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江苏南部麦田杂草群落发生分布规律的数量分析 被引量:28

Quantitative Analysis of Distribution of Weed Communities in Wheat Fields in the South of Jiangsu Province
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摘要 利用七级目测法对江苏省南部麦田81个样点796块田地共计82.7hm2草害进行 调查,采集数据,经转化后,对其进行主成分(PCA)和系统聚类分析,并赋予生态学意义的解 释.研究表明,决定江苏省南部麦田杂草的发生、危害和分布的主要因素是种植制度导致的田间 土壤饱和水分持续时间和土壤酸碱度,生境相似,杂草群落具有趋同性.并就杂草发生的特点提 出杂草综合治理的建议. The outputs of the principal analysis (PCA) and cluster analysis were ecologically interpreted , after the overall weed infestation indexes of all weed populations were statistically analyzed out of weed data, which are collected through assessment by visual scoring the level of weed infestation to wheat in seven scales in 81 sampling sites amounted to 82.7hm2 wheat fields in the south of Jiangsu Province. The result shows that the main factors determined the weed communities and infestation in the wheat fields in this region are saturated soil humidity and soil pH. The similar weed community occurrs under the same environment wherever the sites are in different location. According to the result of PCA and cluster analysis, 81 sampling sites could be divided into three groups. The dry crop group with continuous autumn dry-crop and wheat double cropping system in hill region has such dominant weeds as Galium aparine var. tenerum , Vicia sativa and the second-ranked Avena fatua and Veronica persica; Sclerochloa kengiana and Poly-pogon fugax are the two main weeds occurred in the Yangtse River bank group with continuous rice wheat double cropping system in the bank region of Yangtse River bank; The plain and dyke region group with continuous rice and wheat double cropping system has three dominant weeds as follows, Alopecurus japonicus, Alopecurus aequalis and Beckmannia syzigachn. The suggestion for the integrated weed management is also put forward according to weeds distribution and pattern of each group.
作者 王开金 强胜
出处 《生物数学学报》 CSCD 北大核心 2005年第1期107-114,共8页 Journal of Biomathematics
基金 国家自然科学基金(30170164)高等学校博士点基金(2000030708)资助项目
关键词 麦田杂草群落 主成分和系统聚类分析 杂草综合治理 Weed communities in wheat fields Principal component analysis (PCA) and cluster analysis Integrated weed management
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