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The purpose of this paper is to examine the reduction of crop yield uncertainty using rainfall index insurances. The insurance payouts are determined by a transparent rainfall index rather than actual crop yield of any producer, thereby circumventing problems of adverse selection and moral hazard. The authors consider insurances on rainfall indexes of various months and derive an optimal insurance portfolio that minimizes the income variance for a crop producer.

Various regression models are considered to relate crop yield to monthly mean temperature and monthly cumulative precipitation. A bootstrapping method is used to simulate weather indexes and corn yield in a future year with the correlation between precipitation and temperature incorporated. Based on the simulated scenarios, the optimal insurance portfolio that minimizes the income variance for a crop producer is obtained. In addition, the impact of correlation between temperature and precipitation, availability of temperature index insurance and geographical basis risk on the effectiveness of rainfall index insurance is examined.

The authors illustrate the approach with the corn yield in Illinois east crop reporting district and weather data of a city in the same district. The analysis shows that corn yield in this district is negatively influenced by excessive precipitation in May and drought in June–August. Rainfall index insurance portfolio can reduce the income variance by up to 51.84 percent. Failing to incorporate the correlation between temperature and precipitation decreases variance reduction by 11.6 percent. The presence of geographical basis risk decreases variance reduction by a striking 24.11 percent. Allowing for the purchase of both rainfall and temperature index insurances increases variance reduction by 13.67 percent.

By including precipitation shortfall into explanatory variables, the extended crop yield model explains more fluctuation in crop yield than existing models. The authors use a bootstrapping method instead of complex parametric models to simulate weather indexes and crop yield for a future year and assess the effectiveness of rainfall index insurance. The optimal insurance portfolio obtained provides insights on the practical development of rainfall insurance for corn producers, from the selection of triggering index to the demand of the insurance.

The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather (county, 1980-2011) and yield data (township, 1989-2010) for five counties in Tai’an prefecture.

A survey with farming households is undertaken to obtain local corn prices and production costs to compute the sum insured. CRD indices are developed for five corn-growth phases. Rainfall is spatially interpolated to derive indices for areas that are outside a 25 km radius from weather stations. To lower basis risk, triggers and exits of the payout functions are statistically determined rather than relying on water requirement levels.

The results show that rainfall deficits in the main corn-growth phases explain yield reductions to a satisfying degree, except for the emergence phase. Correlation coefficients between payouts of the CRD indices and yield reductions reach 0.86-0.96 and underline the performance of the indices with low basis risk. The exception is SA-Xintai (correlation 0.71) where a total rainfall deficit index performs better (0.87). Risk premium rates range from 5.6 percent (Daiyue) to 12.2 percent (SA-Xintai) and adequately reflect the drought risk.

This paper suggests that rainfall deficit indices can be used in the future to complement existing indemnity-based insurance products that do not cover drought for corn in Shandong or for CRD indices to operate as a new insurance product.

While the demand for weather‐based agricultural insurance in developed regions is limited, there exists significant potential for the use of weather indexes in developing areas. The purpose of this paper is to address the issue of historical data availability in designing actuarially sound weather‐based instruments.

A Bayesian rainfall model utilizing spatial kriging and Markov chain Monte Carlo techniques is proposed to estimate rainfall histories from observed historical data. An example drought insurance policy is presented where the fair rates are calculated using Monte Carlo methods and a historical analysis is carried out to assess potential policy performance.

The applicability of the estimation method is validated using a rich data set from Iowa. Results from the historical analysis indicate that the systemic nature of weather risk can vary greatly over time, even in the relatively homogenous region of Iowa.

The paper shows that while the kriging method may be more complex than competing models, it also provides a richer set of results. Furthermore, while the application is specific to forage production in Iowa, the rainfall model could be generalized to other regions by incorporating additional climatic factors.

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.

The authors determine the effectiveness of the Rainfall Index Annual Forage Program (RIAFP) in offsetting yield risk of winter annual forage growers. The authors also evaluate the effectiveness in reducing risk of potential alternative weather indices.

The RIAFP is designed to compensate forage producers when yield losses occur. Prior research found weak correlation between the rainfall index and actual winter annual forage yields. The authors use long-term small-plot variety trials of rye, ryegrass, wheat, triticale and oats with rainfall recorded on site and measure the correlation of the index with actual rainfall and actual yields. The alternative indices include frequency of precipitation events and of days with temperature below freezing.

The correlation between actual rainfall and the current RMA index was strongly positive as in previous research. Correlations between forage yields and monthly intervals of the current RMA index were mostly statistically insignificant, and many had an unexpected sign. All indices had some correlations that were inconsistent across time intervals and forage variety. The inconsistent signs suggest a nonlinear relationship with weather and forage yield, indicating that rainfall can be too much or too little. The number of days below freezing has the most potential of the three measures examined.

