TY - JOUR

T1 - Development of mathematical models to predict dry matter intake in feedlot Santa Ines rams

AU - Vieira, Pablo Almeida Sampaio

AU - Pereira, Luiz Gustavo Ribeiro

AU - Azevêdo, José Augusto Gomes

AU - Neves, André Luiz Alves

AU - Chizzotti, Mário Luiz

AU - Dos Santos, Rafael Dantas

AU - De Araújo, Gherman Garcia Leal

AU - Mistura, Claudio

AU - Chaves, Alexandre Vieira

N1 - Funding Information: Financial assistance for this research from Banco do Nordeste do Brasil, Universidade Federal do Vale do São Francisco, Empresa Brasileira de Pesquisa Agropecuária and Conselho Nacional de Desenvolvimento Científico e Tecnológico is gratefully acknowledged.

PY - 2013/5

Y1 - 2013/5

N2 - Mathematical models to predict the dry matter intake (DMI) of feedlot Santa Ines rams were developed and evaluated in this study. The available database had 100 experimental units from 13 studies. Study effect was integrated and random effects of their interactions as components of a hybrid model. The independent variables were initially adjusted to a model which included fixed effects for y-intercept and slope and random effects in y-intercept and slope study, using unstructured covariance model (e.g.: UN-unstructured). Study effect on database was verified, and then a meta-analysis procedure to develop DMI prediction equations was performed. For validation and comparisons between existing prediction equations in the national and international literature, independent data from one survey with 21 animals were used. Validation methods of the observed and predicted DMI were based on linear regression model adjustment of the observed values over predicted values. The following variables: average live weight (ALW), metabolic live weight (MLW0.75), average daily gain (ADG) and average daily gain2 (ADGsq) presented positive correlation with DMI. In contrast diet concentrate level showed a negative correlation. Among eight models examined, the following resulting equation [DMI (g/day)=238.74±114.56 (0.0398)+31.3574±4.2737 (<0.0001)×MLW+1.2623 ±0.2128 (<0.0001)×ADG-5.1837±0.7448 (<0.0001)×CON] has been found as the best fit model to predict DMI in feedlot Santa Ines rams.

AB - Mathematical models to predict the dry matter intake (DMI) of feedlot Santa Ines rams were developed and evaluated in this study. The available database had 100 experimental units from 13 studies. Study effect was integrated and random effects of their interactions as components of a hybrid model. The independent variables were initially adjusted to a model which included fixed effects for y-intercept and slope and random effects in y-intercept and slope study, using unstructured covariance model (e.g.: UN-unstructured). Study effect on database was verified, and then a meta-analysis procedure to develop DMI prediction equations was performed. For validation and comparisons between existing prediction equations in the national and international literature, independent data from one survey with 21 animals were used. Validation methods of the observed and predicted DMI were based on linear regression model adjustment of the observed values over predicted values. The following variables: average live weight (ALW), metabolic live weight (MLW0.75), average daily gain (ADG) and average daily gain2 (ADGsq) presented positive correlation with DMI. In contrast diet concentrate level showed a negative correlation. Among eight models examined, the following resulting equation [DMI (g/day)=238.74±114.56 (0.0398)+31.3574±4.2737 (<0.0001)×MLW+1.2623 ±0.2128 (<0.0001)×ADG-5.1837±0.7448 (<0.0001)×CON] has been found as the best fit model to predict DMI in feedlot Santa Ines rams.

KW - Meta-analysis

KW - Modelling

KW - Nutritional requirements

KW - Ruminants

KW - Santa Ines

UR - http://www.scopus.com/inward/record.url?scp=84876308919&partnerID=8YFLogxK

U2 - 10.1016/j.smallrumres.2012.10.007

DO - 10.1016/j.smallrumres.2012.10.007

M3 - Journal article

AN - SCOPUS:84876308919

VL - 112

SP - 78

EP - 84

JO - Small Ruminant Research

JF - Small Ruminant Research

SN - 0921-4488

IS - 1-3

ER -