BerryPredictor: Improving harvest forecasts, yield predictions and crop productivity by monitoring and optimising zonal phytoclimates in covered strawberry production
BerryPredictor: Improving harvest forecasts, yield predictions and crop productivity by monitoring and optimising zonal phytoclimates in covered strawberry production
批准号:
34299
负责人:
金额:
$119.14万
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --
中文摘要
消费者和零售商对英国种植的高品质草莓的需求不断增加,随着零售商青睐英国产品,这将在英国脱欧后进一步增加。目前,C。英国消费的草莓中有30%是进口的,因此在本土种植季节,有很大的机会取代这些通常是劣质的进口产品,并促进英国经济。然而,在多变和具有挑战性的生长季节实现持续的高产和优质是困难的,如果要优化英国水果生产以满足市场需求,并减少进口,就需要新的种植创新。在典型的有盖台式生产系统中,植物产量和浆果质量存在很大的差异,但其原因尚未完全了解,并且效果的大小尚未令人满意地量化,因此无法管理或预测。这种可变性使种植者无法尽最大努力预测产量,从而为营销策略提供信息,而不准确的预测会导致供应不足或过剩,从而需要从英国以外的地方购买或销毁水果,这两种情况都非常昂贵。我们将开发新的软果种植策略,从如何优化整个种植区域的单株表现的更好的理解。我们将通过纳入高分辨率、卫星衍生的天气输入来完善最近开发的水果收获和成熟模型,数据馈送将用于告知种植者的polytunnel通风策略,以更好地控制生长条件。可变的成熟率和产量将使用一个新的应用程序来捕获,算法将被开发并嵌入到基于云的BerryPredictor工具中,这将使种植者能够在整个种植区域以比目前更高的准确性和精度来预测产量。BerryPredictor还将首次为PO和英国种植者提供实时准确的产量预测数据。大部分研发工作将在NIAB节水技术(WET)中心进行,该中心是一个由行业资助的软水果精准种植示范和KE中心,其职责是展示软水果种植的最新创新,并促进我们的IUK和行业资助项目的项目产出的商业化。BGG商业种植者将提供天气数据来通知模型,并将数据提供给地面实况BerryPredictor。项目产出将使英国种植者(产量、溢价、进口替代)、PO(更好的产品,提高声誉、可靠性和适销性)、超市和消费者(美味、植物营养的英国种植水果)以及更广泛的社会(可持续集约化)受益。
英文摘要
There is increasing consumer and retailer demand for high-quality UK-grown strawberries, and this will increase further post-BREXIT as retailers favour British produce. Currently, c. 30% (Defra) of strawberries consumed in the UK are imported, and so there is a great opportunity to displace these, often inferior, imports during the home-grown season and boost the UK economy. However, achieving consistently high yields and quality across variable and challenging growing seasons is difficult, and new growing innovations are needed if UK fruit production is to be optimised to meet market demand, and imports reduced. There is much variability in plant yield and berry quality over a typical covered table-top production system, but the reasons for this are not fully understood and the magnitude of the effect has not been satisfactorily quantified, and so it cannot be managed or predicted. This variability confounds growers' best efforts to forecast yields to inform marketing strategies, and inaccurate forecasts lead to under-supply or to surpluses, necessitating purchases from outside the UK or fruit destruction, both of which are very costly. We will develop new soft fruit growing strategies from an improved understanding of how to optimise individual plant performance across the growing area. We will refine recently-developed fruit harvest and ripening models by incorporating high resolution, satellite-derived weather inputs, and data feeds will be used to inform growers' polytunnel venting strategies to better control growing conditions. Variable ripening rates and yields will be captured using a new app, and algorithms will be developed and embedded in a cloud-based BerryPredictor tool that will enable growers to forecast yields with much greater accuracy and precision than currently possible, across the entire cropping area. BerryPredictor will also provide POs and UK growers with access to real-time accurate yield prediction profiles for the first time. The majority of the R&D work will be carried out at the NIAB Water Efficient Technologies (WET) Centre, an industry-funded soft fruit precision growing demonstration and KE centre, the remit of which is to showcase the latest innovations in soft fruit growing, and promote the commercialisation of project outputs from our IUK and industry-funded projects. BGG commercial growers will provide weather data to inform the models, and yield data to ground-truth BerryPredictor. Project outputs will benefit UK growers (yield, premium price, import substitution), POs (better product with enhanced reputation, reliability and improved marketability), supermarkets and consumers (flavoursome, phytonutritious UK-grown fruit) and wider society (sustainable intensification).
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国内基金
海外基金
Improving modelling of compact binary evolution.
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批准号:10903001
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项目类别:青年科学基金项目
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资助金额:20.0万元
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批准年份:2009
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负责人:史蒂芬
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依托单位: