EVALUATION OF FEED EFFICIENCY TRAITS WITH POST-WEANING GROWTH AND ULTRASOUND TRAITS IN CENTRAL TEST BULLS Except where reference is made to the work of others, the work described in this thesis is my own or was done in collaboration with my advisory committee. This thesis does not include proprietary or classified information. ______________________________________________ Genevieve Sue Hecht Certificate of Approval: ____________________________ _____________________________ Daryl L. Kuhlers Lisa A. Kriese-Anderson, Chair Professor Professor Animal Sciences Animal Sciences _____________________________ ____________________________ Darrell L. Rankins, Jr. George T. Flowers Professor Interim Dean Animal Sciences Graduate School EVALUATION OF FEED EFFICIENCY TRAITS WITH POST-WEANING GROWTH AND ULTRASOUND TRAITS IN CENTRAL TEST BULLS Genevieve Sue Hecht A Thesis Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Master of Science Auburn, Alabama December 17, 2007 iii EVALUATION OF FEED EFFICIENCY TRAITS WITH POST-WEANING GROWTH AND ULTRASOUND TRAITS IN CENTRAL TEST BULLS Genevieve Sue Hecht Permission is granted to Auburn University to make copies of this thesis at its discretion, upon request of individuals or institutions, and at their expense. The author reserves all publication rights. ____________________________ Signature of Author ____________________________ Date of Graduation iv VITA Genevieve Sue (Davis) Hecht, daughter of James Vernon Davis and Colleen Leslie (Graham) Davis, was born February 10, 1981 in Big Rapids, Michigan. Genevieve grew up on a small beef farm where she was involved in 4-H and FFA. In 1999, she graduated from Chippewa Hills High School in Remus, Michigan. She received her Bachelor of Science Degree in Animal Science from Michigan State University in East Lansing, Michigan in 2003. While there, she was employed at the Michigan State University Purebred Beef Barn and was active in the MSU Block and Bridle Club. After graduation from Michigan State University she received a Graduate Assistantship to attend Auburn University in Auburn, Alabama where she is currently pursuing her Master of Science degree in Animal Breeding and Genetics under the guidance of Dr. Lisa A. Kriese-Anderson. She will graduate in December 2007. In 2005, Genevieve was married to Christopher Adam Hecht and they reside in Reese, Michigan on the family farm. v EVALUATION OF FEED EFFICIENCY TRAITS WITH POST-WEANING GROWTH AND ULTRASOUND TRAITS IN CENTRAL TEST BULLS Genevieve Sue Hecht Master of Science, December 17, 2007 (B.S., Michigan State University, 2003) 105 Typed Pages Directed by Lisa A. Kriese-Anderson Since 1978, individual feed intake has been measured on bulls (n = 2,180) consigned to the Auburn University Bull Test along with weights, heights, scrotal circumference and ultrasound carcass traits. Test length since 1977 was reduced from 140 d to 112 d to 84 d. Eight breeds were analyzed using MTDFREML to estimate heritabilities of and genetic correlations between residual feed intake (RFI) and ADG, scrotal circumference (SC), ultrasound 12 th rib fat thickness (USFAT), ultrasound longissimus muscle area (USREA) and percent intramuscular fat (USIMF). Breeds included were Angus (n = 857), Brangus (n = 41), Charolais (n = 380), Gelbvieh (n = 103), Hereford (n= 192), Limousin (n = 106), Santa Gertrudis (n = 106) and Simmental (n = 395). Traits were analyzed using three-trait analyses and a sire-maternal grandsire vi model with either age or weight as covariates. Fixed effects included length of test, breed and year. (Co)variance estimates were averaged across analyses to arrive at a final estimate. Heritability Estimates of Traits Across Breeds Covariate RFI ADG SC USFAT USREA USIMF FCR Age 0.10 0.17 0.16 0.16 0.09 0.14 0.13 Weight 0.09 0.16 0.17 0.15 0.13 0.13 0.12 Estimates of Genetic Correlations between RFI and Associated Traits Across Breeds Covariate ADG SC USFAT USREA USIMF FCR Age -0.08 0.12 -0.13 -0.77 0.77 0.49 Weight 0.08 0.17 -0.02 -0.70 0.73 0.46 Heritability and genetic correlation estimates of all traits were on the lower end of reported literature estimates. These results may be due to consignment of elite bulls to a central test station. Results also suggest selection of animals with a lower residual feed intake should not increase individual size and should improve feed efficiency. Key Words: Beef Cattle, Feed Efficiency, Genetic Parameters, Ultrasound vii ACKNOWLEDGEMENTS The author would like to thank Dr. Lisa A. Kriese-Anderson for her friendship and guidance throughout this process. Thank you for welcoming and introducing her to the great state of Alabama and its beef cattle industry. She would also like to thank Drs. Darrell L. Rankins and Daryl L. Kuhlers for serving on her committee, and for their assistance with her graduate program. A thank-you also goes to the faculty and staff of the Animal Science Department for their support, assistance, and guidance during her time at Auburn University. The author would also like to thank her fellow graduate students for making her experience at Auburn University worthwhile. The author would like to thank the many animal scientists, extension workers and Auburn University Beef Cattle Unit managers and employees for collecting the data on bulls tested throughout the years. Without their hard work and dedication these data would not have been available. A thank-you also goes to the many beef cattle producers who consigned bulls to the Auburn University Bull Test. A thank-you goes to Mr. and Mrs. Jeremiah Alexander for allowing her to stay with them for the completion of this thesis. The author would like to especially thank her family members who offered love and encouragement throughout the difficult times. A very special thank you goes to her husband Chris, for his love, patience, support, and understanding throughout the entire process. His love and support were essential in the completion of this task. viii Style Manual or Journal Used: Journal of Animal Science Computer Software Used: Microsoft Word 2003 ix TABLE OF CONTENTS LIST OF TABLES............................................................................................................ xii LIST OF FIGURES ......................................................................................................... xiv I. INTRODUCTION ........................................................................................................1 II. REVIEW OF LITERATURE.......................................................................................5 Feed Conversion Ratio.............................................................................................5 Residual Feed Intake................................................................................................8 Genetic Correlations Involving Residual Feed Intake Post-Weaning and at Maturity......................................................................................................10 Residual Feed Intake Effects on Meat Quality and Palatability ................14 Biological Basis for Variation in Residual Feed Intake in Beef Cattle .................14 Synthesis of Potential Mechanisms ...........................................................15 Feed Intake.................................................................................................15 Digestion....................................................................................................16 Body Composition and Metabolism ..........................................................17 Activity ......................................................................................................18 Thermoregulation.......................................................................................19 Feeding Patterns.........................................................................................19 Stress..........................................................................................................20 Other Important Post-Weaning Traits....................................................................21 x Average Daily Gain (ADG).......................................................................21 Scrotal Circumference ...............................................................................22 Real Time Ultrasound............................................................................................22 Heritability Estimates and Genetic Correlations For Ultrasound Measurements ............................................................................................23 Correlations Between Real-Time Ultrasound and Carcass Traits .............25 Variation in Ultrasound Measurements Among Breeds ............................28 Conclusion .............................................................................................................29 Research Objectives...............................................................................................30 III. EVALUATION OF FEED EFFICIENCY TRAITS WITH POST-WEANING GROWTH AND ULTRASOUND TRAITS IN CENTRAL TEST BULLS ........31 Introduction............................................................................................................31 Materials and Methods...........................................................................................32 Experimental Data .....................................................................................32 Data Collection ..........................................................................................34 Data Analysis.............................................................................................35 Model .........................................................................................................36 Residual Feed Intake Analyses ..................................................................39 IV. RESULTS AND DISCUSSION.................................................................................40 Heritability Estimates and Correlations.................................................................40 Phenotypic Correlations Between Residual Feed Intake (RFI) and Associated Traits........................................................................................43 Genetic Correlations Between Residual Feed Intake (RFI) and Associated xi Traits ..........................................................................................................45 Phenotypic Correlations Between Feed Conversion Ratio (FCR) and Associated Traits........................................................................................47 Genetic Correlations Between Feed Conversion Ratio (FCR) and Associated Traits........................................................................................50 Other Phenotypic Correlations...................................................................52 Other Genetic Correlations ........................................................................55 Residual Feed Intake (RFI) Results .......................................................................60 Comparisons Between Residual Feed Intake (RFI) Groups......................60 Breed Effect on Post-Weaning Gain and Ultrasound Measurements........60 Year Effect on Post-Weaning Gain and Ultrasound Measurements..........61 Implications............................................................................................................61 TABLES AND FIGURES .................................................................................................63 LITERATURE CITED ......................................................................................................82 APPENDIX. Calculation of Residual Feed Intake ...........................................................90 xii LIST OF TABLES Table 1. Nutrient analysis of diet fed to bulls by year.......................................................65 Table 2. Estimates of additive (co)variance components of centrally tested bulls adjusted to a common age ................................................................................................................66 Table 3. Estimates of environmental (co)variance components of centrally tested bulls adjusted to a common age..................................................................................................67 Table 4. Estimates of additive (co)variance components of centrally tested bulls adjusted to a common weight...........................................................................................................68 Table 5. Estimates of environmental (co)variance components of centrally tested bulls adjusted to a common weight ............................................................................................69 Table 6. Simple means ? SD for performance and ultrasound traits of bulls used in analyses adjusted by age ....................................................................................................70 Table 7. Simple means ? SD for performance and ultrasound traits of bulls used in analyses adjusted by final weight ......................................................................................71 Table 8. Simple means ? SEM for performance and ultrasound traits of bulls by breed used in analyses adjusted by age........................................................................................72 Table 9. Simple means ? SEM for performance and ultrasound traits of bulls by breed used in analyses adjusted by final weight..........................................................................73 Table 10. Estimates of heritability and genetic and phenotypic correlations of post- weaning traits of centrally tested bulls adjusted to a common age....................................74 Table 11. Estimates of heritability and genetic and phenotypic correlations of post- weaning traits of centrally tested bulls adjusted to a common weight ..............................75 Table 12. Least squares mean ? SEM between residual feed intake (RFI) groups for post- weaning gain and ultrasound traits of central test bulls adjusted for weight.....................76 Table 13. Least squares mean ? SEM between residual feed intake (RFI) groups for post- weaning gain and ultrasound traits of central test bulls adjusted for age ..........................77 xiii Table 14. Least squares mean ? SEM for breed effect of residual feed intake (RFI) on post-weaning gain and ultrasound traits of central test bulls adjusted for weight.............78 Table 15. Least squares mean ? SEM for breed effect of residual feed intake (RFI) on post-weaning gain and ultrasound traits of central test bulls adjusted for age ..................79 Table 16. Least squares mean ? SEM by year of residual feed intake (RFI) on post- weaning gain and ultrasound traits of central test bulls adjusted for weight.....................80 Table 17. Least squares mean ? SEM by year of residual feed intake (RFI) on post- weaning gain and ultrasound traits of central test bulls adjusted for age ..........................81 xiv LIST OF FIGURES Figure 1. Estimates of the percentage contribution of different mechanisms to variation in residual feed intake in beef cattle (Herd et al., 2004) ........................................................63 Figure 2. Contributions of biological mechanisms to variation in residual feed intake as determined from experiments on divergently selected cattle (Richardson and Herd, 2004) ............................................................................................................................................64 1 INTRODUCTION Providing feed to animals is the largest cost input in most animal production systems (Fan et al., 1995; Arthur et al., 2001a; Archer et al., 2002; Basarab et al., 2002; Herd et al., 2003), with 60 to 75% of total feed requirements utilized by the beef animal for maintenance (Arthur et al., 2001a; Basarab et al., 2002). Individual cattle vary in their ability to efficiently utilize feed (Fan et al., 1995; Arthur et al., 2001a). Currently, beef cattle are the least efficient converters of feedstuffs to unit gain among major protein providers. Farm raised fish are most efficient (1.1 pounds of feed to 1.0 pound of gain) followed by poultry (2 pounds of feed to 1.0 pound of gain) and swine (2.5 or 3 pounds of feed to 1.0 pound of gain). Cattle are a distant fourth with a feed conversion ratio (FCR) of 7.5 or 8 pounds of feed to 1.0 pound of gain. Any improvement of the output of beef per unit of feed used over the entire production system would be of significant economic benefit (Herd et al., 2003). Many different facets contribute to the overall efficiency of a beef production system for both the breeding herd and feeder cattle. Growth traits and other production traits (reproductive rate, mature cow size, feed intake, and milk production) must be considered when determining the overall efficiency of the cow herd. Herring and Bertrand (2002) discussed factors influencing overall efficiency of the cow herd. Those factors included age, diet, temperature, breed, growth promoting implants, use of ionophores, and many other management and environmental variables. All of these 2 factors need to be considered when evaluating the overall efficiency of a herd. Cattle able to maintain body condition helps defer input cost, which is the primary feed cost of most cow/calf operations. Cattle able to maintain body condition with the same amount of feed can be defined as cattle with a better feed efficiency. In a feedlot, feed efficiency is one of the primary factors in profit or loss in a pen of cattle. There are several methods to examine and define feed efficiency in cattle. Feed efficiency is most commonly expressed as a ratio of G:F or its inverse feed conversion ratio (FCR). This ratio can also be adjusted to a common body weight to account for differences in size. However, Koch et al. (1963) discussed selection for a trait defined by a ratio may lead to erroneous or unexpected results. Twenty years later, Gunsett (1984) argued that direct selection on FCR may not be the best way to improve efficiency, because: (i) the statistical properties of ratios are poor and selection response can be erratic; (ii) the use of a ratio as a selection criterion results in different responses in the component traits; and (iii) ratios may produce fallacious indications of economic efficiency. Crews (2006) concluded ratios and other measures of efficiency generally suffer from similar limitations in that they are too related to other economically important traits. Both beef cattle (Koch et al., 1963) and poultry (Byerly, 1941) results suggest using net feed efficiency (NFE) or residual feed intake (RFI) as the appropriate measure for examining feed efficiency in livestock species. Residual Feed Intake (RFI) is defined as the difference between the actual feed intake and the expected feed intake requirements for maintenance of body weight and production (Byerly, 1941; Koch et al., 1963). 3 Many studies have examined at least one definition of feed efficiency in beef cattle (Dawson et al., 1955; Shelby et al., 1955; Koch et al., 1963; Fan et al., 1995; Arthur et al., 1999, 2001a,b; McDonagh et al., 2001; Richardson et al., 2001; Archer et al., 2002). In general, most studies utilizing a ratio definition of FCR have suggested FCR is moderately heritable (0.22 to 0.80) with a moderate to high genetic correlation with post-weaning average daily gain (-0.32 to -0.69) and feed consumption (0.71 to 0.79) (Koots et al., 1994a; Archer et al., 1997; Richardson et al., 1998; Herd and Bishop, 2000; Arthur et al., 2001a,b; Herring and Bertrand, 2002; Herd et al, 2003). Many researchers and extension specialists instructed producers to select high average daily gain individuals to improve feed efficiency. This selection strategy probably favored larger framed individuals with excellent appetites. Currently researchers are focusing on RFI as a more appropriate selection tool for improving feed efficiency without increasing mature cow size. One way researchers can collect data on postweaning growth traits is the use of central bull test stations. Central bull test stations are used in many parts of the country to evaluate post-weaning performance of bulls under uniform conditions. The first test stations were used to demonstrate performance concepts and improve growth rate in many breeds of beef cattle. The first full scale evaluation test for gain was conducted with bulls and heifers in 1949-1950 in Texas (Warwick and Cartwright, 1955). With the development of national cattle evaluations conducted by many breeds today, central test stations are now used by seedstock breeders as an additional source of performance records on their bulls. Also, central test stations may serve as a 4 demonstration of how to conduct an on-farm performance test (Dolezal and Silcox, 2004). Central test stations document post-weaning gain performance and provide educational opportunities for prospective bull buyers. The stations also serve as a good source of bulls for commercial and seedstock herds. Test stations not only provide a seedstock producer with a place to market individual bulls, but it also gives the producer an opportunity to advertise their breeding program. It is difficult to obtain measures of individual feed intake on the farm. A few central test stations in America have measured individual feed intake for considerable time. The purpose of this study was to examine feed efficiency traits with post-weaning growth and carcass ultrasound traits in central test bulls in Alabama. 