Journal of Pest Science (2019) 92, 417-428

Gao Hu, Ming-Hong Lu, Don R. Reynolds, Hai-Kou Wang, Xiao Chen, Wan-Cai Liu, Feng Zhu, Xiang-Wen Wu, Feng Xia, Miao-Chang Xie, Xia-Nian Cheng, Ka-Sing Lim, Bao-Ping Zhai and Jason W. Chapman (2019)
Long-term seasonal forecasting of a major migrant insect pest: the brown planthopper in the Lower Yangtze River Valley
Journal of Pest Science 92 (2), 417-428
Abstract: Rice planthoppers and associated virus diseases have become the most important pests threatening food security in China and other Asian countries, incurring costs of hundreds of millions of US dollars annually in rice losses, and in expensive, environmentally harmful, and often futile control efforts. The most economically damaging species, the brown planthopper, Nilaparvata lugens (Hemiptera: Delphacidae), cannot overwinter in temperate East Asia, and infestations there are initiated by several waves of windborne spring or summer migrants originating from tropical areas in Indochina. The interaction of these waves of migrants and synoptic weather patterns, driven by the semi-permanent western Pacific subtropical high-pressure (WPSH) system, is of critical importance in forecasting the timing and intensity of immigration events and determining the seriousness of subsequent planthopper build-up in the rice crop. We analysed a 26-year data set from a standardised light trap network in Southern China, showing that planthopper aerial transport and concentration processes are associated with the characteristics (strength and position) of the WPSH in the year concerned. Then, using N. lugens abundance in source areas and indices of WPSH intensity or related sea surface temperature anomalies, we developed a model to predict planthopper numbers immigrating into the key rice-growing area of the Lower Yangtze Valley. We also demonstrate that these WPSH-related climatic indices combined with early-season planthopper catches can be used to forecast, several months in advance, the severity of that season's N. lugens infestations (the correlation between model predictions and outcomes was 0.59), thus allowing time for effective control measures to be implemented.
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Database assignments for author(s): Jason W. Chapman, Bao Ping Zhai

Research topic(s) for pests/diseases/weeds:
population dynamics/ epidemiology
thresholds/decision-support systems

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Pest and/or beneficial records:

Beneficial	Pest/Disease/Weed	Crop/Product	Country	Quarant.
	Nilaparvata lugens	Rice (Oryza)	China (south)
	Nilaparvata lugens	Rice (Oryza)	China (NE)