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Since the 1990s, there has been a discussion about the use of weather index‐based insurance, also called weather derivatives, as a new instrument to hedge against volumetric risks in agriculture. It particularly differs from other insurance schemes by pay‐offs being related to objectively measurable weather variables. Due to the absence of individual farm yield time series, the hedging effectiveness of weather index‐based insurance is often estimated on the basis of aggregated farm data. The authors expect that there are differences in the hedging effectiveness of insurance on the aggregated level and on the individual farm‐level. The purpose of this paper is to estimate the magnitude of bias which occurs if the hedging effectiveness of weather index‐based insurance is estimated on aggregated yield data.

The study is based on yield time series from individual farms in central Germany and weather data provided by the German Meteorological Service. Insurance is structured as put‐option on a cumulated precipitation index. The analysis includes the estimation of the hedging effectiveness of insurance on aggregated level and on individual farm‐level. The hedging effectiveness is measured non‐parametrically regarding the relative reduction of the standard deviation and the value at risk of wheat revenues.

Findings indicate that the hedging effectiveness of a weather index‐based insurance estimated on aggregated level is considerably higher than the realizable hedging effectiveness on the individual farm‐level. This refers to: hedging effectiveness estimated on the aggregated level is higher than the mean of realized hedging effectiveness on the individual farm‐level and almost every evaluated individual farm in the analysis realizes a lower hedging effectiveness than estimated on the aggregated level of the study area. Nevertheless, weather index‐based insurance designed on the aggregated level can lead to a notable risk reduction for individual farms.

To the authors’ knowledge, this paper is the first that analyzes the influence of crop yield aggregation with regard to the hedging effectiveness of weather index‐based insurance.

The purpose of this paper is to investigate the role that weather shocks can play in the livestock mortality microinsurance take-up when the insured risk has a prevalent covariant component.

The sample consists of 360 rural Ethiopian households. Data were collected in a panel-structure at the end of three agricultural seasons (2011-2013). In the questionnaire, a specific section on insurance was meant to collect information on the farmer’s willingness-to-pay (WTP) for a set of insurance products, including livestock mortality insurance. Two OLS regression models and a quantile regression model were employed to estimate the impact of weather anomalies on the WTP for the insurance product.

The authors find that weather anomalies contribute to changes in the WTP to a large extent. Negative (positive) changes in precipitation (temperature) anomalies can lead to more than a 30 percent reduction in the WTP. This general finding is complemented with the analysis of the conditional distribution of the WTP, which shows that other elements can prevail for low values of the conditional distribution. In this case, the WTP seems to be represented more by the interviewee’s age and basic knowledge of insurance, and village fixed-effects. Basic knowledge of insurance, in particular, can increase WTP by about 60 percent.

This paper has straightforward implications from a policy perspective. It suggests that farmers would prefer an insurance premium that follows the changes in the systemic component. On the contrary, insurance as well as reinsurance companies are usually reluctant to frequently revise their premiums. Financial education programs, farmer-driven design, trust building, and bundling insurance with other financial and non-financial products can increase the value proposition perceived by the farmers. From a marketing perspective, the overall findings suggest that continuous fine-tuning of the contract, transparency, and targeted information campaigns can contribute to increase and stabilize potential customers’ WTP.

To the best of the authors’ knowledge, this is the first paper that considers the impact of weather shocks on the WTP for a livestock mortality insurance product. Livestock is one of the most strategic assets of poor rural households in Africa. This study contributes to the theoretical and empirical literature on the determinants of weather insurance take-up in developing countries and, in particular, the role of spatiotemporal adverse selection and basis risk (e.g. Jensen et al., 2016).

This 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.

Crop 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.

The 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.

This 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.

The purpose of this paper is to assess farmers’ willingness to participate and pay for weather-based index insurance in the Upper East Region of Ghana, and what factors influence the participation and purchase of crop insurance schemes.

