Index-based Insurance Research Articles

Assessing farmers’ intention to adopt drought insurance. A combined perspective from the extended theory of planned behavior and behavioral reasoning theory

Agriculture in Mediterranean climates, such as that of southern Spain, is highly vulnerable to climate change, and particularly to hydrological drought. Financial instruments such as weather index-based insurance have been developed in response to increasing climate risks. In previous studies of intention to adopt this type of insurance, little attention has been paid to farmers’ underlying psychological motivations related to their perceptions of climate change, reasons for or against insurance adoption, attitudes toward insurance, subjective norms, or perceived control over the instrument. To comprehensively address this gap, a combined exploratory model based on the theory of planned behavior and behavioral reasoning theory is proposed. Specifically, the aim of this research is to analyze the psychological factors that determine farmers' intention to adopt drought insurance. The conceptual model is tested on a sample of 146 farmers from an irrigated area in the lower Guadalquivir region of Andalusia. The data are analyzed using partial least squares structural equation modeling (PLS-SEM). Contrary to expectations, the results of the study suggest that belief in climate change does not significantly influence the reasons for or against insurance adoption, nor the attitudes toward or perceived control over it. The recommendations drawn from this study can contribute to the design of more effective policies and communication strategies that address farmers' psychological perceptions and motivations, thereby helping to boost the adoption of drought insurance.

Producer Welfare Benefits of Rating Area Yield Crop Insurance

Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran.

Blended insurance scheme: A synergistic conventional-index insurance mixture

Conventional indemnity-based insurance (“conventional insurance”) and index-based insurance (“index insurance”) represent two primary insurance types, each harboring distinct advantages depending on specific circumstances. This paper proposes a novel blended insurance whose payout is a mixture of the two, to achieve enhanced risk mitigation and cost efficiency. We present the product design framework that employs a multi-output neural network (NN) model to determine both the triggering type and the index-based payout level. The proposed framework is then applied to an empirical case involving soybean production coverage in Iowa. Our results demonstrate this blended insurance could generally outperform both conventional and index insurance in enhancing policyholders' utility.

Integrating rainfall index-based insurance with optimal crop management strategies can reduce financial risks for Australian dryland cotton farmers

Drought undermines the financial sustainability of farmers. While farmers have adopted various strategies to mitigate some drought impacts, they remain exposed to substantial drought risk. Insurance could be useful in managing climatic risks and for encouraging farmers to take sensible risks (e.g., changing their sowing date to increase yield), but it can be costly. Here, we tested whether the integration of a change in sowing date with rainfall index-based insurance could improve farmer profitability and income stability. We used the Agricultural Production Systems Simulator (APSIM)-Cotton model to simulate cotton lint yields for various sowing dates, taking into account different management strategies, across three dry-land cotton research farm sites – Dalby, Goondiwindi, and Theodore – from 1940 to 2022. We designed the index-based insurance payout when the average rainfall received during the growing season falls below a predefined level, such as the 5th, 10th, or 20th percentile of rainfall. Our study, which involved 3.9 million cotton lint simulations and 3,000 rainfall index-based insurance products, showed that combining a shift in sowing date with insurance can lead to an income improvement of up to 21.5% at some study sites. Additionally, in drought years, the income improvement for farmers who combined optimal sowing dates with rainfall index-based insurance was up to 48.0%. The framework developed in this study could aid in devising financial strategies to enhance farming resilience during climate extremes.

Climate Smart Agriculture: A Roadmap to Sustainable Food Security

Climate change (CC) and climate variability (CV) are causing irregular precipitation, rising sea levels, and frequent extreme weather events, which threaten global agricultural crop production through prolonged droughts, floods, and shifting agroclimatic zones. Addressing greenhouse gas emissions and ensuring food security are among the greatest challenges of this century. Climate Smart Agriculture (CSA) emerges as a global strategy to enhance food productivity amid these uncertainties. CSA aims to create climate-resilient agricultural systems that increase soil health, water, and nutrient efficiency, provide stable yields, and reduce greenhouse gas emissions. By improving farmers' incomes and resilience to climate impacts, CSA contributes to both climate change mitigation and adaptation. Future CSA development directions include leveraging advanced internet technology for secure agricultural information, optimizing cropping patterns and management, integrating "internet + weather" services, and implementing agricultural weather index-based insurance. These strategies offer new pathways for ecological protection, green agricultural development, and climate change mitigation.

