Estimation of savanna rangeland productivity: Linking allometric traits to above-ground biomass of palatable grass species in sub-Saharan Africa

Estimating above-ground biomass (AGB) of grasses as a proxy for savanna productivity is vital for land management. Allometric theory suggests plant size metrics can predict biomass across species and ecosystems. However, relationships can be influenced by environmental factors and vary between species. This study quantifies AGB and allometric relationships of five palatable grass species in semi-arid Namibian savannas. To assess how environmental factors influence allometric relationships, we compare them with equations from other drylands and climatic zones. Our approach provides a baseline for non-destructive AGB estimations of palatable grasses in African savannas, supporting large-scale AGB estimation via remote sensing.

For each species, we measured AGB, height, canopy-, and basal area of 100 individuals. Allometric relationships were calculated across and separately for each species using power regression models. In all models, basal area was the best AGB predictor. Including both basal and canopy area improved predictions. Adding height slightly enhanced predictions but differed between species-specific models. Our comparison within drylands and across climate zones showed similar patterns in allometric relationships, but differences in scaling exponents and coefficients highlight the need for site-specific, but not necessarily species-specific models. Our findings suggest opportunities and challenges for using size measures determined by remote sensing. Large-scale AGB prediction of grasses using canopy area is feasible but less precise than using basal area. Including height improves AGB predictions but changes the allometric coefficient to species-specific values, requiring species differentiation. In conclusion, our findings could enhance AGB estimation precision for sustainable land management in southern Africa.