genotypes group? |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2017-03-01 |
description |
Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection. |
topic |
phenomic high-throughput phenotyping phenotyping carbon isotope discrimination reflectance |
url |
http://journal.frontiersin.org/article/10.3389/fpls.2017.00280/full |
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