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ANIMAL PRODUCTION |
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* Igenity Livestock Production Business Unit, Merial Ltd.;
and
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, T6G 2P5, Canada;
and
Agriculture and Agri-Food Canada Research Centre, Lethbridge, Alberta, T1J 4B1 Canada; and
Alberta Agriculture, Food and Rural Development, Lacombe Research Centre, 6000 C&E Trail, Lacombe, Alberta, Canada, T4L 1W1
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: beef cattle • carcass merit • growth efficiency • genetic parameter • performance
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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) imply that growth efficiency of beef cattle is an economically relevant trait. Recently, the availability of automatic feed intake monitoring technology has enabled measurement of individual intakes and allowed ranking of animals based on different indices of efficiency (Archer et al., 1999).
Traditionally, one measure of production efficiency, termed feed efficiency, has been measured as the feed:gain ratio [F:G, the ratio of feed consumed to gain in BW, which is the reciprocal of the efficiency of gain (G:F), and therefore increases as the efficiency of gain decreases and vice versa]. The trait is highly correlated with growth (as is G:F) and confounded with maturity patterns of animals (Kennedy et al., 1993; Archer et al., 1999). As a selection tool, F:G, or G:F, has the potential to increase growth rate in young animals. However, it could also result in substantial increases in the feed intake of the cow herd, resulting in negative impacts on the overall production system efficiency (Dickerson, 1978).
Other measures of production efficiency (Archer et al., 1999) include residual feed intake (RFI), cow-calf efficiency, and partial efficiency of growth (PEG). Residual feed intake is the difference between an animal’s actual intake and its expected intake based on its BW and growth rate over a time period (Koch et al., 1963), and it has been shown to have great potential as an index of efficiency for beef cattle (Archer et al., 1999, 2001a). There is a current need to fully understand how RFI compares to other measures of efficiency (Arthur et al., 2001b) in terms of relationships to performance and carcass merit (Hoque et al., 2006a,b). The objective of this study was to determine the genetic and phenotypic relationships of feed intake and measures of efficiency with growth and carcass merit of beef cattle.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The study was approved by the University of Alberta animal welfare committee and all animals in the study were cared for according to the guidelines of the Canadian Council on Animal Care (CCAC, 1993). Growth, feed intake, ultrasound, and carcass merit data were collected on beef steers over 3 yr (November 2002 to June 2005). Animals were produced from crosses between Angus, Charolais, or Alberta Hybrid Bulls and the University of Alberta’s Hybrid dam line. The dam line was produced from crosses among 3 composite cattle lines, namely beef synthetic 1, beef synthetic 2, and dairy x beef synthetic. Briefly, beef synthetic 1 was composed of 33% each of Angus and Charolais, approximately 20% Galloway, and with the remainder from other beef breeds. Beef synthetic 2 was made up of about 60% Hereford and 40% other beef breeds. The dairy x beef line was composed of approximately 60% dairy breeds (Holstein, Brown Swiss, or Simmental) and 40% beef breeds, mainly Angus and Charolais (Goonewardene et al., 2003). The steers were managed and tested for growth and efficiency under feedlot conditions at the University of Alberta’s Kinsella Research Station using the GrowSafe automated feeding system (GrowSafe Systems Ltd., Airdrie, Alberta, Canada).
Cows and heifers were bred in multiple sire, breeding groups on pasture, and the sire of each calf was later determined in a parentage test using a panel of bovine microsatellite markers. Two tests made up of approximately 80 animals per test were conducted each year. Each animal was identified by means of a plastic tag located in the left ear. All animals had been vaccinated for bovine viral diarrhea and clostridial diseases and treated with a pour-on parasiticide before entry into the test. In each year, steers were fed a backgrounding diet of mainly smooth bromegrass hay (40%) with oats grain (40%) and supplemented with corn grain or barley (20%; as-fed basis) and feedlot mineral supplement to promote a growth rate of approximately 1.0 kg/d. This period was followed by a 30-d pretest adjustment period in which the amount of grain in the backgrounding diet was gradually increased to introduce the animals to the test diet and the feeding system. This was done to allow them to adapt to the diet and to learn to feed from the test facility.
