Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso)

Hounkpatin, Ozias; Hipt, Felix Op de; Bossa, Aymar Yaovi; Welp, Gerhard; Amelung, Wulf

doi:10.1016/j.catena.2018.04.013

Cited by 88 publications

(40 citation statements)

References 80 publications

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“…In this study, the explanatory ability of climate variables to the variation of SOC stocks was about 20% during two periods, and the explanatory ability gradually decreased with the decrease of scale. With the increase of MAP, the annual output of organic matter increases correspondingly, so the organic carbon quality entering the soil also increases [52]. In addition, the RI of climate variables was offset to some extent by related environmental factors such as topography [42].…”

Section: Controls Of Soc Stocksmentioning

confidence: 99%

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m −2 . In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region. soil properties are affected by many environmental factors, it is difficult to predict soil properties accurately and efficiently at the regional scale [8]. As an efficient and low-cost method, digital soil mapping (DSM) technology is able to accurately describe the spatial variability of soil attributes in a region by using a limited amount of sampling data and environmental variables [9]. In order to accurately predict the SOC stocks in a region and analyze its key environmental factors, scholars have carried out a lot of researches [10][11][12]. In fact, the direct relationship between soil properties and environmental factors is non-linear and complex [13,14]. Therefore, machine learning algorithms which can effectively avoid this kind of problem are widely used in DSM mapping [11,15]. For example, on Barro Colorado Island, Panama, Grimm et al. [15] used a random forest model to simulate SOC stocks in 0-10 cm, 10-20 cm, 20-30 cm, and 30-50 cm layers, respectively. Giasson et al. [16] applied a multiple logistic regression model to predict the occurrence of soil types in southern Brazil. Dorji et al. [17] selected regression kriging and equal-area spline profile function to predict the SOC stocks at depths 0-5, 5-15, 15-30, 30-60, and 60-100 cm in montane ecosystems, Eastern Himalayas. In Denmark, Adhikari et al. [18] used regression kriging and 12 environmental variables to simulate the spatial distribution of SOC stocks at five soil layers. Were et al. [19] compared the performance of support vector regression, artificial neural network, and random forest models in predicti...

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Section: Controls Of Soc Stocksmentioning

confidence: 99%

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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“…Additionally, RF requires no assumption of the probability distribution of the target predictors as with linear regression, and is robust against nonlinearity and overfitting, although overfitting may occur in instances where noisy data are being modeled. The caret package was chosen to implement the classification algorithm due to its ability to streamline the model building and evaluation process of a multitude of algorithms [26]. The package reduces the complexity associated with model tuning by first iterating over a range of values of model parameters and then selecting the parameter combination that gives the best performance for building a final model.…”

Section: Geographical Distribution Of Carbon Stock Under Changing CLImentioning

confidence: 99%

“…RF regression [49,50] was also used to build a predictive model using the total carbon stock (biomass + soil) as dependent/response variables and the spectral and terrain/climatic variables as independent variables. Feature selection was carried out using the RF recursive feature elimination algorithm of the R "caret" package [26]. The following variables were finally retained for the carbon stock prediction: temperature, aspect, topographic wetness index, precipitation, elevation, red (spectral band), EVI, near infrared (spectral band), hue index, green (spectral band), NDVI, blue (spectral band), brightness index, redness index, and SAVI.…”

Section: Geographical Distribution Of Carbon Stock Under Changing CLImentioning

confidence: 99%

“…Moreover, the production of a local beer (dolo) results in the use of about 6400 t of firewood per year from the native savanna sites; this also constitutes a major source of degradation of natural resources [62]. Wood harvesting and charcoal production are a major cause of loss of trees in the study area, through clearing, harvesting, and bushfires [26,33]. However, the dominance of trees in lower DBH is very interesting for the survival of the vegetation.…”

Section: Vegetation Structurementioning

confidence: 99%

“…Several studies have been conducted on the potential of soils to sequester carbon [21][22][23][24][25][26] and plant biomass [16,17,[27][28][29][30][31][32] in semi-arid landscapes and their possible contribution to climate change mitigation. However, these studies only focused on the carbon stocks present either in the aboveground biomass or the soil, without integrating all these components at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas

