The fifth assessment report of the intergovernmental panel on climate change (IPCC) estimated that by 2040 agroforestry would offer high potential of carbon (C) sequestration in developing countries. However, the role of tropical agroforestry in C sequestration and in climate change mitigation has only recently been recognized by United Nations Framework Convention on Climate Change (UNFCCC). This is partly due to the lack of reliable estimates on the sequestration potential in biomass and soil carbon pools over time. The aim of this study was to analyze the changes in the biomass and soil carbon pools of three indigenous agroforestry systems in south-eastern Rift Valley escrapment of Ethiopia using CO2FIX (v. 3.2) model. The agroforestry systems studied were Enset ( Ensete ventricosum)-tree, Enset-coffee-tree, and Tree-coffee systems. To run the model, empirical data collected from 60 farms (20 farms for each agroforestry system) and literature were used as inputs to parameterize the model. Simulations were run over a period of 50 years. Average simulated total biomass C stocks was the highest for Tree-coffee system (122 Mg C ha -1), followed by the Enset-coffee-tree (114 Mg C ha -1) and Enset-tree system (76 Mg C ha -1). The tree cohort accounted for 89-97% of the total biomass C stocks in all the studied systems, and the reminder was shared by Enset and coffee cohorts. The total average simulated total C stocks (biomass and soil) were 209, 286 and 301 Mg C ha -1 for Enset-tree, Enset-coffee-tree and Tree-coffee systems, respectively. The soil organic carbon (SOC) stocks accounted for 60-64% of the total carbon in the studied systems. Model validation results showed that long-term (10-40 years) simulated biomass C stocks were within the range of measured biomass C stocks for Enset-tree and Enset-coffee-tree systems, but significantly differed for the Tree-coffee system. The simulated soil and total C stocks were within the range of measured values for all the three systems. The CO2FIX model accurately predicted the SOC and total C stocks in the studied indigenous agroforestry systems, but the prediction of the biomass C stocks could be improved by acquiring more accurate input parameter values for running the model.
