Behavioural Insights into Dairy Farmers’ Adoption of Feeding Innovations
Bibliographic record
Abstract
The Philippine Carabao Center (PCC) has promoted various feeding innovations to enhance buffalo-based dairy enterprise and increase milk production, yet adoption rates have been suboptimal. This study explores the decision-making processes of dairy farmers regarding the adoption of these innovations, focusing on how attitudes and subjective norms influence their intentions to implement PCC-endorsed feed technologies, such as improved forage, concentrate feeding, legume supplementation, and forage ensiling. Data were collected through structured interviews with 60 dairy farmers. The analysis was conducted using the Statistical Package for Social Sciences (SPSS). Results showed that socioeconomic factors and farm characteristics minimally impact the intention to adopt innovations, with land ownership and herd size positively influencing concentrate feeding. Perceived usefulness and difficulty significantly shape farmers' intentions, indicating that constraints like land availability and high production costs hinder the adoption of legume supplementation and forage ensiling. While attitudes toward feeding innovations are generally positive, practical challenges limit their uptake. Social norms, shaped by extension staff and peer farmers, play a significant role in influencing farmers' intentions to adopt these innovations. This study emphasized the need to address practical barriers to enhance the uptake of feeding innovations and improve dairy buffalo 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".