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

优化燕麦产量和质量，实现可持续生产和经济影响 (Opti-Oat)

• 批准号: BB/M027368/1
• 负责人: Eric Ober
• 金额: $ 29.48万
• 依托单位: National Institute of Agricultural Botany
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM027368%2F1
• 关键词: Optimising; oat; yield; quality; deliver

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. 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, contribute to sustainable intensification, reduce supply risk for millers, reduce imports, catalyse food product innovation and stimulate milled product export.

自2008年以来，对食品用高品质燕麦的需求增长了23%以上，预计早餐和健康休闲食品类的需求将进一步增长5%。然而，国内种植燕麦的比例有所下降，主要是因为替代作物的回报率较高。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/公顷的显著产量差距，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，以最大限度地提高回报。该项目将为英国燕麦生产商提供世界领先的农艺“工具”，以最大限度地提高种植者的回报，并利用对食品级燕麦日益增长的需求。在田间季节期间准确地使作物表型化，并将其与最终产量和质量的变化联系起来以提高性能的能力一直是农业工业的长期目标。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，正在迅速成为测量现场和遗传变异的成本效益平台，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URSULA Agriculture和阿伯里斯特威斯大学开发的一种新的多光谱相机系统在小型无人机有效载荷内提供了无与伦比的分辨率，覆盖范围和光学清晰度，并将支持该项目。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（图像处理程序和基于对象的分类算法），这是将无人机系统图像转化为有意义的作物生长和发育数据所必需的。至关重要的是，这些算法将根据在地面上进行的全面测量进行校准。这些创新的方法，结合新颖的高通量谷物质量评估，将应用于监测燕麦作物生长，发育，产量形成和谷物质量的小地块和商业种植领域的选定的现代品种跨越广泛的环境和管理系统的数据。这一独特的数据集将允许解剖品种x环境x管理的相互作用，使用因子回归模型最初开发的大麦。它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，将挖掘这一多年数据集，以解释与试验相关的明确环境和/或生理变量的差异品种敏感性，从而构建燕麦生长指南，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。有重点地传播这些创新工具将使平均产量至少增加1吨/公顷，有助于可持续集约化，减少米莱尔的供应风险，减少进口，促进粮食产品创新，并刺激碾磨产品出口。