Performance of organic farming in developing countries: a case of organic tomato value chain in Lebanon
Bibliographic record
Abstract
The future of food value chains has increasingly been reliant on the wider adoption of sustainable farming practices that include organic agriculture. Organic farming in developed countries is standardized and occupies a niche in agro-food systems. However, such a standard model, when transferred to developing countries, faces difficulty in implementation. This study aims to investigate the factors affecting the expansion of organic agriculture in Lebanon, a Middle Eastern context, and analyzes the economic performance of organic tomato among smallholder farmers. Accordingly, the study was able to determine the production costs, map the organic value chain and assess the profitability of organic tomato by comparing it with the conventional tomato in the same value chain. The study finds organic farming being increasingly expensive primarily due to the inherently high cost of production in Lebanon and the inefficient organization of the organic value chain. As a result, we suggest a blended approach of organic farming with other models, in particular agro-tourism, as a local solution to the sustainability of organic farming in developing countries with limited resources (land and labor) and characterized by long marketing channels. In countries such as Lebanon, a country endowed with rich cultural heritage and natural and beautiful landscapes, the agro-tourism model can harness organic farming and tourism activities. We also propose the adoption of local collective guarantee systems for organic production as a way to alleviate the costs of third-party auditing in Lebanon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".