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

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 (HCA), analytical hierarchy process (AHP), fuzzy logic, and geographic information system (GIS) were integrated to zone map agricultural quality in an arid desert area (Matrouh Governorate, Egypt). Satellite imageries, field surveys, soil analyses employed define eighteen indicators terrain, soil, vegetation qualities, which then reduced through PCA a minimum set (MDS). The original MDS weighted by AHP experts’ opinions. Within GIS, raster layers generated, standardized membership functions (linear non-linear), assembled arithmetic mean sum algorithms produce eight index maps. properties (pH, salinity, organic matter, sand), slope, surface roughness, could adequately express quality. Accordingly, HCA classify zones with significant heterogeneity. Selecting salt-tolerant crops, applying leaching fraction, adopting sulfur applications, performing leveling, micro-irrigation are most recommended practices. Highly (p < 0.01) positive correlations occurred among all developed indices. Nevertheless, coefficient of variation (CV) sensitivity (SI) confirmed better performance from non-linearly scored model. It achieve highest discrimination qualities (CV > 35%) was sensitive (SI = 3.88) potential changes. within this sufficiently represent TDS (R2 0.88 Kappa statistics 0.62), reducing time, effort, cost estimating performance. proposed approach would provide guidelines sustainable land-use planning studied similar regions.