Extreme meteorological disaster effects on grain production is mainly determined by the interaction between danger degree of hazard-induced factors and vulnerability degree of hazard-affected bodies.  This paper treats physical exposure, sensitivity of the response to the impact, and capabilities of disaster prevention and mitigation as a complex system for vulnerability degree of hazard-affected bodies, which included the external shocks and internal stability mechanism.  Hazard-induced factors generate external shocks on grain production systems though exposure and sensitivity of hazard-affected body, and the result can be represented as affected area of grain.  By quantile regression model, this paper depicts the quantitative relationship between hazard-induced factors of extreme meteorological disaster and the affected area in the tail of the distribution.  Moreover, the model of production function have also been utilized to expound and prove the quantitative relationship between the affected area and final grain output under the internal stability mechanism of the agricultural natural resources endowment, the input factors of agricultural production, and the capacity of defending disaster.  The empirical study of this paper finds that impact effects of drought disaster to grain production system presents the basic law of “diminishing marginal loss”, namely, with the constant improvement of the grade of drought, marginal affected area produced by hazard-induced factors will be diminishing.  Scenario simulation of extreme drought impact shows that by every 1% reduction in summer average rainfall, grain production of Jilin Province will fell 0.2549% and cut production of grain 14.69% eventually.  In response to ensure China’s grain security, the construction of the long-term mechanism of agricultural disaster prevention and mitigation, and the innovation of agricultural risk management tools should be also included in the agricultural policy agenda.

This paper aims to evaluate the effectiveness of the Chinese crop insurance program in terms of farmers’ utility and welfare. A simulation model based on the power utility function was first developed to evaluate the effectiveness of crop insurance. Then, the Monte Carlo approach was used to generate the datasets of area, price, yield, cost, and income based on the characteristics of representative farmers, which were clustered and calibrated using the farm-level data of 574 individual farmers from five Chinese provinces. Finally, the effectiveness of Chinese crop insurance was evaluated by comparing the certainty equivalence (CE) of farmers’ utility/welfare under alternative crop insurance scenarios. Government subsidy is a necessary premise for implementing the crop insurance program. The government should subsidize more than 50% of the crop insurance premium to motivate more farmers to participate in the program. The findings also show that the current crop insurance program in China has increased the farmers’ welfare but still need to be improved to achieve the Pareto improvement and to make full use of the financial fund of the government. This paper is believed to not only extend academic research but also has significant implications for policymakers, especially in the context of rapid development of Chinese crop insurance with much issues such as rate, subsidy and coverage level needed to be improved.

Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change, and meanwhile, it is an urgent scientific issue need to be solved in agricultural risk assessment discipline. This paper developed the methodology of flood catastrophe risk assessment, which can be shown as the standard process of crop loss calculation, Monte Carlo simulation, the generalized extreme value distribution (GEV) fitting, and risk evaluation. Data on crop loss were collected based on hectares covered by natural disasters, hectares affected by natural disasters, and hectares destroyed by natural disasters using the standard equation. Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data. Block maxima model (BMM) approach based on the extreme value theory was for modeling the generalized extreme value distribution (GEV) of flood catastrophe loss, and then flood catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling flood catastrophe risk for grain production. The impact of flood catastrophe to grain production in China was significantly serious, and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China. Given the scenario of suffering once-in-a-century flood disaster, for majority of the major-producing provinces, the probability of 10% reduction of grain output is more than 90%. Especially, the probabilities of more than 15% decline in grain production reach up to 99.99, 99.86, 99.69, and 91.60% respectively in Anhui, Jilin, Liaoning, and Heilongjiang. Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.