基于Pearson灰色关联熵的粮食产量主影响因子识别——以河南省为例
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摘要
区域粮食产量主控因子的诊断需要合理遴选评价指标并准确测度气象气候等各影响因素的作用。文中以河南省1989-2014年的农业统计资料为数据源,首先将Pearson相关分析法应用于从资源投入、气候变化、技术进步、制度政策等四个方面构建的河南省粮食产量影响因子评价指标集,以进行评价指标的定量遴选和消除依据主观经验选择评价指标的局限性,然后再应用熵权法改进后的灰色关联分析法来识别河南省粮食产量主影响因子。研究结果显示:基于Pearson系数和灰色关联熵相结合的粮食产量主影响因子识别结果较符合河南省的客观实际,对于与粮食产量关系密切但其影响通常难以量化的气象气候等因子所起的作用也能够较准确地测度。农田有效灌溉面积、耕地面积和粮食作物播种面积三项因子是河南省粮食产量的主控因子,并且农田有效灌溉面积的灰色关联熵最高,耕地面积和粮食作物播种面积的灰色关联熵也明显高出位于其后的其它因子,这表明河南省粮食生产受到区域内水资源匮乏的强烈影响,同时该省当前的粮食产量(产出)是通过投入大量的耕地资源要素并保持较高的作物种植规模而得以实现的,这与农业可持续发展的内涵并不相符。平均气温、日照时数、年降水总量以及化肥农药的使用量和成灾面积也是河南省粮食产量变化的主要因素,这显示出该省粮食生产的现代化水平不高,受气象气候因子的制约作用明显,对灾害的防御和抵抗能力较弱,存在对易于造成农业面源污染问题的化肥农药依赖性较强的现象;农业机械等科技投入要素对河南省1989年以来粮食产量的影响较小,明显低于气温和降水等气象气候因子的作用。
Proposing countermeasures for agricultural sustainable development needs the information about main influence factors of grain production. In this research,Pearson correlation coefficient was firstly computed between grain production and its driving factors one by one. Using a rule of Pearson coefficient value greater than 0. 85,a few of factors were selected to enter the following Grey relation analysis( GRA). Then based on Entropy method,the GRA method was improved and adopted in main factors identification of grain production in Henan province,China by using agro-statistic data of Henan Province from 1989 to 2014. The result shows that:( 1)The entropy-weighted GRA method solved a problem about equal weighting of different factors in the traditional GRA method.( 2) Effective agro-irrigation acreage,cropland acreage and grain crop area were the primary driving factors for grain production in Henan Province. The secondary driving factors included average temperate,sunshine duration,annual precipitation,amount of fertilizer and pesticides,and the disaster acreage.( 3)The level of grain production modernization in Henan Province was not high,which was proved by adopting a lot of fertilizer,pesticides,cultivated land resource and a large planting scale of food crops in keeping stability of grain outputs in the study area.( 4) The meteorological and climatic factors such as temperature and precipitation also had higher impacts on the local grain production.
Proposing countermeasures for agricultural sustainable development needs the information about main influence factors of grain production. In this research,Pearson correlation coefficient was firstly computed between grain production and its driving factors one by one. Using a rule of Pearson coefficient value greater than 0. 85,a few of factors were selected to enter the following Grey relation analysis( GRA). Then based on Entropy method,the GRA method was improved and adopted in main factors identification of grain production in Henan province,China by using agro-statistic data of Henan Province from 1989 to 2014. The result shows that:( 1)The entropy-weighted GRA method solved a problem about equal weighting of different factors in the traditional GRA method.( 2) Effective agro-irrigation acreage,cropland acreage and grain crop area were the primary driving factors for grain production in Henan Province. The secondary driving factors included average temperate,sunshine duration,annual precipitation,amount of fertilizer and pesticides,and the disaster acreage.( 3)The level of grain production modernization in Henan Province was not high,which was proved by adopting a lot of fertilizer,pesticides,cultivated land resource and a large planting scale of food crops in keeping stability of grain outputs in the study area.( 4) The meteorological and climatic factors such as temperature and precipitation also had higher impacts on the local grain production.
