Using Information about Spatial Variability to Improve Estimates of Total Soil Carbon

2006 ◽  
Vol 98 (3) ◽  
pp. 823-829 ◽  
Author(s):  
A. N. Kravchenko ◽  
G. P. Robertson ◽  
S. S. Snap ◽  
A. J. M. Smucker
Keyword(s):  
Spatial Variability ◽  
Soil Carbon ◽  
Total Soil

scholarly journals Spatially Related Sampling Uncertainty in the Assessment of Labile Soil Carbon and Nitrogen in an Irish Forest Plantation

2021 ◽  
Vol 11 (5) ◽  
pp. 2139
Author(s):  
Junliang Zou ◽  
Bruce Osborne
Keyword(s):  
Spatial Variability ◽  
Soil Carbon ◽  
Sampling Effort ◽  
Limited Information ◽  
Sampling Area ◽  
Soil C ◽  
Distribution Maps ◽  
Soil C And N ◽  
C And N ◽  
Highly Correlated

The importance of labile soil carbon (C) and nitrogen (N) in soil biogeochemical processes is now well recognized. However, the quantification of labile soil C and N in soils and the assessment of their contribution to ecosystem C and N budgets is often constrained by limited information on spatial variability. To address this, we examined spatial variability in dissolved organic carbon (DOC) and dissolved total nitrogen (DTN) in a Sitka spruce forest in central Ireland. The results showed moderate variations in the concentrations of DOC and DTN based on the mean, minimum, and maximum, as well as the coefficients of variation. Residual values of DOC and DTN were shown to have moderate spatial autocorrelations, and the nugget sill ratios were 0.09% and 0.10%, respectively. Distribution maps revealed that both DOC and DTN concentrations in the study area decreased from the southeast. The variability of both DOC and DTN increased as the sampling area expanded and could be well parameterized as a power function of the sampling area. The cokriging technique performed better than the ordinary kriging for predictions of DOC and DTN, which are highly correlated. This study provides a statistically based assessment of spatial variations in DOC and DTN and identifies the sampling effort required for their accurate quantification, leading to improved assessments of forest ecosystem C and N budgets.

Topography Influences Management System Effects on Total Soil Carbon and Nitrogen

2009 ◽  
Vol 73 (6) ◽  
pp. 2059-2067 ◽  
Author(s):  
S. Senthilkumar ◽  
A. N. Kravchenko ◽  
G. P. Robertson
Keyword(s):  
Soil Carbon ◽  
Management System ◽  
Carbon And Nitrogen ◽  
Soil Carbon And Nitrogen ◽  
Total Soil

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Tibet Plateau ◽  
Learning Approach ◽  
Carbon Turnover ◽  
Machine Learning Approach ◽  
Qinghai Tibet Plateau ◽  
Soil Carbon Turnover

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

scholarly journals Alaskan soil carbon stocks: spatial variability and dependence on environmental factors

2012 ◽  
Vol 9 (5) ◽  
pp. 5695-5718 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Climate Change ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Active Layer ◽  
High Latitude ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Intergovernmental Panel ◽  
Soil Wetness ◽  
Soc Stocks

Abstract. The direction and magnitude of soil organic carbon (SOC) changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially-resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5). Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m−2, 2–166 kg m−2, and 0–232 kg m−2, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were: land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth System Models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%), followed by discontinuous (28%), isolated (24.3%), and sporadic (23.6%) permafrost areas. Our high resolution mapping of soil carbon stock reveals the potential vulnerability of high-latitude soil carbon and can be used as a basis for future studies of anthropogenic and climatic perturbations.

Leys and soil organic matter:III. The accumulation of macro-organic matter in the soil under different swards

1972 ◽  
Vol 78 (2) ◽  
pp. 333-341 ◽  
Author(s):  
E. A. Garwood ◽  
C. R. Clement ◽  
T. E. Williams
Keyword(s):  
Organic Matter ◽  
Soil Carbon ◽  
Perennial Ryegrass ◽  
White Clover ◽  
Arable Land ◽  
N Fertilizer ◽  
Plant Debris ◽  
Total Soil ◽  
Grazed Swards ◽  
Mesh Sieve

SUMMARYMacro-organic matter (roots and partially decomposed plant debris retained on a 0·25 mm mesh sieve) was measured in soils under various swards. Under a grazed perennial ryegrass/white clover sward, sown on arable land, macro-organic matter in the top 15 cm of soil rose steadily in the first 8 years to 15·8 t/ha, but subsequently declined. Under arable cropping there was great variation with crop and season. Under grass, most of the macro-organic matter accumulated in the top 2 cm of soil, particularly during the first 3 or 4 years. More macro-organic matter was found under perennial ryegrass/white clover than under cocksfoot/white clover swards.After 3 years under grass macro-organic matter accounted for 10% of the total soil carbon, and represented about half the increase in soil carbon.Half, or less, of the nitrogen which accumulated in soil under grass was in the macroorganic matter fraction. The differences between swards which received no N fertilizer and those which received 940 kg/ha over 3 years was small, 16–40 kg N/ha respectively for cut and frequently grazed swards. The ratio of C:N in macro-organic matter under different swards averaged 22:1.

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