Description of the peach fruit growth curve by diphasic sigmoidal nonlinear models

Fernandes, Jaqueline Gonçalves; Silva, Édipo Menezes da; Ribeiro, Thaís Destéfani; Silva, Edilson Marcelino; Fernandes, Tales Jesus; Muniz, Joel Augusto

doi:10.1590/0100-29452022875

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…This rapid expansion in the first stage of development of the coffee fruit can be explained by the phenological scale proposed by Nunes et al (2010), in which the authors point out the rapid fruit growth in this first stage. This process is different in peach fruits, for example, which have increased growth in the second stage (Fernandes et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Bayesian estimation of nonlinear models parameters in the description of growth coffee fruits

et al. 2022

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Coffee is one of the main products of Brazilian agriculture and the country is currently the largest producer and exporter in the world. The coffee fruit has a double sigmoidal growth pattern, however, as well as in other fruits that also show such a growth pattern, the authors generally do not estimate parameters of regression models to describe such curve. In the study of fruit growth curves, the sample size is generally small, so the estimation of the parameters should preferably be done by the Bayesian methodology, since a priori information is incorporated, reducing the effects of having few observations. The Markov Chain Monte Carlo algorithms are the most used computational tool in Bayesian statistics. However, these generate dependent samples, can be complicated to implement and, mainly, to teach. There are also other alternatives to the MCMC algorithms to obtain approximations of integrals of interest in Bayesian inference, the main ones are based on the importance resampling techniques. The objective of this work is to use Bayesian inference with the weighted importance resampling technique in the estimation of parameters of double sigmoidal nonlinear regression models to the description of coffee fruit growth. The double nonlinear logistic model was used in the description of the accumulation of fresh weight in coffee fruits. All prioris used have Beta distribution and were obtained by the called prior of specialist technique. Bayesian methodology was efficient, since it provided parameters with practical interpretation to coffee fruit growth, consistent with the reality. Thus, Bayesian inference by weighted importance resampling was a good alternative for the parameters estimation of nonlinear double sigmoid regression models. The logistic model showed that the growth of coffee fruits is more intense in the first sigmoid (until 162 DAF)of the growth curve and stabilizes in its final weight after 262 daf.

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Section: Resultsmentioning

confidence: 99%

Bayesian estimation of nonlinear models parameters in the description of growth coffee fruits

et al. 2022

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“…In São Paulo state, the second Brazilian peach (Prunus persica) and nectarine (P. persica var. nuscipersica) producer (Fernandes et al, 2022), the training system most adopted by the growers is the Y-Shaped (which has similar plant architecture, e.g., to Ypsilon and Perpendicular-V), with two scaffold branches (Sobierajski et al complete block with five replicates, consisting of six fruits each. The traits considered in this study were yield (Y ha -t ha -1 and Y tree -kg tree -1 ), fruit weight (FW-g), fruit pulp (PY-g), flesh firmness (FF-kg cm -2 ), soluble solids content (SSC-°Brix), titratable acidity (TA-g malic acid 100 ml -1 ) and ratio (SS/TA).…”

Section: Introductionmentioning

confidence: 99%

The Fruiting Wall: An Alternative Training System for Peach Orchards in Southeast Brazil

Sobierajski¹,

Blain²

2022

JAS

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The choice of the training system is a key step to the establishment of new orchards since it affects yield and fruit quality. In this context, the aim of this study was to evaluate the performance of the scion cv. Tropic Beauty in the Fruiting Wall training system compared with the Y-Shaped. The two training systems showed no statistical differences among the years regarding the length of phenological cycles (approximately 140 days). The Fruiting Wall showed higher values for yield per tree (from 80.2 to 112.9%), fruit weight (from 7.6 to 10.3%) and fruit pulp from 9.4 to 12.6%) than Y-Shaped. The mean values for flesh firmness and fruit chemical characteristics ranged over the years for both training systems. Despite the lack of significant differences for fruit chemical characteristics, the observed values were compatible with those expected for the cultivar. Data collected from the Fruiting Wall showed lower variance than those collected from Y-shape. This suggests that the Fruiting Wall leads to a higher uniformity of production and fruit quality than the Y-Shaped. Based on these results, we concluded that the Fruiting Wall improves the peach cv. Tropic Beauty production, particularly for yield by tree and fruit mass.

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Adjustment of mixed nonlinear models on Blackberry fruit growth

Silva,

Tadeu,

Silva

et al. 2023

Rev. Bras. Frutic.

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Blackberry fruits belong to the genus Rubus, are fruits more cultivated in temperate climate in the summer, with low luminosity and low temperature in the winter. These fruits have as characteristic the quickperishingafter harvest and regression models, more specifically, nonlinear models, single or double sigmoid growth curve, are more recommended to model the growth of living beings. Several authors have used these models, considering only the average data of individuals under study; however, they do not consider the variability between them. One way to better capture the variability between individuals is by using mixed-effects nonlinear models that, by definition, combine the fixed and random part in the same model. Data used in this work were diameter and length of ‘Choctaw’ blackberry fruits, . The random effects of models were tested on parameters, with some steps, in order to reach the most appropriate model. For fixed-effects models, the least squares method was used, and for mixed models, the restricted likelihood was used. To reach the model that best fits data, the fit quality criteria (R2, AIC c and TRV) were used. For fruit diameter, the simple sigmoid nonlinear model was the logistic with random effect in ß1 and ß2, and for fruit length, the model was the Logistic + Logistic, with random effect in ß1 and ß4.

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Description of the peach fruit growth curve by diphasic sigmoidal nonlinear models

Cited by 7 publications

References 16 publications

Bayesian estimation of nonlinear models parameters in the description of growth coffee fruits

Bayesian estimation of nonlinear models parameters in the description of growth coffee fruits

The Fruiting Wall: An Alternative Training System for Peach Orchards in Southeast Brazil

Adjustment of mixed nonlinear models on Blackberry fruit growth

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