5 REVIEW OF LITERATURE Measures of Feed Efficiency Feed Conversion Ratio. One definition of feed efficiency is the ratio of G:F or its inverse feed conversion ratio (FCR). FCR is defined as the units of feed consumed by an animal divided by the units of gain over a specific time period. Heritability estimates for average daily gain (ADG), FCR, and residual feed intake (RFI) were examined in a study to determine optimum test length for measuring feed intake and FCR (Archer et al., 1997). Variance components, heritability estimates, phenotypic and genetic correlations, and the efficiency of selection using shortened tests compared with a 119 d test were used as criteria to assess the optimum test length. Data consisted of feed intake and weight records from 760 animals from 78 different sires originating from both research and industry herds. All cattle were given ad-libitum access to feed and individual intake of each animal was recorded. Archer and coworkers (1997) found a 70 d test was required to get an accurate measure of growth rate, FCR, and RFI. In another study, Wang and coworkers (2006) determined ADG, DMI, FCR, and RFI test duration could be shortened to 63, 35, 42, and 63 days, respectively. Measuring traits with a shortened test will defer some costs associated with having a traditional 119 d test, without compromising the accuracy of data being collected on traits being studied. This would also allow for conducting more than two tests a year to collect information on bulls. 6 Heritability estimates for FCR reported by Archer and coworkers (1997) ranged from 0.18 at 28 d to 0.42 at 70 d to 0.36 at 119 d. These heritability estimates were all measured with data collection occurring every two weeks. Heritability estimates reported for FCR also indicated there was little improvement in accuracy past 70 d. Archer et al. (2002) conducted a post-weaning performance test using heifers. Upon completion of the test, heifers entered the cow herd. After the birth of their second calf, cows were not rebred. Approximately ten weeks after the calves were weaned, cows re-entered the test facility to examine FCR on mature cows. Archer and coworkers (2002) reported a moderately heritable estimate (0.26) on mature cows for FCR with a strong genetic correlation (-0.87) with ADG, suggesting selection to improve FCR may affect cow size. Approximately 30 years ago, Woldehawariat et al. (1977) summarized heritability estimates concerning feed efficiency of beef cattle. Various heritability estimates of FCR were reported ranging from 0.26 to 0.80. A heritability estimate of 0.36 was reported for G:F. More recent studies agree FCR is moderately heritable (Koots et al., 1994a and Arthur et al., 2001a). However, Herd and Bishop (2000) reported a smaller heritability estimate for FCR of 0.17 on Hereford bulls. Many literature reports suggest single trait selection for lower feed conversion should result in higher degrees of growth with less feed intake. Koots et al. (1994b) reported genetic correlations in beef cattle between FCR and post-weaning gain (Total Gain, TG), fat thickness and feed intake (FI) were -0.53, -0.24 and 0.38, respectively, suggesting they are moderately related to each other. Arthur et al. (2001a,b) reported 7 similar genetic correlations between FCR and average daily gain (ADG) (-0.62 and -0.46) and FCR and FI (0.31 and 0.64) for Angus and Charolais breeds, respectively. Herd et al. (2003) concluded strong genetic relationships exist between feed intake and FCR measured postweaning. Other genetic correlations reported in the literature vary. Koch et al. (1963) and Woldehawariat et al. (1977) reported positive genetic correlations of 0.79 and 0.23, respectively, between post-weaning ADG and FCR. Arthur et al. (2001a) reported a negative genetic correlation of -0.74, between post- weaning ADG and FCR. However, these recent studies continue to confirm strong genetic relationships between FI and FCR. FCR and ADG were related in a favorable direction in these recent studies. Woldehawariat et al. (1977) also summarized genetic and phenotypic correlation estimates between post-weaning feed efficiency and other traits from literature. Genetic correlation estimates between FCR and ADG ranged from -0.41 to 0.31, suggesting there is a moderate correlation between ADG and FCR. However, the direction of the correlation is unclear. Genetic correlations of -0.34 between FCR and TG on test and 0.23 between FCR and post-weaning ADG were reported by Woldehawariat et al., (1977). The phenotypic correlation between FCR and ADG ranged from -0.26 to 0.55, suggesting there is a moderate correlation between ADG and FCR (Woldehawariat et al., 1977). Again, the direction of the phenotypic correlation is unclear. This uncertainty could be a result of analyzing a ratio trait. Bishop et al. (1991a,b) conducted a divergent selection experiment for FCR using Angus cattle. Angus bull calves were individually fed in a 140 d post-weaning test. The 8 three highest for and three lowest FCR bulls were selected each year and randomly mated to approximately 20 Angus cows. A total of 403 progeny were evaluated in post- weaning trials from 1983 to 1986. Two measures of FCR were used in this study. They were group FCR (unadjusted) for the first 140 d on test and FCR adjusted for maintenance as recommended by BIF (2002). Bishop et al. (1991b) reported heritability estimates of 0.26 for unadjusted group FCR and 0.46 for FCR adjusted for maintenance. Both estimates are similar to previous estimates reported and are moderately heritable. Other heritability estimates for FCR in beef cattle reported in the literature include those of Knapp and Nordskog (1946) of 0.48, Dawson et al. (1955) of 0.32, Carter and Kincaid (1959a) of 0.99, Brown and Gifford (1962) of 0.80, and Brown et al. (1988) of 0.14. Realized heritability estimates for FCR in swine were reported by Dickerson and Grimes (1947) of 0.24, Bernard and Fahmy (1970) of 0.11, Jungst et al. (1981) of 0.09, Webb and King (1983) of 0.007, and Bereskin (1986) of 0.061. In poultry, Wilson (1969) reported a realized heritability estimate for FCR of 0.34 and Pym and Nichols (1979) reported an estimate of 0.44. Once again all of these heritability estimates indicate FCR among species is moderately heritable. Residual Feed Intake. Measuring RFI was first described by Byerly (1941) when examining net efficiency of laying hens. Koch and coworkers (1963) recognized in growing beef cattle that differences exist in weight maintained and weight gain and has an effect on feed requirements. Koch et al. (1963) suggested feed intake could be adjusted for BW and weight gain by dividing feed intake into two different components. Those components are expected and residual portions of feed intake for the given level of 9 production. The residual portion of feed intake describes the amount individuals deviate from the expected level of feed intake. Koch and coworkers (1963) initially found RFI was a heritable trait in beef cattle (0.28 ? 0.11), with efficient animals having a lower or negative value for RFI. Since maintenance and growth requirements are not accounted for by G:F or its inverse FCR, RFI comparisons between animals may be a better measure of efficiency (Kolath et al., 2006). Several heritability estimates of RFI are in the literature. RFI appears to be moderately heritable. Pitchford (2004) provided a summary of RFI heritability estimates. Heritability estimates include 0.27 on dual purpose cattle, a range of 0.08 to 0.36 on growing dairy males, 0.22 for growing dairy females, 0.19 for lactating heifers and a range of 0.0 to 0.16 for lactating cows. Herd and Bishop (2000) reported a similar heritability of 0.16 for Hereford bulls. Archer et al. (1997) reported a RFI heritability estimate of 0.41 on Angus, Hereford, Polled Hereford and Shorthorn heifer and Angus bull progeny. Arthur et al. (2001a,b) reported similar heritability estimates for RFI of 0.39 for Charolais bulls and for Angus bulls and heifers. Archer and coworkers (1997) showed environmental variance estimates for RFI decreased from 0.57 (kg/d) 2 at 7 d to 0.13 (kg/d) 2 at 70 d. After 70 d results show only a small decrease in environmental variance estimates for RFI, suggesting the extra measurement time did not improve accuracy of measurements (Archer et al., 1997). Heritability estimates for RFI reported by Archer and coworkers (1997) ranged from 0.34 at 7 d to 0.62 at 70 d to 0.60 at 119 d. The genetic correlation of RFI between 70 d and 119 d was 0.98. This suggests the same measure of RFI was measured at 70 d and 119 d. Thus, measuring RFI for 70 d is adequate to find genetic differences. 10 Heritability estimates for RFI have been reported in several other species. Mrode and Kennedy (1993) reported heritability estimates ranging from 0.30 to 0.38 in growing boars. Von Felde et al. (1996) reported a smaller RFI heritability estimate of 0.18 also in growing boars. Heritability estimates for RFI of laying hens were reported by Luiting and Urff (1991a,b), Bordas et al. (1992), and Tixier-Boichard et al. (1995) ranging from 0.12 to 0.62. Tixier-Boichard et al. (1995) also reported a heritability estimate for RFI of 0.33 for cockerels, which is within the range of laying hens. Pitchford (2004) also summarized heritability estimates for RFI ranging from 0.16 to 0.28 in mice, 0.28 to 0.33 in sheep and 0.32 in Tribolium. There appears to be sufficient genetic variation to select for RFI (Herring and Bertrand, 2002). Since RFI appears to be a moderately heritable trait, and is similar to estimates for traditional growth traits (Crews, 2006) selecting animals that are naturally efficient may improve overall efficiency of a herd. Genetic correlations between RFI postweaning and maturity. To improve herd efficiency, genetic relationships between feed efficiency traits with mature cow performance traits must be known and understood. Herd et al. (2003) reported no genetic correlation estimate between post-weaning RFI and mature cow size. This correlation suggests selection of cattle with lower post-weaning RFI values will not increase cow size. Conversely, a strong positive genetic correlation between post-weaning FCR with cow size was found suggesting selecting for reduced post-weaning FCR may cause a change in cow size. If cow size increases, nutritional requirements also increase, therefore, FCR may not improve. If cow size decreases, FCR may improve, but decreasing cow size may not be the best method to improve FCR. 11 Mice were allotted to a high or low RFI line (Archer et al., 1998). Mice were housed individually for measurement of post-weaning ad-libitum feed intake and weight measurement. Eight males were retained for sires and every female was retained for the breeding colony at the conclusion of the post-weaning test. Mature measurements were taken on female mice after litters were weaned. Post-weaning and mature traits measured were ADG, mid-weight and daily feed intake. Heritability of RFI postweaning was estimated at 0.27 and 0.29 at maturity. In mice, this suggests genetic variation exists for RFI at post-weaning and maturity. A genetic correlation between RFI post-weaning and RFI maturity was 0.60. Animals ranked for RFI measurements taken post-weaning should remain ranked in a similar fashion at maturity. A correlated improvement in efficiency of mature mice (at maintenance) was noted based on selection for post- weaning low RFI of mature animals. These results suggest post-weaning RFI may be a suitable selection criterion for use in livestock to improve efficiency of young animals and decrease feed costs in the breeding herd (Archer et al., 1998). RFI differences appear to continue into maturity. Herd and coworkers (2003) conducted a comprehensive study examining the response to selection of post-weaning RFI on cow traits and steers that were finished on pasture or a feedlot. Parents were selected and assigned to a low or a high RFI line based on their post-weaning RFI data. At maturity, low RFI cows finished on pasture were 7% heavier, had similar rib fat and rump fat depths, and reared calves of similar BW to the high RFI cows, but consumed no more feed than high RFI cows. The advantage in efficiency of the low RFI cows, when expressed as a ratio of calf BW to cow feed intake, was 15%, suggesting a phenotypic 12 association between post-weaning RFI of the young female and her later efficiency as a cow/calf unit on pasture (Herd et al., 2003). In a feedlot setting, mature cow RFI and feed intake were the only traits that differed over the 70 d test period between the low and high RFI line. There were no significant differences in BW, rib fat depth, or ADG throughout the test period between the low and high RFI lines. Milk yield was measured once using the calf weigh-suckle- weigh method over the test period on cows. There was no difference in milk production between the high and low RFI lines. Herd et al. (2003) suggested females more efficient as weanlings required less feed as mature cows, with no compromise in performance. Parents were selected based on their RFI measurement from a post-weaning test conducted at eight to twelve months of age. Their bull and heifer progeny were then evaluated for post-weaning RFI under the same test regimen used to test their parents (Herd et al., 2003). After five years of selecting animals on post-weaning RFI, the direct response for RFI was -0.54 ? 0.18 kg/day in the low RFI line and 0.70 ? 0.17 kg/day in the high RFI line. Herd et al., (2003) also reported a reduction in daily feed intake with a reduced or improved FCR in the low RFI line as compared to the high RFI line. Yearling weight and post-weaning ADG were not affected by selection on post-weaning RFI. Steer progeny were evaluated for post-weaning RFI following a single generation of divergent selection for post-weaning RFI (Herd et al., 2003). The response to selection of post-weaning traits was examined utilizing steers finished on pasture and in the feedlot. Steer progeny finished on pasture from the low RFI line tended to gain faster than progeny from the high RFI line. Herd et al. (2003) reported no significant difference in daily pasture intake between the selection lines. FCR was 6.4 ? 0.4 kg/kg for the low 13 RFI line and 8.5 ? 0.8 kg/kg for the high RFI line (P < 0.1). A positive regression coefficient of FCR with mid-parent estimated breeding value (EBV) for RFI (2.9?1.5, P < 0.1) provided evidence for low RFI in the parents being genetically associated with superior efficiency of FCR on pasture by their steer progeny (Herd et al., 2003). Angus and crossbred Angus steers were evaluated for growth, feed intake, FCR, and some carcass characteristics in the feedlot phase. This study concluded that steer progeny of low RFI parents grew as fast as or faster than steers of high RFI parents, but ate less feed per unit of gain. The steer progeny also produced carcasses of acceptable fat finish with no compromise in retail meat yield, and as a consequence, should be more profitable to feed in a feedlot (Herd et al., 2003). Some studies reported strong positive genetic correlations between RFI and FCR in beef cattle. Herd and Bishop (2000) reported a genetic correlation of 0.70 and Arthur et al., (2001a,b) reported estimates of 0.85 and 0.66, respectively. Finally Schenkel et al. (2004) reported a genetic correlation between RFI and FCR of 0.69. Similar estimates have been reported for RFI and feed intake: 0.64 (Herd and Bishop, 2000); 0.69 and 0.79 (Arthur et al., 2001a,b) and 0.81 (Schenkel et al., 2004). Phenotypically, RFI was positively correlated with DMI (0.54) and FCR (0.42) but was not phenotypically correlated with BW measurements or ADG (Baker et al., 2006). ADG and BW measurements were similar among RFI groups. High RFI steers had greater DMI (P < 0.004) and FCR (P < 0.002) than did the low RFI steers (Baker et al., 2006). These results suggest that selection for improved (lower) RFI will result in a declining genetic trend for feed intake (Crews, 2006). 14 Residual Feed Intake Effects on Meat Quality and Palatability Baker et al. (2006) studied the effects RFI could have on meat quality and palatability. Data were collected on purebred Angus steers (n = 54). Initial (d 28 of test) ultrasound longissmus muscle area (USREA) showed a positive phenotypic correlation with FCR (0.64) but was not correlated with RFI. Baker et al. (2006) found no differences between high, mid, or low RFI steers for initial (d 28 of test) ultrasound fat thickness (USFAT), 71 d USFAT, initial (d 28 of test) USREA, and 71 d USREA. The study also suggested meat quality and palatability were not different between high and low RFI Angus steers. Biological Basis for Variation in Residual Feed Intake in Beef Cattle Biological mechanisms underlying the variation in feed efficiency in animals with similar body weight and growth weight are not well understood. At least five major processes were identified by Herd et al. (2004) in which variation in efficiency can arise (Figure 1). The existence of genetic variation in RFI offers potential that selection for low RFI will produce progeny that eat less, with no compromise in growth performance. However, the biological basis of such variation is largely unknown. Richardson and Herd (2004) reported results following a single generation of divergent selection for RFI on Angus steer progeny and identified seven major processes contributing to variation in RFI (Figure 2). These authors suggest it was important to identify the biological basis for RFI in beef cattle. Knowing this may lead to a more efficient method of selection for RFI (such as molecular markers) and help ensure selection against RFI will not have unexpected detrimental effects on progeny. 15 Synthesis of Potential Mechanisms. Herd and coworkers (2004) provided percentage breakdowns of mechanisms contributing to phenotypic variation for RFI in beef cattle. Mechanisms (Figure 1) include 9% for heat increment of feeding (HIF); 14% for digestion; 5% for body composition; 5% for activity. The remaining 67% were other factors responsible for variation in RFI. Richardson and Herd (2004) also reported similar estimates of what is currently known about mechanisms contributing to variation in RFI (Figure 2). Richardson and Herd (2004) reported biological variation in RFI may be attributed to body composition (5%), animal feeding patterns (2%), protein turnover, tissue metabolism and stress (37%), heat increment of fermentation (9%), animal digestion (10%), animal activity (10%) and other biological mechanisms that are not fully understood (27%). Johnson and coworkers (2003) would add to the list of traits that receive more attention by researchers and cattle producers. These traits include rate of gain, BW and prolificacy. These authors would also separate metabolism into two components, maintenance and growth metabolism. Including these traits in gain and metabolism will help to ensure minimal or no negative consequences for selection of improved RFI. Feed Intake. Variation in feed intake is associated with variation in maintenance requirements. As feed intake increases, the amount of energy needed to digest feed increases (Herd et al., 2004). The amount of energy expended by the tissues themselves also increases per unit weight of the animal. This is known as heat increment of feeding (HIF). Given that selection for RFI is associated with variation in intake, animals that eat less, at the same level of performance, could be expected to have less energy expended as HIF. 16 Digestion. Increases in level of feed intake relative to maintenance usually decreases digestion of feed, as measured by total tract disappearance. Genetic variation appears to influence total tract digestion of feed (Herd et al., 2004). Young bulls and heifers, phenotypically ranked as low or high for RFI, tended to differ in their ability to digest dry matter by approximately 1% (Richardson et al., 1996). This difference in dry matter digestibility accounted for 14% of the difference in intake between the two groups of cattle. Variation in the supply of amino acids is due in part to variation in efficiency of microbial protein production in the rumen and appearance in the portal vein (Herd et al., 2004). In dairy cows, there is evidence that selection for high milk yield is accompanied by improvement in digestion and/or absorption of dietary energy and protein (Adams and Belyea, 1987). Results summarized by Herd and coworkers (2004) suggest differences in the processes of digestion and substrate availability, at least in portal blood, do occur. Herd and coworkers (2004) concluded these results provide a possible mechanism to explain variation in efficiency of feed utilization, without the need to invoke variation in nutrient utilization. Dry matter digestibility was phenotypically correlated with RFI (-0.44). This determined differences in digestibility that accounted for 19% of the phenotypic variation in RFI (Richardson and Herd, 2004). The direction of the correlation suggests lower RFI values were associated with higher digestibility. Richardson and Herd (2004) suggest some of the differences in digestibility may be associated with differences in rate of passage. 