Repositorio UFRO · Omeka S

Comparison of linear regression models considering heteroscedasticity of fruits and flower buds of highbush blueberry cultivated in Chile

Primer Autor	Salvo, Sonia
Co-autores	Avila, Julio Munoz, Carlos
Título	Comparison of linear regression models considering heteroscedasticity of fruits and flower buds of highbush blueberry cultivated in Chile
Editorial	ELSEVIER
Revista	SCIENTIA HORTICULTURAE
Lenguaje	en
Resumen	To suitably manage the logistics of blueberry production, predictions of the potential yield to be obtained from harvest must be made months in advance. One way to estimate this yield is by predicting the number of fruits to be harvested, which depends on the number of flower buds available after pruning. The number of flower buds per plant is the most explanatory variable for the number of fruits (Salvo et al., 2012). This relationship is highly linear; nevertheless, as the number of buds increases, the prediction error for the fruits increases as well (Salvo et al., 2011). This effect is known as heteroscedasticity, and it negatively affects predictions using linear methods. To control this effect, three models were compared: constant variance, nonconstant variance and transformed variables with constant variance. The model of nonconstant variance uses a variance function (sigma(2)(y) over cap (2 theta)), and its parameters were estimated using the generalised least squares model. With these considerations, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood statistics were reduced between 1 and 10% compared to the constant-variance model. In contrast, the transformed variables model fit the data well but did not fulfil the theoretical error assumptions. Another relevant aspect is that the nonconstant variance model decreased the residual variance on the order of 90% and fulfilled all theoretical error assumptions. Using this model, blueberry producers can obtain more precise fruit predictions, thereby reducing the uncertainty of planning the harvest logistics months in advance. (C) 2012 Elsevier B.V. All rights reserved.
Tipo de Recurso	Artículo original
Description	This research was supported by the Project INNOVA 07CN13PAT-213 "Formulacion de un Modelo Predictivo de Produccion de Arandanos". This research was supported by the Project INNOVA 07CN13PAT-213 "Formulacion de un Modelo Predictivo de Produccion de Arandanos".
doi	10.1016/j.scienta.2012.12.012
Formato Recurso	pdf
Palabras Claves	Vaccinium Corymbosum Variance model Nonconstant variance AIC BIC Log-likelihood DOUGLAS-FIR GROWTH EQUATIONS VARIANCE BIOMASS DENSITY YIELD RATIO Vaccinium Corymbosum Modelo de varianza Varianza no constante AIC BIC Log-verosimilitud ABETO DE DOUGLAS CRECIMIENTO ECUACIONES VARIANZA BIOMASA DENSIDAD RENDIMIENTO RELACIÓN
Ubicación del archivo	http://dx.doi.org/10.1016/j.scienta.2012.12.012
Materias	Vaccinium Corymbosum Variance model Nonconstant variance AIC BIC Log-likelihood DOUGLAS-FIR GROWTH EQUATIONS VARIANCE BIOMASS DENSITY YIELD RATIO Vaccinium Corymbosum Modelo de varianza Varianza no constante AIC BIC Log-verosimilitud ABETO DE DOUGLAS CRECIMIENTO ECUACIONES VARIANZA BIOMASA DENSIDAD RENDIMIENTO RELACIÓN
Disciplinas de la OCDE	Horticultura Estadísticas y Probabilidades
Título de la cita (Recomendado-único)	Comparison of linear regression models considering heteroscedasticity of fruits and flower buds of highbush blueberry cultivated in Chile
Revista/Libro	SCIENTIA HORTICULTURAE
Idioma	en
Identificador del recurso (Mandatado-único)	Artículo original
Versión del recurso (Recomendado-único)	versión publicada
Entidad financiadora	"Formulacion de un Modelo Predictivo de Produccion de Arandanos" INNOVA 07CN13PAT-213
Referencia del Financiador (Mandatado si es aplicable-repetible)	"Formulacion de un Modelo Predictivo de Produccion de Arandanos" INNOVA 07CN13PAT-213
Editorial	ELSEVIER
ISSN	0304-4238
Página de inicio (Recomendado-único)	57
Página final (Recomendado-único)	62
Categoría WOS	Horticultura Horticulture
Formato	pdf
Id de Web of Science	WOS:000315706600008