Agroecology: the science and art of building sustainable agri-food systems. A case study from Costa Rica
Bibliographic record
Abstract
The process of change taking place in most countries’ agricultural and food systems has seen a revival of alternative forms of farming, like agroecology, aiming to replace the environmentally destructive practices of conventional agriculture and produce real and nutritious food. This case study analyzes the process of transition of a small-scale farm located in Costa Rica from conventional farming (using heavy machinery, synthetic chemicals, and fossil fuels) to a carefully integrated, resilient, sustainable and self-sustaining organic, agro-socio-ecological system. It uses UN FAO’s (Food and Agricultural Organization of the United Nations) Tool for Agroecology Performance Evaluation (TAPE) with 10 agroecological elements (diversity, synergies, efficiency, recycling, resilience, culture and food traditions, co-creation and sharing of knowledge, human and social values, circular and solidarity economy, and responsible governance) to assess the farm’s ecological, social and economic performance. Results show that this farm is strong in efficiency, culture and food tradition, co-creation and sharing of knowledge, diversity, and resilience. The average score of the 10 elements is 92.29%, indicating an advanced level of transition to agroecology of the farm. While this score is high, the farm has encountered certain challenges, namely lack of consistent financial and policy support from the government, costly procedures for products and processes certification, and lack of awareness about the benefits of this sustainable farming system. The study recommends that more transdisciplinary research and comparative studies between conventional and agroecological farming are needed to move more agrifood systems toward sustainability.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".