Integrating Participatory Stock and Flow Models with Advanced Process Models for Sustainability Assessment in Small-Scale Tropical Agricultural Systems
Bibliographic record
Abstract
Small-scale agricultural production systems, characterized by significant diversity and multidimensional components, engage in complex interactions that fundamentally influence their long-term sustainability. This study presents an innovative methodology that integrates stock and flow models, developed through participatory processes, with sophisticated process models, to facilitate detailed comparisons and assessments of ecological sustainability in tropical small-scale agricultural systems. Specifically, our approach enhances the intuitive and user-friendly interfaces of system dynamics tools such as Stella Architect and Vensim with the precision and flexibility of advanced process models like DNDC (Denitrification-Decomposition). This integration significantly improves model accessibility for stakeholders—including farmers, extension agents, and community leaders—and increases the accuracy and comprehensiveness of sustainability evaluations in these intricate agricultural environments. Conducted in Mariato, Panama, this research produced several key outcomes that contribute valuable insights to the field of sustainable agricultural practices:We developed a preliminary stock and flow model through participatory engagement with stakeholders, specifically designed to assess the ecological sustainability of small-scale agricultural systems in the tropics.We designed a user-friendly interface in Stella Architect, which enhances the understanding of the complexities inherent in tropical small-scale agriculture for users with diverse educational and professional backgrounds.We pioneered an innovative integration of stock and flow models with process models, creating a comprehensive, accessible, and robust tool for evaluating small-scale agricultural systems in the tropics. This novel tool not only simplifies complex data but also facilitates deeper insights into the ecological dynamics at play, ensuring that sustainable practices are both understood and effectively implemented.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".