Accuracy and precision of total mixed rations fed on commercial dairy farms
Bibliographic record
Abstract
Despite the significant time and effort spent formulating total mixed rations (TMR), it is evident that the ration delivered by the producer and that consumed by the cow may not accurately reflect that originally formulated. The objectives of this study were to (1) determine how TMR fed agrees with or differs from TMR formulation (accuracy), (2) determine daily variability in physical and chemical characteristics of TMR delivered (precision), and (3) investigate the relationship between daily variability in ration characteristics and group-average measures of productivity [dry matter intake (DMI), milk yield, milk components, efficiency, and feed sorting] on commercial dairy farms. Twenty-two commercial freestall herds were visited for 7 consecutive days in both summer and winter months. Fresh and refusal feed samples were collected daily to assess particle size distribution, dry matter, and chemical composition. Milk test data, including yield, fat, and protein were collected from a coinciding Dairy Herd Improvement test. Multivariable mixed-effect regression models were used to analyze associations between productivity measures and daily ration variability, measured as coefficient of variation (CV) over 7d. The average TMR [crude protein=16.5%, net energy for lactation (NEL) = 1.7 Mcal/kg, nonfiber carbohydrates = 41.3%, total digestible nutrients = 73.3%, neutral detergent fiber=31.3%, acid detergent fiber=20.5%, Ca = 0.92%, p=0.42%, Mg = 0.35%, K = 1.45%, Na = 0.41%] delivered exceeded TMR formulation for NEL (+0.05 Mcal/kg), nonfiber carbohydrates (+1.2%), acid detergent fiber (+0.7%), Ca (+0.08%), P (+0.02%), Mg (+0.02%), and K (+0.04%) and underfed crude protein (-0.4%), neutral detergent fiber (-0.6%), and Na (-0.1%). Dietary measures with high day-to-day CV were average feed refusal rate (CV = 74%), percent long particles (CV = 16%), percent medium particles (CV = 7.7%), percent short particles (CV = 6.1%), percent fine particles (CV = 13%), Ca (CV = 7.7%), Mg (CV = 5.2%), and Na (CV = 10%). Every 0.5-percentage-point decrease in daily NEL (CV = 1.2 ± 0.4%) was associated with 3.2 kg/d greater milk yield, 1.0 kg/d greater DMI, and 4.3% greater efficiency of production. Every 5-percentage-point decrease in variability in percent long particles (average percent long = 19.8 ± 6.5; CV = 16.1 ± 6.9%) in the TMR was associated with 1.2 kg/d greater milk yield and a 2.6% increase in efficiency of milk production. These results demonstrate the importance of ensuring TMR consistency to maximize DMI, production, and efficiency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".