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      Socio-economic factors and management regimes as drivers of tree cover change in Nepal

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          Abstract

          Despite the local and global importance of forests, deforestation is driven by various socio-economic and biophysical factors continues in many countries. In Nepal, in response to massive deforestation, the community forestry program has been implemented to reduce deforestation and support livelihoods. After four decades of its inception, the effectiveness of this program on forest cover change remains mostly unknown. This study analyses the spatial and temporal patterns of tree cover change along with a few socio-economic drivers of tree cover change to examine the effectiveness of the community forestry program for conserving forests or in reducing deforestation. We also investigate the socio-economic factors and policy responses as manifested through the community forestry program responsible for the tree cover change at the district level. The total tree cover area in the year 2000 in Nepal was ∼4,746,000 hectares, and our analysis reveals that between 2001 and 2016, Nepal has lost ∼46,000 ha and gained ∼12,200 ha of areas covered by trees with a substantial spatial and temporal variations. After accounting socio-economic drivers of forest cover change, our analysis showed that districts with the larger number of community forests had a minimum loss in tree cover, while districts with the higher proportion of vegetation covered by community forests had a maximum gain in tree cover. This indicates a positive contribution of the community forestry program to reducing deforestation and increasing tree cover.

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          Proximate Causes and Underlying Driving Forces of Tropical Deforestation

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            Global Carbon Budget 2016

            Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry ( E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1 σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), E FF was 9.3 ± 0.5 GtC yr −1 , E LUC 1.0 ± 0.5 GtC yr −1 , G ATM 4.5 ± 0.1 GtC yr −1 , S OCEAN 2.6 ± 0.5 GtC yr −1 , and S LAND 3.1 ± 0.9 GtC yr −1 . For year 2015 alone, the growth in E FF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr −1 , showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr −1 that took place during 2006–2015. Also, for 2015, E LUC was 1.3 ± 0.5 GtC yr −1 , G ATM was 6.3 ± 0.2 GtC yr −1 , S OCEAN was 3.0 ± 0.5 GtC yr −1 , and S LAND was 1.9 ± 0.9 GtC yr −1 . G ATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller S LAND for that year. The global atmospheric CO 2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in E FF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of E FF in 2016, the growth rate in atmospheric CO 2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink ( S LAND ) in response to El Niño conditions of 2015–2016. From this projection of E FF and assumed constant E LUC for 2016, cumulative emissions of CO 2 will reach 565 ± 55 GtC (2075 ± 205 GtCO 2 ) for 1870–2016, about 75 % from E FF and 25 % from E LUC . This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2016 ).
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              Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon.

              Intensive mechanized agriculture in the Brazilian Amazon grew by >3.6 million hectares (ha) during 2001-2004. Whether this cropland expansion resulted from intensified use of land previously cleared for cattle ranching or new deforestation has not been quantified and has major implications for future deforestation dynamics, carbon fluxes, forest fragmentation, and other ecosystem services. We combine deforestation maps, field surveys, and satellite-based information on vegetation phenology to characterize the fate of large (>25-ha) clearings as cropland, cattle pasture, or regrowing forest in the years after initial clearing in Mato Grosso, the Brazilian state with the highest deforestation rate and soybean production since 2001. Statewide, direct conversion of forest to cropland totaled >540,000 ha during 2001-2004, peaking at 23% of 2003 annual deforestation. Cropland deforestation averaged twice the size of clearings for pasture (mean sizes, 333 and 143 ha, respectively), and conversion occurred rapidly; >90% of clearings for cropland were planted in the first year after deforestation. Area deforested for cropland and mean annual soybean price in the year of forest clearing were directly correlated (R(2) = 0.72), suggesting that deforestation rates could return to higher levels seen in 2003-2004 with a rebound of crop prices in international markets. Pasture remains the dominant land use after forest clearing in Mato Grosso, but the growing importance of larger and faster conversion of forest to cropland defines a new paradigm of forest loss in Amazonia and refutes the claim that agricultural intensification does not lead to new deforestation.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                29 May 2018
                2018
                : 6
                : e4855
                Affiliations
                [1 ]Department of Biology, University of Massachusetts Boston , Boston, MA, United States of America
                [2 ]Institute for Agriculture and the Environment, University of Southern Queensland , Toowoomba, QLD, Australia
                [3 ]Ashoka Trust for Research in Ecology and the Environment (ATREE) , Bangalore, India
                Article
                4855
                10.7717/peerj.4855
                5983000
                03b67649-c83b-4bdf-b39f-996e5700a0a9
                ©2018 Shrestha et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 24 January 2018
                : 5 May 2018
                Funding
                Funded by: Rufford Small Grants for Nature Conservation
                Funded by: Nancy Goranson Endowment Fund
                Funded by: Doctoral Dissertation Research Grant from the University of Massachusetts Boston
                This research is funded by Rufford Small Grants for Nature Conservation, Nancy Goranson Endowment Fund, and Doctoral Dissertation Research Grant from the University of Massachusetts Boston. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Biodiversity
                Conservation Biology
                Natural Resource Management
                Forestry

                tree cover,community forestry,nepal,forest loss,deforestation,geographic information system,remote sensing

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