Socio-Economic Evaluation of Improved Forage Technologies in Smallholder Dairy Cattle Farming Systems in Uganda
Bibliographic record
Abstract
Smallholder dairy cattle producers in Uganda face major production constraints including inadequate and poor quality feeds. Forage technologies have been widely recommended to alleviate this problem. This study aimed at comparing profitability of dairy cattle enterprises using improved forage technologies (IFTs) with those using local technologies, and determining factors affecting the use of IFTs among smallholder dairy farmers. Data were collected from 121 farmers in Soroti district. Descriptive statistics, partial budget analysis, probit model, and Ordinary Least Squares were used to analyze data. Results indicated that farmers using IFT had significantly (p<0.01) higher gross margins than those using local feeding methods. Probit model results indicated that profitability of technology influenced the decision to use IFT when interacted with improved cattle breed. The decision to use IFTs had a positive significant (p<0.1) relationship with profitability of dairy cattle enterprises. Policies targeting efficient dissemination of IFTs are recommended to improve profitability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".