CANADIAN HORTICULTURAL GROWERS’ PERCEPTIONS OF BENEFICIAL MANAGEMENT PRACTICES FOR IMPROVED ON-FARM WATER MANAGEMENT
Bibliographic record
Abstract
What factors influence farmers' perceptions of Beneficial Management Practices (BMPs) and why does that matter?Determinants of farmers' adoption of BMPs have been extensively researched.This is how we know that a farmer's decision-making process of adopting a BMP is complex, and it can be influenced by many factors, which can be broadly categorized under farmers' personal characteristics, farm salient features, properties of the BMP considered for adoption, and other contextual factors -social, economic, political, ecological, etc.Although this body of knowledge is comprehensive, it lacks the exploration of factors that contribute to understanding farmers' perceptions of BMPs, which play an important role in adoption decisions.This article focuses on identifying factors that contribute to farmers' perceptions of BMPs as better alternatives for their farms.Data for this study were collected through an online survey, containing responses of 70 fruit and vegetable growers in Ontario and Quebec.An ordered logit regression model was constructed to identify the factors influencing farmers' perception of the proposed BMPs as better alternatives.Results suggest that farmers with more farming experience and higher levels of educational attainment, as well as those without exclusive financial goals, and who perceived the BMPs to be expensive, were less likely to perceive the proposed BMPs as better alternatives in the context of their farm.However, growers gaining a larger percentage of their revenue from the crop under study, and those who thought that making best use of scarce resources (by reducing water use) was important, were more likely to perceive the proposed BMPs as better alternatives.These findings are important because they can provide a glimpse into the determinants of these perceptions which in turn are so influential in the adoption decision-making process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".