Predictive biomass equations of chir pine silvipasture ecosystem of Himalayas, India Bhutia P. L.1,2,*, Gupta B.1, Yadav R. P.3, Islam Sadikul4, Pal Sharmistha2, Khola O. P. S.2, Bhutia K. G.5 1Dr Y S Parmar University of Horticulture and Forestry, Solan-173230, India 2ICAR-Indian Institute of Soil and Water Conservation, Chandigarh-160019, India 3Rani Lakshmi Bai Central Agricultural University, Jhansi -284003, India 4ICAR-Indian Institute of Soil and Water Conservation, Dehradun-248195, India 5Rain Forest Research Institute, Jorhat-785001, India *Corresponding author e-mail: pempadenzongpa66@gmail.com
Online Published on 24 November, 2022. Abstract In the present study, the above-ground herbaceous biomass was examined, and species-specific and multispecies power-law allometric equations for six dominant grass species of chir pine silvipasture ecosystem were developed, considering basal area and number of tillers as a predictor. The mean above ground herbaceous biomass and carbon content were estimated to be 3.02 ± 0.16 Mg ha−1 and 1.36 ± 0.7 Mg C ha−1, respectively. All allometric relationships fitted to similar power-law models, with the basal area as the most influential predictor for the majority of grass species, however, the number of tillers proved to be a good predictor for above ground biomass of Panicum maximum. Although the fit improved when the number of tillers and basal area were combined in the model. Species-specific equations gave much better fits than multispecies allometric equations. A validation test indicated that these models made a precise prediction of grass biomass of the region. Top Keywords Allometric equation, Biomass, Carbon stock, Grassland, Mid-hill region, Silvipasture. Top |