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ANIMAL GENETICS |
* Animal Breeding and Genetics Group, Department of Animal Sciences, Egerton University, 20115 Egerton, Kenya; and
Laboratory of Animal Husbandry Resources, Division of Applied Biosciences, Graduate School of Agriculture, Kyoto University, 606 8502 Kyoto, Japan
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONLITERATURE CITED |
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Key Words: breeding program design • carcass trait • insulin-like growth factor I • Japanese Black cattle • residual feed intake • selection strategy
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONLITERATURE CITED |
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, 2001). In Japanese Black cattle, genetic parameters for, and genetic relationships between, RFI and BW, weight gains, and carcass traits have been estimated, and opportunity exists for improvement of efficiency of gain (Hoque et al., 2006a,b). Determination of RFI requires accurate measurements of DMI, BW, and weight gains on individual animals over a period. This is expensive, and, therefore, it would be beneficial for the beef industry in Japan to identify and test alternative selection criteria that can be measured easily and cheaply and are correlated with BW, weight gains, efficiency of gain, RFI, and carcass traits.
There is evidence that the blood concentration of IGF-I (IGF) polypeptide hormone that regulates growth and cellular metabolism during all developmental stages is correlated to BW, weight gains (Davis et al. 1995), efficiency of gain, RFI (Johnston et al., 2002), and carcass traits (Johnston et al., 2001). There is potential to use IGF as a physiological marker and indirect selection criterion for improvement of RFI in herds of cattle. It is possible to improve RFI by selecting for lower IGF concentration without significantly affecting BW and weight gains (Davis and Simmen, 2006).
Genetic improvement of RFI will help reduce feed costs, which are major determinants of profitability in livestock production enterprises. It is of interest to determine whether inclusion of direct and indirect selection criteria for feed intake would improve the efficiency of Japanese Black cattle breeding programs.
The objective of this study was to determine via a model study the effect of use of direct and indirect selection criteria for feed intake on profitability of Japanese Black cattle selection strategies with restricted test capacity.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONLITERATURE CITED |
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Breeding Objective and Selection Criteria
In this study, the actual beef cattle breeding objective in Japan was considered. Beef production systems are separated into 2 segments (cow-calf and feedlot), which are integrated, because the feedlot segment depends on the cow-calf segment for animals to be fattened for slaughter. Inherent inefficiency of beef production may result from separation of objectives by production segment, which may differ from overall industry objectives (MacNeil and Newman, 1994). Therefore, breeding objectives developed for integrated systems (Hirooka et al., 1998a,b) should meet the goals of both segments and were adopted in this study. Traits in the breeding goal included (Hirooka et al., 1998a,b; Hirooka and Sasaki, 1998) birth weight (BWT, kg), weaning weight (WWT, kg), mature weight (MWT, kg), ADG during the feedlot period (DG, kg/d), LM area (LMA, mm2), rib thickness (RT, mm), s.c. fat thickness (SFT, mm), and marbling score (MS). Economic values for traits of the breeding objectives, representing values of unit changes in traits, whereas other traits are unchanged, were derived from the estimates reported by Hirooka et al. (1998b) and Hirooka and Sasaki (1998) and are presented in Table 1.
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). This was done on the left side of the cold beef carcass. The LMA was measured by grid approximation. The RT is the distance between the LM and pleura membrane measured halfway between the rib ends. Carcass quality was also evaluated based on a visual standard of marbling. The MS was measured by scoring based on the Beef Marbling Standards, which range from 1 to 12, with number 12 being the best (Japan Meat Grading Association, 1988). In addition to information on these traits, it was assumed that records on RFI and IGF concentrations were available for use as direct and indirect selection criteria, respectively, for feed intake. The RFI is the difference between the actual feed intake and the expected feed requirements for body maintenance and production (Arthur et al., 2001). In this study, the selection criterion was RFI during performance testing (RFIpt).
The IGF is synthesized and secreted primarily by the liver and is found circulating in the plasma bound to 1 of 6 binding proteins, which keep the levels of IGF relatively stable (Murphy et al., 1987; Hossner et al., 1997). The IGF concentrations were measured during performance testing at 9 mo of age. The genetic correlations among IGF measurements at different ages ranged from 0.86 to 1.00, indicating that the same genetic mechanisms are involved in determining IGF concentrations at different ages (Davis and Simmen, 1997, 2006; Moore et al., 2005).
