Grassland management and SOC sequestration in Rincón de Corrientes, Argentina

2022-007 focuses on assessing and monitoring carbon sequestration in coffee systems. The project aims to demonstrate the use of non-destructive methods and tools to quantify baseline biomass C and SOC stocks of Robusta coffee farm archetypes in two origins. The objectives of Theme A are threefold: to demonstrate the use of non-destructive methods and tools to quantify baseline biomass C and SOC stocks of Robusta coffee farm archetypes in the two origins; to estimate prospective SOC sequestration potentials and alternative scenarios based on baseline inventories in the two origins; and to scale biomass C and SOC at provincial and district levels according to the representativity of each archetype on the entire Robusta production areas in the two origins. The project employs a modeling framework to achieve its objectives. The framework involves the identification of farm archetypes, mapping of unit of analysis, estimation of baseline C stocks, identification of best-case scenario, quantification of SOC turnover per scenario, comparison of scenarios, and scaling of results to the total area represented by each unit of analysis. The project also involves the use of variables for farm typology, including cropping system, altitude, area, sloped, shade level, tree age, soil erosion, number of trees, offer, intercrop tree number, other trees number, yield, and species diversity. The project identifies and defines four farm archetypes per origin, which are used to explain the spatial distribution of farm archetypes. The archetypes are C1: AF_T (Highest nr wood trees), C2: M (Highest nr fruit trees), C3: AF_T (Mulching), and C4: M (S loped). The project also uses a superimposed layers approach to delineate the unit of analysis and estimate changes in baseline SOC stocks over time. The SOC modeling approach involves the use of the RothC model, which is a process-based SOC model that integrates five soil pools and models C turnover in the topsoil up to 30 cm. The model uses a monthly time step and is compatible with GIS. The RothC model has been applied to different agricultural and agroforestry systems and diverse climate zones globally. It is a widely used model for estimating SOC turnover and has been validated in various studies. The project also includes uncertainty propagation via Monte Carlo to account for the uncertainty associated with the model parameters and inputs. The RothC model is referenced in the project, citing Coleman and Jenkinson (1996, 2014) as the original authors of the model.