Information and analytical support for management decision-making in agribusiness based on agromonitoring
Abstract
Information and analytical support are key efficiency drivers in Ukrainian agribusiness. The study examines how agromonitoring data – satellite indices, soil sensors, weather stations – shape management decisions under climate volatility and military risks.
Problem statement. The core issue is a disconnect between raw agromonitoring data and economically justified decisions. Many farms accumulate NDVI, moisture, and temperature data but lack standardized methods to calculate gross margin per hectare, water productivity, or ROI on agrotechnologies. Low digital literacy among agronomists and fragmented IT systems prevent profit-maximizing actions.
Unresolved aspects. Key gaps include the absence of unified accounting integration between IoT sensors and ERP systems, making real-time labor efficiency and technology ROI calculations impossible. The impact of military disruptions on data reliability remains unexplored. No consensus exists on risk-adjusted return metrics for agromonitoring investments.
Purpose of the article. To substantiate the economic role of agromonitoring in management decision-making for Ukrainian agribusinesses and propose an adaptive framework linking analytical indicators to financial, operational, and marketing strategies.
Presentation of the main material. The article analyzes how vegetation indices and soil data trigger specific decisions: variable rate fertilization (15–25% cost reduction), precision irrigation (20–40% water savings), and harvest logistics (10–20% cost reduction). Economic metrics – gross margin per hectare, technology ROI (150–300% over 3–5 years), risk-adjusted returns (30–40% volatility reduction) – are critically assessed. Implementation phases and organizational resistance are examined. The role of agromonitoring in quality management and export certification is highlighted.
Conclusions. Agromonitoring enhances profitability and adaptability when linked to accounting and marketing decisions. Phased implementation with staff training ensures competitive advantages. Future priorities include AI-based prescriptive analytics and ESG compliance.
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References
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