Effect of management practices on milk yield and live weight changes of indigenous breeds of goats supplemented with groundnut haulms and concentrate in sub humid zone of Nigeria
Bibliographic record
Abstract
These studies were carried out at the Research farm of National Animal Production Research Institute, Shika to investigate the effect of management practices on the milk yield and live weight changes of grazing Red Sokoto and Sahelian goats as influenced by supplementation with groundnut haulms and concentrate. Experiment 1 involved 5 Red Sokoto goats with average weight of 27.3 ± 1.37Kg kept either on-farm or on-station and fed groundnut haulms or concentrate. The result showed that management had significant effect on average daily gain (ADG) of kid at 146.7± 0.62g (P<0.01) and dam weight loss of -24.7±1.26g (P<0.05). Milk yield was less sustained on farm. Week of lactation affected milk yield ( P<0.01). In experiment 2, five goats of each of Red Sokoto or Sahelian breeds were randomly assigned to either groundnut haulms or concentrate as supplement. The results, showed that there were significant effect of breed (P<0.0001) and week (P<0.001) on milk yield, mean dam and kid weights respectively. The Red Sokoto dams had higher milk yield (414.1±47.19ml) than the Sahelian dams (203.2±46.61ml). Similarly, breed and type of supplementation fed showed significant difference (P<0.005) on milk yield. Red Sokoto dams fed concentrate produced more milk (555.1±64.92ml) than Sahelian fed concentrate (295.2±69.51ml) or groundnut haulms (111.4±69.51ml). Also in comparing the kid growth performance between on-farm and on-station, The average daily gain of kids was 112.9g± 0.53 was significantly influenced by week of lactation, while dam lost an average of 11.7±0.65g indicating that kids managed on farm were heavier than those on station. However dam on-station lost less weight than those on-farms. The result showed that supplementation of grazing does with diets of protein source irrespective of management will improve milk yield and that heavier dams supported better kid growth.Keywords: On-farm, On-station, Performance, Red Sokoto, Sahelian..
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".