Understanding and simulating blackgrass seed persistence and soil seed bank dynamics

背景、合理性、重要性：杂草是现代农业和全球粮食安全的主要威胁；目前约有10%（不使用除草剂时为30-70%）的作物产量因杂草而损失（粮农组织）。然而，除草剂技术的有效性受到抗性杂草快速发展的威胁（1,2）。仅在英国，每年因杂草抗除草剂而损失的毛利润就高达4亿英镑，小麦产量每年损失80万吨。随着欧洲越来越严格的除草剂法规，它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形成了庞大而持久的土壤种子库。杂草土壤种子库动态是由种子持久性和农业环境的天气、土壤、栽培作物和农艺实践决定的（3,4）。杂草基因型-环境相互作用通过三个特征定义了种子库持久性的季节性出现模式：(i)季节性休眠周期，（ii）未知动态老化和修复机制的寿命，以及（iii）微生物活动的防御和腐烂(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，拟议项目的重点是受土壤种子库和微生物活动相互作用影响的黑草种子寿命（特征ii和iii）。这与目前正在运行的LIDo项目在特性(i)上具有高度协同作用。通过在这个拟议的LIDo项目中解决（ii）和（iii）特征，学生有可能改进先正达现有的杂草出现预测数学模型。目的和目标：拟议的计划旨在了解土壤种子库中黑草持久性特征（ii）和（iii）的基础机制，并利用这些特征来完善杂草出现模型。目的1。描述黑草的寿命和老化。现有的标准种子寿命测定法将被改进，以提供一种针对黑草的加速老化测定法。这将用于鉴定种子老化/寿命的分子标记基因。目标2。研究不同地点土壤样品和种子表面微生物群落的影响。这将为模拟模型提供与现场相关的输入参数（土壤、温度、湿度、微生物）。目标3。研究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）的结果将使用基于人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，将获得的结果整合到“模拟模型土壤种子库”中，并将其与现有的“实际数据”连接起来。这种跨学科的方法将扩展到更复杂的仿真模型的能力，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