Empirical Data-Based Carbon Stock Projection in Agroforestry for Climate Change Mitigation

Deforestation from monoculture farming significantly contributes to greenhouse gas emissions and ecosystem degradation, highlighting the need for sustainable land management. Agroforestry presents a viable solution for enhancing carbon sequestration. However, many project models rely on assumptions or secondary data, leading to limited accuracy. This research aimed to enhance projections of carbon stock changes by utilizing empirical data from a 12.7-ha tea plantation in West Java, Indonesia. This research established baseline carbon stocks through direct field measurements in a monoculture scenario. Agroforestry interventions involved hardwood species, such as Toona sureni, Altingia excelsa, and Manglietia glauca, in conjunction with coffee crops. Carbon stock accumulation was then projected over ten years using allometric equations and annual growth increments derived from field observations. Results indicated that agroforestry increased carbon sequestration by threefold compared to monoculture, reaching 472.77 t CO2eq/ha by 2032. The findings demonstrated that empirical data-driven modeling resulted in more realistic and reliable projections, enhanced the accuracy of carbon stock predictions, and established agroforestry as a sustainable approach for mitigating climate change.