Understanding and simulating blackgrass seed persistence and soil seed bank dynamics

背景、合理、重要：杂草是现代农业和全球粮食安全的主要威胁；目前约10%(30%-70%)的作物生产因杂草而损失(粮农组织)。然而，除草剂技术的有效性受到抗药性杂草的快速发展的威胁(1，2)。仅在英国，每年因抗除草剂而造成的毛利损失就达4亿GB，小麦产量每年损失80万吨。随着欧洲除草剂法规的收紧，这推动了对更可持续的杂草控制策略的需求。问题杂草的成功至少在一定程度上要归功于巨大而持久的土壤种子库的形成。杂草土壤种子库动态由种子持久性和农业环境的天气、土壤、种植作物和农艺措施决定(3，4)。杂草基因-环境互作通过三个特征决定了种子库的季节性出苗模式：(I)季节性休眠循环，(Ii)通过未知的动态老化和修复机制而长寿，(Iii)通过微生物活动来防御和腐烂(3)。黑草(Alopecurus Myosuroides)是一种仅靠种子繁殖的一年生杂草，被认为是欧洲农业中最具破坏性的杂草(1)。因此，拟议项目的重点是黑草种子寿命，因为它受到与土壤种子库和微生物活动(特征II和特征III)相互作用的影响。这与目前正在进行的关于特征(I)的LIDO项目具有高度的协同性。通过解决这个拟议的LIDO项目中的性状(II)和(III)，学生有可能改进先正达现有的杂草出苗预测数学模型。目的与目标：拟议的项目旨在了解支撑性状(II)和(III)在土壤种子库中持续存在的机制，并利用这些机制来改进杂草出苗模型。目的1.表征黑草地的寿命和老化。现有的标准种子寿命测试将被改进，以提供一种特定于黑草的加速老化测试。目的2.研究土壤样品中微生物群落和不同地点样品种子表面附着对微生物群落的影响。这将为模拟模型提供与田间相关的输入参数(土壤、温度、水分、微生物)。目的3.测试土壤性质和微生物活性对黑草种子寿命的影响。目标1(实验室)和目标2(实地)的结果将使用基于种群的阈值模型进行集体分析(4)。目标4.与先正达的杂草建模专家合作，将所获得的结果整合到“模拟土壤种子库”中，并将其与现有的“真实实地数据”相联系。这种跨学科的方法将把能力扩展到更复杂的模拟模型，从而开发出更有效的黑草控制策略

Background, rational, importance: Weeds are a major threat to modern agriculture and global food security; about 10% (30-70% without herbicides) of crop production is currently lost to weeds (FAO). The effectiveness of herbicide technology is however threatened by the rapid advance of resistant weeds (1,2). The annual cost of weed herbicide resistance in England alone is £0.4 billion in lost gross profit and an annual wheat yield loss of 0.8 million tonnes. Along with tightening herbicide regulations in Europe, it drives a need for more sustainable weed control strategies.Problem weeds owe their success, at least in part, to the formation of large and persistent soil seed banks. Weed soil seed bank dynamics is determined by seed persistence and the agro-environment's weather, soil, cultivated crop and agronomical practices (3,4). Weed genotype-environment interactions define seasonal emergence patterns with seed bank persistence by three traits: (i) seasonal dormancy cycling, (ii) longevity by unknown dynamic ageing and repair mechanisms, and (iii) defense and decay by microbial activity (3). Blackgrass (Alopecurus myosuroides) is an annual weed solely propagated by seed and considered to be the most destructive weed in European agriculture (1). The proposed project is therefore focused on blackgrass seed longevity as affected by interaction with the soil seed bank and microbial activity (traits ii and iii). This is highly synergistic with a currently running LIDo project on trait (i). By addressing traits (ii) and (iii) in this proposed LIDo project, the student has the potential to improve Syngenta's existing mathematical model for weed emergence prediction.Aims & objectives: The proposed programme aims to understand the mechanisms underpinning traits (ii) and (iii) for blackgrass persistence in the soil seed bank and to use these to refine weed emergence models.Objective 1. To characterise longevity and ageing of blackgrass lots. Existing standard seed longevity assays will be refined to provide a blackgrass-specific accelerated ageing assay. This will be used to identify molecular marker genes for ageing/longevity of seed lots.Objective 2. To investigate the effects of microbial communities both in soil samples and adhering to the seed surface of samples from different locations. This will provide field-related input parameters (soil, temperature, moisture, microbes) for the simulation model.Objective 3. To test effects of soil properties and microbial activity on blackgrass seed longevity. Results from objectives 1 (lab) and 2 (field) will be collectively analysed using population-based threshold models (4).Objective 4. To integrate the obtained results into a "simulated model soil seed bank" and connect it to existing "real-field data" in collaboration with Syngenta's weed modelling experts. This interdisciplinary approach will extend the capability to more complex simulation models and thereby develop more effective control strategies for blackgrass