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Spatial Pattern and Influencing Factors of Agricultural Leading Enterprises in Heilongjiang Province, China

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  • Tianli Wang

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yanji Ma

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Siqi Luo

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

As one of the major new agricultural business entities, agricultural leading enterprises (ALEs) are responsible for ensuring national food security, leading agricultural and rural modernization, and increasing farmers’ employment prospects and incomes. From the perspective of headquarters and branches, this study used a point pattern analysis, the local Moran’s index, the rank-size rule, and the geographical detector to depict the spatial pattern of ALEs in Heilongjiang Province, detect influencing factors, and reveal the spatial layout mechanism. The main conclusions are as follows. (1) ALE headquarters and branches in Heilongjiang Province had different location requirements, and their layout orientation, clustering areas, and influencing factors were different. (2) The headquarters displayed a pronounced urban and agglomeration orientation, while branches exhibited a significant farm dependence and raw material orientation. (3) Both the headquarters and the branches showed a significant trend towards spatial clustering. The headquarters were mainly in the Harbin municipal district and surrounding counties, including Wuchang, Zhaodong, and Beilin, which showed a high–high cluster pattern. The branches were mainly in the Sanjiang Plain. Tongjiang, Fujin, Hulin, Mishan, Raohe, Baoqing, and Suibin showed a high–high cluster pattern, while the Harbin municipal district and Bei’an showed a high–low outlier pattern. (4) The ALEs’ regional connection network in Heilongjiang Province was radially distributed with the Harbin municipal district as the centre. The agricultural reclamation system deeply influenced it. (5) The number of supporting enterprises, number of permanent residents, gross domestic product, railway mileage, number of people with Bachelor’s degrees or above, and distance from the provincial capital were the main influencing factors of the headquarters spatial pattern in Heilongjiang Province. The number of state farms in Heilongjiang Province, the per capita grain yield, highway mileage, and distance from the provincial capital were the main influencing factors of the branch spatial patterns in Heilongjiang Province. The interaction results indicated that the explanatory power of two-factor interaction was stronger than that of a single factor regardless of headquarters or branches, and most interaction types were bilinear enhancements. This study aims to provide a decision-making reference for the long-term development of ALEs in Heilongjiang Province at the present stage and accelerate the development of agricultural industrialization in major grain-producing areas.

Suggested Citation

  • Tianli Wang & Yanji Ma & Siqi Luo, 2023. "Spatial Pattern and Influencing Factors of Agricultural Leading Enterprises in Heilongjiang Province, China," Agriculture, MDPI, vol. 13(11), pages 1-23, October.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:11:p:2061-:d:1268661
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    References listed on IDEAS

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    1. Siqi Huang & Nik Hadiyan Nik Azman, 2023. "Enhancing Food Security through Digital Inclusive Finance: Evidence from Agricultural Enterprises in China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    2. Maryline Filippi & Pierre Triboulet & Sébastien Chantelot & Stéphanie Peres, 2015. "The Spatial Distribution of French Agricultural Cooperatives: An Exploratory Spatial Data Analysis," European Planning Studies, Taylor & Francis Journals, vol. 23(4), pages 710-732, April.
    3. Alessandro Corsi & Silvia Novelli & Giacomo Pettenati, 2018. "Producer and farm characteristics, type of product, location: Determinants of on‐farm and off‐farm direct sales by farmers," Agribusiness, John Wiley & Sons, Ltd., vol. 34(3), pages 631-649, June.
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