The Adoption of Sustainable Management Practices by Mexican Coffee Producers
Bibliographic record
Abstract
In order to maintain optimal growing conditions on coffee plots, producers in Mexico are encouraged to renovate their stock of coffee trees, use fertilizer, implement soil conservation measures and manage shade levels. The adoption of these sustainable management practices (SMPs) by smallholder coffee growers has become an important rural development objective, especially as a way to overcome low yields, poverty and land degradation. However, adoption rates for SMPs remain below expected levels, a situation that potentially threatens the long- term viability of the coffee sector in Mexico. To better understand the choices made by producers, a multivariate probit technique was used which modelled the adoption of possibly interrelated SMPs using data from a survey of 119 coffee producers. The analysis reveals that adoption of SMPs is related to the size of coffee holdings, the socio-economic characteristics of producers and the role of social capital, the latter being a key factor in the overall adoption process. Surprisingly, government subsidies to coffee growers were not tied to higher adoption rates, suggesting the need for policy reforms in order to better facilitate the uptake of new practices. The results indicate that efforts aimed at strengthening local institutions and organizing coffee growers into producer associations could increase the adoption of SMPs in smallholder coffee systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".