The test diet in yr 1 was composed of 80% dry-rolled corn, 13.5% alfalfa hay pellet, 5% feedlot supplement (32% CP beef mineral supplement containing 440 mg of monensin/kg, trace minerals and vitamins), and 1.5% canola oil, supplying 2.90 Mcal/kg of ME and 12.5% CP. In yr 2 and 3, the same test procedures were used, but the test diet contained 64.5% barley grain, 20% oat grain, 9.0% alfalfa hay pellet, 5.0% beef feedlot supplement, and 1.5% canola oil, supplying 14.0% CP and 2.91 Mcal/kg of ME. The composition of the test diets as presented in Table 1 was analyzed through digestibility trials and proximate analyses. Corn was used in yr 1 instead of barley and oats because of a feed barley shortage that particular year.
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) in which straw was deliberately fed to animals (instead of as a bedding) indicates that the daily energy intake from straw provided as bedding would be insignificant. Individual animal intakes of straw were therefore not measured and excluded from the calculation of intake. Each year the test diet was sampled weekly and composited monthly. The composite samples were thoroughly mixed and subsampled for measurement of in vivo digestibility. Proximate analysis (Van Soest et al., 1991; Van Soest, 1994) was carried out for DM, GE, fat, CP, ash, Ca, P, NDF, and ADF (Table 1
; Nkrumah et al., 2006). Collection of Feed Intake, Growth, Ultrasound, and Carcass Data
Details of the procedures for the feedlot tests were given by Nkrumah et al. (2004). Feed intake was measured for each animal using the GrowSafe automated feeding system, which has been validated and used previously (Basarab et al., 2003). Individual animal feed intake data were recorded for a total of 84 d, excluding the prestudy adjustment period. Briefly, the system consisted of 10 feed bunks, a data-logging reader panel connected to each feed node, a personal computer, and GrowSafe Data Acquisition and Analysis Software. Wireless communication (Model 4000 R/F, Grow Safe Systems Ltd., Airdrie, Alberta, Canada) allowed for transfer of data between the acquisition unit and a desktop computer in an office located approximately 100 m away. Daily feed intake for each animal as recorded by the GrowSafe system was determined using specially customized software. Data collected (1 to 2% per test) on occasions when the automatic monitoring system failed to function due to power failure, mechanical problems, or failure of a main computer board were excluded from all subsequent analyses.
Body weight measurements of all animals were taken weekly (yr 1) or every 2 wk (yr 2 and 3) and ultrasound measurements of 12–13th rib fat depth, LM area, and marbling score were taken every 28 d. All animals had unrestricted access to feed and water before each weigh period. Ultrasound measurements were recorded with an Aloka 500V real-time ultrasound with a 17-cm, 3.5-MHz linear array transducer (Overseas Monitor Corporation Ltd., Richmond, British Columbia) using procedures detailed by Brethour (1992). Animals were weighed at the end of the efficiency tests and shipped to a commercial packing plant, where they were slaughtered the following day and standard industry carcass data collected after a 24-h chill at –4°C.
Traits and their Derivations
Linear regression in SAS (version 9.1.3, SAS Inst. Inc., Cary, NC) of BW measurements recorded at weekly or 2-wk intervals against time (d) was used to derive ADG, final BW, and midtest metabolic BW (BW0.75) for each steer. The total feed intake of each animal over a 70-d test period was used to compute the daily DMI. The F:G for each animal was computed as the ratio of DMI to ADG on test. The PEG of each animal (energetic efficiency for ADG above maintenance) was computed as the ratio of ADG to the difference between average daily DMI and expected DMI for maintenance (DMIm; Arthur et al., 2001b), where DMIm was computed using the NRC (1996) equations. Residual feed intake was calculated from phenotypic regression (RFIp; Arthur et al., 2001a) or genetic regression (RFIg; Hoque and Oikawa, 2004; Crews, 2005) of ADG and metabolic BW on DMI. Calculation of RFIp considers the phenotypic co(variances) between ADG and metabolic BW, whereas the calculation of RFIg considers the appropriate genetic co(variances). Test group (6 levels) was included as a fixed independent variable in the calculation of RFI. In each case, individual RFI was computed as actual DMI minus the expected DMI predicted from the appropriate phenotypic or genetic regression model. Rate of gain and final ultrasound back-fat, LM area, and marbling score were predicted from regression equations of ultrasound measurements upon time (d) for each individual animal. Carcass traits were evaluated according to the Canadian beef carcass grading system (Agriculture Canada, 1992). Hot carcass weight of each animal was determined as the sum of the weights of the left and right halves of each carcass. Carcass grade fat and LM area were measured at the 12–13th rib of each carcass. Carcass marbling score is a measure of intramuscular fat and can be classified as: 1 to < 2 units = trace marbling (Canada A quality grade); 2 to < 3 units = slight marbling (Canada AA quality grade); 3 to < 4 units = small to moderate marbling (Canada AAA quality grade), and 
4 units = slightly abundant or more marbling (Canada Prime). Lean meat yield is an estimate of saleable meat calculated according to Jones et al. (1984). Yield grade (YG) classes are based on the proportion of lean meat and is classified as YG1 > 59%, YG2 = 54 to 59%, and YG3 <54%.
