The quantum-enhanced agri-ledger: a simulation-based pathway to incentivized climate-smart agronomy

Global cropping systems face the dual challenge of increasing production for a growing population while adapting to climate instability. Current precision agriculture relies heavily on retrospective indicators, such as visual wilting or canopy reflectance, which are lagging signals that appear only after irreversible cellular damage has occurred. Furthermore, existing supply chains lack robust, decentralized mechanisms to verify and reward adherence to sustainable farming practices. Aligning with global objectives to facilitate advances in food systems that maximize production while minimizing waste and resource usage, this study presents the Quantum-Enhanced Agri-Ledger (QAL). A unified computational framework is proposed to help shift farm management from a reactive stance to a preemptive, data-driven paradigm. The potential of this system is assessed through a rigorous simulation study using a complex, multi-variable synthetic dataset representing agronomic stress scenarios. Three primary modeled components are integrated: (1) a theoretical high-sensitivity Quantum Dot Spectrometry Sensor (QDSS) model proposed for the in-field detection of stress-related Volatile Organic Compounds (VOCs); (2) a privacy-focused Federated Learning (FL) model that uses detailed sensor data to forecast crop health; and (3) a novel consensus mechanism, Proof-of-Sustainable-Practice (PoSP), designed to create an immutable record of sustainable intensification efforts. Within the constraints of the simulated environment, the full QAL model demonstrated a mean stress classification accuracy of 96.74% ± 0.38%. While these results incorporate modeled sensor noise and drift, they represent performance under controlled synthetic conditions; real-world operational accuracy may vary depending on physical sensor degradation and unforeseen environmental variability. Regarding yield forecasting, the model achieved a significantly lower Root Mean Square Error (RMSE) of 1.33 ± 0.19 tons/ha compared to standard baseline models. Furthermore, a robustness analysis indicates the model retains functional efficacy ( > 90% accuracy) up to noise levels of σ = 0.10, though performance degrades at higher noise thresholds ( σ = 0.20). A theoretical security analysis suggests the ledger’s integrity against network attacks under the defined constraints. This study provides a simulation-based conceptual blueprint for transparent, incentive-oriented agronomic ecosystems. While the current validation is purely computational, it serves as a foundation for exploring pathways toward the UN SDGs 2, 12, and 15.