Sires in Japan are usually selected based on breeding values estimated using BLUP under an animal model (Sasaki et al., 2006). In this study, sires were selected based on a selection index. Best linear unbiased prediction optimally uses all of the available records on the individual and its relatives, while simultaneously adjusting for environmental effects, such that the accuracy of the predicted breeding values is maximized. Wray and Hill (1989) showed that selection indices can be used to approximate selection on BLUP-EBV under an animal model. Therefore, selection index theory remains a useful tool for approximating the accuracy of BLUP selection and predicting selection response. The method used in the current study will also provide a good approximation of response to BLUP selection.
Breeding Structure
The Japanese Black cattle breeding structure was modeled using the computer program ZPLAN (Karras et al., 1997). The breeding structure was similar to that described by Kahi and Hirooka (2005) and consisted only of 1 tier, in agreement with the current Japanese situation, in which there is no differentiation in the levels of performance and pedigree recording, because breeding occurs in the whole population. Breeding was by AI using semen from progeny-tested bulls with a conception rate of 50% (Hirooka et al., 1998a). Genetic gain was generated in the whole population, in which bull and female selection and production of slaughter progeny are the main activities. Replacement bulls and females were selected on indices that included all available information at the time of selection.
A total population size of 500,000 breeding cows was considered. This is the approximate population size for the Japanese Black cattle in Japan (Sasaki, 2001). The average calving and annual cow survival rates (including both death and culling) were assumed to be 85 and 92%, respectively. The calf survival rate from birth to weaning was assumed to be 98%, whereas the proportion of male and females calves that were suitable for breeding was 0.80 and 0.90, respectively. Male calves from planned matings were preselected based on BW for performance testing in a central station. The average age of male calves at the beginning of performance testing was 210 d (7 mo), and they were tested for 112 d. The total test capacity available for performance testing was 500, whereas the survival rate during performance testing was assumed to be 90%.
Bulls were selected in a 2-stage selection process. Twenty percent of the young bulls selected in the first stage were test-mated with average cows, and their progeny performance was tested for carcass traits (Sasaki, 2001). After a waiting period of 4 yr, 45 bulls were selected from the progeny-tested young bulls based on the carcass records of 25 progeny and information from relatives. The bulls were 5 yr old at the birth of their progeny, with a productive lifetime of 9 yr and a survival rate of 92%. The proportion of bull dams was assumed to be 5% of the total population. The age at first calving and the productive lifetime of cows was 2 and 7 yr, respectively. The replacement rate was assumed to be 33% for breeding females and 20% for breeding males (Mukai, 1994). Calves not used for replacements were shipped to calf markets for fattening and slaughter. Their carcass traits were evaluated at carcass markets. The number of records used to select sires and dams using a selection index was determined by these biological parameters.
Selection Pathways
At the end of performance testing, a proportion of young bulls to breed sires was selected based on their growing ability. Depending on the selection strategy under investigation, this included information on IGF and RFIpt. The selected bulls were used to produce both bulls and cows (bulls to breed sires and bulls to breed dams). In addition, these bulls were used to produce slaughter progeny. There was no differentiation in the use of selected bulls as sires of young bulls, replacement cows, and slaughter progeny. Therefore, intensities of selection were equal for bulls to breed sires, bulls to breed dams, and slaughter progeny. Selection of cows also occurred to breed sires, dams, and slaughter progeny. The intensities of selection were equal for dams and slaughter progeny.
Input Parameters
The ZPLAN program uses economic, statistical, and genetic and phenotypic parameters to calculate the annual genetic gain for the breeding objective, genetic gain for single traits, and returns on investment adjusted for costs (profit) using gene-flow and selection index methodology (Karras et al., 1997). In addition, the program calculates selection indices for breeding animals and applies order statistics to obtain adjusted selection intensities for populations with finite sizes. These calculations assume that parameters and selection strategies remain unchanged during the investment period. Archer et al. (2004) outlined the changes made to the ZPLAN code to calculate response from 2-stage selection following the procedure of Wade and James (1996). The assumed variable costs incurred in the breeding population of 500,000 cows are shown in Table 2. The variable costs were directly related to performance and pedigree recording and were applied to the complete population. These costs affect profitability of the breeding scheme but not genetic response. Costs were based on Hirooka et al. (1998a, b) and when missing for the Japanese situation were based on Kahi et al. (2003).
