Integrated system of performance indicators in the asset management of agricultural enterprises

doi:10.26565/2786-4995-2025-4-09

Denys Omelianenko Sumy National Agrarian University https://orcid.org/0000-0002-3836-7962

DOI: https://doi.org/10.26565/2786-4995-2025-4-09

Keywords: agricultural enterprises, asset management, KPIs, fixed assets, uncertainty, resilience, KRIs

Abstract

The paper proposes a unified system that links performance indicators (KPIs) with early-warning risk indicators (KRIs) to improve asset management under uncertainty in Ukrainian agribusiness.

Problem statement. Volatile input prices, logistics disruptions, climate risks and wartime shocks expose weaknesses of fragmented metrics and hinder transparent, comparable control of assets.

Unresolved issues. Standardization of KPI/KRI definitions for biological and fixed assets, links to risk appetite and capital allocation, data quality/digitalization gaps, and adaptation to national regulations remain insufficiently addressed.

Purpose of the article. To justify the concept, structure and practical guidelines for an integrated KPI-KRI system (IPIS) tailored to agricultural enterprises and harmonized with international practice.

Main material. The article outlines domains and metric passports, index-based tracking for comparability, threshold setting and escalation rules, data governance and reporting cadence, and use cases for capex/opex decisions, liquidity discipline, and stress-response. It adds a benchmarking grid versus EU practice to calibrate thresholds and reporting frequency. A lightweight dashboard is proposed for monthly management review, with quarterly external disclosure to enhance investor communication.

Conclusions. Implementing IPIS strengthens transparency, accelerates corrective actions, and supports efficient capital allocation; success depends on data quality, staff training, and phased integration into management routines. It also improves comparability across farms and seasons, facilitating lender and investor due diligence. Limitations include uneven digital maturity and potential measurement bias, suggesting phased pilots and future research on automated data capture.

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Author Biography

Denys Omelianenko , Sumy National Agrarian University

PhD Student

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