scholarly journals Addressing Fractional Dimensionality in the Application of Weather Index Insurance and Climate Risk Financing in Agricultural Development: A Dynamic Triggering Approach

2019 ◽  
Vol 11 (4) ◽  
pp. 901-915 ◽  
Author(s):  
Calum G. Turvey ◽  
Apurba Shee ◽  
Ana Marr
Keyword(s):  
Agricultural Development ◽  
Seasonal Rainfall ◽  
Climate Risk ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Climate Risks ◽  
Weather Index Insurance ◽  
The Mean ◽  
Rainfall Patterns

Abstract Climate risk financing programs in agriculture have caught the attention of researchers and policy makers over the last decade. Weather index insurance has emerged as a promising market-based risk financing mechanism. However, to develop a suitable weather index insurance mechanism it is essential to incorporate the distribution of underlying weather and climate risks to a specific event model that can minimize intraseasonal basis risk. In this paper we investigate the erratic nature of rainfall patterns in Kenya using Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall data from 1983 to 2017. We find that the patterns of rainfall are fractional, both erratic and persistent, which is consistent with the Noah and Joseph effects that are well known in mathematics. The erratic nature of rainfall emerges from the breakdown of the convergence to a normal distribution. Instead we find that the distribution about the average is approximately lognormal, with an almost 50% higher chance of deficit rainfall below the mean than adequate rainfall above the mean. We find that the rainfall patterns obey the Hurst law and that the measured Hurst coefficients for seasonal rainfall pattern across all years range from a low of 0.137 to a high above 0.685. To incorporate the erratic and persistent nature of seasonal rainfall, we develop a new approach to weather index insurance based upon the accumulated rainfall in any 21-day period falling below 60% of the long-term average for that same 21-day period. We argue that this approach is more satisfactory to matching drought conditions within and between various phenological stages of growth.

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.

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

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>

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.

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 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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