Factors That Affect the Adoption Decision of Conservation Tillage in the Prairie Region of Canada
Bibliographic record
Abstract
The adoption of conservation tillage technology since the 1970s has been one of the most remarkable changes in the production of crops on the Canadian Prairies. The decision whether to adopt conservation tillage technology or not requires the producer to go through a thorough decision‐making process. In Canada, there has been little economic research on the question of what farm, regional, and environmental characteristics affect the adoption decision. Using 1991, 1996, and 2001 Census of Agriculture data together with other data sources we estimate a probit model explaining the adoption decision. We find that important variables include farm size, proximity to a research station, type of soil, and weather conditions. La pratique du semis direct depuis les années 1970 constitue l'un des changements les plus notables de la production des cultures dans les Prairies canadiennes. Avant de décider d'adopter ou non cette pratique, le producteur doit s'engager dans un processus rigoureux de prise de décisions. Au Canada, peu d'études économiques se sont penchées sur les caractéristiques agricoles, régionales et environnementales qui influencent la décision d'adopter ou non. Au moyen des données tirées du Recensement de l'agriculture de 1991, 1996 et 2001, combinées à d'autres sources de données, nous avons estimé un modèle probit pour expliquer la décision d'adopter ou non. Nous avons estimé que les variables importantes incluent la taille de l'exploitation, la proximité d'une station de recherche, le type de sol et les conditions météorologiques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".