树木异速生长
异速滴定
生物量(生态学)
每年落叶的
碳储量
环境科学
固碳
生态学
生物
农学
生物量分配
二氧化碳
气候变化
作者
Pranab Kumar Pati,Priya Kaushik,M. L. Khan,P. K. Khare
标识
DOI:10.1016/j.tfp.2022.100289
摘要
In order to assess the contribution to the overall carbon stock and generate carbon credits under REDD+, it is essential to have an accurate estimation of biomass of different forest components. Forest cover in India is gradually increasing due to the active restoration of degraded land and plantation on waste and barren lands leading to an increase in the abundance of small diameter woody species. However, these were not included in biomass studies due to the non- availability of adequate allometric equations and low carbon stocks compared to mature individuals. We have harvested 589 individuals belonging to 23 woody species at the seedling and sapling stage from a tropical dry deciduous forest and developed species specific allometric equation and general allometric equation for aboveground biomass estimation. Further, the belowground biomass equation of 9 species were also developed using above ground biomass and root to shoot ratio as predictor variable. In the case of general equation, the combination of diameter with height and diameter, height and wood specific gravity exhibited highest adjusted R2 value. In case of species-specific allometric equations, combination of diameter with height predicts above ground biomass more precisely as compared to the diameter and wood specific gravity. Since the estimation of wood specific gravity requires destruction of lower diameter individuals, usage of diameter and height for biomass estimation would help protecting regeneration as both methods yield same results. All the equations developed in the present study for below ground biomass predicts biomass precisely. We suggest use of species specific allometric model developed with diameter and height for estimation of biomass. Further, general models consisting of height and diameter may be used for biomass estimation in case of non-availability of species specific equations without destroying the regeneration.
科研通智能强力驱动
Strongly Powered by AbleSci AI