Statistical Analyses
Performance, F:G, and ultrasound records were available on 464 animals, and carcass merit records were available on 381 animals. The total number of animals and their parents without records used in the analyses was 813. The animals in the study were primarily paternal half-sibs, but some dams were used in multiple years on the same sires making some full siblings. There were a total of 28 half-sib families, with the number of calves per family ranging from 3 to 56 (average = 20). The animals were classified into high, medium, and low RFIp groups based on ± 0.5 SD from the mean. This was done to determine the actual differences in performance and carcass merit among animals belonging to different classes of F:G.
Differences in measures of efficiency, performance, ultrasound, and carcass merit among steers in different classes of RFIp were determined using the MIXED procedure of SAS, and included the fixed effects of RFIp group (high, medium, and low), breed of sire (Angus, Charolais, or hybrid), test group nested within year (6 levels), and random effects of sire and dam of steer. Age of steer on test was included in the model as a linear covariate. Mean separation among RFIp groups for different test traits was carried out by least squares using the PDIFF option of SAS. The PROC CORR of SAS was also used to obtain Pearson partial phenotypic correlations among the traits.
Genetic (co)variances were obtained with the statistical software ASREML (Gilmour et al., 2000). A preliminary univariate animal model for each trait was fitted to obtain starting (co)variance parameters that were then fitted in subsequent REML bivariate analyses. Pairwise bivariate analyses were performed for each efficiency trait against the other test traits. The 2-trait animal models used to estimate (co)variance components included all the systematic effects listed above as well as random additive polygenic and residual effects, and a linear covariate for age. Genetic variances and heritability estimates for any particular trait were calculated as the average value of the estimates from all pairwise bivariate analyses performed against all traits, whereas their SE were the medians of the SE estimates.
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RESULTS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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. Animals in the study averaged 1.46 kg/d (range 0.78 to 2.24 kg/d) for ADG, 10.45 kg/ d (range 6.2 to 14.5 kg/d) for DMI, and 7.36 kg of DM/ kg of gain (range 4.97 to 14.3 kg of DM/kg of gain). Phenotypic RFI ranged from –2.73 kg/d (most efficient) to 2.86 kg/d (least efficient), a difference of 5.59 kg of DM/d of feed. Similarly, RFIg ranged from –3.12 kg of DM/d to 3.72 kg of DM/d, a difference of 6.84 kg of DM/d between the most and least efficient animals. Phenotypic variances for ADG, metabolic BW, DMI, F:G, RFIp, RFIg, and PEG were 0.073, 109.6, 2.59, 1.59, 0.77, 1.02, and 0.0036, respectively. Corresponding heritability estimates were 0.59, 0.31, 0.54, 0.41, 0.21, 0.42, and 0.56, respectively. Heritability estimates for all the traits considered are presented in Table 2.
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. Differences in ultrasound and carcass merit among animals in different RFIp groups are presented in Table 4. It must be noted that animals with greater RFIp, RFIg, and F:G values are less efficient than animals with smaller values. The difference in RFIp between high-RFIp and low-RFIp animals was approximately 1.90 kg of DM/d (P < 0.001). Phenotypic and genetic correlations among feed intake and different measures of efficiency are presented in Table 5. Generally, RFIp and RFIg were highly genetically and phenotypically correlated with each other (r > 0.90), and their corresponding phenotypic (P < 0.001) and genetic correlations with F:G and DMI were high. Least squares means for RFIp, RFIg, F:G, DMI, and ME intake were consistently greater (P < 0.001) for high-RFIp animals than for medium or low-RFIp animals (P < 0.001) and were greater (P < 0.001) for medium-RFIp animals than low-RFIp animals. Both RFIp and RFIg were strongly correlated phenotypically (P < 0.001) and genetically with PEG. Partial efficiency of growth was greater for low-RFIp than medium- or high-RFIp animals (P < 0.001). Also, PEG was smaller (P < 0.001) for medium-RFIp than low-RFIp animals compared with high-RFIp animals.
