ASSESSING THE ECONOMIC EFFICIENCY OF RICE CULTIVATION TECHNOLOGIES UNDER SALINE SOIL CONDITIONS

Abstract

Water scarcity in Central Asia demands rigorous, economically grounded decision frameworks for adopting water-saving technologies. This study presents a hybrid multi-criteria model that fuses the Analytic Hierarchy Process (AHP) for criteria weighting, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking, and Data Envelopment Analysis (DEA) for efficiency benchmarking, with deliberate emphasis on economic performance. Calibrated using farm-level and regional data from Uzbekistan’s Khorezm province and the wider Aral Sea basin, the model evaluates three technologies: conventional furrow irrigation (baseline), deficit irrigation, and sensor-augmented drip systems.

Results reveal that drip irrigation with soil-moisture sensors dominates, attaining a normalized TOPSIS score of 0.82, an internal rate of return of 21–26 %, net present values exceeding US$1,150 ha⁻¹ (10-year horizon, 6 % discount rate), and water productivity gains of 42–52 %. These outcomes surpass deficit irrigation and the baseline by 18–31 % in economic efficiency terms while simultaneously reducing irrigation demand by up to 48 %. Extensive Monte Carlo sensitivity analysis (±20 % variation in input prices, water tariffs, and yield response) confirms the ranking’s robustness in 92 % of iterations.

The proposed framework offers a transparent, replicable, and economically oriented decision-support tool that enables extension services and policymakers to prioritize investments yielding the highest marginal returns on scarce water and capital, thereby strengthening agricultural resilience across Central Asia’s water-constrained environments.