GIS–TOPSIS assessment of soil and water constraints on sunflower in arid Algeria

Abstract Identifying land capable of sustaining oilseed crops under increasing aridity is central to strengthening agricultural resilience in North African drylands. We developed a statistically validated, spatially explicit land‑suitability framework that integrates geographic information systems (GIS) with the technique for order preference by similarity to the ideal solution (TOPSIS) across 34 geo‑referenced sites within the 15.8 km 2 Maader perimeter (Ain Sefra Basin, southwestern Algeria). Eight pedo‑hydrological indicators such as cation exchange capacity (CEC), organic matter (OM), exchangeable sodium percentage (ESP), texture, gypsum, slope, pH, and the Irrigation Water Quality Index were normalized and weighted using the analytic hierarchy process (consistency ratio = 0.07). TOPSIS closeness coefficients ( Ci ; relative proximity to the ideal solution in TOPSIS) ranged from 0.37 to 0.76, classifying the area into one highly suitable site (2.9%), 29 moderately suitable sites (85.3%), and four marginal sites (11.8%); no location was classified as unsuitable. Statistical validation using Kruskal–Wallis tests (CEC: p = 0.00056; OM: p = 0.00313; ESP: p = 0.0272), Pearson correlations (CEC: r = +0.65; OM: r = +0.62; ESP: r = −0.70), principal component analysis, and variance inflation factors (VIF < 2.5) shows that CEC, OM, and ESP are the dominant, independent drivers of suitability. Spatially, a central calcimagnesic belt with high CEC, natural gypsum content, and gentle slopes emerges as a “ready‑to‑scale” zone for sunflower ( Helianthus annuus L.) intensification (observed yields ≈ 2.8 t ha − 1 ), whereas sodic depressions adjacent to the wadi require targeted gypsum amendment (≥6 t ha − 1 ) and subsurface drainage. Sensitivity tests with ±20% perturbation in factor weights confirmed the robustness of the model, with changes in suitability class below 15%. Overall, this GIS–TOPSIS approach is not merely diagnostic; it provides a transparent, scalable, and investment‑ready platform to guide oilseed expansion under climate stress. By combining quantitative rigor with actionable spatial outputs, it supports Algeria's agricultural self‑sufficiency agenda and offers a transferable template for dryland suitability planning across the region.