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. Phenotypically, RFIp was not related to ADG, metabolic BW, final BW, or slaughter weight, and there were no differences (P > 0.10) among different RFIp groups in LS means for ADG, metabolic BW, final BW, or slaughter weight. Similar phenotypic correlations were obtained between RFIg and these traits, except with ADG where a weak but significant negative phenotypic correlation (r = –0.21, P < 0.05) was observed. On the other hand, ADG and metabolic BW tended to be moderately genetically correlated with RFIp (albeit with high SE), but the corresponding genetic correlations with RFIg were not different from zero. Generally, most of the phenotypic and genetic correlations among growth, DM intake, and F:G reported in this study were significantly different from zero. Daily DMI was moderately correlated genetically and phenotypically with F:G, but had strong genetic and phenotypic correlations with ADG and metabolic BW. Feed:gain ratio had strong genetic and phenotypic correlations with ADG, but had only a weak phenotypic correlation with metabolic BW; the genetic correlation of F:G with metabolic BW was not different from zero. Partial efficiency of growth had moderate and weak but significant phenotypic correlations with ADG and metabolic BW, respectively; the corresponding genetic correlations of PEG were strong with ADG and moderate with metabolic BW.
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. Phenotypic correlations of RFIp and RFIg with ultrasound and carcass backfat were moderate (P < 0.01). Compared with animals with low RFIp, animals with high RFIp had greater (P < 0.001) rate of gain in ultrasound backfat, final ultrasound backfat thickness, and carcass grade fat. Ultrasound backfat had moderate genetic correlations with RFIp but its correlation with RFIg was not different from zero. Carcass grade fat, on the other hand, was moderately genetically correlated with RFIp and RFIg. The phenotypic correlation of RFIp and RFIg with ultrasound LM area, carcass LM area, and ultrasound marbling score were not different from zero (P > 0.10). Carcass marbling score had low but significant (P < 0.05) phenotypic correlations with RFIp and RFIg. Consistently, there were no significant differences (P > 0.10) between different RFIp groups in rate of gain or final ultrasound LM area or ultrasound marbling score. Also, no differences were observed among different RFIp groups in carcass LM area or carcass marbling score. However, RFIp and RFIg had moderate positive genetic correlations with ultrasound marbling score, and low to moderate positive genetic correlations with carcass marbling score. On the other hand, both measures of RFI had strong negative genetic correlations with ultrasound LM area and carcass LM area.
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Phenotypic correlations of F:G and PEG with ultrasound and carcass traits were not different from zero (P > 0.10), with the exception of the phenotypic correlations of PEG with ultrasound backfat (r = –0.21, P < 0.01) and carcass grade fat (r = –0.14, P < 0.05). The genetic correlations of F:G with ultrasound and carcass traits were generally different from zero, except correlations with ultrasound marbling, carcass grade fat and carcass yield grade. Genetic correlations of PEG with ultrasound and carcass traits ranged from low to high and were generally different from zero except correlations with ultrasound backfat and carcass weight. With the exception of the genetic and phenotypic correlations of DMI with lean meat yield, daily DMI had positive moderate to high phenotypic and genetic correlations with ultrasound and carcass traits. Lean meat yield had a moderate negative phenotypic correlation and a high negative genetic correlation with daily DMI.
