Evaluating an Agricultural Extension Program Aimed at Improving Biodiversity in Alberta, Canada
Bibliographic record
Abstract
Abstract We evaluate an agricultural extension program aimed at increasing on‐farm biodiversity in Alberta. Using reports prepared for program participants by the extension agency, customized surveys were used to collect data on whether recommended practices were adopted. Data were also collected from producers who were willing but unable to participate in the program due to its unexpected cancellation. A count model compared the number of practices adopted by participants and nonparticipants to evaluate the program's efficacy, and a probit model using participants’ adoption data were used to understand factors affecting adoption. Simple, low‐cost, and easily trialed practices were adopted at high rates, though some higher cost practices associated with observable benefits were also adopted at moderate or high rates. Farm‐specific characteristics, such as size, tenure, or group membership were also significantly linked to the likelihood of adoption. Results suggest that the extension program was successful in encouraging adoption of environmentally beneficial practices with caveats; project completion and quality were not verified, and decreasing marginal returns to extension efforts may have been realized within the small participant pool. Nous évaluons un programme d'éducation permanente en agriculture visant l'augmentation de la biodiversité fermière en Alberta. Utilisant des rapports préparés par les agences d'éducation pour les participants de programmes, des sondages individualisés ont servi à la collecte de données concernant l'adoption de pratiques recommandées. Des données ont aussi été recueillies auprès de producteurs qui auraient voulu participer mais qui n'ont pas profité du programme suivant son annulation inattendue. Un modèle de comptage a servi à comparer le nombre de pratiques adoptées par les participants et non‐participants pour évaluer l'efficacité du programme, et un modèle probit basé sur les données d'adoption par les participants a servi à comprendre les facteurs ayant un impact sur l'adoption. Les pratiques simples, peu coûteuses et faciles à tester ont été hautement adoptées. Certaines pratiques à coûts plus élevés, associées à des avantages observables, ont aussi vu des taux moyens et élevés d'adoption. Les caractéristiques spécifiques aux exploitations agricoles comme la taille, la longévité et l'adhésion à un groupe se sont aussi trouvées liées de façon significative aux probabilités d'adoption. Les résultats suggèrent que le programme d'éducation permanente a réussi à encourager l'adoption de pratiques bénéfiques pour l'environnement avec quelques bémols: la réalisation des projets et leur qualité n'ont pas fait l'objet de vérification, et la diminution des rendements marginaux en fonction des efforts d'éducation pourrait avoir été réalisée dans le cadre d'un petit bassin de participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 teacher head, 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".