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常 栋,葛少华,李朋彦,刘国顺.乡镇尺度烟田土壤有机质时空变异性研究——以襄城县紫云镇为例[J].土壤,2018,50(5):1006-1012. CHANG Dong,GE Shaohua,LI Pengyan,LIU Guoshun.Spatial and Temporal Variation of SOM During Tobacco Growth Season—A Case Study of Ziyun Town of Xiangcheng County[J].Soils,2018,50(5):1006-1012 本文二维码信息
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乡镇尺度烟田土壤有机质时空变异性研究——以襄城县紫云镇为例
Spatial and Temporal Variation of SOM During Tobacco Growth Season—A Case Study of Ziyun Town of Xiangcheng County
投稿时间:2017-05-12  修订日期:2017-06-01
DOI:10.13758/j.cnki.tr.2018.05.021
中文关键词:  土壤有机质  时空变异  烟田  地统计学  地理信息系统
Key Words:SOM  Spatial and temporal variation  Tobacco field  Geostatistics  GIS
基金项目:国家烟草专卖局资助项目(110201101001TS-1)资助。
作者单位E-mail
常 栋 河南省烟草公司平顶山市公司 cd411@outlook.com 
葛少华 烟草行业烟草栽培重点实验室河南农业大学烟草学院  
李朋彦 烟草行业烟草栽培重点实验室河南农业大学烟草学院  
刘国顺 烟草行业烟草栽培重点实验室河南农业大学烟草学院 liugsh1851@163.com 
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中文摘要:
      为探明田块尺度烟田土壤有机质(SOM)的时空变异特征,本研究采用地统计学和地理信息系统(GIS)相结合的方法,分析了豫中襄城县紫云镇某个约4 hm2的烟田2013年和2014年4月、7月、10月合计6个时期耕层SOM的时空变异特征。结果表明:该田块各个时期SOM的变异系数差异较小;每年不同时期SOM的平均含量表现为先降后升的变化趋势,7月的含量显著低于4月和10月。各时期SOM空间最大相关距离范围在26 ~ 203 m;2013年4月和2014年7月SOM具有中等的空间自相关性,其他时期均表现为强烈的空间自相关性。研究田块SOM的稳定高值区主要分布在西部,稳定低值区主要集中在中部;SOM的时间稳定和较稳定级别区域面积很广,占总面积的90.89%。在时空变异研究的基础上,辅以时间稳定性分析能够更好地量化烟田SOM的时空分布,可为研究区更为精确、高效的施肥和田间管理提供决策依据。
Abstract:
      The spatial and temporal variation of soil organic matter(SOM) of tobacco-planting fields at field scale was comprehensively analyzed by using geostatistics method and GIS platform. In total 606 soil samples were collected from Ziyun Town of Xiangcheng County of Henan Province six times in successive two years, and the spatial distribution maps of SOM were edited and analyzed. The results indicated that CV of SOM during each period was small, ranging from 13.69% to 24.56%. The mean content of SOM during each period in a year decreased first and then increased, which was significantly lower in July than those in April and October. The maximal range of spatial correlation of SOM ranged from 26 m to 203 m. SOM showed medium spatial autocorrelation in April 2013 and July 2014, expressed strong spatial autocorrelation in other duration. In the study area, the stable high value of SOM was mainly distributed in the west area while the stable low value appeared in the middle area. The area of SOM stable with time was the largest, accounting for 90.89% of the total study area. The above results prove that spatial and temporal analysis can provide more efficient approach to quantify SOM with the help of time stable analysis and provide a theoretical basis for accurate and efficient fertilization and field management.
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