Food security is the cornerstone that ensures the stable development of a country. Based on panel data of 31 provinces (including autonomous regions and municipalities) in China from 2015 to 2019, we use the mediating effect model to explore the mechanism by which food consumption structure affects food security. The results indicate that grain consumption has a significant promoting effect on food security, while plant and animal food consumption have significant inhibiting effects on food security. Furthermore, agricultural R&D and investment play mediating roles in the impact of food consumption structure on food security. Obvious differences exist in the relationship between food consumption structure and food security between urban and rural areas, as well as between Eastern, Central, and Western regions. Animal food consumption had a negative and significant impact on food security, with a stronger effect on rural residents than on urban residents. Compared with the central and western regions, grain consumption and animal food consumption in the eastern region had a stronger marginal impact on food security. This paper enriches and expands the research on influencing factors of food security from the perspective of consumer demand, which has important theoretical value and practical significance for ensuring food security.

IntroductionContract farming is seen as a tool to create new market opportunities that can address market imperfections in many developing countries and thus increase smallholder income.MethodsThis study examines the impact of contract farming on farm household income using survey data from 610 rural households in China. The propensity score matching method addresses the sample selection bias of participation in contract farming.ResultsContract farming can significantly increase farmers’ income, and both marketing contracts and production-management contracts can substantially increase farmers’ income levels, with production-management contracts having a greater degree of impact. Additional analysis reveals that breeding years, farm size, and training time can significantly affect how contract farming enhances farmers’ income. At the same time, contract farming can also considerably improve farmers’ technical efficiency in agricultural production. Participation in contract farming enhances the tendency to centralize the technical efficiency of agricultural production. Further analysis shows that the technical efficiency of agricultural production partially mediates the effects of contract farming on farm household income.DiscussionContract farming can be an effective institutional arrangement for improving the technical efficiency of farm household production and revenue. We also point out that farmers should strengthen their comprehensive ability levels and actively participate in training to acquire new knowledge and improve their cognitive ability. Simultaneously, according to the characteristics of farmers’ resource endowments, small farmers are encouraged to cooperate with companies in depth and develop contractual contracts in a targeted manner. Promote win-win cooperation and benefit-sharing among various business entities to promote the sustainable and high-quality development of China’s beef cattle industry.

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Credit risk evaluation innovation is of incredible importance to monetary establishments. AI innovation can fundamentally work on the precision and versatility of credit risk evaluation. This paper aims to study the risk assessment of operator big data Internet of Things credit financial management based on machine learning. It proposes machine learning-related algorithms, including the introduction of logistic model and decision tree model, as well as related concepts of credit financial management risk. This paper proposes that big data can be better used to reduce financial risk management problems and proposes specific actions based on the actual situation of the company. This paper selects company A for financial risk management evaluation through case analysis and compares it with three major e-commerce companies. The experimental results show that the earnings per share of company A is between −0.99 and 0. Company A is still in a state of loss in recent years, and there are certain debt risks, operational risks, and capital risks.