Declining soil quality and nutrient imbalances constrain rice productivity in tropical acidic soils. The agricultural reuse of fly ash (FA), an industrial by-product, offers potential as a soil amendment when combined with organic inputs, yet mechanistic understanding of its effects on soil–yield relationships remain limited. Traditional statistical methods often fail to decode non-linear soil-yield relationships, necessitating advanced machine learning (ML) approaches. A field experiment evaluated the integrated effect of FA (10–40 t ha−1), FYM (5 t ha−1), and NPK effects on soil physio-chemical and biological properties and identified key soil predictors driving rice productivity using explainable machine learning. The FA40 + FYM + NPK treatment achieved the highest grain yield (54.0 q ha−1), outperforming NPK alone by 38.5 %. This treatment improved soil porosity (45.5 %), water-holding capacity (37.8 %), available nitrogen (212.9 kg ha−1), available phosphorus (19.6 kg ha−1), and microbial enzyme activities, including urease (22.9 μg NH4+-N g−1 hr−1) and β-glucosidase (15.1 μg pNP g−1 hr−1). Machine learning interpretation revealed β-glucosidase, organic carbon, urease, available phosphorus, and clay content as dominant predictors of yield variation. Conditional partial dependence plots revealed synergistic interactions between β-glucosidase and organic carbon, and between urease and available phosphorus, indicating that carbon turnover and nutrient mineralization jointly regulated yield response. These findings demonstrate that the combination of FA (20–40 t ha−1) with FYM and NPK can improve soil functionality and sustain rice productivity. Explainable modelling provides mechanistic insight for advancing soil health assessment and fertilizer strategies in acidic agroecosystems.
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