Agricultural sustainability lags behind national innovation gains in Eastern Europe

Innovation is tied to a smaller agriculture share of GDP. The relationship between the Global Innovation Index and AVAS_GDP is consistently negative and becomes more pronounced toward the upper quantiles of the agriculture share distribution. This means that in countries or years where agriculture still accounts for a larger slice of GDP, improvements in innovation are associated with a faster shift of value toward other sectors.

A new peer-reviewed analysis published in Agriculture reports that stronger national innovation performance across six Eastern European EU members is linked to a smaller role for agriculture in gross domestic product, while showing little sign of a broader rise in public spending on the sector. The research tracks Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia over more than a decade and shows how the gains from innovation often flow into higher productivity industries outside primary agriculture, unless governments use targeted tools to channel them into farms and rural systems.

The paper, titled “Is Innovation a Driver of Agricultural Sustainability? Evidence from Eastern European Countries Under the SDG 2 Framework,” tests whether national innovation capacity, captured by the Global Innovation Index, aligns with progress on key Sustainable Development Goal 2 indicators, including agriculture’s share in GDP, agriculture’s share of government expenditure, and the Agriculture Orientation Index that compares spending levels with the sector’s economic weight.

What question does the study answer, and how?

Do countries that score higher on national innovation also advance sustainable agriculture, or does innovation mostly speed up a shift of value toward manufacturing and services while agriculture’s weight in the economy falls? The study looks at this through three outcome indicators:

• Agriculture value added as a share of GDP (AVAS_GDP). This captures the macroeconomic weight of agriculture.
• Agriculture’s share of government expenditure (ASGE). This tracks how much of the budget is devoted to the sector.
• Agriculture Orientation Index (AOIG). This compares agriculture’s share of spending with its share of GDP to show whether public finance is aligned with the sector’s role.

The analysis covers 2010 to 2023 and applies panel quantile regression with country fixed effects, with bootstrapped standard errors. This lets the author see how the innovation link behaves across the full distribution of each outcome, rather than only at the mean. The choice of method matters because a single average effect can hide important differences between agriculture-heavy economies and those where farming already plays a smaller role.

The model includes controls for GDP per capita growth, population, and agricultural land share. These controls reduce the risk that innovation is standing in for other structural forces such as urbanization, demographic change, or land endowments. The study also reports diagnostic checks for heteroskedasticity and cross-section dependence, which are common in macro panels and, if ignored, can bias results. By using a distribution-aware design and robust inference, the paper offers a clearer picture of how innovation relates to agriculture under varied country conditions within a shared EU policy setting.

In plain terms, the author is asking three linked questions. First, when a country becomes more innovative, does agriculture’s share of GDP tend to fall, stay the same, or rise. Second, do governments in more innovative countries allocate a larger share of their budgets to agriculture. Third, is public spending better aligned with agriculture’s economic weight in more innovative settings, once institutions are taken into account.

What did the evidence show about structure, spending, and policy alignment?

Innovation is tied to a smaller agriculture share of GDP. The relationship between the Global Innovation Index and AVAS_GDP is consistently negative and becomes more pronounced toward the upper quantiles of the agriculture share distribution. This means that in countries or years where agriculture still accounts for a larger slice of GDP, improvements in innovation are associated with a faster shift of value toward other sectors. That pattern matches the classic path of structural transformation. Rising innovation tends to favor activities with higher capital intensity, stronger knowledge spillovers, and faster productivity growth. Even when farms adopt better tools, other sectors often grow faster, which pulls the overall share of agriculture down.

According to the study, innovation does not clearly move the raw budget share for agriculture. Across the distribution of ASGE, the study finds no systematic link with national innovation scores. In more innovative settings, the slice of total public spending that goes to agriculture does not show a consistent increase or decrease once other factors are controlled. Budget decisions are shaped by fiscal frameworks, political priorities, and multi-year EU programming cycles. General innovation policy often steers money toward education, research organizations, digital infrastructure, and industrial competitiveness, which may or may not include targeted rural and farm components. Without tailored instruments that route resources to the sector, innovation alone does not raise agriculture’s claim on the national budget.

Policy alignment improves with innovation only where institutions are already stronger. The Agriculture Orientation Index behaves differently from the raw budget share. The study reports that AOIG becomes positively related to innovation in the mid to upper quantiles, then the effect fades at the extreme high end. This suggests an interaction between innovation capacity and governance quality. Where agricultural institutions and spending systems already function reasonably well, a stronger national innovation base aligns with a more supportive relative stance toward agriculture. Where orientation and administrative capacity are weak, innovation does little to lift that relative measure. In other words, innovation can amplify alignment, but only after a basic level of institutional readiness has been reached.

