Optimising oat yield and quality to deliver sustainable production and economic impact (Opti-Oat)

自2008年以来，食用型优质燕麦的需求增长了23%以上，预计还会进一步增长，预计每年增长5%。在早餐和健康零食类别中。增加健康和营养食品的可持续生产是英国食品部门的优先事项。然而，国产燕麦的比例有所下降，主要是因为替代间作作物的回报更高。这在很大程度上是因为平均产量和最高产量之间存在3.6吨/公顷的显著差距，这表明大多数种植者没有适当的农艺信息和指南来实现最佳产量和质量，以实现收益最大化。该项目将为英国燕麦生产商提供世界领先的农艺工具，以最大化种植者的回报，并利用对食品级燕麦日益增长的需求。能够在田间季节准确地确定作物的表型，并将其与最终产量和质量的变化联系起来，以提高表现，一直是农业行业的长期目标。围绕无人机系统(UAS)的技术，包括用于图像采集和处理的硬件和软件，正在迅速崛起，成为测量现场和遗传变异的经济有效的平台，但通常很少有用户超越这一步。在拟议工作的背景下，这项技术有可能改变燕麦作物的生产，并提供一个可应用于其他作物的模板。由乌苏拉农业和Aberystwyth大学开发的一种新的多光谱相机系统，在小型UAS有效载荷内提供无与伦比的分辨率、覆盖率和光学清晰度，将支持该项目。该项目将在这些进展的基础上开发专门针对燕麦的定制软件(图像处理例程和基于对象的分类算法)，这是将UAS图像转换为关于生长和发育的有意义的作物数据所必需的。关键是，这些算法将根据地面进行的全面测量进行校准。这些创新方法与谷物质量的新型高通量评估相结合，将被应用于监测燕麦作物生长、发育、产量形成和谷物质量的数据，这些数据来自于跨越广泛环境和管理系统的选定现代品种的小块和商业种植田地。这个独特的数据集将允许通过使用最初为大麦开发的阶乘回归模型来剖析各种x环境x管理交互作用。这将为基于过程的燕麦作物模型的开发提供背景数据，并为模型驱动的管理决策支持工具奠定基础。最后，将挖掘这个多年数据集来解释不同品种对与试验相关的明确环境和/或生理变量的敏感性，从而建立一个燕麦生长指南，类似于广泛采用的小麦和大麦生长指南(HGCA，2008和2005)。这将为整个燕麦生长过程提供适当的细节和深入的知识，并确定关键的作物管理点，以最大限度地提高产量、质量和可持续性。集中推广这些创新工具将使平均产量至少增加1吨/公顷(相当于每年增加1500万GB)。这将有助于可持续集约化，降低磨坊的供应风险，减少进口，促进食品创新和消费者获得健康谷物，并刺激精磨产品出口。

The demand for high quality oats for food use has risen by over 23% since 2008 and is projected to increase further with forecast growth of 5% p.a. in the breakfast and healthy snack foods category. Increasing the sustainable production of healthy and nutritious food is a priority for the UK food sector. However, the percentage of home-grown oats has declined, primarily because returns on alternative break crops are higher. In large part this is because there is a significant yield gap of 3.6t/ha between average and the highest yields, indicating most growers do not have the appropriate agronomic information and guides to achieve optimal yields and quality to maximise returns. This project will provide UK oat producers with world-leading agronomic 'tools' to maximise grower returns and capitalise on the increasing demand for food grade oats. The ability to accurately phenotype crops during the season in field, and to link this with variation in final yield and quality in order to improve performance, has been a long term goal of the agricultural industry. The technology around Unmanned Aircraft Systems (UAS), including the hardware software for image acquisition and processing, is rapidly emerging as a cost-effective platform for measuring in-field and genetic variation, yet commonly few users go beyond this step. In the context of the proposed work, this technology has the potential to transform oat crop production and provide a template that can be applied to other crops. A new multi-spectral camera system, developed by URSULA Agriculture and Aberystwyth University, delivers unparalleled resolution, coverage and optical clarity within a small UAS payload and will underpin this project. This project will build on these advances to develop bespoke software (image processing routines and object-based classification algorithms) specific to oats, which are necessary to translate the UAS imagery into meaningful crop data on growth and development. Critically, these algorithms will be calibrated against comprehensive measurements made on the ground. These innovative approaches, combined with novel high-throughput assessment of grain quality, will be applied to data from the monitoring of oat crop growth, development, yield formation and grain quality on small plots and commercially-grown fields of selected modern varieties spanning a wide range of environments and management systems. This unique dataset will allow the dissection of variety x environment x management interactions by using factorial regression models originally developed for barley. It will provide the background data for development of a process-based Oat Crop Model and lay the foundation for model-driven management decision support tools. Finally, this multi-year dataset will be mined to explain differential varietal sensitivities to explicit environmental and/or physiological variables associated with the trials to allow the construction of an Oat Growth Guide, similar to the widely adopted Wheat and Barley Growth Guides (HGCA, 2008 & 2005). This will give appropriate detail and an in-depth knowledge to the whole oat growth process and identify critical crop management points to maximise yield, quality and sustainability. Focused dissemination of these innovative tools will increase average yields by at least 1 t/ha (equivalent to a £15M uplift p.a. in output from the existing oat land base), contribute to sustainable intensification, reduce supply risk for millers, reduce imports, catalyse food product innovation and consumer access to healthy grains and stimulate milled product export.