Soil Carbon Stocks Decrease following Conversion of Secondary Forests to Rubber (Hevea brasiliensis) Plantations

Blécourt, Marleen de; Brumme, R.; Xu, Jianchu; Corre, Marife D.; Veldkamp, Edzo

doi:10.1371/journal.pone.0069357

Cited by 159 publications

(102 citation statements)

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“…Thus, both are associated with lower soil permeability. Such soil degradation after forest conversion was also observed in similar land-use types in Malaysia (Gharibreza et al 2013), http://www.ecologyandsociety.org/vol21/iss2/art5/ China (de Blécourt et al 2013), and Ghana (Chiti et al 2014). Although the extent of soil degradation was similar between rubber and oil palm plantations, soil characteristics are more heterogeneous in oil palm plantations, i.e., soil organic carbon content is lower in inter-rows (Frazão et al 2013) and frequent and intensive management and harvesting operations increase soil compaction, e.g., on harvesting paths.…”

Section: Environmental Perceptions Of Changes In the Local Water Cyclesupporting

confidence: 56%

Water scarcity and oil palm expansion: social views and environmental processes

Merten¹,

Röll²,

Guillaume³

et al. 2016

E&S

104

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ABSTRACT. Conversions of natural ecosystems, e.g., from rain forests to managed plantations, result in significant changes in the hydrological cycle including periodic water scarcity. In Indonesia, large areas of forest were lost and extensive oil palm plantations were established over the last decades. We conducted a combined social and environmental study in a region of recent land-use change, the Jambi Province on Sumatra. The objective was to derive complementary lines of arguments to provide balanced insights into environmental perceptions and eco-hydrological processes accompanying land-use change. Interviews with villagers highlighted concerns regarding decreasing water levels in wells during dry periods and increasing fluctuations in stream flow between rainy and dry periods. Periodic water scarcity was found to severely impact livelihoods, which increased social polarization. Sap flux measurements on forest trees and oil palms indicate that oil palm plantations use as much water as forests for transpiration. Eddy covariance analyses of evapotranspiration over oil palm point to substantial additional sources of evaporation in oil palm plantations such as the soil and epiphytes. Stream base flow from a catchment dominated by oil palms was lower than from a catchment dominated by rubber plantations; both showed high peaks after rainfall. An estimate of erosion indicated approximately 30 cm of topsoil loss after forest conversion to both oil palm and rubber plantations. Analyses of climatic variables over the last 20 years and of a standardized precipitation evapotranspiration index for the last century suggested that droughts are recurrent in the area, but have not increased in frequency or intensity. Consequently, we assume that conversions of rain forest ecosystems to oil palm plantations lead to a redistribution of precipitated water by runoff, which leads to the reported periodic water scarcity. Our combined social and environmental approach points to significant and thus far neglected eco-hydrological consequences of oil palm expansion.

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Section: Environmental Perceptions Of Changes In the Local Water Cyclesupporting

confidence: 56%

Water scarcity and oil palm expansion: social views and environmental processes

Merten¹,

Röll²,

Guillaume³

et al. 2016

E&S

104

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“…Within the same soil group, SOC is mainly influenced by land-use type and management (e.g. de Blécourt et al, 2013;de Koning et al, 2003;Mekuria et al, 2009;Post and Kwon, 2000), and geomorphological characteristics such as slope and slope position (Chaplot et al, 2005;Corre et al, 2015;Pennock and Corre, 2001). Spatial patterns of SOC are also greatly influenced by small-scale variability in biophysical factors that influence plant productivity and decomposition of soil organic matter (Hook et al, 1991;Stoyan et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales

Blécourt¹,

Corre²,

Paudel

et al. 2017

SOIL

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Abstract. Presently, the lack of data on soil organic carbon (SOC) stocks in relation to land-use types and biophysical characteristics prevents reliable estimates of ecosystem carbon stocks in montane landscapes of mainland SE Asia. Our study, conducted in a 10 000 ha landscape in Xishuangbanna, SW China, aimed at assessing the spatial variability in SOC concentrations and stocks, as well as the relationships of SOC with land-use types, soil properties, vegetation characteristics and topographical attributes at three spatial scales: (1) land-use types within a landscape (10 000 ha), (2) sampling plots (1 ha) nested within land-use types (plot distances ranging between 0.5 and 12 km), and (3) subplots (10 m radius) nested within sampling plots. We sampled 27 one-hectare plots -10 plots in mature forests, 11 plots in regenerating or highly disturbed forests, and 6 plots in open land including tea plantations and grasslands. We used a sampling design with a hierarchical structure. The landscape was first classified according to land-use types. Within each land-use type, sampling plots were randomly selected, and within each plot we sampled within nine subplots. SOC concentrations and stocks did not differ significantly across the four land-use types. However, within the open-land category, SOC concentrations and stocks in grasslands were higher than in tea plantations (P < 0.01 for 0-0.15 m, P = 0.05 for 0.15-0.30 m, P = 0.06 for 0-0.9 m depth). The SOC stocks to a depth of 0.9 m were 177.6 ± 19.6 (SE) Mg C ha −1 in tea plantations, 199.5 ± 14.8 Mg C ha −1 in regenerating or highly disturbed forests, 228.6 ± 19.7 Mg C ha −1 in mature forests, and 236.2 ± 13.7 Mg C ha −1 in grasslands. In this montane landscape, variability within plots accounted for more than 50 % of the overall variance in SOC stocks to a depth of 0.9 m and the topsoil SOC concentrations. The relationships of SOC concentrations and stocks with land-use types, soil properties, vegetation characteristics, and topographical attributes varied across spatial scales. Variability in SOC within plots was determined by litter layer carbon stocks (P < 0.01 for 0-0.15 m and P = 0.03 for 0.15-0.30 and 0-0.9 m depth) and slope (P ≤ 0.01 for 0-0.15, 0.15-0.30, and 0-0.9 m depth) in open land, and by litter layer carbon stocks (P < 0.001 for 0-0.15, 0.15-0.30 and 0-0.9 m depth) and tree basal area (P < 0.001 for 0-0.15 m and P = 0.01 for 0-0.9 m depth) in forests. Variability in SOC among plots in open land was related to the differences in SOC concentrations and stocks between grasslands and tea plantations. In forests, the variability in SOC among plots was associated with elevation (P < 0.01 for 0-0.15 m and P = 0.09 for 0-0.9 m depth). The scale-dependent relationships between SOC and its controlling factors demonstrate that studies that aim to investigate the land-use effects on SOC Published by Copernicus Publications on behalf of the European Geosciences Union. 124 M. de Blécourt et al.: Spatial variability of soil organic carbon need an appropriate samp...

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“…This consolidation of forest types can oversimplify and mask key ecological differences that are important for monitoring forest change, biodiversity, and carbon stocks. Tree plantations and tree crops support much lower levels of biodiversity than mature and secondary forests, and potentially have higher rates of clearing for timber management and soil carbon emissions [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

et al. 2015

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An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) OPEN ACCESSRemote Sens. 2015, 7 5661 of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

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Soil Carbon Stocks Decrease following Conversion of Secondary Forests to Rubber (Hevea brasiliensis) Plantations

Cited by 159 publications

References 30 publications

Water scarcity and oil palm expansion: social views and environmental processes

Water scarcity and oil palm expansion: social views and environmental processes

Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

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