State Support for Agriculture in the Context of Ukraine’s Economic Security: Identification of Key Measures in the EU, Canada, the USA, and New Zealand’s Conceptual Models
Bibliographic record
Abstract
State support for agriculture, as a strategically important sector of Ukraine’s national economy, is the government’s response to adverse conditions, crises, and temporary shocks to ensure the continuity of farming production, which experiences economic instability and significant destructive losses caused by a full-scale invasion. Based on the diversity of existing state support types and the presence of controversial statements regarding the effectiveness of its mechanisms, the paper aims to identify measures approved in countries with a highly developed agricultural sector that will contribute to strengthening economic security if implemented in the strategy of Ukraine’s agricultural policy. For this objective, linear multiple regression models are used, which allow the identify the dominant types of state support in funding volumes in the EU, Canada, New Zealand, the USA, and Ukraine in 2010–2022, which have a statistically significant impact on the value of agricultural products. It was found that only some types of state support have an empirically confirmed effect on the dependent variable, namely financing payments based on the resources used (New Zealand), payments based on both current (Canada) and non-current (USA) area planted, animal numbers and incomes that require or do not require production, support of agricultural knowledge and innovation (EU, Canada), inspections and control (New Zealand, Ukraine), consumer subsidies (USA, Canada). Supplementing existing inspection and control measures in Ukraine, implemented by New Zealand, will minimize the risk of losses due to diseases, pests, or biological threats and increase the international competitiveness of agricultural products, which is crucial for strengthening the country’s foreign economic security. Separate measures of the Canadian model for direct producer support are justified since their adaptation could facilitate the forced relocation of the breeding stock during a full-scale invasion, which positively influences industrial and food security, or expand the diversity of basic food products, reducing import dependence on them. The provision of tax breaks for bioethanol and biofuel production, which demonstrates statistical significance in the USA, if adopted by Ukraine, increases its energy security level, reducing dependence on imported energy sources through renewable energy development. However, when improving support tools, the limited financial resources that are necessary for their implementation should be borne in mind. Therefore, Ukraine should avoid subsidies tied to non-current production indicators and not require production (USA), as they create an additional burden on the state budget, weakening the level of both budgetary and debt security. In addition, the financing of measures to promote Ukrainian agricultural products (in particular, advertising campaigns and participation in international fairs) did not lead to a growth in its value due to extra demand, new sales markets, or increased consumer awareness of the world market, which is likely evidence of the ineffectiveness of implemented actions in 2010–2022. Using results based on successful international practices adapted to real challenges, Ukraine can improve its state agricultural policy, focusing on strengthening industrial, food, social, energy security, and environmental sustainability, depending on priorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".