Aboveground carbon stocks in Madagascar’s vanilla production landscape – exploring rehabilitation through agroforestry in the light of land-use history

Forests and tree-dominated land uses store large amounts of carbon stocks in plant biomass. However, anthropogenic changes in land use and land cover decrease tree cover and associated carbon stocks. Agroforestry has the potential to maintain or restore carbon in plant biomass but the amount will be influenced by various factors that may include land-use history and management practices. However, few studies explicitly address how these factors determine aboveground carbon stocks. Therefore, our study estimates aboveground carbon stocks in different land-use types, across stem diameters and geographic origin of tree species, and its structural controls. We particularly focus on the importance of land-use history in agroforestry systems. We conducted the study in the mosaic landscape of north-eastern Madagascar in old-growth forests, forest fragments, woody fallows, and vanilla agroforests. The agroforests differed in land-use history and were either directly derived from forest or derived from woody fallows after slash-and-burn shifting cultivation. Aboveground carbon stocks were highest in old-growth forests and lowest in woody fallows. Within vanilla agroforests, aboveground carbon stocks were highly variable: forest-derived agroforests stored significantly higher carbon stocks that were mainly stored in native and endemic species, whereas fallow-derived agroforests stored lower carbon stocks that were mainly provided by introduced species. Furthermore, aboveground carbon stocks were mainly controlled by stem density and stem diameter. In conclusion, forest-derived agroforests have the potential to maintain relatively high carbon stocks and a forest-like structure in the landscape, whereas fallow-derived agroforests contribute to convert historically forested open land into permanent tree-dominated land-use systems, thereby restoring carbon stocks. Thus, considering the land-use history of agroforests is important for conservation and restoration agendas.

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Carbon Stocks in Miombo Woodlands: Evidence from over 50 Years

Forests ◽

10.3390/f12070862 ◽

2021 ◽

Vol 12 (7) ◽

pp. 862

Author(s):

Medha Bulusu ◽

Christopher Martius ◽

Jessica Clendenning

Keyword(s):

Land Use ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Stocks ◽

Dry Forest ◽

Miombo Woodlands ◽

Old Growth ◽

Aboveground Carbon ◽

Wide Range ◽

Soc Stocks

Miombo woodlands are extensive dry forest ecosystems in central and southern Africa covering ≈2.7 million km2. Despite their vast expanse and global importance for carbon storage, the long-term carbon stocks and dynamics have been poorly researched. The objective of this paper was to present and summarize the evidence gathered on aboveground carbon (AGC) and soil organic carbon (SOC) stocks of miombo woodlands from the 1960s to mid-2018 through a literature review. We reviewed the data to find out to what extent aboveground carbon and soil organic carbon stocks are found in miombo woodlands and further investigated if are there differences in carbon stocks based on woodland categories (old-growth, disturbed and re-growth). A review protocol was used to identify 56 publications from which quantitative data on AGC and SOC stocks were extracted. We found that the mean AGC in old-growth miombo (45.8 ± 17.8 Mg C ha−1), disturbed miombo (26.7 ± 15 Mg C ha−1), and regrowth miombo (18.8 ± 16.8 Mg C ha−1) differed significantly. Data on rainfall, stand age, and land-use suggested that the variability in aboveground carbon is site-specific, relating to climatic and geographic conditions as well as land-use history. SOC stocks in both old-growth and re-growth miombo were found to vary widely. It must be noted these soil data are provided only for information; they inconsistently refer to varying soil depths and are thus difficult to interpret. The wide range reported suggests a need for further studies which are much more systematic in method and reporting. Other limitations of the dataset include the lack of systematic sampling and lack of data in some countries, viz. Angola and Democratic Republic of the Congo.

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