A qualitative framework to identify variables influencing ecological sustainability in tropical small-scale agriculture
Bibliographic record
Abstract
Small-scale agriculture continues to be the sector with the largest number of food-producing farms worldwide. According to literature, this sector is highly diverse, not highly mechanized, and has low environmental impact. As a result, smallholders play a crucial role in ensuring food security and sustainability. Despite their small scale, these systems must be evaluated and compared based on a wide variety of factors influenced by their specific contexts. Environmental conditions, personal preferences, economic constraints, government regulations, and social norms all contribute to these contexts. A comparison of the ecological sustainability of agricultural systems has shown potential, but is often hindered by substantial limitations. Many of these approaches fail to engage stakeholders comprehensively and elucidate the intricate structures, components, and feedback mechanisms of agricultural ecosystems. Incomplete portrayals of these systems' complex interdependencies lead to inaccurate sustainability assessments. A novel method for analyzing and comparing the ecological sustainability of small farming systems in the tropics is presented using semi-structured interviews, content analysis, and causal loop diagrams. Using interviews, we identified key drivers and challenges in the development of these systems. Through causal loop diagrams, we visualized each system and identified its feedback loops. Several important conclusions have been drawn from the study of these systems in Mariato, Panama: 1.Ecological sustainability is driven by production, regenerative practices, and soil quality 2.Subsistence and respect for nature motivated the farmers 3.Degradation of soil and extreme dry seasons were major challenges 4.All three system types that were compared tended towards equilibrium • A bottom-up approach was used to develop conceptual models for small-scale farming systems in the tropics. • Causal loop diagrams illustrated the interactions between variables affecting the ecological sustainability of agricultural systems. • The methodology identified significant barriers and drivers impacting sustainability. • The methodology was tested for small farms located in Mariato, Panama. • The methodology facilitated the creation of conceptual models representing the shared vision of the stakeholders.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".