Exploiting the Convergence of Evidence in Satellite Data for Advanced Weather Index Insurance Design

2018 ◽  
Vol 11 (1) ◽  
pp. 65-93 ◽  
Author(s):  
Markus Enenkel ◽  
Daniel Osgood ◽  
Martha Anderson ◽  
Bristol Powell ◽  
Jessica McCarty ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Smallholder Farmers ◽  
Agricultural Drought ◽  
Added Value ◽  
Stress Index ◽  
Index Insurance ◽  
Weather Index ◽  
Vegetation Greenness ◽  
Weather Index Insurance

Abstract The goal of drought-related weather index insurance (WII) is to protect smallholder farmers against the risk of weather shocks and to increase their agricultural productivity. Estimates of precipitation and vegetation greenness are the two dominant satellite datasets. However, ignoring additional moisture- and energy-related processes that influence the response of vegetation to rainfall leads to an incomplete representation of the hydrologic cycle. This study evaluates the added value of considering multiple independent satellite-based variables to design, calibrate, and validate weather insurance indices on the African continent. The satellite data include two rainfall datasets, soil moisture, the evaporative stress index (ESI), and vegetation greenness. We limit artificial advantages by resampling all datasets to the same spatial (0.25°) and temporal (monthly) resolution, although datasets with a higher spatial resolution might have an added value, if considered as the single source of information for localized applications. A higher correlation coefficient between the moisture-focused variables and the normalized difference vegetation index (NDVI), an indicator for vegetation vigor, provides evidence for the datasets’ capability to capture agricultural drought conditions on the ground. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset, soil moisture, and ESI show higher correlations with the (lagged) NDVI in large parts of Africa, for different land covers and various climate zones, than the African Rainfall Climatology, version 2 (ARC2), rainfall dataset, which is often used in WII. A comparison to drought years as reported by farmers in Ethiopia, Senegal, and Zambia indicates a high “hit rate” of all satellite-derived anomalies regarding the detection of severe droughts but limitations regarding moderate drought events.

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

scholarly journals Determinants of Participation of Smallholder Farmers in Weather Index Insurance in Embu County, Kenya

2021 ◽  
Vol 25 (3) ◽  
pp. 1-12
Author(s):  
Hezron Nyarindo Isaboke
Keyword(s):  
Mobile Phone ◽  
Smallholder Farmers ◽  
Household Head ◽  
Size Perception ◽  
Index Insurance ◽  
Weather Station ◽  
Weather Index ◽  
Weather Index Insurance ◽  
Rain Season ◽  
Land Size

The study examined how multiple factors influence participation of farmers in Weather Index Insurance WII in Embu County, Kenya. Data wer e collected from a sample of 401 smallholders following multi stage sampling technique The study employed the Cragg ’s Double Hurdle model in determining factors that influence participation and extent of participation in WII. Results revealed th at short rain season, household size, land size, perception of the household head on WII , owners h ip of a mobile phone a nd location of the farm were important factors in explaining participation in WII. The distance to a registered agro veterinary products outlet, insurance premium ,  group membership, the weather station in Runyenjes station and distan c e to the local weather station influenced probability to participate negatively. Similarly, ownership of mobile phone had a positive influence on the extent of participation in WII while the size of the household, distance to a registered agro veterinary p roducts outlet and land size were significant with a negative influence. The findings of this study highlight the importance of shaping farmers’ perceptions to wards WII, promotion of policies that allow for access and use of information and communication t echnologies ( such as mobile phones by the farming households as a pathway to providing smart so lutions to smallholder farmers in dealing with weather rela ted risks . Further, the research recommends for development of policies that would ensure modest WII insurance premiums that are aligned to the unique need s of the smallholder farmers.

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 What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

2018 ◽  
Vol 10 (11) ◽  
pp. 1819 ◽  
Author(s):  
Markus Enenkel ◽  
Carlos Farah ◽  
Christopher Hain ◽  
Andrew White ◽  
Martha Anderson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Smallholder Farmers ◽  
Added Value ◽  
Stress Index ◽  
Financial Instruments ◽  
Sources Of Information ◽  
African Countries ◽  
Rainfall Estimates

Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers.

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.

scholarly journals Farmers’ Demand for Informal Risk Management Strategy and Weather Index Insurance: Evidence from China

2021 ◽  
Author(s):  
Yingmei Tang ◽  
Huifang Cai ◽  
Rongmao Liu
Keyword(s):  
Risk Management ◽  
Field Experiments ◽  
Management Strategies ◽  
Insurance Companies ◽  
Index Insurance ◽  
Insurance Demand ◽  
Weather Index ◽  
Weather Index Insurance ◽  
Risk Management Strategies ◽  
The Government

AbstractIn the absence of formal risk management strategies, agricultural production in China is highly vulnerable to climate change. In this study, field experiments were conducted with 344 households in Heilongjiang (Northeast China) and Jiangsu (East China) Provinces. Probit and logistic models and independent sample T-test were used to explore farmers’ demand for weather index insurance, in contrast to informal risk management strategies, and the main factors that affect demand. The results show that the farmers prefer weather index insurance to informal risk management strategies, and farmers’ characteristics have significant impacts on their adoption of risk management strategies. The variables non-agricultural labor ratio, farmers’ risk perception, education, and agricultural insurance purchase experience significantly affect farmers’ weather index insurance demand. The regression results show that the farmers’ weather index insurance demand and the influencing factors in the two provinces are different. Farmers in Heilongjiang Province have a higher participation rate than those in Jiangsu Province. The government should conduct more weather index insurance pilot programs to help farmers understand the mechanism, and insurance companies should provide more types of weather index insurance to meet farmers’ diversified needs.

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