新疆农业机械化发展水平区划时空格局
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摘要
[目的]以新疆各地区为基本单元,对其农业机械化发展水平进行区划,为分类指导全疆各地区农机化发展提供依据。[方法]以2001~2014年统计数据作为数据源建立了农机化发展水平指标体系,并提出了指标标准值。构建了基于指标标准值、熵和变异系数的指标组合赋权法。依据此法,为各指标赋予权重,从而建立新疆2001~2014年14地区农机化发展水平关系数据模型。以各地州2001、2005、2010及2014年的农机化发展水平作为聚类样本,应用系统聚类法对其进行区划分析。[结果]从时间维度上,4个年段新疆各地州农业机械化发展水平在簇间距离阀3.5、4.4、4.8和5.2条件下区划时,各划分区域农机化发展水平呈现出先高低分化,后趋同的态势。从空间维度上昌吉、巴州、塔城、博州和克拉玛依等5个地州农机化发展水平在不同的区划距离阀和时段内均显现为高度相似特征。[结论]以北疆为主的划分区域中相邻地区农机化发展水平聚集效应较明显。同时区划结果也揭示,乌鲁木齐、哈密、吐鲁番和喀什在4个区划年段中农机化发展水平的高度相似特征。其余各地区在研究年段内农机化发展水平聚集效应较弱。为不同地区农机化发展水平区划、农机化管理和服务政策的制定提供了参考。
Agricultural mechanization plays an important role in the development of modern agricultural in the last 3 decades. The development in agricultural mechanization varied with time due to the invention and adoption of new technologies. At the same time,farmer economic conditions,soil conditions,land distribution and government policies for specific region also play important roles in the development of agricultural mechanization. During the farm mechanization in Xinjiang in the Last decade,the different regions in south and north show different characteristics of development. So,the differentiation pattern of the development level of agricultural mechanization(DLAM) is considered as one of the significant factor for mechanization management. This paper is to plan the agricultural mechanization level of Xinjiang for guiding the development of the mechanization in other areas. The suitable regional DLAM index and standard value were proposed for agricultural mechanization level of Xinjiang based on the statistical yearbook and statistical annual report from 2001 to 2014. Then 14 regions of Xinjiang Urumqi,Karmay,Turpan,Kumul,Changji,Eli,Tarbaghatay,Altay,Bortala,Bazhou,Ahsu,Kezhou,Kashgharand Hotan were selected as sample,and the data model of DLAM for each district in Xinjiang was established by applying combination weighting method on the basis of standard value,entropy and the coefficient of variation. The DLAM data of each region in 2001,2005,2010 and 2014 was collected as sample and the regionalization was carried out by applying system clustering algorithm. The results showed that on the basis of time dimension,there was an emerging trend on DLAM of each region in same area,which shifted from higher to lower differentiation,due to the central's subsidies and autonomous region's support policy. By the space dimension,the similarity of DLAM was higher in Changji,Bazhou,Tarbaghatay and Bortala based on the different threshold and revealed aggregation effects in the northern Xinjiang. It concluded that there were highly similar DLAM features in Urumqi,Kumul,Kashghar,and Turpan,during the regionalization in 2001,2005,2010 and 2014. The aggregation effect was weak in the others six regions during the study period. The results can be taken as a reference to make different policies in the management of agricultural mechanization in Xinjiang.
Agricultural mechanization plays an important role in the development of modern agricultural in the last 3 decades. The development in agricultural mechanization varied with time due to the invention and adoption of new technologies. At the same time,farmer economic conditions,soil conditions,land distribution and government policies for specific region also play important roles in the development of agricultural mechanization. During the farm mechanization in Xinjiang in the Last decade,the different regions in south and north show different characteristics of development. So,the differentiation pattern of the development level of agricultural mechanization(DLAM) is considered as one of the significant factor for mechanization management. This paper is to plan the agricultural mechanization level of Xinjiang for guiding the development of the mechanization in other areas. The suitable regional DLAM index and standard value were proposed for agricultural mechanization level of Xinjiang based on the statistical yearbook and statistical annual report from 2001 to 2014. Then 14 regions of Xinjiang Urumqi,Karmay,Turpan,Kumul,Changji,Eli,Tarbaghatay,Altay,Bortala,Bazhou,Ahsu,Kezhou,Kashgharand Hotan were selected as sample,and the data model of DLAM for each district in Xinjiang was established by applying combination weighting method on the basis of standard value,entropy and the coefficient of variation. The DLAM data of each region in 2001,2005,2010 and 2014 was collected as sample and the regionalization was carried out by applying system clustering algorithm. The results showed that on the basis of time dimension,there was an emerging trend on DLAM of each region in same area,which shifted from higher to lower differentiation,due to the central's subsidies and autonomous region's support policy. By the space dimension,the similarity of DLAM was higher in Changji,Bazhou,Tarbaghatay and Bortala based on the different threshold and revealed aggregation effects in the northern Xinjiang. It concluded that there were highly similar DLAM features in Urumqi,Kumul,Kashghar,and Turpan,during the regionalization in 2001,2005,2010 and 2014. The aggregation effect was weak in the others six regions during the study period. The results can be taken as a reference to make different policies in the management of agricultural mechanization in Xinjiang.
