Weight of individual wheat grains estimated from high-throughput digital images of grain area

Average grain weight (AGW) is a major component of wheat yield. When attempting to elucidate mechanisms behind treatments effects on AGW, the distribution of the weight of individual grains may be critical. Determining the individual weight of thousands of grains in each sample would be unmanageable...

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Detalles Bibliográficos
Autores: Kim, Jinwook, Savin, Roxana, Slafer, Gustavo A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/70765
Acceso en línea:https://doi.org/10.1016/j.eja.2021.126237
http://hdl.handle.net/10459.1/70765
Access Level:acceso abierto
Palabra clave:Thousand grain weight
Grain size
Yield components
Triticum aestivum
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spelling Weight of individual wheat grains estimated from high-throughput digital images of grain areaKim, JinwookSavin, RoxanaSlafer, Gustavo A.Thousand grain weightGrain sizeYield componentsTriticum aestivumAverage grain weight (AGW) is a major component of wheat yield. When attempting to elucidate mechanisms behind treatments effects on AGW, the distribution of the weight of individual grains may be critical. Determining the individual weight of thousands of grains in each sample would be unmanageable. Then, when individual sizes must be considered, researchers either weigh individually a very minor proportion of the grains or determine for the complete sample individual linear dimensions (length, width, area) through an image processing equipment. We aimed to generate a single model equation to trustworthily convert grain linear dimensions to grain weights. Firstly, we used a set of data to build and calibrate a model for the relationship between weight and linear dimensions of individual grains. Then, we validated the model calibrated with independent data. Grain area was a better predictor of grain weight than length and width of grains. Initially, we generated a single linear model but (i) the intercept was incongruently negative and therefore (ii) we forced the linear regression through the origin, but that consistently overestimated the weight of small grains and underestimated large grains. Finally, we fitted the data again with a power curve model and forced the intercept to zero (with the log-transformed data) obtaining the model (ŷ = x1.32) to estimate individual grain weight from grain area. The model was validated with (i) independent data from the same studies used to build the model, (ii) data from other completely independent experiments, and (iii) data from the literature. Considering the diversity of genotypes and environments in the model generation and validation, the proposed power curve model could be trustworthily used to estimate grain weights from measured areas.Funding was provided by projects AGL2015-69595R and RTI2018-096213-B-100 funded by the Agencia Estatal de Investigación (AEI) of Spain. Jinwook Kim held a pre-doctoral research contract from AGAUR (the Agency for Management of University and Research Grants of Catalonia).Elsevier202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.eja.2021.126237http://hdl.handle.net/10459.1/70765http://hdl.handle.net/10459.1/70765reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/grantAgreement/MINECO//AGL2015-69595-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096213-B-I00Reproducció del document publicat a: https://doi.org/10.1016/j.eja.2021.126237European Journal of Agronomy, 2021, vol. 124, p. 126237cc-by, (c) Kim, 2021info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/707652026-05-29T05:05:01Z
dc.title.none.fl_str_mv Weight of individual wheat grains estimated from high-throughput digital images of grain area
title Weight of individual wheat grains estimated from high-throughput digital images of grain area
spellingShingle Weight of individual wheat grains estimated from high-throughput digital images of grain area
Kim, Jinwook
Thousand grain weight
Grain size
Yield components
Triticum aestivum
title_short Weight of individual wheat grains estimated from high-throughput digital images of grain area
title_full Weight of individual wheat grains estimated from high-throughput digital images of grain area
title_fullStr Weight of individual wheat grains estimated from high-throughput digital images of grain area
title_full_unstemmed Weight of individual wheat grains estimated from high-throughput digital images of grain area
title_sort Weight of individual wheat grains estimated from high-throughput digital images of grain area
dc.creator.none.fl_str_mv Kim, Jinwook
Savin, Roxana
Slafer, Gustavo A.
author Kim, Jinwook
author_facet Kim, Jinwook
Savin, Roxana
Slafer, Gustavo A.
author_role author
author2 Savin, Roxana
Slafer, Gustavo A.
author2_role author
author
dc.subject.none.fl_str_mv Thousand grain weight
Grain size
Yield components
Triticum aestivum
topic Thousand grain weight
Grain size
Yield components
Triticum aestivum
description Average grain weight (AGW) is a major component of wheat yield. When attempting to elucidate mechanisms behind treatments effects on AGW, the distribution of the weight of individual grains may be critical. Determining the individual weight of thousands of grains in each sample would be unmanageable. Then, when individual sizes must be considered, researchers either weigh individually a very minor proportion of the grains or determine for the complete sample individual linear dimensions (length, width, area) through an image processing equipment. We aimed to generate a single model equation to trustworthily convert grain linear dimensions to grain weights. Firstly, we used a set of data to build and calibrate a model for the relationship between weight and linear dimensions of individual grains. Then, we validated the model calibrated with independent data. Grain area was a better predictor of grain weight than length and width of grains. Initially, we generated a single linear model but (i) the intercept was incongruently negative and therefore (ii) we forced the linear regression through the origin, but that consistently overestimated the weight of small grains and underestimated large grains. Finally, we fitted the data again with a power curve model and forced the intercept to zero (with the log-transformed data) obtaining the model (ŷ = x1.32) to estimate individual grain weight from grain area. The model was validated with (i) independent data from the same studies used to build the model, (ii) data from other completely independent experiments, and (iii) data from the literature. Considering the diversity of genotypes and environments in the model generation and validation, the proposed power curve model could be trustworthily used to estimate grain weights from measured areas.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.eja.2021.126237
http://hdl.handle.net/10459.1/70765
http://hdl.handle.net/10459.1/70765
url https://doi.org/10.1016/j.eja.2021.126237
http://hdl.handle.net/10459.1/70765
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO//AGL2015-69595-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096213-B-I00
Reproducció del document publicat a: https://doi.org/10.1016/j.eja.2021.126237
European Journal of Agronomy, 2021, vol. 124, p. 126237
dc.rights.none.fl_str_mv cc-by, (c) Kim, 2021
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by, (c) Kim, 2021
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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