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典型草原主要物种生长指标对种群和群落地上生物量的预测

Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe.

作者信息

Huang Xiaojuan, Liu Yongjie, Wang Niya, Li Lan, Hu An, Wang Zhen, Chang Shenghua, Chen Xianjiang, Hou Fujiang

机构信息

State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, Lanzhou University, Lanzhou 730020, China.

出版信息

Plants (Basel). 2020 Oct 5;9(10):1314. doi: 10.3390/plants9101314.

Abstract

The objective was to explore a fast, accurate, non-destructive, and less disturbance method for predicting the aboveground biomass (AGB) of the typical steppe, by using plant height and canopy diameter of the dominant species, , , and , data were observed from 165 quadrats during the peak plant growing season, and the product of plant height (PH) and canopy diameter (PC) were calculated for each species. AGB of population were predicted for the same species and other species through using 2/3 of the measured data, and the optimal predictive equation was linear in terms of determination coefficient. The other 1/3 of the data, which was measured from no grazing paddocks or rotational grazing paddocks, was substituted into the predictive equations for validation. Results showed that PC of one dominant species could be used to predict aboveground biomass (AGB) of the same species or other species well. The predicted and measured values were significantly correlative, and most of the predictive accuracy was above 80%, and not affected by the managements of grassland, grazing or no grazing. A combination of 3 to 6 representative species was used to predict AGB of the community, and the predictive equations with PC of six species as an independent variable were the most optimal because explaining 83.5% variation of AGB. The predictive methods cost 1/15, 1/9, and 1/51 of time, labor, and capital as much as the destructive sample method (quadrat sampling method), respectively, and thus improved the efficiency of field study and protecting the fragile study areas, especially the long-term study sites in grassland.

摘要

目的是探索一种快速、准确、无损且干扰较小的方法,通过利用优势种的株高和冠幅来预测典型草原的地上生物量(AGB)。在植物生长旺季,从165个样方中观测了[具体物种1]、[具体物种2]、[具体物种3]和[具体物种4]的数据,并计算了每个物种的株高(PH)与冠幅(PC)的乘积。利用2/3的实测数据对同一物种和其他物种的种群AGB进行预测,就决定系数而言,最优预测方程为线性方程。将从未放牧围场或轮牧围场测得的另外1/3数据代入预测方程进行验证。结果表明,一种优势种的PC可很好地用于预测同一物种或其他物种的地上生物量(AGB)。预测值与实测值显著相关,大多数预测精度高于80%,且不受草地管理方式(放牧或不放牧)的影响。使用3至6种代表性物种的组合来预测群落的AGB,以6种物种的PC作为自变量的预测方程最为最优,因为其解释了AGB变异的83.5%。与破坏性采样方法(样方采样法)相比,该预测方法在时间、人力和资金方面分别仅花费其1/15、1/9和1/51,从而提高了野外研究效率并保护了脆弱的研究区域,尤其是草地的长期研究地点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56e3/7600926/1a74f979ca21/plants-09-01314-g001.jpg

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