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

Abstract

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, quinone outside inhibitors (QoIs) and succinate dehydrogenase inhibitors (SDHIs)] are currently used for the control of many plant pathogens. This project will focus on three major diseases in Brazil: Asian soybean rust (Phakopsora pachyrhizi), the banana Sigatoka disease complex (Mycosphaerella fijiensis and M. musicola) and wheat blast (Pyricularia graminis-tritici). 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 rationalize 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 characterization 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 optimized 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. (AU)