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The purpose of this paper is to analyze the extent to which weather index‐based insurances can contribute to reducing shortfall risks of revenues of a representative average farm that produces corn or wheat in the North China Plain (NCP). The geographical basis risk is quantified to analyze the spatial dependency of weather patterns between established weather stations in the area and locations where the local weather patterns are unknown.

Data are based on the Statistical Yearbook of China and the Chinese Meteorological Administration. Methods of insurance valuation are burn analysis and index value simulation. Risk reduction is measured non‐parametrically and parametrically by the change of the standard deviation and the value at risk of revenues. The geographical basis risk is quantified by setting up a decorrelation function.

Results suggest significant differences in the potential risk reduction between corn and wheat when using insurance based on a precipitation index. The spatial analysis suggests a potential to expand the insurance around a reference weather station up to community level.

Findings are limited by a weak database in China and, in particular, by the unavailability of individual farm data. Moreover, the low density of weather stations currently limits the examination of the approach in a broader context.

The risk reduction potential of the proposed insurance is encouraging. From a policy point of view, the approach used here can support the adjustment of insurers towards different crops.

This paper is believed to be the first that investigates a weather index‐based insurance designed for an average farm in the NCP and the quantification of geographical basis risk.

The purpose of this paper is to review some challenges of insuring weather risk in agriculture and to discuss potential remedies for these problems.

The paper is developed as a narrative on weather insurance based largely on existing literature.

Weather risks show characteristics that often violate classical requirements for insurability. First, some weather risks, particularly slowly emerging weather perils like drought, are spatially correlated and cause systemic risks. Second, climatic change may increase the volatility of weather variables and lead to non-stationary loss distributions, which causes difficulties in actuarial ratemaking. Third, limited availability of yield and weather data hinders the estimation of reliable loss distributions.

Some of the approaches discussed in this review, such as time diversification, local test procedures and the augmentation of observational data by expert knowledge, can be useful for crop insurance companies to improve their risk management and product design.

This study provides background and development information regarding weather insurance and also presents statistical tools and actuarial methods that support the assessment of weather risks as well as the design of weather and yield insurance products.

The authors investigate whether an index-based weather insurance (WII) product can complement or replace existing traditional crop yield insurance for mitigating farmers' financial risks, with an application to blueberry growers in British Columbia (BC).

A hybrid model combining expected utility (EU) and prospect values is developed to analyse farmers' demand for WII.

While weather data are used to investigate supply elements, a hybrid model combining EU theory and prospect theory (PT) is developed to analyse farmers' demand for WII. On the supply side, a quality index is constructed and the relationship between the quality index and key weather parameters is quantified using a partial least squares structural model. The authors then model weather parameters via time-series analysis and statistical distributions to provide reasonable estimates for calculating actuarially sound insurance premiums for a rainfall indexed, insurance product. This model indicates that decreases in the proportion of a blueberry grower's total revenue and revenue volatility will decrease the possibility that they participate in WII. At the same time, an increase in the value loss aversion coefficient and WII's basis risk further leads to less demand for WII. In short, a grower may decide not to participate in WII at an actuarially fair premium due to the combined effects of the above factors. Overall, while the supply analysis enables us to demonstrate that WII can potentially help in mitigating farmers' financial risks, it turns out that, on the demand side, blueberry growers are unwilling to pay for such a product without large government subsidies.

The authors argue that the demand for insurance may be affected by the level and the volatility of a berry grower's total revenue. Hence, the authors propose a hybrid expression that assumes a farmer seeks to maximize the total utility function to capture the rational and intuitive parts of a farmer's decision-making process. The EU represents rationality and the prospect value represents the intuitive component. Meanwhile, the authors investigate the possibility of using key weather parameters to construct a berry quality index – one that could be applied to other agricultural areas for studying the relationship between weather conditions and product quality.

The purpose of the present paper is to review studies on weather index-insurance as a tool to manage the climate change impact risk on farmers and to explore the study gaps in the currently existing literature by using a systematic literature review.

This study analyzed and reviewed the 374 articles on weather index insurance (WII) based on a systematic literature search on Web of Science and Scopus databases by using the systematic literature review method.

WII studies shifted their focus on growing and emerging areas of climate change impact risk. The finding shows that the impact of climate change risk significantly influenced the viability of WII in terms of pricing and design of WII. Therefore, the cost of WII premium increases due to the uncertainty of climate change impact that enhances the probability of losses related to insured weather risks. However, WII has emerged as a risk management tool of climate insurance for vulnerable agrarian communities. The efficacy of WII has been significantly influenced by repetitive environmental disasters and climate change phenomena.

This study will be valuable for scholars to recognize the missing and emerging themes in WII.

This study will help the policy planners to understand the influence of climate change impact on WII viability.

This study is the original work of the author. An attempt has been made in the present study to systematically examine the viability of WII for insuring the climate change risk.

