Effects of phytase supplementation on growth performance, plasma biochemistry, bone mineralisation and phosphorus utilisation in pre-lay pullets fed various levels of phosphorus
Bibliographic record
Abstract
Context Reducing the environmental impact of animal production is becoming a really hot topic, especially with raised concerns over excessive flows of nitrogen and phosphorus (P) to the environment. Aims The present study was conducted to determine the effects of phytase supplementation on growth, plasma biochemistry, bone mineralisation and P utilisation of pre-lay pullets fed varying levels of non-phytate P. Methods A total of 240 Lohmann pullet chicks were randomly allocated to one of six dietary treatments with eight replicate cages (5 birds per cage) per treatment. Six treatments included three phytase-free diets and three diets supplemented with 1000 U/kg phytase; the non-phytate P levels were 2.75–2.50–2.25, 3.75–3.50–3.25 and 4.75–4.50–4.25 g/kg in the former, and 1.75–1.50–1.25, 2.75–2.50–2.25 and 3.75–3.50–3.25 g/kg in the latter, for the age of 0–4, 4–8 and 8–16 weeks respectively. Key results No significant differences were found for growth performance, plasma biochemistry (calcium, P, alkaline phosphatase and albumin) and bone mineralisation among dietary treatments, but P retention (%) was different (P < 0.001). Analysis of planned contrasts showed that phytase supplementation increased phytate P retention (P < 0.001), and improving the utilisation of phytate P tended most efficiently under low P conditions. Total P retention rate was reduced slightly by phytase supplementation (P < 0.05). Conclusions The results indicated that dietary non-phytate P level could possibly be reduced to 1.75, 1.50 and 1.25 g/kg for 0–4, 4–8 and 8–16 weeks of age respectively after phytase supplementation, without compromising pullet growth and performance during the pre-laying period. Implications The results of this study will contribute to decreasing P excretion by poultry and reducing the potential environmental impact with land application of manure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".