Genetic parameters for production traits and measures of residual feed intake in large white swine.

1999 ◽  
Vol 77 (7) ◽  
pp. 1679 ◽  
Author(s):  
Z B Johnson ◽  
J J Chewning ◽  
R A Nugent
2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 75-76
Author(s):  
Camren l Maierle ◽  
Andrew R Weaver ◽  
Eugene Felton ◽  
Scott P Greiner ◽  
Scott A Bowdridge

Abstract Residual feed intake (RFI) is quickly becoming the preferred measurement of efficiency in many species due to its inherent independence of most other important production traits. Making meaningful improvement in feed efficiency of sheep will require a consistent methodology to accurately identify efficient individuals. Due to difficulty in measuring this trait efforts must be made to incorporate efficiency data in large-scale genetic evaluations. The aim of this study was to evaluate lambs in a feedlot with large-scale genetic evaluations for feed efficiency calculated by residual feed intake (RFI) utilizing a Growsafe™ system. RFI was calculated by subtracting expected intake from actual intake. Expected intake was determined by regressing metabolic body size of mid-test weight. Regression determined ADG on actual intake for individuals in the population. Texel (n = 58) and Katahdin (n = 118) lambs were placed in a feedlot and fed in separate feeding trials, a complete pellet ad libitum as the sole source of nutrition. In this environment Texel and Katahdin lambs had expected ADG values (0.27 kg/day, 0.32 kg/day respectively) and actual intake data (2154.17 g/day, 1909.33 g/day respectively. After a period of adaptation, Texel average intake was determined over a period of 27 consecutive days and used to calculate individual RFI within the test population. Observable ranges of RFI (-0.62 – +0.62) were seen in the Texel lambs. At the start of the Katahdin trial lambs were separated by sex and FEC treatment. After a period of adaptation, Katahdin average intake was determined over a period of 42 consecutive days and used to calculate individual RFI within the test population. Observable ranges of RFI (-0.53 – +0.50) were seen in the Katahdin lambs as well. In both feeding trials RFI appeared to be normally distributed. Use of this technology may be useful in identifying superior individuals for feed efficiency.


1997 ◽  
Vol 1997 ◽  
pp. 31-31
Author(s):  
A.D. Hall ◽  
W.G. Hill ◽  
P.R. Bampton ◽  
A.J. Webb

Until recently, to enable accurate recording of feed intake, pigs were kept in individual pens. The advent of electronic feeders has allowed accurate records of feed intake and feeding patterns in group housing which is more similar to that found in the production environment. The objectives of this study were to estimate genetic parameters for these feeding pattern traits and their correlations with production traits to show potential benefits in selection.


2000 ◽  
Vol 43 (3) ◽  
pp. 287-298
Author(s):  
J. Bizelis ◽  
A. Kominakis ◽  
E. Rogdakis ◽  
F. Georgadopoulou

Abstract. Production and reproduetive traits in Danish Landrace (LD) and Large White (LW) swine were analysed by restricted maximum likelihood methods to obtain heritabilities as well as genetic and phenotypic correlations. Production traits were: age, backfat thickness (BT), muscle depth (MD) and the ratio BT/MD, adjusted to Standard bodyweight of 85 kg. Reproduction traits were: number of pigs born (NB) and number of pigs weaned (NW) per sow and parity. Heritabilities for age, BT, MD and BT/MD were 0.60, 0.44, 0.51 and 0.42 for LD and 0.36, 0.44, 0.37 and 0.45 for LW, respectively. Genetic correlations between age and BT were −0.22 in LD and – 0.44 in LW. The genetic correlation between age and MD was close to zero in both breeds. Genetic correlation between BT and MD were −0.36 and −0.25 in LD and LW, respectively. Heritabilities for NB were 0.25 in LD and 0.13 in LW while heritabilities for NW were close to zero in both breeds. Genetic correlation between NB and NW was 0.46 and 0.70 in LD and LW, respectively.


2013 ◽  
Vol 91 (6) ◽  
pp. 2542-2554 ◽  
Author(s):  
R. Saintilan ◽  
I. Mérour ◽  
L. Brossard ◽  
T. Tribout ◽  
J. Y. Dourmad ◽  
...  

2016 ◽  
Vol 95 (9) ◽  
pp. 1999-2010 ◽  
Author(s):  
L. Drouilhet ◽  
R. Monteville ◽  
C. Molette ◽  
M. Lague ◽  
A. Cornuez ◽  
...  

2018 ◽  
Vol 96 (suppl_2) ◽  
pp. 256-257
Author(s):  
S A Hershorin ◽  
R Manjarin ◽  
A M Emond ◽  
S Id-Lahoucine ◽  
P Fonseca ◽  
...  