Producers should view the winter forage RIAFP as a risk-increasing income-transfer farm program. A product to reduce the risk for forage producers may need to use a crop growth simulation model or another approach that can capture the nonlinearity.

Considerably more data were considered than in past research. Past research did not consider alternative weather indices. The program should be continued if its goal is to serve as disguised income transfer, but it should be discontinued if its goal is to reduce risk.

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.

The paper analyzes the hedging efficiency of weather-indexed insurance for corn production in Northeast of China. The purpose of this paper is to identify the potential weather variables that impact corn yields and to analyze the efficiency of weather-indexed insurance under varying thresholds for payouts (strike values).

Statistical relationships between climate variables and crop yields are used to construct weather-indexed insurance that enable a farmer to hedge against adverse precipitation outcomes. Mean root square loss is used to compare the efficiency of various weather products.

Based on efficiency comparisons, it turns out that in some, but not all circumstances, cumulative rainfall (CR) insurance can be used to hedge weather risk. When CR explains one-third or more of the variation in corn yields, a hedge can offset the revenue loss caused by the corresponding weather risk; but when it explains much less of the yield variation, it is inefficient for hedgers to buy weather insurance. If CR explains variation in crop yields, it is increasingly efficient to employ CR-indexed insurance as strike values decline for put options or increase for call options.

The paper provides a method for calculating the premium for an insurance product that provides a payout if CR in a growing season is too low.

This research is important because it illustrates the potential benefits of using weather insurance as an agricultural risk management strategy in China.

The purpose of this paper is to investigate weather risks facing Chinese farmers, and to determine whether farmers would have a preference for weather insurance over other types of agricultural insurance.

The data are based on 1,564 farm households surveyed in Shaanxi, Henan, and Gansu provinces in Central China between October 2007 and 2008.

Results suggest that the greater risk for farmers is drought followed by excessive rain. Heat is less critical as a risk but more significant than cool weather. Results suggest a strong interest in precipitation insurance with 50 and 44 percent of respondents indicating strong interest in the product. Supplementary results indicate that interest is equal between planting, cultivating, and harvesting. Furthermore, results suggest that farmers are willing to adopt new ideas, and where possible action has already been taken to self‐insure through diversification and other means.

This research is based on primary data gathered in China. However, the authors are limited in the access to Chinese weather station data to illustrate how weather insurance operates. Instead, the authors use weather data from the weather station in Ashland, Kansas which has similarities to the wheat growing regions of China. While the example is for illustrative purposes only, the authors cannot claim that it actually represents premiums that might actually be found in China.

The Chinese Government has within the past year authorized an investigation into agricultural insurance. The burst of research and applications of weather insurance in both developed and developing countries suggest that a wide array of applications could be feasible in China. The results are encouraging because they suggest that farmers in China would have an interest in purchasing weather insurance.

The authors believe that this is the first study conducted on weather insurance in China. The survey instrument is designed to specifically determine what weather risks are important to Chinese farmers and the interest that farmers would have in using such a product.

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.

The purpose of this paper is to empirically examine the financial outcomes from forage production and RI-PRF insurance interval for two locations in Nebraska. Both locations provide historical forage production and precipitation data, allowing the authors to examine the relation between RI-PRF net income and forage production.

The authors focus on evaluating the producer net income and risk (measured as variance of net income) by examining the relation between farm precipitation and production and comparing multiple insurance intervals to no insurance. Each insurance interval will likely have a different relation (basis risk) between observed production and return from insurance and, therefore, a different impact on the variance of net incomes. The impact on variance of net incomes identifies the risk-reducing aspects of RI-PRF insurance intervals. The authors then rank each scenario into four mutually exclusive zones that describe the risk-reducing effectiveness and whether the subsidy is working correctly.

The authors found both risk increasing and decreasing insurance intervals exist at both locations. One insurance scenario (low in BBR) provided the highest net income while increasing risk, suggesting a profit maximizing opportunity. RI-PRF reduces net income risk with intervals insuring during high expected precipitation (growing season); while net income risk increases with intervals insuring low expected precipitation (non-growing season, winter months). The farmer would want to insure during the high expected precipitation months, which coincides with the growing season, since RI-PRF lowers the net income risk. For the government, removing net income risk increasing intervals improves the allocation of government resources.

In this paper, the authors modeled the relation between RI-PRF interval selection using the historical forage production data at two locations in Nebraska. The use of historical forage production data allowed the authors to precisely identify the risk-reducing effectiveness of RI-PRF interval selection.