The control variables help set the context. A higher share of land under agriculture tends to be associated with a larger agriculture share in GDP and a modestly higher budget share devoted to the sector. A larger population is associated with a smaller agriculture share in GDP, which lines up with ongoing urbanization and diversification. By including GDP per capita growth, the model separates pure growth effects from the innovation channel. These patterns confirm that structural factors still matter, even inside a common EU policy and funding framework.

Put together, the findings draw a sharp and practical picture. Innovation supports modernization and economy-wide productivity, and it does so in a way that shifts value toward non-agricultural sectors. It does not, on its own, increase the raw budget allocation for agriculture. It helps align policy with the sector only in places where institutions are already strong enough to catch and apply the gains.

What should policymakers do now to connect innovation with sustainable agriculture?

The paper offers a clear set of implications for governments across the region and for EU partners.

Measure success by productivity and resilience, not by a fixed GDP share

A falling agriculture share in GDP during periods of rising innovation is not, by itself, a sign of decline. It often signals that the rest of the economy is scaling faster. The right scoreboard for agriculture should focus on farm productivity, climate resilience, rural incomes, diversification into higher value chains, and food system stability. Policymakers should avoid trying to hold the GDP share steady and should instead push for quality and competitiveness.

Use targeted instruments to bring innovation to farms

Since general innovation does not automatically increase agriculture’s budget share, governments need tools that send resources and knowledge directly to the sector. The study highlights the role of strong Agricultural Knowledge and Innovation Systems. Priority tools include public agricultural R&D linked to real adoption pathways, impartial extension and digital advisory services, and on-farm experimentation networks that cut the time from research to practice. Smart incentives can support precision fertilization, water-saving irrigation, low-emission livestock systems, and data-driven crop management. These measures convert a country’s innovation base into field-level gains.

Align innovation policy with the Common Agricultural Policy

National innovation strategies and CAP instruments should point in the same direction. That means using multi-year programming to lock in investment in agri-tech pilots, interoperable data platforms, skills programs for young farmers, and regional innovation clusters that include agri-food startups and cooperatives. Strong alignment helps turn periodic funding cycles into a steady flow of knowledge and tools for farms.

Build institutional readiness where it is weakest

The AOIG evidence shows that better alignment comes through in the mid to upper part of the distribution. Countries that are still low on orientation should sequence reforms to lift administrative basics first. That includes program budgeting capacity, co-financing management for EU funds, transparent and impartial advisory networks, and reliable execution of planned spending. Once these foundations are in place, national innovation strength has a better chance to show up in the orientation metrics.

Prevent a digital divide in adoption

Precision tools, remote sensing, and advanced analytics can raise efficiency and cut emissions, but adoption is uneven and can favor larger farms. To avoid leaving small and medium producers behind, governments and partners should lower adoption costs, provide shared digital infrastructure and trusted data governance, and expand training. Procurement pilots and cooperative platforms can help spread benefits across regions and farm sizes.

Track outcomes with richer indicators

The paper notes that its results are associative rather than strictly causal. Future work should add green innovation metrics, agricultural R&D intensity, and farm-level adoption indicators. Linking innovation changes to concrete sustainability outcomes, such as yield stability, carbon intensity, soil health, and biodiversity, would help sharpen policy choices. The study also points to econometric extensions that could strengthen causal claims, such as instrumental-variable designs and common-correlated-effects quantile methods that handle unobserved shocks across countries.

Recognize regional diversity inside a common EU frame

Bulgaria and Romania face different structural and administrative constraints than Czechia or Poland. A single playbook will not fit all. Countries where agriculture still has a large GDP share should focus on productivity and value-chain upgrading to guide a fair transition. Countries where institutions are already mid to high on orientation can lean on innovation to deepen alignment, while improving budget execution and monitoring so that results show up in outcomes on the ground.

Keep the rural lens in broader innovation policy

Investments in connectivity, higher education, research centers, and industrial technology often cluster in cities. To avoid a gap between urban innovation hubs and rural areas, national strategies should reserve space for agri-food challenge programs, regional testbeds, and cross-sector spillover projects that bring manufacturing and energy innovations into farming and logistics. Transport, cold chains, and digital services can carry a large share of the productivity gains to rural communities if policy makes those links a priority.