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时空风险预测以改善蝗虫管理。

Spatiotemporal risk forecasting to improve locust management.

机构信息

CIRAD, UMR CBGP, Montpellier, France; CBGP, INRAE, IRD, CIRAD, Institut Agro Montpellier, Montpellier University, Montpellier, France.

CIRAD, UMR CBGP, Montpellier, France; CBGP, INRAE, IRD, CIRAD, Institut Agro Montpellier, Montpellier University, Montpellier, France.

出版信息

Curr Opin Insect Sci. 2023 Apr;56:101024. doi: 10.1016/j.cois.2023.101024. Epub 2023 Mar 21.

Abstract

Locusts are among the most feared agricultural pests. Spatiotemporal forecasting is a key process in their management. The present review aims to 1) set a common language on the subject, 2) evaluate the current methodologies, and 3) identify opportunities to improve forecasting tools. Forecasts can be used to provide reliable predictions on locust presence, reproduction events, gregarization areas, population outbreaks, and potential impacts on agriculture. Statistical approaches are used for the first four objectives, whereas mechanistic approaches are used for the latter. We advocate 1) to build reliable and reproducible spatiotemporal forecasting systems for the impacts on agriculture, 2) to turn scientific studies into operational forecasting systems, and 3) to evaluate the performance of these systems.

摘要

蝗虫是最令人恐惧的农业害虫之一。时空预测是其管理的关键过程。本综述旨在:1)就该主题建立共同语言;2)评估当前的方法;3)确定改进预测工具的机会。预测可用于可靠地预测蝗虫的存在、繁殖事件、群居区、种群暴发以及对农业的潜在影响。统计方法用于前四个目标,而机械方法用于后一个目标。我们主张:1)为农业影响建立可靠和可重复的时空预测系统;2)将科学研究转化为业务预测系统;3)评估这些系统的性能。

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