Abstract
Accurately characterizing the spatial deviation of hog slaughter across prefecture-level cities within a province relative to the agricultural production base is essential for understanding adjustments in hog-production capacity and for advancing differentiated regional governance. Using prefecture-level hog slaughter, the gross output value of agriculture, forestry, animal husbandry and fishery, and provincial farm-size structure data for 30 mainland Chinese provinces from 2007 to 2023, this study constructs an intra-provincial Ellison-Glaeser (EG) index. Markov transitions, spatial Markov analysis, club convergence and the optimal-parameters-based geographical detector (OPGD) are then used to examine the dynamic evolution of intra-provincial spatial organization and its influencing factors. The results show that: (1) the national mean EG index increased with fluctuations, whereas the median remained broadly stable, indicating that the rise in relative spatial deviation was driven mainly by a small number of high-EG provinces; (2) Qinghai, Xinjiang, Shanghai and Inner Mongolia recorded relatively high EG values, whereas the traditional hog-producing provinces of Sichuan, Hunan, Henan and Hebei recorded relatively low values. The net-exporting areas also had lower EG levels than the major consuming and balanced production-consumption areas, suggesting a closer spatial match between hog slaughter and the agricultural production base in traditional producing regions; (3) EG states displayed strong path dependence and spatial association, with conditional transition probabilities varying across neighboring EG environments, while provinces formed three relatively stable high-, medium- and low-EG evolutionary tiers rather than converging as a whole; and (4) the leading factor varied by period: agricultural industrial structure was most prominent in 2007–2012, mean annual temperature in 2013–2018, and the corn-production base in 2019–2023, although the evidence for the last period was weak. The study extends hog-industry agglomeration research from an intra-provincial relative-spatial-organization perspective and provides evidence for improving production layout and differentiated regional governance.
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Funding
This research was supported by the National Social Science Fund of China (Grant No. 24XJY004), “Research on the Dilemmas and Breakthrough Paths of Digital Transformation in Animal Husbandry in Western China”; the Social Science Planning Office of Inner Mongolia Autonomous Region (Grant No. 2023NDB191), “Research on the Mechanism and Policy Optimization of Digital Technology Empowering High-Quality Development of Animal Husbandry”;the Fundamental Research Funds for Universities Directly Under the Inner Mongolia Autonomous Region (Grant No. NCYWT25052), “Innovation Team for Research on the Modern Industrial System”;the Inner Mongolia Industrial Development Research Base (Grant No. 2023YB005), “Research on the Impact of Digital Management Based on Precision Feeding on the Supply Efficiency of the Animal Husbandry Industry in Inner Mongolia”; the Yellow River Basin High-Quality Economic Development Research Center (Grant No. 24HND02), “Research on the Impact of Crop-Livestock Integration on the High-Quality Development of Agriculture and Animal Husbandry in the Yellow River Basin”; and the Regional Digital Economy and Digital Governance Research Center (Grant No. szzl202622), “Research on the Dynamic Mechanism of Appropriate Digital Technology Selection, the Transition between Old and New Kinetic Energy, and Industrial Transformation and Upgrading in Inner Mongolia.”
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Ma, X., Liang, Y. & Hao, W. Measurement and spatiotemporal evolution of spatial agglomeration in China’s hog industry an EG-index analysis based on prefecture-level data.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-70704-1
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DOI: https://doi.org/10.1038/s41598-026-70704-1
Keywords
- Hog industry
- Spatial agglomeration
- Ellison-Glaeser index
- Spatial Markov
- Optimal-parameters-based geographical detector
- Spatiotemporal evolution
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