欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报 ›› 2011, Vol. 22 ›› Issue (07): 1717-1724.

• 研究报告 • 上一篇    下一篇

扎龙季节性湿草甸土壤养分和土壤微生物特性

马玲1,丁新华1,顾伟1,马伟2**   

  1. 1东北林业大学林学院, 哈尔滨 150040;2黑龙江中医药大学, 哈尔滨 150040
  • 出版日期:2011-07-18 发布日期:2011-07-18

Spatial distribution patterns of soil nutrients and microbes in seasonal wet meadow in Zhalong wetland.

MA Ling1, DING Xin-hua1, GU Wei1, MA Wei2   

  1. 1Northeast Forestry University, Harbin 150040, China;2Heilongjiang University of Chinese Medicine, Harbin 150040, China
  • Online:2011-07-18 Published:2011-07-18

摘要: 以扎龙自然湿地典型季节性湿草甸为试验对象,研究了土壤微生物特性和土壤养分空间分布规律及其主要影响因子.结果表明:土壤养分、微生物群落数量、微生物量碳、氮均呈现明显的垂直分布特征;土壤酶活性的空间分布因受多种因素影响而表现复杂.逐步线性回归分析表明:土壤微生物生物量碳、氮均分别与β-葡萄糖苷酶、脲酶、磷酸酶显著相关(P<0.05);有机碳与放线菌数量、过氧化氢酶活性显著相关(P<0.05);速效钾、全氮、水解性氮、C/N分别与放线菌数量、细菌数量、β-葡萄糖苷酶活性、土壤微生物生物量氮显著相关(P<0.05);全P、pH与土壤微生物活性无显著相关关系(P>0.05).采用主成分分析构建了湿地土壤养分的评价模型及微生物学预测模型.

关键词: 湿地, 土壤酶活性, 土壤微生物生物量, 土壤微生物, 土壤养分, 主成分分析, 新品种(系), 茶用菊, 定植期, 摘心, 产量

Abstract: This paper studied the spatial distribution patterns of soil nutrients and biological characteristics and related major affecting factors in seasonal wet meadow in Zhalong wetland. In the meadow, the soil nutrients, microbial communities, and microbial biomass carbon and nitrogen showed an obvious vertical distribution, but the soil enzyme activities had a complicated spatial distribution due to the effects of multi factors. Stepwise linear regression analysis showed that soil microbial biomass carbon and nitrogen had significant positive correlations with soil β-glucosidase, urease, and phosphatase activities (P<0.05), soil organic carbon had significant correlations with soil actinomycetes and soil catalase activity (P<0.05), soil available K, total N, alkali-hydrolyzable N, and C/N ratio were significantly correlated with soil bacteria (P<0.05), actinomycetes (P<0.05), β-glucosidase activity (P<0.05), and microbial biomass nitrogen (P<0.05), respectively, whereas soil total P and pH had no significant correlations with soil microbial activity (P>0.05). Two models, one for soil nutrients evaluation and another for soil microbiological prediction, were constructed by principal component analysis.

Key words: wetland, soil enzyme activity, soil microbial biomass, soil microorganism, soil nutrient,  , principal component analysis, tea-applied chrysanthemum, planting date, pinching, new varieties (line), yield.