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; Oyama et al., 2004; Hoque et al., 2006a,b). When not available, they were derived from other studies on other beef breeds (Koots et al., 1994a,b; Johnston et al., 2001; Maiwashe et al., 2002). In situations in which estimates of phenotypic, but not genetic, correlations were found and vice versa, the phenotypic and genetic correlations were assumed to be equal (Koots et al., 1994a,b). Information on covariances between IGF and other traits of economic importance in Japanese Black cattle are unavailable. Therefore, parameters involving IGF were obtained from Johnston et al. (2001, 2002), Moore et al. (2005), and Davis and Simmen (2006). The resulting genetic and phenotypic variance-covariance matrices were nonpositive definite. Bending was applied to find positive definite variance-covariance matrices that were a minimum distance from the nonpositive definite matrices (Henshall and Meyer, 2002). The genetic and phenotypic parameters used are presented in Table 1
. Selection Strategies and Index Information
Selection strategies were defined that differed in whether information on IGF and RFIpt was used to make selection decisions and in the number of animals measured for IGF. The base scenario (strategy 1) simulated the current situation in which neither IGF nor RFI are recorded. The selection criteria were those currently used in Japan. During performance testing, a proportion of young bulls to breed sires is selected based on their ability to grow, plus information on weights and carcass traits from their relatives. The other selection paths are also selected based on information on weights and carcass traits from relatives. The criteria therefore included measurements on ADG, DG, BWT, WWT, MWT in females retained for replacement in the herd, LMA, RT, SFT, and MS. All other selection strategies included measurements available in strategy 1, as well as any additional measurements.
In this study, measurement of IGF concentration was only done in males, because increased costs to measure IGF in females would not be compensated for by increased gain in the breeding objective (Kahi et al., 2003; Wood et al., 2004). Strategies 2 and 3 considered measurement at the first stage of selection of IGF and RFIpt, respectively, in bulls. Strategy 4 had both IGF and RFIpt measured during performance testing. In strategies 2 and 4, the assumption was that information on IGF concentration was available on the individual and on the sires for relevant selection pathways and that this had only previously been available for selection of sires. Strategies 5 and 6 were similar to strategies 2 and 4, respectively, but assumed that all male animals were measured for IGF and that relevant selection pathways had previously been selected under this same structure.
In all selection strategies, selection was based on the best index for the available criteria, and sires were selected from the proportion selected after performance testing (first selection stage). For all selection pathways, information sources included records on individuals, paternal half-sibs, sire, dam, and half-sibs of the sire and dam. In addition, for sires, information sources included records on carcass traits of progeny. Table 3 shows the number of records and information sources for indices used to select sires and dams.
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The effect of variation in test capacity, of genetic correlations of IGF with MS and RFI, and of nonzero economic values for RFI were investigated for all relevant selection strategies. The test capacity varied from 200 to 2,000 places. The aim was to determine the optimal capacity needed in the Japanese Black cattle breeding program that incorporates direct and indirect measures of efficiency of gain. The genetic correlations of IGF with MS and RFIpt were based on literature values from other beef cattle breeds. An overestimation of the expected response from selection based on a selection index will arise from sampling error of genetic parameters used in constructing an index (Hazel, 1943). Sensitivity of profitability to changes in the genetic correlations of IGF with MS and RFIpt were investigated. Alternative correlations were examined within the parameter space, because larger variations in correlations would have resulted in a nonpositive definite genetic covariance matrix. This would have necessitated further bending of the matrix, making comparisons of sensitivity difficult. The basis for the sensitivity analysis is that correlation estimates will likely change if estimated in the Japanese Black cattle breed. Such changes will have an effect on the profitability of beef production enterprises in Japan, because carcass value is primarily determined by the degree of marbling (Hirooka and Groen, 1999) and feed cost is a major cost determinant (Hoque et al., 2006a).