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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; Arthur et al., 2001a; Schenkel et al., 2004), indicating the existence of considerable genetic variation in RFI, in younger cattle, to warrant its application in genetic selection to improve efficiency of feed utilization. Arthur et al. (2001a), using data on Angus bulls and heifers, reported heritability estimates of ADG, metabolic BW, F:G, feed intake, and RFIp to be 0.28, 0.40, 0.29, 0.39, and 0.39, respectively. Additionally, Arthur et al. (2001b) reported heritability estimates on young Charolais bulls at 15 mo of age for ADG, BW, feed intake, F:G, RFIp, and PEG to be 0.34, 0.37, 0.48, 46, 0.39, and 0.39, respectively. Thus, the heritability estimate of RFIp in our study was lower compared with the estimates by Arthur et al. (2001a, b) cited above. The genetic variance for RFIp in the current study (0.15) was slightly lower than the value of 0.220 reported by Arthur et al. (2001a) but comparable with that observed (0.149) by Arthur et al. (2001b). On the other hand, the phenotypic variance of RFIp in this study (0.774) was greater than values reported by Arthur et al. (2001a,b; 0.58 and 0.55, respectively). Differences in genetic and phenotypic variances among these studies could be related to differences in genetic background and potentially environmental variances associated with the measurements of intake, of which the type of diet may be most important.
The heritability estimates obtained for the other traits in this study are generally moderate and similar to the estimates by Arthur et al. (2001a, b). Heritability estimates for efficiency and performance traits in similar studies (Renand and Krauss, 2002; Schenkel et al., 2004) were moderate and consistent with the findings of this study. The heritability estimate for RFIg in the current study was greater than reported in Japanese Black cattle (0.25; Hoque and Oikawa, 2004; Hoque et al., 2006b). The heritability estimates obtained from the various studies imply that selection for RFI has the potential to result in genetic change that is comparable with that obtainable from other moderately heritable traits in beef cattle (Crews, 2005). Heritability estimates reported in this study for the measures of ultrasound and carcass merit are consistent and within the range of values previously reported for carcass traits (Bertrand et al., 2001; Crews and Kemp, 2001; Crews et al., 2003) or studies incorporating performance, F:G, and carcass traits (Arthur et al., 2001a; Robinson and Oddy, 2004; Schenkel et al., 2004).
Generally, most of the genetic and phenotypic correlations of daily DMI and F:G with each other and with growth and BW reported in this study were significantly different from zero. These results are consistent with genetic and phenotypic correlations reported in the literature (Herd and Bishop, 2000; Arthur et al., 2001b; Schenkel et al., 2004). The genetic and phenotypic correlations between RFIp and RFIg were greater than 0.90, implying that they are relatively the same trait. Hoque et al. (2006b) reported the genetic and phenotypic correlations between RFIp and RFIg, respectively, to be 0.97 and 0.98 in Japanese black cattle. The genetic correlation between RFIg and RFIp observed in this study imply that approximately 15% of the variation in phenotypic residual feed intake was not captured by the genetic regression. Such variation may be related to differences in genetic background or may be purely environmental in nature and may account for differences in heritability estimates obtained between RFIp and RFIg. The correlations of DMI and F:G with ADG and metabolic BW are similar to published estimates as reviewed by Koots et al. (1994), as well as those reported by Arthur et al. (2001a, b), and recently by Schenkel et al. (2004). However, these correlations are in contrast with several earlier reports (Gill et al., 1986; Meissner et al., 1995, Gibb and McAllister, 1999), which indicated that the correlations between feed intake and gain or between intake and F:G are generally poor in feedlot cattle.
The high genetic correlations of DMI with ADG and metabolic BW imply that a considerable proportion (76%) of the genetic variation in intake is associated with genetic differences in maintenance and growth, and the remainder represents only a small proportion of the total genetic variance. Nevertheless, the respective genetic SD of RFIp and RFIg were 0.39 and 0.67 kg of DM/d, and these represent useful variation that probably reflect between-animal differences in the efficiency of other metabolic processes not accounted for by differences in growth and BW (Herd et al., 2004; Crews, 2005). The strong phenotypic and genetic relationships of daily DMI with growth rate and body size imply that 1-sided selection for faster growth rate and greater finish weights would lead to greater maintenance energy requirements and greater overall feed consumption, especially in mature breeding animals (Archer et al., 1999). Similarly, a 1-sided selection against daily DMI may lead to reductions in growth and BW at maturity, which may be undesirable for the feedlot sector of the beef industry. The genetic and phenotypic relationships among ADG, DMI, and F:G obtained in this and other studies indicate that selection against F:G would reduce the amount of feed required for growth and thus be very beneficial to the feedlot operator. However, a strong correlation of F:G with growth raises questions in terms of its value to the improvement of overall production system efficiency as it may also lead to direct increases in mature BW, resulting in an increase in the cost of maintaining breeding herds (Archer et al., 1999).