17 Body Composition and Metabolism. According to Herd et al. (2004), the deposition of the same weight of lean tissue and fat has different energy costs. There is more variation in the efficiency of depositing lean gain than fat gain. There have been few studies in which contribution of body composition to genetic variation in heat production or feed efficiency has been studied (mice, Archer and Pitchford, 1996; beef cattle, Richardson et al., 1999). These authors found variation in composition was small, relative to variation in heat production. It is useful to consider possible causes of variation in metabolism which impact heat production. Many of these processes contribute to the maintenance energy requirement of an animal (Herd et al., 2004). Some of these processes include demonstrated differences in energy efficiency used for maintenance between animals (Archer et al., 1999). Also, there is evidence that maintenance energy requirement per unit metabolic weight was closely associated with genetic variation in RFI (Herd and Bishop, 2000). Another process includes protein turnover. Protein turnover in living animals is an energetically expensive process and variation in protein metabolism has been shown to accompany genetic selection for growth and other traits in domestic animals (Herd et al., 2004). All together, evidence supports many possible mechanisms of variation in metabolism. Variations in metabolism are principally regulated at the tissue level (Herd et al., 2004). If there are differences in nutrient supply due to variation in digestion and absorption of feed, there may also be associated changes in hormone release and thus tissue responsiveness, over and above the availability of substrate (Herd et al., 2004). 18 Herd and coworkers (2004) suggest the challenge remains in identifying the possible contributors to variation in efficiency associated with other desirable traits. Results show from the RFI selected steer group that chemical composition was correlated with genetic variation in RFI. Steer progeny of low RFI parents have less whole body chemical fat and more whole body chemical protein, as compared to progeny of high RFI parents (Richardson and Herd, 2004). It was estimated these differences contributed 5% of the genetic variation in RFI. Measurements taken on steers following divergent selection for RFI support the hypothesis that rates of protein degradation and protein accretion in the whole body are correlated with variation in RFI in beef cattle. From these measurements Richardson and Herd (2004) concluded more efficient steers possess a more efficient mechanism for protein deposition. Less efficient steers have a greater rate of protein degradation and higher levels of protein catabolism in the liver. With all these factors taken into consideration, Richardson and Herd (2004) concluded it is likely there is genetic association between protein turnover and RFI. Activity. Variation in heat production and energy available for maintenance and growth also occurs as a result of differences in energy expenditure associated with activity (Herd et al., 2004). Activity also contributes to substantial proportions of the variation in RFI in chickens (Braastad and Katle, 1989; Katle, 1991; Luiting et al., 1991). Luiting and coworkers (1991) concluded 79% of the genetic difference in RFI of lines of chickens divergent for RFI could be related to a difference in physical activity. In mice selected for and against RFI post-weaning, there were marked differences in activity 19 pattern, such that more efficient mice were less active than less efficient mice (Herd et al., 2004). Differences in activity can also be associated with variation in RFI in cattle. Herd et al. (2004) reported a phenotypic correlation of 0.32 between RFI, based on activity as measured with a daily pedometer count. Approximately 10% of observed variation in RFI was explained by this measure of activity. Mechanisms associated with variation in activity include work involved in feeding, ruminating and walking at various speeds (Herd et al., 2004). Thermoregulation. The principal route for energy loss in ruminants is evaporative heat loss. To a large extent this is regulated by rate of respiration. No studies to date have examined the relationship between respiration rate and RFI. Postural change and other adaptations such as seeking shelter and huddling do not, by themselves, constitute a large proportion of variation in heat loss, except in extreme situations (Herd et al., 2004). Feeding Patterns. Richardson and Herd (2004) examined feeding patterns in steers bred for high or low RFI values. High RFI steers tended to have a faster decline in the length of average daily feeding sessions, and the high RFI steers had longer eating sessions early in the test as compared to low RFI steers. This, along with the observed difference in profiles for the total time spent on daily feeding, suggest that high RFI steers were standing and feeding longer than low RFI steers. This contributes 2% of the variation associated with RFI (Richardson and Herd, 2004). Studies on monogastric species reveal the potential importance of differences in activity to variation in RFI. DeHaer et al. (1993) found in a study with pigs that total 20 daily feeding time and number of visits to a feeding station showed a positive phenotypic correlation with RFI (0.64 and 0.51, respectively). On a daily basis these results indicate animals ranked by improved RFI spent less time feeding when visiting the feeding station. Stress. Fraser et al. (1975) defined stress as an abnormal or extreme adjustment in the physiology of an animal to cope with adverse effects of its environment and management. Cattle in an intensive husbandry system, such as a feedlot, are potentially subjected to an increased abundance of stressors, such as sudden noise, dust, transportation, mixing, and close proximity to others. Using results for red and white blood cell parameters of steers selected for RFI, high RFI steers may be more susceptible to stress than low RFI steers (Richardson and Herd, 2004). Richardson and Herd (2004) concluded there are many mechanisms contributing to variation of RFI (Figure 2). Further research is required to understand these and other possible biological mechanisms that contribute to RFI. Nkrumah and coworkers (2006) studied the relationship of feedlot FCR, performance, and feeding behavior with metabolic rate, digestion, and energy partitioning in beef cattle ranked by RFI. Differences among the groups of RFI selected steers were found to include efficiency in energy of ADG, FCR, DMI, but not in metabolic BW or ADG. There were no significant differences observed among RFI groups for heat increment of feeding, even though the low RFI steers had 32.6% lower heat increment of feeding. Nkrumah et al. (2006) reported a negative association between RFI and digestibility of dietary crude protein (-0.34) and dry matter (-0.33). 21 There were also no significant results reported for NDF and ADF analyses of diets on RFI levels. The analyses did indicate NDF digestibility was less in high RFI steers than low RFI steers (Nkrumah et al., 2006). Feedlot FCR of steers was also unrelated to any of the metabolic rate and energy partitioning traits. Nkrumah and coworkers (2006) concluded differences in metabolism; mainly digestibility and methane production, heat production, and energy retention are responsible for a major part of the variation among animals in RFI. Other Important Post-Weaning Traits There are many traits that are important for producers to consider when selecting bulls for their breeding program. Traits easily measured include average daily gain (ADG) and scrotal circumference (SC). Improved gains result in heavier market weights, while larger yearling SC measurements may improve heifer fertility. Yearling SC measurements are genetically correlated to age at puberty in subsequent daughters (Moser et al., 1996 and Vargas et al., 1998). Average Daily Gain (ADG). ADG is another way to measure post-weaning growth in livestock. ADG is moderately to highly heritable in beef cattle with estimates ranging from 0.13 to 0.47 (Bishop et al., 1991b; MacNeil et al., 1991; Veseth et al., 1993; Archer et al., 1997; Evans et al., 1999; and Jakobsen et al., 2000), and 0.26 in ram lambs (Cammack et al., 2005). ADG shows a negative genetic correlation with fat thickness (FT) and FCR (-0.20 and -0.43, respectively). This negative correlation indicates that selection for improved ADG may result in lower subcutaneous FT measurements and an improved FCR (MacNeil et al., 1991). 22 Scrotal Circumference. Bull selection for increased scrotal circumference is considered to be a fast way to genetically improve fertility traits in beef cattle (Keeton et al., 1996). Scrotal circumference is found to be highly heritable with estimates reported ranging from 0.16 to 0.78 (Coulter and Foote, 1979; Neely et al., 1982; Knights et al., 1984; Bourdon and Brinks, 1986; Nelson et al., 1986; Lunstra et al., 1988; Smith et al., 1989; Meyer et al., 1990; Kriese et al., 1991a; Meyer et al., 1991; Keeton et al., 1996; Evans et al., 1999; Eler et al., 2004). More importantly, yearling SC is genetically related to more traits of female reproduction. Real-Time Ultrasound Ultrasound is used for live animal carcass prediction. Carcass composition can be determined on all species of livestock using real-time ultrasound technology (Perkins et al., 1997). The first animal evaluation using the application of ultrasound was in 1956 in the United States (Stouffer, 2004). Ultrasound is a non-destructive, humane method to provide quantitative identification of muscle and fatty tissue of the live animal (Perkins et al., 1997). Backfat thickness over the 12 th rib was the first trait evaluated in beef cattle. Currently, cattle evaluated by carcass ultrasound utilize real-time ultrasound technology. Today, the most common carcass traits evaluated with ultrasound include back fat thickness (USFAT) and longissimus muscle area (USREA), rump fat thickness (USRF) and percent intramuscular fat (USIMF) at yearling age. Genetic evaluations for carcass traits based on ultrasound measurements have the potential to increase the rate of genetic progress and reduce the expenses involved in progeny testing. However, it is important to obtain reliable heritability and genetic correlation estimates between carcass measurements on finished steers and ultrasound 23 measurements on yearling bulls (Devitt and Wilton, 2001). Heritability estimates for ultrasound carcass traits have been well published in a variety of research studies (Arnold et al., 1991; Moser et al., 1998; Crews et al., 2003; Carstens and Tedeschi, 2006). Additionally, genetic correlations between progeny carcass traits and yearling ultrasound traits have been published and suggest genetic progress can be made in actual carcass traits with ultrasound-based selection. Heritability Estimates and Genetic Correlations for Ultrasound Measurements. It is industry standard for ultrasound measurements to be taken at yearling age for carcass traits (BIF, 2002). Level of diet and environment can affect the variation seen in populations measured and thus heritability estimates. In general, heritability estimates USFAT, USREA and USIMF are moderately heritable. However, a wide range of estimates can be found in the literature. An early literature report estimated heritability for USFAT at 0.04 and USREA at 0.12 on 385 Hereford bulls (Turner et al., 1990). Using a larger sample size, Arnold and coworkers (1991) reported heritability estimates for USFAT and USREA of 0.26 and 0.25, respectively. These estimates were on a constant weight basis utilizing both Hereford bull (n = 3,089) and heifer (n = 393) data. Devitt and Wilton (2001) reported heritability estimates for yearling ultrasound bull measurements to a constant weight basis also. These estimates were 0.44 for USREA, 0.24 for USIMF, and 0.55 for USFAT. In later literature estimates, Hassen et al. (1998a) reported heritability estimates of 0.05 for USFAT and 0.21 for USREA. Crews and Kemp (2002) reported heritability estimates for bull USREA, heifer USREA, bull USFAT, and heifer USFAT (0.61, 0.49, 0.50, and 0.44, respectively). 24 The literature provides few studies where heritability estimates are adjusted to a common age. Some of the first heritability estimates reported for ultrasound measured traits were moderately heritable. Arnold and coworkers (1991) reported age constant heritability estimates for USFAT (0.26) and USREA (0.28). Earlier literature reports heritability estimates adjusted to a common age for USFAT (0.14) and USREA (0.40) on Brangus cattle (Johnson et al., 1993). Moser and coworkers (1998) reported age constant heritability estimates of 0.11 for USFAT and 0.29 for USREA. More recent literature reports age constant heritability estimates of 0.48 for USREA, 0.23 for USIMF, and 0.52 for USFAT on yearling bull ultrasound data (Devitt and Wilton, 2001). Stelzleni et al., (2002) reported similar ultrasound heritability estimates for USREA, USFAT, and USIMF (0.31, 0.26, and 0.16, respectively) on Brangus bulls and heifers. Ultrasound measured traits adjusted to a common age are all moderately to highly heritable. One article in the literature reported heritability estimates for ultrasound measured traits with backfat thickness held constant. Devitt and Wilton (2001) reported a heritability estimate of 0.48 for USREA and 0.23 for USIMF. These estimates taken on yearling bull ultrasound measurements were moderately to highly heritable. Crews et al. (2003) examined genetic parameters and their live animal indicators in Simmental cattle and found that replacement bull and heifer USFAT resulted in heritabilities of 0.53 and 0.69, respectively. Low heritability estimates were reported earlier in Brangus cattle by Johnson et al. (1993) and Moser et al. (1998) for yearling USFAT when bull and heifer data were combined. Shepard et al. (1996), however, estimated a heritability estimate of 0.56 for yearling USFAT in Angus cattle. Heritability estimates of 0.37 and 0.51 for replacement bull and heifer USREA (Crews et al., 2003) 25 were also reported. These are also similar to previously reported heritability estimates indicating the potential to improve carcass characteristics in the breeding herd?s offspring. Correlations between Real-Time Ultrasound and Carcass Traits. Literature reports genetic correlations between real-time ultrasound measured traits (USFAT, USREA and USIMF) and their corresponding carcass traits (12 th rib fat thickness, longissimus muscle area and marbling score) are highly correlated to one another. This suggests yearling bull ultrasound measured traits can be used to improve progeny carcass characteristics for the feedlot phase. Research shows few reports of genetic correlations between postweaning growth traits and ultrasound measured traits adjusted to live weight. One study reports genetic correlations between ADG and USFAT and USREA (-0.02 and 0.06 respectively) were small because the data were adjusted for live weight (Arnold et al., 1991). When adjusted for age, genetic correlations revealed consistently positive relationships among USFAT with ADG and USREA with ADG (0.23 and 0.33, respectively). The genetic correlation between age constant USFAT with USREA was greater in magnitude (0.48) than weight constant analysis (0.39). When examined to either a weight constant or an age constant basis, backfat measurements in these yearling Hereford cattle were positively correlated with growth rate and size (Arnold et al., 1991). These estimates suggest ultrasound and carcass traits are moderately heritable and selection based on ultrasound measurements could improve progeny carcass measurements. Crews and coworkers (2002) collected real-time ultrasound images on composite bulls (n = 224), steers (n = 116), and heifers (n = 257) three times, including 60 d 26 post-weaning, near one year of age and three to seven days prior to harvest. Real-time ultrasound images were collected by one technician and interpreted by a second technician. The residual correlation between USREA and longissimus muscle area was 0.87 (Crews et al., 2002). Indicating USREA measurements taken post-weaning accurately reflect variability in longissimus muscle area measured at older ages or harvest. These results compare favorably with those of previous studies showing moderate to high correlations between USREA and longissimus muscle area. Smith et al. (1992) reported simple correlations of 0.43 and 0.63 between USREA and longissimus muscle area measurements in two studies. Hassen et al. (1998b) reported correlations of 0.48 and 0.44, respectively, between USREA and longissimus muscle area. Higher correlations of 0.60 (Perkins et al., 1992) and 0.52 to 0.72 for multiple technicians (Herring et al., 1994) have also been reported between USREA and longissimus muscle area. Yearling and USFAT measures resulted in residual correlations of 0.78 and 0.86 with carcass fat thickness, respectively (Crews et al., 2002). A similar correlation (0.89) between USFAT and carcass fat thickness in steers and heifers was reported by Faulkner et al. (1990). High similar correlations (0.70 to 0.82) between USFAT and carcass fat thickness have also been reported in several studies (Perkins et al., 1992; Smith et al., 1992; Herring et al., 1994; Hassen et al., 1998b). Devitt and Wilton (2001) utilized crossbred steer carcass data (n = 843) and yearling bull ultrasound measurements (n = 5,654) to estimate genetic parameters of carcass traits from two different sources and to determine the genetic correlations between steer carcass measurements and bull ultrasound measurements. 