吕国红,
谢艳兵,
温日红,
王笑影^,,
贾庆宇
中国气象局沈阳大气环境研究所 沈阳 110166
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

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出版历程

收稿日期:2018-01-25
录用日期:2018-07-05
刊出日期:2019-04-01

Modeling root biomass of maize in Northeast China

LYU Guohong,
XIE Yanbing,
WEN Rihong,
WANG Xiaoying^,,
JIA Qingyu
Shenyang Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
Funds: the National Natural Science Foundation of China31501215
the Special Scientific Research Fund of Meteorology in Public Welfare of China2016SYIAEZD

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Corresponding author:WANG Xiaoying, E-mail: wangxy_0917@qq.com

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摘要
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。
关键词:玉米/
根系生物量/
根长/
根径
Abstract:It is of great significance to explore root biomass and establish a universal root biomass model for the monitoring and evaluation of the ecosystem biomass. In order to get the real-time information of a root system, the biomass and ecological indexes of maize root system were collected using soil sampling method and root scanning systems at the agricultural meteorological experiment station in Jinzhou City, Liaoning Province in September 2016. The vertical distribution characteristics of root biomass of maize were analyzed and simulation equations were established based on the relationships between root biomass and root ecological indexes. The results showed that the maize root biomass decreased with increase of soil depth. The root biomass of maize mainly concentrated at the soil depth from 0 cm to 30 cm, which accounted for 94.44% of the total root biomass. The simulation accuracy of exponential and power functions of root biomass with root diameter as independent variable established by ordinary least square method was low, and R² was 0.10 and 0.12, respectively. The biomass model constructed by adding root length as an independent variable significantly improved the simulation accuracy, with R² reaching above 0.91. There was heteroscedasticity issue in the models of root biomass established by the ordinary least square method inducing less stable and inaccurate prediction results. This issue could be eliminated by using logarithmic transformation. The biomass model of maize root system with root length and root dimeter together as variables (D²H) had a better simulation effect and a good prediction accuracy, with the coefficient of determination (R²) as 0.90 and the evaluation indexes MAE, SEE and MPE as 4.38 g, 18.68 g and 16.09%, respectively. The correlation coefficient between simulated and measured values was 0.92 (P < 0.01), indicating that this model could be used to simulate the biomass of maize root system in Northeast China. The study results indicated that the difficulty in observing root biomass in real time could be resolved by using root biomass model combined with the minirhizotron method.
Key words:Maize/
Root biomass/
Root length/
Root diameter

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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。
Figure1.Vertical distribution of root biomass for maize
The soil volume for measurement of root biomass is 25 000 cm³.


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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。
Figure2.Biomass estimation models of maize root system and their residual analysis
W: root biomass; D: root diameter; H: root length; R: residual.


下载: 全尺寸图片幻灯片


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。
Figure3.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.


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图4玉米根系生物量模型的实测值和模型估计值
Figure4.Measured and estimated values of root biomass for maize


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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)
Table1.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)
Table2.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.

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