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ARS Home » Midwest Area » West Lafayette, Indiana » National Soil Erosion Research Laboratory » Research » Publications at this Location » Publication #137479

Title: C-FACTOR RESEARCH ON HORTICULTURAL CROPS FOR EROSION PREDICTION MODELS: PHILOSOPHY AND METHODOLOGY OF DATA COLLECTION

Author
item PANICKER, G - ALCORN STATE UNIV.
item TIWARI, S - ALCORN STATE UNIV.
item WEESIES, G - USDA-NRCS
item Stott, Diane
item AL-HUMADI, A - ALCORN STATE UNIV.
item SIMS, C - ALCORN STATE UNIV.
item HUAM, L - ALCORN STATE UNIV.
item IGBOKWE, P - ALCORN STATE UNIV.
item VADHWA, O - ALCORN STATE UNIV.
item JOHNSON, A - ALCORN STATE UNIV.

Submitted to: Extension Service Bulletins
Publication Type: Book / Chapter
Publication Acceptance Date: 5/1/2001
Publication Date: 5/1/2001
Citation: PANICKER, G.K., TIWARI, S.C., WEESIES, G.A., STOTT, D.E., AL-HUMADI, A.H., SIMS, C., HUAM, L.C., IGBOKWE, P., VADHWA, O.P., JOHNSON, A. C-FACTOR RESEARCH ON HORTICULTURAL CROPS FOR EROSION PREDICTION MODELS: PHILOSOPHY AND METHODOLOGY OF DATA COLLECTION. EXTENSION SERVICE BULLETINS. 2001. BULLETIN 01-1. P.40.

Interpretive Summary:

Technical Abstract: Even though research and education systems have transformed agriculture from a traditional to a high technology sector, soil erosion still remains as a major universal problem to agricultural productivity. The Universal Soil Loss Equation (USLE) and its replacement, the Revised Universal Soil Loss Equation (USLE), are the most widely used of all soil erosion prediction models. Of the five factors in RUSLE, t he cover and management C-factor is the most important one from the standpoint of conservation planning because land use changes meant to reduce erosion are represented here. Even though the RUSLE is base on the USDLE, this modern erosion prediction model is highly improved and updated. Alcorn State University entered into a cooperative agreement with the NRCS of USDA in 1988 to conduct C-factor research on vegetable and fruit crops. The main objective of this research is to collect plant growth and residue data that are used to populate databases needed to develop C-factors in RUSLE, and used in database for other erosion prediction and natural resource models. The enormous amount of data collected on leaf area index (LAI), canopy cover, lower and upper biomass, rate of residue decomposition, C:N ratio of samples of residues and destructive harvest and other growth parameters of canopy and rhizosphere made the project the largest data bank on horticultural crops.