BEEF CATTLE HUSBANDRY AND FATTENING PRACTICES: THE CASE OF SMALL-SCALE AND LARGE-SCALE FATTENERS IN KAFTA HUMERA DISTRICT OF TIGRAY, ETHIOPIA
Bibliographic record
Abstract
The study was aimed to investigate beef cattle husbandry and fattening practices in small and large scale fattening units in Kafta Humera district, Ethiopia. A total of 159 cattle fatteners were randomly selected from six tabias of the district. Data were collected on husbandry, fattening practices and other relevant information through household interviews, document reviews. Data were analyzed using proper statistical tools. Almost all the producers (98.5%) use local cattle for fattening activity. Fattening cattle are sourced from market (83%) and own herd (17%). Most farmers use intact oxen (93%), while some use castrated oxen (7%). Majority farms fatten once (78%), some twice (21%) and rare three times (1%) per year with no variation between small and large fattening units. Body condition (71%), feeding length (21%) and anticipated selling price (8%) are the determinant factors for terminating fattening finishing period in that order. Almost all farms practice stall feeding (99.6%), while few farms employ free grazing (0.4%). Sorghum stover and sesame residues are utilized as basal diet and supplemented with traditional sesame oil cakes and Hatela (residues of local brewery). Tap water (94%) and rivers (6%) serve as source of drinking water for fattening cattle. Majority farms (62%) faced animal health problems. Anthrax, blackleg, pasteurollosis, FMD (foot and mouth disease) and lumpy skin diseases were identified as the major diseases of cattle. There is a need to design and implement proper scientific fattening practices to enhance meat animal productivity. This calls for formulating and implementing appropriate research and development interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".