Identification of financial risks in the agricultural sector

Todor Todorov (), Veneta Tabakova-Komsalova, Stoyan Cheresharov () and Stanimir Stoyanov ()
Additional contact information
Todor Todorov: University of Plovdiv “Paisii Hilendarski†, Plovdiv, Bulgaria
Veneta Tabakova-Komsalova: University of Plovdiv “Paisii Hilendarski†, Plovdiv, Bulgaria
Stoyan Cheresharov: University of Plovdiv “Paisii Hilendarski†, Plovdiv, Bulgaria
Stanimir Stoyanov: University of Plovdiv “Paisii Hilendarski†, Plovdiv, Bulgaria

Access Journal, 2026, vol. 7, issue 3, 489-502

Abstract: 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.

Keywords: financial risk identification; agriculture; intelligent systems; financial indicators; risk modelling; digital agriculture; decision support systems (search for similar items in EconPapers)
JEL-codes: C89 G32 O33 Q14 Q16 (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.access-bg.org/journalfiles/journal ... icultural_sector.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aip:access:v:7:y:2026:i:3:p:489-502

DOI: 10.46656/access.2026.7.3(1)

Access Statistics for this article

More articles in Access Journal from Access Press Publishing House
Bibliographic data for series maintained by Mariana Petrova ().