Dimobe

Kouakou

Tondoh

et al. 2018

Land

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West African savannas are experiencing rapid land cover change that threatens biodiversity and affects ecosystem productivity through the loss of habitat and biomass, and carbon emissions into the atmosphere exacerbating climate change effects. Therefore, reducing carbon emissions from deforestation and forest degradation in these areas is critical in the efforts to combat climate change. For such restorative actions to be successful, they must be grounded on a clear knowledge of the extent to which climate change affects carbon storage in soil and biomass according to different land uses. The current study was undertaken in semi-arid savannas in Dano, southwestern Burkina Faso, with the threefold objective of: (i) identifying the main land use and land cover categories (LULCc) in a watershed; (ii) assessing the carbon stocks (biomass and soil) in the selected LULCc; and (iii) predicting the effects of climate change on the spatial distribution of the carbon stock. Dendrometric data (Diameter at Breast Height (DBH) and height) of woody species and soil samples were measured and collected, respectively, in 43 plots, each measuring 50 × 20 m. Tree biomass carbon stocks were calculated using allometric equations while soil organic carbon (SOC) stocks were measured at two depths (0–20 and 20–50 cm). To assess the impact of climate change on carbon stocks, geographical location records of carbon stocks, remote sensing spectral bands, topographic data, and bioclimatic variables were used. For projections of future climatic conditions, predictions from two climate models (MPI-ESM-MR and HadGEM2-ES) of CMIP5 were used under Representative Concentration Pathway (RCP) 8.5 and modeling was performed using random forest regression. Results showed that the most dominant LULCc are cropland (37.2%) and tree savannas (35.51%). Carbon stocks in woody biomass were higher in woodland (10.2 ± 6.4 Mg·ha−1) and gallery forests (7.75 ± 4.05 Mg·ha−1), while the lowest values were recorded in shrub savannas (0.9 ± 1.2 Mg·ha−1) and tree savannas (1.6 ± 0.6 Mg·ha−1). The highest SOC stock was recorded in gallery forests (30.2 ± 15.6 Mg·ha−1) and the lowest in the cropland (14.9 ± 5.7 Mg·ha−1). Based on modeling results, it appears clearly that climate change might have an impact on carbon stock at horizon 2070 by decreasing the storage capacity of various land units which are currently suitable. The decrease was more important under HadGEM2-ES (90.0%) and less under MPI-ESM-MR (89.4%). These findings call for smart and sustainable land use management practices in the study area to unlock the potential of these landscapes to sequestering carbon.

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Reconstructing continental‐scale variation in soil δ¹⁵N: a machine learning approach in South America

Sena-Souza

Houlton

Martinelli³

et al. 2020

Ecosphere

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Soil nitrogen isotope composition (δ15N) is an essential tool for investigating ecosystem nitrogen balances, plant–microbe interactions, ecological niches, animal migration, food origins, and forensics. The advancement of these applications is limited by a lack of robust geospatial models that are capable of capturing variation in soil δ15N (i.e., isotopic landscapes or isoscapes). Due to the complexity of the nitrogen cycle and general scarcity of isotopic information, previous approaches have reconstructed regional to global soil δ15N patterns via highly uncertain linear regression models. Here, we develop a new machine learning approach to ascertain a finer‐scale understanding of geographic differences in soil δ15N, using the South American continent as a test case. We use a robust training set spanning 278 geographic locations across the continent, spanning all major biomes. We tested three different machine learning methods: cubist, random forest (RF), and stochastic gradient boosting (GBM). 10‐fold cross‐validation revealed that the RF method outperformed both the cubist and GBM approaches. Variable importance analysis of the RF framework pointed to biome type as the most crucial auxiliary variable, followed by soil organic carbon content, in determining the model performance. We thereby created a biogeographic boundary map, which predicted an expected multiscale spatial pattern of soil δ15N with a high degree of confidence, performing substantially better than all previous approaches for the continent of South America. Therefore, the RF machine learning framework showed to be a great opportunity to explore a broad array of ecological, biogeochemical, and forensic issues through the lens of soil δ15N.

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Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso)

Cited by 88 publications

References 80 publications

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas

Reconstructing continental‐scale variation in soil δ¹⁵N: a machine learning approach in South America

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Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso)

Cited by 88 publications

References 80 publications

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas

Reconstructing continental‐scale variation in soil δ15N: a machine learning approach in South America

Contact Info

Product

Resources

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Reconstructing continental‐scale variation in soil δ¹⁵N: a machine learning approach in South America