The only report in the literature comparing RFI to PEG in terms of genetic and phenotypic relationships with cattle performance is the study by Arthur et al. (2001b). The relationship of RFI or PEG with each other and with ADG, metabolic BW and DMI obtained in the study by Arthur et al. (2001b) as well as in this study may indicate that selection for PEG or against RFI would be similarly beneficial in terms of the correlated reduction in feed intake with little effect (PEG) or no effect (RFI) on growth rate and no effect on body size. Arthur et al. (2001b) explained that indices of efficiency that incorporate linear combinations of measures of growth and metabolic body size seek to capture the variations among animals in energy utilization for growth and maintenance. This ensures that the cattle resulting from this form of selection would potentially be efficient both as feedlot animals and in the breeding herd. This is not the same with F:G, which is a ratio trait, and selection to reduce F:G may not necessarily be correlated specifically to improvements in efficiency, but may only reflect selection for increased growth rate (Crews, 2005). The argument is that the use of a ratio trait in selection may not translate into equivalent improvements in efficiency mainly because selection pressure may be disproportionately applied to the numerator or to the denominator (usually in favor of the component with the greatest genetic variance).
The current study indicated that animals with high RFIp generally had greater F:G (19% greater) and consumed more feed (18% greater) compared with animals with low RFI, despite the lack of differences in ADG or metabolic BW. This difference in energy intake is even greater when expressed in terms of the ME per unit metabolic BW of animals. The associations also showed that the PEG above maintenance of high RFI animals was 24% compared with 29% in medium RFI and 34% in low RFI animals. High genetic and phenotypic correlations between RFI and PEG observed in this study are not surprising because both traits incorporate components of feed intake due to maintenance and to growth. Arthur et al. (2001b) reported strong genetic and phenotypic correlations between RFIp and PEG. These findings therefore indicate that responses to selection for PEG would be similar to the expected responses to selection against RFI. However, unlike RFI, PEG showed moderate to high genetic and phenotypic correlations with ADG in the current study indicating that, at least in some animals, greater PEG may be related to increased growth rate and subsequently BW. This observation is in contrast to Arthur et al. (2001b) who observed that PEG was not related to rate of growth. This difference in findings between the current study and the study by Arthur et al. (2001b) may be related to the fact that feed requirements for maintenance in the latter study were computed using the French feeding standards formula, whereas expected feed intake for maintenance (required for computing PEG) in the current study was computed from NRC (1996) feeding standards. This may be true because observations have shown that even RFI may not be phenotypically independent of growth and body size when expected feed intake is computed from feeding standards formula, instead of from regression equations (Arthur et al., 2001b).
Relationships between RFI, DMI, and F:G with measures of ultrasound and carcass merit obtained in this study generally agree with published estimates, except that the SE associated with the present estimates were rather high, making it difficult to judge whether the estimates were indeed different from zero; caution should be applied in the interpretation of some of these estimates. The high SE generally reflect the relatively low number of animals with carcass data. Nevertheless, the point estimates do differ from zero in most instances and are useful for judging putative relationships of these traits with intake and efficiency. However, to the best of our knowledge, there is no report in the literature comparing PEG to RFI with respect to effects on carcass merit. Koots et al. (1994) reported a significant genetic correlation between F:G and lean meat yield (r = –0.32). Herd and Bishop (2000) reported significant phenotypic and genetic correlations between RFI and carcass lean percentage (r = –0.43 ± 0.23). In addition, Arthur et al. (2001b) reported weak and moderate phenotypic correlations between feed intake and backfat (r = 0.23) or LM area (r = 0.33), respectively. In the same study, however, phenotypic correlations of RFI or F:G and ultrasound measures of backfat or rib eye area were not different from zero. A study by Richardson et al. (2001) showed that a single generation of selection against RFI was accompanied by a small (
5%) reduction in body fat content.
