Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols

Introduction and objectives Powdery mildew caused by Uncinula necator and Downy mildew produced by Plasmopara viticola are the most common diseases in the North-West Spain vineyards. Knowledge of airborne spore concentrations could be a useful tool in the Integrated Pest Management protocols in...

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Main Authors: María Fernández-González, David Ramos-Valcárcel, María Jesús Aira, Francisco Javier Rodríguez-Rajo
Format: Article
Language:English
Published: Institute of Rural Health 2015-12-01
Series:Annals of Agricultural and Environmental Medicine
Subjects:
Online Access:http://www.journalssystem.com/aaem/Prediction-of-biological-sensors-appearance-with-ARIMA-models-as-a-tool-for-Integrated-Pest-Management-protocols,72389,0,2.html
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spelling doaj-71eb992e072d44f08678fd39c88a42392020-11-24T23:41:37ZengInstitute of Rural HealthAnnals of Agricultural and Environmental Medicine1232-19661898-22632015-12-0123112913710.5604/12321966.119686872389Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocolsMaría Fernández-González0David Ramos-Valcárcel1María Jesús Aira2Francisco Javier Rodríguez-Rajo3Department of Plant Biology and Soil Sciences, Sciences Faculty of Ourense, University of Vigo, Ourense, SpainDepartment of Informatics, University of Vigo, Ourense, SpainDepartment of Botany, Pharmacy Faculty, University of Santiago of Compostela, Santiago of Compostela, SpainDepartment of Plant Biology and Soil Sciences, Sciences Faculty of Ourense, University of Vigo, Ourense, SpainIntroduction and objectives Powdery mildew caused by Uncinula necator and Downy mildew produced by Plasmopara viticola are the most common diseases in the North-West Spain vineyards. Knowledge of airborne spore concentrations could be a useful tool in the Integrated Pest Management protocols in order to reduce the number of pesticide treatments, applied only when there is a real risk of infection. Material and Methods The study was carried out in a vineyard of the D. O. Ribeiro, in the North-West Spain, during the grapevine active period 2004–2012. A Hirts-type volumetric spore-trap was used for the aerobiological monitoring. Results During the study period the annual total U. necator spores amount ranged from the 578 spores registered in 2007 to the 4,145 spores sampled during 2008. The highest annual total P. viticola spores quantity was observed in 2010 (1,548 spores) and the lowest in 2005 (210 spores). In order to forecast the concentration of fungal spores, ARIMA models were elaborated. Conclusions The most accurate models were an ARIMA (3.1.3) for U. necator and (1.0.3) for P. viticola. The possibility to forecast the spore presence 72 hours in advance open an important horizon for optimizing the organization of the harvest processes in the vineyard.http://www.journalssystem.com/aaem/Prediction-of-biological-sensors-appearance-with-ARIMA-models-as-a-tool-for-Integrated-Pest-Management-protocols,72389,0,2.htmlagronomyARIMAintegrated pest managementphytopatologyPlasmopara viticolaUncinula necator
collection DOAJ
language English
format Article
sources DOAJ
author María Fernández-González
David Ramos-Valcárcel
María Jesús Aira
Francisco Javier Rodríguez-Rajo
spellingShingle María Fernández-González
David Ramos-Valcárcel
María Jesús Aira
Francisco Javier Rodríguez-Rajo
Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols
Annals of Agricultural and Environmental Medicine
agronomy
ARIMA
integrated pest management
phytopatology
Plasmopara viticola
Uncinula necator
author_facet María Fernández-González
David Ramos-Valcárcel
María Jesús Aira
Francisco Javier Rodríguez-Rajo
author_sort María Fernández-González
title Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols
title_short Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols
title_full Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols
title_fullStr Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols
title_full_unstemmed Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols
title_sort prediction of biological sensors appearance with arima models as a tool for integrated pest management protocols
publisher Institute of Rural Health
series Annals of Agricultural and Environmental Medicine
issn 1232-1966
1898-2263
publishDate 2015-12-01
description Introduction and objectives Powdery mildew caused by Uncinula necator and Downy mildew produced by Plasmopara viticola are the most common diseases in the North-West Spain vineyards. Knowledge of airborne spore concentrations could be a useful tool in the Integrated Pest Management protocols in order to reduce the number of pesticide treatments, applied only when there is a real risk of infection. Material and Methods The study was carried out in a vineyard of the D. O. Ribeiro, in the North-West Spain, during the grapevine active period 2004–2012. A Hirts-type volumetric spore-trap was used for the aerobiological monitoring. Results During the study period the annual total U. necator spores amount ranged from the 578 spores registered in 2007 to the 4,145 spores sampled during 2008. The highest annual total P. viticola spores quantity was observed in 2010 (1,548 spores) and the lowest in 2005 (210 spores). In order to forecast the concentration of fungal spores, ARIMA models were elaborated. Conclusions The most accurate models were an ARIMA (3.1.3) for U. necator and (1.0.3) for P. viticola. The possibility to forecast the spore presence 72 hours in advance open an important horizon for optimizing the organization of the harvest processes in the vineyard.
topic agronomy
ARIMA
integrated pest management
phytopatology
Plasmopara viticola
Uncinula necator
url http://www.journalssystem.com/aaem/Prediction-of-biological-sensors-appearance-with-ARIMA-models-as-a-tool-for-Integrated-Pest-Management-protocols,72389,0,2.html
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