Artificial Neural Networks for Predicting Mango Response to Potassium-Enriched Biochar Under Drought Conditions

Sustainable water stress management in arid and semi-arid regions requires precise understanding of soil-plant interactions when implementing biochar-based strategies. This study developed an Artificial Neural Networks (ANN) model to predict mango productivity under drought conditions using potassium-enriched biochar (KEB), addressing a significant knowledge gap in biochar application modeling. KEB was made by pyrolyzing a maize straw-banana peel mix (1:3) at 500 degrees C for 3 h. A two-year field experiment evaluated four potassium sources, i.e., C (control), KS (K2SO4), KEB (potassium-enriched biochar), and KF (potassium feldspar), under two irrigation regimes representing 80% (normal) and 50% (drought) of available soil moisture. Potassium release patterns between KEB and KS (R-2 = 0.83-0.97), both superior to other treatments. Drought stress significantly impaired soil quality, reducing mango fruit yield by 20% and decreasing soil microbial biomass carbon (MBC) and dehydrogenase enzyme activity by 22% and 14%, respectively. However, KEB application enhanced soil quality under water stress by improving MBC, dehydrogenase enzyme activity, and soil organic carbon (SOC), resulting in an 82% yield increase. KEB treatment also elevated chlorophyll content, proline levels, and soluble carbohydrates, enhancing drought tolerance through improved osmotic adjustment in mango leaves. ANN modeling identified optimal conditions for maximizing fruit yield, with 125% KEB application providing the best results. The model established critical threshold values: SOC (3.1 g kg(- 1)), MBC (365.66 mg kg(- 1)), chlorophyll (3.7 mg kg(- 1)), soluble carbohydrates (38.25 mg kg(- 1)), and phosphorus (2.78 mg kg(- 1)). These findings highlight the dual role of KEB as a sustainable soil amendment and the utility of ANN as a decision-support tool for precision agriculture. Integrating KEB with intelligent modeling approaches offers a promising strategy for improving resilience and productivity in mango orchards under water-limited conditions.