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Temperature-driven cotton verticillium wilt: a beta model for risk assessment from laboratory insights to climate scenarios

文献类型： 外文期刊

作者： Zhang, Tianyi 1 ; Zheng, Bangyou 3 ; Xie, Zongming 4 ; Zhang, Tao 6 ; Feng, Hongjie 7 ; Zhou, Jinglong 7 ; Ouyang, Fang 8 ;

作者机构： 1.Chinese Acad Sci, Inst Atmospher Phys, Lab Earth Syst Numer Modeling & Applicat, Beijing, Peoples R China

2.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing, Peoples R China

3.CSIRO Plant Ind, Queensland Biosci Precinct, Brisbane, Qld, Australia

4.Xinjiang Acad Agr & Reclamat Sci, Inst Cotton Res, Shihezi, Peoples R China

5.Minist Agr & Rural Affairs, Key Lab Cotton Biol & Genet Breeding Northwest Inl, Shihezi, Peoples R China

6.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Xinjiang, Peoples R China

7.Chinese Acad Agr Sci, Inst Cotton Res, Natl Key Lab Cotton Biobreeding & Integrated Utili, Anyang, Peoples R China

8.Hebei Univ, Coll Life Sci, Baoding 071002, Hebei, Peoples R China

9.Chinese Acad Sci, Inst Zool, State Key Lab Integrated Management Pest Rodents, Beijing, Peoples R China

关键词： verticillium wilt; laboratory; Beta; model validation; climate change; China

期刊名称：PEST MANAGEMENT SCIENCE （ 影响因子：3.8； 五年影响因子：4.3 ）

ISSN： 1526-498X

年卷期： 2025 年 81 卷 3 期

页码：

收录情况： SCI

摘要： BACKGROUNDVerticillium wilt is a critical disease affecting cotton in the Xinjiang province, a region producing 90% cotton in China. Defining the specific temperature thresholds for disease prevalence is essential but has remained unclear.RESULTSThis study aimed to establish a model to quantify the relationship between temperature and cotton verticillium wilt disease risk. Through a controlled temperature experiment, we identified a nonlinear temperature relationship, with an optimal temperature of 26.5 degrees C. Then a beta model, parameterized from these findings, was validated against historical regional disease data, confirming its ability to accurately reflect interannual variations in disease occurrence and its direct applicability from laboratory to regional scales. We then utilized the model to project future disease risks under two Shared Socioeconomic Pathways (SSP) climate scenarios. The projections estimate a 4.8% to 10.1% increase in disease risks in Xinjiang by the 2080s under SSP1-2.6 and SSP5-8.5 scenarios, respectively.CONCLUSIONThis research offers a valuable predictive tool for cotton verticillium wilt risks, informing strategic decisions for cotton production in the face of climate change. The successful application of a laboratory-derived model at a regional scale marks a significant advancement in plant disease risk assessment, underscoring temperature as a dominate factor in cotton disease dynamics. (c) 2024 Society of Chemical Industry.

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