The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.
Descrição do crescimento de frutos de pequizeiro por modelos não linearesResumo -O pequizeiro é uma espécie nativa do cerrado brasileiro, com ampla distribuição geográfica, cujo fruto é bastante apreciado na culinária, compondo pratos tradicionais. Em geral, o fruto do pequi é consumido quando maduro, na forma in natura ou nos diversos, produtos derivados tais como óleos, licores, doces, sorvetes, entre outros, envolvendo importante atividade socioeconômica geradora de emprego e renda na agricultura familiar. Este trabalho teve por objetivo avaliar o ajuste dos modelos Brody, Gompertz, Logístico e Von Bertalanffy no crescimento e no desenvolvimento de frutos de pequi, com base em suas características físicas, como diâmetro longitudinal e transversal, e massa fresca obtidos ao longo do tempo. Os parâmetros foram estimados por meio de rotinas do software R, utilizando-se do método de mínimos quad rados e o processo iterativo de Gauss-Newton. O ajuste dos modelos foi comparado, utilizando os critérios: desvio-padrão residual, coeficiente de determinação ajustado e critério de informação de Akaike corrigido. Em geral, os dados não apresentaram estrutura de erros correlacionados, e o modelo Von Bertalanffy não se ajustou aos dados de massa fresca. Os modelos apresentaram boa qualidade no ajuste aos dados de crescimento de pequi, exceto o modelo Brody para a variável massa fresca. Os modelos Gompertz e Logístico foram os que melhor descreveram as variáveis, sendo o Gompertz o mais indicado para descrever os dados de diâmetro e de massa. Com base no ajuste, observou-se um crescimento inicial lento até cerca de 20 dias após a antese; após este período, o fruto desenvolveu-se de modo mais acelerado até 90 dias e, então, apresentou tendência à estabilidade até o final da avaliação, aos 117 dias após a antese, com caráter sigmoide da curva. O fruto de pequizeiro obteve valores finais, em média, de 7,1 cm de diâmetro transversal, 6,8 cm de diâmetro longitudinal e 113g de massa fresca. Termos de indexação: Modelos de crescimento, medidas biométricas, curvas sigmoides, ajuste de modelos, pequi.Abstract-Pequi tree is a species native to the Brazilian cerrado, with wide geographic distribution, whose fruit is very appreciated in cooking, composing traditional dishes. In general, pequi fruit is consumed when ripe in the fresh form or in the various derived products such as oils, liqueurs, sweets, ice creams among others, involving important socioeconomic activity generating employment and income in family agriculture. The aim of this study was to evaluate the adjustment of Brody, Gompertz, Logistic and Von Bertalanffy models in the growth and development of pequi fruits based on their physical characteristics such as longitudinal and cross-sectional diameter and fresh mass obtained over time. Parameters were estimated using R software routines, using the least squares method and the Gauss-Newton iterative process. The adjustment of models was compared using the following criteria: residual standard deviation, adjusted determinati...
Agricultural management is a viable way for recycling animal residues in feedlots. Thesubstances that make up organic residues change the dynamics of the organic matter decompositionin the soil. Information on carbon mineralization curves allows seeking improvements in soil qualityand, consequently, in crop productivity. The Stanford & Smith Nonlinear Model is the most usedto describe C mineralization of organic residues in the soil. This model considers organic residuesare composed of substances that are mineralized exponentially. The Cabrera Model considers twofractions, one composed of substances that are mineralized exponentially and other composed ofmore resistant substances with constant mineralization. The objective of this work was to comparenonlinear models that describe carbon mineralization, considering residues on surface or incorporatedinto the soil. The data evaluated were from an experiment with oat straw, liquid swine manure, andswine litter bedding. The Stanford & Smith and Cabrera Models were used considering structure offirst order autoregressive errors - AR(1), when necessary. The fittings were compared using the AkaikeInformation Criterion (AIC). The Cabrera Model was more adequate to describe C mineralization infour treatments (soil + incorporated liquid swine manure; soil + oat straw on surface + liquid swinemanure on surface; soil + incorporated straw; and soil + straw on surface). The Stanford & SmithModel was better in three treatments (soil + incorporated straw + incorporated liquid swine manure;swine litter bedding on surface; and incorporated swine litter bedding). None of the models describedthe treatment soil + liquid swine manure on surface.Keywords: Decomposition. Half-life. Stanford & Smith Model. Cabrera Model.
The aim of this study was to describe the growth curve of “Aurora 1” peaches using fruit height and diameter data over time through diphasic sigmoidal models constructed from eight combinations of the following models: Brody, Gompertz and Logistic. Data were obtained from an experiment carried out in 2005 in the municipality of Vista Alegre do Alto, São Paulo, Brazil. The parameters of models were adjusted by the least squares method using the Gauss-Newton algorithm implemented in the R software. Assumptions of normality, homogeneity and independence of residues were verified based on Shapiro-Wilk, Breush and Pagan and Durbin-Watson tests, respectively. The goodness of fit of models was verified according to the corrected Akaike information criterion (AICc), residual standard deviation (RSD), asymptote adjustment index (AI) and nonlinearity measures. All models adjusted for both fruit height and diameter variables met the assumptions of normality, independence and homoscedasticity of errors. In addition, all of them present good quality of fit to fruit height and diameter data, since they presented AI values close to one and low RSD values and non-linearity measures. However, the double Gompertz (GG) and the Logistic + Gompertz (LG) models presented, respectively, the best quality of fit to fruit height and diameter data in relation to the other models. It could be concluded that all diphasic sigmoidal models evaluated showed good fit to height and diameter data and can be used to describe the growth curve of “Aurora-1” peaches, according to goodness of fit criteria. However, it is important to highlight that GG and LG models presented the best quality of fit and can be selected to describe the height and diameter growth of “Aurora 1” peach fruits, respectively, with maximum expected growth close to 63 mm in height and 48 mm in diameter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.