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作 者:姜鹏[1,2] 张绍轩[1] 任佳佳[1] 王襄平[3] 孟京辉[3] 谷建才[1,2] 陆贵巧[1]
机构地区:[1]河北农业大学,保定071000 [2]河北省林木种质资源与森林保护重点实验室,保定071000 [3]北京林业大学林学院,北京100083
出 处:《生态学报》2015年第9期2937-2945,共9页Acta Ecologica Sinica
基 金:国家科技支撑计划项目(2011BAD38B05)
摘 要:以木兰林管局北沟林场内典型落叶松-杨桦混交林、落叶松人工林、白桦天然次生林、山杨天然次生林为研究对象,利用分层切割法和分层挖掘法对华北落叶松、白桦、山杨的生物量进行测定,并通过解析木进行了生长量的测定,从而建立生物量、生长量模型对林分的碳储量和固碳能力进行了估算。其研究结果表明:落叶松-杨桦混交林较落叶松人工林、白桦天然次生林、山杨天然次生林具有一定幅度的增产效益。落叶松-杨桦混交林中落叶松、白桦、山杨表现均优于各自的人工林或天然林,平均胸径分别高出6.7%、12.8%、4.1%,平均树高分别高出12.1%、1.4%、11.1%。落叶松-杨桦混交林中落叶松、白桦、山杨的固碳量增幅分别为29.74%、28.36%、34.52%;落叶松人工林固碳量增幅27.09%;白桦天然次生林固碳量增幅26.34%;山杨天然次生林固碳量增幅26.24%。落叶松-杨桦混交林中落叶松、白桦、山杨固碳量的增幅分别高于所对应树种的2.65%、2.02%、8.28%。A mixed Dahurian larch and polar birch forest,artificial Larix principis-rupprechtii forest,natural Betula platyphylla secondary forest,and natural Populus davidiana secondary forest,which are all part of the Bei-gou Forest Farm in Mulan Forestry Bureau were studied here. The layered cut method was used to measure aboveground biomass in all four forests. The layered mining method was used to measure underground biomass. The trunks were divided into segments 0. 5m in diameter and 5 cm thick. The north-south diameter of each disk was recorded. Tree roots were divided into 0. 5 cm layers,and the crown projection area was dug up. Fresh weights of the trunks( with bark),branches,leaves,and roots of model trees were recorded in the open air. Samples of each organ were dried to( 105℃) constant weight and weighed. To determine biomass,leaf and branch samples were collected from different parts of the crown cover. They were weighed and dried,and their moisture content was calculated. For trunks,disk sections were obtained and weighed before and after drying. Moisture content was calculated. The leaf,branch,and root biomass of each model tree was calculated. Incrementswere measured in analytic trees. Then models of biomass and increments were established. Carbon reserve and carbon sequestration were estimated. Significant power functions and logistic equations were chosen as biomass prediction models.DBH( diameter at breast height) served as an independent variable and the napierian logarithm of biomass and total biomass of organs as the dependent variable. Optimization was performed according to regression curve and residual sum of squares( SSE),discriminant coefficient( R2),total relative error( RS),average relative error( E1),absolute value of average relative error( E2),AIC( Akaike Information Criterion),and BIC( Bayesian Information Criterion). DBH increments were measured with analytic trees,wooden cores,and DBH increments of the past five years as a baseline. A power functio
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