A MODEL FOR DETERMINING PREMIUM RATES IN INDEXBASED CROP INSURANCE

The paper deals with index-based crop insurance as a tool for managing flood and drought risks in agriculture. We introduce a novel model for determining premium rates in index-based crop insurance which combines the loss cost method and the average yield method. The proposed model was applied to data related to the production of selected crops in Serbia to calculate unique premium rates for index-based insurance for cereals, industrial crops and fodder plants. The paper also outlines the prerequisites for index-based crop insurance to become technically, operationally, and financially feasible in Serbia. We propose the introduction of mandatory index-based crop insurance, along with necessary legislative amendments and subsidization of insurance premiums.

Index Insurance for Forage, Pasture, and Rangeland: A Review of Developed (USA and Canada) and Developing (Kenya and Ethiopia) Countries

Index insurance for forage, pasture, and rangeland has gained ground in policy and academic circles. Stakeholders promote it as an innovative risk management tool for enhancing resilience to drought-induced perils and providing a way for consumption smoothing to livestock producers in drought vulnerable ecosystems. Index insurance, which avoids market failures such as moral hazard, adverse selection, and transactional cost, has been piloted and implemented all over the world. To support future development and research on index-based insurance in livestock systems, operational index insurance for forage, pasture, and rangeland systems in developed (USA and Canada) and developing (Kenya and Ethiopia) countries are reviewed and compared. This paper finds some similar characteristics (huge subsidy payments—ranging from 50 to 100 percent, significant government role, low adoption, insufficient payouts, data challenges, etc.), of this product between the two regions. A major difference between the PRF and NDVI is the number of choices available to users of rainfall index insurance who face close to 3000 choice options, while NDVI users have less than 5 choice options available for them. Based on these insights, we highlight opportunities where the two regions can benchmark and improve upon their respective index insurance schemes—index-based livestock insurance (IBLI) in developing and rainfall index insurance for forage in developed regions.

Mitigating Drought Financial Risk for Water Supply Sector through Index-Based Insurance Contracts

Mitigating Drought Financial Risk for Water Supply Sector through Index-Based Insurance Contracts

Be Happy, Be Loyal? Exploring Drivers for Renewal of Mobile-Delivered Index Insurance

Agricultural microinsurance is a promising risk management tool for smallholder farmers. However, adoption rates remain low and only a small share of farmers renew their policy after the first period. With the increasing availability of cell phones, mobile-delivered insurance is gaining importance on the market. As for any agricultural microinsurance, it is essential for the longevity of a mobile-delivered insurance scheme to retain a solid customer base. To date, it is unknown what drives the decision to renew a mobile-delivered agricultural microinsurance policy. We address this question by performing mean comparisons and logistic regressions based on primary data collected from 479 smallholder farmers in Mali who purchased a mobile-delivered weather index-based insurance in 2020. Results show that the level of satisfaction with the insurance product was considerably higher among farmers who renewed. We found low levels of understanding of the product among all clients, but especially among those who did not renew. Both factors were confirmed as drivers for renewal. Consistent with previous findings, receiving a payout had the strongest effect on the decision to renew. We conclude that additional measures to foster client satisfaction as well as to promote understanding of agricultural insurance among smallholder farmers are highly recommended.

Effects of weather index insurance adoption on household food consumption and investment in agricultural inputs in Ethiopia

Weather index insurance is an innovative pro-poor climate risk management strategy that can benefit the poor. But evidence on the welfare impact of this innovation is scant. This study examines the impact of adoption of weather index-based insurance (IBI) for crops on household food consumption and investment in agricultural inputs using the difference-in-difference estimator. Results indicate that adoption of insurance has causally increased household food consumption and investment in high-risk high-return agricultural inputs. Further, accounting for the intensity of adoption, results suggest that repeated uptake of weather index insurance has cumulative lasting effect on household food consumption and investment in agricultural inputs.

Transformative crop insurance solution with big earth data: Implementation for potato in India

