Effect of market information quality, sharing and utilisation on the innovation behaviour of smallholder pig producers
Bibliographic record
Abstract
Although pig farming can accelerate Uganda’s economic development, the value chain is undeveloped with poorly organized informal markets. Buyers take advantage of farmers paying low prices, pointing to the poor quality of pigs and pork. Farmer innovation can remedy this situation by enabling farmers to reduce costs, improve pig productivity and quality of pigs and pork. Leveraging farmer innovation behaviour calls for appropriate agricultural information. However, the effect of market information quality, sharing, and utilization on the innovation behaviour of pig producing farmers is not fully known. This study sought to determine the effect of information quality, sharing, and utilisation on the innovation behaviour of pig producing farmers in Northern Uganda. A cross-section survey of 239 respondents selected through multiple stages of purposive and random sampling was done. Data were analysed by Structural Equation Modeling (SEM). The results show that information quality contributes significantly to innovation behaviour directly (β = 0.247; P < 0.01) as well as indirectly through the partial mediation of information utilization (β = 0.176; 95% CI = 0.040∼0.349). Therefore, interventions that seek to enhance smallholder farmer innovation should provide quality information and support farmers to utilise it.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".