Skyline system for photosynthetic crop phenotyping

用于光合作物表型分析的 Skyline 系统

• 批准号: BB/X019179/1
• 负责人: Stephen Rolfe
• 金额: $ 32.13万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX019179%2F1
• 关键词: Skyline; system; photosynthetic; crop; phenotyping

Plants capture energy from sunlight and use this to fix carbon dioxide, producing the carbohydrates that we eat. Although we have selected and bred crop plants for hundreds of years, their performance is still not optimal. By improving photosynthesis, or maintaining photosynthetic rates in the face of stresses (e.g. pests and pathogens, drought, extremes of temperature), we can increase food security to address the concerns of rising world populations and losses due to climate change.The emerging field of plant phenomics seeks to make high-throughput measurements of plant structure and function over time. Whilst great advances have been made using drones and robots to measure the structure and pigmentation of plants using specialised cameras, measuring photosynthesis is much more challenging. The 'gold standard' for measuring photosynthesis is gas exchange, where we can measure carbon dioxide taken up by the leaves and water loss through small pores known as stomata. This requires leaves to be clamped in an air-tight chamber, which is time consuming and doesn't capture the variation in the environment and subsequent changes in photosynthetic rates that occur throughout the day, or from day-to-day as the weather changes. It also only measures a small part of the leaf rather than the whole plant canopy.In this project we will deploy a 'Skyline' system that allows gas exchange measurements to be made automatically, from the whole crop canopy, multiple times per day. The system uses a cradle that runs along a pair of cables that can automatically lower a chamber onto plants and capture gas exchange. This will allow us to capture a true picture of photosynthesis in crops in the field (or protected environments such as greenhouses) over the course of a growing season. Whenever a measurement is taken, an image is also captured using a multispectral camera, of the sort normally deployed by drone. This will enable us to accurately correlate true measurements of photosynthetic rate made using the Skyline system with high-throughput imaging approaches that can be deployed on a much larger scale by drones. Making these correlations is particularly important when plants are modified in some way or are subject to stresses such as disease. In this project we will test the system in a protected environment to see how plants which have been genetically modified to improve photosynthetic rate perform in a fluctuating environment over an extended period. We will also look at conventional wheat varieties that, in the laboratory, show different responses to the important pathogen, Septoria tritici,to see whether these differences are maintained in the field, preserve photosynthetic rates and ultimately yield. This approach is essential in translating laboratory based research into the field and ultimately, improving the UK's food security.The system will also be made accessible to the wider UK plant phenomics and crop breeding communities as part of PhenomUK-RI - a new project that addresses how the UK can exploit the developing field of plant phenomics to improve agriculture.

植物从阳光中获取能量，并利用这些能量来固定二氧化碳，产生我们吃的碳水化合物。虽然我们已经选择和培育了数百年的农作物，但它们的性能仍然不是最佳的。通过改善光合作用，或在面对压力（如害虫和病原体、干旱、极端温度）时保持光合作用速率，我们可以增加粮食安全，以解决世界人口增长和气候变化造成的损失等问题。植物表型组学这一新兴领域旨在对植物的结构和功能进行高通量测量。虽然无人机和机器人已经取得了很大的进步，可以利用专门的相机来测量植物的结构和色素沉着，但测量光合作用却更具挑战性。测量光合作用的“黄金标准”是气体交换，我们可以测量叶片吸收的二氧化碳和通过被称为气孔的小孔隙流失的水分。这需要将叶子夹在一个气密的房间里，这很耗时，而且不能捕捉到全天或每天天气变化时环境的变化和光合速率的变化。它也只测量叶片的一小部分，而不是整个植物冠层。在这个项目中，我们将部署一个“Skyline”系统，该系统允许每天多次从整个作物冠层自动进行气体交换测量。该系统使用了一个支架，它沿着一对电缆运行，可以自动将一个腔室降低到植物上，并捕获气体交换。这将使我们能够捕捉到作物在田间（或受保护的环境，如温室）整个生长季节光合作用的真实情况。每当进行测量时，也会使用无人机通常部署的多光谱相机捕获图像。这将使我们能够准确地将使用Skyline系统进行的光合速率的真实测量与高通量成像方法相关联，而高通量成像方法可以通过无人机在更大的范围内部署。当植物以某种方式被改造或受到疾病等压力时，建立这些相关性尤为重要。在这个项目中，我们将在一个受保护的环境中测试这个系统，看看经过基因改造以提高光合作用速率的植物在长时间波动的环境中是如何表现的。我们还将研究传统的小麦品种，在实验室中，它们对重要的病原体——小麦Septoria tritici——表现出不同的反应，看看这些差异是否在田间保持，保持光合速率和最终产量。这种方法对于将基于实验室的研究转化为实地研究并最终改善英国的粮食安全至关重要。作为PhenomUK-RI项目的一部分，该系统还将提供给更广泛的英国植物表型学和作物育种社区。PhenomUK-RI是一个新项目，旨在解决英国如何利用植物表型学的发展领域来改善农业。