0484 Phosphorus utilization on dairy farms in Manitoba
Bibliographic record
Abstract
A survey was performed on 19 dairy farms in Manitoba representing a range of sizes, feeding, housing and management systems to identify factors that affect the utilization of dietary phosphorus (P) by lactating dairy cows. Each farm was visited once to collect milk, blood, feed and feces samples as well as production data from 10 early/peak, 10 mid-lactation, and 10 late lactation cows. Phosphorus content of feed (FEED P), feces (FECAL P), milk (MILK P), blood (BLOOD P) and calcium content of feed (Ca) were determined. Pearson correlation analysis among the various measures was conducted using SAS 9.4 software. The average P contents (DM basis) of FEED P, FECAL P, MILK P, and BLOOD P was 0.41% (0.34 and 0.53%), 0.76% (0.30 and 1.35%), 0.09% (0.05 and 0.12%) and 2.04 mmol/L (1.34 to 3.04 mmol/L), respectively. The output of P in milk (P OUT) which is obtained by multiplying MILK P with daily milk yield (MY), was positively correlated with the MILK P, FEED P, MY, Parity, Ca and negatively correlated with days in milk (DIM). But no correlation was observed with the BLOOD P and FECAL P. FEED P was positively correlated with the MILK P, FECAL P, MY, and negatively correlated with Ca and DIM. BLOOD P was positively correlated with Ca and DIM, but not correlated with any other measures (Table 1). Results show that the P content of diets, and feces vary considerably among cows and among farms, suggesting that the P contents in diets and feces can be reduced. Furthermore, more than 50% of the animals received diets which contain excess P levels than the NRC (2001) recommendations. The P content of blood was not indicative of P output in milk, nor the P contents of the diet or feces. The output of P in milk was highly correlated with the yield and P content of milk, but a 1% increase in the dietary P content lead to a 0.21% increase in P OUT. Better understanding of the dynamics between these dietary and cow factors can help to develop efficient P management strategies to improve P utilization and avoid excess P use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".