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生态学杂志 ›› 2004, Vol. ›› Issue (4): 179-183.

• 技术与方法 • 上一篇    下一篇

羊草草原地上生物量的预测

刘正恩1, 王昱生2, 潘介正2, 葛剑平1   

  1. 1. 北京师范大学生命科学学院, 北京, 100875;
    2. 东北农业大学, 哈尔滨, 150030
  • 收稿日期:2003-05-11 修回日期:2003-09-12 出版日期:2004-08-10
  • 基金资助:
    国家自然科学基金项目(202046);教育部博士点基金项目(273101);教育部重点项目资助(272004)

Prediction of aboveground biomass of Leymus chinensis grassland

LIU Zhengen1, WANG Yusheng2, PAN Jiezheng2, GE Jianping1   

  1. 1. Life Science College of Beijing Normal University, Beijing 100875, China;
    2. Northeast Agricultural University, Harbin 150030, China
  • Received:2003-05-11 Revised:2003-09-12 Online:2004-08-10

摘要: 采用数量化理论对羊草草原地上生物量进行了预测,建立了2个数学预测模型。检验结果表明,地上生物量预测值与实测值的相关性显著,地上生物量的鲜重和干重的复相关系数分别达到了0.97和0.95。在影响羊草草原地上生物量的生态因素和植被特征因素中,按其对生物量形成的贡献大小(以得分范围衡量),贡献最大的为测定生物量时前一个月的降雨量,其次为土壤全氮含量,再次为测定生物量时前一个月≥10℃的积温,小于这三个生态因子影响大小的因素依次为草群总盖度、草群平均高度和羊草种群生长状况。所建模型不但包含了生态因素对地上生物量的影响,而且也包含了植被特征因素对地上生物量的影响,为准确预测草地生物量提供了一条新途径.

关键词: 有机物料, 黄瓜, 苗期病害, 土壤微生物

Abstract: Two mathematical models were built for predicting aboveground biomass of Leymus chinensis grassland according to quantification theory.Test results showed that the correlation between predicted aboveground biomass and field survey aboveground biomass was remarkable.The multiple correlation coefficients of aboveground biomass of fresh weight and dry weight were 0.97,0.95,respectively. Of the six selected factors,including three ecological features and three vegetation features,the most important element affecting the aboveground biomass,according to their scoring ranges,was monthly rainfall before measuring.The second and third important elements were total nitrogen content of soil(depth <30cm) and monthly accumulation of temperature≥10℃ before measuring.The intensities of other factors affecting the biomass were total coverage of the community,mean height of the community and Leymus chinensis population growth state.In the models,not only the ecological features were used for the prediction,but the vegetation features were also used for the prediction.This provided a new path to predict grassland aboveground biomass accurately.

Key words: Organic materials, Cucumber, Seedling disease, Soil microorganism

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