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Modeling the Effects of the Environment and the Host Plant on the Ripe Rot of Grapes, Caused by the Colletotrichum Species

文献类型： 外文期刊

作者： Ji, Tao 1 ; Salotti, Irene 1 ; Dong, Chaoyang 2 ; Li, Ming 3 ; Rossi, Vittorio 1 ;

作者机构： 1.Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod DIPROVES, Via E Parmense 84, I-29122 Piacenza, Italy

2.Tianjin Climate Ctr, Tianjin 300074, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr NERCITA, Beijing 100097, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： life cycle; epidemiology; disease modeling; model validation

期刊名称：PLANTS-BASEL （ 影响因子：3.935； ）

ISSN：

年卷期： 2021 年 10 卷 11 期

页码：

收录情况： SCI

摘要： Ripe rot caused by Colletotrichum spp. is a serious threat in many vineyards, and its control relies mainly on the repeated use of fungicides. A mechanistic, dynamic model for the prediction of grape ripe rot epidemics was developed by using information and data from a systematic literature review. The model accounts for (i) the production and maturation of the primary inoculum; (ii) the infection caused by the primary inoculum; (iii) the production of a secondary inoculum; and (iv) the infection caused by the secondary inoculum. The model was validated in 19 epidemics (vineyard x year combinations) between 1980 and 2014 in China, Japan, and the USA. The observed disease incidence was correlated with the number of infection events predicted by the model and their severity (rho = 0.878 and 0.533, respectively, n = 37, p & LE; 0.001). The model also accurately predicted the disease severity progress during the season, with a concordance correlation coefficient of 0.975 between the observed and predicted data. Overall, the model provided an accurate description of the grape ripe rot system, as well as reliable predictions of infection events and of disease progress during the season. The model increases our understanding of ripe rot epidemics in vineyards and will help guide disease control. By using the model, growers can schedule fungicides based on the risk of infection rather than on a seasonal spray calendar.

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