What the hay: predicting equine voluntary forage intake using a meta-analysis approach
Bibliographic record
Abstract
To properly formulate diets, the ability to accurately estimate feed intake is critical as the amount of feed consumed will influence the amount of nutrients delivered to the animal. Inaccurate intake estimates may lead to under- or over-feeding of nutrients to the animal. Individual differences in equine forage intake are well-known, but predictive equations based on animal and nutritional factors are not comprehensive. The objective of the present study was to consolidate the current body of knowledge in the published literature on voluntary forage DM intake (VFDMI) in equines and conduct a meta-analysis to identify driving factors, sources of heterogeneity, and develop predictive equations. Therefore, a systematic literature search was applied and identified 61 publications which met the inclusion criteria. From each study, the outcomes of interest (e.g., forage intake), diet composition (e.g., forage information, nutrient composition), and animal factors (e.g., sex, age, breed, BW, exercise level) were extracted. Forage intake was analyzed as two different outcome variables: (1) VFDMI in kg/d and (2) VFDMI in g/kg BW. Linear mixed model analysis treating study as a random effect was applied, using a backward-stepping approach to identifying potential driving variables for VFDMI (both units) where all terms have P < 0.1. The best fitting models for VFDMI included similar factors (also across kg/d and g/kg BW) such as forage quality (i.e., neutral detergent fiber or CP content), forage type (i.e., grass, legume, or mixed), the animals' size category (i.e., horses vs ponies), and some management factors (i.e., pasture access). As anticipated, forage intake increased when higher quality forages were fed (i.e., lower neutral detergent fiber or higher CP), potentially due to improved digestibility. Additionally, VFDMI increased as BW increased but ponies increased their VFDMI more per every kg increase in BW compared to horses. Lastly, pasture access (i.e., grazing) may influence VFDMI such that pastured animals consume less than stalled animals, possibly due to the time it takes to graze forage. In conclusion, equations to predict equine VFDMI with high accuracy and precision (concordance correlation coefficient = 0.82 - 0.95; root mean squared error RMSE = 0.82-5.49) were developed which could be applied in practice by equine nutritionists or owners and managers. The results of this meta-analysis confirm that animal traits and forage quality have a significant impact on the VFDMI of equines and should be accounted for when formulating diets to ensure nutritional requirements are met.
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 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) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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".