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基于GIS的滨海盐渍化农田土壤空间变异及其分区管理 被引量:11

Spatial variability and zoning management of coastal salinized farmland soil based on GIS
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摘要 针对滨海盐渍化农田盐碱瘠薄、土壤属性空间变异大、粗放管理效益低的现实,研究管理分区精准划分方法,采取差异化措施,提升盐渍化土地利用水平。该文以无棣县农田为研究区,采用网格法结合土地利用现状定点野外采样、室内化验分析获取土壤属性数据,运用ArcGIS 10.2地统计方法分析土壤属性的空间变异特征;在MATLAB R2016a中采用模糊c⁃均值聚类法(FCM)计算各样点的模糊隶属度,通过插值预测模糊隶属度的空间分布,基于最大隶属度原则进行分区;通过变异性分析和最小极差法(LSR)差异显著性检验,对分区结果进行精度验证。结果表明:无棣县农田土壤总体呈轻中度盐渍化,有效氮含量偏低,有机质、有效磷含量中等,速效钾含量较高;有机质、有效氮、有效磷、速效钾和含盐量呈中等变异性(变异系数25.0%—52.3%),空间变异性较大,应分区调控;速效钾、含盐量和pH的块金效应值小于25%,主要受土壤质地、地下水矿化度等结构因素影响,有机质、有效氮和有效磷的块金效应值在50%—75%之间,受耕作方式、施肥等人为因素影响较大。将全县农田划分为3类管理区,估算面积分别为2.56万hm^2、1.76万hm^2、3.24万hm^2;各分区土壤养分变异系数分别为23.9%—51.5%、15.9%—50.3%、14.7%—33.0%,检验结果表明各分区间差异显著,而各分区内部变异性明显低于未分区。管理分区与土壤属性空间分布特征具有较高的拟合度,分区结果可以作为差异化管理的作业单元。研究结果为各分区内部统一、不同分区间差异化管理提供了依据,研究有助于推进滨海盐渍化农田精准化管理水平的提高。 Considering the fact of the barren coastal saline farmland,the large spatial variability of soil properties,and the low benefit of extensive management,the precise division method of management zones is studied,to take differential measures to improve the level of land use of saline land.In this paper,taking farmland in Wudi County as a research area,we used grid method combined with fixed⁃point field sampling of land use status and laboratory analysis to obtain soil attribute data.We applied geo⁃statistical method to analyze the spatial variation characteristics of soil attributes in ArcGIS 10.2.Fuzzy c⁃means algorithm(FCM)was used to calculate the fuzzy membership degree of each sample point in MATLAB R2016a.The interpolation was used to predict the spatial distribution of the fuzzy membership degree.The partition was carried out based on the principle of maximum membership degree.The accuracy of partition results was verified by variability analysis and LSR difference significance test.The results showed that the farmland soil in Wudi County was slightly and moderately salinized with low content of available nitrogen,medium content of organic matter and available phosphorus,and high content of available potassium.The content of organic matter,available nitrogen,available phosphorus,available potassium,and salt showed a moderate variability(coefficient of variation 25.0%—52.3%).The spatial variability was relatively large to be regulated in different zones.The nugget effect values of available potassium,salt content and pH were less than 25%,which were mainly influenced by the soiltexture,groundwatersalinityandother structural factors.The nugget effect values of organic matter,available nitrogen,and available phosphorus were between 50%and 75%,which were greatly influenced by cultivation methods,fertilization and other human random factors.Therefore,the farmland in the county was divided into three zones with estimated area of 25.6 thousand hm^2,17.6 thousand hm^2 and 32.4 thousand hm^2.The coefficient of variation of soil nutrients in the three zones was 23.9%—51.5%,15.9%—50.3%,and 14.7%—33.0%,respectively.The results showed that there were significant differences among the three zones,while the variability within the zones was significantly lower than that in the non⁃zones.And the management zoning and the spatial distribution characteristics of soil attributes have a high degree of fitness.The results of zoning can be used as the operational units of differentiated management.The results provide a basis for the unified management within each division and differentiated management among different zones,which is helpful to improve the precision management level of coastal saline farmland.
作者 朱昌达 高明秀 王文倩 李俊翰 姚宇 周娜娜 ZHU Changda;GAO Mingxiu;WANG Wenqian;LI Junhan;YAO Yu;ZHOU Nana(College of resources and environment,Shandong Agricultural University,Tai′an 271018,China;National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources,Tai′an 271018,China)
出处 《生态学报》 CAS CSCD 北大核心 2020年第19期6982-6990,共9页 Acta Ecologica Sinica
基金 山东省重点研发计划(软科学)项目(2019RZB01015) 山东省重大科技创新工程项目(2017CXGC0301) 国家科技支撑计划(2013BAD05B06⁃5)。
关键词 盐渍化农田 空间变异 分区管理 模糊c⁃均值聚类 最大隶属度 最小极差法 saline farmland spatial variability management zone fuzzy c⁃means algorithm maximum degree of membership least significant range method
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