引文
[1]卢秉福,韩卫平,朱明.农业机械化发展水平评价方法比较.农业工程学报,2015,31(16):46~49
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[4]李晓,林正雨,何鹏,等.区域现代农业规划理论与方法研究.西南农业学报,2010,23(03):953~958
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[6]邓依萍,刘涛.新疆节水农业区划及分区对策研究.节水灌溉,2008,(10):8~11
[7]靳雪,胡继连.虚拟水视角下的农业区划研究——以黄淮海地区为例.中国农业资源与区划,2011,32(02):53~57
[8]Riveiro J A,Marey M F,Marco J L,et al.Procedure for the classification and characterization of farms for agricultural production planning:Application in the Northwest of Spain.Computers and Electronics in Agriculture,2008,61(2):169~178
[9]Berkel D B V,Verburg P H.Sensitising rural policy:Assessing spatial variation in rural development options for Europe.land Use Policy,2011,28(3):447~459
[10]张宗毅,曹光乔,易中懿.“十二五”农业机械化发展区域划分研究.中国农业资源与区划,2011,32(4):50~55
[11]袁烨城,周成虎,覃彪,等.多层次格网模型的近邻指数聚类生态区划算法与实验——以新疆北部地区区划为例.地球信息科学学报,2011,13(1):1~11
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[13]陶红军,陈体珠.农业区划理论和实践研究文献综述.中国农业资源与区划,2014,35(2):59~66
[14]邓依萍,刘涛.新疆节水农业区划及分区对策研究.节水灌溉,2008,(10):8~11
[15]白人朴,杨敏丽.农业机械化发展水平评价指标体系及评价标准研究.农业机械化软科学研究成果汇编(农业部农业机械化管理司),2001
[16]郑文钟,何勇,岑益郎.基于粗糙集和模糊聚类的农机化水平评价方法.农业机械学报,2006,37(2):58~61
[17]骆健民,郑文钟,何勇.浙江省农业机械化发展水平评价.浙江大学学报:农业与生命科学版,2007,33(2):217~221
[18]胡赛,蒲春玲,汪霖,等.基于熵值法的乌什县农用地利用效益评价研究.中国农业资源与区划,2016,37(3):111~115
[19]邸娜.开放条件下中国大豆种子产业安全状况评估.中国农业资源与区划,2016,37(2):199~203,209
[20]Ni J,Li P,Wei C.Potentialities evaluation of regional land consolidation based on AHp and entropy weight method.Transactions of the Chinese Society of Agricultural Engineering,2009,25(5):202~209
[21]储莎,陈来.基于变异系数法的安徽省节能减排评价研究.中国人口.资源与环境,2011,S1:512~516
[22]田盛丰.聚类分析方法.计算机研究与发展,1992,(3):16~20
[2]田扬戈,边馥苓.基于概念聚类和面向属性归纳的区划分析.武汉大学学报:信息科学版,2005,30(1):86~88
[3]马忠玉,高如嵩.农业区划学科理论体系雏形探讨.中国农业资源与区划,1991,12(2):79~83
[4]李晓,林正雨,何鹏,等.区域现代农业规划理论与方法研究.西南农业学报,2010,23(03):953~958
[5]陶陶,罗其友.农业的多功能性与农业功能分区.中国农业资源与区划,2004,25(01):45~49
[6]邓依萍,刘涛.新疆节水农业区划及分区对策研究.节水灌溉,2008,(10):8~11
[7]靳雪,胡继连.虚拟水视角下的农业区划研究——以黄淮海地区为例.中国农业资源与区划,2011,32(02):53~57
[8]Riveiro J A,Marey M F,Marco J L,et al.Procedure for the classification and characterization of farms for agricultural production planning:Application in the Northwest of Spain.Computers and Electronics in Agriculture,2008,61(2):169~178
[9]Berkel D B V,Verburg P H.Sensitising rural policy:Assessing spatial variation in rural development options for Europe.land Use Policy,2011,28(3):447~459
[10]张宗毅,曹光乔,易中懿.“十二五”农业机械化发展区域划分研究.中国农业资源与区划,2011,32(4):50~55
[11]袁烨城,周成虎,覃彪,等.多层次格网模型的近邻指数聚类生态区划算法与实验——以新疆北部地区区划为例.地球信息科学学报,2011,13(1):1~11
[12]韩渊丰.中国区域地理.广州:广东高等教育出版社,2000
[13]陶红军,陈体珠.农业区划理论和实践研究文献综述.中国农业资源与区划,2014,35(2):59~66
[14]邓依萍,刘涛.新疆节水农业区划及分区对策研究.节水灌溉,2008,(10):8~11
[15]白人朴,杨敏丽.农业机械化发展水平评价指标体系及评价标准研究.农业机械化软科学研究成果汇编(农业部农业机械化管理司),2001
[16]郑文钟,何勇,岑益郎.基于粗糙集和模糊聚类的农机化水平评价方法.农业机械学报,2006,37(2):58~61
[17]骆健民,郑文钟,何勇.浙江省农业机械化发展水平评价.浙江大学学报:农业与生命科学版,2007,33(2):217~221
[18]胡赛,蒲春玲,汪霖,等.基于熵值法的乌什县农用地利用效益评价研究.中国农业资源与区划,2016,37(3):111~115
[19]邸娜.开放条件下中国大豆种子产业安全状况评估.中国农业资源与区划,2016,37(2):199~203,209
[20]Ni J,Li P,Wei C.Potentialities evaluation of regional land consolidation based on AHp and entropy weight method.Transactions of the Chinese Society of Agricultural Engineering,2009,25(5):202~209
[21]储莎,陈来.基于变异系数法的安徽省节能减排评价研究.中国人口.资源与环境,2011,S1:512~516
[22]田盛丰.聚类分析方法.计算机研究与发展,1992,(3):16~20