Multivariate analysis and GIS approaches for modeling and mapping soil quality and land suitability in arid zones

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Abstract

Assessing soil quality marks the initial step in precision farming and agricultural management. Developing countries like Egypt face numerous hurdles in ensuring food security due to increasing populations and limited agricultural resources. A geographic information system (GIS) and multivariate analysis were utilized in the current work to evaluate and map a soil quality index (SQI). Moreover, the land suitability of the land for two plantations of the tree's oak ( Quercus robur), and pine ( Pinus silvestris), respectively was assessed using a parametric approach. A total of 82 soil profiles were selected to fulfill the objectives of the study. Based on the samples' PC scores, and agglomerative hierarchical clustering (AHC, the data was divided into two clusters: Cluster I and Cluster II, which collectively account for approximately 57% and 43% of the total data, respectively.. . The findings indicated that land suitability for planting Q. robur planted identified 2.14% of the research area as highly suitable (S1), 37.98% as moderately suitable (S2), and 59.89% as not suitable (N). Furthermore, the assessment of suitability for P. silvestris indicated that 50.88% of the investigated area was classified into: S1, 48.73% as S2, and 0.39% as N, which means it is not suitable for conservation activities. The research identified that soil depth beside excessive salinity and calcium carbonate as the primary soil constraints in the area in both clusters. The average soil depth, ECd and CaCO3 were 113.62 ± 12.41, 17.27 ± 10.23, 16.83 ± 6.57 in Cluster 1 and 45.43 ± 15.21, 22.42 ± 12.43, 21.55 ± 5.63 in Cluster II. The study demonstrates that integrating multivariate analysis with GIS enables a precise and streamlined assessment of the Soil Quality Index (SQI). Soil suitability modelling underscores the importance of implementing efficient management practices to attain agricultural sustainability in arid regions, particularly amidst intensive land utilization pressures

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Principal component analysis: a review and recent developments.

Ian Jolliffe, Jorge Cadima (2016)

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.