A survey of 200 farmers in the region was carried out in 2018 to measure demographic information, farm characteristics, risks and risk-management practices and attitudes to crop insurance programs. The survey also captured maximum willingness to pay (WTP) for crop insurance. The double-bounded contingent valuation technique was used to estimate the WTP for crop insurance and the variables that affected WTP.

Farmers, in general, had an indifferent attitude to crop insurance in the region, but were willing to participate in the crop insurance programme, and were willing to pay between 7.5 and 12.5 per cent of the cost of growing maize as a premium for crop insurance. Demographic and economic variables did not impact WTP, but attitude towards crop insurance, farm diversification and frequency of drought negatively impacted on the WTP for crop insurance.

Education programs could be undertaken to improve the attitude and understanding towards crop insurance, as some farmers perceived the programme as not trustworthy, and others did not truly understand the operation of the programme.

Drought can have a significant impact on household welfare, particularly in food insecure countries or regions. Crop insurance can provide a method of securing income for farmers allowing them to purchase food rather than other choices, such as removing children from education to reduce household expenses, improving the long-term welfare of the farm household.

This paper considers willingness to participate and WTP for a crop insurance programme in Ghana, it is one of a small number of papers that consider attitude to, and willingness to participate and WTP for crop insurance in developing countries. The value of the research is the expanded understanding of farmer attitude to crop insurance and their lack of knowledge of crop insurance operations.

Novel micro‐insurance schemes are emerging to help the poor better deal with droughts and other disasters. Climate change is projected to increase the intensity and frequency of disasters and is already adding stress to actual and potential clients of these schemes. As well, insurers and reinsurers are increasingly getting worried about increasing claim burdens and the robustness of their pricing given changing risks. The purpose of this paper is to review and suggest ways to methodologically tackle the challenges regarding the assessment of drought risk and the viability of index‐based insurance arrangements in the light of changing risks and climate change.

Based on novel modeling approaches, the authors take supply as well as demand side perspectives by combining risk analysis with regional climate projections and linking this to household livelihood modeling and insurance pricing. Two important examples in Malawi and India are discussed, where such schemes have been or are about to be implemented.

The authors find that indeed micro‐insurance instruments may help low‐income farming households better manage drought risk by smoothing livelihoods and reducing debt, thus avoiding poverty traps. Yet, also many obstacles to optimal design, viability and affordability of these schemes, are encountered. One of those is climate change and the authors find that changing drought risk under climate change would pose a threat to the viability of micro‐insurance, as well as the livelihoods of people requesting such contracts.

The findings and suggestions may corroborate the case for donor support for existing or emerging insurance arrangements helping the poor better cope with climate variability and change. Furthermore, a closer linkage between climate and global change models with insurance and risk management models should be established in the future, which could be beneficial for both sides.

Using cotton yield, and rainfall data from Tajikistan, the purpose of this paper is to investigate the magnitude of weather induced revenue losses in cotton production. Hereby the authors look at different risk aggregation levels across political regions (meso-level). The authors then design weather index insurance products able to compensate revenue losses identified and analyze their risk reduction potential.

The authors design different weather insurance products based on put-options on a cumulated precipitation index. The insurance products are modeled for different inter-regional and intra-regional risk aggregation and risk coverage scenarios. In this attempt the authors deal with the common problem of developing countries in which yield data is often only available on an aggregate level, and weather data is only accessible for a low number of weather stations.

The authors find that it is feasible to design index-based weather insurance products on the meso-level with a considerable risk reduction potential against weather-induced revenue losses in cotton production. Furthermore, the authors find that risk reduction potential increases on the national level the more subregions are considered for the insurance product design. Moreover, risk reduction potential increases if the index insurance product applied is designed to compensate extreme weather events.

The findings suggest that index-based weather insurance products bear a large risk mitigation potential on an aggregate level. Hence, meso-level insurance should be recognized by institutions with a regional exposure to cost-related weather risks as part of their risk-management strategy.