The breeding objective comprises breeding values and economic values for traits that directly influence either income or costs of the production enterprise. Currently, the breeding objective in Japan is aimed mainly at output traits, with little emphasis placed on reducing inputs. In this case, the economic values for traits associated with inputs (e.g., feed intake) are zero, and, for that matter, the assumption is that such traits are not included in the breeding objective. However, feed costs are the single largest expense in most beef production enterprises, and it is important to estimate the actual economic value of feed intake and test its effect on profitability at the industry level. All simulations were repeated assuming nonzero economic values for RFI of cow (RFIc) and RFI of feedlot animals (RFIf). The economic values (v) for RFIc and RFIf were calculated in yens (¥)· kg–1·day–1 and were derived using the following equation:
 | [1] |
where EIi = the GE intake in MJ; pi = the price of feed in ¥ per MJ of ME; 18.4 = the conversion factor of the metabolizability of the GE of a feed at maintenance to dietary ME values (AFRC, 1993); qi = the metabolizability of feed; and PRC = the mean length of the reproductive cycle of a cow in days (2,435 d). The first PRC is defined as the interval from replacement to weaning of the first calf. The growing period is included in the first PRC. For second and later reproductive cycles, PRC is defined as the interval from weaning of 1 calf to weaning of the next calf. The EIi for cows and feedlot animals in MJ was 113,000 and 119,000, respectively (Hirooka et al., 1998a). Correspondingly, pi in ¥ per MJ of ME was 4.51 and 4.10. The qi was assumed to be 0.57 and 0.6 for cows and feedlot animals, respectively. No information is available on covariances of RFIc and RFIf with other traits in Japanese Black cattle. Genetic and phenotypic parameters involving RFIc and RFIf were obtained from Kahi et al. (2003). The economic values and genetic and phenotypic parameters involving RFIc and RFIf are also presented in Table 1. Sensitivity of profitability to changes in the genetic correlations of IGF with MS and RFIpt, and of IGF and MS with RFIc and RFIf, was investigated when RFI was included in the breeding objective with nonzero economic values.
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RESULTS AND DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONLITERATURE CITED |
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The index accuracy, annual monetary genetic gain and costs, returns, and profit per cow varied according to the selection strategy (Table 4). Strategy 1 had the lowest and strategy 6 the highest index accuracies, annual monetary genetic gains, costs, and returns per cow in the population. The profit per cow was lowest (¥67,568) in strategy 5 and highest (¥68,556) in strategy 4. A comparison of strategies 1 and 2 shows that additional genetic gain and profitability were generated when information on IGF in the performance-tested young bulls was included in the selection criteria. Inclusion of IGF in the selection criteria provides a breeder with a possible method to select for efficiency of gain without the cost of measuring feed intake. Use of IGF allows for earlier selection of bulls, but the influence of decreased generation intervals on genetic gain and profitability were not modeled in this study.
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). Thus, there is a need to include information on feed intake either alone or coupled with IGF as additional selection criteria during performance testing. The genetic correlations of IGF with RFI and carcass traits is desirable, and use of these traits during selection of young bulls will ensure that genetically superior candidates are not selected against early in the selection process.
Strategy 2 can be compared with strategy 5, because the difference between these strategies was in the number of animals on which IGF measurement is taken. Similarly, strategy 4 can be compared with strategy 6. The annual genetic gain and returns per cow were greater in strategies 5 and 6 than in strategies 2 and 4, respectively. However, the costs associated with measuring IGF in strategies 5 (¥15,862) and 6 (¥15,874) had high effect on profit and, therefore, on the implementation of these strategies in a breeding program for Japanese Black cattle. The increase in costs to measure IGF in all males would not be compensated for by the increased gain in the objective. This is contrary to other studies, which have reported greater profitability in strategies in which more animals were measured for IGF (Kahi et al., 2003; Wood et al., 2004). Those studies also reported over 7.6% greater (relative to the base scenario) profitability in strategies that included measurement of RFI. This study reported a difference of 1.5% (Table 4). The lower differences in this study are apparently due to differences in the genetic and economic parameters used and the number of traits included in the selection criteria (in both selection stages) and breeding objective. Further, the difference could also be due to differences in population size and breeding structure and, hence, number of selection pathways modeled, breeds, number of animals measured for IGF and RFI in different selection stages, and information available during each selection stage. Because of the importance of sire selection to genetic response and profitability, the major causes of differences could be associated with sire selection pathways and are related to the number of animals measured for IGF and RFI and in which selection stage information on IGF and RFI was included in the selection criteria.
