scholarly journals Spatial Variability of Soil Organic Carbon and Total Nitrogen in Desert Steppes of China’s Hexi Corridor

2021 ◽  
Vol 9 ◽  
Author(s):  
Xuyang Wang ◽  
Yuqiang Li ◽  
Yulong Duan ◽  
Lilong Wang ◽  
Yayi Niu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Total Nitrogen ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Elevation Gradient ◽  
Dominant Factor ◽  
Desert Steppe ◽  
Carbon And Nitrogen

Stock estimates are critical to quantifying carbon and nitrogen sequestration, quantifying greenhouse gas emissions, and understanding key biogeochemical processes (i.e., soil carbon and nutrient cycling). Many studies have assessed soil organic matter and nutrients in different ecosystems. However, the spatial distribution of carbon and nitrogen and the key influencing factors in arid desert steppe remain unclear. Here, we investigated the soil organic carbon (SOC) and soil total nitrogen (STN) to a depth of 100 cm at 126 sites in a desert steppe in northwestern China. SOC and STN contents decreased with increasing depth; the highest average SOC and STN contents were 12.70 and 0.65 g kg−1 in the surface 5 cm, and the lowest were from 80 to 100 cm (4.49 and 0.16 g kg−1, respectively). SOC density (SOCD) and STN density (STND) to a depth of 100 cm averaged 8.94 and 0.45 kg m−2, respectively. The top 1 m of the soils stored approximately 1,041 Tg SOC and 52 Tg STN in the study area. Geostatistical analysis showed strong and moderate spatial autocorrelation for SOCD in different soil layers, but the autocorrelation for STND gradually weakened with increasing depth. SOCD and STND decreased from southwest to northeast in the study area, along an elevation gradient. Both were significantly positively correlated with topographic variables, precipitation, and the normalized-difference vegetation index, but negatively correlated with temperature and aridity. More than 40% of the SOCD and STND spatial variation was explained by elevation, which was the dominant factor. The data and high-resolution maps from this study will support future soil carbon and nitrogen analyses.

scholarly journals Alteration of carbon, nitrogen, and phosphorus stoichiometry and their related enzymes as affected by increased soil coarseness

2016 ◽  
Author(s):  
Ruzhen Wang ◽  
Linyou Lü ◽  
Courtney A. Creamer ◽  
Heyong Liu ◽  
Xue Feng ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Enzyme Activities ◽  
Microbial Biomass Carbon ◽  
Soil Parameters ◽  
Available Phosphorus ◽  
Carbon And Nitrogen ◽  
Acid Phosphomonoesterase

Abstract. Soil coarseness decreases ecosystem productivity, ecosystem carbon and nitrogen stocks, and soil nutrient contents in sandy grasslands. To gain insight into changes in soil carbon and nitrogen pools, microbial biomass, and enzyme activities in response to soil coarseness, a field experiment of sand addition was conducted to coarsen soil with different intensities: 0 % sand addition, 10 %, 30 %, 50 %, and 70 %. Soil organic carbon and total nitrogen decreased with the intensification of soil coarseness across three depths (0–10 cm, 10–20 cm, and 20–40 cm) by up to 43.9 % and 53.7 %, respectively. At 0–10 cm, soil microbial biomass carbon (MBC) and nitrogen (MBN) declined with soil coarseness by up to 44.1 % and 51.9 %, respectively, while microbial biomass phosphorus (MBP) increased by as much as 73.9 %. Soil coarseness significantly decreased the activities of β-glucosidase, N-acetyl-glucosaminidase, and acid phosphomonoesterase by 20.2 %–57.5 %, 24.5 %–53.0 %, and 22.2 %–88.7 %, respectively. Soil coarseness enhanced microbial C and N limitation relative to P, indicated by the ratios of β-glucosidase and N-acetyl-glucosaminidase to acid phosphomonoesterase (and MBC:MBP and MBN:MBP ratios). As compared to laboratory measurement, values of soil parameters from theoretical sand dilution was significantly lower for soil organic carbon, total nitrogen, dissolved organic carbon, total dissolved nitrogen, available phosphorus, MBC, MBN, and MBP. Phosphorus immobilization in microbial biomass might aggravate plant P limitation in nutrient-poor grassland ecosystems as affected by soil coarseness. We conclude that microbial C:N:P and enzyme activities might be good indicators for nutrient limitation of microorganisms and plants.

