Understanding the reproductive performance of a dairy cattle herd by using both analytical and systemic approaches: a case study based on a system experiment
Bibliographic record
Abstract
Reproductive performance has recently been a growing concern in cattle dairy systems, but few research methodologies are available to address it as a complex problem in a livestock farming system. The aim of this paper is to propose a methodology that combines both systemic and analytical approaches in order to better understand and improve reproductive performance in a cattle dairy system. The first phase of our methodology consists in a systemic approach to build the terms of the problem. It results in formalising a set of potential risk factors relevant for the particular system under consideration. The second phase is based on an analytical approach that involves both analysing the shapes of the individual lactation curves and carrying out logistic regression procedures to study the links between reproductive performance and the previously identified potential risk factors. It makes it possible to formulate hypotheses about the biotechnical phenomena underpinning reproductive performance. The last phase is another systemic approach that aims at suggesting new practices to improve the situation. It pays particular attention to the consistency of those suggestions with the farmer's general objectives. This methodology was applied to a French system experiment based on an organic low-input grazing system. It finally suggested to slightly modify the dates of the breeding period so as to improve reproductive performance. The formulated hypotheses leading to this suggestion involved both the breed (Holstein or Montbéliarde cows), the parity, the year and the calving date with regard to the turnout date as the identified risk factors of impaired performance. Possible use of such a methodology in any commercial farm encountering a biotechnical problem is discussed.
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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".