引文
[1]宋小青,欧阳竹.1999-2007年中国粮食安全的关键影响因素[J].地理学报,2012,67(6):793-803.
[2]周志刚,郑明亮.基于对数均值迪氏指数法的中国粮食产量影响因素分解[J].农业工程学报,2015,31(2):1-6.
[3]徐志宇,宋振伟,邓艾兴,等.近30年我国主要粮食作物生产的驱动因素及空间格局变化研究[J].南京农业大学学报,2013,36(1):79-86.
[4]王树涛,李新旺,门明新,等.基于改进灰色关联度法的河北省粮食波动影响因素研究[J].中国农业科学,2011,44(1):176-184.
[5]胡文海.我国中部地区粮食生产特征及其对我国粮食安全的影响[J].地理研究,2008,27(4):885-896.
[6]范建刚,谢斌.西北五省区粮食生产灰色关联比较分析[J].干旱区资源与环境,2007,21(8):121-125.
[7]熊健.影响我国粮食生产主要因素的灰色关联动态分析[J].农业经济问题,1997(1):42-44.
[8]张利国,鲍丙飞.我国粮食主产区粮食全要素生产率时空演变及驱动因素[J].经济地理,2016,36(3):147-152.
[9]熊伟,林而达,蒋金荷,等.中国粮食生产的综合影响因素分析[J].地理学报,2010,65(4):379-406.
[10]温晓慧,张新润.中国粮食生产的预测模型研究[J].统计与决策,2010(22):25-27.
[11]Wang X B,Cai D X,Grant C,et al.Factors controlling regional grain yield in China over the last 20 years[J].Agronomy for Sustainable Development,2015,35:1127-1138.
[12]Li B J,He C H,Hu L P,et al.Dynamical analysis on influencing factors of grain production in Henan province based on grey systems theory[J].Grey Systems:Theory and Application,2012,2(1):45-53.
[13]何春花.河南省粮食生产影响因素的量化分析与对策[D].郑州市:河南农业大学,2009.
[14]杨铭,刘青利.基于改进广义灰色关联度的河南省粮食生产影响因素分析[J].西南师范大学学报:自然科学版,2015(9):174-180.
[15]陈祺琪,李君,梁保松.基于灰色多元线性回归的河南省粮食产量预测[J].河南农业大学学报,2012,46(4):448-452.
[16]Zhen H E.Analysis of driving factors of grain output in Henan Province based on grey correlation model[J].Agricultural Science&Technology,2009,10(3):123-126,147.
[17]何春花,胡丽平,李炳军.河南省粮食生产影响因素的灰色关联动态分析[J].农业系统科学与综合研究,2011,27(3):311-315.
[18]赵奇,范春丽,杨玉珍.2004-2012年河南五地市粮食产量影响因子的灰色关联分析[J].河南科学,2014(12):2486-2490.
[19]LI Q F,Kang G L,LI X F Factors influencing grain production of Henan Province based on gray correlation[J].Asian Journal of Agricultural Research,2009,1(5):23-27.
[20]效赛丽,朱秀英,邓蒙芝,等.基于HP滤波法的河南省粮食产量波动分析[J].河南农业大学学报,2014,48(6):785-789.
[21]李青松,邓素君,徐国劲,等.河南省粮食产量波动特征及影响因素分析[J].中国农学通报,2015(18):226-230.
[22]郭慧萍.河南省粮食综合生产能力影响因素的量化分析结果及其政策含义[J].河南工业大学学报:社会科学版,2016,12(1):1-6.
[23]杨红旗,温建,雷丽娟,等.基于农业生产条件的河南粮食产量组合模型研究[J].安徽农业科学,2009,37(28):3971-3973.
[24]熊淑萍,卢中民,吴克远,等.河南省小麦和玉米产量及其有关气候因子分析[J].河南农业大学学报,2014,48(2):123-129.
