ALTERNATIVE PROGENY TESTING PROGRAMS FOR WEANING WEIGHT AND EASE OF CALVING IN BEEF CATTLE

1980 ◽  
Vol 60 (3) ◽  
pp. 609-620 ◽  
Author(s):  
G. F. S. HUDSON ◽  
L. R. SCHAEFFER ◽  
J. W. WILTON

Progeny test programs were characterized by numbers of young bulls, successful matings (conceptions) per young bull, herds in which each young sire was used, proven bulls used as reference sires and herd size. Matings to reference sires were calculated as a percentage of the number of young sire matings. Designs were simulated and mixed-model equations formed and added to equations obtained from the Canadian ROP program for years 1971–1978. The average standard error of prediction (SEP) of young bulls was used for comparing designs. Increasing matings per young sire decreased SEP curvilinearly. The curvilinearity was greater when either herd size or herds per young sire was low. Increasing herd size and herds per young sire improved connectedness between sires and reduced SEP. Number of young sires affected SEP only when fewer than 10 bulls were sampled. Number of young sire matings were more important in reducing SEP than percent reference sire matings. If number of young sire matings was adequate to achieve a desired level of SEP, 10% reference sire matings were sufficient. The number of reference sires used did not affect SEP. Design patterns affected SEP of weaning weight (h2 = 0.25) and calving ease (h2 = 0.10) similarly. SEP was not affected by the number of proven sires in a breed (Maine Anjou, 133 sires or Blonde d’Aquitaine, 39 sires) or by the connectedness between proven sires.

1982 ◽  
Vol 62 (4) ◽  
pp. 1057-1062
Author(s):  
G. H. CROW ◽  
W. E. HOWELL

Genetic aspects of maternal influence on weaning weights in beef cattle were examined using analyses within breed and parity of dam (first, second, third and fourth and greater parities) of Angus, Charolais and Hereford Record of Performance data. A mixed model which included herd-year and maternal grandsire (MGS) was used. The data were adjusted for calf sex within breed and parity of dam prior to analysis. The heritability of dam influence on her offspring weaning weight averaged 0.23 for first parity data of the three breeds. Heritability for second and third parities of the three breeds were lower than this but averaged 0.16 in parity four and greater. MGSs contributed significantly to variation in weaning weights. Their contribution, however, is a composite of both direct and maternal genetic effects. Key words: Beef cattle, weaning weight, maternal, variance components, heritability


2007 ◽  
Vol 50 (5) ◽  
pp. 464-475
Author(s):  
R. J. C. Cantet ◽  
M. J. Suarez ◽  
S. M. Leguizamón ◽  
E. P. Cappa ◽  
A. N. Birchmeier

Abstract. Evidence is presented for "generalized autoregressive conditional heteroskedasticity" processes (GARCH(1,1)), in the residuals of beef cattle growth traits. This process can account for differences in variance at different time points, with the advantage of using a parsimonious parametrization. Data used were 10271 birth weights (BW), 19992 weaning weights (WW) and 9717 weight at 18 months (FW), from five herds registered in the national evaluation of the Brangus breed in Argentina. The residuals calculated from the 2005 genetic evaluation were regressed on Julian dates by least squares. From a second set of residuals out of the linear regression model, Maximum Likelihood estimation via the Fisher scoring algorithm was used to estimate the GARCH(1,1) parameters. Eight out of fifteen one-sided Lagrange multiplier statistics significantly (P < 0.05) rejected the hypothesis of null GARCH(1,1) parameters in the genetic evaluation residuals. Incorporating these effects in genetic evaluation is feasible due to the diagonal covariance matrix induced by the process on each trait, which simplifies building the mixed model equations.


1992 ◽  
Vol 70 (8) ◽  
pp. 2359-2365 ◽  
Author(s):  
D. R. Notter ◽  
B. Tier ◽  
K. Meyer
Keyword(s):  

2015 ◽  
Vol 133 (2) ◽  
pp. 92-104 ◽  
Author(s):  
M.L. Santana ◽  
A.B. Bignardi ◽  
J.P. Eler ◽  
J.B.S. Ferraz

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