Prediction equations for the energy values of soybean meal for pigs at the growing phase with ex-post validation

Autores

  • Arlindo Garcia da Silva (85) 999574783

Palavras-chave:

Digestible energy. Mathematical models. Metabolizable energy. Protein feedstuffs.

Resumo

The aim of this study was to determine and validate prediction equations for digestible (DE) and metabolizable
energy (ME) of soybean meal for growing pigs. The prediction equations were developed using data from chemical composition,
digestibility and metabolizability of soybean meal samples (n = 25) evaluated in essays at Embrapa Suínos e Aves. The equations
were estimated through regression analysis, using the REG procedure of SAS and adjusted R² was the criterion of choice to
select the best models. Two equations were estimated for DE and two for ME. To validate the equations, one experiment
with two essays was performed to determine the values of DE and ME of five samples of soybean meal. In each essay, 24
growing pigs with an initial weight of 54.20 ± 1.28 kg at first and 54.60 ± 2.26 kg at second essay, were sorted in a complete
randomized block design with 6 treatments (1 reference diet and 5 test diets) and 4 replicates. Considering the lowest prediction
error (ep
), the equations to predict the DE and ME of soybean meal were: DE = 48153 – 1586.1(PB) + 744.5(EE) + 363.6(FB)
- 1398.3(MM) + 15.5(PB2
) – 170.8(EE2
) – 29.3(FB2
) + 5.4(FDA2
) – 2.5(FDN2
) + 90.6(MM2
) – 8.2(EEFDA) + 33(EEFDN),
with R² = 0.88 and ep =
2.32 and ME = 12692 – 2397.7(MM) – 56.8(EE2
) + 164.9(MM2
) – 102.2(EEFB) – 12.25(EEFDA)
+ 67.6(EEFDN) + 5.5(PBFB) – 2.9(PBFDN) with R2 = 0.65 and ep
= 1.69. Based on the chemical composition data and
correlations, it was possible to establish prediction equations for the values of digestible and metabolizable energy of soybean
meal for pigs, being necessary validation for greater accuracy of the models.

Biografia do Autor

Arlindo Garcia da Silva, (85) 999574783

 

             

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Publicado

2015-08-28

Edição

Seção

Zootecnia