桉树生物量估算模型及与IPCC法的对比分析
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摘要
为构建适用于区域桉树人工林生物量异速生长方程,收集整理桉树林的生物量文献数据,拟合测树因子(胸径和树高)与地上、地下和单株生物量间的回归关系。结果表明:单自变量模型中,基于胸径因子的方程拟合优度高于树高因子。双自变量模型中,树高因子的添加仅对单株生物量拟合优度提高了0.7%～1.5%。模型的预测值与实测值的比较及相容性分析表明,方程lnW=-2.833+2.301ln D+0.352 1lnH对地上生物量预测效果最优,精度达94.6%;方程lnW=-5.175+0.939ln D~2H对地下生物量预测效果最优,精度达66.8%;方程lnW=-2.960+0.896ln DH~2对单株生物量预估效果最优,精度达95.5%。分量模型与单株模型相容性较好。桉树人工林BCEF和R的平均值分别为0.634 1(n=65,SD=0.132)和0.205 6(n=76,SD=0.089)。IPCC法对林分生物量估算精度高于异速生长方程,达到95.3%。因此,建议采用IPCC生物量估算参数法进行区域尺度桉树人工林生物量估算。
In order to construct allometric equations for large scale Eucalyptus plantations,biomass data were obtained from published papers and subject to meta-analysis to obtain regression relationships of various factors with aboveground,belowground and total-tree biomass.The results showed that a single independent variable equation based on DBH performed better than one based on tree height.In double independent variable models,the use of tree height in combination with DBH only improved the R~2 of total-tree biomass by 0.7% to 1.5%,but did not improve correlations obtained with other component equations.Verification analysis of predicted and measured values of the models showed that the equation ln W=-2.833+(2.301~*ln D)+0.3521~*ln H has the best predictive ability for aboveground biomass with an accuracy of 94.6%,while ln W=-5.175+0.939~*ln D~2H has the best predictive ability for underground biomass with a correlation of 66.8%,and ln W=-2.960+0.896~*ln DH~2 has the best predictive ability for total-tree biomass with a correlation of 95.5%.The derived component model was comparable with total-tree biomass model.The means of BCEF(Biomass Conversion and Expansion Factor)and R(Root:shoot Ratio)were 0.634(n=65,SD=0.132)and 0.206(n=76,SD=0.089),respectively.The accuracy of the forest biomass estimation by the IPCC method was higher than that of the allometric equation,which was 95.3% Therefore,it is recommended to use the IPCC(Intergovernmental Panel on Climate Change)method for estimating the biomass of Eucalyptus plantations on regional scales.
In order to construct allometric equations for large scale Eucalyptus plantations,biomass data were obtained from published papers and subject to meta-analysis to obtain regression relationships of various factors with aboveground,belowground and total-tree biomass.The results showed that a single independent variable equation based on DBH performed better than one based on tree height.In double independent variable models,the use of tree height in combination with DBH only improved the R~2 of total-tree biomass by 0.7% to 1.5%,but did not improve correlations obtained with other component equations.Verification analysis of predicted and measured values of the models showed that the equation ln W=-2.833+(2.301~*ln D)+0.3521~*ln H has the best predictive ability for aboveground biomass with an accuracy of 94.6%,while ln W=-5.175+0.939~*ln D~2H has the best predictive ability for underground biomass with a correlation of 66.8%,and ln W=-2.960+0.896~*ln DH~2 has the best predictive ability for total-tree biomass with a correlation of 95.5%.The derived component model was comparable with total-tree biomass model.The means of BCEF(Biomass Conversion and Expansion Factor)and R(Root:shoot Ratio)were 0.634(n=65,SD=0.132)and 0.206(n=76,SD=0.089),respectively.The accuracy of the forest biomass estimation by the IPCC method was higher than that of the allometric equation,which was 95.3% Therefore,it is recommended to use the IPCC(Intergovernmental Panel on Climate Change)method for estimating the biomass of Eucalyptus plantations on regional scales.
