Using the Taguchi experimental design for assessing within-field variability of surface run-off and soil erosion risk.

Design of experiment Heterogeneous field conditions Rainfall simulator erosion-prone areas

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
01 Jul 2022
Historique:
received: 21 11 2021
revised: 02 03 2022
accepted: 10 03 2022
pubmed: 19 3 2022
medline: 17 5 2022
entrez: 18 3 2022
Statut: ppublish

Résumé

Water erosion is one of the soil degradation processes driven by environmental and field factors such as rainfall intensity, slope gradient, dynamics of vegetation cover, soil characteristics, and management practices. Most of the studies assess the separate contribution of these factors under controlled conditions. However, there is a lack of adequate knowledge regarding the complex interactions between prevailing factors and soil erosion processes under heterogeneous field conditions. This study investigated 16 combinations of 5 factors at 4 levels of each factor on the soil erosion process using Taguchi's fractional factorial experiment design, identifying the factor combinations resulting in maximum sediment yield, runoff, organic carbon, and nitrogen losses. We considered the factors: Soil organic matter and silt content (SiltOM), vegetation cover (VC), slope steepness (SS), rainfall intensity (RI), and depth to a loamy layer (DLL). The interactive effects of these factors and their combinations were visualized from the analysis of signal-to-noise (S/N) responses. Results indicated that interactions between the selected factors and soil erosion processes exist and multiple linear regression models were developed to predict sediment yields, runoff, carbon, and nitrogen losses at the sub-field scale. Results revealed that 1) RI with 40.6% showed the highest contribution to sediment yield followed by SS (23.8%), VC (17.74%), SiltOM (14.77%), and DLL (3.17%), indicating a strong rainfall-erosion relationship; 2) the combination of levels of factors generating highest sediment yield was determined; 3) A simple multiple linear regression model developed for predicting local sediment yield showed the highest agreement with field observations (R

Identifiants

pubmed: 35302038
pii: S0048-9697(22)01660-6
doi: 10.1016/j.scitotenv.2022.154567
pii:
doi:

Substances chimiques

Soil 0
Carbon 7440-44-0
Nitrogen N762921K75

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

154567

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Ahsan Raza (A)

University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, Katzenburgweg 5, 53115 Bonn, Germany. Electronic address: araza@uni-bonn.de.

Hella Ahrends (H)

University of Helsinki, Department of Agricultural Sciences\, 00014 Helsinki, Finland.

Muhammad Habib-Ur-Rahman (M)

University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, Katzenburgweg 5, 53115 Bonn, Germany; Department of Agronomy, MNS- University of Agriculture, Multan, Pakistan.

Hubert Hüging (H)

University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, Katzenburgweg 5, 53115 Bonn, Germany.

Thomas Gaiser (T)

University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, Katzenburgweg 5, 53115 Bonn, Germany.

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Classifications MeSH