Skip to main content
ARS Home » Research » Publications at this Location » Publication #98476


item STARR, G
item LAL, R
item Owens, Lloyd
item Malone, Robert - Rob
item KIMBLE, J

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/1/1998
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The loss of soil organic carbon (SOC) in runoff and eroded sediments from watersheds is difficult to assess and methods of evaluating this component of the carbon cycle need to be developed. Because SOC is transported along with sediments, its loss may be highly correlated with soil and nutrient losses by erosion. This hypothesis has been tested using 13 years of runoff and soil erosion data from two conservation tillage watersheds of the North Appalachian Experimental Watersheds in Coshocton, OH. Flumes equipped with Coshocton wheel samplers were used to collect flow-weighted sub-samples of runoff that were analyzed for SOC, N, P, K, and soil losses in rainstorm events. The SOC losses, ranging from 2 (10)**-4 to 20 kg ha**-1, depended on the severity of the storm and were highly correlated (R**2 = 0.75) in a power law relationship with soil loss (2 (10)**-4 to 2 (10)**+3 kg ha**-1) with no-till and chisel till watersheds exhibiting nearly identical regression models. The C enrichment generally declined with increasing soil loss although the exact mathematical function is difficult to develop. The C/N ratio also varied widely, but with no evident trend. The SOC losses were highly correlated with those of N, P, and K (R**2 ranging from 0.65 to 0.92) with the highest correlation being between SOC and K. Much of the transport of soil, nutrients, and SOC occurs during infrequent, severe rainstorms. These data suggest that it may be possible to develop empirical relationships by which SOC losses by erosion could be assessed from soil erosion for specific soil and management systems. Understanding transport mechanisms may lead to development of conceptual models for predicting C dynamics in agriculture.