The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions

The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions

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Improving durum wheat performance to abiotic stresses is often limited by a lack of proper monitoring methods in support of crop management and efficient phenotyping tools for breeding. The objectives of this study were (1) comparing the performance under contrasting water treatments of different physiological traits, which evaluate plant growth and water status; and (2) understanding how these traits can predict grain yield (GY) performance under contrasting water conditions. Thus, five modern durum wheat genotypes were subjected to rainfed (RF) and supplemental irrigation (SI) treatments. Two categories of physiological traits were tested; (1) the vegetation indices the Normalized Difference Vegetation Index (NDVI) and the Normalized Green Red Difference Index (NGRDI); and (2) the stable carbon and oxygen isotope compositions (δ13C and δ18O) of different plant parts. The NGRDI at anthesis and the δ13C of mature grains were the traits best correlated (positively and negatively, respectively) with GY. Both traits in combination explained at least 50% of variability in GY within each water treatment. The produced path models for RF and SI conditions highlighted the particular role of NGRDI and δ13C in predicting GY. In addition, the study showed the potential of using vegetation indices derived from digital Red-Green-Blue (RGB) images as a low-cost technique for assessing aerial biomass (AB) and GY under different water availabilities. © 2015 Elsevier B.V.

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Bibliographic Details
Main Authors: Elazab, A., Bort, J., Zhou, B., Serret, M. D., Nieto-Taladriz García, María Teresa, Araus, J. L.
Format: artículo biblioteca
Language:English
Published: Elsevier 2015
Subjects:Digital RGB imaging, Durum wheat, Path models, Vegetation indices, Stable isotopes, Grain yield,
Online Access:http://hdl.handle.net/20.500.12792/1181
http://hdl.handle.net/10261/291730
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spelling dig-inia-es-10261-2917302023-02-20T07:21:30Z The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions Elazab, A. Bort, J. Zhou, B. Serret, M. D. Nieto-Taladriz García, María Teresa Araus, J. L. Digital RGB imaging Durum wheat Path models Vegetation indices Stable isotopes Grain yield Improving durum wheat performance to abiotic stresses is often limited by a lack of proper monitoring methods in support of crop management and efficient phenotyping tools for breeding. The objectives of this study were (1) comparing the performance under contrasting water treatments of different physiological traits, which evaluate plant growth and water status; and (2) understanding how these traits can predict grain yield (GY) performance under contrasting water conditions. Thus, five modern durum wheat genotypes were subjected to rainfed (RF) and supplemental irrigation (SI) treatments. Two categories of physiological traits were tested; (1) the vegetation indices the Normalized Difference Vegetation Index (NDVI) and the Normalized Green Red Difference Index (NGRDI); and (2) the stable carbon and oxygen isotope compositions (δ13C and δ18O) of different plant parts. The NGRDI at anthesis and the δ13C of mature grains were the traits best correlated (positively and negatively, respectively) with GY. Both traits in combination explained at least 50% of variability in GY within each water treatment. The produced path models for RF and SI conditions highlighted the particular role of NGRDI and δ13C in predicting GY. In addition, the study showed the potential of using vegetation indices derived from digital Red-Green-Blue (RGB) images as a low-cost technique for assessing aerial biomass (AB) and GY under different water availabilities. © 2015 Elsevier B.V. 2023-02-20T07:21:30Z 2023-02-20T07:21:30Z 2015 artículo Agricultural Water Management 158: 196-208 (2015) 0378-3774 http://hdl.handle.net/20.500.12792/1181 http://hdl.handle.net/10261/291730 10.1016/j.agwat.2015.05.003 en none Elsevier
institution INIA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-inia-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del INIA España
language English
topic Digital RGB imaging
Durum wheat
Path models
Vegetation indices
Stable isotopes
Grain yield
Digital RGB imaging
Durum wheat
Path models
Vegetation indices
Stable isotopes
Grain yield
spellingShingle Digital RGB imaging
Durum wheat
Path models
Vegetation indices
Stable isotopes
Grain yield
Digital RGB imaging
Durum wheat
Path models
Vegetation indices
Stable isotopes
Grain yield
Elazab, A.
Bort, J.
Zhou, B.
Serret, M. D.
Nieto-Taladriz García, María Teresa
Araus, J. L.
The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
description Improving durum wheat performance to abiotic stresses is often limited by a lack of proper monitoring methods in support of crop management and efficient phenotyping tools for breeding. The objectives of this study were (1) comparing the performance under contrasting water treatments of different physiological traits, which evaluate plant growth and water status; and (2) understanding how these traits can predict grain yield (GY) performance under contrasting water conditions. Thus, five modern durum wheat genotypes were subjected to rainfed (RF) and supplemental irrigation (SI) treatments. Two categories of physiological traits were tested; (1) the vegetation indices the Normalized Difference Vegetation Index (NDVI) and the Normalized Green Red Difference Index (NGRDI); and (2) the stable carbon and oxygen isotope compositions (δ13C and δ18O) of different plant parts. The NGRDI at anthesis and the δ13C of mature grains were the traits best correlated (positively and negatively, respectively) with GY. Both traits in combination explained at least 50% of variability in GY within each water treatment. The produced path models for RF and SI conditions highlighted the particular role of NGRDI and δ13C in predicting GY. In addition, the study showed the potential of using vegetation indices derived from digital Red-Green-Blue (RGB) images as a low-cost technique for assessing aerial biomass (AB) and GY under different water availabilities. © 2015 Elsevier B.V.
format artículo
topic_facet Digital RGB imaging
Durum wheat
Path models
Vegetation indices
Stable isotopes
Grain yield
author Elazab, A.
Bort, J.
Zhou, B.
Serret, M. D.
Nieto-Taladriz García, María Teresa
Araus, J. L.
author_facet Elazab, A.
Bort, J.
Zhou, B.
Serret, M. D.
Nieto-Taladriz García, María Teresa
Araus, J. L.
author_sort Elazab, A.
title The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
title_short The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
title_full The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
title_fullStr The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
title_full_unstemmed The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
title_sort combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions
publisher Elsevier
publishDate 2015
url http://hdl.handle.net/20.500.12792/1181
http://hdl.handle.net/10261/291730
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