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

该项目将研究真菌病原菌Zymoseptoria triacetylum（Zt）与其宿主植物小麦之间的相互作用。Zt引起三孢壳针孢菌斑点病（STB），这是欧洲小麦最具破坏性的疾病，也是全球小麦生产的最大限制之一。这种疾病在英国特别严重，因为气候条件有利。由于Zt能够迅速进化对杀菌剂的抗性并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，面对病原体臭名昭著的适应能力，没有任何单一的控制措施是持久的，因此，两种关键的控制方法-杀真菌剂和小麦抗病基因-需要以一种不仅优化短期控制效果，而且优化长期可持续性的方式结合起来。这个跨学科项目将利用大规模实地实验与新型高通量表型分析技术、生物信息学分析、最先进的机器学习和数学建模方法的强大组合，为实现这一目标做出重大贡献。在第一阶段，将进行连续两年的田间试验，以调查STB在大量不同基因型小麦中的流行发展。将使用传统的视觉评估和新型数字表型分析方法来测量疾病数量，并记录每日天气数据。流行病发展的数据将与研究中的小麦群体已有的基因组数据相关联，这样你就有可能确定小麦中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来联合收割机结合三种类型的数据（疾病测量、天气数据和小麦基因组数据），并构建预测季节性STB流行发展的模型。最后，在该项目的第三阶段，前两个阶段的成果将被纳入一个建模框架（流行病学/进化模型），该框架描述病原体种群如何随着时间的推移而变化，与小麦植物的宿主种群相互作用。该模型将考虑两种控制措施的效果：杀菌剂和小麦中的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最大限度地提高种植者的净效益。然后，您将能够将结果与连续几个生长季节的长期预测净效益进行比较，同时考虑疾病水平，天气变量和小麦基因组。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.