Wartime Ukraine Reveals What It Really Takes to Track Farm Sustainability

When the European Union began expanding its Farm Accountancy Data Network into a broader Farm Sustainability Data Network, the idea seemed straightforward enough: take the accounting machinery that has quietly collected farm economics data for decades and stretch it to cover environmental and social indicators as well. But a new study from wartime Ukraine shows just how fragile that seemingly simple extension can be. Researchers at the Leibniz Institute of Agricultural Development in Transition Economies (IAMO) in Halle, Germany, together with a colleague based at the State Tax University in Irpin, Ukraine, interviewed 53 accounting professionals and managers at predominantly medium and large Ukrainian agricultural enterprises between June and August 2025. Their findings, published in Discover Sustainability, suggest that the success of any FSDN-style monitoring system depends less on the elegance of its indicators and more on whether the institutions surrounding the data can earn the trust of the people who must produce it.

The Farm Sustainability Data Network concept represents a significant evolution in how agricultural statistics are gathered. The original Farm Accountancy Data Network, or FADN, has long served as the European Union’s backbone for farm economic data, collecting standardized accountancy information that informs agricultural policy across the bloc. Extending this into multi-domain sustainability monitoring means adding indicators for soil health, greenhouse gas emissions, biodiversity, water use, and social conditions alongside the familiar balance sheets and income statements. On paper, the logic is compelling: farms already maintain detailed records, so piggybacking sustainability metrics onto existing accounting routines should minimize additional burden. The Ukrainian study set out to test whether that logic survives contact with reality, and it chose one of the most demanding environments imaginable for doing so.

Ukraine offers a uniquely instructive case. Its agricultural sector is substantial by European standards, dominated by medium and large enterprises that already operate sophisticated accounting functions. At the same time, the full-scale war has strained every dimension of enterprise operations, from labor availability to energy supply to the physical security of fields and facilities. If sustainability data collection can be made workable under these conditions, the argument goes, it should be feasible almost anywhere. Conversely, if the barriers that emerge in Ukraine turn out to be structural rather than situational, that is a warning for the entire European project of sustainability monitoring. The researchers therefore framed their interviews not as a survey of Ukrainian readiness per se, but as a stress test of the institutional assumptions baked into the FSDN model.

The first and perhaps most striking finding is what the respondents did not say. Contrary to any expectation that war-weary managers would reject sustainability reporting outright as an unwelcome distraction, the interviewees generally did not object to the principle of it. Their skepticism was more precise and, in some ways, more difficult to address. They questioned whether new reporting requirements could be integrated into existing routines without duplicating administrative work that their teams were already performing at capacity. They doubted whether environmental indicators, which lack the standardized measurement conventions of financial accounting, could be measured consistently across enterprises and over time. And they worried about what would happen to commercially sensitive data once it left their hands.

That last concern deserves particular attention because it cuts to the heart of how data networks function. Financial data collection under FADN operates within a well-established framework of confidentiality rules and trusted intermediaries, typically national liaison agencies that anonymize and aggregate the information before it is used. Sustainability data raises the stakes in new ways. Detailed records of fertilizer application, pesticide use, land management practices, and labor conditions are not merely statistical inputs; they are commercially valuable and potentially reputationally sensitive. An enterprise that reports higher emissions than a competitor, or that discloses practices a rival keeps quiet about, could find itself disadvantaged in markets or targeted by regulators. Without credible guarantees about how such data will be protected, used, and shared, rational managers have every incentive to report inaccurately or to resist participation altogether.

The interviews revealed a consistent set of preferences that map onto this underlying logic. Respondents favored voluntary participation, or at most a mixed model combining voluntary and mandatory elements, rather than an immediate compulsory scheme. They wanted external technical support to help them build the necessary measurement and reporting capabilities, and they expected cost compensation to offset the real resources that data collection would consume. Above all, they wanted clear rules governing data use, specifying who could access the information, for what purposes, and under what safeguards. Taken together, these preferences indicate something the researchers describe as relational readiness: the perception that a monitoring system is workable depends not on the capacity of individual enterprises in isolation, but on the fit between that capacity and the surrounding data-governance architecture.

This reframing of readiness is the study’s central conceptual contribution. Much of the policy discussion around sustainability monitoring treats data quality as a technical problem, to be solved with better indicator definitions, standardized measurement protocols, and digital reporting tools. The Ukrainian evidence suggests a different picture. Data quality, on this account, is an institutional outcome rather than a property of individual reports. An enterprise may be perfectly capable of producing accurate numbers, but if it distrusts the system receiving them, fears duplication of effort, or cannot verify how its figures will be handled, the data it submits will be degraded in predictable ways: rounded, delayed, defensively conservative, or quietly omitted. The quality of the network is therefore produced jointly by enterprises and institutions, not unilaterally by either.

War, the researchers found, intensifies these concerns but does not create them. The scarcity of labor, capital, and managerial attention under wartime conditions makes any additional administrative burden harder to absorb, and the disruption of supply chains and record-keeping complicates the provenance of environmental data. Yet the core worries about measurement consistency, data protection, and administrative duplication would exist in peacetime as well. This matters for policy design because it means the lessons from Ukraine are not exotic artifacts of an exceptional situation. They are magnified versions of the challenges that any country introducing FSDN-type monitoring will face, visible in Ukraine precisely because the margin for institutional error there is so thin.

The authors are careful about the limits of their findings. Fifty-three semi-structured interviews with accounting professionals and enterprise managers cannot establish sector-wide readiness, nor do the results validate any particular implementation model. The enterprises studied were predominantly medium and large, so smaller operations, which face different capacity constraints, remain outside the frame. What the study does offer is a set of design principles for early implementation. Indicators should be chosen for clear provenance, meaning their origin and measurement basis can be traced and verified. They should interoperate with records enterprises already maintain, rather than demanding parallel bookkeeping. Quality-control pathways should be transparent, so participants can see how their data will be checked and corrected. And participation should deliver credible reciprocal value, giving enterprises a concrete return, whether analytical insight, benchmarking, or policy influence, for the data they contribute.

There is a broader lesson here that extends well beyond agriculture. Around the world, governments and international bodies are racing to build sustainability monitoring systems, from corporate climate disclosure regimes to biodiversity tracking frameworks, and nearly all of them assume that the hard part is defining the right metrics. The Ukrainian case suggests the hard part is institutional: building the trust, the routines, and the reciprocal value that make accurate reporting rational for the people on the ground. Data networks are social systems as much as technical ones. If Europe’s sustainability monitoring ambitions are to succeed, the experience of Ukrainian agricultural enterprises, gathered in the most difficult circumstances imaginable, may prove to be one of the most valuable evidence bases available. The researchers’ conclusion is quietly radical: before asking farms to report on sustainability, first build a system they can believe in.

Subject of Research: Readiness and data quality risks in FSDN-type sustainability monitoring among Ukrainian agricultural enterprises under wartime conditions

Article Title: Toward FSDN-type sustainability monitoring: readiness and data quality risks in Ukrainian agricultural enterprises under wartime conditions

Article References: Metelytsia, V., Valentinov, V., & Gagalyuk, T. (2026). Toward FSDN-type sustainability monitoring: readiness and data quality risks in Ukrainian agricultural enterprises under wartime conditions. Discover Sustainability. https://doi.org/10.1007/s43621-026-04853-8

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04853-8

Keywords: Farm Sustainability Data Network, FADN, sustainability monitoring, Ukraine, agricultural enterprises, data quality, data governance, wartime economy, environmental indicators, farm accounting, institutional trust, agricultural policy