Farm characteristics and management routines related to cow longevity: a survey among Swedish dairy farmers
Bibliographic record
Abstract
BACKGROUND: Longevity is an important trait for increasing the profitability of dairy production. Long cow longevity is also essential to reduce the environmental impact of milk production, and to maintain positive consumer attitude. Genetic selection for increased longevity has been effective, but the phenotypic trend of longevity in Swedish dairy cows has not been improved. The objective of this study was to identify herd characteristics and management routines that are associated with the average cow longevity in a herd. To obtain this information, a questionnaire was developed and sent out to 661 Swedish dairy farmers. RESULTS: The response rate was 35%. Seventeen of the 62 characteristics investigated had either a univariable association with the outcome (days from birth to culling) at P < 0.15, or were identified as confounders in the causal diagram and were therefore considered as candidates for the multivariable analysis. Multiple imputation was used to fill in the missing data from the questionnaires, and this increased the number of usable observations in the multivariable modeling from 156 to 228. Only a few of the investigated herd characteristics and management routines were associated with average cow longevity. The results demonstrated that using herd health advisory services shortened the average longevity, while using breeding advisory services prolonged the average longevity in the herd. Furthermore, having a greater interest in animal breeding (i.e. genetic selection) decreased the longevity, and calling the veterinarian when discovering an unhealthy cow increased the average longevity. Higher age of the farmer was also associated with longer average herd longevity. CONCLUSIONS: The herd average cow longevity was only associated with some of the farm characteristics and management routines studied. The results demonstrate that the use of advisory services and farmers' attitudes could be targeted for increasing the herd longevity. Further, the results indicate that other e.g. qualitative factors influencing farmers' decisions play an important role.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".