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262 Ability of Model that Predict Growing-Finishing Pigs Requirements to Predict Dietary Phosphorus Use in Replacement Gilts
Author(s) -
Piterson Floradin,
C. Pomar,
P. Schlegel,
Marion Lautrou,
Marie-Pierre Létourneau-Montminy
Publication year - 2022
Publication title -
journal of animal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.928
H-Index - 156
eISSN - 1525-3015
pISSN - 0021-8812
DOI - 10.1093/jas/skac064.038
Subject(s) - zoology , energy requirement , phosphorus , body weight , mean squared prediction error , chemistry , biology , mathematics , endocrinology , regression , statistics , organic chemistry
A modeling approach to predict dynamics of body phosphorus (P) and calcium (Ca) independent of soft-tissue growth in growing-finishing pigs has been developed (Lautrou et al., 2020). Considering that the feeding of replacement gilts differs from that of growing pigs, this study was investigated to evaluate the ability of this model and other available models for growing-finishing pigs (INRAE of Jondreville and Dourmad, 2005; NRC, 2012; CVB of Bikker et Block, 2017) to predict body P and Ca retention in gilts. Data from gilts fed in 2 phases (Growing (C): 55-95 kg, finishing: 95-140 kg) was used to evaluate the models. In the first phase, the gilts received a diet that provided 100% of the Ca and P requirements (C100; 2.1 g of digestible P) ad libitum. In the finishing phase, the gilts received a control diet (F100; 2.1 g of digestible P) or a rich diet providing 160% of the requirements (F160; 3.5 g of digestible P), and daily feed intake and the energy content was reduced for a gain of 700 g/d. Body composition data were used to evaluate the predictive ability of the model according to the mean square error of prediction (MSEP) and its subdivision into central tendency error (CTE), regression (RE) and disturbance (DE). At 95 kg, the proposed model adequately predicted body P (MSPE=2.26%, CTE=0.016%, RE=13.4% and DE=86.5%), while the other models had a higher CTE, thus indicating an underestimation. At 140 kg, accuracy of predictions decreased for all models, with the proposed model overestimating body P and the other models underestimating it. These results will help adapt existing models to replacement gilts.

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