USGS Science⌕ Search

USGS · 70159364

Distribution of light and heavy fractions of soil organic carbon as related to land use and tillage practice

Abstract

Mass distributions of different soil organic carbon (SOC) fractions are influenced by land use and management. Concentrations of C and N in light- and heavy fractions of bulk soils and aggregates in 0–20 cm were determined to evaluate the role of aggregation in SOC sequestration under conventional tillage (CT), no-till (NT), and forest treatments. Light- and heavy fractions of SOC were separated using 1.85 g mL −1 sodium polytungstate solution. Soils under forest and NT preserved, respectively, 167% and 94% more light fraction than those under CT. The mass of light fraction decreased with an increase in soil depth, but significantly increased with an increase in aggregate size. C concentrations of light fraction in all aggregate classes were significantly higher under NT and forest than under CT. C concentrations in heavy fraction averaged 20, 10, and 8 g kg −1 under forest, NT, and CT, respectively. Of the total SOC pool, heavy fraction C accounted for 76% in CT soils and 63% in forest and NT soils. These data suggest that there is a greater protection of SOC by aggregates in the light fraction of minimally disturbed soils than that of disturbed soil, and the SOC loss following conversion from forest to agriculture is attributed to reduction in C concentrations in both heavy and light fractions. In contrast, the SOC gain upon conversion from CT to NT is primarily attributed to an increase in C concentration in the light fraction.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhengxi Tan, R. Lal, L. Owens, R. C. Izaurralde. 2007. Distribution of light and heavy fractions of soil organic carbon as related to land use and tillage practice. https://doi.org/10.1016/j.still.2006.01.003

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery

Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover ( f R ) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify f R at scale has been limited. In this study we produced annual maps of f R for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs ( n = 895) classified to f R using SamplePoint software. Univariate linear regressions were used to compare photograph f R to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in f R estimation in terms of coefficient of determination ( R 2 = 0.869) and root mean square error (RMSE = 0.111), when R 2 and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest f R estimation performance ( R 2 = 0.795; RMSE = 0.141), suggesting that SINDRI can map f R accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a multiple linear regression analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of f R estimation significantly, particularly for SWIRA ( R 2 = 0.767; RMSE = 0.144), but also NDTI ( R 2 = 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for f R estimation, SWIRA has the potential to improve f R estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global f R dating back to 1982.

Soil and Tillage Research↗