Cargando…

Prediction equations for digestible and metabolizable energy concentrations in feed ingredients and diets for pigs based on chemical composition

OBJECTIVE: The objectives were to develop prediction equations for digestible energy (DE) and metabolizable energy (ME) of feed ingredients and diets for pigs based on chemical composition and to evaluate the accuracy of the equations using in vivo data. METHODS: A total of 734 data points from 81 e...

Descripción completa

Detalles Bibliográficos
Autores principales: Sung, Jung Yeol, Kim, Beob Gyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asian-Australasian Association of Animal Production Societies 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876718/
https://www.ncbi.nlm.nih.gov/pubmed/32819083
http://dx.doi.org/10.5713/ajas.20.0293
Descripción
Sumario:OBJECTIVE: The objectives were to develop prediction equations for digestible energy (DE) and metabolizable energy (ME) of feed ingredients and diets for pigs based on chemical composition and to evaluate the accuracy of the equations using in vivo data. METHODS: A total of 734 data points from 81 experiments were employed to develop prediction equations for DE and ME in feed ingredients and diets. The CORR procedure of SAS was used to determine correlation coefficients between chemical components and energy concentrations and the REG procedure was used to generate prediction equations. Developed equations were tested for the accuracy according to the regression analysis using in vivo data. RESULTS: The DE and ME in feed ingredients and diets were most negatively correlated with acid detergent fiber or neutral detergent fiber (NDF; r = −0.46 to r = −0.67; p<0.05). Three prediction equations for feed ingredients reflected in vivo data well as follows: DE = 728+ 0.76×gross energy (GE)−25.18×NDF (R(2) = 0.64); ME = 965+0.66×GE−24.62×NDF (R(2) = 0.60); ME = 1,133+0.65×GE−29.05×ash−23.17×NDF (R(2) = 0.67). CONCLUSION: In conclusion, the equations suggested in the current study would predict energy concentration in feed ingredients and diets.