Prediction of grain diameter and grain mass in white oat using morphological and phytosanitary variables
Keywords:
Avena sativa L, machine learning, regression tree, indirect selectionAbstract
This study aimed to evaluate the possibility of predicting grain diameter and grain mass in white oat using morphological and phytosanitary variables. A total of 478 white oat lines were evaluated in the municipality of Augusto Pestana, Rio Grande do Sul, Brazil. The lines were sown on April 23, 2023, using an augmented block design. After flowering, leaf rust (LR, %), stem rust (SR, %), and leaf spot (LS, %) were assessed. At physiological maturity, lodging (LOD, %) was evaluated, and ten plants were collected to measure plant height (PH, cm), grain diameter (GD, mm), hundred grain mass (HGM, g), and grain mass (GM, g). Pearson correlation coefficients were calculated, with significance determined by Student’s t-test at the 5% level, and a correlation network was constructed. Regression tree analysis was used to predict GD and GM. The results indicated that these variables can be predicted based on morphological traits. Hundred grain mass was the main determinant of grain diameter, whereas plant height and hundred grain mass determined grain mass. Phytosanitary variables showed low predictive contribution in the models used, reinforcing the importance of selecting more efficient and productive genotypes under field conditions.
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