27 Age constant genetic correlations between crossbred steer carcass data and yearling bull ultrasound measurements were also reported by Stelzleni et al. (2002). These genetic correlations were between steer longissimus muscle area and USREA (0.66), steer marbling score and USIMF (0.80), steer backfat and USFAT (0.88), and steer ADG and bull ADG (0.72). Similar genetic correlations were reported by Moser et al. (1998) between carcass longissimus muscle area and USREA (0.66) and carcass backfat with USFAT (0.69) with age held constant. Devitt and Wilton (2001) also reported genetic correlation estimates with backfat held constant. Genetic correlations between steer carcass traits and yearling bull ultrasound measurements with backfat held constant were steer REA with USREA (0.57), steer marbling with USIMF (0.68) and steer ADG with bull ADG (0.87) (Devitt and Wilton, 2001). Finally, Devitt and Wilton (2001) looked at genetic correlation estimates with weight held constant. Genetic correlations between steer carcass traits and yearling bull ultrasound traits adjusted to a common weight were 0.75 between steer REA and USREA, 0.68 between steer marbling score and USIMF, and 0.91 between steer BF and USFAT (Devitt and Wilton, 2001). All moderate to high genetic correlations reported by Devitt and Wilton (2001), regardless of which trait was held constant, were similar overall in sign and magnitude. A year later, Crews and Kemp (2002) reported similar positive genetic correlations between ultrasound measured traits and steer carcass traits. The genetic correlations reported between bull USREA and carcass REA, heifer USREA and carcass 28 REA, bull USFAT and carcass fat thickness, and heifer USFAT and carcass fat thickness were 0.71, 0.67, 0.23, and 0.66, respectively. Most literature concludes USREA and USFAT measurements taken near weaning and yearling ages could be used to predict corresponding carcass measurements in beef steers, bulls, and heifers. Predictions based on yearling measurements were more accurate for fat thickness; however, predictions based on weaning vs. yearling measurements were similar for muscle area (Crews et al., 2002). All literature reported indicates genetic progress can be made in actual carcass traits with ultrasound-based selection (Devitt and Wilton, 2001). Ultrasound measured traits in the breeding herd were consistent with carcass measured traits in the finishing herd (Crews and Kemp, 2002). Variation in Ultrasound Measurements Among Breeds. Breed differences have been shown by many studies for reproduction, growth and carcass traits. One study detected ultrasound trait differences (Bergen et al., 1997) among breeds. Measurements were taken on British (Angus, Hereford and Shorthorn) and Continental (Charolais and Simmental) breeds of cattle during a post-weaning performance test. Breed differences were detected (P < 0.05) for end of test ultrasound measurements. Charolais and Simmental bulls had less fat than British breed bulls, but did not differ from each other at the end of test. Among the British breed bulls, Angus and Shorthorn bulls were fatter than Hereford bulls. Continental breeds had larger USREA than British breeds but did not differ from each other. Within the British breeds, Angus and Shorthorn bulls did not differ from each other but had larger USREA than Hereford bulls (Bergen et al., 1997). Bergen and coworkers (1997) concluded the moderate heritability of these traits, 29 combined with their high degree of within-breed phenotypic variation, indicates that ultrasound may make a valuable addition to genetic improvement programs for carcass traits. Conclusion There are many definitions of feed efficiency that are used in the beef cattle industry. The most popular definition used is the ratio of G:F or its inverse FCR. G:F and FCR were reported to be moderately heritable throughout the literature. However, there has been increasing interest in RFI recently. RFI is defined as the difference between an animal?s actual feed intake and expected feed intake for their level of production. Heritability estimates of RFI were also reported to be moderately heritable throughout the literature. RFI may be a better efficiency comparison tool among individuals because it takes into consideration size of the animal where G:F or FCR does not. Many underlying biological mechanisms occur in individual animals that cause certain animals to have better efficiency than others. Digestion of feed, metabolism and animal activity level are some of the biological mechanisms that can differ in individuals. These underlying biological mechanisms are not fully understood and further research is needed. ADG was also reported to be moderately heritable, while SC was reported to be highly heritable throughout the literature suggesting that genetic improvement can be made with these traits. Post-weaning ultrasound measured traits were moderately to highly heritable. Post-weaning ultrasound measurements (USFAT, USREA, and USIMF) were found to be genetically correlated to carcass trait (12 th rib fat thickness, 30 longissimus muscle area, and marbling score) estimates taken on individuals post-harvest. This suggests that genetic progress can be made in actual carcass traits with ultrasound- based selection. Individual performance records collected on bulls in a central test can be added to National Cattle Evaluation (NCE) models to predict EPD?s. Producers can then use their respective breed?s EPD?s to select a total package herd sire for their breeding program. Research Objectives The purpose of this research is to: 1. Determine heritability estimate of RFI in bulls measured at a central test. 2. Determine genetic correlations of RFI in central test bulls with other postweaning measures of growth, efficiency and product end point. 3. Determine phenotypic and genetic trends for RFI in central test bulls. 4. Determine relationships of traits in low and high RFI bulls. 31 EVALUATION OF FEED EFFICIENCY TRAITS WITH POST-WEANING GROWTH AND ULTRASOUND TRAITS IN CENTRAL TEST BULLS Introduction Central bull test stations are used in many parts of the country to evaluate post- weaning performance of bulls under uniform conditions. The first test stations were used to demonstrate performance concepts and improve growth rate in many breeds of beef cattle. The first full scale evaluation test for gain was conducted with bulls and heifers in 1949 in Texas (Warwick and Cartwright, 1955). With the development of national cattle evaluations conducted by many breeds today, central test stations are now used by seedstock breeders as an additional source of performance records on their bulls. There are several ways to examine and define feed efficiency in cattle; many times being expressed as a ratio of G:F or its inverse feed conversion ratio (FCR). Another measure of efficiency, residual feed intake (RFI) is defined as the difference between the actual feed intake and the expected feed intake requirements for maintenance of body weight and production (Koch et al., 1963). Since maintenance and growth requirements are not accounted for by G:F, RFI comparisons between animals may be a better measure of efficiency (Kolath et al., 2006). Many studies have examined at least one of the definitions of feed efficiency in beef cattle (Dawson et al., 1955; Koch et al., 1963; Arthur et al., 1999, 2001a,b). In general, studies utilizing a ratio definition of feed efficiency and RFI have suggested feed 32 efficiency and RFI are moderately heritable (0.22 to 0.80 and 0.14 to 0.62, respectively). With RFI being moderately heritable, genetic change can be achieved based on selection of low RFI bulls. The objective of this study was to determine heritability estimates of and phenotypic and genetic correlations between post-weaning growth and ultrasound carcass measurements of bulls consigned in a full feed bull test. Also, bulls were ranked for RFI to determine relationships of traits measured between low RFI bulls and high RFI bulls. MATERIALS AND METHODS Experimental Data Data were collected on bulls consigned to the Auburn University Bull Test from 1977 to 2004. All bulls were consigned by individual breeders located primarily in the Southeastern United States. Guidelines for full-feed central bull test programs were followed as outlined by the Beef Improvement Federation (BIF, 2002). A total of 2,277 bulls from 26 breeds were evaluated at the test station since 1977. For this analysis 2,180 records on eight breeds were utilized. The eight breeds included in the analyses were Angus, Brangus, Charolais, Gelbvieh, Hereford, Limousin, Santa Gertrudis, and Simmental. Bulls were housed at the Beef Cattle Evaluation facility on the Auburn University campus. The facility, constructed in 1976, consists of 8 pens with 12 Calan-Gates installed in each pen. Individual feed intake was measured for a maximum of 96 bulls per evaluation. One evaluation was held each year. Bulls were delivered in late July to early August each year. After a 21 day acclimation period, bulls were weighed on test. 33 Bulls remained at the test facility until sale day. Depending on year, bulls were sold via auction from January through March. Bulls had inside and outside access with inside pen dimensions of 6.096 meters wide by 9.144 meters long. Water was provided using automatic water troughs with one trough supplying water to two pens. Outside pen dimensions have changed over the years to maximize bull health and minimize environmental impact. Until 2002, outside pens consisted of a dirt and stone foundation. In 2002, common bermudagrass (Cynodon dactylon) was planted to minimize nutrient runoff, rock upheaval and increase foot health of bulls. Currently, outside pens are 54.864 meters wide by 92.6592 meters long and divided into three 18.288 meter strips. Bulls were allowed access to one strip per pen weekly. This allowed grass coverage to be maintained for the duration of the test. From 1977 to 1989 length of test was 140 d. In 1990, length of test was shortened to 112 d. In 2000, test length was shortened to 84 d. Bulls were fed twice daily with access to ad-libitum amounts of the diet. Enough feed was placed in each bunk to ensure 0.45 to 2.27 kg remained in each bunk prior to the next feeding. Feed weights were recorded at each feeding. Orts were taken as necessary between weigh days. Orts were always measured each weigh day. Throughout the years, the composition of the feed has remained fairly consistent. Diet ingredients changed due to availability and cost. All diets were formulated for a constant level of total digestible nutrients (TDN) and protein (CP). Table 1 describes TDN, CF, and CP levels of the diet since 1977. 34 Data Collection At bull delivery, initial weight, hip height and scrotal circumference were measured. A general health exam was also performed by Auburn University College of Veterinary Medicine personnel. Bulls were allotted into one of the eight pens by breed, hip height and weight. Bulls not meeting entry requirements for weight (2.5 pounds weight per day of age), scrotal circumference and health were excused from the test. Appropriate BIF guidelines for full feed bull evaluations were followed throughout the years. Bulls had an adjustment period of 21 d to become accustomed to the facility, calan gate and diet. Bulls unable to adjust to calan gates by d 21 were excused from the test. At the end of 21 d, bulls were weighed and measured for hip height on two consecutive days. The weights and heights were averaged for an on test weight and height. Subsequent measurements were taken every 28 d until the end of the evaluation. Bulls were again measured on subsequent days at the end of the evaluation. Final scrotal circumference was also taken at this time. At each weigh period, daily feed intakes, weight and hip height were used to determine FCR, average daily gain (ADG), weight per day of age (WDA) and frame score. Feed intake data was used to determine residual feed intake (RFI). RFI values for this study were estimated as outlined by Okine et al., (2004) and Archer (Personal Communication, 2005, 2007) and detailed in Appendix A. At the end of the feeding evaluation, adjusted yearling weight was also calculated (BIF, 2002). From 1985 to 1991, fat thickness measurements were taken on bulls. These estimates were obtained ultrasonically or by using a probe at the 12 th rib. Beginning in 35 1992, real time ultrasound measurements of carcass composition were taken. From 1992 to 1998, measures of 12 th rib fat thickness (USFAT) and longissimus muscle area (USREA) were routinely taken. Beginning in 1998, measures of percent intramuscular fat (USIMF) were added. In general, ultrasound measures were taken between 56 d and 84 d of the feed evaluation using an Aloka 210 real-time ultrasound machine in the beginning (1985 - 1993) and an Aloka 500 real-time ultrasound machine (Corometrics Medical Systems, Wallingford, CT, 17.2 cm transducer) after 1993. Dates were adjusted yearly to ensure bulls fit required age windows of appropriate national breed associations. All ultrasound information has been collected by the same technician, since 1992. Data Analysis Data were edited using SAS (SAS Institute Inc., Cary, NC) to check means, minimum numbers and maximum numbers for errors. Prior to editing, there were 2,277 bulls in the dataset. Edits reduced the number of records available for analysis to 2,180. Breeds to analyze were determined by the total number of each breed and the representation of each breed across years. Eight breeds were included in the final dataset. They included Angus, Brangus, Charolais, Gelbvieh, Hereford, Limousin, Santa Gertrudis, and Simmental. Further editing of the data set eliminated bulls with incomplete or unknown pedigrees or data. Data were analyzed using age of bull at sale date and final test weight as a covariate. Bulls with missing birth dates were removed from the final data set when age was used as a covariate. When final weight was used as a covariate, those bulls that did not have an age were included because they had a final weight. 36 Model A sire-maternal grandsire (sire-mgs) model was used to estimate (co)variance components of the data using MTDFREML (Boldman et al., 1993). A series of three- trait multiple trait analyses were used to estimate all (co)variance components. (Co)variance components were used to form estimates of heritability and genetic correlations. The basic sire-mgs model used was: Y ijklm = length of test i +year j + breed k + s l + mgs m + e ijklm Where: i = length of test fixed effect j = year of test fixed effect k = breed of bull on test fixed effect l = random sire effect m = random maternal grandsire effect and covariates of final test weight or age of bull at sale date were used. The general form of the mixed model matrix equations for the sire-mgs model was: X?X X?Z 1 X?Z 2 X?? y Z? 1 X Z? 1 Z? 1 Z? 1 Z? 2 +G -1 = Z? 1 s 1 y Z? 2 X Z? 2 Z? 1 Z? 2 Z? 2 m 1 Z? 2 gs 37 Where X = Incidence matrix that relates fixed effects to vector of observations y Z 1 = Incidence matrix that relates random effects of sire to the model Z 2 = Incidence matrix that relates random effects of maternal grandsire effects to the model G -1 = Inverse of numerator relationship matrix including (co)variance components ? = Fixed effects of breed, year and length of test s 1 = Random effects of sire mgs 1 = Random effects of maternal grandsire y = Vector of observations y ^ ^ ^ ^ ^ ^ The (co)variance matrix for random effects was: 38 s A? 2 s A? s,MGS 0 V mgs = A? s,MGS A? 2 MGS 0 e 0 0 I? 2 e Where A = Numerator relationship matrix for all sires and mgs in the analysis I = Identity matrix for residual effects ? 2 s = The variance of sire effects ? s,mgs = The covariance between sire effects and MGS effects ? 2 mgs = The variance of MGS effects ? 2 e = The variance of residual effects Analyses were stopped when the variance of function values (-2 log L) in the simplex were equal to 1 X 10 -9 . Each analysis was then restarted using the estimates of parameters as new priors to verify a local minimum was not reached. All models converged to a global minimum when there was no change in function values (-2 log L) (Boldman et al., 1993). A maximum of 3,739 animals were contained in the inverse of the numerator relationship matrix (A -1 ), with final weight as a covariate. Fewer animals were included in A -1 when ultrasound traits were analyzed. There were 2,962 animals included in USFAT analyses. USREA analyses included 2,045 animals and USIMF analyses contained 1,100 animals were in A -1 . A maximum of 3,725 animals were included in A -1 with age as covariate. Once again, fewer animals were included in A -1 when ultrasound traits were analyzed. There 39 were 2,957 animals included in USFAT analyses. USREA analyses included 2,045 animals and USIMF analyses included 1,100 animals in A -1 . With age as covariate a total of 958 sires and 1,111 MGS were in the final data set. There were 398 sires and 378 MGS with more than one progeny. With final weight as a covariate a total of 962 sires and 1,115 MGS were in the final data set. There were 400 sires and 379 MGS with more than one progeny. Traits analyzed included total gain on test (TG), FCR, RFI, ADG, USFAT, USREA, USIMF and scrotal circumference (SC). (Co)variances were converted from ? 2 s , ? 2 m , and ? sm to ? 2 a and ? a1a2 values. (Co)variance components were averaged across analyses for each trait to determine final estimates which can be seen in Tables 2, 3, 4, and 5. These values were used to estimate heritabilities and correlations. Residual Feed Intake Analyses Bulls were classified as high RFI (RFI ? 0) or low RFI (RFI < 0) individuals. Because of the inherent nature of RFI, half of the bulls were classified into each category. To evaluate differences of other measured traits in high and low RFI bulls, GLM models in SAS (SAS Institute Inc., Cary, NC) were run to examine TG, ADG, adjusted 365 d weight (YW), initial weight (IW), USFAT, USREA, USIMF, frame score (FS), feed intake (FI), scrotal circumference (SC), FCR and RFI. Fixed effects were year, breed, length of test, and RFI classification. Covariates were again age at sale date or final weight. The LSMEANS procedure of SAS (SAS Institute Inc., Cary, NC) was used to separate means. 40 RESULTS AND DISCUSSION Raw means of performance data for each covariate are contained in Table 6 and Table 7. On average bulls were 405 d of age when sold and had an average final weight of 586 kg. Bulls had similar means for FCR, ADG, SC, USIMF, YWT, USREA, USFAT and RFI for age and weight adjusted analyses. Raw means of performance data by breed for each covariate are in Table 8 and Table 9. From these simple means, Charolais, Gelbvieh, Limousin, Hereford, and Santa Gertrudis breeds had lower RFI values than other breeds. British breeds (Angus and Hereford) on average were fatter than Continental breeds (Charolais, Gelbvieh, Limousin and Simmental) and had smaller longissimus muscle areas. Heritability Estimates and Correlations Heritability estimates and phenotypic and genetic correlations among traits are found in Tables 10 and 11. Heritability estimates adjusted to a common age or weight were similar in magnitude, except for USREA. In all cases, except TG, heritability estimates were lower than published literature reports. Heritability estimates for efficiency based traits adjusted to a common age were 0.13 for FCR and 0.10 for RFI (Table 10). Heritability estimates adjusted to a common weight were 0.12 for FCR and 0.09 for RFI (Table 11). Most heritability estimates for FCR and RFI are moderate (Koch et al., 1963; Woldehawariat et al., 1977; Herd and Bishop, 2000; and Pitchford, 2004). However, Brown and coworkers (1988) reported a 41 smaller heritability estimate of 0.14 for FCR. Pitchford (2004) also reported a smaller heritability estimate for RFI in growing dairy males of 0.08. Heritability estimates reported in the literature for FCR range from 0.14 to 0.80 (Woldehariat et al., 1977; Brown et al., 1988; Herd and Bishop, 2000 and Arthur et al., 2001a). Arthur and coworkers (2001b) reported heritability estimates for FCR on Charolais bulls at 15 and 19 months of age (0.46 and 0.31, respectively). Age and weight adjusted heritability estimates reported in this study were comparable to earlier, smaller heritability estimates of 0.14 by Brown et al. (1988) and 0.15 by Herring and Bertrand (2002). Heritability estimates reported in the literature for beef cattle RFI range from 0.16 to 0.41 (Koch et al., 1963; Archer et al., 1997; Herd and Bishop, 2000; Arthur et al., 2001a). Pitchford (2004) reported a range of heritability estimates for RFI on growing dairy males from 0.08 to 0.36. Estimates found in this study fall into the lower range of these values. One explanation for these low heritability estimates is the data structure. Although bulls were reared together post-weaning, initial contemporary group (CG) structure was lost. Pre-weaning differences in management and selection strategies were not accounted for in the model. Also, field data heritability estimates are generally lower than heritability estimates from designed studies because there are more people involved (beef unit manager, bull test supervisor and student employees) with data collection throughout the years. With a designed study, usually the same person(s) is collecting the data so there should be less room for error. 