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Cells to Fields: crop movement characterisation across scales of order

从细胞到田地：跨秩序尺度的作物运动特征

基本信息

批准号：
BB/X00595X/1
负责人：
Erik Murchie
金额：
$ 12.54万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX00595X%2F1
关键词：
Cells Fields crop movement characterisation

项目摘要

There is an urgent need to improve crop yield (tonnes per hectare) in order to meet the needs of a growing global population and declining fertile agricultural land base. This proposal tackles a much-ignored factor in agriculture and plant science. Plants 'move' in response to moderate wind and this occurs on a daily basis, sometimes continually during growth. Wind induced canopy movement has a large number of effects on plant biology including the alteration of the plant microenvironment with consequences for photosynthesis and plant productivity. For example we found that movement, affected by mechanical properties, has a strong effect on the rate at which light levels change in the canopy, by altering 'light fleck' properties. This has strong implications for canopy photosynthesis (Burgess et al (2016) Frontiers in Plant Science 7, 1392; Burgess et al (2021) Plant. Cell Environ. 44, 3524-3537). It appears that movement enables the production of more rapid 'lightflecks' and increases light levels in lower leaf levels, enhancing photosynthesis at the canopy level. We recently developed the techniques to generate high resolution 3D models of field grown wheat and rice canopies and developed approaches for 'tracking' moving canopies using the detection of wheat ears (Gibbs et al. Plant Physiology 181, 28-42 (2019)). Despite work on mechanical failure in high wind speeds, we currently have no methods for quantitative assessment of canopy motion in the field resulting from lower windspeeds that can be used to predict its influence on photosynthesis and yield/productivity over seasons. This presents a glaring critical knowledge gap, with methodologies needed across agriculture and ecology. There are likely to be many applications, especially remote sensing of canopy movement in relation to climate change. A chance meeting in 2021 between physicists working on lung cilia motion (U.Cambridge) and crop / plant scientists working on field canopy motion (U. Nottingham) led to the idea that microscopic imaging methods could be scaled up to whole plant canopies. Cilia are fine hair like structures that protrude from a variety of cells and beat back and forth, in a manner reminiscent of plant leaves moving in the wind. After some tests in the field in 2021, it was confirmed that the method would be applicable but we lack resources to continue. The multiDDM approach developed in U. Cambridge at the microscopic level simplifies biological movement to a core set of parameters that represent motion across all length scales. Within this programme, the multiDDM approach will be applied to large scale video capture of field-grown wheat plants. Using plots of wheat with contrasting canopy mechanical properties, we will set up platforms in the field at several scales: frames and tripods (1-2m); cherry picker (5- 6m) and drones (>10m). We will capture videos of movement in relevant periods of growth alongside windspeed sensor data placed close to the cameras. We also will collect physiological and photosynthesis data for the models. Using the field data, the MultiDDM method will be used to generate frequencies and amplitudes of oscillation according to windspeed. The Nottingham group will utilise their existing knowledge of 3D canopy modelling to simulate light fluctuations accordingly. These light fluctuations will be used to model photosynthesis of the crop canopy over critical growth stages and provide the first quantitative prediction of the role of windspeed in crop yield. We expect this will provide new targets for crop breeding. We will make these methods available to the community and publish findings, extending the method to other aspect of movement and features of canopies such as humidity and temperature and gas exchange.

迫切需要提高作物产量（每公顷吨数），以满足不断增长的全球人口和日益减少的肥沃农业土地基础的需要。这一提议解决了农业和植物科学中一个被忽视的因素。植物会对中等程度的风做出“移动”反应，这种情况每天都会发生，有时在生长过程中会持续发生。风引起的冠层运动对植物生物学有许多影响，包括改变植物微环境，从而影响光合作用和植物生产力。例如，我们发现受机械特性影响的运动，通过改变“光斑”特性，对树冠中光照水平变化的速率有很大影响。这对冠层光合作用具有强烈的影响（Burgess et al（2016）Frontiers in Plant Science 7，1392; Burgess et al（2021）Plant.细胞环境44，3524-3537）。看来，运动能够产生更快的“光照”，并增加较低叶片水平的光照水平，增强冠层水平的光合作用。我们最近开发了生成田间生长的小麦和水稻冠层的高分辨率3D模型的技术，并开发了使用麦穗检测来“跟踪”移动冠层的方法（Gibbs et al. Plant Physiology 181，28-42（2019））。尽管在高风速下的机械故障的工作，我们目前还没有方法定量评估冠层运动在现场造成的较低的风速，可用于预测其对光合作用和产量/生产力的影响，在季节。这显示了一个明显的关键知识差距，农业和生态学需要各种方法。可能有许多应用，特别是与气候变化有关的树冠层运动的遥感。2021年，研究肺纤毛运动的物理学家（美国剑桥大学）和研究田间冠层运动的作物/植物科学家（美国剑桥大学）之间的一次偶然会面。诺丁汉）导致了显微成像方法可以扩大到整个植物冠层的想法。纤毛是从各种细胞中伸出的细毛状结构，来回跳动，让人想起风中移动的植物叶子。于二零二一年进行实地测试后，确认该方法将适用，但我们缺乏资源继续进行。多DDM方法是在美国开发的。剑桥在微观水平上将生物运动简化为一组核心参数，这些参数代表了所有长度尺度上的运动。在该计划中，multiDDM方法将应用于田间生长的小麦植株的大规模视频捕获。使用具有对比冠层机械特性的小麦地块，我们将在田间设置几种规模的平台：框架和三脚架（1- 2米）;樱桃采摘机（5- 6米）和无人机（> 10米）。我们将拍摄相关生长时期的运动视频，以及放置在摄像机附近的风速传感器数据。我们还将收集模型的生理和光合作用数据。使用现场数据，将使用MultiDDM方法根据风速生成振荡的频率和振幅。诺丁汉研究小组将利用他们现有的3D树冠建模知识来相应地模拟光线波动。这些光的波动将被用来模拟作物冠层在关键生长阶段的光合作用，并提供第一个定量预测风速在作物产量中的作用。我们希望这将为作物育种提供新的目标。我们将向社会提供这些方法，并公布研究结果，将该方法扩展到其他方面的运动和树冠特征，如湿度和温度以及气体交换。