摘要
基于创新、协调、绿色、开放、共享五维高质量发展的视角,运用区位熵、熵权法和障碍度模型,对2021年江西省南昌市153家省级农业龙头企业高质量发展水平进行了评价与分析.结果表明:从区域分布看,全市农业龙头企业呈现非平衡的态势,青山湖区和青云谱区的龙头企业集聚程度较高;从行业发展看,种植业、养殖业、加工业和服务业的整体高质量发展水平呈现逐级降低的趋势;从障碍因子分析,种植业发展主要受制于绿色和协调维度,养殖业则面临开放维度的最大障碍,而加工业和服务业则在绿色和开放维度的障碍度相对较高.为此,应从强化产业集聚、推进创新发展、加快绿色转型、加强企农联结、加大开放合作等方面提升农业龙头企业的高质量发展水平,从而推进农业产业化经营.
Abstract
Based on the perspective of five-dimensional high-quality development of"innovation,coordination,greenness,openness and sharing",The high-quality development level 153 provincial agricultural leading enterprises in Nanchang City,Jiangxi Province in 2021 were evaluated and analyzed by the entropy of location,entropy weight method and the obstacle degree model.The results show that from the perspective of regional distribution,the leading agricultural enterprises in the city present a non-balanced situation,with a higher degree of agglomeration of leading enterprises in Qingshanhu District and Qingyunpu District.From the perspective of industry development,the overall high-quality development level of planting,farming,processing and service industries presents a step-by-step decreasing trend.From the perspective of obstacle factor analysis,the development of plantation industry is mainly restricted by the factors of green and coordination dimension,while the breeding industry faces the biggest obstacle of openness dimension,and the processing industry and service industry have relatively high obstacles in green and openness dimension.Therefore,we should enhance the high-quality development of agricultural leading enterprises in terms of strengthening industrial agglomeration,promoting innovative development,accelerating green transformation,strengthening enterprise-farmer links,and increasing openness and cooperation,so as to promote the industrialized operation of agriculture.
基金项目
国家自然科学基金项目(72063018)
江西省蔬菜产业技术体系资助项目-产业经济岗位(JXARS-06)
南昌市社会科学规划项目(YJ202309)
南昌市社会科学规划项目(GL202303)
江西农业大学经济管理学院2024年研究生创新专项资金项目(JG2024009)
江西省2023年度研究生创新专项资金项目(YC2023-S400)
江西省2023年度研究生创新专项资金项目(YC2023-B140)
国家科技期刊平台
NSTL
万方数据