Canada’s Environmental Farm Plan: Evaluating Implementation, Use of Services, and the Influence of Social Factors
Bibliographic record
Abstract
Canada’s Environmental Farm Plan (EFP) is a voluntary, self-administered education and risk assessment tool that assists farmers in developing customized action plans to address environmental risks on their farms. During 2010-11 a study was undertaken in Ontario to evaluate the level of implementation of the EFP, the use of related services and resources, and social factors influencing implementation and services used. A confidential survey of 189 Ontario farmers with EFPs revealed high levels of implementation and significant investments of time and money to reduce environmental risks and improve environmental conditions. Farmers completed or were implementing 67.5% (median) of their action plans, up from 55% reported in a survey in 1999. Farmers invested an average of C$69,600 per farm in agri-environmental activities (of which 73% was drawn from their own funds) and spent 130 hours of their time per farm. Percent implemented, time and cost are all much higher compared to the survey in 1999. Farmers used many existing services in preparing and implementing their EFPs. In 2010, social factors significantly influenced motivation, preferences and service needs including education, age and main commodity produced. Also in 2010, 95% percent of farmers reported perceived environmental improvements on their farm operations. The results emphasize the importance of combining risk assessment, education and financial incentives as well as offering a range of program services to appeal to the varied needs of different farmers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".