Non-Genetic Factors Affecting Production Traits in Murrah Buffaloes
Bibliographic record
Abstract
The present investigation was undertaken to estimate the effect of non-genetic factors on different production traits of Murrah buffaloes maintained at Buffalo Research Centre, Chaudhary Charan Singh Haryana Agricultural University, Hisar, India. A total of 1128 lactation records of 326 Murrah buffalo were targeted to explore the effect of non-genetic factors. The production traits considered for the present study were lactation yield (LY), lactation length (LL), 305 days milk yield (305 MY), peak yield (PY), and days to attain peak yield (DAPY). The highest CV (%) was obtained for PY. The overall least squares means were 2118.10 ± 25.54 kg, 296.60 ± 3.23 days, 2053.88 ± 21.80 kg, 11.08 ± 0.08 kg, and 61.72 ± 1.02 days for LY, LL, 305 MY, PY and DAPY, respectively. The period of calving revealed a highly significant (P<0.01) effect on targeted traits except for LL. Animals in the fourth lactation revealed significantly the highest LY and PY. The effect of the season of calving was highly significant (P<0.01) on all the traits under study. Performances of animals calved during summer seasons were excellent for the traits under the present study. The effect of parity was highly significant (P<0.01) for all the traits under study except for DAPY where it was non-significant. The significant effects of different non-genetic factors like period of calving, the season of calving, and parity of animals on different production traits of Murrah buffaloes indicate that adjustment of effect of non-genetic factors is important for accurate and unbiased estimates of genetic parameters and selection of superior animals.
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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".