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

对用于控制害虫、杂草和病原体的化学药剂的抗药性威胁到有效的作物保护，从而威胁到粮食安全。更严格的监管和新产品供应的放缓也减少了可供选择的化学产品种类。这导致了对更少的杀菌剂和作用模式的更大依赖，增加了对更多抗药性病例的选择。有效的作物保护产品有限，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。为了延长现有和新的植保产品的有效寿命，需要进化智能的病虫害综合管理策略。根据不同的剂量率、杀菌剂的交替使用和混合使用来减少抗性选择的策略已被提倡。然而，关于哪种策略是最有效的，仍然存在争议，需要关于抗性选择的基本进化过程的更多经验数据。目前，三类关键的杀菌剂：氮唑类、QOI类和SDHI类：用于控制许多植物病原菌。该项目将重点关注巴西的三种主要疾病：小麦稻瘟病(稻瘟病菌)、亚洲大豆锈病(厚根褐腐病)和香蕉赤霉病复合体(稻瘟病菌和番茄根霉病菌)。在所有四种病原体中都检测到对一个或多个杀菌剂组的抗药性，但巴西国内发生抗药性或赋予抗药性的分子机制尚未在所有情况下都知道。此外，人们对疾病流行的爆发知之甚少，因此，无法实现适当的抗药性战略和最佳疾病控制。为了使杀菌剂投入合理化(例如产品选择、剂量率、喷洒频率和时机、杀菌剂的混合/交替)，并测试旨在减少病害接种量(例如大豆无作物时期的影响)并延缓对现有和新型杀菌剂的抗性演变和传播的抗药性策略，需要高通量监测工具，能够定量测量病原菌水平和检测抗病等位基因，并结合病害预测。我们将开发实时疾病监测，使用自动孢子捕获和病原体DNA检测。将评估巴西病原菌分离株的杀菌剂抗药性状况和分子机制，并通过抗药性等位基因的实验进化和功能特征预测进一步的抗药性进化。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技术。将创建一个在线门户，与农民、农化行业和其他利益相关者分享工具、结果和建议。改进的疾病预测和优化的疾病管理战略将通过避免不必要的(没有流行预测)或无效的(高抗药性)杀菌剂的使用，并在需要时延长杀菌剂的效力，从而使种植者(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)受益。

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.