Bayesian inference for the fitting of dry matter accumulation curves in garlic plants

Macedo, Leandro Roberto de; Cecon, Paulo Roberto; Silva, Fabyano Fonseca e; Nascimento, Moysés; Puiatti, Guilherme Alves; Oliveira, Ana Carolina Ribeiro de; Puiatti, Mário

doi:10.1590/s0100-204x2017000800002

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The Bayesian analysis have been used in plant breeding for present itself as a robust statistical procedure that shows information richness and the possibility of several applications (Bastiaansen et al 2012;Almeida et al 2016;Macedo et al 2017).…”

Section: Introductionmentioning

confidence: 99%

The Eberhart and Russel’s Bayesian method used as an instrument to select maize hybrids

et al. 2018

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Adaptability and stability analysis methods that use a priori information allow identifying and selecting potentially productive genotypes with greater accuracy. The aim of the current study is to use the Eberhart and Russel' Bayesian method as an instrument to analyze the adaptability and stability of hybrid maize cultivars and to assess the efficiency of using the distribution of informative and non-informative priors to select cultivars. Twenty-five (25) hybrid maize cultivars were assessed in 11 environments located in the Brazilian Northeastern region, during 2012 and 2013, according to a complete randomized block design, with two repetitions. The Eberhart and Russel's methodology was performed in the GENES software, whereas the Bayesian procedure was implemented in the free software R, by using the MCMCregress function of the MCMCpack package. The adaptability and stability parameters values and the credibility intervals have shown that the Eberhart and Russel's method via Bayesian technique has shown greater stability-estimation accuracy and greater efficiency in recommending cultivars adapted to favorable and unfavorable environments. The Bayesian methods using priories informative (M1) and few informative (M2) distributions have presented the same genotype classifications in the comparison between a priori distributions; however, according to the Bayes Factor, the M1 was the most adequate distribution to help finding more reliable estimates.

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Section: Introductionmentioning

confidence: 99%

The Eberhart and Russel’s Bayesian method used as an instrument to select maize hybrids

et al. 2018

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“…Among the advantages of the Bayesian approach, stands out satisfactory modeling even with a relatively small sample and obtaining credibility intervals (MARTINS FILHO et al, 2008;SILVA et al, 2020;MACEDO et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Although, there are applied researches in the literature presenting the fitting of regression models using Bayesian methods, such researches generally use linear and nonlinear models . They are limited to the assumption of residual independence (ANDRADE FILHO et al;2010;MARTINS FILHO et al;2008;SILVA et al, 2020;MACEDO et al, 2017) or consider autocorrelation and use linear models (CHIB & GREENBERG, 1994;MENZEFRICKE, 1999). Specifically, in the study of coffee tree growth, there is no research in the literature using a Bayesian approach considering both nonlinear models and residual autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

Bayesian modeling of the coffee tree growth curve

et al. 2022

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When modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time.

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Nonlinear quantile regression to describe the dry matter accumulation of garlic plants

et al. 2020

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The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

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Bayesian inference for the fitting of dry matter accumulation curves in garlic plants

Cited by 6 publications

References 14 publications

The Eberhart and Russel’s Bayesian method used as an instrument to select maize hybrids

The Eberhart and Russel’s Bayesian method used as an instrument to select maize hybrids

Bayesian modeling of the coffee tree growth curve

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants

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