Feasibility of an Early Warning Tool for Locust Outbreaks in East Africa

东非蝗灾预警工具的可行性

• 批准号: 10020115
• 金额: $ 2.59万
• 依托单位: DTIME LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10020115
• 关键词: Feasibility; Early; Warning; Tool; Locust

Since 2019, countries in East Africa have been facing the biggest locust outbreaks in history, which damaged thousands of kilometres of cropland and drastically reduced crop yields. Such pest and disease outbreaks are threatening food security and livelihoods of millions of people. African small-scale farmers are particularly vulnerable as they are largely dependent on crop production for livelihoods and food.Besides, the intensity and frequency of such ecological shocks are projected to increase with climate change, resulting in an upsurge in agricultural losses. This will have severe consequences for food security and nutritional health for not only the poorest in Africa but also for people across the world.Crop pest and disease outbreaks including Locust occurrences can be predicted with the help of the existing earth observation data and intelligent models. We, at Dtime, plan to develop a mobile and web-based Early-Warning Tool that will provide warning of crop pest and disease outbreaks using big data and cutting-edge machine learning algorithms, in addition to a deep understanding of crop pest/disease distribution and ecology. This tool will inform farmers, agriculture officers, non-government organisations such as FAO, government bodies and other stakeholders in the value chain about the probability of locust emergence and arrival, movement patterns, and outbreak at local-scales (1-10km) and landscape scales (100-500km). Farmers and other stakeholders will then be able to anticipate and adapt in order to prevent crop losses. The tool will help FAO and other organisations manage locusts much more effectively resulting in reduced use of pesticides, hence minimising the costs of pest control and environmental impact.Our team is developing a range of intelligent forecast models using algorithms such as Convolutional Neural Networks and Random Forest Classification. At this stage, we focus on building models for East Africa. We will use the locust models and data pipelines to model other devastating pests (e.g., Fall armyworm) and diseases (e.g., Cassava mosaic disease) in east Africa and across the world.We will engage with the insurance sector, value chain stakeholders, commercial farmers and advertisements to fund the tool, as we want this tool to be entirely free for small-scale farmers. We also plan to look at funding options from governments and regional organisations such as the FAO as the tool will significantly reduce the costs of pest and disease control by the FAO and World bank which was over $ 6 million for Locust control in 2020\.

自2019年以来，东非国家一直面临着历史上最大规模的蝗灾，破坏了数千公里的农田，使作物产量大幅下降。这种虫害和疾病的暴发正在威胁着数百万人的粮食安全和生计。非洲小农尤其脆弱，因为他们的生计和食物在很大程度上依赖于农作物生产。此外，这种生态冲击的强度和频率预计将随着气候变化而增加，导致农业损失激增。这不仅对非洲最贫穷的人，而且对世界各地的人民，都将对粮食安全和营养健康造成严重后果。农作物病虫害和疾病的爆发，包括蝗虫的发生，可以借助现有的地球观测数据和智能模型进行预测。我们计划在Dtime开发一个基于移动和网络的预警工具，除了深入了解作物病虫害/疾病分布和生态外，还将使用大数据和尖端机器学习算法提供作物病虫害爆发的警告。这一工具将向农民、农业官员、粮农组织等非政府组织、政府机构和价值链上的其他利益攸关方通报蝗虫发生和到达的可能性、移动模式以及局部规模(1-10公里)和景观规模(100-500公里)的暴发。农民和其他利益攸关方将能够预测和适应，以防止作物损失。该工具将帮助粮农组织和其他组织更有效地管理蝗虫，从而减少杀虫剂的使用，从而将虫害控制和环境影响的成本降至最低。我们的团队正在使用卷积神经网络和随机森林分类等算法开发一系列智能预测模型。在这个阶段，我们专注于为东非建立模型。我们将使用蝗虫模型和数据管道来模拟东非和世界各地的其他毁灭性害虫(如秋粘虫)和疾病(如木薯花叶病)。我们将与保险部门、价值链利益相关者、商业农民和广告合作，为该工具提供资金，因为我们希望该工具对小规模农民完全免费。我们还计划研究来自各国政府和区域组织(如粮农组织)的资金选择，因为该工具将大大降低粮农组织和世界银行控制虫害和疾病的成本。2020年，世界银行用于蝗虫控制的资金超过600万美元。