Abordagem Bayesiana das curvas de crescimento de duas cultivares de feijoeiro

Filho, Sebastião Martins; Silva, Fabyano Fonseca e; Carneiro, Antônio Policarpo Souza; Muniz, Joel Augusto

doi:10.1590/s0103-84782008000600004

Cited by 20 publications

(20 citation statements)

References 2 publications

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“…A similar result was reported by Puiatti et al (2013), who identified and grouped the nonlinear regression models that best fitted the description of the total dry matter accumulation of garlic plant over time, where L showed better performance than that of the B, G, L, M, M1, M2, vB, and Meloun III models. The L model fitted the data well in several experiments with nonlinear regression models, for the description of growth curves or nutrient accumulation, as in Pôrto et al (2007) for onion cultivation, Maia et al (2009) for banana trees, and Martins Filho et al (2008) who also reported great adjustments for the L model using the Bayesian methodology for the growth data of two bean cultivars.…”

Section: Resultsmentioning

confidence: 88%

“…Nonlinear regression models have been shown as adequate to describe these curves, both by the frequentist and by the Bayesian approaches, which have parameters with biological interpretation, such as asymptotic weight and growth velocity (Martins Filho et al, 2008). Puiatti et al (2013) and Reis et al (2014) indicated high-fitting quality for the logistic, Gompertz, and Von Bertalanffy models, in garlic accessions, using the frequentist approach.…”

Section: Introductionmentioning

confidence: 99%

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

Macedo

Cecon

Silva³

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to identify nonlinear regression models that best describe dry matter accumulation curves over time, in garlic (Allium sativum) accessions, using Bayesian and frequentist approaches. Multivariate cluster analyses were made to group similar accessions according to the estimates of the parameters with biological interpretation (β 1 and β 3 ). In order to verify if the obtained groups were equal, statistical tests were applied to assess the parameter equality of the representative curves of each group. Thirty garlic accessions were used, which are kept by the vegetable germplasm bank of Universidade Federal de Viçosa, Brazil. The logistic model was the one that fit best to data in both approaches. Parameter estimates of this model were subjected to the cluster analysis using Ward's algorithm, and the generalized Mahalanobis distance was used as a measure of dissimilarity. The optimal number of groups, according to the Mojena method, was three and four, for the frequentist and Bayesian approaches, respectively. Hypothesis tests for the parameter equality from estimated curves, for each identified group, indicated that both approaches highlight the differences between the accessions identified in the cluster analysis. Therefore, both approaches are recommended for this kind of study.

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Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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

Macedo

Cecon

Silva³

et al. 2017

Pesq. agropec. bras.

Self Cite

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“…The modeling works on vegetables as well as on beans aim to evaluate all the cycle of a specific species or to model the growth of different cultivars or according to the application of different cultural managements (Urchei et al, 2000;Martins Filho et al, 2008;Vieira et al, 2008;Vieira Neto et al, 2013).…”

Section: Palavras-chavementioning

confidence: 99%

Nonlinear regression and plot size to estimate green beans production

2016

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The objectives of this work were to adjust nonlinear regression models for the green beans production and to identify the plot size which provides the best explanation and adjustment to the models. The authors used two field and two protected environment (plastic tunnel) trials in the autumn-winter and spring-summer seasons. The logistic and von Bertalanffy models were adjusted for average weight of green beans accumulated after multiple harvests and with different plot sizes. The models presented similar estimates and the same parameters estimates in all the plot sizes. The logistic model provided estimates closest to the reality, showing the best description performance for the average weight of pods during the productive cycle. In the autumn-winter season, plot size of 14 basic units (28 plants in the cultivation line direction) in the field and of two basic units (four plants in the cultivation line direction) under the plastic tunnel provide a good quality in the models adjustment. In the springsummer season, the plot sizes are made of six basic units (12 plants in the cultivation line) in the field and seven basic units (14 plants in the cultivation line) under the plastic tunnel.

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“…O ajuste de modelos não-lineares, do ponto de vista metodológico, é bastante explorado por diversos autores nas mais diversas áreas, por meio das abordagens clássicas (SAVIAN et al, 2007a(SAVIAN et al, , 2007bMENDES, et al, 2008;MAIA et al, 2009;) e bayesiana (MUNIZ et al, 2007;MARTINS FILHO et al, 2008;SILVA et al, 2008;SAVIAN et al, 2009). Na análise de regressão usual, geralmente, não se usa uma informação a priori sobre uma possível relação de ordem na variável resposta.…”

Section: Dentro Do Contexto Nutricional a Suplementação De Micromineunclassified