Strategies for selecting soybean genotypes using mixed models and multivariate approach

Andrade, AndrÃ©a Carla Bastos; Silva, Alysson Jalles da; Ferraudo, AntÃ nio SÃ©rgio; UnÃada-Trevisoli, Sandra Helena; Mauro, AntÃ nio Orlando Di

doi:10.5897/ajar2015.9715

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…Interaction analysis allows the maximization of selection gains, when testing homozygous clones or lines. Similar results were obtained by other authors (Pinheiro et al, 2013;Rocha et al, 2015;Andrade et al, 2016), who also found low-heritability estimates for soybean GY. The value of interaction variance (V G×A ), when higher than the genotypic variance (V G ), also contributes to the low values of the h 2 g estimates.…”

Section: Resultssupporting

confidence: 92%

“…The simultaneous use of mixed models based on REML/BLUP and multivariate methods enables the exploration of different adaptability and stability concepts, thereby complementing the collected data, thus increasing the efficacy of the selection of superior genotypes (Andrade et al, 2016). This study differs from other published ones on the parameters of soybean adaptability, stability, and yield performance because it combines the methods of mixed-models and GGE biplots, in order to assess cultivars widely grown in the Brazilian macroregions of adaptation 1 and 2.…”

Section: Introductionmentioning

confidence: 99%

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Agronomic performance of modern soybean cultivars in multi-environment trials

Matei

Benin

Woyann

et al. 2017

Pesq. agropec. bras.

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The objective of this work was to evaluate the productive performance, and the adaptability and stability parameters of modern soybean (Glycine max) cultivars in multi-environment trials, as well as to identify the ideal genotypes for eight growing environments in Brazil. A randomized complete block experimental design was carried out, with three replicates, for the evaluation of 46 soybean cultivars in eight environments, in the microregions of adaptation 102, 201, and 202, in the 2014/2015 crop season. A complex genotype x environment interaction occurred, with changes in the ranking of genotypes among locations. The NA 5909 RG, M6410IPRO, NS 5959 IPRO, NS6823RR, M5917IPRO, NS 6767 RR, and 6563RSF IPRO cultivars showed the highest mean yields. The NA 5909 RG, NS6823RR, M6410IPRO, and NS 5959 IPRO cultivars showed high adaptability and stability and high grain yield, in the evaluated environments, and were ranked next to the ideal genotype for the analyzed environments. There are modern soybean cultivars, which are adapted, stable, and highly productive, for cultivation in the microregions 102, 201, and 202 for soybean crop adaptation in Brazil.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Agronomic performance of modern soybean cultivars in multi-environment trials

Matei

Benin

Woyann

et al. 2017

Pesq. agropec. bras.

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“…Andrade et al. (2016) studied F 6 generation soy strains in Jaboticabal, SP, Brazil, and obtained higher CV estimates for a number of branches (69.5%), whereas Oliveira et al. (2019), studying the genetic diversity of soy strains developed by the genetic improvement program of the Federal University of Tocantins aiming at biodiesel production, reported CVs ranging from 2.5 to 28.2% for the agronomic characters evaluated.…”

Section: Resultsmentioning

confidence: 99%

Genetic parameters and selection indexes in F2 and F2:3 soybean populations

et al. 2021

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The evaluation of genetic parameters in early generations select individuals in F2 and F2:3 and advancing only progenies with the potential to generate pure productive lines. The objectives of this study were to estimate genetic and phenotypic parameters in F2 and F2:3 populations derived from UFUS 7415 and TMG 7161 RR, evaluate different genotype selection strategies, and select progenies to improve agronomic traits. Populations in the F2 and F2:3 generations derived from a biparental cross between the parents UFUS 7415 and TMG 7161 RR were evaluated. The genetic parameters of 360 individuals in the F2 generation and 150 genotypes of each parent were evaluated. Heritability varied from 4.66% for the number of seeds per pod to 86.06% for plant height at flowering. The greatest selection gain was obtained for plant height at flowering (29.68%), related to grain production. In the F2:3 generation, 145 genotypes were evaluated and their parents, in a randomized block design with two replications. The correlations between characters and the genetic gains obtained by direct and indirect selection and based on the genotype–ideotype distance index and the Mulamba and Mock index of the sum of “ranks” were evaluated. With direct selection, selection gains ranged from –5.34 to 16.19%. The genotype–ideotype distance index showed a total selection gain of 40.49%, while the Mulamba and Mock index of the sum of ranks obtained a total gain of 39.35%. Twenty‐three genotypes were selected for the characters evaluated with both indexes in the F3 generation.

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“…The mixed model has been applied to the analysis of cultivar trial data for many crops such as soybean (Andrade et al 2016), corn (So and Edwards 2009; Hu and Spilke 2011), wheat (Friesen et al 2016), rape (Hu et al 2014), and canola (Beeck et al 2010). Results are not always consistent due to differences in crop cultivars, trial environments, adopted variance-covariance structure of models, and data sufficiency.…”

Section: Introductionmentioning

confidence: 99%

Optimal models in the yield analysis of new flax cultivars

Jia

Booker

2018

Can. J. Plant Sci.

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Multi-environment trials are conducted to evaluate the performance of cultivars. In a combined analysis, the mixed model is superior to an analysis of variance for evaluating and comparing cultivars and dealing with an unbalanced data structure. This study seeks to identify the optimal models using the Saskatchewan Variety Performance Group post-registration regional trial data for flax. Yield data were collected for 15 entries in post-registration tests conducted in Saskatchewan from 2007 to 2016 (except 2011) and 16 mixed models with homogeneous or heterogeneous residual errors were compared. A compound symmetry model with heterogeneous residual error (CSR) had the best fit, with a normal distribution of residuals and a mean of zero fitted to the trial data for each year. The compound symmetry model with homogeneous residual error (CS) and a model extending the CSR to higher dimensions (DIAGR) were the next best models in most cases. Five hundred random samples from a two-stage sampling method were produced to determine the optimal models suitable for various environments. The CSR model was superior to other models for 396 out of 500 samples (79.2%). The top three models, CSR, CS, and DIAGR, had higher statistical power and could be used to access the yield stability of the new flax cultivars. Optimal mixed models are recommended for future data analysis of new flax cultivars in regional tests.

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Strategies for selecting soybean genotypes using mixed models and multivariate approach

Cited by 10 publications

References 21 publications

Agronomic performance of modern soybean cultivars in multi-environment trials

Agronomic performance of modern soybean cultivars in multi-environment trials

Genetic parameters and selection indexes in F2 and F2:3 soybean populations

Optimal models in the yield analysis of new flax cultivars

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