Abstract
Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.
Highlights
IntroductionIn some cases, numerous soil variables are required to assess soil quality
Precision agriculture is based on the use of a set of techniques and technologies devised to assess the spatial variability of soil and plant properties to facilitate and optimize soil management, which often requires the use of several variables to support decisionmaking [1,2]
Soil quality is affected by agricultural practices and climatic conditions, which, in turn, affect the physical, chemical, and fertility properties of the soil
Summary
In some cases, numerous soil variables are required to assess soil quality. Because some of these variables can be redundant, the ability to identify key parameters/variables can reduce both the time and costs of in situ and laboratory analyses and optimize models and procedures for spatio-temporal soil assessment [3]. In this context, principal component analysis (PCA) is recognized as one of the most widely used methods for reducing the number of variables by identifying those that are most
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