Poverty caused by disasters poses a great challenge to consolidate the achievements of poverty alleviation. Livelihood resilience is the key factor for farmers to resist risks and get rid of poverty. Therefore, this study used the China Family Panel Studies (CFPS) database. Firstly, we examined the impact of natural disasters on the poverty vulnerability of farmers. Secondly, taking livelihood resilience and its decomposition dimensions as threshold variables, we examined the mechanism of livelihood resilience between natural disasters and poverty. The results show that natural disaster shocks, natural disaster intensity, and natural disaster frequency all had a significant positive effect on farm households’ vulnerability to poverty. The threshold test shows that natural disasters had larger effects on the poverty vulnerability of the farmers with lower buffer capacity, self-organizing capacity, and learning capacity. When the livelihood resilience value exceeded the third threshold, the impact of natural disasters on the poverty vulnerability of farmers turned from positive to negative. When the buffer capacity exceeded the third threshold, the impact of natural disasters on poverty vulnerability turned from positive to negative; when the self-organizing capacity exceeded the first threshold, the impact of natural disasters on poverty vulnerability turned from positive to negative; when the learning capacity exceeded the third threshold, the impact of natural disasters on poverty vulnerability turned from positive to negative. Therefore, it is suggested that appropriate policies should be needed to support farmers’ livelihood resilience and address disaster-induced poverty by improving farmers’ buffer capacity, self-organizing capacity, and learning capacity. Focusing on farmers’ livelihood resilience, government should establish a policy support system aimed at improving farmers’ buffer capacity, self-organizing capacity, and learning capacity, that will help farmers to escape from disaster-induced poverty.

The Chinese government encourages rural economic entities to use farmland management rights as collateral for loans, which helps to alleviate multi-level financing needs in rural areas. Based on the panel data of counties in Hubei Province, this paper adopts the Difference-in-Differences (DID) and the intermediary effect model to evaluate the impact of farmland management rights mortgage loans (FMRML) on the agri-food industrial agglomeration (AIA) in China. The study found that the pilot policy has significantly promoted the AIA. Moreover, the regression results remain robust after conducting the placebo test and the Propensity Score Matching Difference-in-Differences (PSM-DID) model, which demonstrates that the improvement effect is stable and long-lasting. From the heterogeneity analysis, it can be seen that the policy of FMRML has a more significant effect on the AIA in mountainous and hilly areas. By further analysis of the mechanism of action, it can be concluded that the pilot policy promotes the AIA by enhancing agricultural specialized production. The main findings can provide information for policymakers in China. The recommendations we have summarized encompass gradually expanding the scope of the pilot policy of FMRML, advancing the institutionalization and legalization of the policy, and promoting agricultural production specialization.

In 2020, China announced the successful completion of its poverty alleviation mission, noting that the focus of China’s poverty alleviation mission has shifted from eliminating absolute poverty to alleviating relative poverty. Due to global warming and frequent natural disasters, natural disaster shocks have seriously affected farmers’ livelihoods and aggravated relative poverty. Based on 5,804 rural household samples from the China Family Panel Studies, the impact of natural disasters on farmers’ relative poverty was investigated using the logit model. In addition, the interaction terms between the impact and intensity of natural disasters, non-agricultural employment and productive investment were included in the model. The results show that: 1) Natural disaster shocks and natural disaster intensities had a significant positive impact on farmers’ relative poverty. 2) Migrating for work and stable employment effectively alleviated the positive impact of natural disaster shocks and natural disaster intensities on farmers’ relative poverty, respectively. 3) Productive investment weakened the positive impact of natural disaster shocks on farmers’ relative poverty. 4) Scale management effectively alleviated the positive impact of natural disaster shocks on farmers’ relative poverty, but the moderating effect of scale management was not significant in areas with high disaster intensities.

Vulnerability research is an active option for fisheries to adapt to climate change. Based on the vulnerability analysis framework of the vulnerability scoping diagram, a vulnerability evaluation index system for inland fisheries in China was constructed in three dimensions, including exposure, sensitivity and adaptive capacity. The entropy method was used to evaluate the flood disaster vulnerability of China’s inland fisheries from 2010 to 2019 and its decomposition. The temporal and spatial differences between vulnerability and its decomposition were analyzed. Kernel density estimation and factor contribution model were used to analyze the changing trend of vulnerability and main influencing factors. The results show that: during the study period, the vulnerability of inland fisheries in China to flood disasters showed a fluctuating downward trend, and the high vulnerability areas were mainly distributed in South China and the middle and lower reaches of the Yangtze River; the exposure index first decreased and then increased, and the high-exposure regions were mainly concentrated in the middle and lower reaches of the Yangtze River; the sensitivity index first decreased and then increased, and the high-sensitivity areas were concentrated in North-east China, the middle and lower reaches of the Yangtze River, and South China; the adaptive capacity index showed a downward trend, and the areas with lower adaptive capacity were concentrated in the South-west and North-west. From the factor contribution model, the economic losses of fishery floods and the affected area had the greatest impact on the exposure index; fingerling production and freshwater fishery production had the greatest impact on the sensitivity index; the index with a lower contribution to the adaptive capacity index was the total power of fishery machinery and fishery technology promotion. Therefore, building reservoirs, optimizing aquaculture layout and promoting fishery modernization are the keys to reducing the vulnerability of inland fisheries to flood disasters.

Flood is one of the main problems faced by agricultural production in China. The research of agriculture’s floods vulnerability is the premise of scientifically dealing with floods. Based on the vulnerability assessment framework of “sensitivity-exposure-adaptability,” this paper selects 14 evaluation indicators from three aspects: sensitivity, exposure, adaptability, and the index weights which are determined by the entropy weight method to evaluate the sensitivity, resilience, and vulnerability of flood In terms of time, China’s overall flood vulnerability shows a trend of increasing first and then decreasing. From a spatial point of view, the number of highly vulnerable areas is relatively small which are mainly concentrated in Henan, Hubei, Anhui and other provinces, and most areas of the country are at low and mild levels. From the factor analysis model, the main contributing factors of agricultural flood exposure, sensitivity and adaptability are soil erosion control area, forest coverage rate, total reservoir capacity and total power of agricultural machinery. Therefore, controlling soil erosion, increasing forest coverage, further improving water conservancy facilities and strengthening agricultural mechanization level are the keys to reduce vulnerability of agricultural floods.