scholarly journals Effect of legume and grazing intensity on soil organic carbon and total nitrogen stock in Himalayan rangeland

Our Nature ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 1-8
Author(s):  
Dil Kumar Limbu ◽  
Madan Koirala ◽  
Zhanhuan Shang
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Nitrogen Concentration ◽  
Grazing Intensity ◽  
Soil Bulk Density ◽  
Rangeland Management ◽  
Carbon And Nitrogen ◽  
Ground Biomass ◽  
Below Ground

Organic carbon and total nitrogen are important components of global carbon and nitrogen cycle in rangeland ecology.Objective of this study is to identify and quantify the present status of carbon and nitrogen pool in Himalayan rangeland and to make recommendations for enhancing carbon and nitrogen storage for rangeland management. To meet the aforementioned objectives, the field study was conducted in 2011 -2013. The study showed that soil organic carbon was highest in legume seeding sub-plot in top soil (28.53 ± 2.6) t/ha of heavily grazed area. Similarly, total nitrogen was highest in bottom soil (2.81 ± 0.16) t/ha in legume seeding sub-plot of enclosed un-grazed area. Usually, heavily grazed and legume seeding sub-plots had more soil organic carbon and total nitrogen concentration compared to others. The value of above ground biomass was in increasing trend with decreasing grazing intensity but for below ground biomass, it was just the reverse. On the basis of the results of this study, the grazing intensity is positively correlated with above ground and below ground biomass and soil organic carbon but no correlation with soil total nitrogen and soil bulk density.

Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra

Soil Research ◽  
2013 ◽  
Vol 51 (8) ◽  
pp. 577 ◽  
Author(s):  
J. A. Baldock ◽  
B. Hawke ◽  
J. Sanderman ◽  
L. M. Macdonald
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Total Nitrogen ◽  
Diffuse Reflectance ◽  
Inorganic Carbon ◽  
Soil Samples ◽  
Mid Infrared ◽  
Organic And Inorganic Carbon ◽  
Mir Spectra

Quantifying the content and composition of soil carbon in the laboratory is time-consuming, requires specialised equipment and is therefore expensive. Rapid, simple and low-cost accurate methods of analysis are required to support current interests in carbon accounting. This study was completed to develop national and state-based models capable of predicting soil carbon content and composition by coupling diffuse reflectance mid-infrared (MIR) spectra with partial least-squares regression (PLSR) analyses. Total, organic and inorganic carbon contents were determined and MIR spectra acquired for 20 495 soil samples collected from 4526 locations from soil depths to 1 m within Australia’s agricultural regions. However, all subsequent MIR/PLSR models were developed using soils only collected from the 0–10, 10–20 and 20–30 cm depth layers. The extent of grinding applied to air-dried soil samples was found to be an important determinant of the variability in acquired MIR spectra. After standardisation of the grinding time, national MIR/PLSR models were developed using an independent test-set validation approach to predict the square-root transformed contents of total, organic and inorganic carbon and total nitrogen. Laboratory fractionation of soil organic carbon into particulate, humus and resistant forms was completed on 312 soil samples. Reliable national MIR/PLSR models were developed using cross-validation to predict the contents of these soil organic carbon fractions; however, further work is required to enhance the representation of soils with significant contents of inorganic carbon. Regional MIR/PLSR models developed for total, organic and inorganic carbon and total nitrogen contents were found to produce more reliable and accurate predictions than the national models. The MIR/PLSR approach offers a more rapid and more cost effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil carbon and total nitrogen content provided that the soils are well represented by the calibration samples used to build the predictive models.

scholarly journals Estimating the Spatial Distribution of Soil Organic Carbon and Soil Total Nitrogen Stocks by Remote Sensing Methods: A Case Study in a Forest-dominated Alpine Watershed, Western China

