Extensive Field Surveys, Spatiotemporal Analysis, and Response of Fungicidal Chemistries Reveal Dynamic Nature of Colletotrichum gloeosporioides, a Cause of Mango Anthracnose Disease

文献类型： 外文期刊

第一作者： Mansoor, Talha

作者： Mansoor, Talha;Abdurrehman, Muhammad Nasir;Subhani, Muhammad Nasir;Malik, Muhammad Tariq;Abbas, Aqleem;Hasnain, Ammarah;Rao, Muhammad Junaid;Alrefaei, Abdulwahed Fahad;Naqvi, Syed Atif Hasan;Zhou, Lei

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关键词： Anthracnose; Colletotrichum gloeosporioides management; Fungicides assay; Regression model

期刊名称：ERWERBS-OBSTBAU （ 影响因子：1.3； 五年影响因子：1.4 ）

ISSN： 0014-0309

年卷期： 2023 年

页码：

收录情况： SCI

摘要： Mango (Mangifera indica L.) is an important fruit crop of Pakistan, known as the "King of Fruits" due to its shape, color, aroma, and taste and mangoes from the province of Punjab in particular are considered one of the world's most delicious fruit commodity. A comprehensive survey was conducted to explore the current status of mango anthracnose disease caused by Colletotrichum gloeosporioides in mango orchards of the most unique environmentally favored subtropical region known as Multan. The spatiotemporal dynamics of the disease for its modeling and management through various chemistries was conducted. Disease prevalence was recorded with characteristic symptoms of anthracnose including necrotic spots on leaves, panicles, and fruits, as well as the fungus potentially causing dieback on twigs in the eastern region of Multan with 17.66% disease intensity. The model developed here indicated 95% variability in the progression of disease and concluded that the most responsive environmental variables were wind speed, relative humidity (morning), and sunshine hours, which seemed to be more significant for disease advancement with an R-2 value of 0.93 as shown through step-wise regression analysis. Evaluation of chemistries revealed that Score, followed by Topsin-M and Bavistin, was the most effective against the pathogenic fungus. An integrated strategy is needed to control mango anthracnose, utilizing the evidence from the regression model by studying meteorological variables.

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