The authors are the first to investigate the potential of weather index insurance for different risk aggregation levels in developing countries.

Farmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.

This research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.

First, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.

This research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

Examine 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.

Based 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.

The 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).

The 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.

Meteorological 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.

Agriculture insurance is the panacea for the farming community. Many policy interventions were implemented for stimulating agriculture insurance access to farmers in India. However, access to agriculture insurance constantly remained one of the major challenges to Indian policy planners. The goal of the present paper is to explore current policy interventions in the area of agriculture insurance in India.

The present paper reviews and analyzes the evidence literature through a content analysis method on development and performance analysis perspective of existing agriculture insurance schemes in India.

Agriculture insurance is a significant risk management policy, but this is not easily reachable to the majority of farmers in India. The government of India introduces a novel agriculture scheme every decade, but every crop insurance scheme was inconsistent and ineffective owing to operational defects. Agriculture insurance in India is still developing in terms of coverage, scope, and exposure, but farmers' dissatisfaction about agriculture insurance turned out to be a negative word of mouth. Insurance illiteracy and farmers' preference for agriculture relief payments are the main reasons for limited access to agriculture insurance. The current crop insurance schemes are improperly operated because of implementation issues at the state level.

This paper will be useful for researchers and academicians to analyze the past and present status of crop insurance in India.

The paper is the unique work of the authors as it has attempted to present India's journey with agriculture insurance. An effort is made in the present study to provide a comprehensive and holistic developmental and performance analysis perspective of agriculture insurance in India.

Based on the Normalized Difference Vegetation Index (NDVI)-based insurance developed by the Ministry of Agriculture, Livestock and Fisheries of Argentina (MAGyP) with technical assistance of the World Bank (WB), the purpose of this paper is to evaluate the out-of-sample performance of the NDVI-based insurance in Bahía Blanca Department of the south west of Buenos Aires (SWBA) Province in Argentina, by calculating the technical premium with the methodology developed by MAGyP-WB and NDVI information up to 2007, and analyzing the results that would have been obtained in 2008 and 2009.

With the available NDVI information (1982-2009), the authors uses the out-of-sample method to analyze the rating of the contract and the reasonability of the premium payment through a comparison of the frequency and severity of payouts in the NDVI coverage with the losses suffered by droughts in SWBA. Specifically, it has been taken the data until 2006 to set the Triggers and Exits values and to calculate the premium rates, and the payout and loss ratio in 2007 was then analyzed. A similar analysis was done for years 2007-2008 and 2008-2009.

According with the rating methodology described in this paper, payouts determined by the NDVI-based insurance fit with falls in forage production and reduced meat production yields, which confers reasonability to this tool as a coverage option for cattle and fodder producers. Definite technical premiums based on 1982-2007 period, capture the occurrence of severe drought events, defining a risk profile that is consistent with the used information. Adding more observations to the sample, this profile is redefined, showing the sensitivity of the results to the quality and quantity of data used in the analysis.

There is a subjective assessment in the determination of the sample and the weighting of this information. The election of the period to be considered, how to incorporate the changes in the patterns of climate behavior in the medium- and long-term and the expected effects in the NDVI, etc. will impact on the values of resulting technical premiums. In the specific case of this analysis, the fact of not having considered in the sample two extreme years (2008 and 2009) has a concrete implication in the obtained premiums. In this paper, only the Bahía Blanca Department was considered. Rainfall pattern and extensive grazing of natural grassland in the SWBA area might vary from Department to Department, so the results and payouts might change according to these circumstances.

The situation shown in the findings could be seen as an underestimation of the risk to which the producer is exposed and should be taken into account for further researches and insurance products to be designed. When the considered data series shows atypical or extreme values and these are incorporated into the sample, significant changes can be registered in the triggers and premiums.

This paper analyzes how a risk profile is redefined depending on the sample considered and shows the sensitivity of the results to used data when using index-based insurance.