[25]Chen C,Zhou G S,Pang Y M.Impacts of climate change on maize and winter wheat yields in China from 1961 to 2010 based on provincial data[J].Journal of Agricultural Science,2015,153:825-836.
[26]Ju H,Lin E D,Wheeler T,et al.Climate change modeling and its roles to Chinese crops yield[J].Journal of Integrative Agriculture,2013,12(5):892-902.
[27]史文娇,陶福禄,张朝.基于统计模型识别气候变化对农业产量贡献的研究进展[J].地理学报,2012,67(9):1213-1222.
[28]李治国.近40a河南省农业气象灾害对粮食生产的影响研究[J].干旱区资源与环境,2013,27(5):126-130.
[29]赵丹丹,翟石艳.1951-2012年河南省气候变化对冬小麦和玉米产量的影响[J].中国农学通报,2015(29):152-157.
[30]Pearson W R,Lipman D J.Improved tools for biological sequence comparison[J].Proceedings of the National Academy of Sciences,1988,85(8):2444-2448.
[31]邓聚龙.灰色系统理论教程[M].武汉:华中理工大学出版社,1990.
[32]张岐山,郭喜江,邓聚龙.灰关联熵分析方法[J].系统工程理论与实践,1996,26(8):7-11.
[33]田民,刘思峰,卜志坤.灰色关联度算法模型的研究综述[J].统计与决策,2008(1):24-27.
[34]刘思峰,蔡华,杨英杰,等.灰色关联分析模型研究进展[J].系统工程理论与实践,2013,33(8):2041-2046.
[35]赵旭,叶剑平,薛姝.基于改进灰色关联分析法的湖南省城市土地可持续利用评价[J].水土保持通报,2013,33(3):265-269.
[36]汤淏,王腊春.基于熵权法的南京市水资源可持续利用评价[J].四川环境,2010,29(1):75-79.
[37]李晔,崔岩.区域粮食生产灰色关联分析[J].河南科学,2009,27(2):218-221.
[38]崔静,王秀清,辛贤.气候变化对中国粮食生产的影响研究[J].经济社会体制比较,2011(2):54-60.
[2]周志刚,郑明亮.基于对数均值迪氏指数法的中国粮食产量影响因素分解[J].农业工程学报,2015,31(2):1-6.
[3]徐志宇,宋振伟,邓艾兴,等.近30年我国主要粮食作物生产的驱动因素及空间格局变化研究[J].南京农业大学学报,2013,36(1):79-86.
[4]王树涛,李新旺,门明新,等.基于改进灰色关联度法的河北省粮食波动影响因素研究[J].中国农业科学,2011,44(1):176-184.
[5]胡文海.我国中部地区粮食生产特征及其对我国粮食安全的影响[J].地理研究,2008,27(4):885-896.
[6]范建刚,谢斌.西北五省区粮食生产灰色关联比较分析[J].干旱区资源与环境,2007,21(8):121-125.
[7]熊健.影响我国粮食生产主要因素的灰色关联动态分析[J].农业经济问题,1997(1):42-44.
[8]张利国,鲍丙飞.我国粮食主产区粮食全要素生产率时空演变及驱动因素[J].经济地理,2016,36(3):147-152.
[9]熊伟,林而达,蒋金荷,等.中国粮食生产的综合影响因素分析[J].地理学报,2010,65(4):379-406.
[10]温晓慧,张新润.中国粮食生产的预测模型研究[J].统计与决策,2010(22):25-27.
[11]Wang X B,Cai D X,Grant C,et al.Factors controlling regional grain yield in China over the last 20 years[J].Agronomy for Sustainable Development,2015,35:1127-1138.
[12]Li B J,He C H,Hu L P,et al.Dynamical analysis on influencing factors of grain production in Henan province based on grey systems theory[J].Grey Systems:Theory and Application,2012,2(1):45-53.
[13]何春花.河南省粮食生产影响因素的量化分析与对策[D].郑州市:河南农业大学,2009.
