Pure line selection in a heterogeneous soybean cultivar

Amaral, Lígia de Oliveira; Bruzi, Adriano Teodoro; Resende, Pedro Milanez de; Silva, Karina Barroso

doi:10.1590/1984-70332019v19n3a39

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The joint analysis demonstrated interactions due not only to the sites but also due to the crop season or agricultural year. The genotype by environment interaction on morpho-agronomic traits has been widely studied in soybean (Soares et al 2015;Silva et al 2015;Gesteira et al 2018;Amaral et al 2019). Reports on physiological seed quality attributes are comparatively scarce (Keep et al 2016;Martins et al 2016;Zambiazzi et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Breeding for yield and seed quality in soybean

et al. 2021

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Currently, there is demand for high yield soybean cultivars associated with seeds of high physiological quality. The launch of high yield soybean cultivars associated with high quality seeds in soybean is big challenge due to the lack of a straight correlation between agronomic traits and seed quality. The genetic and phenotypic parameters associated with the physiological quality of seeds could help the soybean breeders to development the strategies to achieve this goal. This research aims to estimate the genetic and phenotypic parameters associated with traits related to the physiological quality of seeds soybean progenies, as well as to quantify the effect of selection for grain yield on the physiological quality of seeds. A total of 131 progenies were evaluated from the 2015/2016, and from these, 51 progenies were evaluated at the 2016/2017 season. Physiological quality was evaluated under controlled conditions using germination tests, seedling length tests, tetrazolium tests and emergence tests. Heritability, expected genetic gains, variance components and correlated response were estimated. The variance components due to variation in the progenies were significant for most traits, showing variability. The interaction of progeny by environment was significant, suggesting that environmental factors influence seed production and quality. The magnitudes of heritability and expected selection gain suggested that physiological quality can be successfully improved. Estimates of the correlation responses showed that selection for grain yield is also reflected in gains in the seed quality.

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Section: Discussionmentioning

confidence: 99%

Breeding for yield and seed quality in soybean

et al. 2021

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Application of Artificial Neural Networks to Predict Genotypic Values of Soybean Derived from Wide and Restricted Crosses for Relative Maturity Groups

Amaral,

Miranda,

Souza

et al. 2023

Agronomy

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The primary objective of soybean-breeding programs is to develop cultivars that offer both high grain yield and a maturity cycle tailored to the specific soil and climatic conditions of their cultivation. Therefore, predicting the genetic value is essential for selecting and advancing promising genotypes. Among the various analytical approaches available, deep machine learning emerges as a promising choice due to its capability to predict the genetic component of phenotypes assessed under field conditions, thereby enhancing the precision of breeding decisions. This study aimed to determine the efficiency of artificial neural networks (ANNs) in predicting the genetic values of soybean genotypes belonging to populations derived from crosses between parents of different relative maturity groups (RMGs). We characterized populations with broad and restricted genetic bases for RMG traits. Data from three soybean populations, evaluated over three different agricultural years, were used. Genetic values were predicted using the multilayer perceptron (MLP) artificial neural network and compared to those obtained using the best unbiased linear prediction from variance components using restricted maximum likelihood (RR-BLUP). The MLP neural network efficiently predicted genetic values for the relative maturity group trait for genotypes belonging to populations of broad and restricted crosses, with an R2 of 0.999 and root-mean-square error (RMSE) of 0.241, and for grain yield, there was an R2 of 0.999 and an RMSE of 0.076. While the percentage of coincident superior genotypes remained relatively consistent, a significant difference was observed in their ranking order. The genetic gain with selection estimated using MLP was higher by 30–110% compared to RR-BLUP for the relative maturity group trait and 90–500% for grain yield. Artificial neural networks (ANNs) showed higher efficiency than RR-BLUP in predicting the genetic values of the soybean population. Local selection at intermediate latitudes is conducive to developing lines adaptable for regions at higher and lower latitudes.

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