Study of statistic stability to select high-yielding and stable peach genotypes

Maulión, Evangelina; Valentini, Gabriel Hugo; Ornella, Leonardo; Pairoba, Claudio F.; Daorden, Maria Elena; Cervigni, Gerardo Domingo Lucio

doi:10.1016/j.scienta.2014.06.026

Cited by 8 publications

(11 citation statements)

References 45 publications

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“…Since peach yield is sensitive to climatic variability because change in temperature and precipitation can alter blooming and fruit development period, a significant effect of E on the model is expected. Maulión et al (2014) have already indicated that the interaction between rain and heat accumulation during fruit development period was associated with performance instability. Later, a portion of yield variation was explained by heat accumulation during fruit development period, rainfall during floral bud endo-dormancy and rainfall from floral bud endo-to ecodormancy .…”

Section: Resultsmentioning

confidence: 99%

“…Since the presence of GE is a natural phenomenon in MET crops, including this effect in the final model to analyze agronomic data is a common fact, the full model in sorghum (Omer and Singh 2017), wheat (Mohammadi et al 2015), cotton , soybean (Volpato et al 2019), among many others, was used. Although the GE characterization for main crops has been studied and reported extensively for many years, GE studies in peach started recently (Citadin et al 2014;Maulión et al 2014). Maulión et al (2014) showed that peach yield is a character in which COI predominates over NCOI.…”

Section: Resultsmentioning

confidence: 99%

“…Although the GE characterization for main crops has been studied and reported extensively for many years, GE studies in peach started recently (Citadin et al 2014;Maulión et al 2014). Maulión et al (2014) showed that peach yield is a character in which COI predominates over NCOI. These authors, using the Cruz and Castoldi (1991) 5).…”

Section: Resultsmentioning

confidence: 99%

“…Small discrepancies between ̂ and G-BP order indicates that simultaneous selection for high yield and stability could be made considering only the G-BP, without the calculation of ̂ since it will simply delay the selection process. Previously, Maulión et al (2014) showed that fruit yield was strongly correlated with superiority (Pi) (Lin and Binns 1988), reliability indexes (Ii) (Eskridge, 1990), with non-parametric measurements for the genotype selection index (GSI) (Farshadfar 2008) and the rank sum (RS) (Farshadfar et al 2011). However, Bayesian prediction can be used in unbalanced experiments or trials with missing data, but not the Pi, Ii, GSI and RS measurements.…”

Section: Resultsmentioning

confidence: 99%

“…Peach yield is a character whose main characteristic is its COI (Maulión et al 2014;Angelini et al 2019), so that its statistical modeling is a real challenge. Measured and genetic parameters have been studied in reproductive characters (Souza et al 1998a) and associated with peach fruits (Souza et al 1998b) using LMM under the frequentist paradigm.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Angelini¹,

Eb²,

Gs³

et al. 2021

Preprint

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Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-by-environment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the LMM under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters. According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis’s method was the most appropriate to estimate heritability. It was calculated at \(0.80,\) and selecting above 5% of the genotypes, the realized gain of 14.81 kg.tree1 was attained. Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Angelini¹,

Eb²,

Gs³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Angelini

Bortolotto²,

Faviere³

et al. 2022

Euphytica

Self Cite

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Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-byenvironment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the LMM under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters.According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis's method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.81 kg.tree 1 was attained.Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.

show abstract

Determination of stable safflower genotypes in variable environments by parametric and non-parametric methods

Afzal

Hassan

Ahmed

et al. 2021

Journal of Agriculture and Food Research

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Study of statistic stability to select high-yielding and stable peach genotypes

Cited by 8 publications

References 45 publications

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Determination of stable safflower genotypes in variable environments by parametric and non-parametric methods

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