Método de comparação de modelos de regressão não-lineares em bananeiras

Maia, Emanuel; Siqueira, Dalmo Lopes de; Silva, Fabyano Fonseca e; Peternelli, Luiz Alexandre; Salomão, Luiz Carlos Chamhum

doi:10.1590/s0103-84782009000500012

Cited by 22 publications

(22 citation statements)

References 11 publications

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“…measures the adherence of the estimated data to the obtained data (Maia et al, 2009). The calculations were performed with the help of Microsoft Office Excel® application and software statistic R (R Development Core Team, 2017).…”

Section: Methodsmentioning

confidence: 99%

Gompertz and Logistic Models to the Productive Traits of Sunn Hemp

de¹,

Filho²,

Chaves³

et al. 2017

JAS

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Studies on growth models for productive character of sunn hemp are important to know the behavior of the culture. Therefore, the objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of productive traits of sunn hemp in two sowing periods. Two uniformity trials were performed. The evaluations began on October the 29 th 2014 and December the 16 th 2014, totaling 94 and 76 evaluation days for periods 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for first period from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: fresh matter leaf, stem, root, shoot, and total, and dry matter leaf, stem, root, shoot, and total. For both models the confidence interval was calculated of parameters a, b and c. The adjustment quality of the Gompertz and Logistic models was verified by the determination coefficient, the Akaike information criteria, residual standard deviation, mean absolute deviation, mean absolute percentage error and mean prediction error. The Gompertz model when compared between the sowing periods through the confidence interval of the parameters, for the productive traits, differs. The same result was found for the Logistic model. The growth models of Gompertz and Logistic presented good adjustment quality.

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Section: Methodsmentioning

confidence: 99%

Gompertz and Logistic Models to the Productive Traits of Sunn Hemp

de¹,

Filho²,

Chaves³

et al. 2017

JAS

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show abstract

“…Therefore, nonlinear growth models such as the Spillman, Mitscherlich, logistic (Verhulst), Gompertz, and Richards, among others (REGAZZI, 2003), are advantageous when compared to linear models because they more accurately predict plant growth. Among these, the logistic growth model has been considered the most suitable in recent studies on plant growth (MAIA et al, 2009;PUIATTI et al, 2013;PRADO et al, 2013). Growth predictions for China pinks in different substrates prior to flowering are important for planning commercial production because they will allow growers to estimate distribution dates and calculate when to start production.…”

Section: Introductionmentioning

confidence: 99%

Logistic growth models of China pinks, cultivated on seven substrates, as a function of degree days

et al. 2016

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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: 86%

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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Método de comparação de modelos de regressão não-lineares em bananeiras

Abstract: Método de comparação de modelos de regressão não-lineares em bananeiras Method of comparison of models non-linear regression in bananas trees

Cited by 22 publications

References 11 publications

Gompertz and Logistic Models to the Productive Traits of Sunn Hemp

Gompertz and Logistic Models to the Productive Traits of Sunn Hemp

Logistic growth models of China pinks, cultivated on seven substrates, as a function of degree days

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

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