Vol. 7, Issue 11, Part A (2025)

Sugarcane cultivation in this region is characterized by intensive nutrient extraction, heavy reliance on nitrogen-dominated fertilizer regimes, residue removal, and variable soil fertility, all of which contribute to accelerated depletion of labile SOC fractions and long-term soil degradation. Balanced fertilization ensuring an optimal supply of macro-, secondary-, and micronutrients based on soil-crop demand has been shown to enhance carbon inputs through improved root biomass, rhizodeposition, and residue quality. These changes directly influence the distribution of carbon among fast-cycling pools such as dissolved organic carbon, microbial biomass carbon, and particulate organic matter, and more stable pools such as mineral-associated organic carbon. Balanced fertilization supported by AI also improves cane yield and sugar productivity by harmonizing nutrient supply with crop uptake dynamics, particularly under conditions of climatic variability typical of northwest India. However, balanced fertilization increases microbial biomass carbon (MBC), particulate organic carbon (POC), and dissolved organic carbon (DOC), which act as sensitive early indicators of changes in SOC status. Enhanced nutrient stoichiometry reduces C: N imbalances, leading to improved microbial efficiency and higher carbon use efficiency (CUE), promoting conversion of labile carbon into mineral-associated organic carbon (MAOC) and aggregate-protected carbon.

Evidence from sugarcane regions of Northwest India indicates that AI-optimized balanced fertilization can increase cane yield by 10-18%, improve nitrogen recovery efficiency by 15-25%, and elevate labile SOC fractions by 8-20% compared with conventional blanket recommendations. Improved nutrient synchrony also reduces decomposition hotspots and moderates seasonal fluctuations in MBC and DOC, supporting greater carbon stabilization. Overall, the reviewed evidence indicates that integrating AI with balanced fertilization offers a promising pathway to enhance productivity while rebuilding SOC stocks and stabilizing labile carbon pools in sugarcane-based systems. The approach will be most effective when combined with residue retention, organic amendments, real-time monitoring, and region-specific calibration of AI models. Strengthening these components can accelerate sustainable intensification and long-term soil health restoration in northwest India.