Do high-resolution satellite indices at field level reduce basis risk of satellite-based weather index insurance?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wienand Kölle ◽  
Matthias Buchholz ◽  
Oliver Musshoff
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Content Type ◽  
Farm Level ◽  
Insurance Contracts ◽  
Weather Index Insurance ◽  
Field Level

PurposeSatellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.Design/methodology/approachIn this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.FindingsThe results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.Originality/valueTo the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

scholarly journals Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design

2021 ◽  
Vol 13 (9) ◽  
pp. 5207
Author(s):  
Zed Zulkafli ◽  
Farrah Melissa Muharam ◽  
Nurfarhana Raffar ◽  
Amirparsa Jajarmizadeh ◽  
Mukhtar Jibril Abdi ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Insurance Contracts ◽  
Weather Index Insurance ◽  
Streamflow Statistics

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.

Factors affecting farmers’ willingness to purchase weather index insurance in the Hainan Province of China

2015 ◽  
Vol 75 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Jia Lin ◽  
Milton Boyd ◽  
Jeffrey Pai ◽  
Lysa Porth ◽  
Qiao Zhang ◽  
...  
Keyword(s):  
Background Information ◽  
Index Insurance ◽  
Probit Regression ◽  
Weather Index ◽  
Basis Risk ◽  
Content Type ◽  
Factors Affecting ◽  
Weather Index Insurance ◽  
Main Challenge ◽  
Willingness To Purchase

Purpose – The purpose of this paper is to explain the factors affecting farmers’ willingness to purchase weather index insurance for crops in China, in the Province of Hainan, and to also provide additional background information on weather index insurance. Design/methodology/approach – A survey of 134 farmers was undertaken in Hainan, China, regarding their willingness to purchase weather index insurance. A probit regression model was used, and a number of variables were included to explain willingness of farmers to purchase weather index insurance. Findings – In total, 11 of 15 variables in the model are found to be statistically significant in explaining farmers’ willingness to purchase weather index insurance. Research limitations/implications – First, farmers’ interest in weather index insurance may be limited due to basis risk. Second, some farmers may not sufficiently understand weather index insurance and so may not purchase it, and a considerable portion of farmers may also require a subsidy if they are to purchase weather insurance. Practical implications – Weather index insurance may provide a lower cost alternative than traditional crop insurance, however, basis risk remains a main challenge. Originality/value – This is the first study to quantitatively study the factors affecting the willingness of farmers to purchase weather index insurance for agriculture in the province of Hainan, China.

Quantifying spatial basis risk for weather index insurance

2012 ◽  
Vol 14 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Michael T. Norton ◽  
Calum Turvey ◽  
Daniel Osgood
Keyword(s):  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Weather Index Insurance

Mapping the customer centric weather index insurance service design using quality function deployment

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pankaj Singh ◽  
Gaurav Agrawal
Keyword(s):  
Quality Function Deployment ◽  
Service Design ◽  
Index Insurance ◽  
Importance Rating ◽  
Customer Requirements ◽  
Weather Index ◽  
Quality Function ◽  
Content Type ◽  
Weather Index Insurance ◽  
Design Requirements

PurposeThe present paper aims to propose a framework on weather index insurance (WII) service design by using quality function deployment (QFD).Design/methodology/approachThis study utilizes QFD technique to propose a customer oriented framework on WII service design. In initial phase, customer and design requirements were gathered to derive the relationship between customers' and managers' voice for construct the house of quality (HOQ). Later on, prioritized customer and design requirements as QFD outcome were utilized to develop the action plan matrix in order to suggest the future action plans.FindingsThis study proposed a customer centric framework on WII service design to address the customer requirements. Findings show that adequate claim payments, hassle free prompt claim payment and transparency in losses computation are prioritized customer requirements with highest importance rating, whereas, accurate claim estimation, claim management system and advancement of technology are prioritized service design necessities with highest importance rating.Research limitations/implicationsThe proposed WII service design can enhance the quality of WII service by attain the higher standards of WII service in order to completely satisfy the customers.Practical implicationsThe proposed WII service design can provide a solution to the problems faced by WII industry by improve the customer's service experience and satisfaction.Originality/valueBased on best of author's knowledge, this paper first proposed a framework on WII service design by integrating customer and design requirements by using QFD.

