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Novel real-time disease surveillance and fungicide resistance monitoring tools to foster a smart and sustainable crop protection platform in Brazil

新型实时疾病监测和杀菌剂抗性监测工具，将在巴西打造智能且可持续的作物保护平台

基本信息

批准号：
BB/S018867/2
负责人：
Bart Fraaije
金额：
$ 29.5万
依托单位：
National Institute of Agricultural Botany
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FS018867%2F2
关键词：
Novel real time disease surveillance

项目摘要

Resistance to chemical agents used to control pests, weeds and pathogens is a threat to effective crop protection and therefore to food security. Tighter regulations and a slowing pipeline of new products have also reduced the range of available chemical classes. This has led to a greater dependence on fewer fungicides and mode of actions, increasing the selection for further cases of resistance. The limited availability of effective crop protection products, coupled with lack of genetic resistance in major crop varieties, is making key pathogens increasingly difficult to control. In order to prolong the effective life of current and new crop protection products, evolution-smart integrated pest management strategies are needed. Strategies based on different dose rates, alternations and mixtures of fungicides have been advocated to reduce the selection of resistance. However, debate continues as to which strategies are most effective and there is a need for more empirical data on the fundamental evolutionary processes underlying the selection of resistance.Three key fungicide classes: azoles, QoIs and SDHIs: are currently used for the control of many plant pathogens. This project will focus on three major diseases in Brazil: wheat blast (Pyricularia graminis-tritici), Asian soybean rust (Phakopsora pachyrhizi) and the banana Sigatoka disease complex (Mycosphaerella fijiensis and M. musicola). Resistance to one or more fungicide groups has been detected in all four pathogens, but the occurrence of resistance within Brazil or the molecular mechanisms conferring resistance are not yet known in all cases. In addition, onset of disease epidemics is poorly understood and, therefore, appropriate anti-resistance strategies and optimal disease control cannot be achieved. In order to rationalise fungicide inputs (e.g. product choice, dose rate, spray frequency and timing, and mixing/alternation of fungicides), and to test anti-resistance strategies aiming to reduce disease inoculum (for example effect of crop free periods of soybean) and delay evolution and spread of resistance against current and new fungicides, high throughput monitoring tools, enabling quantitative measurement of pathogen levels and detection of fungicide resistant alleles, in combination with disease forecasting, are needed. We will develop real-time disease surveillance, using automated spore trapping with pathogen DNA detection.The status and molecular mechanisms of fungicide resistance in Brazilian pathogen isolates will be assessed, and further resistance evolution predicted through experimental evolution and functional characterisation of resistant alleles. We will then develop molecular diagnostics for rapid, high-throughput monitoring of fungicide resistance. An online portal to share tools, results and recommendations with farmers, agrochemical industry and other stake holders will be created. Improved disease forecasting and optimised disease management strategies would benefit growers (lower production costs), consumers (food safety, residue reduction) and the environment (reduced pesticide applications), by avoiding unnecessary (no epidemic forecast) or ineffective (high levels of resistance) fungicide applications, and prolonging the effectiveness of fungicides for when they are needed.

对用于控制害虫、杂草和病原体的化学药剂的抗性是对有效作物保护的威胁，从而对粮食安全构成威胁。更严格的监管和新产品开发的放缓也减少了可用化学类别的范围。这导致对更少的杀菌剂和作用方式的更大依赖，增加了对进一步耐药病例的选择。有效作物保护产品的供应有限，加上主要作物品种缺乏遗传抗性，使得关键病原体越来越难以控制。为了延长现有和新型作物保护产品的有效寿命，需要进化智能的害虫综合治理策略。人们提倡采用基于不同剂量率、交替使用和混合使用杀菌剂的策略，以减少抗药性的选择。然而，关于哪种策略最有效的争论仍在继续，需要更多关于选择抗性的基本进化过程的经验数据。三种主要的杀菌剂：唑类、qoi类和sdis类，目前被用于控制许多植物病原体。该项目将重点关注巴西的三种主要疾病：小麦稻瘟病（稻瘟病）、亚洲大豆锈病（厚根锈病）和香蕉叶斑病复发病（斐济Mycosphaerella fijiensis和M. musicola）。在所有四种病原体中都发现了对一种或多种杀菌剂群的耐药性，但在巴西境内发生的耐药性或在所有病例中产生耐药性的分子机制尚不清楚。此外，人们对疾病流行的发病知之甚少，因此无法实现适当的抗耐药性战略和最佳的疾病控制。为了使杀菌剂投入合理化（例如产品选择、剂量率、喷洒频率和时间以及杀菌剂的混合/交替），并测试旨在减少疾病接种（例如大豆无作物期的影响）和延迟对现有和新的杀菌剂的抗性进化和传播的抗抗性战略，高通量监测工具，能够定量测量病原体水平和检测抗杀菌剂等位基因，与疾病预测相结合，是必要的。我们将开发实时疾病监测，使用带有病原体DNA检测的自动孢子捕获。将评估巴西病原菌分离株杀菌剂抗性的现状和分子机制，并通过实验进化和抗性等位基因的功能特征预测进一步的抗性进化。然后，我们将开发用于快速、高通量监测杀菌剂耐药性的分子诊断方法。将建立一个在线门户网站，与农民、农用化学工业和其他利益相关者分享工具、结果和建议。改进疾病预测和优化疾病管理战略将有利于种植者（降低生产成本）、消费者（食品安全、减少残留）和环境（减少农药使用），避免不必要的（没有流行病预测）或无效的（高抗性）杀菌剂的使用，并在需要时延长杀菌剂的有效性。