Estimativas de repetibilidade de híbridos diploides (AA) de bananeira

Lessa, Lauro Saraiva; Lêdo, Carlos Alberto da Silva; Amorim, Édson Perito; Silva, Sebastião de Oliveira e

doi:10.1590/s0100-204x2014000200005

Cited by 14 publications

(14 citation statements)

References 24 publications

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“…The F-test also revealed a significant effect for measurements, which indicates that there has been a change in the mean of the genotypes over the years. These results are similar to those obtained for other perennial crops (Lopes et al, 2001;Degenhardt et al, 2002;Danner et al, 2010;Bruna et al, 2012;Negreiros et al, 2014;Lessa et al, 2014;Teodoro et al, 2016) and reinforce the need to study the minimum number of measurements to select superior Brazil nut genotypes for a productive increase. The Scott-Knott test clustered the 75 Brazil nut genotypes in three homogeneous groups for the number of fruits and two for the dry mass of seeds ( Table 2).…”

Section: Methodssupporting

confidence: 85%

Research Article Minimum number of measurements for evaluating Bertholletia excelsa.

Baldoni¹,

Tonini²,

Tardin³

et al. 2017

Genet. Mol. Res.

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ABSTRACT. Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of Brazil nut tree (Bertholletia excelsa) genotypes based on fruit yield. For this, we assessed the number of fruits and dry mass of seeds of 75 Brazil nut genotypes, from native forest, located in the municipality of Itaúba, MT, for 5 years. To better estimate r, four procedures were used: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (CPCOR), principal component analysis based on the phenotypic variance and covariance matrix (CPCOV), and structural analysis based on the correlation matrix (mean r -AECOR). There was a significant effect of genotypes and measurements, which reveals the need to study the minimum number of measurements for selecting superior Brazil nut genotypes for a production increase. Estimates of r by ANOVA were lower than those observed with the principal component methodology and close to AECOR. The CPCOV methodology provided the highest estimate of r, which resulted in a lower number of measurements needed to identify superior Brazil nut genotypes for the number of fruits and dry mass of seeds. Based on this methodology, three measurements are necessary to predict the true value of the Brazil nut genotypes with a minimum accuracy of 85%.

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Section: Methodssupporting

confidence: 85%

Research Article Minimum number of measurements for evaluating Bertholletia excelsa.

Baldoni¹,

Tonini²,

Tardin³

et al. 2017

Genet. Mol. Res.

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“…The process usually involves a large number of experiments, with several stages and evaluation of different characteristics, which implies the use of considerable labor and time. As an alternative to overcome such limitations, the estimation of the repeatability coefficient (r) can be used to reduce the number of evaluations (CRUZ; REGAZZI; CARNEIRO, 2012;LESSA et al, 2014;NEGREIROS et al, 2014). Thus, the determination of the minimum number of multiple measures is used to carry out the selection with a certain degree of accuracy and efficiency, with minimum costs and efforts.…”

Section: Unmentioning

confidence: 99%

Analysis of Structures of Covariance and Repeatability in Guava Segreganting Population

