Skyline system for photosynthetic crop phenotyping
Skyline system for photosynthetic crop phenotyping
批准号:
BB/X019179/1
负责人:
Stephen Rolfe
金额:
$32.13万
依托单位:
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
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
植物从阳光中获取能量,并利用这些能量来固定二氧化碳,产生我们食用的碳水化合物。虽然我们已经选育了几百年的农作物,但它们的表现仍然不是最优的。通过改善光合作用,或在面临逆境(如病虫害、干旱、极端温度)时保持光合作用,我们可以提高粮食安全,以解决世界人口增加和气候变化造成的损失的问题。植物表型组学新兴领域寻求随着时间的推移对植物结构和功能进行高通量测量。虽然使用无人机和机器人来测量植物的结构和色素作用已经取得了很大的进步,但测量光合作用的难度要大得多。测量光合作用的“黄金标准”是气体交换,我们可以测量树叶吸收的二氧化碳和通过称为气孔的小孔损失的水分。这需要将树叶夹在一个密闭的房间里,这很耗时,而且无法捕捉到环境的变化以及随后发生的全天或随着天气变化而发生的光合作用速率的变化。它也只测量一小部分树叶,而不是整个植物树冠。在这个项目中,我们将部署一个‘Skyline’系统,允许从整个作物树冠每天多次自动进行气体交换测量。该系统使用一个支架,它沿着一对电缆延伸,可以自动将密封室降低到植物上,并捕获气体交换。这将使我们能够捕捉到田间(或温室等受保护环境)作物在整个生长季节中光合作用的真实图景。每当进行测量时,也会使用通常由无人机部署的多光谱相机拍摄图像。这将使我们能够准确地将使用Skyline系统进行的光合作用速率的真实测量与高通量成像方法相关联,这些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,建立这些关联尤为重要。在这个项目中,我们将在受保护的环境中测试该系统,以了解经过基因改造以提高光合作用速率的植物在较长一段时间内在波动环境中的表现。我们还将观察在实验室中对重要病原菌SepVictoria tritici表现出不同反应的常规小麦品种,看看这些差异是否在田间保持、保持光合作用速率并最终获得产量。这种方法对于将基于实验室的研究转化为现场研究并最终提高英国的食品安全至关重要。作为PhenomUK-RI的一部分,该系统还将向更广泛的英国植物表型组学和作物育种社区开放。PhenomUK-RI是一个新项目,旨在解决英国如何利用正在发展的植物表型组学领域来改善农业。
英文摘要
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.
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Sinusoidally modulated fluorescence imaging for stress detection in plants
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批准号:BB/X003299/1
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项目类别:Research Grant
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资助金额:$22.7万
-
财政年份:2023
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负责人:Stephen Rolfe
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依托单位:
国内基金
海外基金
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