scholarly journals Geochemical Fingerprint and Soil Carbon of Sandy Alfisols

Soil Systems ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 59 ◽  
Author(s):  
Jenifer L. Yost ◽  
Eric E. Roden ◽  
Alfred E. Hartemink
Keyword(s):  
Particle Size ◽  
Soil Carbon ◽  
Soil Depth ◽  
Soil Carbon Storage ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Weathering Indices ◽  
Geochemical Fingerprint ◽  
High Concentrations ◽  
Carbon Concentrations

Soil carbon storage is affected by particle-size fractions and Fe oxides. We assessed soil carbon concentrations in different particle-size fractions, determined the soil chemical composition of the soil, and weathering and mineralogy of sandy soils of the Wisconsin Central Sands, USA. Three land uses were studied (agriculture, forest, and prairie). The soils contained a minimum of 830 g sand kg−1 up to 190 cm soil depth. Approximately 46% of the sand was in the 250–500 μm fraction, and 5% was <125 μm. Soil carbon ranged from 5 to 13 g kg−1 in the topsoil, and decreased with depth. The <45 μm fraction tended to have high concentrations of carbon, ranging from 19 to 43 g kg−1 in the topsoil. Silicon content was over 191 g Si kg−1, and was lowest in the Bt horizons (191–224 g Si kg−1). Up to 29 g Fe kg−1 and 39 g Al kg−1 were present in the soil, and were highest in the Bt horizons. These soils were mostly quartz, and diopside was found throughout the soil profiles. Weathering indices, such as the Ruxton Ratio, showed that the C horizons were the least weathered and the Bt horizons were more weathered. We conclude that most of the carbon in these soils is held in the <45 μm fraction, and soil carbon and total Fe were lowest in the coarser size fractions.

Data synthesis of carbon distribution in particle size fractions of tropical soils: Implications for soil carbon storage potential in croplands

Geoderma ◽  
2018 ◽  
Vol 313 ◽  
pp. 41-51 ◽  
Author(s):  
Kenji Fujisaki ◽  
Lydie Chapuis-Lardy ◽  
Alain Albrecht ◽  
Tantely Razafimbelo ◽  
Jean-Luc Chotte ◽  
...  
Keyword(s):  
Particle Size ◽  
Soil Carbon ◽  
Carbon Storage ◽  
Tropical Soils ◽  
Soil Carbon Storage ◽  
Carbon Distribution ◽  
Data Synthesis ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Storage Potential

DISTRIBUTION OF CARBON, NITROGEN, SULPHUR AND PHOSPHORUS IN PARTICLE-SIZE SEPARATES FROM GLEYSOLIC SOILS

1980 ◽  
Vol 60 (4) ◽  
pp. 783-786 ◽  
Author(s):  
A. A. HINDS ◽  
L. E. LOWE
Keyword(s):  
Particle Size ◽  
Soil Carbon ◽  
Organic P ◽  
Soil N ◽  
Dispersion Method ◽  
Ultrasonic Dispersion ◽  
Soil Material ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Fine Clay

Levels of C, N, S and organic P (Po) were determined in fine, medium and coarse clay- and silt-size separates obtained from five Gleysolic soils by an ultrasonic dispersion method. Contents of C, N, S and Po increased with decreasing particle size, with average C values increasing from 3.7% in the silt to 10.1% in fine clay fractions. The corresponding increases for N, S and Po were 0.26–1.17%, 0.037–0.178% and 0.043–0.172%, respectively. C/N and C/S ratios decreased with decreasing particle size, indicating a relative enrichment of N and S in the finer particle-size fractions. N/S showed little variation with particle size, while C/Po ratios were erratic. The three clay fractions accounted on average for 31.3% of the soil material, and for 39.1% of soil carbon. In contrast, the clay fractions together accounted on average for 52–59% of soil N, S and Po.

Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB201-WB211 ◽  
Author(s):  
S. Buchanan ◽  
J. Triantafilis ◽  
I. O. A. Odeh ◽  
R. Subansinghe
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Soil Mapping ◽  
Additive Models ◽  
Digital Soil Mapping ◽  
Unbiased Estimation ◽  
Ancillary Data ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Log Ratio

The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.

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