In the studies by Kahi et al. (2003) and Wood et al. (2004), number of animals measured for IGF in the first stage of selection, which occurred immediately after weaning, was not restricted, and measurement of IGF was used as a screening test in a 2-stage selection procedure to identify animals to test for RFI. Measurement of RFI was done postweaning in the second stage of selection. In this study, the number of sires measured for IGF and RFI was restricted, and these traits were selection criteria in the first stage of selection. The second stage of selection was based on progeny testing, in which bulls were selected based on the carcass records on progeny and information from relatives. Therefore, in this study, the increase in profitability as a result of measurement of IGF and RFI was small, because the increase in costs associated with measuring these traits was only compensated for by the limited gain in the breeding objective as a result of improvements in traits, especially carcass traits, and greater accuracy of first-stage selection. In the studies by Kahi et al. (2003) and Wood et al. (2004), an increase in profitability also came from improvements in traits and greater accuracy of first- and second-stage selection and from a decrease in the number of animals needing to be placed into feed tests in the second stage of selection and increased returns from lower feed costs.
Recording of feed intake that is used to calculate RFI is expensive, and, therefore, a 2-stage selection policy has been used by previous authors (Kahi et al., 2003; Archer et al., 2004; Wood et al., 2004). In this policy, potential breeding animals are first evaluated for production traits of interest or even for physiological or genetic markers for efficiency of gain, after which selected individuals are tested for efficiency of gain (Arthur and Herd, 2005). As described above, this 2-stage selection policy is different from the one applied in the current study. This study has demonstrated that evaluating animals for production traits of interest and physiological markers for efficiency of gain, and testing for efficiency of gain in the same selection stage, yielded additional benefits over and above the benefits from strategies that did not include testing for efficiency of gain. Results of the current study assume conditions in which the performance test capacity is limited. Caution is consequently needed in extrapolating from these results to conditions under which the performance test capacity is not restricted, and progeny testing, especially for carcass traits, is not applied in the second stage of selection.
Table 5 shows returns from annual genetic gain in individual traits in the breeding objective for the different selection strategies. The returns were greatest for MS and lowest for BWT for all selection strategies. Inclusion of IGF and RFIpt in the selection criteria in strategies 2 to 6 resulted in increased returns from annual genetic gain in carcass traits. The genetic relationships assumed between most carcass traits and RFIpt and IGF in this study were favorable (Table 1). When unfavorable genetic relationships exist between traits of high economic value, collection of sufficient information on these traits through progeny testing is important, because accurate information on both traits enables selection of outlier animals that do not follow the general antagonistic relationship (Archer and Barwick, 2001; Archer et al., 2004). In this study, information on carcass traits was collected through progeny testing, and this further led to positive returns for carcass traits.
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Figure 1 shows the effect of varying the test capacity on genetic gain and profit per cow. Only results for strategies 1, 2, 3, and 4 are presented. Increasing the test capacity increased the value of genetic gain in all strategies, and the differences between strategies were small at greater test capacities. Improvements in genetic gain at high test capacities came from an increase in selection intensities in the second stage of selection, greater accuracy of first- and second-stage selection, and improvement in individual traits. In contrast, profit per cow increased with increase in test capacity, reaching an optimum at 900 places, and then decreased as the test capacity increased to 2,000. The effect of test capacity on profitability came not only from an increase in selection intensities in the second stage of selection and greater accuracy of first- and second-stage selection but also from an increase in the number of animals for IGF measurement needing to be placed into feed tests, as well as reduced returns from greater measurement costs.
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reported an optimal test capacity from 900 to 1,000 places when direct and indirect measures of efficiency of gain were not incorporated into the breeding program. This study should serve as an encouragement for the beef industry in Japan to increase the number of young bulls that are performance-tested. Usually, the cost of measurement is of lesser importance when considered across the whole breeding program, especially where application of relatively expensive measurements is targeted to animals likely to be most influential on the population (Archer et al., 2004). However, the importance of a large increase in cost to the breeding program should not be underestimated, because this can be a significant barrier to adoption of beneficial technologies in breeding (Kahi et al., 2003).
Table 6 shows the sensitivity of profit per cow to changes in genetic correlations of IGF with MS and RFIpt for strategies 4 and 6. In strategy 4, the profit per cow was not sensitive to changes in the genetic correlations between IGF and RFIpt. Similarly, strategy 6 was not sensitive to changes in the genetic correlation between IGF and RFIpt, but only when the genetic correlation between IGF and MS was from –0.45 to 0.20. Strategy 4 was less sensitive to changes in the genetic correlation between IGF and MS than strategy 6. In strategy 4, the change in sign of the genetic correlations between IGF and MS resulted in very small effects on profit per cow. In contrast, these changes resulted in larger effects on profit per cow in strategy 6, a strategy that assumed all males were measured for IGF. This study has shown that genetic correlations between IGF and MS are more important than correlations between IGF and RFIpt, especially when more animals are measured for IGF. With little information on IGF, the favorable relationship between IGF and MS will not have a significant effect on profit per cow. Use of reliable estimates of genetic correlations is important to avoid over- or underestimating the expected response to selection.
