Item


Modelling of weather parameters to predict russet on ‘Golden Delicious’ apple

Russet on ‘Golden Delicious’ apple (Malus × domestica Borkh.) fruit is a physiological disorder that causes major economic losses to growers. Large variations occur in the severity of russet from one year to another. In Girona (Spain), good correlations were found between the annual severity of russet at harvest and several weather parameters measured shortly after full bloom. A specific statistical methodology for the analysis of compositional data (CoDa) was used to establish these correlations. The most important factor was the percentage of time at relative humidity values > 55% from 30 – 34 d after full bloom (DAFB), which yielded a high correlation (R = 0.80). The percentage of rainy days from 0 – 34 DAFB was also positively correlated with the severity of russet (R = 0.80). Ordinal logit regression models that included these two climatic variables strongly predicted a low, moderate, or high annual severity of russet. Understanding the effects of weather on russet, and developing predictive models may help to manage the marketing of this apple variety which is prone to russet in some areas of cultivation

This research was supported by the Spanish Ministry of Science and Innovation under Projects MTM2009 13272 and MTM2012-33236, and by the Agència de Gestió d’Ajuts Universitaris i de Recerca of the Generalitat de Catalunya (Ref. 2009SGR424)

Taylor & Francis

Manager: Ministerio de Ciencia e Innovación (Espanya)
Ministerio de Economía y Competitividad (Espanya)
Author: Barceló i Vidal, Carles
Bonany, J.
Martín Fernández, Josep Antoni
Carbó, J.
Abstract: Russet on ‘Golden Delicious’ apple (Malus × domestica Borkh.) fruit is a physiological disorder that causes major economic losses to growers. Large variations occur in the severity of russet from one year to another. In Girona (Spain), good correlations were found between the annual severity of russet at harvest and several weather parameters measured shortly after full bloom. A specific statistical methodology for the analysis of compositional data (CoDa) was used to establish these correlations. The most important factor was the percentage of time at relative humidity values > 55% from 30 – 34 d after full bloom (DAFB), which yielded a high correlation (R = 0.80). The percentage of rainy days from 0 – 34 DAFB was also positively correlated with the severity of russet (R = 0.80). Ordinal logit regression models that included these two climatic variables strongly predicted a low, moderate, or high annual severity of russet. Understanding the effects of weather on russet, and developing predictive models may help to manage the marketing of this apple variety which is prone to russet in some areas of cultivation
This research was supported by the Spanish Ministry of Science and Innovation under Projects MTM2009 13272 and MTM2012-33236, and by the Agència de Gestió d’Ajuts Universitaris i de Recerca of the Generalitat de Catalunya (Ref. 2009SGR424)
Format: application/pdf
Document access: http://hdl.handle.net/10256/13741
Language: eng
Publisher: Taylor & Francis
Collection: info:eu-repo/semantics/altIdentifier/doi/10.1080/14620316.2013.11513016
info:eu-repo/semantics/altIdentifier/issn/1462-0316
info:eu-repo/semantics/altIdentifier/eissn/2380-4084
info:eu-repo/grantAgreement/MICINN//MTM2009-13272/ES/Analisis Estadistico De Datos Composicionales Y Otros Datos Con Espacio Muestral Restringido/
info:eu-repo/grantAgreement/MINECO//MTM2012-33236/ES/METODOS ESTADISTICOS EN ESPACIOS RESTRINGIDOS/
Rights: Tots els drets reservats
Subject: Anàlisi multivariable
Multivariate analysis
Correlació (Estadística)
Correlation (Statistics)
Pomes -- Malalties i plagues -- Mètodes estadístics
Apples -- Diseases and pests -- Statistical methods
Title: Modelling of weather parameters to predict russet on ‘Golden Delicious’ apple
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

Subjects

Authors