ContextCrop insurance has become an indispensable risk management tool in the agricultural sector because globally crops are being exposed to multiple hazards. The lack of reliable crop yield data has impacted the sustenance of area-yield crop insurance schemes. Index-based insurance, which links pay-outs to crop performance proxies rather than measured losses, is being explored to improve the effectiveness of crop insurance contracts. ObjectiveThis paper presents an innovative crop insurance scheme that has replaced the existing ‘area-yield’ approach using bias-prone crop yield estimates with the ‘area-crop performance approach’ using objectively measured satellite indices. MethodSatellite-based crop mapping, satellite and weather-based crop health indicators, field data collection and analysis, composite index generation, and insurance loss assessment are major tasks in the project. Data of Sentinel-1 and 2 satellites, weather datasets and mobile app-based field data from transplantation to harvesting of the crop constituted a huge repository of the database in this project. Metrics derived from established satellite indices, such as NDVI, LSWI and Backscatter, along with weather indices, were synthesized into a composite index of crop performance called Crop Health Factor (CHF). The input data matrix of the CHF model included eight input indicators. After data normalization, weights for these indicators were generated using the entropy technique, a proven method of information measurement that produces balanced relationships and unbiased weights. The CHF was first generated with data from the past years (2016–2019), and the resulting weights were then applied to the normalized data of the current year (2020). ResultsThe current crop insurance scheme, using CHF data instead of yield data, was implemented in the state of West Bengal, India, covering about 500,000 ha of potato across and one thousand insurance units in the 2020 crop season. The CHF and yield data from past years showed similar patterns in the majority of cases. The indemnity level was set at 70 % of the normal CHF, which was the average CHF of past years. Loss assessment and compensation payouts for the current year were determined by the extent of CHF reduction beyond the indemnity level. SignificanceThis new index-insurance scheme has many advantages over the conventional yield-based scheme in terms of transparency, objectivity and ease of implementation. There is scope for improving the composite index with additional features. Such technology-driven index-insurance schemes for field crops are expected to bring a paradigm shift in the crop insurance sector, giving rise to new business models and benefitting all the stakeholders.

Application of Index Insurance in Iran's Agriculture: case of wheat growers.

Drought-induced risk endangers farmers in arid and semi-arid regions. Insurance is recognized as an appropriate policy alternative to support farmers facing with financial losses associated with production reduction. In this context, present study developed an ex ante index-based insurance program to deal with drought-induced risk of production losses. We applied this model to wheat growers in Iran. After the calibration of the contract parameters, an insurance scheme was optimized and tested. We showed that optimal insurance contracts generate low gain of certain equivalent income, high compensation, and a high basis risk. The best contract was not proportional to the complexity of the proposed index. The insurance program studied is recommended as a proper alternative for currently applying yield insurance.

Integrating rainfall index-based insurance with optimal crop management strategies can reduce financial risks for Australian dryland cotton farmers

Integrating rainfall index-based insurance with optimal crop management strategies can reduce financial risks for Australian dryland cotton farmers

Index-based insurance to mitigate current and future extreme events financial losses for water utilities

Mitigation and management strategies for extreme weather events become increasingly important for the development and maintenance of society as these events become more frequent and intense. Drought and flood management strategies have focused on structural measures; however, they are not sufficient to prevent water supply disruptions and economic losses. In this concept, adaptation plays the role of anticipating the adverse financial impacts of extreme weather events and taking appropriate measures to minimize them. Therefore, insurance is a valuable adaptation tool to offset unexpected losses and prevent financial damage from turning into long-term economic damage. We simulated multi-year and multi-risk index-based insurance for a Water Supply System, responsible for providing water to 7.2 million people. Our methodology comprises (1) characterizing the indexed variable, (2) calculating economic losses by Loss Distribution Approach (LDA) for current and future periods, and (3) estimating risk premiums based on expected losses of the low, medium, and high coverage levels. Our findings indicate a linear relationship between premium and coverage level, i.e, the higher the premium, the higher the insurance coverage level. We suggested a premium fee for each evaluated scenario, which would be introduced into the water bill as a novel way to pool risk among customers and protect them from surcharge fluctuations.

Basis risk and the demand for catastrophic rainfall insurance

Abstract Rainfall is an important source of covariate shock in developing countries. Insurance against a rainfall index has, therefore, held much promise as a formal insurance product to protect the livelihoods of poor farmers. But how good is rainfall as a measure of covariate shocks? The imperfect association between them has been flagged as a reason for low demand for index insurance. Using district crop yields and rainfall data for India, we find that tail dependence characterizes the association between aggregate crop yields and rainfall. Could this property be exploited to design catastrophic loss insurance programs that have low basis risk? Using simulations of the estimated copulas, we show that the value of index-based insurance relative to actuarial cost is higher for insurance against extreme losses (of the index) than for insurance against all losses. We conclude that index insurance could find greater acceptability if it only insured extreme losses.