et al. 2017

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-The present study was conducted with the objective of analyzing the covariance structure and repeatability estimates of the variables related to guava productivity, such as fruit weight (FW), fruit number (FN) and fruit production (FP) of three harvests, in 95 genotypes of a segregating population. The study also aims to choose the most appropriate covariance structure of the observations within the same individual by means of AIC (Akaike's Information Criterion) and SBC (Schwarz's Bayesian Criterion) criteria. A covariance structure between repeated measures could be incorporated into the statistical model, with the self-regression and compound symmetry forms being the most adequate. The values of repeatability coefficients obtained for FW (0.25), FN (0.14), and FP (0.29) were considered low, indicating that the three harvests were not sufficient to select the best individuals with greater accuracy for the study population. For the variables PF and FP, estimates of accuracy around 0.50 could be obtained from five measurements, while for the variable FN more harvests would be necessary. These values indicate that in guava-segregating populations, evaluations in the first harvests are not enough to select more stable genotypes for the variables considered in this study.Keywords: Covariance structure. Coefficient of repeatability. Psidium guajava. ANÁLISE DE ESTRUTURAS DE COVARIÂNCIAS E REPETIBILIDADE EM POPULAÇÃO SEGREGANTE DE GOIABEIRARESUMO -O presente estudo foi conduzido com o objetivo de analisar estrutura de covariância e estimativas de repetibilidade das variáveis relacionadas à produtividade da goiabeira como massa dos frutos (MF), número de frutos (NF) e produção de frutos (PROD) de três safras, em 95 genótipos de uma população segregante. E por meio dos critérios de AIC (Akaike's Information Criterion) e SBC (Schwarz's Bayesian Criterion) escolher a estrutura de covariância mais adequada das observações dentro de um mesmo indivíduo. Foi possível incorporar ao modelo estatístico, uma estrutura de covariância entre medidas repetidas, sendo as formas autorregressiva e simetria composta as mais adequadas. Os valores dos coeficientes de repetibilidade obtidos para PF (0,25), NF (0,14) e PROD (0,29) foram considerados baixos, indicando que para a população em estudo as três safras realizadas não foram suficientes para selecionar os melhores indivíduos com maior acurácia. Para as variáveis PF e PROD, a partir de cinco medições seria possível a obtenção de estimativas de acurácias em torno de 0,50. Enquanto que para a variável NF, observou-se que mais safras seriam necessárias. Estes valores indicam que em populações segregantes de goiabeira, avaliações nas primeiras safras, não são suficientes para seleção de genótipos mais estáveis para as variáveis consideradas neste estudo.Palavras-chave: Estrutura de covariância. Coeficiente de repetibilidade. Psidium guajava.

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“…The superiority of the repeatabilitycoefficient estimates obtained by the Principal Components method based on the covariance matrix have been reported in many forage species: Penissetum purpureum (CAVALCANTE et al, 2012); Panicum maximum (MARTUSCELLO et al, 2007;LÉDO et al, 2008); Brachiaria brizantha (MARTUSCELLO et al, 2013), as well as in fruits, like banana (LESSA et al, 2014).…”

Section: Resultsmentioning

confidence: 87%

Repeatability estimates and minimum number of evaluations for selection of elephant-grass genotypes for herbage production

et al. 2020

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In forage-plants breeding, the selection of superior genotypes has been undertaken through successive harvests in previously established intervals. However, this process involves many steps, the evaluation of many traits, and a great spending with costs and labor. Thus the estimate of the repeatability is essential in improvement of perennials, it allows predicting genotypic value of the individual, the minimum number of evaluations in the selection of genotypes and minimizes resources and time in the selection of promising individuals. The objective of this study was to estimate the repeatability coefficient for morphological traits in elephant grass and determine the number of evaluations needed for phenotypic selection more efficient. The experimental randomized block design with 53 genotypes and two replications. The repeatability coefficients were estimated for variables plant height, number of tillers, stem diameter and dry matter yield, using the methods of Anova, Principal Components and Structural Analysis. We observed significant differences between genotypes (P <0.01) for all variables. The main components provide larger estimates of repeatability when compared to other methods. Estimates of the repeatability coefficients are of high magnitude average for the variables plant height (0.44) number of tillers (0.44) and stem diameter (0.63) and low magnitude for dry matter production (0.27). The Principal Components method requires five, five, two and eleven measurements for plant height, number of tillers, stem diameter and dry matter yield, respectively, with 80% reliability.

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Estimativas de repetibilidade de híbridos diploides (AA) de bananeira

Cited by 14 publications

References 24 publications

Research Article Minimum number of measurements for evaluating Bertholletia excelsa.

Research Article Minimum number of measurements for evaluating Bertholletia excelsa.

Analysis of Structures of Covariance and Repeatability in Guava Segreganting Population

Repeatability estimates and minimum number of evaluations for selection of elephant-grass genotypes for herbage production

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