The modeling of pasture conservation and of its impact on stream water quality using Partial Least Squares-Path Modeling.

Autor: Oliveira CF; Federal University of Triângulo Mineiro, Institute of Technological and Exact Sciences (ICTE), Uberaba, MG 38015-360, Brazil., do Valle Junior RF; Federal Institute of Triângulo Mineiro, Uberaba Campus, Geoprocessing Laboratory, Uberaba, MG 38064-790, Brazil. Electronic address: renato@iftm.edu.br., Valera CA; Coordenadoria Regional das Promotorias de Justiça do Meio Ambiente das Bacias dos Rios Paranaíba e Baixo Rio Grande, Rua Coronel Antônio Rios, 951, Uberaba, MG 38061-150, Brazil. Electronic address: carlosvalera@mpmg.mp.br., Rodrigues VS; Federal University of Triângulo Mineiro, Institute of Technological and Exact Sciences (ICTE), Uberaba, MG 38015-360, Brazil., Sanches Fernandes LF; Center for Research and Agro-environmental and Biological Technologies, University of Trás-os-Montes e Alto Douro, Ap. 1013, 5001-801 Vila Real, Portugal. Electronic address: lfilipe@utad.pt., Pacheco FAL; Center of Chemistry of Vila Real, University of Trás-os-Montes e Alto Douro, Ap. 1013, 5001-801 Vila Real, Portugal. Electronic address: fpacheco@utad.pt.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2019 Dec 20; Vol. 697, pp. 134081. Date of Electronic Publication: 2019 Aug 24.
DOI: 10.1016/j.scitotenv.2019.134081
Abstrakt: Cattle grazing is a major source of income across the globe, and therefore conservation of pastures is vital to society. Pasture conservation requires the full understanding of factors contributing to their degradation, which is facilitated through panoramic analyses capable to handle all factors and capture their relationships at once. In this study, Partial Least Squares - Path Modeling (PLS-PM) was used to accomplish that task. The study area was the Environmental Protection Area of Uberaba River Basin (525 km 2 ), located in the state of Minas Gerais, Brazil, and extensively used for livestock pasturing (51%). The selected (15) contributing factors comprised soil characteristics (e.g., organic matter, phosphorus content), runoff indicators (e.g., percentage of sand and clay in the soil), environmental land use conflicts (deviations of actual from natural uses), stream water quality parameters (e.g., oxidation-reduction potential-ORP, turbidity), and pasture conservation indicators (extent of degraded pasture within a pre-defined buffer). These measured variables were assembled into 5 conceptual (latent) variables to form the PLS-PM model, namely Groundcover, Pasture Conservation, Surface Runoff, Environmental Land Use Conflicts and Water Quality. The results elected Groundcover as prominent contributor to Pasture Conservation, because of its largest regression (path) coefficient (β = 0.984). The most influent measured variable was organic matter. Surface Runoff (β = -0.108) and Environmental Land Use Conflicts (β = -0.135) contribute to pasture degradation. The role of conflicts is, however, limited to predefined areas where the deviations of actual from natural uses are more expressive. Pasture Conservation contributes unequivocally to improved Water Quality (β = 0.800), expressed as high ORP. The PLS-PM model was free from multi-collinearity problems and model fits (R 2 ) were high. This gives us confidence to implement conservation measures and improved management techniques based on the PLS-PM results, and to transpose the model to other areas requiring pasture quality improvements.
(Copyright © 2019 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: