Mitigating basis risk in weather index-based crop insurance: harnessing models and big data to enable climate-resilient agriculture in India

降低基于天气指数的农作物保险的基差风险：利用模型和大数据实现印度的气候适应型农业

基本信息

批准号：
NE/R014094/1
负责人：
Timothy Foster
金额：
$ 36.04万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FR014094%2F1
关键词：
Mitigating basis risk weather index

项目摘要

Livelihoods of millions of smallholder farmers across the developing world are under threat from extreme weather events, such as droughts, floods, and heatwaves, with risks projected to increase significantly in future years due to climate change. Crop insurance protects farmers against financial risks posed by extreme weather events, and has been widely advocated as a tool to help farmer households to escape poverty traps and invest in climate-smart high-productivity agriculture. Yet, to date, the success and uptake of crop insurance schemes across the developing world has been extremely limited. Several reasons can be identified for problems in scaling crop insurance in developing countries. Traditional indemnity-based insurance schemes require time-consuming verification of actual losses experienced by individual farmers resulting in high transaction costs, claims disputes and delays that deter farmers from purchasing insurance. To counteract these issues, governments and insurers seek to develop more cost-effective and reliable tools to determine when, and at what level, insurance should payout to farmers if an extreme weather event occurs. Parametric insurance, for example weather index-based insurance, triggers payouts based on pre-established relationships between meteorological indices and crop yields, removing the need for expensive crop loss assessments. However, a major challenge for current weather index-based insurance is that payouts often are poorly correlated with farmers' actual yield losses, a problem known as 'basis risk', creating a major barrier to use of index insurance for climate risk mitigation.In this context, how can scientists contribute to the design of smarter index insurance products that meet the needs of farmers, insurers, and governments? The overall aim of this project is to improve the current poor performance of index-based crop insurance by using state-of-the-art environmental modelling and big datasets to reduce basis risk and better protect farmers against weather risks. Our proposed research will develop novel weather index-based insurance contracts that reliably and accurately predict weather-related crop yield losses by combining crop growth modelling, satellite and smartphone imagery of crop growth status, and high-resolution gridded estimates of spatial weather variability. Importantly, our work will produce novel tools and approaches that address two stated needs of the index insurance sector: (i) to reduce temporal basis risk by designing weather index triggers that reflect accurately how yield sensitivity to extreme events varies during the growing season, and (ii) to minimise spatial basis risk by exploiting datasets that capture spatial heterogeneity in weather conditions, crop development, field conditions and management practices. Working in collaboration with HDFC ERGO General Insurance, a major provider of weather index-based insurance for smallholder farmers across India, we will apply these approaches to design and test new weather index-based insurance products to protect farmers in the major agricultural states of Punjab and Haryana - the breadbasket of India - against combined production risks from extreme temperature and heavy rainfall events. Leveraging unique field data collected through the recent IFPRI-HDFC Picture-Based Crop Insurance (PBI) Project, we will conduct agro-economic impact evaluations to quantify reductions in basis risk, increases in farmer welfare and demand for insurance from our new contracts relative to both current index insurance products and government area-yield insurance schemes. Our work will contribute directly to improvements in the quality of index insurance for farmers in India, and, more broadly, will provide the scientific foundations for weather index-based insurance to more effectively support climate-smart smallholder agriculture across the developing world.

发展中国家数百万小农的生计正受到干旱、洪水和热浪等极端天气事件的威胁，由于气候变化，预计未来几年风险将大幅增加。农作物保险保护农民免受极端天气事件带来的财务风险，并被广泛倡导为帮助农户摆脱贫困陷阱和投资气候智能型高产农业的工具。然而，迄今为止，发展中国家农作物保险计划的成功和采用程度极其有限。发展中国家在扩大农作物保险方面出现的问题有几个原因。传统的基于赔偿的保险计划需要耗时地核实个体农民所经历的实际损失，从而导致高昂的交易成本、索赔纠纷和延误，从而阻碍农民购买保险。为了解决这些问题，政府和保险公司寻求开发更具成本效益和可靠的工具，以确定发生极端天气事件时保险应何时以及以何种水平向农民支付。参数保险，例如基于天气指数的保险，根据气象指数和农作物产量之间预先建立的关系触发赔付，从而无需进行昂贵的农作物损失评估。然而，当前基于天气指数的保险面临的主要挑战是，赔付往往与农民的实际产量损失相关性较差，这一问题被称为“基础风险”，为使用指数保险缓解气候风险造成了主要障碍。在此背景下，科学家如何为设计更智能的指数保险产品做出贡献，以满足农民、保险公司和政府的需求？该项目的总体目标是通过使用最先进的环境模型和大数据集来改善基于指数的农作物保险目前表现不佳的情况，以降低基础风险并更好地保护农民免受天气风险的影响。我们提出的研究将开发基于天气指数的新型保险合同，通过结合作物生长模型、作物生长状态的卫星和智能手机图像以及空间天气变化的高分辨率网格估计，可靠、准确地预测与天气相关的作物产量损失。重要的是，我们的工作将产生新颖的工具和方法，以满足指数保险行业的两个既定需求：（i）通过设计准确反映生长季节期间对极端事件的收益率敏感性如何变化的天气指数触发器来降低时间基础风险，以及(ii) 通过利用捕捉天气条件、作物发育、田间条件和管理实践的空间异质性的数据集，最大限度地减少空间基础风险。我们与 HDFC ERGO General Insurance（一家为印度小农提供基于天气指数的保险的主要提供商）合作，将应用这些方法来设计和测试新的基于天气指数的保险产品，以保护旁遮普邦主要农业州的农民哈里亚纳邦——印度的粮仓——应对极端气温和强降雨事件的综合生产风险。利用通过最近的 IFPRI-HDFC 图片农作物保险 (PBI) 项目收集的独特现场数据，我们将进行农业经济影响评估，以量化基本风险的降低、农民福利的增加以及新合同相对于保险需求的增加。当前的指数保险产品和政府地区收益保险计划。我们的工作将直接有助于提高印度农民的指数保险质量，更广泛地说，将为基于天气指数的保险提供科学基础，以更有效地支持发展中国家的气候智能型小农农业。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identifying links between monsoon variability and rice production in India through machine learning.

DOI：
10.1038/s41598-023-27752-8
发表时间：
2023-02-10
期刊：
Scientific reports
影响因子：
4.6
作者：
通讯作者：

Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

DOI：
10.1088/1748-9326/ab5ebb
发表时间：
2019-12
期刊：
Environmental Research Letters
影响因子：
6.7
作者：
Ben Parkes;Thomas P. Higginbottom;K. Hufkens;Francisco Ceballos;B. Kramer;T. Foster
通讯作者：
Ben Parkes;Thomas P. Higginbottom;K. Hufkens;Francisco Ceballos;B. Kramer;T. Foster

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

使用作物模型和物候监测提高指数保险的绩效