An integrated MCDM decision-support framework for evaluating drought-resilient maize production strategies under arid conditions

Abstract This study evaluates drought-resilient maize production strategies under arid conditions using a Smart Adaptive Balanced Ranking Yoke (SABRY), a transparent sequential workflow that combines conventional statistical inference, entropy-weighted TOPSIS ranking, robustness assessment, and a separately reported conditional scenario analysis. Five treatments were assessed over two growing seasons in Nubaria, Egypt, using three independent field plots per treatment in each season. The primary empirical ranking used three criteria supported by replicated treatment data: crop yield (C1), water-use efficiency (C2), and soil moisture retention (C3). Seven additional criteria, C4-C10, were retained only in a separate conditional scenario analysis because traceable source-level measurements were not available in the study records. Replicated responses were evaluated using analysis of variance (ANOVA) followed, where appropriate, by Tukey's honestly significant difference (HSD) test. Entropy-derived weights and TOPSIS were applied to the primary C1-C3 matrix, whereas the extended ten-criterion analysis was treated as scenario-based. White plastic mulch (A3) ranked first in both seasons, followed by black plastic mulch (A2), the water-retaining agent treatment (A1), straw mulch (A4), and the untreated control (A5). The secondary ten-criterion scenario placed A2 first under the specified C4-C10 inputs; this result is conditional and is not experimentally validated. Sensitivity analysis showed that A2 remained first in 97.1% of ±30% one-at-a-time cases and in 85.7%/85.4% of the 2022/2023 cases under simultaneous ±30% perturbation. White plastic mulch increased numerical grain yield from 6345.73 ± 160 kg/ha under the control to 9561.35 ± 190ᵃ kg/ha in 2022 and to 10,500 kg/ha in 2023. The experimentally supported conclusion is limited to the measured C1-C3 responses under the investigated field conditions.