Reproductive Performance of Water Buffalo Cows: A Review of Affecting Factors
Bibliographic record
Abstract
This article aims to review both the economic impact of reproductive failures on the profitability of water buffalo systems and the effect of different factors on the reproductive performance of water buffaloes. Besides, an overview of various non-hormonal alternatives to improve reproductive performance is made. The optimal reproductive efficiency in water buffaloes implies calving to conception interval around 90 days to reach a calving interval of 400 days, with longer calving intervals having a negative impact on profitability. Reproductive efficiency is the consequence of the interaction of genetic and non-genetic factors, and the recognition of these factors by analyzing the reproductive information must be a priority. Although each factor's impact can be of greater or lesser magnitude depending on the conditions of each herd, some factors like nutrition, milk yield, body condition score, negative energy balance, parity, bull presence, low estrus intensity, and season can be considered high-impact factors. Not all factors are common among farms; therefore each farm must implement a program for the identification, control, and prevention of reproductive problems, especially during early lactation, to prevent a long anestrus; and when artificial insemination is used, so that it is done at the correct time with respect to the beginning of estrus to enhance fertility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".