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Food security is crucial for national stability and public welfare. Since the 21st century, China’s grain production has been significantly influenced by the rapid process of urbanization. In this context, this paper systematically measures the multidimensional coupling patterns and dynamic coupling processes between urbanization and grain production from 2000 to 2022, and preliminarily summarizes the complex coupling mechanisms within the Chinese context. The goal is to provide scientific references for achieving high-quality coordinated development of urbanization and grain production in China. The study reveals the following key findings: (1) The coupling relationship between urbanization and grain production exhibits both regional heterogeneity and temporal variability, demonstrating specific levels of coupling and dynamic processes under distinct spatiotemporal conditions. (2) Between 2000 and 2022, both urbanization and grain production patterns in China underwent significant reconstruction, with the coupling coordination level displaying a long-term spatial pattern of “high in the north, low in the south; high in the east, low in the west.” Although there is an overall upward trend in coupling coordination states, spatial imbalances and dimensional heterogeneity persist. (3) Since the beginning of the 21st century, the dynamic coupling processes between provincial urbanization and grain production have primarily manifested as two types: simultaneous increase (with urbanization outpacing grain production) and urban increase accompanied by grain production decrease. Various dynamic coupling types exhibit significant spatial clustering, and the multidimensional dynamic coupling processes reveal notable similarities. (4) The evolution of coupling states demonstrates an overall trend of optimization, with clear bidirectional migration trends observed in coupling dynamics, primarily transitioning from simultaneous increase (urbanization outpacing grain production) to urban increase with grain production decrease, and vice versa. (5) The formation of the complex coupling relationship between urbanization and grain production in the Chinese context is fundamentally influenced by changes in population quantity and structure between urban and rural areas, shifts in land use, economic transformation, regional specialization, technological interactions, and factor mobility. These influences exhibit significant negative effects in the domains of population, land, and economy, while showcasing notable positive effects in terms of technology and factor mobility.
Food security is crucial for national stability and public welfare. Since the 21st century, China’s grain production has been significantly influenced by the rapid process of urbanization. In this context, this paper systematically measures the multidimensional coupling patterns and dynamic coupling processes between urbanization and grain production from 2000 to 2022, and preliminarily summarizes the complex coupling mechanisms within the Chinese context. The goal is to provide scientific references for achieving high-quality coordinated development of urbanization and grain production in China. The study reveals the following key findings: (1) The coupling relationship between urbanization and grain production exhibits both regional heterogeneity and temporal variability, demonstrating specific levels of coupling and dynamic processes under distinct spatiotemporal conditions. (2) Between 2000 and 2022, both urbanization and grain production patterns in China underwent significant reconstruction, with the coupling coordination level displaying a long-term spatial pattern of “high in the north, low in the south; high in the east, low in the west.” Although there is an overall upward trend in coupling coordination states, spatial imbalances and dimensional heterogeneity persist. (3) Since the beginning of the 21st century, the dynamic coupling processes between provincial urbanization and grain production have primarily manifested as two types: simultaneous increase (with urbanization outpacing grain production) and urban increase accompanied by grain production decrease. Various dynamic coupling types exhibit significant spatial clustering, and the multidimensional dynamic coupling processes reveal notable similarities. (4) The evolution of coupling states demonstrates an overall trend of optimization, with clear bidirectional migration trends observed in coupling dynamics, primarily transitioning from simultaneous increase (urbanization outpacing grain production) to urban increase with grain production decrease, and vice versa. (5) The formation of the complex coupling relationship between urbanization and grain production in the Chinese context is fundamentally influenced by changes in population quantity and structure between urban and rural areas, shifts in land use, economic transformation, regional specialization, technological interactions, and factor mobility. These influences exhibit significant negative effects in the domains of population, land, and economy, while showcasing notable positive effects in terms of technology and factor mobility.
Water, land, and other environmental conditions restrict marginal land (ML) conversion into newly cultivated land. Accurately evaluating ML’s development and utilization potential (DUP) can provide critical support for increasing new cultivated land and ensuring food security. This study focuses on Northwest China, using spatial identification of different types of ML based on remote sensing images, and constructs a county-level DUP evaluation model through contiguous characteristics and restrictive factors to determine new cultivated-land potential, water demand, and liftable grain production. The results show that the DUP of ML in Northwest China is 12.59 million ha, with low-efficiency cultivated land (LCL) and two types of restoration land (TTRL) accounting for 3.29% and 5.95%, and other marginal land (OML) making up 90.76%. The total water demand for ML development and utilization is 69.87 billion cubic meters, which can increase grain production by 62.31 million tons. The coordinated development of water, land, and food promotes an increase in grain production, with water resources being the main restrictive factor. This model effectively evaluates DUP and provides a scientific basis for promoting the rational use of water and land resources. Further research should set up more detailed water resource utilization strategies and scenarios as well as find more development and utilization techniques.
Excessive non-grain production of farmland (NGPF) seriously affects food security and hinders progress toward Sustainable Development Goal 2 (Zero Hunger). Understanding the spatial distribution and influencing factors of NGPF is essential for food and agricultural management. However, previous studies on NGPF identification have mainly relied on high-cost methods (e.g., visual interpretation). Furthermore, common machine learning techniques have difficulty in accurately identifying NGPF based solely on spectral information, as NGPF is not merely a natural phenomenon. Accurately identifying the distribution of NGPF at a grid scale and elucidating its influencing factors have emerged as critical scientific challenges in current literature. Therefore, the aims of this study are to develop a grid-scale method that integrates multisource remote sensing data and spatial factors to enhance the precision of NGPF identification and provide a more comprehensive understanding of its influencing factors. To overcome these challenges, we combined multisource remote sensing images, natural/anthropogenic spatial factors, and the maximum entropy model to reveal the spatial distribution of NGPF and its influencing factors at the grid scale. This combination can reveal more detailed spatial information on NGPF and quantify the integrated influences of multiple spatial factors from a microscale perspective. In this case study of Foshan, China, the area under the receiver operating characteristic curve is 0.786, with results differing by only 1.74% from the statistical yearbook results, demonstrating the reliability of the method. Additionally, the total error of our NGPF identification result is lower than that of using only natural/anthropogenic information. Our method enhances the spatial resolution of NGPF identification and effectively detects small and fragmented farmlands. We identified elevation, farming radius, and population density as dominant factors affecting the spatial distribution of NGPF. These results offer targeted strategies to mitigate excessive NGPF. The advantage of our method lies in its independence from negative samples. This feature enhances its applicability to other cases, particularly in regions lacking high-resolution grain crop-related data.
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