Cells to Fields: crop movement characterisation across scales of order

迫切需要提高作物产量(每公顷吨)，以满足全球人口不断增长和肥沃的农业土地基础不断减少的需要。这项建议解决了农业和植物科学中一个被忽视的因素。植物在温和的风中“移动”，这种情况每天都会发生，有时在生长过程中也会持续发生。风引起的冠层移动对植物生物学有很大的影响，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，运动受机械特性的影响，通过改变光斑的特性，对冠层中光线水平的变化速度有很大的影响。这对冠层光合作用有很强的影响(Burgess等人(2016)《植物科学前沿》7,1392；Burgess等人(2021)植物。牢房环境。44、3524-3537)。看起来，移动能够产生更快的“光斑”，并增加下部叶片的光照水平，增强冠层的光合作用。我们最近开发了生成田间种植的小麦和水稻树冠的高分辨率3D模型的技术，并开发了使用小麦穗检测来‘跟踪’移动树冠的方法(Gibbs等人。植物生理学181，28-42(2019))。尽管研究了高风速下的机械故障，但我们目前还没有方法来定量评估由于风速较低而导致的田间冠层运动，从而可以预测其对光合作用和产量/生产力的影响。这造成了一个明显的关键知识鸿沟，农业和生态领域都需要方法论。可能会有许多应用，特别是与气候变化有关的树冠移动的遥感。2021年，致力于研究肺纤毛运动的物理学家(U.Cambridge)和研究田间树冠运动的作物/植物科学家(U.Nottingham)偶然相遇，导致了显微成像方法可以扩大到整个植物冠层的想法。纤毛是细毛状的结构，从各种细胞中突起，来回跳动，让人想起植物的叶子在风中移动的方式。经过2021年的实地测试，确认该方法是适用的，但我们缺乏继续下去的资源。剑桥大学在微观层面上开发的多DDM方法将生物运动简化为代表所有长度尺度上的运动的一组核心参数。在这一方案中，多DDM方法将应用于对田间种植的小麦植株进行大规模视频捕获。利用具有不同冠层机械性能的小麦地块，我们将在田间建立几个比例的平台：框架和三脚架(1-2米)；樱桃采摘机(5-6米)和无人机(&gt；10米)。我们将捕捉相关生长时期的运动视频，并将风速传感器数据放置在相机附近。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方法根据风速产生振荡的频率和幅度。诺丁汉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，并首次对风速在作物产量中的作用提供定量预测。我们预计这将为作物育种提供新的目标。我们会把这些方法提供给社会人士，并发表研究结果，把这些方法推广至其他方面，例如雨篷的运动和特征，例如湿度、温度和气体交换。

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.