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，食品用优质燕麦的需求增长了 23% 以上，并且预计将进一步增长，早餐和健康休闲食品类别的预计年增长率为 5%。然而，本土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/公顷的显着产量差距，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化回报。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。能够在田间准确地对作物进行表型分析，并将其与最终产量和质量的变化联系起来以提高性能，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软件，正在迅速成为测量现场和遗传变异的经济高效的平台，但通常很少有用户能够超越这一步骤。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，并提供可应用于其他作物的模板。由乌苏拉农业和阿伯里斯特威斯大学开发的新型多光谱相机系统可在小型无人机有效载荷内提供无与伦比的分辨率、覆盖范围和光学清晰度，并将为该项目提供支持。该项目将在这些进步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），这对于将无人机图像转化为有意义的作物生长和发育数据是必要的。至关重要的是，这些算法将根据地面的综合测量进行校准。这些创新方法与新颖的谷物质量高通量评估相结合，将应用于监测小块土地和商业种植田中选定现代品种的燕麦作物生长、发育、产量形成和谷物质量的数据。范围的环境和管理系统。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品种 x 环境 x 管理的相互作用。它将为开发基于流程的燕麦作物模型提供背景数据，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，以解释与试验相关的明确环境和/或生理变量的不同品种敏感性，以便构建燕麦生长指南，类似于广泛采用的小麦和大麦生长指南（HGCA， 2008 年和 2005 年）。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。重点推广这些创新工具将使平均产量提高至少 1 吨/公顷，有助于可持续集约化，降低磨粉厂的供应风险，减少进口，促进食品创新并刺激磨粉产品出口。