HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Eltz, F. L. F. Articles by Norton, L. D. Search for Related Content PubMed Articles by Eltz, F. L. F. Articles by Norton, L. D. Agricola Articles by Eltz, F. L. F. Articles by Norton, L. D.

Surface Roughness Changes as Affected by Rainfall Erosivity, Tillage, and Canopy Cover

Depto. de Solos, CCR, Univ. Fed. de Santa Maria, 97119-900 Santa Maria, RS, Brazil

L. Darrell Norton^*

USDA-ARS Natl. Soil Erosion Res. Lab., 1196 Soil Building, Purdue Univ., West Lafayette, IN 47907-1196

* Corresponding author ( nortond{at}ecn.purdue.edu).

ABSTRACT

Surface roughness and canopy cover are important factors in preventing soil erosion. There is limited information on how soil surface roughness changes as a function of natural rainfall erosivity and canopy cover by plants. We hypothesized that canopy cover, tillage systems, and cumulative rainfall erosivity (CRE) would have unique effects on roughness. We tested this hypothesis on a Miami silt loam soil (fine-silty, mixed, mesic Typic Hapludalf) using a portable laser microtopographer. Tillage treatments of conventional (moldboard plowing + disking), chisel plowing, and chisel plowing + dragging a chain produced three initial roughness levels. Surface cover was none (fallow) or soybean [Glycine max (L.) Merr.]. Random roughness (RR), standard deviation (SD), tortuosity (T), and fractal roughness functions, expressed by the fractal index (D) and the crossover length (l), were calculated from microtopography data. Chisel tillage had the greatest initial values of surface roughness, followed by chisel + chain and conventional tillage, as measured by the l index. All indices but D generally decreased with CRE. The RR and SD indices decreased quadratically with CRE, with decreases of 38 and 36%, respectively, from initial values after 200 units of CRE, while the T and l indices decreased exponentially, with decreases of 40 and 60%, respectively, from initial values after 200 units of CRE. Soybean cover lowered soil surface roughness 7% less than fallow, as measured by the l index. The l index was 50, 71, and 205% more sensitive to changes in CRE than RR, SD, and T indices, respectively. The fractal roughness functions, with D and l indices calculated, were the best approaches to characterize surface roughness at small scales, such as existing plant rows, mainly due to l index sensitivity to changes in CRE.

Received for publication October 18, 1995.

This article has been cited by other articles:

				R. G. Moreno, M. C. D. Alvarez, A. S. Requejo, and A. M. Tarquis Multifractal Analysis of Soil Surface Roughness Vadose Zone J., May 27, 2008; 7(2): 512 - 520. [Abstract] [Full Text] [PDF]

				E.C. Kamphorst, V. Jetten, J. Guérif, J. Pitkänen, B.V. Iversen, J.T. Douglas, and A. Paz Predicting Depressional Storage from Soil Surface Roughness Soil Sci. Soc. Am. J., September 1, 2000; 64(5): 1749 - 1758. [Abstract] [Full Text]