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Effect of microbial phytase supplementation on P digestibility in pigs: a meta-analysis.

Pia Rosenfelder-KuonWolfgang SiegertMarkus Rodehutscord
Published in: Archives of animal nutrition (2019)
The objectives of this meta-analysis were to determine to which extent phosphorus (P) digestibility and digestible P concentration in pig diets were increased by phytase supplementation and to quantify factors that potentially influence effects of phytase supplementation. A data set with a total of 547 data lines was compiled from 88 experiments published in 74 peer-reviewed papers between 2007 and April 2019. An exponential model was determined as more suitable to describe the response of P digestibility to phytase supplementation than a polynomial model. Phytase supplementation increased P digestibility by 25.6 percentage points (standard error (SE) = 1.54) to a plateau at 64.9% (SE = 1.82). The digestible P concentration was increased by phytase supplementation in the order of 1.01 g/kg (SE = 0.102) to a plateau at 2.62 g/kg (SE = 0.122). Goodness-of-fit criteria were R2 = 0.780 and root mean square error = 7.55% for P digestibility, and R2 = 0.691 and root mean square error = 0.48 g/kg for digestible P concentration. Consideration of further factors such as mineral P supplementation (yes or no), ad libitum vs. restrictive feeding, mixed diets vs. single feed ingredients, sex and age of pigs did not increase the accuracy of prediction in this data set. Some of these traits exhibited responses, but they likely are artefacts generated through the imbalanced structure of the data set. Effects of dietary total P, phytate (InsP6), InsP6-P to total P ratio, and Ca on the effect of supplemented phytase were not quantifiable. The present meta-analysis showed that responses to phytase supplementation can be well predicted although variation in P digestibility and digestible P concentration in the data set was high. Overall, predicted effects of phytase on P digestibility well corresponded to predictions made 25 years ago.
Keyphrases
  • systematic review
  • electronic health record
  • big data
  • gene expression
  • risk assessment
  • data analysis
  • deep learning
  • heavy metals
  • sewage sludge