2026-04-17T15:02:55Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/707652024-12-05T21:31:48Zcom_2072_3622col_2072_479130

00925njm 22002777a 4500 dc Kim, Jinwook author Savin, Roxana author Slafer, Gustavo A. author 2021-03-17T07:18:32Z 2021-01-29 2021-03-17T07:18:32Z 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. 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). http://hdl.handle.net/10459.1/70765 Thousand grain weight Grain size Yield components Triticum aestivum Weight of individual wheat grains estimated from high-throughput digital images of grain area