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《Subtropical Soil and Water Conservation》 2017-01
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Study on Biomass Estimate Method of Pinusmassoniana Lamb in Changting County of Fujian Province

Xie Yanmin;Soil Conservation Station of Changting County;  
Based on the investigation of Pinusmassoniana Lamb sample plot in Changting County and the estimate module of biomass per plant of Pinusmassoniana Lamb frequently used in eight provinces of China,it was calculated and compared the ground Pinusmassoniana Lamb biomass(W),taking the diameter at breast height(D),tree height(H) and tree numbers(T) as arguments.The results show that,the total biomass amount of Jiangxi Province is highest in eight estimate modules,with the highest coefficient of variation of biomass.While the total biomass amount of Anhui Province is lowest in eight estimate modules,with the lowest coefficient of variation of biomass.From the comparison of total amount of biomass,among the different modules,the biomass calculated by the module of Jiangxi Province is highest followed by Guangdong Province.The biomass amounts calculated by the modules of Guizhou,Fujian,Guangxi,Hubei and Hunan Provinces are similar while the biomass amount calculated by the module of Anhui Province is lowest.For the different quadrats,the calculation difference reached highest by using quadrat 3 modules,followed by quadrat 1,2 and 10.The differences calculated by quadrat 11 and 12 modules were lowest.After the comparison of the biomass coefficient of variation calculated by the different modules,it is found that the coefficient of variation calculated by the module of Jiangxi Province is always highest,the coefficient of variation calculated by the modules of Fujian,Guizhou,Hubei,Hunan and Guangxi Provinces are relatively concentrated,followed by the Guangdong Province.The coefficient of variation calculated by the biomass module of Anhui Province is still lowest.Therefore,during the study of biomass estimate,it is necessary to establish the most suitable module based on the actual eco-environment of study areas so as to get the most accurate biomass value.
【Fund】: 国家自然科学基金(41571415/41071281)资助
【CateGory Index】: S718.55+6
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