Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle

Authors

C. B. Williams, USDA, ARS, US Meat Animal Research CenterFollow
G. L. Bennett, US Meat Animal Research CenterFollow
T. G. Jenkins, USDA, ARS, US Meat Animal Research CenterFollow
L. V. Cundiff, US Meat Animal Research CenterFollow
C. L. Ferrell, USDA, ARS, US Meat Animal Research Center

Date of this Version

2006

Document Type

Article

Citation

J. Anim. Sci. 2006. 84:1310–1316

Comments

Copyright 2006 American Society of Animal Science. All rights reserved.

Abstract

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFR_mcg, CFR_mc, CFR_g, CFR_mg, and CFR_m, respectively) and DECI (DFR_mcg, DFR_mc, DFR_g, DFR_mg, and DFR_m, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) × CFR_mcg [R2 = 0.44, residual SD (s_y.x) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) × DFR_mcg (R2 = 0.53, s_y.x = 0.612 kg/d). Heritability of OFI was 0.27 ± 0.12, and heritabilities ranged from 0.33 ± 0.12 to 0.41 ± 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFR_mcg, CFR_mc, CFR_g, CFR_mg, CFR_m, DFR_mcg, DFR_mc, DFR_g, DFR_mg, and DFR_m were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P < 0.001 for all phenotypic correlations); and 0.95 ± 0.07, 0.82 ± 0.13, 0.89 ± 0.09, 0.95 ± 0.07, 0.91 ± 0.09, 0.96 ± 0.07, 0.89 ± 0.09, 0.88 ± 0.09, 0.96 ± 0.06, and 0.96 ± 0.07, respectively. Phenotypic and genetic correlations between CFR_mcg and DFR_mcg, CFR_mc and DFR_mc, CFR_g and DFR_g, CFR_mg and DFR_mg, and CFR_m and DFR_m were 0.98, 0.94, 0.99, 0.98, and 0.95 (P < 0.001 for all phenotypic correlations), and 0.99 ± 0.004, 0.98 ± 0.017, 0.99 ± 0.004, 0.99 ± 0.005, and 0.97 ± 0.021, respectively. The strong genetic relationships between OFI and CFR_mcg, CFR_mg, DFR_mcg, and DFR_mg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFR_mcg, and DFR_mcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency.