Soil Quality Assessment Using Multivariate Approaches: A Case Study of the Dakhla Oasis Arid Lands

Salman A. H. Selmy, Salah H. Abd Al-Aziz, Raimundo Jiménez-Ballesta, Francisco Jesús García-Navarro and Mohamed E. Fadl
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Salman A. H. Selmy: Department of Soils and Water, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
Salah H. Abd Al-Aziz: Department of Soils and Water, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
Raimundo Jiménez-Ballesta: Department of Geology and Geochemistry, Autonomuos University of Madrid, 28019 Madrid, Spain
Francisco Jesús García-Navarro: Higth Technical School of Agricultural Engineers, University of Castilla-La Mancha, 13007 Ciudad Real, Spain
Mohamed E. Fadl: Division of Scientific Training and Continuous Studies, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt

Land, 2021, vol. 10, issue 10, 1-22

Abstract: A precise evaluation of soil quality (SQ) is important for sustainable land use planning. This study was conducted to assess soil quality using multivariate approaches. An assessment of SQ was carried out in an area of Dakhla Oasis using two methods of indicator selection, i.e., total data set (TDS) and minimum data set (MDS), and three soil quality indices (SQIs), i.e., additive quality index (AQI), weighted quality index (WQI), and Nemoro quality index (NQI). Fifty-five soil profiles were dug and samples were collected and analyzed. A total of 16 soil physicochemical parameters were selected for their sensitivity in SQ appraising to represent the TDS. The principal component analysis (PCA) was employed to establish the MDS. Statistical analyses were performed to test the accuracy and validation of each model, as well as to understand the relationship between the used methods and indices. The results of principal component analysis (PCA) showed that soil depth, gravel content, sand fraction, and exchangeable sodium percentage (ESP) were included in the MDS. High positive correlations (r ≥ 0.9) occurred between SQIs calculated using TDS and/or MDS under the three models. Moreover, the findings showed highly significant differences ( p < 0.001) among SQIs within and between TDS and MDS. Approximately 80 to 85% of the total study area based on TDS, as well as 70 to 75%, according to MDS, were identified as suitable soils with slight limitations on soil quality grade (Q3, Q2, and Q1), while the remaining 20 to 30% had high to severe limitations (Q4 and Q5). The highest sensitivity (SI = 2.9) occurred by applying WQI using MDS and indicator weights based on the variance of PCA. Furthermore, the highest linear regression value (R 2 = 0.88) between TDS and MDS was recorded using the same model. Because of its high sensitivity, such a model could be used for monitoring SQ changes caused by agricultural practices and environmental factors. The findings of this study have significant guiding implications and practical value in assessing the soil quality using TDS and MDS in arid areas critically and accurately.

Keywords: factor analysis; minimum data set; indexing models; indicator selection; land use (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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