Identification of financial risks in the agricultural sector

Financial risk in agriculture is generated by the interaction of market, credit, liquidity, climate and institutional factors. These risks affect farm income stability, debt-servicing capacity, investment returns and long-term sustainability. Recent climate extremes, input-price shocks and agricultural price volatility have increased the need for early-warning tools that combine financial and agronomic information. Although digital agriculture platforms, FMIS/ERP systems, satellite monitoring, IoT sensors and AI analytics improve data availability, many platforms still lack an integrated module that translates accounting data into interpretable financial-risk categories. This study addresses that gap by proposing a rule-based financial risk identification model designed for subsequent integration into digital agriculture platforms. Objectives: Objectives: This study develops a methodological model and a prototype system for identifying financial risks in agricultural enterprises. Methods/Approach: The model constructs parametric, index-based and interval spaces from standard financial indicators and implements them as production rules in the FIRA prototype. Results: The prototype calculates key risk ratios, assigns them to predefined interval states and generates an explainable financial-risk profile for an enterprise. Discussion: The approach complements digital agriculture and FMIS/ERP research by adding a transparent financial early-warning module that can be integrated with platforms such as ZEMELA. Conclusions: The proposed approach provides a practical basis for monitoring liquidity, solvency and cash-flow risk in agricultural enterprises. Its main value is decision support: it can assist farmers, advisers, lenders and insurers in early screening and follow-up financial planning. Empirical validation across farms and subsectors remains a necessary next step.