Optimizing maize cultivation through Fuzzy AHP: Balancing land suitability, land use, and environmental sustainability

Abstract

This study optimized maize cultivation in Gowa Regency, South Sulawesi, Indonesia, a tropical region with diverse topography and environmental constraints, by integrating Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and spatial autocorrelation analysis to assess land suitability. Using a two-stage Fuzzy AHP, 12 criteria (e.g., slope, landslide risk, rainfall) were normalized via fuzzy membership functions and weighted through expert pairwise comparisons in a GIS framework, with spatial autocorrelation identifying clustering patterns. A 30-meter resolution dataset covering topographic, soil, climatic, land use, and environmental risk factors, prioritized slope, landslide risk, and rainfall, yielding a consistent model (CR = 0.0093). The suitability map classified 1.35% (2,445 ha) as highly suitable (S1), 18.1% (32,868 ha) as moderately suitable (S2), 49.1% as marginally suitable (S3), and 31.45% as unsuitable (N). Spatial autocorrelation (Moran’s I = 0.81, p = 0.001) revealed S1/S2 hotspots in the northern plains, ideal for maize expansion, and N coldspots in the eastern highlands, limited by steep slopes and landslide risks. Overlay analysis highlighted land-use conflicts, with moderately suitable land in settlements and unsuitable land in nature reserves, underscoring the need for integrated planning. The framework prioritizes low-risk S1/S2 hotspots for cultivation, restricts high-risk zones, and promotes sustainable practices like terracing and agroforestry for marginal lands. This replicable methodology offers policymakers and farmers actionable insights to enhance maize productivity while ensuring environmental resilience in tropical landscapes. Policymakers should enforce zoning to protect S1/S2 hotspots and subsidize sustainable practices.

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