Advanced technologies for efficient crop management: A participatory approach with application at farm scale

高效作物管理的先进技术：在农场规模应用的参与式方法

• 批准号: BB/P004628/1
• 负责人: Mathew Williams
• 金额: $ 52.32万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP004628%2F1
• 关键词: Advanced; technologies; efficient; crop; management

Changing climate, variable yields, and resource constraints are challenging UK agriculture and global food security. Our goal is to enhance sustainable and efficient production for two crops of major importance in the UK, wheat and potatoes. We will work with farmers and end users in the application and deployment of novel crop sensing and diagnostic technologies. We will develop a tool that can predict and diagnose crop response to water and nutrient related limits. In turn, this knowledge will guide crop management and help to inform stakeholders from across the arable supply chain about the best approaches for land management towards more sustainable and efficient production, using precision agriculture. We will test remote sensing technologies and couple them with methods for analysing crop and soil processes. We will deploy sensors at farm sites on fixed towers and unmanned aerial vehicles (UAV), and compare these against satellite sensors with global coverage. We will evaluate whether changes in leaf temperature, fluorescence and reflectance are related to yield reductions, comparing sensor data against field measurements of plant growth, yield and ecophysiology, and plant and soil temperature and moisture. We will understand how, and under what circumstances, sensors and platforms can be employed to determine and diagnose crop yield limits. Links to simulation process modelling will provide a rich set of diagnostics related to the plant-soil system, and forecast its sensitivity to management changes. Data assimilation approaches will allow model updates and improvements based on field observations and sensor output, to generate more reliable, near real-time and robust analyses. Our technologies will underpin a crop diagnostic system, indicating crop water and nutrient status, quantifying reductions to yield, that can be used at sub-field to farm scale, with a clear quantification of reliability. Working with farmers, our technologies will be combined to generate a decision support tool, with capacity for (i) immediate (near-real time) mapping of crop stress, and its likely impact on crop yield and (ii) providing detailed spatial information on optimal management interventions to support decision making for sustainable high yield. This work directly addresses a priority of the UK research councils to support UK farming with high quality and practical research to support consistent high returns from crop production against a background of changing climate and increasingly competitive global markets. Our deliverables will provide advanced diagnostics for farmers, and guide cost effective strategies for water and nutrient management for consistent yield.

不断变化的气候，可变收益率和资源限制正在挑战英国农业和全球粮食安全。我们的目标是增强英国，小麦和土豆主要重要性的两种主要重要性的可持续生产。我们将与农民和最终用户合作，以​​应用和部署新颖的作物感测和诊断技术。我们将开发一种可以预测和诊断作物对水和养分相关限制的反应的工具。反过来，这些知识将指导作物管理，并帮助来自可耕种供应链的利益相关者了解最佳的土地管理方法，以使用精确农业来实现更可持续和高效的生产。我们将测试遥感技术，并将它们与分析作物和土壤过程的方法进行杂交。我们将在固定塔和无人机（UAV）的农场遗址部署传感器，并将这些传感器与卫星传感器与全球覆盖范围进行比较。我们将评估叶片温度，荧光和反射率的变化是否与降低的降低有关，将传感器数据与植物生长，产量和生态生理学的现场测量以及植物以及土壤温度和水分进行比较。我们将了解如何以及在什么情况下，传感器和平台来确定和诊断作物产量限制。与仿真过程建模的链接将提供与植物土壤系统有关的丰富诊断，并预测其对管理变化的敏感性。数据同化方法将允许基于现场观察和传感器输出的模型更新和改进，从而生成更可靠的实时和强大分析。我们的技术将支持农作物诊断系统，表明农作物水和养分状态，量化降低的产量，可在子场到农场规模上使用，并清楚地量化可靠性。与农民合作，我们的技术将合并以生成决策支持工具，并具有（i）（i）立即（接近现实的时间）作物压力映射的能力，以及其对作物产量的可能影响，以及（ii）提供有关最佳管理干预措施的详细空间信息，以支持决策制定可持续高收益。这项工作直接解决了英国研究委员会的优先事项，以支持高质量和实践研究的英国农业，以支持农作物产量的一致回报，而在不断变化的气候和竞争激烈的全球市场的背景下。我们的可交付成果将为农民提供先进的诊断，并指导水和营养管理的成本效益策略，以保持一致的产量。

项目成果

A systematic approach to identifying key parameters and processes in agroecosystem models

• DOI: 10.1016/j.ecolmodel.2017.12.009
• 发表时间: 2018-01
• 期刊: Ecological Modelling
• 影响因子: 3.1
• 作者: V. Myrgiotis;R. Rees;C. Topp;M. Williams

Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

• DOI: 10.3390/rs12111843
• 发表时间: 2020-06
• 期刊: Remote. Sens.
• 作者: A. Revill;Anna Florence;A. MacArthur;S. Hoad;R. Rees;M. Williams

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

• DOI: 10.3390/rs11172050
• 发表时间: 2019-09-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: Revill, Andrew;Florence, Anna;Williams, Mathew
• 通讯作者: Williams, Mathew

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

表征遥感 LAI 产品的误差和偏差：以华南热带和亚热带常绿森林为例

• DOI: 10.3390/rs12193122
• 发表时间: 2020
• 期刊: Remote Sensing
• 影响因子: 5
• 作者: Zhao Yuan;Chen Xiaoqiu;Smallman Thomas Luke;Flack-Prain Sophie;Milodowski David T.;Williams Mathew
• 通讯作者: Williams Mathew

UAV-Based Approaches for Crop Parameter Retrievals

基于无人机的作物参数检索方法

• 发表时间: 2018
• 作者: Revill A