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

Modeling root biomass of maize in Northeast China

  • 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。

     

    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.

     

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