Multi-Indicator and Geospatial Based Approaches for Assessing Variation of Land Quality in Arid Agroecosystems

Ahmed S Abuzaid, Yasser S. A. Mazrou, Ahmed A El Baroudy, Zheli Ding and Mohamed S. Shokr
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Ahmed S Abuzaid: Soils and Water Department, Faculty of Agriculture, Benha University, Benha 13518, Egypt
Yasser S. A. Mazrou: Applied College—Muhyle, King Khalid University, Abha 62587, Saudi Arabia
Ahmed A El Baroudy: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
Zheli Ding: Haikou Experimental Station, Chinese Academy of Tropical Agricultural Sciences, Haikou 570000, China
Mohamed S. Shokr: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt

Sustainability, 2022, vol. 14, issue 10, 1-19

Abstract: Novel spatial models for appraising arable land resources using data processing techniques can increase insight into agroecosystem services. Hence, the principal component analysis (PCA), hierarchal cluster analysis (HCA), analytical hierarchy process (AHP), fuzzy logic, and geographic information system (GIS) were integrated to zone and map agricultural land quality in an arid desert area (Matrouh Governorate, Egypt). Satellite imageries, field surveys, and soil analyses were employed to define eighteen indicators for terrain, soil, and vegetation qualities, which were then reduced through PCA to a minimum data set (MDS). The original and MDS were weighted by AHP through experts’ opinions. Within GIS, the raster layers were generated, standardized using fuzzy membership functions (linear and non-linear), and assembled using arithmetic mean and weighted sum algorithms to produce eight land quality index maps. The soil properties (pH, salinity, organic matter, and sand), slope, surface roughness, and vegetation could adequately express the land quality. Accordingly, the HCA could classify the area into eight spatial zones with significant heterogeneity. Selecting salt-tolerant crops, applying leaching fraction, adopting sulfur and organic applications, performing land leveling, and using micro-irrigation are the most recommended practices. Highly significant ( p < 0.01) positive correlations occurred among all the developed indices. Nevertheless, the coefficient of variation (CV) and sensitivity index (SI) confirmed the better performance of the index developed from the non-linearly scored MDS and weighted sum model. It could achieve the highest discrimination in land qualities (CV > 35%) and was the most sensitive (SI = 3.88) to potential changes. The MDS within this index could sufficiently represent TDS (R 2 = 0.88 and Kappa statistics = 0.62), reducing time, effort, and cost for estimating the land performance. The proposed approach would provide guidelines for sustainable land-use planning in the studied area and similar regions.

Keywords: GIS; fuzzy logic; multivariate statistical analysis; AHP; land quality index (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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