Predicting the seasonal flight activity of Myzus persicae, the main aphid vector of Virus Yellows in sugar beet

• Published in: Pest management science Vol. 79; no. 11; pp. 4508 - 4520
• Main Authors: Luquet, Martin, Sylvain, Poggi, Buchard, Christelle, Plantegenest, Manuel, Tricault, Yann
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.11.2023; Wiley
• Subjects: Aphidoidea; Colonization; Computer Science; Data acquisition; Flight; Flight activity; Growing season; Insects; Life Sciences; Management methods; Mathematical models; Meteorological data; Mitigation; Model updating; Modeling and Simulation; Myzus persicae; Phytopathology and phytopharmacy; Risk assessment; Seasons; Seed treatments; Suction; Sugar; Sugar beets; Vegetal Biology; Viruses; Weather forecasting; Winter; Yellows; Epidemiological surveillance; Sugar beet; Virus Yellows; Regression model; Forecasting; Myzus persicae
• ISSN: 1526-498X; 1526-4998
• DOI: 10.1002/ps.7653

Virus Yellows (VY), a disease caused by several aphid-borne viruses, is a major threat to the global sugar beet production. Following the ban of neonicotinoid-based seed treatments against aphids in Europe, increased efforts are needed to monitor and forecast aphid population spread during the sugar beet growing season. In particular, predicting aphid flight seasonal activity could allow anticipation of the timing and intensity of crop colonisation and contribute to the proper implementation of management methods. Forecasts should be made early enough to assess risk, but can be updated as the season progresses to refine management. Based on a long-term suction-trap dataset gathered between 1978 to 2014, we built and evaluated a set of models to predict the flight activity features of the main VY vector, Myzus persicae, at any location in the French sugar beet production area (circa 4.10 ha). Flight onset dates, length of flight period and cumulative abundance of flying aphids were predicted using climatic and land-use predictors as well as geographical position. Our predictions outperformed current models published in the literature. The importance of the predictor variables varied according to the predicted flight feature but winter and early spring temperature always played a major role. Forecasts based on temperature were made more accurate by adding predictors related to aphid winter reservoirs. In addition, updating the model parameters to take advantage of new weather data acquired during the season improved the flight forecast. Our models can be used as a tool for the mitigation in sugar beet crops. This article is protected by copyright. All rights reserved.