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Spatial and temporal patterns of rice planthopper populations in South and Southwest China

Lookup NU author(s): Dr Jin XingORCiD

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2022 Elsevier B.V. Rice planthoppers (RPH) are important pest that cause severe yield losses of rice production in China. South and Southwest China are the main infestation areas on the annual spread of RPH. Identifying the spatial and temporal patterns in migration and subsequent development can provide insight into underlying mechanisms driving the spread of RPH and assist governments with prioritizing areas to achieve proactive management and prevention. Across rice production regions in South and Southwest China, RPH population were recorded by light traps and field surveys at 195 counties from March to October in 2000 to 2019. We first measured the spatial patterns in RPH immigrant populations and field populations with spatial autocorrelation analysis. Then, spatial hotspot analysis was undertaken to highlight dense RPH regions, and significant hot spots were further extracted for comparing the spatial and temporal patterns according to RPH species. The results revealed that RPH population were highly aggregated in space over large geographical distances up to 700 km approximately. Geographic patterns and hot spots of populations varied substantially with time and species, and various spatial patterns might be determined by inherent properties of RPH and rice eco-systematic. Overall, these results provided essential information to improving and optimizing the monitoring network for RPH. The findings from this research may provide helpful information to enhance proactive management against RPH and offer sustainable management practitioners new opportunities to design, develop, and implement optimal pest control strategies to protect rice production in China.


Publication metadata

Author(s): Zhang H, He B, Xing J, Lu M

Publication type: Article

Publication status: Published

Journal: Computers and Electronics in Agriculture

Year: 2022

Volume: 194

Print publication date: 01/03/2022

Online publication date: 11/02/2022

Acceptance date: 24/01/2022

Date deposited: 26/04/2022

ISSN (print): 0168-1699

ISSN (electronic): 1872-7107

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.compag.2022.106750

DOI: 10.1016/j.compag.2022.106750

ePrints DOI: 10.57711/wd2y-zj10


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