Author(s):  
Hadi Esfandyari ◽  
Just Jensen

Abstract Rate of gain and feed efficiency are important traits in most breeding programs for growing farm animals. Rate of gain (GAIN) is usually expressed over a certain age period and feed efficiency is often expressed as residual feed intake (RFI), defined as observed feed intake (FI) minus expected feed intake based on live weight (WGT) and GAIN. However, the basic traits recorded are always WGT and FI and other traits are derived from these basic records. The aim of this study was to develop a procedure for simultaneous analysis of the basic records and then derive linear traits related to feed efficiency without retorting to any approximations. A bivariate longitudinal random regression model was employed on 13,791 individual longitudinal records of WGT and FI from 2,827 bulls of six different beef breeds tested for own performance in the period from 7 to 13 months of age. Genetic and permanent environmental covariance functions for curves of WGT and FI were estimated using Gibbs sampling. Genetic and permanent covariance functions for curves of GAIN were estimated from the first derivative of the function for WGT and finally the covariance functions were extended to curves for RFI, based on the conditional distribution of FI given WGT and GAIN. Furthermore, the covariance functions were extended to include GAIN and RFI defined over different periods of the performance test. These periods included the whole test period as normally used when predicting breeding values for GAIN and RFI for beef bulls. Based on the presented method, breeding values and genetic parameters for derived traits such as GAIN and RFI defined longitudinally or integrated over (parts of) of the test period can be obtained from a joint analysis of the basic records. The resulting covariance functions for WGT, FI, GAIN and RFI are usually singular but the method presented here do not suffer from the estimation problems associated with defining these traits individually before the genetic analysis. All results are thus estimated simultaneously, and the set of parameters are consistent.


2011 ◽  
Vol 51 (7) ◽  
pp. 615 ◽  
Author(s):  
Craig R. G. Lewis ◽  
Kim L. Bunter

This study examined the effects of season on genetic parameters for production and reproductive traits and quantified within contemporary group effects of temperature on these traits using linear and plateau-linear regression models. From 2003 onwards, data were available on ~60 000 gilts for the routinely recorded production traits (BF: back fat; LADG: lifetime average daily gain) and ~45 000 litters for the sow reproductive traits (TB: total born; NBA: number born alive; BWT: average piglet birthweight). A subset of gilts were also recorded for test period daily gain (TADG), daily feed intake (ADI) and feed conversion ratio (FCR) and, later, as sows (n ~2000) for average daily lactation feed intake (LADI). Least-squares means for some production and reproductive traits significantly differed between seasons: summer and winter means were 2.28 ± 0.017 vs 2.54 ± 0.011 kg/day for ADI, 2.80 ± 0.022 vs 3.21 ± 0.011 kg/kg for FCR, and 1.61 ± 0.02 vs 1.54 ± 0.02 kg for BWT. However, some statistically significant differences (due to large n) were biologically insignificant. Trait variation also differed between seasons, but heritability estimates did not significantly differ from each other. Heritabilities were (summer vs winter): BF: 0.43 ± 0.03 vs 0.41 ± 0.02; LADG: 0.18 ± 0.02 vs 0.16 ± 0.02; TADG: 0.12 ± 0.10 vs 0.08 ± 0.06; ADI: 0.37 ± 0.15 vs 0.22 ± 0.07; FCR: 0.14 ± 0.11 vs 0.17 ± 0.06; TB: 0.09 ± 0.01 vs 0.10 ± 0.01; NBA: 0.06 ± 0.01 vs 0.07 ± 0.01 and BWT: 0.37 ± 0.03 vs 0.32 ± 0.04. Genetic correlations between the same trait recorded in different seasons were generally very high (>0.70), with the exception of TB, where the genetic correlation between spring and autumn was 0.65 ± 0.09, suggesting a genetic component to the effect of seasonal infertility on litter size. Regression models demonstrated that two selection lines had different responses to increasing temperature, despite concurrent selection in the same environment. Plateau-linear models were generally better than linear models for describing changes to production traits with temperature. Based on maximum temperature at the end of performance testing, the estimated temperature thresholds above which lifetime growth performance was compromised were 25.5 and 32.5°C in the two lines. There were only small linear relationships between reproductive traits and temperature. Overall, the ongoing acclimatisation to the thermal environment and the partial confounding of contemporary group with temperature variables (season explained 62% of variation in average daily temperature) are potentially contributing factors to the lack of major differences in heritability estimates between seasons, and the relatively small regression coefficients for the effects of temperature on performance. Nevertheless, temperature can be demonstrated to affect phenotypic outcomes within contemporary groups using commercial data.


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