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福建省特色现代农业发展分异特征与聚类类型分析
引用本文:陈志峰,王海平,曾玉荣.福建省特色现代农业发展分异特征与聚类类型分析[J].中国农业资源与区划,2020,41(1):105-112.
作者姓名:陈志峰  王海平  曾玉荣
作者单位:福建省农业科学院农业经济与科技信息研究所,福州350003,福建省农业科学院农业经济与科技信息研究所,福州350003,福建省农业科学院农业经济与科技信息研究所,福州350003
基金项目:福建省自然科学基金项目“县域特色农业产业的竞争力分析及类型选择研究”(2016J01334); 福建省科技计划项目——省属公益类科研院所专项“福建省县域优势特色农业产业选择与政策引导研究——基于28个县的农业主导产业调整研究”(2019R1033 8)
摘    要:目的]福建省具有丰富的农业多样性特征和特色“农情”,为了寻找发展因素的关联和耦合、延长产业链、提高附加值,稳步推进福建省区域特色现代农业发展。方法]文章从地理学和经济学视角,构建了BPDS概念模型和评价指标体系,对福建省及9地市的现代农业发展水平进行评价,在此基础上运用系统聚类分析方法从指标因素和空间布局等方面解析福建现代农业发展的分异和聚类情况,为制定农业可持续发展措施提供决策参考。结果](1)从发展指数看,福建省现代农业发展水平在起步阶段和入初步发展阶段临界点上。9地市水平指数在0.487~0.561,其中6个地市处于初步发展阶段,3个地市还处于起步阶段。4个准则要素指标方面存在较大的指数分异,其中投入水平要素与其他3个要素水平差距较大;(2)从区域空间看,主要呈南北高中间低的空间特征,龙岩、泉州和莆田3个地区最低。不同空间区域环境使不同地区各有优势,在分指标间存在较大分异;(3)从水平类型看,福建省现代农业发展水平按聚类情况可分实力高值区、过渡中值区和潜力低值区为3个类型。依据区位与资源特点,在实力高值区以生产加创汇的特征性农业,过渡中值区以发展服务型农业为主,潜力低值区以生态农业生产为主。结论]经济水平和人才等实力因素和资源禀赋等潜力因素在现代农业发展中具有重要的作用,是产生地区分异的重要因素。同时,生产条件优势和服务体系优势在现代农业的集群形成等具有潜在优势。

关 键 词:特色现代农业  发展水平  BPDS概念模型  分异特征  空间聚类
收稿时间:2018/5/23 0:00:00

THE ANALYSIS OF DIFFERENTIATION CHARACTERISTICS AND CLUSTERING TYPES OF DEVELOPMENT LEVEL OF MODERN AGRICULTURE IN FUJIAN
Chen Zhifeng,Wang Haiping and Zeng Yurong.THE ANALYSIS OF DIFFERENTIATION CHARACTERISTICS AND CLUSTERING TYPES OF DEVELOPMENT LEVEL OF MODERN AGRICULTURE IN FUJIAN[J].Journal of China Agricultural Resources and Regional Planning,2020,41(1):105-112.
Authors:Chen Zhifeng  Wang Haiping and Zeng Yurong
Institution:(Institute of Agricultural Economics and Information,Fujian Academy of Agricultural Sciences,Fuzhou,Fujian350003,China)
Abstract:There are the characteristics of abundant agricultural biodiversity and different agricultural situation in Fujian province,so we aims to explore the association and coupling for development factors,extend the industrial chain,increase added value,and steadily promote the development of modern agriculture.From the perspective of geography and economics,we constructed BPDS conceptual mode and evaluation index system,evaluated the development level of modern agriculture in Fujian province and the nine cities,analyzed the differentiation of Fujian modern agricultural development level from index factors and spatial distribution by using the cluster analysis method,so as to provide references for sustainable development.The results showed that(1)In terms of development index,the development level of modern agriculture in Fujian province just entered the preliminary stage of development.The level index of nine cities was all between 0.487 and 0e.561,six of them were in the preliminary stage of development,and the 3 cities were at the beginning.There were a big index gap among the four criteria elements,which the gap between the level of input factor and the other three was large;(2)In terms of regional space,it was mainly characterized as the middle low,the South and North high;Longyan,Quanzhou and Putian were the lowest of regions and in the middle of Fujian,and next to each other.Because the conditions of different space regions had their advantages,the spatial differentiation of the sub index was different.(3)In terms of level type,the development level of modern agriculture in Fujian province could be divided into 3 types,High value area,transitionally median value area and potentially low value area based on clustering.According to the characteristics of location and resource,the characteristic agriculture was about on production and foreign exchange in the high value area,the transitionally median area was mainly to develop service agriculture,and the potentially low value area was mainly based on ecological agriculture.The potential factors,such as economic level and talent,and resource endowment have an important role in the development of modern agriculture,and are the important factors to produce regional differentiation.Moreover,the advantages of production conditions and service system have potential advantages in the formation of modern agricultural clusters.
Keywords:characteristic modern agriculture  development level  Conceptual Mode of BPDS  differentiation characteristics  spatial clustering
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