Effect of Year, Season and Parity on Milk Production Traits in Murrah Buffaloes
Bibliographic record
Abstract
The objective of this study was to find out the effects of year, season and parity on milk production traits i.e. total lactation milk yield (TLMY), 305 day milk yield (305d MY) and average fat percentage etc. in Murrah buffaloes under organized herd. Records of 515 Murrah buffaloes maintained at Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, Punjab (India) during the period of 2004-2008 were used. Average TLMY, 305d MY and fat percentage were recorded to be 2229.87± 93.7 kg, 2147.6 ± 87.06 kg and 7.12 ±0.11%. The TLMY was found to be significantly affected by season (P≤0.05) but not by year and parity. The highest milk yield was obtained in buffaloes calving in winter followed by rainy and summer. Milk yield of buffaloes in winter was significantly higher than that of animals in summer (P≤0.05). The TLMY increased over the years with highest milk yield in the year 2006 (2345.1±99.32kg). Similar results were obtained for 305d MY, where only the season was found significant (P≤0.05). The average fat percentage was significantly (P≤0.05) affected by year and season. Milk fat percentage of buffaloes calved in winter was significantly (P≤0.05) higher than that of calved in summer. Similarly milk fat percentage varied significantly among the parities with no consistent increase over the advancement of the parities. In this study season was found to have a significant effect on 305MY and fat % but not on the total lactation milk yield.
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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 | 0.000 | 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) | 0.001 | 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".