Predição e seleção indireta da massa de grãos da panícula de aveia branca em ambientes com e sem adubação
DOI:
https://doi.org/10.24221/jeap.10.4.2025.8058.233-243Keywords:
Avena sativa, Agronomic traits, Plant breeding, Grain yield, Logistic regressionAbstract
Indirect plant selection can be enhanced by easily measured traits correlated with traits of interest. This study aimed to determine whether panicle grain weight can be predicted using easily measured agronomic traits in environments with and without fertilization. Logistic regression analysis was used to evaluate the performance of predictive models in the indirect selection of white oat plants for greater panicle grain weight. Plant height, panicle insertion height, panicle length, panicle weight, panicle grain number, and panicle grain weight were evaluated for 570 white oat plants. Pearson's correlation and partial correlation were performed between the traits. Simple and multiple stepwise forward linear regression were applied to predict NGP and MGP. Logistic regression with cross-validation was used to assess the accuracy rate of plants selected under selection pressures (5 to 50%), considering the regression models developed. Panicle grain weight can be predicted using panicle weight, with a coefficient of determination equal to or greater than 0.93, regardless of the presence or absence of fertilization. White oat plants with greater panicle grain weight can be selected simply by weighing the panicle, without the need for threshing, achieving accuracy greater than 85% at selection pressures equal to or greater than 20%, and over 90% at pressures equal to or greater than 35%, regardless of the presence or absence of fertilization.Downloads
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Copyright (c) 2025 Paola Notargiacomo Ceolin, Murilo Vieira Loro, Ivan Ricardo Carvalho, Diovana Thays Schlösser, Dani Antonini Bromberger, Angélica Guareschi, Vitória Larrosa Bueno, Pedro Henrique Pereira Nunes
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