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Rooting pattern and equations for estimating biomasses of Hardwickia binata and Colophospermum mopane trees in agroforestry system in Indian desert

G. Singh and Bilas Singh

In addition to conserving soil and water, improving land-use efficiency and increasing economic returns, agroforestry practice is also one of the better options of sequestering atmospheric CO2 and contributing to mitigate climate change effects with the secondary benefits of food security. We studied root growth pattern and biomass allocation in roots, stem, branches and foliage (twig+leaves) of 18-year old Colophospermum mopane J. Kirk ex Benth. and Hardwickia binata Roxb and developed equations for precise carbon accounting, environmental health monitoring and sustainable management of agroforestry systems in dry areas. Roots of both these species mined the area >1.5 times the canopy area. Roots of C. mopane were more confined to top 80 cm soil layer and almost parallel to soil surface and appeared to be more competitive as compared to that in H. binata, where roots were relatively deep penetrating. Biomass allocation to roots and foliage decreased with increase in tree total biomass. Such decrease was at the cost of increased branch biomass in H. binata and both branch and stem biomass in C. mopane. Among the linear and nonlinear equations developed for estimating above ground biomass, root biomass and total biomass using diameter at breast height (DBH) and height as the predictors, DBH alone was sufficient to predict these biomasses. Inclusion of height in the models did not improve the results. Average total dry biomass ranged between 4.49 to 135.85 kg per tree for H. binata and 5.91 to 130.41 kg per tree for C. mopane trees. Biomass accumulation in stem was higher (45.7%) in H. binata than in C. mopane (28.6%) trees. A reverse trend was observed in case of foliage, the contribution of which to the total biomass was 40.2% in C. mopane and 23.5% in H. binata trees. Findings on rooting pattern cautioned in selecting agroforestry tree species, whereas predicting standing biomass more accurately for carbon accounting may be beneficial in promoting tree cover and help mitigate climate change effects.