2021 ◽  
Author(s):  
chao wang ◽  
Chuanyan Zhao ◽  
Kaiming Li ◽  
Shouzhang Peng ◽  
Ying Wang
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Vegetation Index ◽  
Multiple Linear Regression Model ◽  
Western China ◽  
Linear Regression Models ◽  
Soil Total Nitrogen

Abstract Soil organic carbon and soil total nitrogen stocks are important indicators for evaluating soil health and stability. Accurately predicting the spatial distribution of soil organic carbon and total nitrogen stocks is an important basis for mitigating global warming, ensuring regional food security, and maintaining the sustainable development of ecologically fragile areas. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0–30 cm) into three soil layers of 0–10 cm, 10–20 cm, and 20–30 cm to carry out soil organic carbon and soil total nitrogen stocks estimation experiments in the Qilian Mountains in western China. A multiple linear regression model and a stepwise multiple linear regression model were used to estimate soil organic carbon and soil total nitrogen stocks. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results indicated that these two linear regression models showed good performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen stocks. In topsoil, remote sensing technology could be used to predict the soil properties in layers; however, as the soil depth increased, the performance of the linear regression models gradually decreased.

scholarly journals Soil Carbon and Nitrogen Stock as Affected by Agricultural Wastes in a Typic Haplusult of Owerri, Southeastern Nigeria

2016 ◽  
Vol 4 (7) ◽  
pp. 592
Author(s):  
Stanley Uchenna Onwudike ◽  
Bethel Ugochukwu Uzoho ◽  
Bernadine Ngozi Ndukwu ◽  
Innocent Uzoma Opara ◽  
Ojinere Clitton Anyamele
Keyword(s):  
Organic Carbon ◽  
Soil Carbon ◽  
Total Nitrogen ◽  
Chemical Properties ◽  
Southeastern Nigeria ◽  
Carbon And Nitrogen ◽  
Saw Dust ◽  
Soil Carbon And Nitrogen ◽  
Nitrogen Stock ◽  
Poultry Droppings

We evaluated the effect of saw dust ash (SDA) and poultry droppings (PD) on soil physico-chemical properties, soil carbon and nitrogen stock and their effects on the growth and yield of okra (Abelmoshus esculentus) on a typic haplusult in Owerri, Imo State Southeastern Nigeria. The experiment was a factorial experiment consisted of saw dust ash applied at the rates of 0, 5 and 10 t/ha and poultry droppings applied at the rates of 0, 5 and 10 t/ha. The treatments were laid out in a randomized complete block design and replicated four times. Results showed that plots amended with 10 t/ha PD + 10 t/ha SDA significantly reduced soil bulk density from 1.37 – 1.07 g/cm3, increased soil total porosity from 48.4 – 59.7% and the percentage of soil weight that is water (soil gravimetric moisture content) was increased by 68.4%. There were significant improvements on soil chemical properties with plots amended with 10 t/ha PD + 10 t/ha SDA recording the highest values on soil organic carbon, soil total nitrogen and exchangeable bases. Plots amended with 10 t/ha PD + 10 t/ha SDA significantly increased soil carbon stock by 24% and soil nitrogen stock by 49.5% more than other treatments. There was significant increase in the growth of okra when compared to the un-amended soil with application of 10 t/ha PD + 10 t/ha SDA increasing the fresh okra pod yield by 78.5%. Significant positive correlation existed between SCS and organic carbon (r = 0.6128), exchangeable Mg (r= 0.5035), total nitrogen (r = 0.6167) and soil pH (r = 0.5221). SNS correlated positively with organic carbon (r = 0.5834), total nitrogen (r= 0.6101) and soil pH (r = 5150). Therefore applications of these agro-wastes are effective in improving soil properties, increasing soil carbon and nitrogen stock. From the results of the work, application of 10 t/ha PD + 10 t/ha SDA which was the treatment combination that improved soil properties and growth performances of okra than other treatments studied is hereby recommended for soil carbon and nitrogen stock improvement and okra production in the region.

