AI-driven decision support system enhances productivity, water use efficiency, and soil sustainability of strategic crops in sandy soils

Sandy soils in Egypt's newly reclaimed lands face multiple challenges due to their low water-holding capacity, nutrient leaching, and high evapotranspiration, all of which threaten sustainable crop production. This study evaluated an artificial intelligence-driven decision support system (AI-DSS) for managing irrigation and fertilization in wheat ( Triticum aestivum L.), maize ( Zea mays L.), and sugar beet ( Beta vulgaris L.) over three consecutive seasons (2022–2025). The AI-DSS integrated real-time soil moisture, nutrient, and weather data using Random Forest and LSTM models to optimize input scheduling. Compared to conventional farmer practices (CFP), AI-DSS led to yield increases of up to 13.1 % and improvements in water use efficiency (WUE) by up to 15.5 %, particularly in sugar beet during 2024. Partial factor productivity (PFP) also increased significantly, especially in maize. Post-harvest soil analysis indicated higher residual levels of nitrogen (+13.6–19.3 %), phosphorus (+22.7–25.0 %), and organic matter (+17.9–22.0 %), along with a 13–19 % reduction in soil salinity. Economic assessments showed an 8.5–15.0 % increase in the benefit–cost ratio (BCR). Additionally, nitrate leaching was substantially reduced under AI-DSS, mitigating environmental risks. These results underscore the potential of AI-driven management to enhance productivity, input-use efficiency, and soil sustainability in coarse-textured soils of arid regions. • AI-DSS improved crop yields by up to 13.1 % in sandy soils of arid Egypt. • WUE rose by 15.5 %, notably in sugar beet. • In maize, AI-driven scheduling boosted PFP by 28 %. • Nitrate leaching decreased by 40–45 %, reducing environmental risk. • Soil N, P, and OM improved post-harvest.