Understanding and reducing lodging in maize and rice.

恶劣天气会导致农作物连根拔起或茎折断，这一过程称为倒伏。这意味着作物没有充分发挥其潜力，从而减少了种子产量(产量)。倒伏使作物更容易受到真菌的感染，真菌会产生被称为真菌毒素的有毒化学物质，使谷物无法使用。倒伏的这些影响大大降低了作物的价值，而且可能会有额外的谷物烘干成本。通过采取适当的行动(例如，作物品种的选择和如何管理)，农民有可能减少倒下的可能性。在过去的20年里，通过开发现实的倒伏过程模型，在改善对英国种植的谷类作物(如小麦)倒伏的理解和控制方面取得了很大进展，这些模型已被用于制定实用的畜牧业战略，使农民能够减少倒伏。对玉米和水稻倒伏过程的了解不如英国谷类作物先进，玉米和水稻作物倒伏通常会使产量减少高达40%，这是作物生产力的主要制约因素，特别是对中低收入国家。这个项目将利用世界领先的英国倒伏科学专业知识来制定理解和缓解策略，使玉米和水稻产区能够最大限度地减少倒伏。这一战略将提高玉米和水稻生产系统对气候变化的适应能力，并通过减少倒伏作物中霉菌毒素的产生来生产更安全的食品。水稻和玉米的形态都与已经为倒伏过程建模的作物截然不同。将开发新的倒伏模式，考虑到水稻的大悬穗、高分蘖和浅根系统；以及玉米的粗中空茎、有支撑的根系、更高的身高和大的叶片。此外，不同地区的气候特征(风和降雨)将被纳入模式中。这些倒伏模型将利用盆栽植物的风洞试验和田间试验进行开发和测试，并将制定一个框架，以确定倒伏风险最高的地区和田地，其中将考虑地形特征如何影响作物上的风流，以及如何使用地球观测技术(例如来自卫星的植被指数图)来识别作物倒伏风险最大的田地。人们经常发现，具有大树冠(植被指数)的作物在生长季节后期倒伏的风险更大。关于风流的理解、EO技术和新的住宿模型将被合并到一个单一的集成系统中。然后，该系统将用于帮助农民降低倒伏风险；1)战略上，在倒伏风险高的地区种植抗倒伏品种；2)战术上，减少氮肥，优先考虑在生长季出现高倒伏风险的田地(甚至部分田地)的收获。倒伏模型将用于了解作物饲养、环境参数(风、降雨和土壤类型)的变化以及对气候变化的预测将如何影响全球不同地区玉米和水稻的倒伏风险。这项工作将确定哪些作物参数对倒伏风险影响最大，因此应该成为植物育种者的目标。

Severe weather can cause crops to become uprooted or their stems to break, a process called lodging. This means that the crops do not grow to their full potential which reduces the quantity of seed they produce (the yield). Lodging makes crops more susceptible to infection by fungi which can produce toxic chemicals known as mycotoxins which render the grain unusable. These impacts of lodging substantially reduce the value of a crop and there can be additional costs of drying the grain. By taking appropriate action (e.g. choice of crop variety and how it is managed) it is possible for farmers to reduce the likelihood of lodging. Over the past 20 years great advances have been made with improving understanding and control of lodging in cereal crops (e.g. wheat) grown in the UK through the development of realistic models of the lodging process which have been used to develop practical husbandry strategies enabling farmers to reduce lodging. Understanding of the lodging process in maize and rice is less advanced than in UK cereal crops and lodging in maize and rice crops commonly reduces yields by up to 40% representing a major constraint for crop productivity, particularly for low/middle income countries.This project will use world leading UK expertise in lodging science to develop understanding and mitigation strategies that enable maize and rice producing regions to minimise lodging. This strategy will increase the resilience of maize and rice production systems to climate variability and produce safer food by reducing mycotoxin development in lodged crops. Both rice and maize have very different morphologies from the crops that have already been modelled for the lodging process. New lodging models will be developed that take account of the large hanging panicles, high tiller numbers and shallow rooting system of rice; and the thick hollow stem, braced root system, much taller stature and large leaves of maize. Additionally characteristics of the climate (wind and rainfall) of different regions will be built into the models. These lodging models will be developed and tested using wind tunnel tests on pot grown plants and field tests.A framework for identifying regions and fields with the highest risk of lodging will be produced which will account for how landscape features affect wind flow over crops and how Earth Observation (EO) technologies (e.g. vegetation index maps from satellites) may be used to identify the fields with crops that are most at risk to lodging. Frequently it is found that crops with large canopies (vegetation indices) have a greater risk to lodging later in the growing season. The understanding about wind flow, EO technologies and the new lodging models will be combined into a single integrated system. This system will then be used to help farmers to mitigate lodging risk; 1) strategically by planting lodging resistant varieties in regions with a high risk of lodging and 2) tactically by reducing nitrogen fertiliser and prioritising harvest for fields (and even part fields) which are shown to have developed a high risk of lodging during the growing season. The lodging models will be used to understand how variation in crop parameters caused by changes in crop husbandry, environmental parameters (wind, rainfall and soil type) and predictions of climate change will affect lodging risk in maize and rice within different global regions. This work will identify which crop parameters have the greatest influence on lodging risk and therefore should be targeted by plant breeders.