A Comparison of Climate Smart Food Systems in Armenia, Georgia, and Moldova: Policy Implications for Armenia
Bibliographic record
Abstract
In May 2020, the European Commission introduced the Farm to Fork (F2F) strategy (European Commission, 2020), a bold initiative aimed at overhauling Europe’s food systems with a strong focus on sustainability and long-term environmental, human, and planetary health goals - in line with the objectives of the EU Green Deal (European Commission, 2021). The profound impacts of industrial food systems on climate change, biodiversity, and public health are often overlooked. Globally, food systems account for nearly one-third of greenhouse gas emissions (Crippa et al. 2021), are the primary causes of biodiversity loss (Boakes, et al., 2024), and play a substantial role in health conditions such as cardiovascular diseases, cancer, and type 2 diabetes. To build on these efforts, the EU Strategic Dialogue on Agriculture recently introduced a document of recommendations called “A Shared Prospect for Farming and Food in Europe.”( European Commission, 2024). The initiative aims to reform the EU Common Agricultural Policy (CAP), create Just Transition and Nature Restoration Funds, and advocate for more sustainable diets – new directions for advancing the Farm to Fork agenda that will shape European policy in the future. Food systems are critical for ensuring food security, supporting sustainable development, and addressing the challenges posed by climate change. This paper explores lessons learned from three GUMA project countries: Armenia, Georgia, and Moldova. A qualitative analysis was conducted using data gathered from diverse sources, including official statistical agencies, international donor organization frameworks, and sectorial data. In addition, for Armenia specifically, the study incorporates insights from surveys of key actors across state, academic, and private sectors. All three countries face challenges related to rising temperatures leading to heat stress and droughts, soil health and degradation, and the prevalence of smaller farm sizes. Additional issues include low levels of organic production, limited access to markets and finance, underdeveloped or no agricultural extension services, significant post-harvest food loss and waste, food insecurity, and insufficient adoption of healthy and sustainable diets. Furthermore, these challenges are worsening because of the lack of governmental or international incentives to promote climate-friendly and sustainable farming programs and gaps in governance and strategic planning. The study highlights key lessons from climate-smart food systems in Armenia, Georgia, and Moldova, offering comparative insights and actionable recommendations to guide policy development and advance sustainable agricultural practices in Armenia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".