Using a regional climate model to develop index-based drought insurance for sovereign disaster risk transfer

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Roman Hohl ◽  
Ze Jiang ◽  
Minh Tue Vu ◽  
Srivatsan Vijayaraghavan ◽  
Shie-Yui Liong
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Index Insurance ◽  
Weather Index ◽  
Content Type ◽  
Risk Transfer ◽  
Central Java ◽  
Weather Index Insurance

PurposeExamine the usability of rainfall and temperature outputs of a regional climate model (RCM) and meteorological drought indices to develop a macro-level risk transfer product to compensate the government of Central Java, Indonesia, for drought-related disaster payments to rice farmers.Design/methodology/approachBased on 0.5° gridded rainfall and temperature data (1960–2015) and projections of the WRF-RCM (2016–2040), the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are calculated for Central Java over different time spans. The drought indices are correlated to annual and seasonal rice production, based on which a weather index insurance structure is developed.FindingsThe six-month SPI correlates best with the wet season rice production, which generates most output in Central Java. The SPI time series reveals that drought severity increases in future years (2016–2040) and leads to higher payouts from the weather index structure compared to the historical period (1960–2015).Practical implicationsThe developed methodology in using SPI for historical and projected periods allows the development of weather index insurance in other regions which have a clear link between rainfall deficit and agricultural production volatility.Originality/valueMeteorological drought indices are a viable alternative for weather index insurance, which is usually based on rainfall amounts. RCM outputs provide valuable insights into future climate variability and drought risk and prolong the time series, which should result in more robust weather index insurance products.

Near real-time identification of extreme events for weather index insurance using machine learning algorithms

2021 ◽  
Author(s):  
Luigi Cesarini ◽  
Rui Figueiredo ◽  
Beatrice Monteleone ◽  
Mario Martina
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Index Insurance ◽  
Model Parameters ◽  
Weather Index ◽  
Basis Risk ◽  
Weather Index Insurance ◽  
Extreme Flood

<p>A steady increase in the frequency and severity of extreme climate events has been observed in recent years, causing losses amounting to billions of dollars. Floods and droughts are responsible for almost half of those losses, severely affecting people’s livelihoods in the form of damaged property, goods and even loss of life. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. In this type of insurance, payouts are triggered when an index calculated from one or multiple environmental variables exceeds a predefined threshold. Thus, contrary to traditional insurance, it does not require costly and time-consuming post-event loss assessments. Its ease of application makes it an ideal solution for developing countries, where fast payouts in light of a catastrophic event would guarantee the survival of an economic sector, for example, providing the monetary resources necessary for farmers to sustain a prolonged period of extreme temperatures. The main obstacle to a wider application of this type of insurance mechanism stems from the so-called basis risk, which arises when a loss event takes place but a payout is not issued, or vice-versa.</p><p>This study proposes and tests the application of machine learning algorithms for the identification of extreme flood and drought events in the context of weather index insurance, with the aim of reducing basis risk. Neural networks and support vector machines, widely adopted for classification problems, are employed exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, leveraging datasets from multiple sources with low latency, covering a time period between 2000 and 2019. Using rainfall (GSMaP, CMORPH, CHIRPS, CCS, PERSIANN and IMERG) and soil moisture (ERA5) data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the number of information provided during model training proved to be beneficial to the performances, improving their classification accuracy and confirming the ability of these algorithms to exploit big data. Results highlight the potential of machine learning for application within index insurance products.</p>

Modelling the barriers of weather index insurance service adoption integrating expert mining and ISM Fuzzy-MICMAC

