Forecasting ARIMA models for atmospheric vineyard pathogens in Galicia and Northern Portugal: Botrytis cinerea spores

María Fernández-González 1, Francisco Javier Rodríguez-Rajo 2, Victoria Jato 2, María Jesús Aira 3, Helena Ribeiro 4, Manuela Oliveira 5, Iida Abreu 4
1 - Department of Plant Biology and Soil Sciences. Sciences Faculty of Ourense. University of Vigo. Ourense Spain
2 - Department of Plant Biology and Soil Sciences. Sciences Faculty of Ourense. University of Vigo. Ourense Spain
3 - Department of Botany. Pharmacy Faculty. University of Santiago of Compostela. Santiago of Compostela
4 - Centro de Geologia. Universidade do Porto & Departamento de Biologia, Sciences Faculty of Porto
5 - Centro de Geologia. Universidade do Porto & Departamento de Biologia, Sciences Faculty of Porto.
Ann Agric Environ Med
2012; 19 (2):
ICID: 1001805
Article type: Original article
 
 
Botrytis cinerea is the cause of the most common disease in the Galician and Portuguese vineyards. Knowledge of the spore levels in the atmosphere of vineyards is a tool for forecasting models of the concentration of spores in order to adjust the phytosanitary treatments to real risk infection periods. The presented study was conducted in two vineyards, one located in Cenlle (Spain) and other in Amares (Portugal), from 2005-2007. A volumetric trap, model Lanzoni VPPS-2000, was used for the aerobiological study. Phenological observations were conducted on 20 vines of three grape varieties in Cenlle (Treixadura, Godello and Loureira) and in Amares (Trajadura, Loureiro and Pedernã), by using the BBCH scale. The highest total spore concentrations during the grapevine cycle were recorded in 2007 in both locations (Cenlle:16,145 spores; Amares:1,858 spores), and the lowest, in 2005 in Cenlle (1,700 spores) and in Amares (800 spores) in 2006. In Cenlle, the best adjusted model was an ARIMA (0,2,2), including the relative humidity four days earlier, while in Amares there was an ARIMA (1,2,3), considering the relative humidity three days earlier and rainfall two days earlier. The t-test showed no significant difference between observed and predicted data by the model.
PMID 22742797 - click here to show this article in PubMed
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