42 Heritability estimates for postweaning growth traits adjusted to a common age or weight were low for ADG, TG and SC. Literature estimates for ADG range from 0.13 to 0.47 (Bishop et al., 1991b; MacNeil et al., 1991; Fan et al., 1995; Archer et al., 1997; Jakobsen et al., 2000) with most being moderate in size. Using Hereford and Angus cattle Fan et al. (1995) reported ADG estimates of 0.16 and 0.43 for heritability. Herring and Bertrand (2002) also reported a lower heritability estimate of 0.28 for ADG. The same trends were seen for TG adjusted for either covariate (Table 10 and 11). Low heritability estimates for TG have been reported previously especially when breed association field data were analyzed (Kriese et al., 1991a,b). However, with designed studies TG is generally found to be moderately heritable (Koch et al. 1963, 2004; Woldehawariat, 1977). Most literature reports heritability estimates of SC to be moderate to highly heritable ranging from 0.36 to 0.78 (Coulter and Foote, 1979; Bourdon and Brinks, 1986; Meyer et al., 1991; Evans et al., 1999; and Eler et al., 2004). However, this study found low heritability estimates for SC. Kriese and coworkers (1991a) reported SC heritability estimates on Brangus and Hereford cattle field data (0.16 and 0.53, respectively). The low heritability estimates could be a function of the type of bulls consigned to central test stations. Breeders generally consign only their best bulls, thus decreasing additive genetic variation. Additionally, since eight breeds were present in the data, the fixed effect of breed in the model could be accounting for significant amounts of the variation present. USFAT, USREA and USIMF heritability estimates were also low in magnitude adjusted for either covariate (Table 10 and 11). Arnold and coworkers (1991) reported 43 age and weight adjusted heritability estimates on Hereford bulls and heifers for USFAT (0.23 and 0.26) and USREA (0.33 and 0.25). In 1990, Turner and coworkers reported heritability estimates for USFAT (0.04) and USREA (0.12) on yearling Hereford bulls. However, Arthur et al. (2001a) reported much larger heritability estimates for USFAT (0.35) and USREA (0.27). The estimates reported in this study, adjusted to a common age or weight, were lower than most heritability estimates reported throughout the literature involving ultrasound measured traits. Phenotypic Correlations Between RFI and Associated Traits. Phenotypic correlations between RFI and postweaning growth traits (ADG and TG) were small or zero when adjusted to a common age or weight (Table 10 and 11). These results are in agreement with previously reported literature (Koch et al., 1963; Jenson et al., 1992a; Arthur et al., 2001a,b; Basarab et al., 2003; and Carstens and Tedeschi, 2006). This suggests RFI as a measure of efficiency is independent of gain in growing bulls. Phenotypic selection of individual bulls with improved RFI should not affect the size of animal. RFI had a strong positive phenotypic correlation with FCR (0.61 and 0.60) when adjusted to a common age and weight, respectively. Herd and Bishop (2000) reported a phenotypic correlation between RFI and FCR of 0.61 on Hereford cattle while Arthur et al. (2001a) reported a correlation of 0.53. Carstens and Tedeschi (2006) reported a similar phenotypic correlation between RFI and FCR of 0.56, while Baker et al. (2006) reported a phenotypic correlation of 0.42. These phenotypic correlations are related in a 44 favorable direction, indicating that phenotypic selection for improved RFI will result in improved FCR. RFI was phenotypically uncorrelated with SC for both age and weight adjusted analyses (0.02 and 0.04, respectively). Arthur et al. (2001a) also reported a low phenotypic correlation of 0.10 between RFI and SC. RFI does not appear to phenotypically influence SC size. Most phenotypic correlations between RFI and ultrasound measured traits adjusted to a common age or weight were similar in sign and magnitude, except USFAT. RFI had a positive phenotypic correlation with USFAT (0.12) and USIMF (0.13) and a negative phenotypic correlation with USREA (-0.17), when adjusted to a common age. When adjusted to a common weight, RFI had a positive phenotypic correlation with USFAT (0.35) and USIMF (0.13) and a negative phenotypic correlation with USREA (-0.16). Literature reports of phenotypic correlations between RFI and USFAT and USREA are variable. Arthur et al. (2001a) reported a low phenotypic correlation between RFI and USFAT (0.14). Carstens and Tedeschi (2006) reported a smaller correlation of 0.11. Baker et al. (2006) reported a phenotypic correlation of 0.00 between RFI and USFAT, while Crews and coworkers (2003) reported a negative phenotypic correlation between RFI and USFAT. Phenotypic correlations reported in this study between RFI and USFAT (0.12 and 0.35) were positive, indicating cattle with improved RFI will tend to be leaner at the 12 th and 13 th rib. Arthur et al. (2001a) reported a low phenotypic correlation between RFI and USREA (0.06). Carstens and Tedeschi (2006) reported RFI was not phenotypically 45 correlated to USREA (0.00), while Baker et al. (2006) reported a small negative phenotypic correlation between RFI and USREA (-0.09). The correlations reported in this study for age and weight adjusted analyses between RFI and USREA is similar in sign to that of Baker et al. (2006), but is greater in scale (-0.17 and -0.16, respectively). These results indicate cattle with improved phenotypic RFI tended to produce a larger longissimus muscle area. Basarab et al. (2003) reported a phenotypic correlation between RFI and USIMF of 0.13, which was reported in this study when adjusted to a common age or weight. These results indicate as RFI improves in cattle their intramuscular fat will increase. This seems to suggest that cattle with improved RFI values marbled better, which mean these cattle have an opportunity to improve their quality grade. Genetic Correlations Between RFI and Associated Traits. Literature suggests RFI is independent of size reporting genetic correlations of zero or close to zero (Herd and Bishop, 2000; Arthur et al., 2001a,b). Genetic correlations between RFI and ADG and TG adjusted to a common age or weight reported in this study are similar to other estimates (-0.08 and -0.06 or 0.08 and 0.10, respectively). Arthur et al. (2001a,b) reported genetic correlations between RFI and ADG of -0.04 and -0.10, respectively, while Herd and Bishop (2000) reported a slight positive genetic correlation of 0.09. This study, along with most literature, suggests selection for improved RFI should not affect animal size. RFI had a positive genetic correlation with FCR for both age and weight adjusted analyses (0.49 and 0.46, respectively). Most literature reports strong positive genetic correlations between RFI and FCR (Fan et al., 1995; Herd and Bishop, 2000; Arthur et 46 al., 2001a,b). Arthur et al. (2001a,b) reported a genetic correlation between RFI and FCR of 0.66 involving Angus cattle and 0.85 involving Charolais cattle. Herd and Bishop (2000) reported a similar genetic correlation (0.70) between RFI and FCR. Schenkel et al. (2004) reported a correlation of 0.69, while Fan et al. (1995) reported much larger genetic correlations of 0.90 involving Angus cattle and 1.00 involving Hereford cattle. This favorable strong genetic correlation is indicative that both traits are measures of efficiency. RFI had a positive genetic correlation with SC when adjusted to a common age or weight (0.12 and 0.17, respectively). Studies indicate that RFI is independent of SC. Arthur et al. (2001a) reported a negative genetic correlation between RFI and SC (-0.03). These findings are not surprising. Since SC is genetically correlated to growth (Kriese et al., 1991a,b), and RFI is not related to growth, no strong genetic correlations should be present between RFI and SC. RFI had a strong positive genetic correlation with USIMF (0.77 for age adjusted analysis and 0.73 for weight adjusted analysis). However RFI was negatively correlated genetically to USFAT (-0.13 for age adjusted analysis and -0.02 for weight adjusted analysis) and USREA (-0.77 for age adjusted analysis and -0.70 for weight adjusted analysis). Nkrumah et al. (2007) reported genetic correlations between two different calculations of RFI and USIMF. RFI was calculated using a phenotypic regression and genetic regression. Genetic correlations between phenotypic RFI and USIMF (0.32) and genetic RFI and USIMF (0.44) were reported (Nkrumah et al., 2007). The results reported in this study were much greater in magnitude (0.77 and 0.73) than those reported 47 in literature, indicating a strong genetic relationship between RFI and USIMF. This could be a result of low numbers of USIMF measurements. Cattle with an improved RFI have the potential genes to reduce their intramuscular fat. This may cause slaughter cattle to not marble as well, and could potentially affect quality grade. Arthur et al. (2001a) reported a genetic correlation between RFI and USFAT (0.l7). Schenkel et al. (2004) reported a similar correlation of 0.16 between RFI and USFAT, while Basarab et al. (2004) reported a negative genetic correlation of -0.24. Nkrumah et al. (2007) reported genetic correlations between phenotypic RFI and USFAT and genetic RFI and USFAT to be 0.35 and -0.04, respectively. The correlations reported in this study for age or weight adjusted analysis (-0.13 and -0.02, respectively) were similar to what Basarab et al. (2004) reported. Results indicate selection for improved RFI may be genetically associated with an increased potential for subcutaneous fat deposition at the 12 th and 13 th rib. Arthur et al. (2001a) reported a genetic correlation between RFI and USREA of 0.09, while Schenkel et al. (2004) reported a negative genetic correlation of -0.17. Nkrumah et al. (2007) reported genetic correlations between phenotypic RFI and USREA and genetic RFI and USREA (-0.52 and -0.65, respectively). Results published in this study are similar in sign and magnitude (-0.77 and -0.70) to those reported in the literature. These genetic correlations indicate that selection for improved RFI may increase longissimus muscle area. Phenotypic Correlations Between FCR and Associated Traits. Phenotypic correlations between FCR and ADG and TG of -0.50 and -0.63, respectively, adjusted to a common age were reported in this study. Phenotypic correlations between FCR and 48 ADG and TG, -0.70 and -0.74, respectively, adjusted to a common weight were also reported. Woldehawariat et al. (1977) reported a range of phenotypic correlations between FCR and ADG (-0.26 to 0.55). Arthur et al. (2001a,b) reported phenotypic correlations on Angus and Charolais cattle between FCR and ADG (-0.74 and -0.54, respectively). Baker et al. (2006) and Carstens and Tedeschi (2006) reported similar phenotypic correlations between FCR and ADG (-0.65 and -0.60, respectively), while Nkrumah et al. (2004 and 2007) reported correlations of -0.63 and -0.69 between FCR and ADG. These high negative phenotypic correlations suggest that selection for favorable phenotype (improved FCR) will increase gain in growing bulls. There were no phenotypic correlations reported in the literature between FCR and TG. The phenotypic correlation between FCR and TG was -0.63 when adjusted to a common age and -0.74 when adjusted to a common weight. However, TG and ADG are the same trait. One would expect phenotypic correlations between FCR and ADG or TG to be very similar. The phenotypic correlation between FCR and SC, adjusted to a common age and weight, was 0.01. Arthur et al. (2001a) reported a phenotypic correlation between FCR and SC of 0.00. The results reported in this study are similar to that reported by Arthur et al. (2001a) indicating bulls ranked for favorable phenotype (improved FCR) had no effect on SC size. Phenotypic correlations adjusted to a common age between FCR and USFAT (0.11), USREA (-0.09) and USIMF (0.11) were reported in this study. Phenotypic correlations adjusted to a common weight between FCR and USFAT, USREA and USIMF were 0.10, -0.06 and 0.12, respectively. 49 Arthur et al. (2001a) reported a phenotypic correlation between FCR and USFAT of 0.08, while Nkrumah and coworkers (2004) reported a much larger correlation between FCR and USFAT (0.21). Carstens and Tedeschi (2006) reported a phenotypic correlation between FCR and USFAT of 0.11, while Baker et al. (2006) reported a correlation of 0.13, which is similar to what was reported in this study for age and weight adjusted analyses (0.11 and 0.10, respectively). These results indicate there is little to no phenotypic correlation between FCR and ultrasound measured traits. Arthur et al. (2001a) reported a phenotypic correlation between FCR and USREA of 0.03. Baker et al. (2006) reported a correlation of 0.12, while Carstens and Tedeschi (2006) reported a correlation of 0.11 between FCR and USREA. Meanwhile, Nkrumah et al. (2004) reported a slight negative phenotypic correlation between FCR and USREA (-0.08), which is similar to what was reported in this study for age or weight adjusted analyses (-0.09 and -0.06, respectively). These results suggest there is no phenotypic correlation between FCR and USREA. There were no papers that reported phenotypic correlations between FCR and USIMF. However, Nkrumah et al. (2004) reported a phenotypic correlation between FCR and ultrasound marbling (USMAR) of 0.10, indicating FCR was independent of USMAR. The correlations reported from this study between FCR and USIMF, adjusted to a common age or weight was 0.11 and 0.12, respectively which was similar to what Nkrumah et al. (2004) reported. 50 Genetic Correlations Between FCR and Associated Traits. Overall genetic correlations between FCR and postweaning growth traits were similar in sign and magnitude when adjusted to a common age or weight. However, genetic correlations between FCR and ultrasound measured traits varied in sign and magnitude. Genetic correlations between FCR and ADG and TG, adjusted to a common age were -0.60 and -0.76, respectively. Correlations between FCR and ADG and TG, adjusted to a common weight were -0.82 for both. Arthur et al. (2001a,b) reported a genetic correlation between FCR and ADG on Angus and Charolais cattle (-0.62 and -0.46, respectively). Herd and Bishop (2000) reported a similar genetic correlation between FCR and ADG (-0.62) and MacNeil et al. (1991) reported a correlation of -0.43. Koch et al. (1963) reported a correlation of 0.79, while Woldehawariat et al. (1977) reported genetic correlations ranging from -0.41 to 0.31. These estimates were similar in sign and magnitude with most estimates reported throughout the literature. Woldehawariat et al. (1977) also reported a genetic correlation between FCR and TG of -0.34. Koots et al. (1994b) reported a slightly higher correlation of -0.53 between FCR and TG. The correlations reported in this study for age and weight adjusted analyses were similar in sign and slightly higher (-0.76 and -0.82, respectively) than those reported throughout the literature. However these results, coupled with correlations between FCR and ADG do show gain traits are highly related with FCR. The genetic correlation between FCR and SC adjusted to a common age was -0.04. This study also reported a genetic correlation adjusted to a common weight of 0.15. Genetic correlations reported throughout the literature were inconsistent also. Arthur et al. (2001a) reported a genetic correlation of -0.10 between FCR and SC, while 51 Woldehawariat et al. (1977) reported a larger correlation of 0.48. The genetic correlations reported in this study indicate SC was independent of FCR. Since SC and growth are genetically correlated (Kriese et al. 1991a) and FCR is correlated with growth we would expect to see a genetic correlation between FCR and SC. Genetic correlations between FCR and ultrasound measured traits (USFAT, USREA and USIMF), adjusted to a common age were -0.05, -0.47 and 0.22, respectively. Weight adjusted genetic correlations between FCR and USFAT (0.01) and USREA (-0.39) and USIMF (0.19) were also reported in this study. Arthur et al. (2001a) reported genetic correlations between FCR and USFAT of 0.03, while Koots et al. (1994b) reported a correlation of -0.24. Correlations reported in this study for age and weight adjusted analyses were -0.05 and 0.01 between FCR and USFAT. These genetic correlations suggest selection for improved FCR should be independent of subcutaneous fat deposition at the 12 th and 13 th rib. Arthur et al. (2001a) reported a genetic correlation between FCR and USREA of -0.12. The estimates reported in this study for age and weight adjusted traits were -0.47 and -0.39, respectively. These results suggest selection for improved FCR may increase longissimus muscle area. There were no genetic correlations between FCR and USIMF reported in the literature. However, the results reported in this study were 0.19 and 0.22 for age and weight adjusted analyses, respectively. These results indicate selection for improved FCR may reduce the amount of fat deposited within the longissimus muscle area. 52 Other Phenotypic Correlations. In general, phenotypic correlations between postweaning growth traits (TG and ADG) and ultrasound measured traits (USFAT and USREA) differed in sign and magnitude except USIMF when adjusted to a common age or weight. Phenotypic correlations adjusted to a common age between TG and USFAT (0.11), USREA (0.21) and USIMF (-0.06) were reported in this study. Johnson et al. (1993) reported an age constant phenotypic correlation between TG and USFAT (0.07). There were no other papers reporting age-adjusted phenotypic correlations. TG and USFAT were slightly correlated (0.11) in this study and similar to what Johnson et al. (1993) reported. This low result indicates little relationship between gain on test and subcutaneous fat depostition. Johnson et al. (1993) also reported an age constant phenotypic correlation between TG and USREA (0.07). The correlation from this study between TG and USREA (0.21) was much greater in magnitude than what was reported in the literature. This estimate indicates that as growing bulls gained weight on test their longissimus muscle areas increased in size. There were no reports of phenotypic correlations between TG and USIMF in the literature. The correlation between TG and USIMF adjusted to a common age in this study was weak (-0.06) indicating intramuscular fat was phenotypically independent of gain in young growing bulls. This study also reported a phenotypic correlation between TG and SC adjusted to a common age (0.22). Johnson et al. (1993) reported a correlation between TG and SC of 0.18, which is similar in sign and magnitude to what was reported in this study. These 53 results indicate that SC size is phenotypically correlated in a favorable way with gain in centrally tested bulls at a common age. There were no phenotypic correlations adjusted to a common weight reported throughout the literature. Phenotypic correlations between TG and USFAT, USREA and USIMF were -0.06, -0.04 and -0.07, respectively. All of these phenotypic correlations reported between TG and ultrasound measured traits were low and indicate gain on test was phenotypically independent of ultrasound measured traits. Phenotypic correlations between ADG and ultrasound measured traits (USFAT and USREA) differed in sign and magnitude except for USIMF for age or weight adjusted analyses. Age adjusted phenotypic correlations between ADG and USFAT, USREA and USIMF were 0.08, 0.21 and -0.07, respectively. Weight adjusted phenotypic correlations between ADG and USFAT, USREA and USIMF were -0.06, -0.05 and -0.16, respectively. Carstens and Tedeschi (2006) reported a phenotypic correlation between ADG and USFAT of 0.06. The age adjusted correlation reported in this study was similar to what Carstens and Tedeschi (2006) reported and indicates ADG was independent of subcutaneous fat deposition. The weight adjusted correlation reported in this study was similar in magnitude but differed in sign compared to Carstens and Tedeschi?s (2006) estimate. Phenotypic correlations for age and weight adjusted analyses between ADG and USREA were 0.21 and -0.05, respectively in this study. Carstens and Tedeschi (2006) reported a phenotypic correlation between ADG and USREA of 0.08. Age adjusted phenotypic correlation between ADG and USREA (0.21) reported in this study indicates 54 as bulls gain more per day on test their longissimus muscle area will also increase in size. However, the weight adjusted correlation indicates that longissimus muscle area size was phenotypically independent of how much weight the bull gained on an average daily basis. The phenotypic correlations reported in this study between ADG and USIMF for age and weight adjusted analyses was -0.07 and -0.16, respectively. There were no phenotypic correlations between ADG and USIMF reported throughout the literature. Phenotypic correlations from this study between ADG and USIMF indicate post-weaning ADG was phenotypically independent of USIMF. The age adjusted phenotypic correlation reported in this study between ADG and SC was 0.21, while the weight adjusted phenotypic correlation was 0.05. Age adjusted phenotypic correlation between ADG and SC indicates bulls ranked for the best ADG tended to phenotypically have larger SC. However, weight adjusted phenotypic correlation between ADG and SC indicated ADG was phenotypically independent of SC. This is understandable since at heavier weights, SC is not going to get much larger. Phenotypic correlations between ultrasound traits were similar in sign and magnitude for age and weight adjusted analyses, except the correlation between USFAT and USREA. Phenotypic correlations adjusted to a common age between USFAT and USREA and USIMF were 0.11 and 0.35, respectively. Phenotypic correlations adjusted to a common weight between USFAT and USREA and USIMF were -0.01 and 0.36, respectively. The phenotypic correlation between USREA and USIMF was -0.10 when adjusted to a common age or a common weight. 