Hamed Etezadi, Viacheslav Adamchuk, Yacine Bouroubi, Maxime Leduc, MarcOlivier Gasser
A robust decision-support infrastructure is fundamental to modern nitrogen management, where recommendations must be both site-specific and economically resilient under uncertain environmental and market conditions. This study presents a modular, pre-computed decision support system that delivers near real-time nitrogen rate recommendations by combining agronomic similarity, probabilistic yield modelling, and scenario-based economic optimization. Instead of fitting a single model on demand for each user query, the system pre-computes millions of combinations of soil, weather, and management conditions, storing yield response, error distributions, and expected profit metrics in a relational database. At runtime, user inputs are converted into binned feature keys, and the closest precomputed records are retrieved, allowing nitrogen rate profitability to be evaluated in seconds without additional regression or simulation. To illustrate the proposed concept, a hybrid soil module links legacy soil maps, soil organic matter (SOM) estimates, weather factors and similarity search to historical field trials, enabling the reuse of existing agronomic information at scale. The architecture is explicitly modular: each component, similarity search, yield response modelling, uncertainty treatment, and profit calculation, can be updated or replaced without altering the rest of the system. This design supports the integration of richer data sources (e.g., improved SOM models, climate scenarios, or alternative crops) and the extension of new analytical modules beyond nitrogen. The system was evaluated across nitrogen rate scenarios ranging from 0 to 250 kg N ha⁻¹, achieving response times of under 30 s, thus enabling timely evaluation of management decisions under uncertainty. Overall, the proposed framework provides a practical path toward scalable, real-time, and uncertainty-aware decision support for precision agriculture.