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Record W4413385244 · doi:10.3168/jds.2025-26642

Understanding challenges and strengths in the post–dairy farm surplus calf value chain: An interview study

2025· article· en· W4413385244 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock Management and Performance Improvement
Canadian institutionsNative Mental Health Association of Canada
FundersNational Institute of Food and AgricultureOhio State University
KeywordsValue (mathematics)Agricultural scienceBusinessStatisticsBiologyMathematics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.300
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it