55 Stelzleni et al. (2002) reported a phenotypic correlation between USFAT and USREA and USIMF of 0.16 and 0.17, respectively. The phenotypic correlation between USFAT and USREA adjusted to a common age or weight in this study was small and indicates no phenotypic relationship between USREA and USFAT. However, the results reported in this study between USFAT and USIMF were greater in magnitude than what Stelzleni et al. (2002) reported. Indicating subcutaneous fat deposition increases in growing bulls, intramuscular fat in the longissimus muscle area will also tend to phenotypically increase. Stelzleni et al. (2002) reported a phenotypic correlation between USREA and USIMF of -0.08. The results reported in this study between USREA and USIMF were similar in sign and magnitude to those reported in the literature indicating there was little or no relationship between performances of these traits. Phenotypic correlations between ultrasound measured traits and SC were similar for both age and weight adjusted analyses. There were no phenotypic correlations reported in the literature between ultrasound measured traits and SC. The correlations reported in this study were small and close to zero, regardless of sign, indicating SC size was phenotypically independent of ultrasound measured traits. Other Genetic Correlations. TG in this study was slightly to moderately genetically correlated to the ultrasound measured traits. Genetic correlations between TG and USFAT, USREA and USIMF were 0.20, 0.10 and 0.04, respectively when adjusted to a common age. Johnson et al. (1993) reported age adjusted genetic correlations between TG and USFAT (0.44) and USREA (0.43). These estimates were higher than what was found in this study. Results reported in this study indicate selection for increased weight gain will 56 result in slightly fatter animals and larger longissimus muscle areas. There were no genetic correlations between TG and USIMF reported throughout the literature. The genetic correlation (0.04) in this study between TG and USIMF was small suggesting that intramuscular fat was independent of gain in growing bulls. TG was genetically correlated to SC (0.19) when adjusted to a common age. Johnson et al. (1993) reported an age constant genetic correlation between TG and SC of 0.38. This estimate is larger than what was reported in this study, but indicates that selection for increased SC size would result in growing bulls with larger gains. These results were not unexpected because SC tends to be correlated with growth in bulls. Weight adjusted genetic correlations between TG on test and ultrasound measured traits indicate TG had no genetic impact on ultrasound measured traits. Genetic correlations between TG and USFAT (-0.01), USREA (0.04) and USIMF (0.11) were estimated in this study. There were no weight adjusted genetic correlations between TG and ultrasound measured traits reported in literature. The genetic correlation between TG and SC was 0.01 when adjusted to a common weight. This estimate was much lower than the age adjusted estimate reported earlier (0.19). Literature reports that gain and SC size is correlated, however the correlation reported here indicates SC size is independent of gain. Age adjusted genetic correlations between ADG and USFAT, USREA and USIMF were 0.23, 0.07 and 0.05, respectively. Arnold et al. (1991) reported age adjusted genetic correlations between ADG and USFAT (0.23) and USREA (0.33). The genetic correlation between ADG and USFAT indicate subcutaneous fat deposition is positively correlated to average daily weight gain in young growing bulls. However, the 57 genetic correlation between ADG and USREA (0.07) reported in this study adjusted to a common age was lower than the estimate (0.33) reported by Arnold et al. (1991). The small positive genetic correlation between ADG and USREA in this study may indicate yearling bulls gaining more weight on a daily basis was genetically independent of longissimus muscle area size. There were no genetic correlations reported in literature between ADG and USIMF. The small positive correlation between ADG and USIMF (0.05) adjusted to a common age indicates bulls gaining more weight on a daily basis were genetically independent to the amount of intramuscular fat deposited in their longissimus muscle area. The genetic correlation between ADG and SC, adjusted to a common age, was related in a favorable direction (0.21). This moderate correlation between ADG and SC suggests that as bulls on test gain more weight on a daily basis their SC measurement will also increase. This was not unexpected since SC has been reported to be correlated with postweaning growth traits in growing bulls. The genetic correlations between ADG and ultrasound measured traits were small and positive when adjusted to a common weight. The correlations between ADG and USFAT, USREA and USIMF were 0.03, 0.08, and 0.17, respectively in this study. Arnold et al. (1991) reported genetic correlations adjusted to a common weight between ADG and USFAT (-0.02) and USREA (0.06). Arnold and coworkers (1991) estimates were similar to estimates reported in this study. This suggests post-weaning growth was independent of ultrasound measured traits when adjusted to a weight basis. 58 There were no genetic correlations between ADG and USIMF adjusted to a common weight reported in literature. There was a slight genetic correlation between ADG and USIMF in this study (0.17). This correlation implies that as a bull?s ADG increases on test, the bull has the genetic potential to increase the amount of intramuscular fat deposited in their longissimus muscle. The genetic correlation between ADG and SC adjusted to a common weight was 0.04. This correlation is small and indicates that SC size was independent of ADG of centrally tested bulls. This was surprising to find since growth traits and SC size has been found to be genetically correlated to each other in the literature. Genetic correlations were also reported in this study between the ultrasound measured traits for age and weight adjusted analyses. Once again the genetic correlations adjusted to a common weight were lower but similar in sign than those adjusted to a common age. Genetic correlations adjusted to a common age between USFAT and USREA and USIMF were 0.18 and 0.34, respectively. Genetic correlations adjusted to a common weight between USFAT and USREA and USIMF were 0.07 and 0.45, respectively. The genetic correlation adjusted to a common age between USREA and USIMF was -0.24. The genetic correlation adjusted to a common weight between USREA and USIMF was -0.07. Stelzleni and coworkers (2002) reported a genetic correlation between USFAT and USREA of -0.09. This correlation was lower than what was found in this study for both age and weight adjusted analyses (0.18 and 0.07, respectively). The correlations from this study indicate that as longissimus muscle area increases in size the bull tends to 59 deposit more subcutaneous fat. The low negative correlation reported by Stelzleni and coworkers (2002) implies no correlation between USFAT and USREA. Stelzleni et al. (2002) also reported a genetic correlation between USFAT and USIMF of 0.36. This correlation was similar in sign and magnitude to what was reported in this study for both age and weight adjusted analyses (0.34 and 0.45, respectively). These genetic correlations imply that as bulls deposit more subcutaneous fat they have the genetic potential to deposit more intramuscular fat in their longissimus muscle also. Stelzleni et al. (2002) reported a genetic correlation between USREA and USIMF of -0.25. This correlation was similar to the age adjusted correlation reported in this study (-0.24) but was much smaller in magnitude than the weight adjusted correlation reported (-0.07). These genetic correlations indicate that as bull?s longissimus muscle area increases they tend to deposit less intramuscular fat in their longissimus muscle. Genetic correlations between ultrasound measured traits (USFAT, USREA and USIMF) and SC varied in sign and magnitude for age and weight adjusted analyses. Genetic correlations adjusted to a common age between SC and USFAT, USREA and USIMF were 0.03, -0.24 and 0.27, respectively. Genetic correlations adjusted to a common weight between SC and USFAT and USREA were -0.13 and -0.20, respectively. A genetic correlation adjusted to a common weight was not calculated between SC and USIMF. There were no genetic correlations between SC and ultrasound measured traits reported in the literature. 60 Residual Feed Intake (RFI) Results Comparison Between RFI Groups. No significant differences were detected between low RFI and high RFI group bulls for size and growth traits (ADG, initial weight (IW), frame score (FS), adjusted 365 day weight (YW), SC, USIMF and TG) when final weight or age was used as a covariate (Table 12 and Table 13). Significant differences (P < 0.05) were seen in low RFI and high RFI group bulls for USFAT, USREA, FCR, feed intake (FI), and RFI when final weight or age was used as a covariate (Table 12 and Table 13). The low RFI group bulls were leaner, had a larger longissimus muscle area, better FCR while consuming less feed over the duration of the test than the high RFI group bulls. These results were inconsistent with most studies done involving comparison of low RFI groups and high RFI groups in beef cattle (Herd et al., 2003; Baker et al., 2006). Breed Effect on Post-Weaning Gain and Ultrasound Measurements. Differences among breeds can be seen in Table 14 and Table 15. As expected, there were breed differences (P < 0.05) among the following traits: ADG, IW, FS, YW, USFAT, USREA, USIMF, SC, FCR, and TG. Continental breeds are larger framed cattle than British breeds and consequently tend to weigh more. Also, Continental breeds of cattle are generally leaner and have a larger longissimus muscle area than British breeds of cattle. The results published in this study for both weight and age adjusted traits was consistent with the findings reported in previous studies with beef cattle (Marshall, 1994; Bidner et al., 2002). 61 Year Effect on Post-Weaning Gain and Ultrasound Measurements. Least squares means can be seen in Table 16 and Table 17 for the main effect of year of RFI on post-weaning gain and ultrasound measured traits. As expected there were differences (p < 0.05) among years for traits analyzed (ADG, IW, FS, YW, USFAT, USREA, USIMF, SC, FCR, and TG). Implications Results of this study indicate all traits analyzed were low to moderately heritable. Almost all heritability estimates were lower than published reports, except TG. TG was within published estimates for field data. Most heritability estimates reported throughout the literature were from designed studies not field data estimates like what was reported in this study. Genetic correlations of RFI adjusted to a weight or age basis with TG, ADG, USFAT and SC were low or uncorrelated. However genetic correlations of RFI adjusted to a weight or age basis with FCR, USIMF and USREA were correlated. Selection for improved RFI would cause an increase in longissimus muscle area, a decrease in marbling and an improvement in FCR. There was no difference between low RFI and high RFI groups when adjusted to a weight or age basis for most post-weaning growth and ultrasound measured traits. However, differences were detected between low RFI and high RFI group bulls for USFAT, USREA, FCR, RFI and FI. Low RFI bulls had less subcutaneous fat thickness, larger longissimus muscle areas, better FCR and RFI values while consuming less feed throughout the duration of the test. Results indicate selection of lower RFI animals 62 should not cause a change in size or weight of the animal. Ultrasound traits should remain similar in size and measure also. Finally, these individual performance records collected on bulls centrally tested can be added to NCE models to predict EPD?s. Producers can then use their respective breed?s EPD?s to select a total package bull for their breeding program. Digestion, 14% Heat Increment of Feeding, 9% Body Composition (Energy Retention), 5% Activity, 5% Other Processes (e.g. Protein turnover, ion pumping, proton leakage), 67% Figure 1. Estimates of the percentage contribution of different mechanisms to variation in residual feed intake in beef cattle (Herd et al., 2004). 63 Body Composition, 5% Feeding Patterns, 2% Protein Turnover, Tissue Metabolism and Stress, 37% Heat Increment of Fermentation, 9% Digestibility, 10% Activity, 10% Other, 27% Figure 2. Contributions of biological mechanisms to variation in residual feed intake as determined from experiments on divergently selected cattle (Richardson and Herd, 2004). 64 65 Table 1. Nutrient analysis of diet fed to bulls by year a Year TDN, % CP, % CF b , % 1978 ? 1984 71.50 12.30 16.10 1985 ? 1986 70.52 12.00 16.10 1987 ? 1988 68.97 12.10 19.27 1989 ? 1990 69.80 12.12 18.38 1991 ? 1992 69.46 12.49 19.18 1993 ? 1994 70.00 12.72 18.41 1995 70.07 12.77 17.67 1996 ? 2004 71.03 Not < 12.50 Not > 20.00 a Percent dry matter basis b Crude fiber, % 66 Table 2. Estimates of additive (co)variance components of centrally tested bulls adjusted to a common age a . Trait TG FCR RFI ADG USFAT USREA USIMF SC TG b 1841 FCR c -18.82 0.34 RFI d -1.61 0.19 0.44 ADG e - -0.13 -0.02 0.15 USFAT f 0.69 -0.002 -0.007 0.007 0.01 USREA g 3.28 -0.21 -0.39 0.02 0.01 0.59 USIMF h 0.86 0.06 0.25 0.009 0.01 -0.09 0.24 SC i 15.19 -0.04 0.15 0.15 0.005 -0.36 0.25 3.65 a Variance on diagonal and covariance on off-diagonal b Total gain over duration of test (final weight ? initial weight) c Feed conversion ratio defined as kilograms of feed required to put on one kilogram of gain d Residual feed intake as outlined by Appendix A e Average daily gain, kg/d f Ultrasound fat thickness taken at 12 th and 13 th rib g Ultrasound longissimus muscle area, cm 2 h Ultrasound percent intramuscular fat i Scrotal circumference, cm 67 Table 3. Estimates of environmental (co)variance components of centrally tested bulls adjusted to a common age a Trait b TG FCR RFI ADG USFAT USREA USIMF SC TG 9029 FCR -88 2.28 RFI 0.93 1.84 3.77 ADG - -0.64 0.01 0.77 USFAT 1.52 0.04 0.06 0.01 0.04 USREA 51.25 -0.16 -0.51 0.49 0.05 5.69 USIMF -8.60 0.18 0.10 -0.10 0.08 -0.24 1.50 SC 93.27 0.09 0.04 0.81 0.04 1.30 -1.02 19.33 a Variance on the diagonal and covariance on the off diagonal b See Table 2 for trait abbreviations 68 Table 4. Estimates of additive (co)variance components of centrally tested bulls adjusted to a common weight a . Trait b TG FCR RFI ADG USFAT USREA USIMF SC TG 1237 FCR -17.41 0.36 RFI 2.20 0.17 0.40 ADG - -0.17 0.02 0.11 USFAT -0.03 0.0002 -0.001 0.001 0.01 USREA 1.30 -0.20 -0.37 0.02 0.004 0.71 USIMF 1.77 0.06 0.22 0.03 0.12 -0.03 0.23 SC 0.60 0.17 0.21 0.02 -0.02 -0.32 - 3.65 a Variance on the diagonal and covariance on off-diagonal b See Table 2 for trait abbreviations 69 Table 5. Estimates of environmental (co)variance components of centrally tested bulls adjusted to a common weight a Trait b TG FCR RFI ADG USFAT USREA USIMF SC TG 6627 FCR -94.82 2.55 RFI 2.86 1.94 3.84 ADG - -0.83 0.02 0.59 USFAT -0.89 0.04 0.06 -0.01 0.03 USREA -8.45 -0.03 -0.37 -0.12 -0.0085 4.52 USIMF -9.67 0.22 0.12 -0.11 0.08 -0.28 1.51 SC 21.40 -0.09 -0.31 0.18 -0.02 0.02 - 17.48 a Variance on the diagonal and covariance on off-diagonal b See Table 2 for trait abbreviations 70 Table 6. Simple means ? standard deviations for performance and ultrasound traits of bulls used in analyses adjusted by age Trait N Mean Age, days 2,008 405 ? 31 WWT a , kg 1,905 299 ? 37 YWT b , kg 1,995 540 ? 53 ADG c , kg?d -1 1,998 1.73 ? 0.27 FCR d 1,998 7.54 ? 1.12 USFAT e , mm 1,830 7.98 ? 3.46 USIMF f , % 475 3.17 ? 0.86 SC g , cm 1,574 36.32 ? 2.91 USREA h , cm2 1,012 95.48 ? 10.52 TG i ,kg 1,998 205 ? 35 RFI j 1,998 0.00 ? 1.05 a Weaning weight adjusted to 205 days and adjusted for age of dam using national breed association adjustments b Yearling weight adjusted to 365 days as outlined by BIF (2002) c Average daily gain on test d Feed efficiency defined as kilograms of feed required to put on one kilogram of gain e Ultrasound Fat Thickness measured at the 12 th and 13 th rib f Ultrasound percent Intramuscular Fat measured in the ribeye muscle g Scrotal circumference measured at conclusion of test h Ultrasound longissimus muscle area measured at the 12 th and 13 th rib i Total gain over duration of test (final test weight ? on test weight) j Residual feed intake as outlined by Appendix A 71 Table 7. Simple means ? standard deviations for performance and ultrasound traits of bulls used in analyses adjusted by final weight Trait N Mean FW a , kg 2,005 586 ? 60 WWT b , kg 1,912 299 ? 37 YWT c , kg 2,002 540 ? 53 ADG d , kg?d -1 2,005 1.73 ? 0.28 FCR e 2,005 7.56 ? 0.62 USFAT f , mm 1,836 7.96 ? 3.46 USIMF g , % 475 3.17 ? 0.86 SC h , cm 1,576 36.32 ? 2.91 USREA i , cm 2 1,012 95.48 ? 10.19 TG j , kg 2,005 205 ? 35 RFI k 2,005 0.00 ? 1.05 a Final weight (average of two consecutive weigh days at conclusion of test) b Weaning weight adjusted to 205 days and adjusted for age of dam using national breed association adjustments c Yearling weight adjusted to 365 days as outlined by BIF (2002) d Average daily gain on test e Feed efficiency defined as kilograms of feed required to put on one kilogram of gain f Ultrasound Fat Thickness measured at the 12 th and 13 th rib g Ultrasound percent Intramuscular Fat measured in the ribeye muscle h Scrotal circumference measured at conclusion of test i Ultrasound longissimus muscle area measured at the 12 th and 13 th rib j Total gain over duration of test (final test weight ? on test weight) k Residual feed intake as outlined in the Appendix 72 Table 8. Simple means ? SEM for performance and ultrasound traits of bulls by breed used in analyses adjusted by age Breed Angus Brangus Charolais Gelbvieh Limousin Hereford Santa Gertrudis Simmental Trait N Mean N Mean N Mean N Mean N Mean N Mean N Mean N Mean Age, days 829 408 ? 1.07 33 389 ? 5.74 337 402 ? 1.68 100 384 ? 2.43 74 405 ? 3.01 183 412 ? 2.58 85 399 ? 2.98 367 405 ? 1.50 WWT a , kg 771 296 ? 1.38 32 293 ? 5.11 322 303 ? 1.99 91 305 ? 3.22 71 290 ? 3.09 177 268 ? 2.29 84 298 ? 3.30 357 317 ? 1.64 YWT b , kg 825 541 ? 1.90 33 522 ? 6.15 334 549 ? 2.62 98 548 ? 4.31 73 509 ? 4.10 183 488 ? 3.32 84 524 ? 4.21 365 565 ? 2.24 ADG c kg?d -1 825 1.74 ? 0.01 33 1.58 ? 0.03 334 1.79 ? 0.02 100 1.80 ? 0.02 73 1.63 ? 0.02 183 1.50 ? 0.02 85 1.57 ? 0.02 365 1.81 ? 0.01 FCR d 825 7.67 ? 0.04 33 7.83 ? 0.18 334 7.25 ? 0.07 100 7.08 ? 0.09 73 6.92 ? 0.09 183 7.76 ? 0.11 85 7.67 ? 0.11 365 7.65 ? 0.07 USFAT e , mm 771 10.16 ? 0.12 29 8.02 ? 0.58 314 5.62 ? 0.11 89 4.82 ? 0.20 67 5.48 ? 0.23 151 9.69 ? 0.22 70 7.30 ? 0.31 339 5.90 ? 0.11 USIMF f , % 240 3.59 ? 0.06 1 2.16 ? 0.00 79 2.77 ? 0.06 37 2.68 ? 0.07 20 2.47 ? 0.10 14 2.93 ? 0.10 3 2.63 ? 0.26 81 2.77 ? 0.07 SC g , cm 632 36.24 ? 0.11 30 36.01 ? 0.48 281 36.07 ? 0.15 95 35.23 ? 0.24 65 33.25 ? 0.26 70 35.11 ? 0.34 57 35.14 ? 0.49 344 38.02 ? 0.15 USREA h , cm 2 457 92.41 ? 0.41 14 88.98 ? 2.47 182 99.00 ? 0.79 69 95.73 ? 1.27 39 106.17 ? 1.83 19 79.83 ? 1.90 12 90.37 ? 1.75 220 98.83 ? 0.61 TG i , kg 825 204 ? 1.19 33 189 ? 3.90 334 212 ? 1.91 100 192 ? 3.91 73 188 ? 3.55 183 200 ? 2.50 85 208 ? 3.56 365 211 ? 1.84 RFI j 825 0.28 ? 0.03 33 0.12 ? 0.22 334 -0.28 ? 0.06 100 -0.17 ? 0.13 73 -1.07 ? 0.12 183 -0.39 ? 0.06 85 -0.26 ? 0.11 365 0.15 ? 0.06 a Weaning weight adjusted to 205 days and adjusted for age of dam using National Breed Association adjustments b Yearling weight adjusted to 365 days as outlined by BIF (2002) c Average daily gain of bulls for entire test period d Feed conversion ratio defined as pounds of feed required to put on one pound of gain e Ultrasound fat thickness measured at the 12 th and 13 th rib f Ultrasound percent intramuscular fat measured in the longissimus muscle area g Scrotal circumference measured at conclusion of test h Ultrasound longissimus muscle area measured at the 12 th and 13 th rib i Total gain over duration of test (final test weight ? on test weight) j Residual feed intake as outlined in Appendix A 73 Table 9. Simple means ? SEM for performance and ultrasound traits of bulls by breed used in analyses adjusted by final weight Breed Angus Brangus Charolais Gelbvieh Limousin Hereford Santa Gertrudis Simmental Trait a N Mean N Mean N Mean N Mean N Mean N Mean N Mean N Mean FW, kg 825 591 ? 2.14 33 547 ? 9.83 334 594 ? 2.86 100 566 ? 4.64 73 554 ? 4.95 183 542 ? 3.94 87 565 ? 5.13 370 610 ? 2.88 WWT, kg 771 296 ? 1.38 32 293 ? 5.11 322 303 ? 1.99 91 305 ? 3.23 71 290 ? 3.09 177 268 ? 2.29 86 298 ? 3.24 362 317 ? 1.64 YWT, kg 825 541 ? 1.90 33 522 ? 6.15 334 549 ? 2.62 98 548 ? 4.31 73 509 ? 0.36 183 488 ? 3.32 86 524 ? 4.25 370 564 ? 2.26 ADG kg?d -1 825 1.74 ? 0.01 33 1.58 ? 0.03 334 1.79 ? 0.02 100 1.80 ? 0.02 73 1.63 ? 0.02 183 1.50 ? 0.02 87 1.56 ? 0.02 370 1.80 ? 0.01 FCR 825 7.67 ? 0.04 33 7.83 ? 0.18 334 7.25 ? 0.07 100 7.08 ? 0.09 73 6.92 ? 0.09 183 7.76 ? 0.11 87 7.69 ? 0.11 370 7.67 ? 0.07 USFAT, mm 771 10.16 ? 0.12 29 8.02 ? 0.58 314 5.62 ? 0.11 89 4.82 ? 0.20 67 5.48 ? 0.23 151 9.69 ? 0.22 72 7.29 ? 0.30 343 5.86 ? 0.11 USIMF, % 240 3.59 ? 0.06 1 2.16 ? 0.00 79 2.77 ? 0.06 37 2.68 ? 0.07 20 2.47 ? 0.10 14 2.93 ? 0.10 3 2.63 ? 0.26 81 2.77 ? 0.07 SC, cm 632 36.24 ? 0.11 30 36.01 ? 0.48 281 36.07 ? 0.15 95 35.23 ? 0.24 65 33.24 ? 0.26 70 35.11 ? 0.34 58 35.06 ? 0.49 345 38.03 ? 0.15 USREA, cm 2 457 92.41 ? 0.41 14 88.98 ? 2.47 182 99.00 ? 0.79 69 95.73 ? 1.27 39 106.17 ? 1.83 19 79.83 ? 1.90 12 90.37 ? 1.75 220 98.83 ? 0.61 TG, kg 825 204 ? 1.19 33 189 ? 3.90 334 212 ? 1.91 100 192 ? 3.91 73 188 ? 3.55 183 200 ? 2.50 87 208 ? 3.49 370 211 ? 1.83 RFI 825 0.28 ? 0.03 33 0.12 ? 0.22 334 -0.28 ? 0.06 100 -0.17 ? 0.13 73 -1.07 ? 0.12 183 -0.39 ? 0.06 87 -0.26 ? 0.11 370 0.17 ? 0.06 a See Table 8 for trait abbreviations 74 Table 10. Estimates of heritability and genetic and phenotypic correlations of post-weaning traits of centrally tested bulls adjusted to a common age a Trait b TG FCR RFI ADG USFAT USREA USIMF SC TG 0.17 -0.63 0.00 - 0.11 0.21 -0.06 0.22 FCR -0.76 0.13 0.61 -0.50 0.11 -0.09 0.11 0.01 RFI -0.06 0.49 0.10 0.00 0.12 -0.17 0.13 0.02 ADG - -0.60 -0.08 0.17 0.08 0.21 -0.07 0.21 USFAT 0.20 -0.05 -0.13 0.23 0.16 0.11 0.35 0.04 USREA 0.10 -0.47 -0.77 0.07 0.18 0.09 -0.10 0.08 USIMF 0.04 0.22 0.77 0.05 0.34 -0.24 0.14 -0.12 SC 0.19 -0.04 0.12 0.20 0.03 -0.24 0.27 0.16 a Heritability estimates on the diagonal, genetic correlations below the diagonal and phenotypic correlations above the diagonal b See Table 2 for trait abbreviations 75 Table 11. Estimates of heritability and genetic and phenotypic correlations of post-weaning traits of centrally tested bulls adjusted to a common weight a Trait b TG FCR RFI ADG USFAT USREA USIMF SC TG 0.16 -0.74 0.03 - -0.06 -0.04 -0.07 0.05 FCR -0.82 0.12 0.60 -0.70 0.10 -0.06 0.12 0.01 RFI 0.10 0.46 0.09 0.02 0.35 -0.16 0.13 0.04 ADG - -0.82 0.08 0.16 -0.06 -0.05 -0.08 0.05 USFAT -0.01 0.01 -0.02 0.03 0.15 -0.01 0.36 -0.04 USREA 0.04 -0.39 -0.70 0.08 0.07 0.13 -0.10 -0.03 USIMF 0.04 0.19 0.73 0.17 0.45 -0.07 0.13 - SC 0.01 0.15 0.17 0.04 -0.13 -0.20 - 0.17 a Heritability estimates on the diagonal, genetic correlations below the diagonal and phenotypic correlations above the diagonal b See Table 2 for trait abbreviations 76 Table 12. Least squares mean ? SEM between residual feed intake (RFI) groups for post- weaning gain and ultrasound traits of central test bulls adjusted for weight. Group a a Trait b Low-RFI High-RFI P >F ADG 1.70 ? 