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Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices – namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil moisture Index (SSI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI) – are used to evaluate the conditional probability. Non-stationary analysis is carried out for SPEI and SSI to incorporate the impact of large-scale oscillations and regional hydrological variability. Copula analysis is performed between drought conditions and various crop yield anomalies over Maharashtra, India, during 1998–2015. The outcomes suggest that SPEI is a significant drought indicator over the maximum number of districts in all the crops. Sea Surface Temperature (SST) and Indian Summer Monsoon Index (ISMI) are selected as suitable covariates to model the non-stationarity in the SPEI time series. The drought risk is estimated to increase with drought severity for all of the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk. [ABSTRACT FROM AUTHOR]

Weather index-based insurance schemes are gaining attention as instruments for agricultural risk management. A key difference between these insurance schemes and more traditional ones is that the former can cope more effectively with adverse selection and moral hazard issues, yielding less expensive insurance contracts. In this article, we argue that index-based crop insurance schemes can be particularly promising in Bolivia and discuss the essential technical requirements and methodological steps for igniting supply of these policies. Using daily rainfall data between 1967 and 2017, pilot insurance schemes for wheat and potato crops are developed for Anzaldo, one of Bolivia's poorest agricultural-dependent rural municipalities. These policies are compared with the country's current public fully subsidized crop-insurance program, which builds on traditional schemes. We prove that index-based schemes that offer variable reimbursing according to climate-induced crop damage allow to manage similar climate risks with significantly lower policy prices. [ABSTRACT FROM AUTHOR]

Impacts of climatic and non-climatic risks are on the rise in the major maize-growing counties of western Kenya. We conducted a questionnaire survey of sampled maize farmers in Nandi, West Pokot, Uasin Gishu and Trans Nzoia counties, achieving 210 responses. We used a stepwise regression model to evaluate the factors influencing farmers' perceptions of farming risks. Results show that most of the sampled farmers were aware of the risks, and perceived reduced rainfall with erratic patterns to be the major climatic risk in crop production. The non-climatic factors were identified as inadequate farm size, limited extension services, land degradation and low soil fertility. The determinant factors that influence farmers' perception of climatic and non-climatic risks affecting crop production were age, farm size, income, crop production cost, marital status, the highest level of education and farming experience. Understanding the risks faced in crop production and determinants of farmers' perceptions can be important in the development and dissemination of sustainable agronomic strategies tailored towards improving crop production. [ABSTRACT FROM AUTHOR]

Rainstorms are one of the frequent natural disasters which have significant impact on agricultural production and people's lives. Considering the comprehensive influence of various factors on the rainstorm events, this study extracts four elements of rainstorms in the Liaoning Province based on meteorological data collected between 1957 and 2015. Combined four elements of rainstorms by copula function, the cumulative probability of the rainstorm events and the probability of their return period can be analysed. The results show that the joint return period of multiple factors combination is less than the return period of a single factor. The degree of accuracy of the joint return period is higher, which is consistent with the actual situation. During periods of 5, 10, 20, and 50 years, the occurrence probability varies according to the idea 'the central region is higher, while the eastern and western regions are lower'. Special attention must be paid to the low values of the region in future rainstorm disaster prevention and mitigation work. Based on return periods, the agricultural risk grade can be divided into 5 levels. In ascending order, they are level I (Fuxin, Shenyang, Tieling), II (Dalian, Jinzhou, Panjin, Liaoyang), III (Huludao, Fushun), IV (Chaoyang, Yingkou, Anshan, Benxi), and V (Dandong). [ABSTRACT FROM AUTHOR]