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dc.contributor.authorKim, Jinwook
dc.contributor.authorSavin, Roxana
dc.contributor.authorSlafer, Gustavo A.
dc.date.accessioned2021-03-17T07:18:32Z
dc.date.issued2021-01-29
dc.identifier.issn1161-0301
dc.identifier.urihttp://hdl.handle.net/10459.1/70765
dc.description.abstractAverage 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.
dc.description.sponsorshipFunding 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).
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relationMINECO/PN2013-2016/AGL2015-69595R
dc.relationMINECO/PN2017-2020/RTI2018-096213-B-100
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1016/j.eja.2021.126237
dc.relation.ispartofEuropean Journal of Agronomy, 2021, vol. 124, p. 126237
dc.rightscc-by-nc-nd, (c) Elsevier, 2021
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es
dc.subjectThousand grain weight
dc.subjectGrain size
dc.subjectYield components
dc.subjectTriticum aestivum
dc.titleWeight of individual wheat grains estimated from high-throughput digital images of grain area
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2021-03-17T07:18:32Z
dc.identifier.idgrec031100
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccess
dc.identifier.doihttps://doi.org/10.1016/j.eja.2021.126237
dc.date.embargoEndDate2023-01-29


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cc-by-nc-nd, (c) Elsevier, 2021
Except where otherwise noted, this item's license is described as cc-by-nc-nd, (c) Elsevier, 2021