0.01 1.69 ? 0.01 0.50 IW 381 ? 0.98 382 ? 1.21 0.52 FS 6.83 ? 0.03 6.79 ? 0.04 0.40 YW 537 ? 1.37 535 ? 1.66 0.34 USFAT 7.33 ? 0.13 8.45 ? 0.17 0.0001* USREA 96.62 ? 0.65 93.28 ? 0.78 0.001* USIMF 2.80 ? 0.11 3.00 ? 0.10 0.16 SC 35.67 ? 0.12 36.01 ? 0.15 0.08 FCR 7.11 ? 0.04 8.10 ? 0.05 0.0001* TG 203 ? 0.98 202 ? 1.21 0.52 FI 1435 ? 4.31 1621 ? 5.30 0.0001* RFI -0.90 ? 0.03 0.79 ? 0.04 0.0001* a Low-RFI group = RFI < 0, High-RFI group = RFI ? 0 b Average daily gain (ADG, kg/d), initial weight (IW, kg), frame score (FS), yearling weight adjusted to 365 days as outlined by BIF (2002) (YW, kg), ultrasound fat thickness (USFAT, mm), ultrasound longissimus muscle area (USREA, sq cm), ultrasound percent intramuscular fat (USIMF), scrotal circumference (SC, cm), feed conversion ratio (FCR), total gain on test (TG, kg), total feed intake on test (FI, kg), residual feed intake (RFI, kg) *Means are significantly different (P < 0.05) 77 Table 13. Least squares mean ? SEM between residual feed intake (RFI) groups for post- weaning gain and ultrasound traits of central test bulls adjusted for age. Group a a Trait b Low-RFI High-RFI P >F ADG 1.67 ? 0.01 1.65 ? 0.01 0.28 IW 374 ? 1.59 371 ? 1.94 0.23 FS 6.7 ? 0.03 6.7 ? 0.04 0.30 YW 527 ? 1.85 524 ? 2.23 0.34 USFAT 7.11 ? 0.13 8.22 ? 0.17 0.0001* USREA 95.41 ? 0.69 91.76 ? 0.83 0.0007* USIMF 2.82 ? 0.11 2.99 ? 0.10 0.23 SC 35.44 ? 0.12 35.72 ? 0.16 0.17 FCR 7.08 ? 0.04 8.12 ? 0.05 0.0001* TG 199 ? 1.14 197 ? 1.40 0.28 FI 1414 ? 5.76 1587 ? 7.04 0.0001* RFI -0.87 ? 0.03 0.78 ? 0.04 0.0001* a Low-RFI group = RFI < 0, High-RFI group = RFI ? 0 b Average daily gain (ADG, kg/d), initial weight (IW, kg), frame score (FS), yearling weight adjusted to 365 days as outlined by BIF (2002) (YW, kg), ultrasound fat thickness (USFAT, mm), ultrasound longissimus muscle area (USREA, sq cm), ultrasound percent intramuscular fat (USIMF), scrotal circumference (SC, cm), feed conversion ratio (FCR), total gain on test (TG, kg), total feed intake on test (FI, kg), residual feed intake (RFI, kg) *Means are significantly different (P < 0.05) 78 Table 14. Least squares mean ? SEM for breed effect of residual feed intake (RFI) on post-weaning gain and ultrasound traits of central test bulls adjusted for weight Trait a Breed ADG IW FS YW USFAT USREA USIMF SC FCR TG FI RFI Angus 1.71 ? 0.01 ab 380 ? 0.78 b 6.1 ? 0.03 d 536 ? 1.06 bc 10.39 ? 0.10 ab 92.24 ? 0.36 c 3.62 ? 0.05 a 36.29 ? 0.10 b 7.66 ? 0.03 ab 204 ? 0.78 b 1553 ? 3.41 a 0.14 ? 0.02 a Brangus 1.62 ? 0.03 c 392 ? 3.49 a 6.9 ? 0.11 bc 535 ? 4.86 bc 9.75 ? 0.42 b 89.72 ? 2.06 cd - 36.55 ? 0.41 b 7.85 ? 0.15 a 192 ? 3.49 c 1501 ? 15.32 cd 0.00 ? 0.11 bc Charolais 1.75 ? 0.01 a 376 ? 1.15 c 7.1 ? 0.04 b 539 ? 1.58 ab 5.85 ? 0.14 d 97.91 ? 0.57 b 2.88 ? 0.08 bc 35.94 ? 0.14 b 7.37 ? 0.05 c 209 ? 1.15 a 1527 ? 5.04 bc -0.09 ? 0.04 bc Gelbvieh 1.75 ? 0.02 a 375 ? 2.23 c 6.9 ? 0.07 c 543 ? 3.08 a 5.39 ? 0.25 d 98.73 ? 0.91 b 2.79 ? 0.11 bc 36.31 ? 0.25 b 7.45 ? 0.09 bc 210 ? 2.23 a 1550 ? 9.80 a 0.05 ? 0.07 ab Limousin 1.64 ? 0.03 c 387 ? 3.06 a 6.9 ? 0.10 c 516 ? 4.19 d 6.20 ? 0.40 d 109.56 ? 1.48 a 2.55 ? 0.16 c 33.90 ? 0.35 d 7.62 ? 0.13 abc 198 ? 3.06 c 1491 ? 13.46 d -0.33 ? 0.10 d Hereford 1.71 ? 0.02 ab 380 ? 1.82 bc 6.1 ? 0.06 d 533 ? 2.51 c 11.06 ? 0.38 a 84.84 ? 1.77 d 3.11 ? 0.19 b 35.16 ? 0.31 c 7.57 ? 0.08 abc 205 ? 1.82 ab 1536 ? 8.02 ab -0.14 ? 0.06 cd Santa Gertrudis 1.67 ? 0.02 bc 385 ? 2.22 ab 7.4 ? 0.07 a 543 ? 3.07 a 8.68 ? 0.32 c 89.23 ? 2.18 cd 2.49 ? 0.41 c 34.86 ? 0.31 c 7.68 ? 0.09 ab 199 ? 2.22 bc 1520 ? 9.78 bcd -0.13 ? 0.07 bcd Simmental 1.71 ? 0.01 ab 380 ? 1.15 b 7.1 ? 0.04 b 543 ? 1.58 a 5.80 ? 0.13 d 97.36 ? 0.51 b 2.88 ? 0.08 bc 37.68 ? 0.13 a 7.63 ? 0.05 ab 204 ? 1.15 b 1545 ? 5.06 a 0.03 ? 0.04 b a See Table 2 for trait abbreviations Columns with different superscripts differ at P < 0.05 79 Table 15. Least squares mean ? SEM for breed effect of residual feed intake (RFI) on post-weaning gain and ultrasound traits of central test bulls adjusted for age Trait a Breed ADG IW FS YW USFAT USREA USIMF SC FCR TG FI RFI Angus 1.72 ? 0.01 b 382 ? 1.27 b 6.1 ? 0.03 c 542 ? 1.45 b 10.51 ? 0.10 a 92.78 ? 0.39 c 3.61 ? 0.05 a 36.43 ? 0.10 b 7.59 ? 0.04 a 206 ? 0.91 b 1560 ? 4.60 a 0.12 ? 0.02 a Brangus 1.53 ? 0.04 d 372 ? 5.69 bc 6.6 ? 0.12 b 508 ? 6.63 d 9.17 ? 0.43 b 86.82 ? 2.23 d - 35.90 ? 0.42 c 7.91 ? 0.16 a 182 ? 4.10 e 1441 ? 20.64 e 0.03 ? 0.11 abc Charolais 1.75 ? 0.01 a 379 ? 1.88 b 7.2 ? 0.04 a 542 ? 2.16 b 5.89 ? 0.15 c 98.16 ? 0.62 b 2.87 ? 0.08 bc 36.00 ? 0.15 c 7.32 ? 0.05 b 210 ? 1.35 a 1535 ? 6.81 b -0.10 ? 0.04 cd Gelbvieh 1.70 ? 0.02 bc 367 ? 3.65 c 6.7 ? 0.08 b 527 ? 4.22 c 5.19 ? 0.26 d 97.67 ? 0.98 b 2.79 ? 0.11 bc 36.13 ? 0.26 bc 7.53 ? 0.10 ab 204 ? 2.63 bc 1522 ? 13.24 bc 0.07 ? 0.07 ab Limousin 1.53 ? 0.03 d 345 ? 4.96 d 6.6 ? 0.11 b 489 ? 5.67 de 5.47 ? 0.41 cd 105.11 ? 1.58 a 2.55 ? 0.16 c 32.88 ? 0.36 e 7.56 ? 0.14 abc 184 ? 3.57 e 1383 ? 18.00 f -0.31 ? 0.10 d Hereford 1.65 ? 0.02 c 362 ? 2.96 c 5.9 ? 0.06 d 516 ? 3.41 d 10.74 ? 0.39 a 82.47 ? 1.92 d 3.11 ? 0.19 b 34.70 ? 0.33 d 7.59 ? 0.08 a 198 ? 2.14 cd 1488 ? 10.76 d -0.12 ? 0.06 cd Santa Gertrudis 1.63 ? 0.02 c 380 ? 3.68 b 7.3 ? 0.08 a 530 ? 4.25 c 8.36 ? 0.34 b 87.48 ? 2.36 cd 2.52 ? 0.41 c 34.67 ? 0.33 d 7.72 ? 0.10 a 195 ? 2.65 d 1502 ? 13.36 cd -0.09 ? 0.07 bcd Simmental 1.74 ? 0.01 ab 390 ? 1.15 a 7.2 ? 0.04 a 551 ? 2.18 a 5.98 ? 0.13 c 98.16 ? 0.55 b 2.87 ? 0.08 bc 37.93 ? 0.13 a 7.59 ? 0.05 a 208 ? 1.37 ab 1570 ? 6.90 a 0.02 ? 0.04 bc a See Table 2 for trait abbreviations Columns with different superscripts differ at P < 0.05 80 Table 16. Least squares mean ? SEM by year of residual feed intake (RFI) on post-weaning gain and ultrasound traits of central test bulls adjusted for weight Trait a Year ADG IW FS YW USFAT USREA USIMF SC FCR TG FI RFI 1978 1.57 ? 0.03 hij 367 ? 3.01 ijk 5.4 ? 0.10 lm 502 ? 4.12 h - - - - 7.04 ? 0.13 no 217 ? 3.01 def 1530 ? 13.24 g -0.12 ? 0.09 ab 1979 1.43 ? 0.02 l 386 ? 2.68 de 5.3 ? 0.09 m 491 ? 3.67 i - - - - 9.34 ? 0.11 a 198 ? 2.68 jk 1782 ? 11.79 ab -0.10 ? 0.08 b 1980 1.49 ? 0.02 kl 379 ? 2.66 fg 5.6 ? 0.08 l 508 ? 3.64 h - - - - 8.13 ? 0.11 cd 205 ? 2.66 hi 1623 ? 11.70 f -0.06 ? 0.08 ab 1981 1.56 ? 0.02 ij 366 ? 2.60 ijk 6.3 ? 0.08 j 510 ? 3.55 h - - - - 7.63 ? 0.11 hijk 218 ? 2.60 def 1650 ? 11.41 ef -0.02 ? 0.08 ab 1982 1.56 ? 0.02 ij 366 ? 2.60 ijk 6.3 ? 0.08 j 507 ? 3.56 h - - - - 8.00 ? 0.11 cdef 219 ? 2.60 def 1738 ? 11.42 cd -0.02 ? 0.08 ab 1983 1.53 ? 0.02 jk 370 ? 2.69 hij 5.5 ? 0.09 lm 503 ? 3.69 h - - - 36.98 ? 0.30 b 8.51 ? 0.11 b 214 ? 2.69 efg 1807 ? 11.84 a -0.06 ? 0.08 ab 1984 1.61 ? 0.02 ghi 360 ? 2.69 klmn 6.0 ? 0.09 k 525 ? 3.68 g - - - - 7.78 ? 0.11 efghi 224 ? 2.69 abcd 1718 ? 11.81 d -0.04 ? 0.08 ab 1985 1.57 ? 0.02 hij 366 ? 2.82 ijkl 6.2 ? 0.09 jk 521 ? 3.92 g 7.59 ? 0.31 efgh - - 37.78 ? 0.31 b 7.69 ? 0.12 fghij 219 ? 2.82 cdef 1666 ? 12.39 e -0.16 ? 0.09 b 1986 1.58 ? 0.02 hij 364 ? 2.87 jkl 6.7 ? 0.09 i 531 ? 3.92 fg 7.63 ? 0.32 efgh - - 37.20 ? 0.32 b 7.39 ? 0.12 jklm 221 ? 2.87 cde 1620 ? 12.60 f -0.14 ? 0.09 b 1987 1.61 ? 0.02 ghi 359 ? 2.36 lmn 7.3 ? 0.08 cdef 526 ? 3.23 g 7.14 ? 0.28 ghij - - 38.71 ? 0.28 a 7.86 ? 0.10 defgh 226 ? 2.36 abc 1762 ? 10.39 bc -0.03 ? 0.07 ab 1988 1.59 ? 0.02 ghij 361 ? 2.44 klm 7.3 ? 0.08 def 530 ? 3.34 g 7.51 ? 0.28 fgh - - 36.97 ? 0.28 b 8.05 ? 0.10 cde 224 ? 2.44 bcd 1785 ? 10.72 ab -0.02 ? 0.08 ab 1989 1.64 ? 0.02 g 354 ? 2.32 h 7.5 ? 0.07 bc 548 ? 3.17 de 6.65 ? 0.28 ij - - 35.60 ? 0.28 cdef 7.16 ? 0.10 mno 231 ? 2.32 a 1637 ? 10.18 ef -0.01 ? 0.07 ab 1990 1.63 ? 0.02 gh 355 ? 2.33 mn 7.5 ? 0.07 bcd 548 ? 3.19 de 7.77 ? 0.28 efg - - 36.05 ? 0.28 cd 7.85 ? 0.10 defgh 229 ? 2.33 ab 1790 ? 10.26 ab 0.01 ? 0.07 ab 1991 1.78 ? 0.02 def 386 ? 2.31 def 7.7 ? 0.07 a 548 ? 3.16 de 7.74 ? 0.28 efg - - 35.45 ? 0.28 cdefg 7.46 ? 0.10 jkl 199 ? 2.31 ijk 1468 ? 10.16 h -0.07 ? 0.07 ab 1992 1.82 ? 0.02 cd 382 ? 2.29 efg 7.4 ? 0.07 cde 549 ? 3.13 de 6.47 ? 0.28 j 101.03 ? 0.93 a - 35.47 ? 0.28 cdefg 7.69 ? 0.10 ghij 203 ? 2.29 hij 1542 ? 10.06 g 0.12 ? 0.07 a 1993 1.64 ? 0.02 g 402 ? 2.30 c 7.4 ? 0.07 cde 541 ? 3.14 ef 8.36 ? 0.28 cde 94.17 ? 0.95 de - 35.97 ? 0.26 cd 8.08 ? 0.10 cd 183 ? 2.30 l 1471 ? 10.09 h -0.09 ? 0.07 b 1994 1.74 ? 0.02 f 390 ? 2.27 d 7.3 ? 0.07 ef 543 ? 3.11 de 8.08 ? 0.27 def 92.06 ? 0.95 ef - 35.68 ? 0.26 cde 7.95 ? 0.10 cdefg 195 ? 2.27 k 1534 ? 9.99 g -0.06 ? 0.07 ab 1995 1.80 ? 0.02 de 383 ? 2.32 efg 7.6 ? 0.07 ab 564 ? 3.17 ab 7.35 ? 0.28 ghij 100.94 ? 0.97 ab - 36.09 ? 0.26 c 8.14 ? 0.10 c 202 ? 2.32 hij 1626 ? 10.19 f -0.14 ? 0.07 b 1996 1.91 ? 0.02 ab 371 ? 2.27 hi 7.1 ? 0.07 fg 551 ? 3.40 cd 7.35 ? 0.28 fghi 97.22 ? 0.93 c - 35.46 ? 0.25 cdefg 7.02 ? 0.10 no 214 ? 2.27 fg 1484 ? 9.99 h -0.07 ? 0.07 ab 1997 1.79 ? 0.02 de 385 ? 2.34 def 6.9 ? 0.07 hi 523 ? 3.20 g 8.63 ? 0.26 bcd 96.39 ? 0.92 cd - 34.50 ? 0.26 h 7.19 ? 0.10 lmn 200 ? 2.34 ijk 1415 ? 10.28 i -0.12 ? 0.07 b 1998 1.77 ? 0.02 def 387 ? 2.35 de 7.0 ? 0.07 gh 551 ? 3.22 cd 8.79 ? 0.27 bc 96.08 ? 0.93 cd 3.89 ? 0.14 a 34.79 ? 0.26 gh 7.11 ? 0.10 mno 198 ? 2.35 jk 1392 ? 10.33 i -0.09 ? 0.07 b 1999 1.87 ? 0.03 bc 376 ? 2.91 gh 6.8 ? 0.09 hi 550 ? 3.99 cde 9.40 ? 0.33 ab 98.34 ? 1.11 bc 2.20 ? 0.11 e 35.23 ? 0.32 defgh 6.84 ? 0.12 o 208 ? 2.91 gh 1407 ? 12.81 i -0.11 ? 0.09 b 2000 1.76 ? 0.02 ef 388 ? 2.39 de 7.2 ? 0.08 efg 547 ? 3.27 de 9.94 ? 0.27 a 90.73 ? 0.94 fg 2.96 ? 0.10 c 35.10 ? 0.27 efgh 7.52 ? 0.10 ijk 197 ? 2.39 k 1461 ? 10.50 h -0.01 ? 0.08 ab 2001 1.93 ? 0.02 a 421 ? 2.46 b 7.4 ? 0.08 cde 572 ? 3.37 a 9.87 ? 0.28 a 96.52 ? 0.97 cd 2.90 ? 0.10 c 34.88 ? 0.28 fgh 7.01 ? 0.10 no 163 ? 2.46 m 1141 ? 10.82 j 0.05 ? 0.08 ab 2002 1.89 ? 0.02 ab 427 ? 2.70 b 7.3 ? 0.09 efg 569 ? 3.73 a 6.98 ? 0.30 ghij 89.48 ? 1.03 g 3.01 ? 0.11 b 34.81 ? 0.30 fgh 7.00 ? 0.11 no 157 ? 2.70 m 1088 ? 11.86 k -0.14 ? 0.09 b 2003 1.76 ? 0.02 def 437 ? 2.56 a 6.8 ? 0.08 hi 559 ? 3.50 bc 7.69 ? 0.29 efg 90.43 ? 1.00 fg 2.65 ? 0.10 d 34.64 ? 0.29 h 7.36 ? 0.11 klm 147 ? 2.56 n 1077 ? 11.26 k -0.07 ? 0.08 ab 2004 1.91 ? 0.02 ab 424 ? 2.35 b 7.2 ? 0.07 efg 558 ? 3.21 bc 6.85 ? 0.27 hij 90.95 ? 0.93 fg 2.69 ? 0.10 d 35.23 ? 0.26 efgh 6.50 ? 0.10 p 161 ? 2.35 m 1039 ? 10.31 l -0.14 ? 0.07 b a See Table 2 for trait abbreviations Columns with different superscripts differ at P < 0.05 81 Table 17. Least squares mean ? SEM by year of residual feed intake (RFI) on post-weaning gain and ultrasound traits of central test bulls adjusted for age Trait a Year ADG IW FS YW USFAT USREA USIMF SC FCR TG FI RFI 1978 1.44 ? 0.03 o 314 ? 4.87 l 5.0 ? 0.11 lm 473 ? 5.57 mn - - - - 6.94 ? 0.14 klm 202 ? 3.51 fgh 1397 ? 17.70 i -0.01 ? 0.09 abc 1979 1.28 ? 0.03 p 317 ? 4.41 kl 4.8 ? 0.10 m 458 ? 5.05 o - - - - 9.17 ? 0.12 a 180 ? 3.18 l 1613 ? 16.02 de -0.10 ? 0.09 bc 1980 1.33 ? 0.03 p 326 ? 4.24 jkl 5.1 ? 0.09 kl 469 ? 4.84 no - - - - 8.13 ? 0.12 bcd 187 ? 3.05 jkl 1485 ? 15.38 h -0.02 ? 0.08 abc 1981 1.47 ? 0.03 no 328 ? 4.35 jk 6.0 ? 0.09 ij 488 ? 4.98 lm - - - - 7.57 ? 0.12 fgh 207 ? 3.14 def 1555 ? 15.80 fg 0.01 ? 0.08 abc 1982 1.50 ? 0.03 no 333 ? 4.26 j 6.2 ? 0.09 i 496 ? 4.87 jkl - - - - 7.89 ? 0.12 def 211 ? 3.07 cde 1658 ? 15.45 c -0.04 ? 0.08 abc 1983 1.48 ? 0.03 no 333 ? 4.44 j 5.3 ? 0.10 k 496 ? 5.08 kl - - - 35.73 ? 0.32 def 8.34 ? 0.12 b 208 ? 3.20 def 1722 ? 16.13 b -0.07 ? 0.09 abc 1984 1.53 ? 0.03 mn 337 ? 4.40 ij 5.8 ? 0.10 j 506 ? 5.03 jk - - - - 7.81 ? 0.12 defg 215 ? 3.17 cd 1654 ? 15.97 cd -0.04 ? 0.09 abc 1985 1.52 ? 0.03 no 347 ? 4.66 hi 6.0 ? 0.10 ij 509 ? 5.41 j 7.05 ? 0.33 efgh - - 37.10 ? 0.33 b 7.67 ? 0.13 efgh 213 ? 3.36 cd 1617 ? 16.91 cde -0.16 ? 0.09 c 1986 1.55 ? 0.03 lm 353 ? 4.69 gh 6.7 ? 0.10 h 526 ? 5.38 hi 7.23 ? 0.33 defgh - - 36.66 ? 0.33 bc 7.38 ? 0.13 hij 218 ? 3.38 bc 1595 ? 17.04 ef -0.13 ? 0.09 c 1987 1.59 ? 0.02 kl 348 ? 3.88 hi 7.3 ? 0.08 cdef 525 ? 4.43 i 6.87 ? 0.29 fgh - - 38.30 ? 0.29 a 7.81 ? 0.11 efg 224 ? 2.79 b 1738 ? 14.07 b -0.03 ? 0.08 abc 1988 1.61 ? 0.03 jkl 362 ? 4.00 fg 7.4 ? 0.09 bcd 539 ? 4.57 defgh 7.40 ? 0.29 fgh - - 36.79 ? 0.29 bc 7.99 ? 0.11 cde 227 ? 2.88 ab 1793 ? 14.52 a -0.04 ? 0.08 abc 1989 1.65 ? 0.02 ijk 363 ? 3.80 fg 7.5 ? 0.08 ab 548 ? 4.34 bcd 6.66 ? 0.29 gh - - 35.72 ? 0.29 def 7.22 ? 0.11 ijk 232 ? 2.74 a 1656 ? 13.79 c -0.01 ? 0.07 abc 1990 1.66 ? 0.02 hi 371 ? 3.81 ef 7.6 ? 0.08 ab 556 ? 4.36 ab 7.78 ? 0.29 cde - - 36.12 ? 0.29 cd 7.87 ? 0.11 def 234 ? 2.75 a 1829 ? 13.85 a 0.00 ? 0.07 abc 1991 1.77 ? 0.02 def 393 ? 3.80 d 7.7 ? 0.08 a 543 ? 4.34 cdefg 7.62 ? 0.29 cde - - 35.40 ? 0.29 defg 7.55 ? 0.11 gh 198 ? 2.74 gh 1481 ? 13.79 h -0.06 ? 0.07 abc 1992 1.83 ? 0.02 cd 394 ? 3.76 d 7.4 ? 0.08 bcd 549 ? 4.30 bcd 6.49 ? 0.29 h 101.02 ? 1.01 a - 35.62 ? 0.29 def 7.77 ? 0.11 efg 204 ? 2.71 efg 1569 ? 13.65 fg 0.13 ? 0.07 a 1993 1.64 ? 0.02 ijk 405 ? 3.76 c 7.4 ? 0.08 bc 540 ? 4.31 defg 8.24 ? 0.29 bc 93.29 ? 1.04 ef - 35.77 ? 0.27 de 8.11 ? 0.11 bcd 184 ? 2.71 kl 1479 ? 13.66 h -0.08 ? 0.07 bc 1994 1.75 ? 0.02 efg 398 ? 3.73 cd 7.3 ? 0.08 cdef 544 ? 4.26 cdef 7.94 ? 0.28 bcd 91.04 ? 1.03 fg - 35.57 ? 0.27 def 7.99 ? 0.10 cde 196 ? 2.69 hi 1552 ? 13.53 g -0.05 ? 0.07 abc 1995 1.70 ? 0.02 ghi 359 ? 3.78 g 7.3 ? 0.08 cde 535 ? 4.32 efghi 6.63 ? 0.28 gh 97.00 ? 1.02 bc - 35.22 ? 0.27 efgh 8.23 ? 0.11 bc 190 ? 2.72 ijk 1554 ? 13.70 fg -0.11 ? 0.07 c 1996 1.90 ? 0.02 ab 376 ? 3.74 e 7.1 ? 0.08 fg 546 ? 4.67 bcde 7.22 ? 0.29 defgh 96.38 ? 1.00 bcd - 35.29 ? 0.26 defgh 7.12 ? 0.11 jk 212 ? 2.69 cd 1489 ? 13.57 h -0.06 ? 0.07 abc 1997 1.81 ? 0.02 cde 377 ? 3.85 e 7.0 ? 0.08 g 532 ? 4.40 ghi 8.32 ? 0.28 bc 94.17 ? 1.03 de - 33.97 ? 0.28 j 6.79 ? 0.11 lm 202 ? 2.77 fgh 1407 ? 13.98 i -0.12 ? 0.07 c 1998 1.75 ? 0.02 efg 392 ? 3.86 d 7.0 ? 0.08 g 542 ? 4.42 cdefg 8.55 ? 0.28 b 94.65 ? 1.00 cde 3.89 ? 0.14 a 34.60 ? 0.28 hij 7.07 ? 0.11 jkl 196 ? 2.78 hi 1398 ? 14.02 i -0.06 ? 0.07 abc 1999 1.93 ? 0.03 a 399 ? 4.76 cd 7.0 ? 0.10 g 563 ? 5.44 a 9.54 ? 0.34 a 98.90 ? 1.20 ab 2.21 ? 0.12 e 35.50 ? 0.34 defg 6.66 ? 0.13 m 215 ? 3.43 cd 1466 ? 17.27 h -0.10 ? 0.09 bc 2000 1.74 ? 0.02 fg 390 ? 3.92 d 7.2 ? 0.08 defg 539 ? 4.48 defgh 9.65 ? 0.28 a 88.97 ? 1.01 g 2.96 ? 0.10 b 34.83 ? 0.28 ghi 7.46 ? 0.11 hi 195 ? 2.82 hij 1461 ? 14.23 h 0.01 ? 0.08 abc 2001 1.85 ? 0.03 bc 427 ? 4.13 b 7.0 ? 0.09 g 537 ? 4.72 defghi 9.48 ? 0.30 a 94.73 ? 1.06 cde 2.90 ? 0.10 bc 34.74 ? 0.30 ghij 7.33 ? 0.12 hij 153 ? 2.97 m 1130 ? 14.99 j 0.11 ? 0.08 ab 2002 1.87 ? 0.03 abc 443 ? 4.48 a 7.1 ? 0.10 efg 554 ? 5.17 abc 6.85 ? 0.32 fgh 88.86 ? 1.12 g 3.01 ? 0.11 b 34.91 ? 0.32 fghi 7.12 ? 0.12 jk 155 ? 3.23 m 1112 ? 16.27 jk -0.12 ? 0.09 c 2003 1.73 ? 0.03 fgh 441 ? 4.22 a 6.7 ? 0.09 h 543 ? 4.83 cdefg 7.36 ? 0.30 defg 88.66 ? 1.08 g 2.65 ? 0.10 d 34.41 ? 0.30 ij 7.51 ? 0.12 ghi 143 ? 3.04 n 1075 ? 15.33 k -0.03 ? 0.08 abc 2004 1.84 ? 0.02 bc 423 ? 3.88 b 7.0 ? 0.08 g 533 ? 4.43 fghi 6.43 ? 0.28 h 88.90 ? 0.99 g 2.70 ? 0.10 d 34.90 ? 0.28 fghi 6.70 ? 0.11 m 153 ? 2.79 m 1022 ? 14.07 l -0.08 ? 0.08 bc a See Table 2 for trait abbreviations Columns with different superscripts differ at P < 0.05 82 LITERATURE CITED Adams, M. W., and R. L. Belyea. 1987. Nutritional and energetic differences of dairy cows varying in milk yield. J. Anim. Sci. 70(Supplement 1): 182. Archer, J. A., and W. S. Pitchford. 1996. Phenotypic variation in residual feed intake of mice at different ages and its relationship with efficiency of growth, maintenance and body composition. Anim. Sci. 63: 149-157. Archer, J. A., P. F. Arthur, R. M. Herd, P. F. Parnell, and W. S. Pitchford. 1997. Optimum postweaning test for measurement of growth rate, feed intake, and feed efficiency in British breed cattle. J. Anim. Sci. 75: 2024-2032. Archer, J. A., W. S. Pitchford, T. E. Hughes, and P. F. Parnell. 1998. Genetic and phenotypic relationships between food intake, growth, efficiency and body composition of mice post weaning and at maturity. Anim. Sci. 67: 171-182. Archer, J. A., E. C. Richardson, R. M. Herd, and P. F. Arthur. 1999. Potential for selection to improve efficiency of feed use in beef cattle: A review. Austr. J. Agric. Res. 50: 147-161. Archer, J. A., A. Reverter, R. M. Herd, D. J. Johnston, and P. F. Arthur. 2002. Genetic variation in feed intake and efficiency of mature beef cows and relationships with postweaning measurements. In: Proc. 7 th World Congr. Genet. Appl. Livest. Prod., Montpellier, France. comm. no. 10-07. Arnold, J. W., J. K. Bertrand, L. L. Benyshek, and C. Ludwig. 1991. Estimates of genetic parameters for live animal ultrasound, actual carcass data, and growth traits in beef cattle. J. Anim. Sci. 69: 985-992. Arthur, P. F., J. A. Archer, R. M. Herd, E. C. Richardson, S. C. Exton, C. Oswin, K. C. P. Dibley, and D. A. Burton. 1999. Relationship between postweaning growth, net feed intake and cow performance. In: Proceedings of the thirteenth conference association for the advancement of animal breeding and genetics. p 484-487. Arthur, P. F., J. A. Archer, D. J. Johnston, R. M. Herd, E. C. Richardson, and P. F. Parnell. 2001a. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J. Anim. Sci. 79: 2805-2811. Arthur, P. F., G. Renand, and D. Krauss. 2001b. Genetic and phenotypic relationships among different measures of growth and feed efficiency in young Charolais bulls. Live. Prod. Sci. 68: 131-139. Baker, S. D., J. I. Szasz, T. A. Klein, P. S. Kuber, C. W. Hunt, J. B. Glaze Jr., D. Falk, R. Richard, J. C. Miller, R. A. Battaglia, and R. A. Hill. 2006. Residual feed intake of purebred Angus steers: Effects on meat quality and palatability. J. Anim. Sci. 84: 938-945. 83 Basarab, J. A., M. A. Price, and E. K. Okine. 2002. Commercialization of net feed efficiency. Pages 183-194 in Proceedings of the 23rd Western Nutritional Conference, Edmonton, Alberta, Canada. Basarab, J. A., M. A. Price, J. L. Aalhus, E. K. Okine, W. M. Snelling, and K. L. Lyle. 2003. Residual feed intake and body composition in young growing cattle. Can. J. Anim. Sci. 83: 189-204. Basarab, J. A., E. K. Okine, and S. S. Moore. 2004. Residual feed intake: Animal performance, carcass quality and body composition. Pages 40 - 51 in 2004 Florida Ruminant Nutrition Symposium, Florida. Bereskin, B. 1986. A genetic analysis of feed conversion efficiency and associated traits in swine. J. Anim. Sci. 62: 910-917. Bergen, R. D., J. J. McKinnon, D. A. Christensen, N. Kohle, and A. Belanger. 1997. Use of real-time ultrasound to evaluate live animal carcass traits in young performance-tested beef bulls. J. Anim. Sci. 75: 2300-2307. Bernard, C., and M. H. Fahmy. 1970. Effect of selection on feed utilization and carcass score in swine. Can. J. Anim. Sci. 50: 575-584. BIF. 2002. Guidelines for uniform beef improvement programs (8 th Rev. Ed.). Univ. of Georgia, Athens. Bidner, T. D., W. E. Wyatt, P. E. Humes, D. E. Franke, and D. C. Blouin. 2002. Influence of Brahaman derivative breeds and Angus on carcass traits, physical composition, and palatability. J. Anim. Sci. 80: 2126-2133. Bishop, M. D., M. E. Davis, W. R. Harvey, G. R. Wilson, and B. D. VanStavern. 1991a. Divergent selection for postweaning feed conversion in Angus beef cattle: I. Mean comparisons. J. Anim. Sci. 69: 4348-4359. Bishop, M. D., M. E. Davis, W. R. Harvey, G. R. Wilson, and B. D. VanStavern. 1991b. Divergent selection for postweaning feed conversion in Angus beef cattle: II. Genetic and phenotypic correlations and realized heritability estimate. J. Anim. Sci. 69: 4360-4367. Boldman, K. G., L. A. Kriese, L. D. Van Vleck, and S. D. Kachman. 1993. A manual for use of MTDFREML. A set of programs to obtain estimates of variances and covariances., USDA-ARS, Clay Center, NE. Bordas, A., M. Tixier-Boichard, and P. Merat. 1992. Direct and correlated responses to divergent selection for residual food intake in Rhode Island Red laying hens. British Poultry Sci. 33: 741-754. Bourdon, R. M. and J. S. Brinks. 1986. Scrotal circumference in yearling Hereford bulls: adjustment factors, heritabilities and genetic, environmental and phenotypic relationships with growth traits. J. Anim. Sci. 62: 958-967. Braastad, B. O., and J. Katle. 1989. Behavioural differences between laying hen populations selected for high and low efficiency of food utilisation. Brit. Poul. Sci. 30: 533-544. Brown, A. H., Jr., Z. B. Johnson, J. J. Chewning, and C. J. Brown. 1988. Relationships among absolute growth rate, relative growth rate and feed conversion during postweaning feedlot performance tests. J. Anim. Sci. 66: 2524-2529. Brown, C. J., and W. Gifford. 1962. Estimates of heritability and genetic correlations among certain traits of performance tested beef bulls. J. Anim. Sci. 21: 388. 