Ohio horse industry survey: feeding and housing management practices
Bibliographic record
Abstract
Abstract Equine industry housing and feeding management strategies vary widely. Management choices are important as horses spend most of their time in housing environments and demonstrating ingestive/foraging behavior. As of 2023, over 1.4 million Ohioans identified as horse owners and/or enthusiasts. The objectives of this survey were to determine demographics of the Ohio horse industry, commonly used sources of information, knowledge gaps regarding equine management practices, and to explore what may influence equine management choices. Using Qualtrics (Provo, UT), a 52-question online, anonymous survey was made available to Ohio horse owners and industry personnel through local horse organizations and social media from October to December 2023. Data were summarized using descriptive statistics (mean, percentage, frequency) and relationships between variables were explored using Pearson chi-square tests or Kruskal-Wallis H and Mann-Whitney U tests in SPSS (Armonk, NY). A total of 1,011 usable survey responses were collected. Most respondents had between 1 to 10 yr of horse experience (64%) and identified as primarily white (63%), females (61%), between 35 to 44 yr of age (31%). Quarter Horses (29%) were the most represented breed. Overall, the primary sources of equine management information were internet (15%), veterinarians (14%), and personal contacts (12%). There were differences between respondents’ main source of equine information based on horse owners’ experience level (X2 = 60; P < 0.01) and awareness of resources provided by Ohio State University (OSU) Extension (X2 = 80; P < 0.01). Respondents’ familiarity/use of body condition scoring differed based on awareness of OSU Extension resources (H = 234; P < 0.01). For housing management, most respondents either stalled horses with unlimited turnout (31%) or group housed horses on pasture (32%). For feeding management, most respondents fed concentrates (96%), primarily measuring concentrates either by weight (42%) or visual estimation (46%). However, forages were more commonly fed by visual estimation (52%) rather than by weight (18%). Feeding forage twice per day was most common, regardless of access to pasture (49%) or not (30%). Concentrates were more commonly provided once per day (41%) with 21% feeding twice per day. The results from this study can be used to assist in developing educational opportunities and resources and to design horse management research to benefit Ohio’s equine stakeholders.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 0.001 |
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".