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东北玉米根系生物量模型的构建

吕国红 谢艳兵 温日红 王笑影 贾庆宇

吕国红, 谢艳兵, 温日红, 王笑影, 贾庆宇. 东北玉米根系生物量模型的构建[J]. 中国生态农业学报(中英文), 2019, 27(4): 572-580. doi: 10.13930/j.cnki.cjea.180115
引用本文: 吕国红, 谢艳兵, 温日红, 王笑影, 贾庆宇. 东北玉米根系生物量模型的构建[J]. 中国生态农业学报(中英文), 2019, 27(4): 572-580. doi: 10.13930/j.cnki.cjea.180115
LYU Guohong, XIE Yanbing, WEN Rihong, WANG Xiaoying, JIA Qingyu. Modeling root biomass of maize in Northeast China[J]. Chinese Journal of Eco-Agriculture, 2019, 27(4): 572-580. doi: 10.13930/j.cnki.cjea.180115
Citation: LYU Guohong, XIE Yanbing, WEN Rihong, WANG Xiaoying, JIA Qingyu. Modeling root biomass of maize in Northeast China[J]. Chinese Journal of Eco-Agriculture, 2019, 27(4): 572-580. doi: 10.13930/j.cnki.cjea.180115

东北玉米根系生物量模型的构建

doi: 10.13930/j.cnki.cjea.180115
基金项目: 

国家自然科学基金项目 31501215

公益性行业(气象)科研专项基金 2016SYIAEZD

详细信息
    作者简介:

    吕国红, 主要研究方向为生态气象

    通讯作者:

    王笑影, 主要研究方向为生态气象。E-mail:wangxy_0917@qq.com

  • 中图分类号: S513

Modeling root biomass of maize in Northeast China

Funds: 

the National Natural Science Foundation of China 31501215

the Special Scientific Research Fund of Meteorology in Public Welfare of China 2016SYIAEZD

More Information
  • 摘要: 开展根系生物量的观测和研究,建立通用性的根系生物量模型对于开展生态系统生物量的监测和评估具有重要意义。为得到根系生物量的实时信息,2016年9月末利用挖土法和根系扫描系统,获取玉米根系的生物量及生态指标,分析了玉米根系生物量的垂直分布特征并建立了根系生物量与根系生态指标之间的模拟方程。结果表明:玉米根系生物量主要集中于0~30 cm,占玉米根系垂直分布量的94.44%。利用普通最小二乘法建立根系生物量模型均存在异方差问题,增加根长作为自变量建立的根系生物量模型显著提高了模拟精度,决定系数(R2)达0.91以上。采用对数转换消除方程的异方差及比较不同的模拟方程后发现,玉米根系生物量与根径和根长的组合变量(D2H)建立的指数函数是模拟玉米根系生物量的最优方程,决定系数(R2)最高,为0.90,平均绝对误差(MAE)、估计值的标准误差(SEE)、平均预估误差(MPE)均最小,满足了模拟方程的精度要求。对该方程进一步验证发现,模拟值和实测值之间的相关系数为0.92,说明此模型能较好地模拟根系生物量。利用根系生物量模型结合微根管法,可解决根系生物量实时观测难的问题。
  • 图  1  玉米根系生物量的垂直分布

    根系生物量测量的土壤体积为25 000 cm3

    Figure  1.  Vertical distribution of root biomass for maize

    The soil volume for measurement of root biomass is 25 000 cm3.

    图  2  玉米根系生物量模型的构建及残差分析

    W:根系生物量; D:根径; H:根长; R:残差。

    Figure  2.  Biomass estimation models of maize root system and their residual analysis

    W: root biomass; D: root diameter; H: root length; R: residual.

    图  3  消除异方差后玉米根系生物量模型的构建及残差分析

    W:根系生物量; D:根径; H:根长; R:残差。

    Figure  3.  Biomass estimation models of maize root system and their residual analysis after eliminating heteroscedasticity

    W: root biomass; D: root diameter; H: root length; R: residual.

    图  4  玉米根系生物量模型的实测值和模型估计值

    Figure  4.  Measured and estimated values of root biomass for maize

    表  1  玉米根系生物量回归模型及其拟合优度评价(基于生物量乘以100后计算)

    Table  1.   Root biomass regression models and evaluation of the goodness in fitting for maize (based on multiplying by 100 for root biomass)

    公式
    Formula
    参数估计值
    Parametric
    estimated
    values
    拟合优度评价指标
    Evaluation indicators for fitting
    goodness
    β1 Β2 R2 MAE
    (g)
    SEE
    (g)
    MPE
    (%)
    W=β1eβ2D 5.31 1.48 0.10 14.17 34.72 42.85
    W=β1Dβ2 27.79 1.63 0.12 13.68 34.36 42.40
    W=β1Hβ2 0.00 2.11 0.91 5.37 9.20 11.35
    W=β1(D2H)β2 0.96 0.85 0.95 4.36 18.58 15.85
    W:根系生物量; D:根径; H:根长; R2:决定系数; MAE:平均绝对误差; SEE:估计值的标准误差; MPE:平均预估误差。W: root biomass; D: root diameter; H: root length; R2: coefficient of determination; MAE: mean absolute error; SEE: standard error of estimate; MPE: mean prediction error.
    下载: 导出CSV

    表  2  消除异方差后玉米根系生物量回归模型及其拟合优度评价(基于生物量乘以100后计算)

    Table  2.   Root biomass regression models and evaluation of the goodness in fitting for maize after eliminating heteroscedasticity (based on multiplying by 100 for root biomass)

    公式
    Formula
    参数估计值
    Parametric
    estimated
    value
    拟合优度评价指标
    Evaluation indicator
    for fitting goodness
    β1 β2 R2 MAE
    (g)
    SEE
    (g)
    MPE
    (%)
    W=β1eβ2D 1.93 0.65 0.32 10.27 35.57 43.89
    W=β1Dβ2 1.42 2.66 0.35 10.36 35.72 44.08
    W=β1Hβ2 0.57 -0.35 0.20 10.74 34.69 42.81
    W=β1(D2H)β2 1.04 -1.02 0.90 4.38 18.68 16.09
    W:根系生物量; D:根径; H:根长; R2:决定系数; MAE:平均绝对误差; SEE:估计值的标准误差; MPE:平均预估误差。W: root biamass; D: root diameter; H: root length; R2: coefficient of determination; MAE: mean absolute error; SEE: standard error of estimate; MPE: mean prediction error.
    下载: 导出CSV
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  • 收稿日期:  2018-01-25
  • 录用日期:  2018-07-05
  • 刊出日期:  2019-04-01

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