[14]杨铭,刘青利.基于改进广义灰色关联度的河南省粮食生产影响因素分析[J].西南师范大学学报:自然科学版,2015(9):174-180.
[15]陈祺琪,李君,梁保松.基于灰色多元线性回归的河南省粮食产量预测[J].河南农业大学学报,2012,46(4):448-452.
[16]Zhen H E.Analysis of driving factors of grain output in Henan Province based on grey correlation model[J].Agricultural Science&Technology,2009,10(3):123-126,147.
[17]何春花,胡丽平,李炳军.河南省粮食生产影响因素的灰色关联动态分析[J].农业系统科学与综合研究,2011,27(3):311-315.
[18]赵奇,范春丽,杨玉珍.2004-2012年河南五地市粮食产量影响因子的灰色关联分析[J].河南科学,2014(12):2486-2490.
[19]LI Q F,Kang G L,LI X F Factors influencing grain production of Henan Province based on gray correlation[J].Asian Journal of Agricultural Research,2009,1(5):23-27.
[20]效赛丽,朱秀英,邓蒙芝,等.基于HP滤波法的河南省粮食产量波动分析[J].河南农业大学学报,2014,48(6):785-789.
[21]李青松,邓素君,徐国劲,等.河南省粮食产量波动特征及影响因素分析[J].中国农学通报,2015(18):226-230.
[22]郭慧萍.河南省粮食综合生产能力影响因素的量化分析结果及其政策含义[J].河南工业大学学报:社会科学版,2016,12(1):1-6.
[23]杨红旗,温建,雷丽娟,等.基于农业生产条件的河南粮食产量组合模型研究[J].安徽农业科学,2009,37(28):3971-3973.
[24]熊淑萍,卢中民,吴克远,等.河南省小麦和玉米产量及其有关气候因子分析[J].河南农业大学学报,2014,48(2):123-129.
[25]Chen C,Zhou G S,Pang Y M.Impacts of climate change on maize and winter wheat yields in China from 1961 to 2010 based on provincial data[J].Journal of Agricultural Science,2015,153:825-836.
[26]Ju H,Lin E D,Wheeler T,et al.Climate change modeling and its roles to Chinese crops yield[J].Journal of Integrative Agriculture,2013,12(5):892-902.
[27]史文娇,陶福禄,张朝.基于统计模型识别气候变化对农业产量贡献的研究进展[J].地理学报,2012,67(9):1213-1222.
[28]李治国.近40a河南省农业气象灾害对粮食生产的影响研究[J].干旱区资源与环境,2013,27(5):126-130.
[29]赵丹丹,翟石艳.1951-2012年河南省气候变化对冬小麦和玉米产量的影响[J].中国农学通报,2015(29):152-157.
[30]Pearson W R,Lipman D J.Improved tools for biological sequence comparison[J].Proceedings of the National Academy of Sciences,1988,85(8):2444-2448.
[31]邓聚龙.灰色系统理论教程[M].武汉:华中理工大学出版社,1990.
[32]张岐山,郭喜江,邓聚龙.灰关联熵分析方法[J].系统工程理论与实践,1996,26(8):7-11.
[33]田民,刘思峰,卜志坤.灰色关联度算法模型的研究综述[J].统计与决策,2008(1):24-27.
[34]刘思峰,蔡华,杨英杰,等.灰色关联分析模型研究进展[J].系统工程理论与实践,2013,33(8):2041-2046.
[35]赵旭,叶剑平,薛姝.基于改进灰色关联分析法的湖南省城市土地可持续利用评价[J].水土保持通报,2013,33(3):265-269.
[36]汤淏,王腊春.基于熵权法的南京市水资源可持续利用评价[J].四川环境,2010,29(1):75-79.
[37]李晔,崔岩.区域粮食生产灰色关联分析[J].河南科学,2009,27(2):218-221.
[38]崔静,王秀清,辛贤.气候变化对中国粮食生产的影响研究[J].经济社会体制比较,2011(2):54-60.