Recently, Schenkel et al. (2004) reported positive phenotypic and genetic correlations (r = 0.17, 0.16) between RFIp and backfat thickness, but there were no correlations between RFIp with intramuscular fat. The same authors found negative genetic correlations between F:G (r = –0.28) and RFIp (r = –0.17) with LM area. Robinson and Oddy (2004) reported genetic correlations of 0.48, 0.38, and 0.61 between rib fat (same as grade fat in the current study) and RFIp, F:G, and DMI, respectively. The corresponding respective genetic correlations between grade fat and RFIp, F:G, and DMI in the current study were –0.24, 0.20, and 0.23. The same authors also reported genetic correlations of 0.22, 0.08, and 0.39 between intramuscular fat and RFIp, F:G, and DMI, respectively. A serial slaughter study by Basarab et al. (2003) indicated that RFI computed from regression of ADG and metabolic BW on intake showed weak correlations with carcass fat (r = 0.14), carcass lean (r = –0.21), gain in backfat thickness (r = 0.22), gain in marbling score (r = 0.22), and empty body fat (r = 0.26). Differences in carcass merit, such as less marbling on efficient cattle, may not be considered a favorable response by the beef cattle industry. More recently, Hoque et al. (2006a) reported that the RFIp and RFIg measured on Japanese black cattle was negatively genetically correlated with the carcass weight and marbling score of their progeny, but this association with marbling score may be peculiar to this breed’s unique ability to deposit intramuscular fat because the association was not reported in the other studies listed above that used other breeds.
Thus, evidence from this and other studies generally point toward a potential for small (5 ± 2%) reductions in carcass fatness and rate of gain in subcutaneous fat coupled with a slight improvement in carcass lean meat yield and yield grade (4 to 5%) following selection against RFI. However, results on the differences in carcass merit between high, medium, and low RFI groups indicate that, whereas low RFI is associated with an increased lean meat yield and yield grade, the animals have more than adequate backfat thickness and do not stand any risk of being downgraded for lack of external fat cover. In addition, differences in marbling score among the various groups were not significant. The observed phenotypic relationships of PEG with carcass and ultrasound merit in this study are comparable to the relationships of RFI with carcass merit. The reported differences in carcass merit and body composition in this and other studies for animals differing in RFI may account for only a small proportion of the observed variations in efficiency between these animals. Other sources of variation such as differences in heat increment (especially associated with feeding and visceral metabolism), level of feeding activity and feeding behavior, nutrient turnover, and digestive functions may account for part of the variation in RFI (Oddy and Herd, 2001; Richardson et al., 2001; Johnson et al., 2003). Further research efforts are therefore required to characterize the sources of variation between animals differing in RFI.
Residual feed intake computed from phenotypic regression was phenotypically independent of ADG and metabolic BW, but the corresponding genetic correlation may be different from zero. However, though a weak phenotypic relationship was observed between RFIg and ADG, genetic RFI was genetically independent of ADG and metabolic BW. Although RFI shows small but significant relationships with carcass fatness and leanness, the efficient animals had adequate carcass fatness and did not stand any risk of being downgraded for lack of external fatness. The relationships of carcass and ultrasound merit with PEG in beef cattle may be similar to the relationships with RFI. Animals with low RFI may show significant reductions in the energy requirement for maintenance and increase the PEG above maintenance. Partial efficiency of growth may be similarly robust (compared with RFI or F:G) as a measure of efficiency in feedlot animals, but its potential relationships with growth rate may be a disadvantage to production efficiency in mature animals due to the potential for increased feed consumption. Results of this study imply that, at least in feedlot animals, selection against RFI should be preferred over other measures of efficiency. More studies are required to determine the potential impacts of selection based on RFI on the matured animal desirable traits such as fertility and reproduction. Other studies may be required to examine the potential interactions between diet or other environmental factors and RFI.
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Footnotes |
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2 Corresponding author: stephen.moore{at}ualberta.ca
Received for publication November 21, 2006. Accepted for publication May 18, 2007.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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E. L. Sherman, J. D. Nkrumah, C. Li, R. Bartusiak, B. Murdoch, and S. S. Moore Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle J Anim Sci, January 1, 2009; 87(1): 37 - 45. [Abstract] [Full Text] [PDF]
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W. G. Bergen Measuring in vivo intracellular protein degradation rates in animal systems J Anim Sci, April 1, 2008; 86(14_suppl): E3 - E12. [Abstract] [Full Text] [PDF]
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