84 Byerly, T. C. 1941. Feed and other costs of producing market eggs. Tech. Bull. No. A1, The University of Maryland, College Park. Cammack, K. M., K. A. Leymaster, T. G. Jenkins, and M. K. Nielsen. 2005. Estimates of genetic parameters for feed intake, feeding behavior, and daily gain in composite ram lambs. J. Anim. Sci. 83: 777-785. Carstens, G. E. and L. O. Tedeschi. 2006. Defining feed efficiency in beef cattle. Pages 12-21 in Proceedings of Beef Improvement Federation 38 th Annual Research Symposium and Annual Meeting, Choctaw, Mississippi. Carter, R. C., and C. M. Kincaid. 1959a. Estimates of genetic and phenotypic parameters in beef cattle. II. Heritability estimates from parent-offspring and half-sib resemblances. J. Anim. Sci. 18: 323-330. Carter, R. C., and C. M. Kincaid. 1959b. Estimates of genetic and phenotypic parameters in beef cattle. III. Genetic and phenotypic correlations among economic characters. J. Anim. Sci. 18: 331-335. Coulter, G. H., and R. H. Foote. 1979. Bovine testicular measurements as indicators of reproductive performance and their relationship to productive traits in cattle: A review. Therio. 11: 297-311. Crews, D. H., Jr., and R. A. Kemp. 2002. Genetic evaluation of carcass yield using ultasound measures on young replacement beef cattle. J. Anim. Sci. 80: 1809- 1818. Crews, D. H., Jr., N. H. Shannon, R. E. Crews, and R. A. Kemp. 2002. Weaning, yearling, and preharvest ultrasound measures of fat and muscle area in steers, bulls, and heifers. J. Anim. Sci. 80: 2817-2824. Crews, D. H., Jr., E. J. Pollak, R. L. Weaber, R. L. Quaas, and R. J. Lipsey. 2003. Genetic parameters for carcass traits and their live animal indicators in Simmental cattle. J. Anim. Sci. 81: 1427-1433. Crews, D. H., Jr. 2006. The genetics of feed efficiency in beef cattle. Pages 22-31 in Proceedings of Beef Improvement Federation 38 th Annual Research Symposium and Annual Meeting, Choctaw, Mississippi. Dawson, W. M., T. S. Yao, and A. C. Cook. 1955. Heritability of growth, beef characters and body measurements in milking Shorthorn steers. J. Anim. Sci. 14: 208-217. DeHaer, L. C. M., P. Luiting, and H. L. M. Aarts. 1993. Relations among individual (residual) feed intake, growth performance and feed intake pattern of growing pigs in group housing. Live. Prod. Sci. 36: 233-253. Devitt, C. J. B., and J. W. Wilton. 2001. Genetic correlation estimates between ultrasound measurements on yearling bulls and carcass measurements on finished steers. J. Anim. Sci. 79: 2790-2797. Dickerson, G. E., and J. C. Grimes. 1947. Effectiveness of selection for efficiency of gain in Duroc swine. J. Anim. Sci. 6: 265-287. Dolezal, S. L., and R. Silcox. 2004. Postweaning evaluation programs for beef bulls. Tech. Bull. No. ANSI-3002, Oklahoma State University, Stillwater. Eler, J. P., J. A. II V. Silva, J. L. Evans, J. B. S. Ferraz, F. Dias, and B. L. Golden. 2004. Additive genetic relationships between heifer pregnancy and scrotal circumference in Nellore cattle. J. Anim. Sci. 82: 2519-2527. 85 Evans, J. L., B. L. Golden, R. M. Bourdon, and K. L. Long. 1999. Additive genetic relationships between heifer pregnancy and scrotal circumference in Hereford cattle. J. Anim. Sci. 77: 2621-2628. Fan, L. Q., D. R. C. Bailey, and N. H. Shannon. 1995. Genetic parameter estimation of postweaning gain, feed intake, and feed efficiency for Hereford and Angus bulls fed two different diets. J. Anim. Sci. 73: 365-372. Fan, Y. K., J. Croom, V. L. Christensen, B. L. Black, A. R. Bird, L. R. Daniel, B. W. McBride, and E. J. Eisen. 1997. Jejunal glucose uptake and oxygen consumption in turkey poults selected for rapid growth. Poul. Sci. 76: 1738-1745. Faulkner, D. B., D. F. Parrett, F. K. McKeith, and L. L. Berger. 1990. Prediction of fat cover and carcass composition from live and carcass measurements. J. Anim. Sci. 68: 604-610. Fraser, D., J. S. D. Richie, and A. F. Frase. 1975. The term "stress" in the veterinary context. Brit. Vet. J. 131: 653-662. Gunsett, F. C. 1984. Linear selection to improve traits defined as ratios. J. Anim. Sci. 59: 1185-1193. Hassen, A., D. E. Wilson, and G. H. Rouse. 1998a. Evaluation of carcass, live, and real- time ultrasound measures in feedlot cattle: I. Assessment of sex and breed effects. J. Anim. Sci. 76: 273-282. Hassen, A., D. E. Wilson, R. L. Willham, G. H. Rouse, and A. H. Trenkle. 1998b. Evaluation of ultrasound measurements of fat thickness and longissimus muscle area in feedlot cattle: Assessment of accuracy and repeatability. Can. J. Anim. Sci. 78: 277-285. Herd, R. M., and S. C. Bishop. 2000. Genetic variation in residual feed intake and its association with other production traits in British Hereford cattle. Live. Prod. Sci. 63: 111-119. Herd, R. M., J. A. Archer, and P. F. Arthur. 2003. Reducing the cost of beef production through genetic improvement in residual feed intake: Opportunity and challenges to application. J. Anim. Sci. 81 (E suppl. 1): E9-E17. Herd, R. M., V. H. Oddy, and E. C. Richardson. 2004. Biological basis for variation in residual feed intake in beef cattle. 1. Review of potential mechanisms. Aust. J. Exp. Agric. 44: 423-430. Herring, W. O., D. C. Miller, J. K. Bertrand, and L. L. Benyshek. 1994. Evaluation of machine, technician, and interpreter effects on ultrasonic measures of backfat and longissimus muscle area in beef cattle. J. Anim. Sci. 72: 2216-2226. Herring, W. O., and J. K. Bertrand. 2002. Multi-trait prediction of feed conversion in feedlot cattle. Pages 89-97 in Proceedings of Beef Improvement Federation 34th Annual Research Symposium and Annual Meeting. Jakobsen, J. H., P. Madsen, J. Jensen, G. A. Pedersen, and P. H. Petersen. 2000. Genetic parameters for average daily gain, area of M. longissimus dorsi, feed efficiency and feed intake capacity in young bulls of dairy populations. Acta. Agri. Scan., A 50(3): 146-152. Jensen, J., I. L. Mao, and B. B. Andersen. 1992a. Phenotypic and genetic relationships between residual energy intake and growth, feed intake, and carcass traits of young bulls. J. Anim. Sci. 70: 386-395. 86 Jensen, J., I. L. Mao, B. Bech-Anderson, and P. Madsen. 1992b. Phenotypic and genetic relationships between residual energy intake and growth, feed intake, and carcass traits of young bulls. J. Anim. Sci. 70: 386-395. Johnson, D. E., C. L. Ferrel, and T. G. Jenkins. 2003. The history of energetic efficiency research: Where have we been and where are we going? J. Anim. Sci. 81 (E. Suppl. 1): E27-E38. Johnson, Z. B., R. R. Schalles, M. E. Dikeman, and B. L. Golden. 1993. Genetic parameter estimates of ultrasound-measured longissimus muscle area and 12th rib fat thickness in Brangus cattle. J. Anim. Sci. 71: 2623-2630. Jungst, S. B., L. L. Christian, and D. L. Kuhlers. 1981. Response to selection for feed efficiency in individually fed Yorkshire boars. J. Anim. Sci. 53: 323-331. Katle, J. 1991. Selection for efficiency of food utilisation in laying hens: Causal factors for variation in residual food consumption. Brit. Poul. Sci. 32: 955-969. Keeton, L. L., R. D. Green, B. L. Golden, and K. J. Anderson. 1996. Estimation of variance components and prediction of breeding values for scrotal circumference and weaning weight in Limousin cattle. J. Anim. Sci. 74: 31-36. Knapp, B., Jr., and A. W. Nordskog. 1946. Heritability of growth and efficiency in beef cattle. J. Anim. Sci. 5: 62-70. Knights, S. A., R. L. Baker, D. Gianola, and J. B. Gibb. 1984. Estimates of heritabilities and of genetic and phenotypic correlations among growth and reproductive traits in yearling Angus bulls. J. Anim. Sci. 58: 887-893. Koch, R. M., L. A. Swigher, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22: 486-494. Koch, R. M., L. V. Cundiff, K. E. Gregory, and L. D. Van Vleck. 2004. Genetic response to selection for weaning weight or yearling weight or yearling weight and muscle score in Hereford cattle: Efficiency of gain, growth, and carcass characteristics. J. Anim. Sci. 82: 668-682. Kolath, W. H., M. S. Kerley, J. W. Golden, and D. H. Keisler. 2006. The relationship between mitochondrial function and residual feed intake in Angus steers. J. Anim. Sci. 84: 861-865. Koots, K. R., J. P. Gibson, C. Smith, and J. E. Wilton. 1994a. Analyses of published genetic parameter estimates for beef production traits. 1. Heritability. Anim. Bre. Abst. 62: 309-338. Koots, K. R., J. P. Gibson, and J. W. Wilton. 1994b. Analyses of published genetic parameter estimates for beef production traits. 2. Phenotypic and genetic correlations. Anim. Bre. Abst. 62: 825-853. Kriese, L. A., J. K. Bertrand, and L. L. Benyshek. 1991a. Age adjustment factors, heritabilities and genetic correlations for scrotal circumference and related growth traits in Hereford and Brangus bulls. J. Anim. Sci. 69: 478-489. Kriese, L. A., J. K. Bertrand, and L. L. Benyshek. 1991b. Genetic and environmental growth trait parameter estimates for Brahman and Brahman-derivative cattle. J. Anim. Sci. 69: 2362-2370. Luiting, P. and E. M. Urff. 1991a. Residual feed consumption in laying hens. 1. Quantification of phenotypic variation and repeatabilities. Poult. Sci. 70: 1655- 1662. 87 Luiting, P. and E. M. Urff. 1991b. Residual feed consumption in laying hens. 2. Genetic variation and correlations. Poult. Sci. 70: 1663-1672. Luiting, P., J. W. Scrama, W. Van Der Hel, E. M. Urff, P. G. J. J. Van Boekholt, E. M. W. Van Den Elsen, and M. W. A. Vestergen. 1991. Metabolic differences between white leghorns selected for high and low residual feed consumption. In: C. Wenk and M. Boessinger (eds.) Energy metabolism of farm animals. p 384- 387. European Association for Animal Production, Kartause, Switzerland. Lunstra, D. D., K. E. Gregory, and L. V. Cundiff. 1988. Heritability estimates and adjustment factors for the effects of bull age and age of dam on yearling testicular size in breeds of bulls. Ther. 30: 127-136. MacNeil, M. D., D. R. C. Bailey, J. J. Urick, R. P. Gilbert, and W. L. Reynolds. 1991. Heritabilities and genetic correlations for postweaning growth and feed intake of beef bulls and steers. J. Anim. Sci. 69: 3183-3189. Marshall, D. M. 1994. Breed differences and genetic parameters for body composition traits in beef cattle. J. Anim. Sci. 72: 2745-2755. McDonagh, M. B., R. M. Herd, E. C. Richardson, V. H. Oddy, J. A. Archer, and P. F. Arthur. 2001. Meat quality and the calpain system of feedlot steers following a single generation of divergent selection for residual feed intake. Aust. J. Exp. Agri. 41: 1013-1021. Meyer, K., K. Hammond, P. F. Parnell, M. J. Mackinnon, and S. Sivarajasingham. 1990. Estimates of heritability and repeatability for reproductive traits in Australian beef cattle. Live. Prod. Sci. 25: 15-30. Meyer, K., K. Hammond, M. J. Mackinnon, and P. F. Parnell. 1991. Estimates of covariances between reproduction and growth in Australian beef cattle. J. Anim. Sci. 69: 3533-3543. Moser, D. W., J. K. Bertrand, L. L. Benyshek, M. A. McCann, and T. E. Kiser. 1996. Effects of selection for scrotal circumference in Limousin bulls on reproductive and growth traits of progeny. J. Anim. Sci. 74: 2052-2057. Moser, D. W., J. K. Bertrand, I. Misztal, L. A. Kriese, and L. L. Benyshek. 1998. Genetic parameter estimates for carcass and yearling ultrasound measurements in Brangus cattle. J. Anim. Sci. 76: 2542-2548. Mrode, R. A. and B. W. Kennedy. 1993. Genetic variation in measures of food efficiency in pigs and their genetic relationships with growth rate and backfat. Anim. Prod. 56: 225-232. Neely, J. D., B. H. Johnson, E. U. Dillard, and O. W. Robinson. 1982. Genetic parameters for testes size and sperm number in Hereford bulls. J. Anim. Sci. 55: 1033-1040. Nelson, T. C., R. E. Short, J. J. Urick, and W. L. Reynolds. 1986. Heritabilities and genetic correlations of growth and reproductive measurements in Hereford bulls. J. Anim. Sci. 63: 409-417. Nkrumah, J. D., J. A. Basarab, M. A. Price, E. K. Okine, A. Ammoura, S. Guercio, C. Hansen, C. Li, B. Benkel, B. Murdoch, and S. S. Moore. 2004. Different measures of energetic efficiency and their phenotypic relationships with growth, feed intake, and ultrasound and carcass merit in hybrid cattle. J. Anim. Sci. 82: 2451- 2459. 88 Nkrumah, J.D., Okine, E.K., Mathison, G.W., Schmid, K., Li, C., Basarab, J.S., Price, M.A., Wang, Z., and Moore, S.S. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84: 145-153. Nkrumah, J. D., J. A. Basarab, Z. Wang, C. Li, M. A. Price, E. K. Okine, D. H. Crews, Jr., and S. S. Moore. 2007. Genetic and phenotypic relationships of feed intake and different measures of feed efficiency with growth and carcass merit of beef cattle. J. Anim. Sci. Published online on May 25, 2007. doi: 10.2527/jas.2006-767 NRC. 1996. Nutrient requirements of beef cattle. National Academy Press, Washington D.C. Okine, E. K., J. A. Basarab, L. A. Goonewardene, and P. Mir. 2004. Residual feed intake and feed efficiency: Differences and implications. Pages 27-38 in Proceedings of Florida Ruminant Nutrition Symposium Perkins, T. L., R. D. Green, and K. E. Hamlin. 1992. Evaluation of ultrasonic estimates of carcass fat thickness and longissimus muscle area in beef cattle. J. Anim. Sci. 70: 1002-1010. Perkins, T., A. Meadows, and B. Hayes. 1997. Study guide for the ultrasonic evaluation of beef cattle for carcass merit. Ultrasound guidelines council study guide sub- committee. Available online at: http://www.cuplab.com/index.cfm. Pitchford, W. S. 2004. Genetic improvement of feed efficiency of beef cattle: What lessons can be learnt from other species. Aust. J. Exp. Agri. 44: 371-382. Pym, R. A. E., and P. J. Nichols. 1979. Selection for food conversion in broilers: Direct and correlated responses to selection for body weight gain, food consumption and food conversion ratio. Brit. Poul. Sci. 20: 73. Richardson, E. C., and R. M. Herd. 2004. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection. Austr. J. Exp. Agri. 44: 431-440. Richardson, E. C., R. M. Herd, P. F. Arthur, J. Wright, G. Xu, K. C. P. Dibley, and V. H. Oddy. 1996. Possible physiological indicators for net feed conversion efficiency in beef cattle. Anim. Prod. Aust. 21: 103-106. Richardson, E. C., R. M. Herd, J. A. Archer, R. T. Woodgate, and P. F. Arthur. 1998. Steers bred for improved net feed efficiency eat less for the same feedlot performance. Anim. Prod. Aust. 22: 213-216. Richardson, E. C., R. M. Herd, V. H. Oddy, R. T. Woodgate, J. A. Archer, and P. F. Arthur. 1999. Body composition explains only part of the intake difference between high and low efficiency Angus steers. In: Recent Advances in Animal Nutrition in Australia. 12: 4A. Richardson, E. C., R. M. Herd, V. H. Oddy, J. M. Thompson, J. A. Archer, and P. F. Arthur. 2001. Body composition and implications for heat production of Angus steer progeny of parents selected for and against residual feed intake. Aust. J. Exp. Agri. 41: 1065-1072. SAS. 1998. Sas user's guide: Statistics (version 8). SAS Institute Inc., Cary, NC. Schenkel, F. S., S. P. Miller, and J. W. Wilton. 2004. Genetic parameters and breed differences for feed efficiency, growth, and body composition traits of young beef bulls. Can. J. Anim. Sci. 84: 177-185. 89 Shelby, C. E., R. T. Clark, and R. R. Woodward. 1955. The heritability of some economic characteristics of beef cattle. J. Anim. Sci. 14: 372-385. Shepard, H. H., R. D. Green, B. L. Golden, K. E. Hamlin, T. L. Perkins, and J. B. Diles. 1996. Genetic parameter estimates of live animal ultrasonic measures of retail yield indicators in yearling breeding cattle. J. Anim. Sci. 74: 761-768. Smith, B. A., J. S. Brinks, and G. V. Richardson. 1989. Estimation of genetic parameters among breeding soundness examination components and growth traits in yearling bulls. J. Anim. Sci. 67: 2892. Smith, M. T., J. W. Oltjen, H. G. Dolezal, D. R. Gill, and B. D. Behrens. 1992. Evaluation of ultrasound for prediction of carcass fat thickness and longissimus muscle area in feedlot steers. J. Anim. Sci. 70: 29-37. Stelzleni, A. M., T. L. Perkins, A. H. Brown, Jr., F. W. Pohlman, Z. B. Johnson, and B. A. Sandelin. 2002. Genetic parameter estimates of yearling live animal ultrasonic measurements in Brangus cattle. J. Anim. Sci. 80: 3150-3153. Stouffer, J. R. 2004. History of ultrasound in animal science. J. Ultra. Med. 23: 577-584. Tixier-Boichard, M., D. Boichard, E. Groeneveld, and A. Bordas. 1995. Restricted maximum liklihood estimates of genetic parameters of adult male and adult female Rhode Island Red chickens divergently selected for residual feed consumption. Poult. Sci. 74: 1245-1252. Turner, J. W., L. S. Pelton, and H. R. Cross. 1990. Using live animal ultrasound measures of ribeye area and fat thickness in yearling Hereford bulls. J. Anim. Sci. 68: 3502- 3506. Vargas, C. A., M. A. Elzo, C. C. Chase Jr., P. J. Chenoweth, and T. A. Olson. 1998. Estimation of genetic parameters for scrotal circumference, age at puberty in heifers, and hip height in Brahman cattle. J. Anim. Sci. 76: 2536-2541. Veseth, D. A., W. L. Reynolds, J. J. Urick, T. C. Nelsen, R. E. Short, and D. D. Kress. 1993. Paternal half-sib heritabilities and genetic, environmental, and phenotypic correlation estimates from randomly selected Hereford cattle. J. Anim. Sci. 71: 1730-1736. Von Felde, A., R. Roehe, H. Looft, and E. Kalm. 1996. Genetic association between feed intake and feed intake behaviour at different stages of growth of group-housed boars. Livest. Prod. Sci. 47: 11-22. Wang, Z., Nkrumah, J.D., Li, C., Basarab, J.A., Goonewardene, L.A., Okine, E.K., Crews, Jr., D.H., and Moore, S.S. 2006. Test duration for growth, feed intake, and feed efficiency in beef cattle using the GrowSafe System. J. Anim. Sci. 84: 2289- 2298. Warwick, B. L., and T. C. Cartwright. 1955. Heritability of rate of gain in young growing beef cattle. J. Anim. Sci. 14: 363-371. Webb, A. J., and J. W. B. King. 1983. Selection for improved food conversion ratio on ad libitum group feeding in pigs. Anim. Prod. 37: 375-385. Wilson, S. P. 1969. Genetic aspects of feed efficiency in broilers. Poul. Sci. 48: 487-495. Woldehawariat, G., M. A. Talamantes, R. R. Petty, Jr., and T. C. Cartwright. 1977. A summary of genetic and environmental statistics for growth and conformation characters of young beef cattle. Texas Agricultural Experiment Station Technical Report 103, Texas A&M University, College Station. 90 APPENDIX CALCULATION OF RESIDUAL FEED INTAKE (RFI) A. General a. Daily feed intake was converted to total feed intake of each animal during the entire feeding period. b. Convert total feed intake to total energy intake by multiplying total Dry Matter (DM) intake by metabolizable energy of the diet fed determined by indirect calorimetry. i. Look up energy values of feedstuffs in diet using nutrient requirements of beef cattle (National Research Council, 1996). The following is a list of feedstuffs used to calculate RFI for Auburn University BCIA bull test. 1. Corn = 3.25 Mcal kg -1 2. Cottonseed Hulls = 1.52 Mcal kg -1 3. Oats = 2.78 Mcal kg -1 4. Soybean Meal = 3.04 Mcal kg -1 5. Molasses = 2.60 Mcal kg -1 6. Cottonseed Meal = 2.71 Mcal kg -1 7. Barley Grain #2 = 3.03 Mcal kg -1 8. Fat = 7.30 Mcal kg -1 c. Change pounds of each ingredient to a percent of ingredient in diet by dividing pounds of each ingredient into total pounds of diet. i. Example: Pounds of ingredient ? Total pounds of diet = % of ingredient in diet d. Multiply percent of ingredient in diet by NRC values looked up. i. Example: Corn = 0.30 * 3.25 = 0.975 ii. Then take the sum of all feedstuffs calculated previously (in d.i). e. Take the sum (from d.ii) and multiply it by total feed intake (kg). This number is the total energy intake. f. Convert total energy intake (from e) to Mj by multiplying it by 4.184 g. Total energy intake is then divided by 10 to give total DM intake standardized to an energy density of 10 MJ ME kg -1 DM. h. Total standardized feed intake (SFI) is then divided by the number of days on test to give average standardized daily feed intake (SFI, kg d -1 ). i. Calculate mid-weight (MWT): MWT = Final Weight ? (0.50 * Days on Test * Average Daily Gain) j. Calculate metabolic mid-weight (MMWT): MMWT = (MWT) 0.73 k. Convert MMWT to Kg: MMWT ? 2.20462 91 l. Convert daily feed intake to Kg: total feed intake(kg)/days on test m. Convert ADG from pounds per day to kg per day: ADG (lbs/d)/2.20462 n. Next calculate expected feed intake (EFI, kg d -1 ) i. Calculate expected feed intake (EFI) using a regression equation in a statistical analysis software program (SAS, SAS Inst. Inc., Cary, NC). 1. Model fitted is basically of the form: a. Y i = a + b 1 ADG i + b 2 MMWT + e i Where Yi = SFI for animal i a = regression intercept b 1 = partial regression coefficient of SFI on ADG b 2 = partial regression coefficient on MMWT e i = residual error in SFI of animal i ii. Regress feed intake against some descriptor of maintenance (e.g. bodyweight to the power of 0.73) and production (e.g. growth rate). The predicted value from this regression is the expected feed intake. 1. Measures of average daily gain (ADG, kg d -1 ) and metabolic mid-weight (MMWT, kg 0.73 ) are used to model daily EFI. o. Calculate RFI by the following equation: RFI = Average standardized feed intake per day (from h) ? expected feed intake (from n.ii.1)