Understanding challenges and strengths in the post–dairy farm surplus calf value chain: An interview study
Bibliographic record
Abstract
Surplus calves are animals produced by the dairy industry but not retained on the farm as herd replacements, namely, male calves and excess females. These animals primarily enter dairy-beef or veal production systems. In recent years, surplus calf production has come under scrutiny due to welfare concerns, such as health outcomes and housing. To design and implement effective interventions, it is critical to understand the perspectives of industry stakeholders (i.e., calf marketers and calf raisers). However, little research has been conducted in the post-dairy farm surplus calf value chain in the United States. Therefore, the objective of this study was to understand surplus calf marketer and calf raiser perspectives of the strengths and challenges within the surplus calf system in the United States. Twenty-two telephone interviews were conducted from June 2023 to January 2024. Participants included 7 dairy-beef raisers, 6 veal industry stakeholders, 5 livestock market representatives, and 4 calf dealers. Individuals were located throughout the Northeast and Midwest United States. The interview was designed to take ∼20 min to complete. Mean (range) interview duration was 31 min (11 to 69). Interviews were recorded, anonymized, and transcribed. Transcripts were then analyzed using inductive thematic analysis. Most participants expressed satisfaction with their day-to-day management strategies (e.g., health and nutrition programs, proficient personnel). When questioned about challenges or opportunities for improvement, participants discussed labor issues, lack of industry expertise in advisors, and concerns regarding long-distance transport as a stressor for calves. Disaggregation and lack of communication between stakeholders was perceived to cause difficulties during surplus calf transport. Improving communication between stakeholders may improve transport conditions and subsequently address other challenges expressed by participants, such as calf health on arrival at calf raisers. Future work to streamline calf marketing, as well as bolstering resources available to calf growers, may be beneficial to the industry. Additional qualitative work to ensure a broad representation of stakeholder perspectives, particularly from other regions, may yield additional research avenues.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".