Plant sensing to determine environmental impacts on developmental processes leading to crop yield

Throughout their life cycle, plants are subjected to many adverse environmental conditions including low light levels and periods of drought or extreme temperatures which can dramatically affect plant survival and limit productivity. In order to cope with such stresses, plants adjust metabolically and physiologically. Unanticipated variation in crop development is already in evidence in a range of crop varieties resulting in yield instability with significant negative impacts on the rural economy, environment and wellbeing. Cherry and blueberry are prime examples where, depending on season, a condition known as Cherry June Drop can occur where unripe fruit fall from the tree to excessive levels, drastically reducing yield. Similarly in blueberry widely varying yield is achieved depending on season with factors such as bud initiation being important. Currently no methods exist to understand when and how a plant's development has been disrupted or to characterise the key environmental signals responsible. The lack of knowledge in these two areas severely limits the capacity for active crop management to optimise yield or to breed for future environmental resilience. This work will use a field based plant and environmental monitoring approach to develop environmental models of blueberry bud initiation and cherry June Drop. We will attempt to identify signals that arise from the plants short-term responses to environmental conditions ('sensing'), to identify the point(s) at which the plant's development leads to the unwanted phenotype (excessive June Drop or excessive vegetative bud development). Blueberry and Cherry are key crops with great potential for UK production but which currently supply only 7% and 5% respectively of the market with UK fruit. Current expansion particularly in cherry is presently impeded by this unpredictable developmental phenotype, 'June Drop', which can lead to fruit losses of 80%. In blueberry, yield varies as much as 50% across seasons. Project outputs will allow, for the first time, the ability to carry out in-field environmental monitoring and crop phenotyping to understand environmental factors controlling crop production and develop bespoke crop management systems that will mitigate the effect of environmental variation and ensure future crop yield stabilisation and for cherry to encourage new plantations to reduce imports. The outputs would also have application to a wide range of other crops where other phenotypic disorders can be detected and methods developed for mitigation and also for plant breeding where varieties can be selected based on imaging signals of plant responses to environmental conditions.