Area Yield Index Research Articles

Crop index insurance as a tool for climate resilience: lessons from smallholder farmers in Nigeria

Crop farmers in arid and semi-arid regions face a covariate risk that is of paramount importance, namely, the threat of catastrophic crop loss triggered by natural disasters and climate hazards. Such an occurrence not only jeopardizes the livelihoods of these farmers but may also lead to persistent poverty. Promoting sustainable development requires control over climate-related shocks, which negatively affect the most vulnerable population in the developing world. This study investigates the option of choosing crop area-yield index insurance to mitigate the adverse effects associated with climate stress. In addition, we assessed the amount farmers are willing to pay for an insurance product that insures yields below 70% of the local average in the study area. Our results show that access to extension services and economic association membership offers farmers social capital and encourages their decision to purchase index insurance. We also find that the long-run welfare impact of index insurance policy on vulnerable households could be significant as an alternative insurance mechanism to traditional insurance. This study contributes to filling the gaps on the uptake of index insurance and provide guidance to policymakers in their approach to mitigating the effects of climate change on crop production in Nigeria.

Rice crop insurance in Indonesia: adaptation to climate and farm production support

Indonesia’s Rice Crop Insurance (AUTP) scheme has successfully protected farmers from significant crop losses due to natural disasters. However, the current amount of AUTP’s compensation is still unable to accommodate farmer’s financing costs (i.e., unpaid micro-loan and its interests) to support crops production. This results in higher micro-loan risk and hinder the sustainability of farming enterprises. In this regard, the existing People’s Business Credit (KUR) should supposedly be accessible as a micro-loan source to help farmers fund their farms. This study has two objectives: (1) formulate an integration scheme between KUR and AUTP, and (2) determine the appropriate insurance premiums to meet the farmer’s operational and financing costs. This research used 100,000 Monte Carlo Simulations using lognormal distributions with assumptions based on the results of focus group discussion and in-depth interviews with farmer groups, the local Agriculture Service, and micro-loan distributors, as well as the data from the Ministry of Agriculture from the period of 2018–2020. Additionally, Individual Area Yield Index (I-AYI) policy and loss ratio is used to determine and evaluate the new integrated crop insurance premiums. The study revealed that the farmers expect affordable, accessible, and beneficial insurance products with premium subsidies bundled with KUR. Therefore, the government should develop an integration of crop insurance with KUR and determine the affordable premium calculations along with the insurance companies. Based on the simulation results, the total pure premium is estimated around IDR 1 million for a minimum KUR loan of IDR 8 million (suitable for farming costs).

A Case Study on Disaster Insurance in Kenya

This study aims to provide a comprehensive outlook on the active disaster insurance scene in the East African country, Kenya. It begins with a geographic and economic analysis of the country followed by its historical challenges with natural disasters, namely droughts. The study is both qualitative and quantitative in nature and mostly deals with secondary sources of data. Establishing the current status of the country, the discussion brings into light the three insurance programs that have gained prominence there in the last decade: Kenyan Livestock Insurance Program (KLIP), Area Yield Index Insurance (AYII), and Kilimo Salama or Safe Agriculture. These programs use state of the art remote sensing technology to determine the availability of pasture, crop yields, etc. through standardized vegetation indices. Once the indices reach a predetermined minimum threshold, they trigger the payout mechanisms of the respective programs, economically uplifting the vulnerable communities involved and avoiding potential disaster. The programs are partially subsidized by the government which allows ease of adoption for local communities and helps stabilize the economy by keeping the agriculture and livestock sectors in balance. The study also acknowledges the limitations faced by the programs in order to provide a more realistic depiction and aims to encourage the piloting of similar programs in other developing nations like Bangladesh.
 Res. Agric., Livest. Fish.8(1): 57-64, April 2021

Determining Critical Yield Index of Area Yield Insurance based on Basis Risk Constraint

