Food security is an important issue affecting people’s lives and social stability. Clarifying levels of food security and the factors affecting it (social, economic, agricultural, climatic) can help improve regional food security. The spatiotemporal patterns and driving factors of food security vary at different scales. There is, however, a lack of research that considers the various factors affecting food security at multiple scales. This study, therefore, analyzed dynamic spatiotemporal changes in food security at small (city), medium (province), and large (country) scales; identified hot and cold areas of food security; and revealed the main factors affecting food security at different scales. A food security index (FSI) was built based on the coupling of grain yield, population, and GDP, and spatial analysis was used to evaluate dynamic spatiotemporal changes in China’s food security from 1980 to 2017. Further, the relationship between food security and its driving factors was quantitatively analyzed using stepwise regression. The results showed greater heterogeneity in food security at the smaller scale than at the larger scale. The key factors affecting food security varied substantially at different scales: the added value of tertiary industry dominated the prefecture level, and gross agricultural output value was the main factor at the provincial and national levels. Multiple-scale research can reveal the status and primary factors of food security and provide a decision-making basis for improving regional food security.

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Understanding the dynamic changes of relationships between ecosystem services (ESs) and their dominant factors can effectively adjust human activities to adapt proactively to global climate change. In this study, the Huang-Huai-Hai Plain (HHHP) was selected to assess the dynamics of four key ESs (NPP, net primary productivity; WY, water yield; SC, soil conservation; FP, food production) from 2000 to 2020. The constraint lines of interactions among ESs were extracted based on a segmented quantile regression model. On this basis, the effects of both human activities and natural factors on the key features of the interactions between ESs were quantified with the help of automatic linear model. The results indicated that two types of constraint relationships, including exponential and humped-shaped, existed among the six pairs of ESs. In the past two decades, small changes in NPP thresholds would lead to large variations in other ESs thresholds. Precipitation and normalized difference vegetation index were the key factors to determine the constraint strength of ESs in the HHHP. The potential maximum value of WY in the HHHP could be increased by adjusting landscape shape to make it more complicated. This study helps to improve the potential of target ESs and provides a decision-making basis for promoting regional sustainable development.