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土壤墒情诊断理论和方法

侯彦林1,2*,李敬亚1,米长虹1,黄治平1,刘书田1,2,郑宏艳1,丁健1,王铄今2#br#   

  1. 1农业部环境保护科研监测所, 天津 300191; 2北部湾环境演变与资源利用教育部重点实验室 (广西师范学院), 广西地表过程与智能模拟重点实验室 (广西师范学院), 南宁 530001)
  • 出版日期:2017-12-10 发布日期:2017-12-10

Theory and method of soil moisture diagnosis.

HOU Yan-lin1,2*, LI Jing-ya1, MI Chang-hong1, HUANG Zhi-ping1, LIU Shu-tian1,2, ZHENG Hong-yan1, DING Jian1, WANG Shuo-jin2#br#   

  1. (1Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China; 2Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Guangxi Teachers Education University), Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation (Guangxi Teachers Education University), Nanning 530001, China).
  • Online:2017-12-10 Published:2017-12-10

摘要: 本文对土壤墒情诊断理论和方法进行了简述,并介绍了国内墒情监测、诊断和预报现状。重点介绍了所建立的平衡法、统计法、差减统计法、比值统计法、间隔天数统计法、移动统计法6个独立的墒情诊断模型及其联合应用的综合诊断模型。应用2012—2014年7省23个县87个监测点的数据建模,并应用2015年的数据进行了验证。结果表明:所有模型构建简单,参数获得容易;综合模型优于6个独立模型。所建立的6个独立模型和综合模型适用于基于降水量的土壤墒情诊断和预测。

关键词: 生长模型, 种间互作, 小麦/蚕豆间作, 磷水平

Abstract: This paper reviewed the theory and methods of soil moisture diagnosis, and introduced the status quo of soil moisture monitoring, diagnosis and prediction in China. Six independent models were emphatically presented, which included balance diagnostic model, statistical diagnostic model, subtractive statistical diagnostic model, ratio statistical diagnostic model, interval days statistical diagnostic model and movable statistical diagnostic model. Meanwhile, an integrated diagnosis-model by combined application of the six independent models was also introduced. Models were established by the data of 87 monitoring sites in 23 counties in 7 provinces during the period of 2012-2014, and were validated by the data of 2015. The results showed that all the models were simple and the parameters were easy to obtain. Integrated diagnostic model was superior to the 6 independent models. In conclusion, the 6 independent models and the integrated diagnostic model were suitable for the diagnosis and prediction of soil moisture based on precipitation.

Key words: wheat and faba bean intercropping, species interaction, growth pattern, P level.