Effect of Heat Stress on Milk Production and Composition in Murrah Buffaloes
Bibliographic record
Abstract
Temperature humidity index (THI) is widely used to assess the effect of temperature and relative humidity on performance in animals. In summer the THI was between 74 – 89 with average value of 81.18. in winter months THI ranged between 49 -70 with the average of 60. The results showed a significant effect of heat stress on daily milk yield and milk composition. In the present study the daily milk yield decreases from 4.46 to 3.65kg, heat stress reduced milk yield by 18.2%. There was a significant effect of heat stress on milk composition. Heat stress significantly reduced milk fat content from 8.3% during the winter to 7.19% during the summer. Milk protein percentage significantly decreased as a result of summer heat stress (3.08 vs.2.9 %, respectively for the winter and summer). In the present study the SNF decreases from 9.08 to 9.05 %, heat stress reduced SNF % as the THI value went from ≤ 74 to ≥ 83 in summer. Results showed that milk production is a function of THI. The negative slope of regression line indicates that milk production fat%, protein% and SNF% decreases as THI increases. This regression indicates that in general for each point increase in THI value. There was decrease in milk yield of 0.028kg per buffalo per day. Heat stress environments have been associated with depression in milk fat%, protein% and SNF%. There was decrease in milk fat of 0.046% per buffalo per day. There was also decrease in milk protein of 0.00014 % per buffalo per day. The decrease in milk SNF of 0.0047 % per buffalo per day.
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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) | 0.001 | 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".