Decision-Making in Agriculture: Why do Farmers Decide to Adopt a New Practice?
Bibliographic record
Abstract
Current rates of environmental degradation demand changes to the way in which food is produced. Transforming agricultural production requires both the development and the adoption of new practices that facilitate high yields at least environmental cost. Many beneficial practices have already been developed and their limited adoption now constrains their potential to deliver sustainable agriculture. Greater understanding is needed of why farmers decide to adopt or reject different practices. The Technology Acceptance Model (TAM) has been used in an agricultural context to examine adoption. The TAM posits that perceptions of a practice’s usefulness (PU) and its ease of use (PEOU) drive its adoption. In this thesis, the TAM was first revised such that adoption was considered as being composed of five stages to reflect the preparatory and trial phases that precede the full-scale adoption of agricultural practices. An empirical study was then conducted to investigate farmers’ attitudes in the Southern Ontario region towards agrominerals and cover cropping – two practices that show promise in maintaining soil health at low environmental cost. PU and PEOU were found to be significant drivers of the adoption of agrominerals. However, PEOU did not have a significant direct effect on farmers’ decisions to continue using cover crops. A longitudinal study that applies the revised TAM is needed to ascertain whether it is effective in explaining the adoption process, particularly in the latter stages of adoption when PEOU appears to be of less importance and PU alone appears to largely drive farmers’ decision-making. The concern participants showed for the potential environmental impacts of agriculture highly varied with those showing greater concern reporting greater intentions of adopting agrominerals. Socio-economic and agro-ecological factors were found not to be correlated to adoption. This study demonstrated the need to increase knowledge sharing between farmers and scientists to facilitate the transition towards sustainable agricultural production.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".