The purpose of this paper is to review research on weather index insurance (WII) for mitigating the weather risk in agriculture and to identify research gaps in current available literature through integrative review.

This paper is based on the integrative review method as proposed by Whittemore and Knafl. QualSysts tool was adopted for assessing the quality appraisal of articles. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Detailed critical analysis of content reveals that WII studies are growing and shifting from traditional to the newest themes. Efficacy of WII is significantly influenced by the impacts of climate change. This paper generates a conceptual framework by synthesizing the published literature on WII.

This paper will be used to improve the WII practices and influence public policy. It is also beneficial in research by contributing to the systematic body of knowledge and useful for researchers to analyze the past and present status with future prospects of further studies on WII.

The paper is the original work of the author. To the best of authors’ knowledge, this is the first paper on integrative review on the efficacy of WII. An attempt has been made in the current paper to critically examine the studies of WII.

The purpose of this paper to develop an empirical methodology for managing spatial basis risk in weather index insurance by studying the fundamental causes for differences in weather risk between distributed locations.

The paper systematically compares insurance payouts at nearby locations based on differences in geographical characteristics. The geographic characteristics include distance between stations and differences in altitude, latitude, and longitude.

Geographic differences are poor predictors of payouts. The strongest predictor of payout at a given location is payout at nearby location. However, altitude has a persistent effect on heat risk and distance between stations increases payout discrepancies for precipitation risk.

Given that payouts in a given area are highly correlated, it may be possible to insure multiple weather stations in a single contract as a “risk portfolio” for any one location.

Spatial basis risk is a fundamental problem of index insurance and yet is still largely unexplored in the literature.

Agricultural weather index insurance (WII) has been introduced in pilot or experimental form in many countries. However, the effective demand for WII is often limited by the impact of the basis risk. Thus, the purpose of this paper is to propose a new type of double trigger product, named “supplement” type, to reduce basis risk and improve the performance of the standalone WII.

Two measures of performance are introduced by the certainty equivalent income of expected utility theory. Through the Monte Carlo experiments and empirical study, this paper compares the performance of three types of double trigger products.

The findings indicate that the supplement type can significantly improve the performance of the single weather index product. First, it covers the downside basis risk and the catastrophic basis risk when the standalone WII fails to do so, especially in case of extreme losses. Second, it is superior when the correlation between the weather index and the yield index is not so strong, and can further enhance the performance of insurance even when the weather index and the yield index are highly correlated, for which the standalone WII could perform well.

The supplement type double trigger product proposed in this paper as an enhancement version finds a more preferable way to improve the standalone WII with relative lower complexity.

The purpose of this paper is to assess the losses of weather‐related insurance at different regional levels. The possibility of spatial diversification of insurance is explored by estimating the joint occurrence on unfavorable weather conditions in different locations, looking particularly at the tail behavior of the loss distribution.

Joint weather‐related losses are estimated using copulas. Copulas avoid the direct estimation of multivariate distributions but allow for much greater flexibility in modeling the dependence structure of weather risks compared with simple correlation coefficients.

Results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a large regional scale. Thus the possibility to reduce risk exposure by increasing the trading area of insurance is limited.

The empirical findings are limited by a rather weak database. In that case the estimation of high‐dimensional copulas leads to large estimation errors.

The paper includes implications for the quantification of systemic weather risk which is important for the rate making of crop insurance and reinsurance.

This paper's results highlight how important the choice of the statistical approach is when modeling the dependence structure of weather risks.

Although the microfinance sector in developing countries has seen an impressive development in recent years, many small-scale farmers in rural areas are still undersupplied with capital. One of the main reasons for this undercapitalization is the exposure to weather risks. Weather index insurance is assumed to bear high potential for accelerating agricultural lending. The index design hereby is of particular importance. The purpose of this paper is to estimate the influence of evapotranspiration and precipitation indices on the credit risk of farmers in Madagascar.

The authors base the analysis on a unique borrower data set provided by a commercial microfinance institution in Madagascar and weather data provided by CelsiusPro. In this context, evapotranspiration and precipitation indices both at aggregated bank level and at branch level are identified and their influence on credit risk of small-scale rice farmers is estimated.

The results show that the weather-related part of the credit risk of farmers can be better explained by an evapotranspiration then by a precipitation index. The precipitation index underestimates the weather influence on credit risk especially during the harvesting season. The results suggest a potential for weather index insurance which is based on an evapotranspiration index. The results are of similar importance for developed and developing countries.

The results suggest that, should insurance be considered as an appropriate risk management instrument for the farmers or the bank, weather index insurance has the potential to mitigate a certain part of the credit risk. The authors also find that the focus on precipitation-based index insurance products would underestimate the weather influence on credit risk. Furthermore, the results suggest that insurance products should be tailored to branches to be most effective.

To the authors’ knowledge, this is the first study that compares the explanatory values of evapotranspiration and precipitation indices in general and for the credit risk of small-scale farmers in particular.

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.