Sustainable cost of quinoa production in Bolivia: A foodscape approach integrating the recovery of agricultural heritage (COSPH)
Bibliographic record
Abstract
This study analyzes the sustainable production cost, integrating the recovery of agricultural heritage (COSPH), for quinoa cultivation in the Bolivian High Plateau (Altiplano), seeking to answer: How much does it cost to make quinoa production sustainable over time in Bolivia? And, How does this change when considering agricultural heritage conservation? Specifically, the study evaluates how good agricultural practices (GAP) can mitigate climate change impacts and whether they are cost-effective, integrating the costs of agricultural heritage, which are particularly important for the quinoa real (royal quinoa) crop in Bolivia. Methodologically, the research combines a microeconomic model of imperfect competition calibrated for quinoa - capturing price differentiation based on sustainability and heritage conservation - with the NL-CROP model (Non-Linear Crop Optimization Model), which simulates non-linear interactions between climate, soil, and farming practices. Key findings show that GAP significantly reduce yield losses: under moderate climate conditions, productivity declines decrease from 5-7% to 1.8-2%, while in extreme events, losses drop from 16- 30% to 2.5-6.2%, attributed to sustainable soil management. GAP remain viable in scenarios with up to two to three standard deviations, where profit margins cover additional costs. However, in severe crises (50% yield losses), negative margins (-4.7%) make agricultural insurance necessary (premiums of 7-10%), as well as tailored policies to balance climate adaptation with smallholders' economic viability. When heritage conservation costs are included (COSPH), results show improved resilience (yield loss reduced to 10.5% under a moderate climate scenario) at a moderate additional cost (5.75% compared to 5%), suggesting that preserving agroecological heritage contributes to long-term sustainability. These findings highlight the strategic role of combining sustainable agriculture with the protection of cultural landscapes in vulnerable highland farming in Bolivia.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".