Repeatability coefficients and number of measurements for evaluating traits in strawberry

Diel, Maria Inês; Lúcio, Alessandro Dal’Col; Olivoto, Tiago; Pinheiro, Marcos Vinícius Marques; Krysczun, Dionatan Ketzer; Sari, Bruno Giacomini; Schmidt, Denise

doi:10.4025/actasciagron.v42i1.43357

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…In this context, the coefficient of repeatability has been extensively adopted in genetic breeding programs of several fruit species (Alcoforado, Pedrozo, Mayer, & Lima-Primo, 2019;Andrade Júnior et al, 2020;Catarina et al, 2020;Diel et al, 2020;Jesus, Lima, Souza, & Girdardi, 2021;Malikouski et al, 2021). The coefficient of repeatability indicates whether multiple measurements of a quality trait in a genotype are the same over time or space, based on the contribution of the genotype to the total variance (Cruz, Regazzi, & Carneiro, 2012), which allows the estimation of an optimal number of fruits to ensure high accuracy in quality trait analysis.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

Vilvert,

Freitas,

Ferreira

et al. 2023

Acta Sci. Agron.

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Although acerola (Malpighia emarginata DC.) is a tropical fruit of high interest due to its high ascorbic acid content and attractive sensory attributes, fruit production is characterized by high genetic variability. Additionally, the use of new biometric tools for acerola breeding is scarce. This study aimed to estimate genetic parameters and the coefficient of repeatability, as well as determine the optimal number of fruits for quality trait analyses in different acerola genotypes, using different approaches. Twenty-three (Experiment I) and thirty-five (Experiment II) genotypes were evaluated in a randomized block design with four replicates and three plants per plot. Twenty fruits per plant were harvested and evaluated for the following quality traits: diameter, mass, skin color (lightness, chroma and hue), firmness, soluble solids (SS), titratable acidity (TA), SS/TA ratio, and ascorbic acid content. The genetic parameters and the coefficient of repeatability were estimated for each experiment using classical and Bayesian methods. Both approaches achieved similar results on estimating variance components, genetic parameters and the coefficient of repeatability. Genetic parameters showed favorable conditions for acerola selection. The coefficient of repeatability was high for all acerola quality traits. A total of 17 fruits are required for the effective selection of acerola genotypes with an accuracy of 95%.

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Section: Introductionmentioning

confidence: 99%

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

Vilvert,

Freitas,

Ferreira

et al. 2023

Acta Sci. Agron.

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“…The repeatability coeffi cient can be estimated through different methods, such as, for instance, the method of analysis of variance, the method of principal components, and the method of structural analysis (GUILHEN et al, 2019;RODRIGUES et al, 2020). These methods have been applied in the most diverse cultures (DIEL et al, 2020;FERREIRA et al, 2020;PEDROZO et al, 2011). In soybean, these methods were used in the evaluation of morphological descriptors (MATSUO et al, 2012), evaluation of the content of oil and protein (JIANG, 2020), associations among methods of analysis of adaptability, stability and productivity (WOYANN et al, 2018), and others.…”

Section: Introductionmentioning

confidence: 99%

Repeatability and the optimal number of measurements for screening of soybean cultivars under water deficit

Duarte¹,

Ferreira²,

Silva³

2022

Revista Ciência Agronômica

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This study aimed at determining the optimal number of replications and measurements necessary for the screening of soybean cultivars subjected to three conditions of water availability during the initial stage of development. Two experiments were carried out in a greenhouse. A randomized block design was used, in a factorial scheme with two factors (cultivar and water availability), six replications, ten cultivars and three conditions of water availability (control condition, in which the sand was moistened with water; stress control in the water potential of -0.2 MPa and stress condition in the water potential of -0.4 MPa). At 20 days after cultivation, measurements were taken, subsequently estimating the repeatability coeffi cient, of the number of necessary measurements and of the determination coeffi cient. The repeatability coeffi cient was estimated by distinct statistical procedures, such as method of analysis of variance, principal components with basis on the matrices of phenotypic variances and covariances; principal components with basis on the matrices of correlation and structural analysis. For the determination of the number of replications, the repeatability coeffi cients ranged from low to high in experiment I, except for the root variables, which presented high values in both experiments. In experiment II, in turn, the magnitudes of the determination coeffi cients were above 90%. For plant height, number of leaves, root length and dry matter of the aerial part, only two replications would be necessary to obtain 80% confi dence. Regarding the number of measurements per parcel, only one plant could be evaluated in each parcel, with 85% confi dence for both experiments. Only two replications and one measurement per parcel are enough for high estimates of repeatability. This way, adequate effi ciency is obtained in the selection of superior materials for tolerance to water defi cit, during the initial stages of soybean development.

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MGIDI: toward an effective multivariate selection in biological experiments

Olivoto¹,

2020

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Motivation Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains. Results We propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes/treatment with an ideotype defined a priori as a multi-trait genotype-ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists’ points of view. Our experimental results indicate that MGIDI can effectively select superior treatments/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions towards an effective multivariate selection in biological experiments. Availability and implementation The source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi(). Supplementary information Supplementary data are available at Bioinformatics online.

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Repeatability coefficients and number of measurements for evaluating traits in strawberry

Cited by 5 publications

References 29 publications

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

Repeatability and the optimal number of measurements for screening of soybean cultivars under water deficit

MGIDI: toward an effective multivariate selection in biological experiments

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