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The purpose of this paper is to empirically investigate the impact of weather index insurance on agricultural technology adoption in rural China.

A field experiment was conducted with 344 rural households/farmers in Heilongjiang and Jiangsu Provinces, China. DID model was used to evaluate farmers’ technology adoption with and without index insurance.

The results show that weather index insurance has a significant effect on the technology adoption of rural households; there is a regional difference in this effect between Heilongjiang and Jiangsu. Weather index insurance promotes technology adoption of rural households in Heilongjiang, while has limited impact on those in Jiangsu. Weather, planting scale and risk preference are also important factors influencing the technology adoption of rural households.

This research is subject to some limitations. First, the experimental parameters are designed according to the actual situation to simulate reality, but the willingness in the experiment does not mean it will be put into action in reality. Second, due to the diversity of China’s climate, geography and economic environment, rural households are heterogeneous in rural China. Whether the conclusion can be generalized beyond the study area is naturally questionable. A study with more diverse samples is needed to gain a fuller understanding of index insurance’s effects on farmers in China.

This research provides a rigorous empirical analysis on the impact of weather index insurance on farmers’ agricultural technology adoption through a carefully designed field experiment.

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.

The purpose of this paper is to present readers with information on the state of provision of agricultural insurance as a means of protecting financial investment in agricultural productivity in Ghana.

The paper reviews interventions in the provision of agricultural insurance in Ghana and then examines what is currently being done in this area. The paper looks at issues arising from empirical evidence on agricultural insurance provision and links them to scholarly articles on these issues.

This paper shows that there has been considerable effort from the German Development Cooperation, the Ghana National Insurance Commission and government ministries and agencies, the Insurance sector in Ghana and stakeholder institutions leading to the creation of an agricultural insurance provider in Ghana. It is, however, evident from the results that the system is facing major challenges resulting primarily from the inability of the state to provide the needed policy and regulatory support that will assist the insurance sector in the development and delivery of the agricultural insurance products.

Even though there has been some research that has touched on agricultural insurance in Ghana, none of them has actually examined the current systems of providing the insurance since its inception. The paper therefore fills the gap of providing information on the current ongoing interventions for the provision of agricultural insurance for individuals and organizations that invest in the agricultural sector in Ghana.

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.

The purpose of this paper is to explain the factors affecting farmers’ willingness to purchase weather index insurance for crops in China, in the Province of Hainan, and to also provide additional background information on weather index insurance.

A survey of 134 farmers was undertaken in Hainan, China, regarding their willingness to purchase weather index insurance. A probit regression model was used, and a number of variables were included to explain willingness of farmers to purchase weather index insurance.

In total, 11 of 15 variables in the model are found to be statistically significant in explaining farmers’ willingness to purchase weather index insurance.

First, farmers’ interest in weather index insurance may be limited due to basis risk. Second, some farmers may not sufficiently understand weather index insurance and so may not purchase it, and a considerable portion of farmers may also require a subsidy if they are to purchase weather insurance.

Weather index insurance may provide a lower cost alternative than traditional crop insurance, however, basis risk remains a main challenge.

This is the first study to quantitatively study the factors affecting the willingness of farmers to purchase weather index insurance for agriculture in the province of Hainan, China.

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.

Maize production in China is exposed to pronounced yield risks, in particular weather risk, which is one of the most important and least controllable sources of risk in agriculture. The purpose of this paper is to analyze the extent to which weather index-based insurance can contribute to reducing the revenue risk in maize production caused by yield variations. An average farm producing maize is analyzed for each of eight Chinese provinces, six of which are part of the Northern Plains of China.

Data are based on the Statistical Yearbook of China and the Chinese Meteorological Administration. The used method of insurance pricing is burn analysis. Hedging effectiveness of precipitation index-based insurance is measured by the relative reduction of the standard deviation (SD) and the Value at Risk of maize revenues.

Results reveal that precipitation index-based insurance can cause a reduction of up to 15.2 percent of the SD and 38.7 percent of the Value at Risk with a 90 percent confidence level of maize revenues in the study area. However, there are big differences in the hedging efficiencies of precipitation index-based insurance measured at different weather stations in the various provinces. Therefore, it is recommended for insurance providers to analyze the hedging effectiveness of weather index-based insurance with regard to the geographical location of their reference weather station if they would like to offer weather index-based insurance products.

The absence of individual, long-term yield data in the study area prevents the evaluation of risk on individual farms. Thus, the hedging effectiveness can only be analyzed on an aggregated level of yield data and can rather be modeled for an average farm of a particular province.

To the author's knowledge, this paper is the first that investigates the hedging effectiveness of precipitation index-based insurance designed for reducing revenue risk of maize production in eight Chinese provinces.

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.

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.