Influence of elaeicultural agroecosystem types on carbon storage in a monomodal rainforest agroecological zone
摘要
Over the past four decades, the forest ecosystem of Ngwei municipality has undergone extensive degradation associated with the establishment of various oil palm plantations, including agroforestry-based village oil palm plantations (VP) and monoculture elitist oil palm plantations (EP). This study quantified carbon stocks and biodiversity across four land-use types: reference forest (FO), abandoned oil palm plantations (AB), village oil palm plantations (VP), and elitist oil palm plantations (EP). Sampling methods combining stratified, quadrat, and multi-storey approaches allowed measurement of above- and below-ground biomass. Our results show that reference forests (FO) contained the highest carbon stocks but exhibited lower species richness than village oil palm groves, highlighting their pivotal role in carbon storage and underscoring the urgent need for their conservation. Abandoned oil palm plantations (AB) showed intermediate carbon stocks (25 tC·ha⁻1) and biodiversity levels, indicating natural regeneration potential but highlighting the necessity for active monitoring to prevent soil degradation and biodiversity loss. Village oil palm plantations (VP) have lower carbon stocks than abandoned plantations (AB) (18 tC·ha⁻1), but nevertheless maintain relatively high floristic richness (301 species), suggesting potential for agroecological intensification to enhance ecosystem services and support local livelihoods. Elitist oil palm plantations (EP) had the lowest carbon stocks (7 tC·ha⁻1) and reduced biodiversity, signaling a need for targeted interventions to mitigate ecological impacts. These findings emphasize the essential role of preserving remaining forests, fostering regeneration in abandoned plantations, and implementing sustainable agroecological practices in village oil palm plantations to optimize carbon sequestration and biodiversity conservation within oil palm landscapes. However, the absence of historical baseline data limits the full understanding of long-term carbon dynamics, highlighting the need for future longitudinal studies to track carbon stock changes before and after forest conversion.