Area yield index insurance at district level faces heterogeneous basis risk due to geographical conditions which implies to obtain unprecise critical index . Clustering and zone-based area yield scheme can reduce heterogeneous basis risk that leads to determine the suitable alternative for . On the previous research, we have obtained 7 clusters and 2 level of paddy productivity based on clustering assumption from primary data in Java. The suitable clustering assumption for calculating is cluster based assumption, which gives the homogeneous paddy productivity under 7 clusters in Java. Therefore, our goal is to develop area yield index at district level (cluster based) with minimize basis risk at certain constraints for paddy farmer productivity in Java Indonesia. There are some methods for calculating such as mean, median, winsor mean, one sigma, two sigma and (first quartile) method on the basis risk constraints using confusion matrix. Furthermore, two basis risk constraints are the difference between overpayment and shortfall is not extremely far, and total basis risk does not exceed 20% of its total claim occurrence. Two sigma method has the lowest basis risk, overpayment, and shortfall, but it has lowest pure premium, small probability of claim, and low range of claim. Hence, we consider to use (first quartile) method as alternative and suitable method to calculate that satisfied two basis risk constraints. In conclusion, our research provides analytical calculation for area yield index at district level with pure premium as Rp 152,151 using ( method), which is sufficient to cover the total claim and consistent with the simulation.

Cluster based area yield scheme for crop insurance policy in Java

Zone based area yield scheme becomes a potential in improving the development of area yield index for crop insurance policy in Indonesia. The characteristics of productivity varies among municipalities in Indonesia. Therefore, it will be challenging to apply area yield index policy without developing clusters, especially in Java which is considered as one of the central productivities of paddy in Indonesia. Clustering the productivity is one of the solutions to reduce the heterogeneous basis risk within municipalities of a province. Clustering areas is recommended in order to determine accurate critical yield index leading to a more precise ratemaking in the process of developing area yield index policy. In order to define the clusters, we have not only considered average and volatility of productivity over the past ten years, but also integrated the spatial effect of municipalities. Ward-like hierarchical method shows that clustering of municipalities within each province provides better homogeneity. It has been found that there are 12 clusters in Java tend to have low (8.33%), middle (50%), and high (41.67%) productivity. Therefore, we suggest that the Ministry of Agriculture design the area yield index policy based on these clusters and each cluster will have different critical productivity index and adjusted premium.

Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland

The need for accurate and meaningful agricultural data as the means of making vibrant policies and informed decisions, is an increasing concern for policymakers in developing countries such as Ethiopia, where such information is usually scarce. In Ethiopia, the impacts of climate change on crop yields are rarely available at the lowest administrative levels such as wards/villages, for the benefits of the grassroots' populace. Thus, this research sought to evaluate the use of crop water requirements in the estimation of crops' yield. Food and Agriculture Organization's CROPWAT 8.0 application was used to pre-determine the possibility, preceding the use of crop yield estimation model (CROWRAYEM). Both CROPWAT 8.0 and CROWRAYEM had high coefficients of determination when tested with a survey data of barley and sorghum farmers' yield for the 2015 to 2018 cropping seasons in semi-arid southern Tigray, northern Ethiopia. Furthermore, the infusion of the crop yield into a recently published area yield index insurance payout structure increases the functionality of the proposed yield estimated model (CROWRAYEM).

Grain Area Yield Index Insurance Ratemaking Based on Time–Space Risk Adjustment in China

The foundation and sustainable development of agricultural insurance involve accurately determining a premium and establishing a dynamic premium adjustment mechanism that matches the agricultural production risk. Based on the theoretical analysis of the impact of time–space risk adjustment on agricultural insurance ratemaking, we constructed a pure premium ratemaking model based on time-varying risk adjustment and a safety premium ratemaking model based on spatially dependent risk adjustment. Choosing the county grain area-yield index insurance (GAYI) in China as the research object, we obtained the following results: (1) the risk of grain yield per unit area (YPUA) and pure premium rate in most counties decreased significantly with time-varying adjustment, and we observed differences between regions; (2) grain’s spatially dependent risk has a strong negative adjustment effect on the loading factor, but the expansion of insurance underwriting can still rapidly reduce the safety premium rate, mainly due to the reduction in the spatially dependent risk; and (3) based on time-varying risk adjustment and underwriting expansion, the reduction effect of premium rates is obvious, which supports the sustainable commercial operation of agricultural insurance. These research results help to clarify the relationships of premium rates and provide implications on the sustainability of catastrophe management.

