scholarly journals Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data

2019 ◽  
Vol 11 (13) ◽  
pp. 3569 ◽  
Author(s):  
Li Qi ◽  
Shuai Wang ◽  
Qianlai Zhuang ◽  
Zijiao Yang ◽  
Shubin Bai ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Environmental Variables ◽  
Spatial Variations ◽  
Liaoning Province ◽  
Mean Annual Precipitation ◽  
Topographic Wetness Index ◽  
Infrared Band ◽  
Wetness Index

Quantification of soil organic carbon (SOC) and pH, and their spatial variations at regional scales, is a foundation to adequately assess agriculture, pollution control, or environmental health and ecosystem functioning, so as to establish better practices for land use and land management. In this study, we used the random forest (RF) model to map the distribution of SOC and pH in the topsoil (0–20 cm) and estimate SOC and pH changes from 1982 to 2012 in Liaoning Province, Northeast China. A total of 10 covariates (elevation, slope gradient, topographic wetness index (TWI), mean annual temperature (MAT), mean annual precipitation (MAP), visible-red band 3 (B3), near-infrared band 4 (B4), short-wave infrared band 5 (B5), normalized difference vegetation index (NDVI), and land-use data) and a set of 806 (in 1982) and 973 (in 2012) soil samples were selected. Cross-validation technology was used to test the performance and uncertainty of the RF model. We found that the prediction R2 of SOC and pH was 0.69 and 0.54 for 1982, and 0.63 and 0.48 for 2012, respectively. Elevation, NDVI, and land use are the main environmental variables affecting the spatial variability of SOC in both periods. Correspondingly, the topographic wetness index and mean annual precipitation were the two most critical environmental variables affecting the spatial variation of pH. The mean SOC and pH decreased from 18.6 to 16.9 kg−1 and 6.9 to 6.6, respectively, over a 30-year period. SOC distribution generated using the RF model showed a decreasing SOC trend from east to west across the city in the two periods. In contrast, the spatial distribution of pH showed an opposite trend in both periods. This study provided important information of spatial variations in SOC and pH to agencies and communities in this region, to evaluate soil quality and make decisions on remediation and prevention of soil acidification and salinization.

Effects of land use and topography on spatial variety of soil organic carbon density in a hilly, subtropical catchment of China

Soil Research ◽  
2017 ◽  
Vol 55 (2) ◽  
pp. 134 ◽  
Author(s):  
Huanyao Liu ◽  
Jiaogen Zhou ◽  
Qingyu Feng ◽  
Yuyuan Li ◽  
Yong Li ◽  
...  
Keyword(s):  
Land Use ◽  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Paddy Fields ◽  
Accurate Estimation ◽  
Carbon Density ◽  
Topographic Wetness Index ◽  
Soil Organic Carbon Density ◽  
Effects Of Land Use

A good understanding the effects of environmental factors on the spatial variety of soil organic carbon density (SOCD) helps achieve a relatively accurate estimation of the soil organic carbon stock of terrestrial ecosystems. The present study analysed the SOCD of 1033 top soil samples (0–20cm) from the Jinjing catchment located in subtropical China. Spatial variability of SOCD was estimated using a geostatistics method and a geographically weighted regression (GWR) model, and the major environmental factors affecting SOCD were also explored. In the present study, SOCD had a moderate spatial dependence and the best-fitting model was exponential with a nugget-to-sill ratio of 60.72% and a range of 182m. Land use types (woodlands, paddy fields and tea fields) and topography (elevation, slope, topographic wetness index (TWI)) affected the spatial variation of SOCD. Mean SOCD in the paddy fields was higher than in woodland and tea fields (3.50 vs 3.24 and 2.81kgCm–2 respectively; P<0.05). In addition, SOCD was generally higher in the valleys of paddy fields (with low slope and high TWI) and the hills of woodland (with high elevation and increased slope). GWR generated the spatial distribution of SOCD more accurately than ordinary kriging, inverse distance weighted, multiple linear regression model, and linear mixed-effects model. The results of the present study could enhance our understanding of the effects of land use and topography on SOCD, and improve the accuracy in predicting SOCD by GWR in small catchments of complex land use and topography.

scholarly journals Relating extent of colluvial soils to topographic derivatives and soil variables in a Luvisol sub-catchment, Central Bohemia, Czech Republic

2014 ◽  
Vol 9 (No. 2) ◽  
pp. 47-57 ◽  
Author(s):  
T. Zádorová ◽  
D. Žížala ◽  
V. Penížek ◽  
Š. Čejková
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Soil Organic Carbon Content ◽  
Soil Variables ◽  
Colluvial Soil ◽  
Colluvial Soils

Colluvial soils, resulting from accelerated soil erosion, represent a significant part of the soil cover pattern in agricultural landscapes. Their specific terrain position makes it possible to map them using geostatistics and digital terrain modelling. A study of the relationship between colluvial soil extent and terrain and soil variables was performed at a morphologically diverse study site in a Luvisol soil region in Central Bohemia. Assessment of the specificity of the colluviation process with regard to profile characteristics of Luvisols was another goal of the study. A detailed field survey, statistical analyses, and detailed digital elevation model processing were the main methods utilized in the study. Statistical analysis showed a strong relationship between the occurrence of colluvial soil, various topographic derivatives, and soil organic carbon content. A multiple range test proved that four topographic derivatives significantly distinguish colluvial soil from other soil units and can be then used for colluvial soil delineation. Topographic wetness index was evaluated as the most appropriate terrain predictor. Soil organic carbon content was significantly correlated with five topographic derivatives, most strongly with topographic wetness index (TWI) and plan curvature. Redistribution of the soil material at the study site is intensive but not as significant as in loess regions covered by Chernozem. Soil mass transport is limited mainly to the A horizon; an argic horizon is truncated only at the steepest parts of the slope.

