斯里兰卡巨桉生长参数、茎生物量和碳的异速生长关系的构建

S. Subasinghe
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引用次数: 2

摘要

通过建立人造森林来加强碳储存已被认为是减少不断增加的大气二氧化碳水平的一种缓解办法。因此,本研究利用异速生长关系,以努沃勒埃利耶和巴杜拉地区为研究对象,对大桉主茎生物量和碳储量进行了估算。测量了样本树的树径和总数,并使用先前建立的同一物种的个体模型估计了茎体积。采用岩心样品估算茎生物量,采用Walkley-Black法测定碳含量。最后将生物量值分别转化为碳值。以年龄为解释变量,采用非线性回归分析构建模型。采用线性回归方法,以体积为解释变量,建立地上生物量、茎生物量和碳的预测模型。对于线性和非线性类型,使用r2和拟合线形图检验模型质量。结果表明,第7年茎秆生物量和碳值分别为110.8 kg和68.7 kg。第40年时茎生物量和碳值分别为1095.8 kg和679.4 kg。20龄时茎生物量碳含量为62.0%。指数模型比logistic模型更能准确地预测林分直径、高度、茎体积、生物量和碳含量随林分年龄的变化。r2值和拟合的线形图表明所选模型质量良好。利用茎体积预测茎生物量和碳的线性模型也显示出较高的准确性,r2值均在97.9%以上。
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Construction of allometric relationships to predict growth parameters, stem biomass and carbon of Eucalyptus grandis growing in Sri Lanka
Enhancement of carbon storage through the establishment of man-made forests has been considered as a mitigation option to reduce increasing atmospheric CO 2 levels. Therefore the present study was carried out to estimate the biomass and carbon storages of the main stem of Eucalyptus grandis using allometric relationships using the plantations of Nuwara Eliya and Badulla districts. Tree diameter and total were measured for the samples trees and stem volume was estimated using a previously built individual model for the same species. Stem biomass was estimated using core samples and carbon was determined using Walkley-Black method. Finally the biomass values were converted separately to the carbon values. Non-liner regression analysis was employed for the construction of models which had age as the explanatory variable. Linear regression was used in order to build the models to predict the above ground and stem biomass and carbon using volume as the explanatory variable. For both linear and non-linear types, the model quality was tested using R 2 and fitted line plots. According to the results, stem biomass and carbon values at the 7 th year were 110.8 kg and 68.7 kg respectively. Stem biomass and carbon values at the 40 th year were 1,095.8 kg and 679.4 kg respectively. The carbon content at the age 20 was 62.0% from the stem biomass. Exponential models were proven to be better than the logistic models to predict the diameter, height, stem volume, biomass and carbon with age. R 2 values and the fitted line plots indicated that the selected models are of high quality. Linear models built to predict the stem biomass and carbon using stem volume also showed the high accuracy of these models which had R 2 values above 97.9%.
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