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The index accuracy, annual monetary genetic gain and costs, and returns and profit per cow in the population for the different selection strategies are shown in Table 7. In all selection strategies, there was a moderate increase in genetic gain and costs, as well as in returns and profit per cow, when the economic value for RFI was nonzero. A comparison of Tables 4 and 7 indicates that the increase ranged from ¥108 to ¥114 for genetic gain and ¥1,100 to ¥1,154 for return and profit per cow in the population. Table 8 shows returns from annual genetic gain in individual traits in the breeding objective for the different selection strategies. The returns from all traits in the breeding objective are worth examining, particularly the returns from the highly valued MS. Results presented in Tables 5 and 8 show that nonzero economic values for RFI make little difference in returns from all traits apart from MS, which had lower returns in this case. This result implies that when RFI is included in the breeding objective, the lower returns from MS would be compensated for by increased gain in the objective associated mainly with improvements in RFIc and RFIf.
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. Table 9 shows the sensitivity of profit per cow to changes in genetic correlations of IGF with MS, RFIc, and RFIf for strategies 4 and 6 when RFI is included in the breeding objective. Both strategies were less sensitive to variation in the genetic correlations of IGF with RFIc and RFIf. In contrast, these strategies were more sensitive to variation in the genetic correlations of MS with RFIc and RFIf. This sensitivity stresses the importance of knowing the genetic variances for RFIc and RFIf and covariance with MS under conditions in Japan, where the degree of marbling and feed costs determine the overall efficiency of beef production enterprises. To our knowledge, information on genetic variances for RFIc and RFIf and covariance with MS for the Japanese Black breed is currently not available.
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). Theoretically, genetic improvement of efficiency of gain will have the additional benefit of reducing greenhouse emissions by livestock, leading to improved environmental sustainability (Arthur and Herd, 2005). Koch et al. (1963) first proposed RFI as a trait to be used for the improvement of efficiency of gain. However, whether feed intake is expressed as actual or residual for inclusion in selection indices has no effect on the outcome for a given breeding objective, provided appropriate genetic parameters are used (Kennedy et al., 1993). Therefore, both approaches are appropriate. In Japanese Black cattle, RFI has been recommended as a direct selection criterion for improvement of efficiency of gain (Hoque et al., 2006a), and it was therefore appropriate to use an index containing RFI.
In this study, IGF was used as an indicator trait that is predictive of RFI, and its usefulness in reducing costs and selection of more profitable animals was demonstrated. Selection should be directed toward reduced IGF to produce more efficient cattle with reduced RFI values (Davis and Simmen, 2006). For the Japanese beef industry to benefit fully from breeding programs aimed at improving the efficiency of feed utilization, there is need to include RFI in the breeding objective and use IGF and RFI test data separately or together to estimate RFI breeding values. The use of IGF measurements must be preceded by accurate estimation of breed-specific estimates of variances and covariances between IGF and growth and carcass traits under conditions in Japan. This study provides a reasonable basis for general guidelines on the inclusion of IGF and RFI measurement in Japanese beef cattle breeding programs.
In conclusion, the results presented here indicate that even when the test capacity is restricted, it would be profitable for Japanese Black cattle breeders to incorporate measurement of IGF concentration and RFI in the selection of young bulls during performance testing to identify animals to be progeny-tested. Profit was maximized when measurements of IGF concentration and RFI were incorporated together in the selection index and when 900 places were available for performance testing. Inclusion of RFI in the breeding objective with nonzero economic value is important under conditions in Japan. However, this should be preceded by accurate estimation of variances of, and covariances between, RFI and growth and carcass traits. These results apply to the Japanese Black breed but, in the absence of more breed-specific information, may also have application in other beef breeds.
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Footnotes |
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2 Corresponding author: a.kahi{at}incip.org
Received for publication October 31, 2006. Accepted for publication June 1, 2007.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONLITERATURE CITED |
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p), heritabilities (along the diagonal), and genetic (below diagonal) and phenotypic (above diagonal) correlations among and between selection criteria and traits in the breeding objective
), 2 (
), 3 (
), and 4 (x).