引文
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[14]冯宗炜.中国森林生态系统的生物量和生产力[M].北京:科学出版社,1999.
[15]胥辉.林木生物量模型研究评述[M].昆明:云南科技出版社,2002.
[16]罗云建,王效科,逯非.中国主要林木生物量模型手册[M].北京:中国林业出版社,2015.
[17]WANG C.Biomass allometric equations for 10co-occurring tree species in Chinese temperate forests[J].Forest Ecology and Management,2006,222(1):9-16.
[18]JENKINS J C,CHOJNACKY D C,HEATH L S,et al.National-scale biomass estimators for United States tree species[J].Forest Science,2003,49(1):12-35.
[19]TER-MIKAELIAN M T,KORZUKHIN M D.Biomass equations for sixty-five North American tree species[J].Forest Ecology and Management,1997,97(1):1-24.
[20]ZIANIS D,MENCUCCINI M.On simplifying allometric analyses of forest biomass[J].Forest Ecology and Management,2004,187(2):311-332.
[21]MONTAGU K D,DUTTMER K,BARTON C V M,et al.Developing general allometric relationships for regional estimates of carbon sequestration-an example using Eucalyptus pilularis from seven contrasting sites[J].Forest Ecology and Management,2005,204(1):115-129.
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[25]潘嘉雯,林娜,何茜,等.我国3个桉树人工林种植区生产力影响因素[J].生态学报,2018,28(19):6932-6940.
[26]张利丽,王志超,陈少雄,等.不同林龄尾巨桉人工林的生物量分配格局[J].西北农林科技大学学报(自然科学版),2017,45(6):61-68.
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[28]周建辉,郑磊,张婧,等.不同林龄尾巨桉林木碳贮量研究[J].桉树科技,2013,30(4):11-14.
[29]周群英,陈少雄,韩斐扬,等.短周期尾巨桉能源林生物量与能量特征研究[J].热带亚热带植物学报,2013,21(1):45-51.
[30]郑海水,曾杰,周再知,等.胶园防护林更新树种的选择[J].广东林业科技,1997,13(1),15-21.
[31]韩斐扬,周群英,陈少雄.6年生11种桉树无性系生物量与炭化研究[J].桉树科技,2012,29(3):1-8.
[32]杨卫星,何斌,卢开成,等.桂西南连续年龄序列尾巨桉人工林的生物生产力[J].农业研究与应用,2016(3):6-11.
[33]叶绍明,郑小贤,谢伟东,等.萌芽更新与植苗更新对尾巨桉人工林收获的影响[J].南京林业大学学报(自然科学版),2007,31(3):43-46.
[34]陈婷,温远光,孙永萍,等.连栽桉树人工林生物量和生产力的初步研究[J].广西林业科学,2005,34(1):8-12.
[35]朱宇林,温远光,曹福亮,等.短周期尾巨桉连栽林分生产力的研究[J].江西农业大学学报,2006,28(1):90-94.
[36]李春宁,韦建宏,付军,等.2种更新方式尾巨桉中龄林地上部分生物生产力比较分析[J].农业研究与应用,2017(6):17-20.
[37]吴华静,田丰,桂凌健,等.南宁七坡林场尾巨桉人工林生物量的初步研究[J].广西科学院学报,2014,30(4):233-237.
[38]邓龙江.连栽对尾巨桉萌芽林生物量和生产力的影响[D].南宁:广西大学,2015.
[39]梁宏温,温远光,吴国喜,等.连栽对尾巨桉短轮伐期人工林生长量和生产力动态的影响[J].福建林业科技,2008,35(3):14-18.
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[41]朱宾良,李志辉,陈少雄.不同林分密度对尾巨桉生物产量及生产力的影响研究[J].湖南生态科学学报,2007,13(4):11-14.
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[44]刘正富.巨尾桉人工林生长、生物量及植物多样性的动态变化[D].南宁:广西大学,2012.
[45]陶玉华,隆卫革,马麟英,等.柳州市马尾松、杉木、桉树人工林碳储量及其分配[J].广东农业科学,2011,38(22):42-45.
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