Factors Influencing Partial and Complete Adoption of Organic Farming Practices in Saskatchewan, Canada
Bibliographic record
Abstract
Using a sample of organic producers in Saskatchewan, Canada, this study uses a Tobit model to identify the factors that discourage or encourage the complete adoption of organic farming and to assess why farmers differ in the share of total cultivated crop area they allocate to organic practices. In particular, the study evaluates the effect of transaction costs on the decision to convert partially or completely from conventional to organic practices. The results highlight the importance of lowering certain transaction costs to encourage the adoption of organic management practices. Significant transaction costs were found to include infrastructure and services, satisfaction with marketer performance, marketing problems, and Internet use. Results suggest that farmers with smaller land holdings are more inclined to undertake complete adoption. While the education levels of organic farmers show no significant effect on the probability of adoption, younger organic farmers allocate significantly less of their cultivated area to organic practices. À partir d'un échantillon de producteurs de cultures biologiques de la Saskatchewan, au Canada, nous avons utilisé un modèle Tobit pour déterminer les facteurs qui encouragent ou découragent l'adoption totale des pratiques agricoles biologiques et les raisons pour lesquelles le pourcentage des superficies consacrées aux cultures biologiques varie d'un producteur à l'autre. Nous avons également examiné les répercussions des coûts de transaction sur la décision de passer, en totalité ou en partie, des pratiques conventionnelles aux pratiques biologiques. Les résultats ont fait ressortir l'importance de réduire certains coûts de transaction afin d'encourager l'adoption des méthodes agronomiques biologiques. Les coûts de transaction les plus importants incluaient les infrastructures et les services, l'efficacité des intermédiaires, les problèmes liés à la mise en marché et l'utilisation d'Internet. Les résultats autorisent à penser que les producteurs agricoles qui possèdent de petites superficies sont plus enclins à adopter en totalité l'agriculture biologique. Alors que le niveau de scolarité des producteurs ne semble pas avoir de répercussions sur la probabilité d'adopter l'agriculture biologique, les jeunes producteurs de cultures biologiques consacrent beaucoup moins de leurs superficies cultivées à la culture biologique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".