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      Carbon Sequestration Potential of Agroforestry Systems in Degraded Landscapes in West Java, Indonesia

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      Forests
      MDPI AG

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          Abstract

          When restoring degraded landscapes, approaches capable of striking a balance between improving environmental services and enhancing human wellbeing need to be considered. Agroforestry is an important option for restoring degraded land and associated ecosystem functions. Using survey, key informant interview and rapid carbon stock appraisal (RaCSA) methods, this study was conducted in five districts in West Java province to examine potential carbon stock in agroforestry systems practiced by smallholder farmers on degraded landscapes. Six agroforestry systems with differing carbon stocks were identified: gmelina (Gmelina arborea Roxb.) + cardamom (Amomum compactum); manglid (Magnolia champaca (L.) Baill. ex Pierre) + cardamom; caddam (Neolamarckiacadamba (Roxb.) Bosser) + cardamom; caddam + elephant grass (Pennisetum purpureum Schumach.); mixed-tree + fishpond; and mixed-tree lots. Compared to other systems, mixed-tree lots had the highest carbon stock at 108.9 Mg ha−1. Carbon stock variations related to species density and diversity. Farmers from research sites said these systems also prevent soil erosion and help to restore degraded land. Farmers’ adoption of agroforestry can be enhanced by the implementation of supportive policies and measures, backed by scientific research.

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          Most cited references35

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          Tree allometry and improved estimation of carbon stocks and balance in tropical forests.

          Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contribution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regression models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees >or= 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional relationships between aboveground biomass and the product of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regression model involving wood density and stem diameter only. Our models were tested for secondary and old-growth forests, for dry, moist and wet forests, for lowland and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5-6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprecedented dataset, these models should improve the quality of tropical biomass estimates, and bring consensus about the contribution of the tropical forest biome and tropical deforestation to the global carbon cycle.
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            Agroforestry for ecosystem services and environmental benefits: an overview

            Shibu Jose (2009)
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              Agroforestry solutions to address food security and climate change challenges in Africa

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                Author and article information

                Contributors
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                Journal
                Forests
                Forests
                MDPI AG
                1999-4907
                June 2021
                May 31 2021
                : 12
                : 6
                : 714
                Article
                10.3390/f12060714
                0f618c03-0759-4769-a165-92f3599cfc5e
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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