scholarly journals Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 487
Author(s):  
Kingsley JOHN ◽  
Isong Abraham Isong ◽  
Ndiye Michael Kebonye ◽  
Esther Okon Ayito ◽  
Prince Chapman Agyeman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Catchment Area ◽  
Vegetation Index ◽  
Model Performance ◽  
Soil Nutrient ◽  
Machine Learning Algorithms ◽  
Topographic Wetness Index ◽  
Wetness Index

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

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

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1023 ◽  
Author(s):  
Shuai Wang ◽  
Qianlai Zhuang ◽  
Zijiao Yang ◽  
Na Yu ◽  
Xinxin Jin
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Forest Soil ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Carbon Stocks ◽  
Boosted Regression Trees ◽  
Liaoning Province ◽  
Soc Stocks

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.

scholarly journals Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

2021 ◽  
Vol 13 (15) ◽  
pp. 8332
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Slope Aspect ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Slope Gradient ◽  
Phosphorus And Potassium ◽  
Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

scholarly journals Topographic Position, Land Use and Soil Management Effects on Soil Organic Carbon (Vineyard Region of Niš, Serbia)

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1438
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Layer ◽  
Spatial Distribution ◽  
Soil Erosion ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Compaction ◽  
Soil Management ◽  
Forest Land ◽  
Topographic Position

Spatial distribution of soil organic carbon (SOC) is the result of a combination of various factors related to both the natural environment and anthropogenic activities. The aim of this study was to examine (i) the state of SOC in topsoil and subsoil of vineyards compared to the nearest forest, (ii) the influence of soil management on SOC, (iii) the variation in SOC content with topographic position, (iv) the intensity of soil erosion in order to estimate the leaching of SOC from upper to lower topographic positions, and (v) the significance of SOC for the reduction of soil’s susceptibility to compaction. The study area was the vineyard region of Niš, which represents a medium-sized vineyard region in Serbia. About 32% of the total land area is affected, to some degree, by soil erosion. However, according to the mean annual soil loss rate, the total area is classified as having tolerable erosion risk. Land use was shown to be an important factor that controls SOC content. The vineyards contained less SOC than forest land. The SOC content was affected by topographic position. The interactive effect of topographic position and land use on SOC was significant. The SOC of forest land was significantly higher at the upper position than at the middle and lower positions. Spatial distribution of organic carbon in vineyards was not influenced by altitude, but occurred as a consequence of different soil management practices. The deep tillage at 60–80 cm, along with application of organic amendments, showed the potential to preserve SOC in the subsoil and prevent carbon loss from the surface layer. Penetrometric resistance values indicated optimum soil compaction in the surface layer of the soil, while low permeability was observed in deeper layers. Increases in SOC content reduce soil compaction and thus the risk of erosion and landslides. Knowledge of soil carbon distribution as a function of topographic position, land use and soil management is important for sustainable production and climate change mitigation.

scholarly journals Dynamics of soil organic carbon in the steppes of Russia and Kazakhstan under past and future climate and land use

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Susanne Rolinski ◽  
Alexander V. Prishchepov ◽  
Georg Guggenberger ◽  
Norbert Bischoff ◽  
Irina Kurganova ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Organic Carbon ◽  
Land Use Change ◽  
Soil Organic Carbon ◽  
Arable Land ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Scenarios ◽  
The Impact

AbstractChanges in land use and climate are the main drivers of change in soil organic matter contents. We investigated the impact of the largest policy-induced land conversion to arable land, the Virgin Lands Campaign (VLC), from 1954 to 1963, of the massive cropland abandonment after 1990 and of climate change on soil organic carbon (SOC) stocks in steppes of Russia and Kazakhstan. We simulated carbon budgets from the pre-VLC period (1900) until 2100 using a dynamic vegetation model to assess the impacts of observed land-use change as well as future climate and land-use change scenarios. The simulations suggest for the entire VLC region (266 million hectares) that the historic cropland expansion resulted in emissions of 1.6⋅ 1015 g (= 1.6 Pg) carbon between 1950 and 1965 compared to 0.6 Pg in a scenario without the expansion. From 1990 to 2100, climate change alone is projected to cause emissions of about 1.8 (± 1.1) Pg carbon. Hypothetical recultivation of the cropland that has been abandoned after the fall of the Soviet Union until 2050 may cause emissions of 3.5 (± 0.9) Pg carbon until 2100, whereas the abandonment of all cropland until 2050 would lead to sequestration of 1.8 (± 1.2) Pg carbon. For the climate scenarios based on SRES (Special Report on Emission Scenarios) emission pathways, SOC declined only moderately for constant land use but substantially with further cropland expansion. The variation of SOC in response to the climate scenarios was smaller than that in response to the land-use scenarios. This suggests that the effects of land-use change on SOC dynamics may become as relevant as those of future climate change in the Eurasian steppes.

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