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温室番茄蒸腾量与其影响因子的相关分析及模型模拟
引用本文:姚勇哲,李建明,张荣,孙三杰,陈凯利.温室番茄蒸腾量与其影响因子的相关分析及模型模拟[J].应用生态学报,2012,23(7):1869-1874.
作者姓名:姚勇哲  李建明  张荣  孙三杰  陈凯利
作者单位:1. 西北农林科技大学园艺学院,陕西杨凌,712100
2. 西北农林科技大学食品科学与工程学院,陕西杨凌,712100
基金项目:“十二五”国家科技支撑计划项目(2011BAD12B03-03)资助
摘    要:采用盆栽方法,研究不同灌溉量处理下温室番茄日蒸腾量与单株总叶面积、土壤相对含水量、空气温度、空气相对湿度、太阳辐射等因子的相关关系,并建立日蒸腾量的回归模型.结果表明:番茄日蒸腾量与单株总叶面积、土壤相对含水量、空气温度、空气相对湿度和太阳辐射等因子呈显著的线性关系,各因子之间存在复杂的相互作用;土壤水分状况是番茄蒸腾量的主要决策因子,决策系数为27.4%;日最低空气相对湿度是主要限制因子,决策系数为-119.7%;番茄日蒸腾量预测值和实测值的回归系数平方值(R2)为0.81,回归估计标准误差(RMSE)和相对误差(RE)分别为68.52 g和19.4%.根据通径分析筛选主要影响因子建立的番茄日蒸腾量回归模型能够较好地模拟温室番茄日蒸腾量.

关 键 词:温室番茄  设施蔬菜  蒸腾作用  影响因子  多元线性回归

Greenhouse tomato transpiration and its affecting factors:Correlation analysis and model simulation
YAO Yong-zhe,LI Jian-ming,ZHANG Rong,SUN San-jie,CHEN Kai-li.Greenhouse tomato transpiration and its affecting factors:Correlation analysis and model simulation[J].Chinese Journal of Applied Ecology,2012,23(7):1869-1874.
Authors:YAO Yong-zhe  LI Jian-ming  ZHANG Rong  SUN San-jie  CHEN Kai-li
Institution:1(1College of Horticulture,Northwest A & F University,Yangling 712100,Shaanxi,China;2College of Food Science and Technology,Northwest A & F University,Yangling 712100,Shaanxi,China)
Abstract:A pot experiment was conducted to study the correlations between the daily transpiration of greenhouse tomato and the related affecting factors such as total leaf area per plant,soil relative moisture content,air temperature,relative humidity,and solar radiation under different treatments of supplementary irrigation.A regression model for the daily transpiration of greenhouse tomato was established.There existed significant linear correlations between the daily transpiration and the test affecting factors,and the affecting factors had complicated mutual effects.Soil relative moisture content was the main decision factor of the transpiration,with the decision coefficient being 27.4%,and daily minimum relative humidity was the main limiting factor,with the decision coefficient being-119.7%.The square value of the regression coefficient(R2) between the predicted and measured tomato daily transpiration was 0.81,root mean squared error(RMSE) was 68.52 g,and relative prediction error(RE) was 19.4%,suggesting that the regression model established by using the main affecting factors selected through path analysis could better simulate the daily transpiration of greenhouse tomato.
Keywords:greenhouse tomato  protected vegetable  transpiration  affecting factor  multiple linear regression  
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