Smart control of crop diseases: how can we best combine fungicides and plant resistance genes?

该项目将研究真菌致病菌小麦酵母菌（Zt）与其寄主植物小麦之间的相互作用。Zt引起小麦黑斑病（STB），这是欧洲最具破坏性的小麦病害，也是全球小麦生产的最大制约因素之一。由于有利的气候条件，这种疾病在英国尤其严重。由于Zt能够迅速进化出对杀菌剂的抗性，并且能够适应抗病小麦品种和环境条件，因此控制STB变得越来越困难。人们认识到，面对病原菌臭名昭著的适应能力，没有单一的控制措施是持久的，因此，两种关键的控制方法-杀菌剂和小麦抗病基因-需要以一种既能优化短期控制效果又能优化长期可持续性的方式结合起来。这个跨学科项目将通过大规模现场实验与新型高通量表型技术、生物信息学分析、最先进的机器学习和数学建模方法的有力结合，为实现这一目标做出重大贡献。在第一阶段，将连续两年进行田间试验，调查大量不同基因型小麦的STB流行情况。疾病的数量将使用传统的视觉评估和新的数字表型方法来测量，并且将记录每日天气数据。流行病发展的数据将与所研究的小麦群体已有的基因组数据联系起来，这样就有可能确定小麦对STB抗性的新遗传基础。在第二阶段，将使用强大的机器学习技术将三种类型的数据（疾病测量数据、天气数据和小麦基因组数据）结合起来，构建预测季节性STB流行发展的模型。最后，在该项目的第三阶段，前两个阶段的结果将被纳入一个建模框架（流行病学/进化模型），该模型描述病原体种群在与小麦植物宿主种群的相互作用中如何随时间变化。该模型将考虑两种防治措施的效果：杀菌剂和小麦STB抗性基因。这将使您能够优化杀菌剂处理方案和抗病小麦品种的选择，从而在单个生长季节的短期内使种植者的净效益最大化。然后，考虑到疾病水平、天气变量和小麦基因组，你将能够将结果与在多个连续生长季节的较长期内预测的净收益进行比较。研究结果将为软件产品建立科学基础，种植者可以使用该软件产品来指导他们选择杀菌剂应用和小麦品种。

The project will investigate the interaction between the fungal pathogen Zymoseptoria tritici (Zt) and its host plant wheat. Zt causes septoria tritici blotch (STB), the most damaging disease of wheat in Europe and one of the largest constraints on wheat production globally. The disease is especially serious in the UK because of conducive climatic conditions. It is becoming increasingly difficult to control STB, because Zt is capable of rapidly evolving resistance to fungicides and adapting to disease-resistant wheat varieties and environmental conditions. It is recognized that no single control measure is durable in the face of the pathogen's notorious adaptive capacity, hence the two key control methods - fungicides and disease resistance genes in wheat - need to be combined in a manner that optimizes not only control efficacy in the short term, but also their sustainability in the longer term. This interdisciplinary project will make a major contribution to this goal using a powerful combination of large-scale field experimentation with novel high-throughput phenotyping techniques, bioinformatic analyses, state-of-the-art machine learning and mathematical modelling approaches. In the first phase, a field experiment will be conducted during two consecutive years to investigate the STB epidemic development in a large number of different wheat genotypes. The amount of disease will be measured using both the conventional visual assessments and novel digital phenotyping approaches, and the daily weather data will be recorded. The data on epidemic development will be linked to genomic data already available for the wheat population under study, and in this way you are likely to identify new genetic bases of STB resistance in wheat. In the second phase, powerful machine learning techniques will be used to combine the three types of data (disease measurements, weather data and wheat genomic data) and construct a model predicting the seasonal STB epidemic development. Finally, in the third phase of the project, the outcomes of the two previous phases will be incorporated into a modelling framework (epidemiological/evolutionary model) that describes how the pathogen population changes over time in its interaction with the host population of wheat plants. The model will incorporate the effect of two control measures: fungicides and STB resistance genes in wheat. This will allow you to optimize choices of fungicide treatment programmes and disease-resistant wheat cultivars that maximize net benefit of growers over a short term of a single growing season. You will then be able to compare the outcomes with the predicted net benefit over a longer term of a number of consecutive growing seasons, taking into account disease levels, weather variables and wheat genomes. The outcomes will establish the scientific basis for the software product that growers can use to guide their choices regarding fungicide applications and wheat cultivars.