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pankaj Singh ◽  
Gaurav Agrawal
Keyword(s):  
Structural Model ◽  
Index Insurance ◽  
Second Phase ◽  
Literature Study ◽  
Weather Index ◽  
Content Type ◽  
Service Adoption ◽  
Customer Base ◽  
Weather Index Insurance ◽  
Fuzzy Micmac

PurposeThe purpose of this study is to explore and prioritize the barriers that affect weather index-insurance (WII) adoption among customers by utilizing interpretive structural modelling (ISM) and fuzzy-MICMAC.Design/methodology/approachThis paper utilized the combined approach in two phases. In first phase comprehensive literature study and expert mining method have been performed to identify and validate WII adoption barriers. In second phase, ISM has been utilized to examine the direct relationships among WII adoption barriers in order to develop a structural model. Further, fuzzy-MICMAC method has been utilized to analyse indirect relationships among barriers to explore dependence and driver power.FindingsThis study has identified 15 key barriers of WII adoption among customers and developed a structural model based on binary direct relationship using ISM. Later, the outcomes of ISM model have been utilized for analysing the dependence and driver power of each WII adoption barriers in cluster form using fuzzy-MICMAC. The customer awareness related WII adoption barrier are mainly at the top level, WII demand related barriers are in the centre and WII supply related barriers at the bottom level in ISM model.Practical implicationsThe findings offered important insights for WII insurers to understand mutual relationships amongst WII adoption barriers and assists in developing strategy to eliminate dominant key barriers in order to enhance their customer base.Originality/valueBased on best of author's knowledge this paper firstly integrates the ISM fuzzy-MICMAC method into identification and prioritization of barriers that affects WII adoption among customers.

scholarly journals Incorporating Satellite Data Into Weather Index Insurance

2016 ◽  
Vol 97 (10) ◽  
pp. ES203-ES206 ◽  
Author(s):  
Emily Black ◽  
Helen Greatrex ◽  
Matthew Young ◽  
Ross Maidment
Keyword(s):  
Satellite Data ◽  
Index Insurance ◽  
Weather Index ◽  
Weather Index Insurance

The impact of weather index insurance on agricultural technology adoption evidence from field economic experiment in China

2019 ◽  
Vol 11 (4) ◽  
pp. 622-641
Author(s):  
Yingmei Tang ◽  
Yue Yang ◽  
Jihong Ge ◽  
Jian Chen
Keyword(s):  
Technology Adoption ◽  
Field Experiment ◽  
Rural China ◽  
Rural Households ◽  
Agricultural Technology ◽  
Index Insurance ◽  
Weather Index ◽  
Content Type ◽  
Weather Index Insurance ◽  
The Impact

Purpose The purpose of this paper is to empirically investigate the impact of weather index insurance on agricultural technology adoption in rural China. Design/methodology/approach A field experiment was conducted with 344 rural households/farmers in Heilongjiang and Jiangsu Provinces, China. DID model was used to evaluate farmers’ technology adoption with and without index insurance. Findings The results show that weather index insurance has a significant effect on the technology adoption of rural households; there is a regional difference in this effect between Heilongjiang and Jiangsu. Weather index insurance promotes technology adoption of rural households in Heilongjiang, while has limited impact on those in Jiangsu. Weather, planting scale and risk preference are also important factors influencing the technology adoption of rural households. Research limitations/implications This research is subject to some limitations. First, the experimental parameters are designed according to the actual situation to simulate reality, but the willingness in the experiment does not mean it will be put into action in reality. Second, due to the diversity of China’s climate, geography and economic environment, rural households are heterogeneous in rural China. Whether the conclusion can be generalized beyond the study area is naturally questionable. A study with more diverse samples is needed to gain a fuller understanding of index insurance’s effects on farmers in China. Originality/value This research provides a rigorous empirical analysis on the impact of weather index insurance on farmers’ agricultural technology adoption through a carefully designed field experiment.

Sign in / Sign up

Export Citation Format

Share Document