scholarly journals An exploratory spatial analysis of soil organic carbon distribution in Canadian eco-regions

2014 ◽  
Vol XL-2 ◽  
pp. 205-212 ◽  
Author(s):  
S.-Y. Tan ◽  
J. Li
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ecological Factors ◽  
Spatial Data Analysis ◽  
Slope Aspect ◽  
Climatic Data ◽  
Spatial Error

As the largest carbon reservoir in ecosystems, soil accounts for more than twice as much carbon storage as that of vegetation biomass or the atmosphere. This paper examines spatial patterns of soil organic carbon (SOC) in Canadian forest areas at an eco-region scale of analysis. The goal is to explore the relationship of SOC levels with various climatological variables, including temperature and precipitation. The first Canadian forest soil database published in 1997 by the Canada Forest Service was analyzed along with other long-term eco-climatic data (1961 to 1991) including precipitation, air temperature, slope, aspect, elevation, and Normalized Difference Vegetation Index (NDVI) derived from remote sensing imagery. In addition, the existing eco-region framework established by Environment Canada was evaluated for mapping SOC distribution. Exploratory spatial data analysis techniques, including spatial autocorrelation analysis, were employed to examine how forest SOC is spatially distributed in Canada. Correlation analysis and spatial regression modelling were applied to determine the dominant ecological factors influencing SOC patterns at the eco-region level. At the national scale, a spatial error regression model was developed to account for spatial dependency and to estimate SOC patterns based on ecological and ecosystem factors. Based on the significant variables derived from the spatial error model, a predictive SOC map in Canadian forest areas was generated. Although overall SOC distribution is influenced by climatic and topographic variables, distribution patterns are shown to differ significantly between eco-regions. These findings help to validate the eco-region classification framework for SOC zonation mapping in Canada.

scholarly journals Estimation of soil organic carbon in agricultural fields: A remote sensing approach

2022 ◽  
Vol 43 (1) ◽  
pp. 73-84
Author(s):  
R. Madugundu ◽  
◽  
K.A. Al-Gaadi ◽  
E. Tola ◽  
M. Edrris ◽  
...  
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Prediction Models ◽  
Bare Soil ◽  
Agricultural Fields ◽  
Landsat 8 ◽  
Linear Regression Models

Aim: In view of the importance of Soil Organic Carbon (SOC) in agricultural management, a study was conducted to develop a digital SOC map using remotely sensed spectral indices. The present study was conducted on the Tawdeehiya Farms, located in the central region of Saudi Arabia between Al-Kharj and Haradh cities. Methodology: Landsat-8 (L8) and Sentinel-2 (S2A) satellite images were used for the characterization of SOC stocks in the topsoil layer (0-10 cm) of the experimental fields. Soil samples were randomly collected from six (50 ha each) agricultural fields and analyzed in the laboratory for SOC (SOCA) following Walkley and Black method. While, vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI), NDVIRedEdge, Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), and Reduced Simple Ratio (RSR) were computed and subsequently used for the development of SOC prediction models. Results: Univariate linear regression technique was employed for the recognition of a suitable band/VI for SOC (SOCP) mapping. The SWIR-1 band of both L8 (R2 = 0.86) and S2A (R2 = 0.77) data was promising for predicting SOC with 16% (S2A) and 18% (L8) of BIAS. Interpretation: The NDVI and BSI (for L8 data) and BSI and RSR (for S2A data) were found most suitable VI in the prediction of SOC. The R2 values of linear regression models were 0.68 (BSI) and 0.78 (RSR), indicating that nearly 68% and 78% of the SOC could be predicted through L8 and S2A datasets, respectively.

Soil Organic Carbon and Nitrogen Variations with Vegetation Succession in Passively Restored Freshwater Wetlands

Wetlands ◽  
2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Yu An ◽  
Yang Gao ◽  
Xiaohui Liu ◽  
Shouzheng Tong ◽  
Bo Liu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Vegetation Succession ◽  
Freshwater Wetlands ◽  
Carbon And Nitrogen
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