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
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
154567Informations 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.