An examination of factors influencing producer adoption of HT canola
Bibliographic record
Abstract
This thesis develops a conceptual framework to determine the probability of adopting HT canola when producers are assumed heterogeneous. The model is based on the framework developed by Fulton and Keyowski (1999), but is modified from a deterministic model to a probabilistic model. The study also considers the gross returns from adopting HT canola. Canola production in Manitoba, Canada is chosen as the region of analysis for the empirical component of the study. In 2002, 74 per cent of total canola acres in Manitoba were devoted to HT canola production. Factors such as soil type, producer risk profile, experience, productivity, and management ability are considered as potential determining factors which distinguish adopters of HT technology from non-adopters. Based on an initial assessment of Manitoba canola data, which shows the incomplete adoption of HT technology in Manitoba, a model is developed which considers adoption of a new technology as a function of the characteristics of the adopters. The conceptual model is tested empirically in two-stages. The first stage employs Ordinary Least Squares analysis to estimate the expected yield of different canola varieties to determine whether producers realize a benefit from the adoption of HT varieties. A logit analysis is conducted in the second stage, and considers different attributes of producers – such as risk aversion, management ability, productivity and expected yields – to determine the probability of producers adopting HT technology. The results show two primary findings. First, certain HT varieties can be shown to give producers higher returns. Second, differentiating characteristics of producers are key in determining the likely adoption of HT canola.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".