Association between milk urea nitrogen and first service conception in smallholder dairy farms under heat and humidity stress
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: The study was to evaluate the relationship between the first service conception (FSC) and milk urea nitrogen (MUN) in smallholder dairy farms under heat and humidity stress. MATERIALS AND METHODS: Dairy cows from 43 dairy farms giving birth between November 2014 and April 2015 (n=295) contributed to the study. All cows were sampled monthly to measure milk compositions, and we collected additional farm data from farmers through a questionnaire. The first service during the first 120 days of lactation was the outcome of interest in this study. Multivariable logistic regression determined significant associations with FSC. RESULTS: The overall FSC was 22% and the mean MUN concentration was 11.55 mg/dl. The final FSC model included MUN concentration, the season of breeding, and protein energy ratio (PE ratio) in the diet. The odds of FSC were reduced by approximately 10% for each mg/dl higher MUN on the day of the milk sample that was nearest to the artificial insemination (AI) day. The odds of FSC were nearly 3 times higher when the first insemination occurred in winter compared to summer first services. Taking into account the nutritional factors, the odds of FSC were nearly 70% higher with an increase in PE ratio of 10 g of crude protein/Mcal from the mean of 35.90 g. CONCLUSION: This study of smallholder dairy farmers in the hot and humid climate of Thailand confirmed that season, nutritional management, and MUN concentration were associated with FSC. MUN appears to be a useful indicator to monitor the effects of diet on reproductive performance from this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it