Soybean productivity, stability, and adaptability through mixed model methodology

Evangelista, Jeniffer Santana Pinto Coelho; Alves, Rodrigo Silva; Peixoto, Marco Antônio; Resende, Marcos Deon Vilela de; Teodoro, Paulo Eduardo; Silva, Felipe Lopes da

doi:10.1590/0103-8478cr20200406

Cited by 7 publications

(7 citation statements)

References 14 publications

(15 reference statements)

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“…The use of HMRPGV proved to be a viable alternative for T. grandiflorum, as it allows us to infer the adaptability and stability of genotypic values. We found high levels of coincidence between the best genotypes in HMRPGV and μ + g, what indicates the efficiency of the method [13].…”

Section: Discussionmentioning

confidence: 65%

“…Currently, the Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) mixed model method is the standard for analyses of genotype x environment (GE) interaction [12][13] and repeated measures [14][15]. There are numerous reasons for its use, including the fact that it enables the simultaneous estimates of variance components and prediction of genetic values.…”

Section: Introductionmentioning

confidence: 99%

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Theobroma grandiflorum breeding optimization based on repeatability, stability and adaptability information

Chaves

Alves

Alves³

et al. 2021

Preprint

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The cultivation of Theobroma grandiflorum in the Brazilian Amazon is mainly conducted by family farmers who use a range of different management strategies. Thus, breeding programs of the species must address the challenge of developing cultivars that are adapted to and stable in a variety of cultivation environments. In this context, this study aimed to estimate the optimum number of harvests for genetic selection of T. grandiflorum progenies and identify the most promising ones in terms of productivity, stability, and adaptability. The trials were implemented in three environments, using a randomized complete block design, with 25 full-sib progenies, five replications, and three plants per plot. The traits mean number of fruits/plant, mean fruit production/plant, and rate of infection with witches’ broom (Moniliophthora perniciosa) were evaluated over 11 harvests. The Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) mixed model method was used to estimate genetic parameters and predict genetic values, which were then applied to assess stability and adaptability. The results show that there is genetic variability among the studied T. grandiflorum progenies and that accurate genetic selection aiming at recombination is effective after three harvests, for recombination, or eleven harvests for identification of recommended progenies. Six progenies were selected that met the requirements for productivity, stability, and adaptability to different cultivation environments. These results can be used to optimize and advance T. grandiflorum breeding programs.

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Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Theobroma grandiflorum breeding optimization based on repeatability, stability and adaptability information

Chaves

Alves

Alves³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 77%

“…Currently, the Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) mixed model method is the standard for analyses of genotype x environment (GE) interaction (Coelho et al 2020;Evangelista et al 2021) and repeated measures (Resende et al 2014;Ferreira et al 2020). There are numerous reasons for its use, including the fact that it enables the simultaneous estimates of variance components and prediction of genetic values.…”

Section: Introductionmentioning

confidence: 99%

Theobroma Grandiflorum Breeding Optimization Based on Repeatability, Stability and Adaptability Information

Chaves

Alves

Alves³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The cultivation of Theobroma grandiflorum in the Brazilian Amazon is mainly conducted by family farmers who use a range of different management straegies. Thus, breeding programs of the species must address the challenge of developing cultivars that are adapted to and stable in a variety of cultivation environments. In this context, this study aimed to estimate the optimum number of harvests for genetic selection of T. grandiflorum progenies and identify the most promising ones in terms of productivity, stability, and adaptability. The trials were implemented in three environments, using a randomized complete block design, with 25 full-sib progenies, five replications, and three plants per plot. The traits mean number of fruits/plant, mean fruit production/plant, and rate of infection with witches’ broom (Moniliophthora perniciosa) were evaluated over 11 harvests. The Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) mixed model method was used to estimate genetic parameters and predict genetic values, which were then applied to assess stability and adaptability. The results show that there is genetic variability among the studied T. grandiflorum progenies and that accurate genetic selection aiming at recombination is effective after three harvests, for recombination, or eleven harvests for identification of recommended progenies. Six progenies were selected that met the requirements for productivity, stability, and adaptability to different cultivation environments. These results can be used to optimize and advance T. grandiflorum breeding programs.

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“…Environmental stratification was performed using the routine developed by Rocha et al (2018) in R (R Development Core Team 2020). Selection gains, considering the genotypes selected by the FAI-MCMC index, were obtained using the following equation (Evangelista et al 2021):…”

Section: Environmental Stratification and Genetic Recommendationmentioning

confidence: 99%

Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

Evangelista

Peixoto

Coelho

et al. 2021

Crop Breed. Appl. Biotechnol.

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The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus, the objectives of this study were: i) propose the Multi-Environment Index Based on Factor Analysis and Ideotype-Design/Markov Chain Monte Carlo (FAI/ MCMC index), and ii) apply it for soybean genotypes recommendation. To this end, a data set with 30 soybean genotypes evaluated in 10 environments for grain yield trait was used. Variance components, genetic parameters and genetic values were estimated through MCMC algorithm. Environmental stratification was conducted by factor analyses and the selection of soybean genotypes was performed using the FAI/MCMC index. The results indicated the existence of genotypic variability and G×E interaction. The environments were grouped into three factors. The predicted genetic gains from indirect selection was 4.81%. Thus, our results suggest that the FAI/MCMC index can be successfully used in soybean breeding.

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Soybean productivity, stability, and adaptability through mixed model methodology

Cited by 7 publications

References 14 publications

Theobroma grandiflorum breeding optimization based on repeatability, stability and adaptability information

Theobroma grandiflorum breeding optimization based on repeatability, stability and adaptability information

Theobroma Grandiflorum Breeding Optimization Based on Repeatability, Stability and Adaptability Information

Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

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