Spatial variation and distribution of soil organic carbon in an urban ecosystem from high-density sampling

CATENA ◽  
2021 ◽  
Vol 204 ◽  
pp. 105364
Author(s):  
Pingping Zhang ◽  
Yunqiang Wang ◽  
Hui Sun ◽  
Lijun Qi ◽  
Hao Liu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Urban Ecosystem ◽  
High Density

Spatial variation in soil organic carbon and nitrogen at two field sites under crop and pasture rotations in southern New South Wales, Australia

Soil Research ◽  
2018 ◽  
Vol 56 (8) ◽  
pp. 780 ◽  
Author(s):  
Mark Conyers ◽  
Beverley Orchard ◽  
Susan Orgill ◽  
Albert Oates ◽  
Graeme Poile ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
New South ◽  
New South Wales ◽  
Wagga Wagga ◽  
Soil Cores ◽  
South Wales ◽  
Sampling Protocols ◽  
The Mean

Estimating the likely variance in soil organic carbon (OC) at the scale of farm fields or smaller monitoring areas is necessary for developing sampling protocols that allow temporal change to be detected. Given the relatively low anticipated soil OC sequestration rates (<0.5 Mg/ha.0.30 m/year) for dryland agriculture it is important that sampling strategies are designed to reduce any cumulative errors associated with measuring soil OC. The first purpose of this study was to evaluate the spatial variation in soil OC and nitrogen (N), in soil layers to 1.50 m depth at two monitoring sites (Wagga Wagga and Yerong Creek, 0.5 ha each) in southern New South Wales, Australia, where crop and pasture rotations are practiced. Four variogram models were tested (linear, spherical, Gaussian and exponential); however, no single model dominated across sites or depths for OC or N. At both sites, the range was smallest in surface soil, and on a scale suggesting that sowing rows (stubble) may dominate the pattern of spatial dependence, whereas the longer ranges appeared to be associated with horizon boundaries. The second purpose of the study was to obtain an estimate of the population mean with 1%, 5% and 10% levels of precision using the calculated variance. The number of soil cores required for a 1% precision in estimation of the mean soil OC or N was impractical at most depths (>500 per ha). About 30 soil cores per composite sample to 1.50 m depth, each core being at least 10 m apart, would ensure at least an average of 10% precision in the estimation of the mean soil OC at these two sites, which represent the agriculture of the region.

Spatial variability of soil organic carbon in a typical watershed in the source area of the middle Dan River, China

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 41 ◽  
Author(s):  
Guo-Ce Xu ◽  
Zhan-Bin Li ◽  
Peng Li ◽  
Ke-Xin Lu ◽  
Yun Wang
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Soil Depth ◽  
Source Area ◽  
Soil Layers ◽  
Land Use Types

Soil organic carbon (SOC) plays an important role in maintaining and improving soil fertility and quality, in addition to mitigating climate change. Understanding SOC spatial variability is fundamental for describing soil resources and predicting SOC. In this study, SOC content and SOC mass were estimated based on a soil survey of a small watershed in the Dan River, China. The spatial heterogeneity of SOC distribution and the impacts of land-use types, elevation, slope, and aspect on SOC were also assessed. Field sampling was carried out based on a 100 m by 100 m grid system overlaid on the topographic map of the study area, and samples were collected in three soil layers to a depth of 40 cm. In total, 222 sites were sampled and 629 soil samples were collected. The results showed that classical kriging could successfully interpolate SOC content in the watershed. Contents of SOC showed strong spatial heterogeneity based on the values of the coefficient of variation and the nugget ratio, and this was attributed largely to the type of land use. The range of the semi-variograms increased with increasing soil depth. The SOC content in the soil profile decreased as soil depth increased, and there were significant (P < 0.01) differences among the three soil layers. Land use had a great impact on the SOC content. ANOVA indicated that the spatial variation of SOC contents under different land use types was significant (P < 0.05). The SOC mass of different land-use types followed the order grassland > forestland > cropland. Mean SOC masses of grassland, forestland, and cropland at a depth of 0–40 cm were 5.87, 5.61, and 5.07 kg m–2, respectively. The spatial variation of SOC masses under different land-use types was significant (P < 0.05). ANOVA also showed significant (P < 0.05) impact of aspect on SOC mass in soil at 0–40 cm. Soil bulk density played an important role in the assessment of SOC mass. In conclusion, carbon in soils in the source area of the middle Dan River would increase with conversion from agricultural land to forest or grassland.

Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale

2010 ◽  
Vol 74 (3) ◽  
pp. 906-914 ◽  
Author(s):  
Umakant Mishra ◽  
Rattan Lal ◽  
Desheng Liu ◽  
Marc Van Meirvenne
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Regional Scale ◽  
Carbon Pool ◽  
Organic Carbon Pool

scholarly journals Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

2014 ◽  
Vol 1 (1) ◽  
pp. 757-802 ◽  
Author(s):  
B. A. Miller ◽  
S. Koszinski ◽  
M. Wehrhan ◽  
M. Sommer
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Spatial Association ◽  
Direct Approach ◽  
Indirect Approach ◽  
Global Issues ◽  
Soc Stock ◽  
Estimated Error ◽  
Soc Stocks

Abstract. The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m−2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Spatial variation and driving mechanism of soil organic carbon components in the alluvial/sedimentary zone of the Yellow River

2021 ◽  
Vol 31 (4) ◽  
pp. 535-550
Author(s):  
Guodong Li ◽  
Junhua Zhang ◽  
Lianqi Zhu ◽  
Huiwen Tian ◽  
Jiaqi Shi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Yellow River ◽  
The Yellow River ◽  
Driving Mechanism

scholarly journals Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

SOIL ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 217-233 ◽  
Author(s):  
B. A. Miller ◽  
S. Koszinski ◽  
M. Wehrhan ◽  
M. Sommer
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Spatial Association ◽  
Direct Approach ◽  
Indirect Approach ◽  
Global Issues ◽  
Soc Stock ◽  
Estimated Error ◽  
Soc Stocks

Abstract. The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m−2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

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