A Bootstrap Simulation for comparison of Group Risk Plan and Multi-Peril Crop Insurance Policy

Agricultural insurance is one of the solutions for farmers to avoid risks such as climate change, pest, disease and price fall which increase the risk of crop failure. Indonesian Ministry of Agriculture (MoA) and Jasindo have been using subsidized Multi-Perils Crop Insurance (MPCI) and faced some disadvantages that mainly due to high risk moral hazard, adverse selection, and high administrative costs. Our research provides an alternative policy area yield index insurance known as Group Risk Plan (GRP) to handle this problem. To find homogeneous basis risk in GRP, the data was classified into two categories: up to and above two hectares. Bootstrapping approach was used to find MPCI and GRP indemnity amount for each term of harvesting period. In addition, both parametric estimation and goodness of fit test indicated appropriate distribution used by MPCI and GRP in each term of harvesting period as well. To compare the aggregate loss distribution, the conditional value at risk CV aR(X) measures the mean of excess loss exceeding V aR(X). It reflects how risky and measures the amount of money insurance company should retain. Simulations indicated that MPCI has greater CV aR(X) than GRP, which implies the higher likelihood of loss. The insurance company should have enough reserve for MPCI to cover that loss. On the other hand, the GRP policy that minimizes moral hazard and adverse selection thereby reduces administrative costs and offers potential to market at lower costs, may encourage farmers to do good farming practices that will result in the lower likelihood of loss. Therefore, GRP could be considered as an alternative crop insurance policy in Indonesia.

Area Yield Index Insurance or Farm Yield Crop Insurance? Chinese Perspectives on Farmers' Welfare and Government Subsidy Effectiveness

AbstractWe evaluate the performance of area yield crop insurance (AYCI) and farm yield crop insurance (FYCI) using farm‐level yield data from China, focusing on their effects on farmers' welfare, and their cost‐effectiveness in terms of government subsidy. Given a subsidy rate sufficient to generate a politically acceptable participation level, the price advantage of AYCI may no longer offset its higher basis risk, and consequently FYCI may be preferred by farmers. From the government's perspective, AYCI is the cheapest option to maintain reasonable farmer participation in insurance, but is not necessarily the most cost‐effective choice. Our findings suggest that, contrary to an assumption that informs many developing country agricultural insurance programmes, AYCI schemes are not necessarily preferred to FYCI. Decisions on the structure of a national agricultural insurance programme should be based on careful consideration of local conditions.

Coping with systemic risk in index‐based crop insurance

AbstractThe implementation of index‐based crop insurance is often impeded by the existence of systemic risk of insured losses. We assess the effectiveness of two strategies for coping with systemic risk: regional diversification and securitization with catastrophe (CAT) bonds. The analysis is conducted in an equilibrium pricing framework which allows the optimal price of the insurance and the number of traded contracts to be determined. We also explore the role of basis risk and risk aversion of market agents. The model is applied to a hypothetical area yield insurance for rice producers in northeast China. If yields in two regions are positively correlated, we find that enlarging the insured area leads to higher insurance premiums. Unless capital market investors are very risk averse, a CAT bond written on an area yield index outperforms regional diversification in terms of certainty equivalents of both farmers and insurers.

Evaluating the Potential of Index Insurance Schemes to Reduce Crop Yield Risk in an Arid Region

AbstractWe evaluate yield risk reduction through weather index, area yield index and farm yield insurance contracts for wheat farms in Kazakhstan by employing data from 1980 to 2002. We use the usual mean variance (MV) approach and also a second‐degree stochastic dominance (SSD) criterion. While MV is not necessarily consistent with the expected utility (EU) theory, SSD results only in a minimum but EU‐consistent benefit from insuring. Differences in the estimation results for both approaches underline the advantage of applying both criteria to analyse the risk‐reducing potential of crop insurance. Bootstrapping results show that none of the analysed insurance schemes provides statistically significant risk reduction for every single farm. In addition, weather‐based index insurance is found to provide less risk reduction than area yield insurance based on the rayon (county) yield. Moreover, rayon yield index insurance can reduce yield risk more effectively for Kazakhstan's wheat producers than farm yield insurance with a low strike yield.