Inferring multiple coffee flowerings in Central America using farmer data in a probabilistic model

Coffee (Coffea arabica L.) is a climate-sensitive crop; rainfalls may trigger flowering event occurrences, and extreme rainfall during a flowering day can cause considerable yield reductions. Multiple flowering events can occur in the span of 12 months; the number varies from year to year. This paper introduces a Bayesian network model capable of inferring coffee flowering events in coffee areas in the Pacific Region of Central America based on observed data for coffee flowering and precipitation. The model structure was determined based on expert knowledge, and the model parametrization was learned from 53 years of data registered in the region. Data from four farms in the region were used for model validation. The model's performance in the inference of flowering intensity was good (spherical payoff of 0.78 out of maximal 1.00), and the model was able to depict expected behaviors for single and multiple flowerings. Further, comprehensive new details on the dynamics of multiple flowerings within a crop season were obtained, e.g., that a large flowering event tends to occur more quickly (8 to 10 days) after rain than a small flowering (10 to 13 days). We believe that this Bayesian network model has the potential to evolve and support the development of agricultural index-based insurance to deal with yield losses due to extreme rainfall during flowering. The use of longer farm records for model building may also serve to increase farmers' trust in the reliability of the tool.

Evaluation of the Effects of Introducing Risk Management Tools in Agricultural Development: The Case of PADAER Senegal

This study aims to assess the effects of risk management tools on the agricultural performance of rural producers benefiting from the joint support of the Senegalese state and the International Fund for Agricultural Development (IFAD) through the co-financed project PADAER. Data collection covers two regions in Senegal: Kolda and Tambacounda. The sample comprises 1167 farmers, including 379 beneficiaries of the index-based insurance facilitated by PADAER (Programme d’Appui au Développement Agricole et à l’Entreprenariat Rural). The quasi-experimental method known as the propensity score matching method was used to determine the impact of subscribing to index-based insurance on the farmer’s production, agricultural investments, and annual income. Although the results of the estimates show that the project has not yet had any effect on production, without the intervention of this project, farmers would have recorded a loss of about USD 115 (FCFA 57,600). Not only did the index-based insurance for the harvest facilitated by the PADAER allow the beneficiary to cover this loss and realize a gain estimated at USD 25 (FCFA 12,749), but the added value of this paper is that it measures the effects of agricultural index-based insurance in Africa using real-world statistical data.

Systematic process for crop insurance development: area-yield rice insurance with machine learning technology implementation in Thailand

PurposeThis paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.Design/methodology/approachCrop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.FindingsThe framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.Originality/valueThis paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.

Does weather index-based insurance adoption influence Cocoa Output? An endogenous swicth regression approach

ABSTRACT Agriculture is the most weather-dependent and risky sector where it is unpredictable when a season will be profitable during harvest. Droughts and floods may increase the risk of weather-related catastrophes in agriculture. Weather index Insurance has been widely recommended for decades to control weather risk. 600 Ashanti cocoa producers were surveyed to see how weather index insurance adoption affects cocoa output in Ghana. The endogenous switching regression (ESR) technique coupled with probit models were employed in this study. Cocoa bean production was 30.8 percent whiles only 15.8% of cocoa farmers knew about weather index insurance. The average annual farm income, off-farm, and remittances were 7.40, 7.26, and 6.61, respectively. Weather index insurance adoption and cocoa output were positively influenced by cocoa farm age, gender, education, awareness, and access to finance. We found that weather index insurance negatively influences cocoa output in Ghana due to inadequate information on insurance among farmers, lack of education, lack of awareness, and access to credit. Our findings will help Ghana Agricultural Insurance Pool and the government improve accessibility, affordability, and awareness for cocoa growers in the region and beyond. The findings will add to weather index insurance literature and improve agriculture risk.

A Review of Climate-Smart Agriculture: Recent Advancements, Challenges, and Future Directions

Global climate change has posed serious threats to agricultural production. Reducing greenhouse gas (GHG) emissions and ensuring food security are considered the greatest challenges in this century. Climate-smart agriculture (CSA) is a concept that can provide a solution to the challenges that agricultural development faces. It can do so in a sustainable way by increasing adaptability, decreasing GHG emissions, and ensuring national food security. So far, little research has systematically reviewed the progresses in CSA in developing and in developed countries. A review on the recent advancements, challenges, and future directions of CSA will be quite timely and valuable. In this paper, the definition and development goals of CSA are identified. Then, the recent advancements of CSA in developing and in developed countries are reviewed. The existing problems and challenges in CSA are analyzed and pointed out. Finally, the proposals on prospects and directions for CSA in the future are proposed. Using advanced internet technology to ensure agricultural information security, improvement of cropping patterns, and management techniques, carrying out “internet + weather” service and improving the quality of agricultural service, and conducting agricultural weather index-based insurance are considered as the main direction of future development of CSA. This review provides new ideas and strategies for strengthening ecological environmental protection, promoting agricultural green development, and mitigating climate change.