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Genetic Evaluation for Growth Traits, Reproductive Performance and Meat Tenderness in Beef Cattle

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Genetic Evaluation for Growth Traits, Reproductive Performance and Meat Tenderness in Beef Cattle
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PRAHARANI, LISA
Copyright Date:
2008

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Beef ( jstor )
Beef cattle ( jstor )
Breeding ( jstor )
Cattle ( jstor )
Dams ( jstor )
Estrus ( jstor )
Heifers ( jstor )
Heritability ( jstor )
Phenotypic traits ( jstor )
Zebu ( jstor )
City of Gainesville ( local )

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University of Florida
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University of Florida
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Copyright Lisa Praharani. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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12/31/2005
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436098625 ( OCLC )

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GENETIC EVALUATION FOR GROWTH TRAITS, REPRODUCTIVE PERFORMANCE AND MEAT TE NDERNESS IN BEEF CATTLE By LISA PRAHARANI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Lisa Praharani

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With all my love to my husband, Christian Rogahang, and to my children, Gloria, Imanuel and Gracia.

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ACKNOWLEDGMENTS First of all, the author would like to thank God for His miraculous strength, mercy and grace which has brought her to this point of her life. She has learned of His wonderful love over years while studying at Gainesville. To God be the Glory. The author wishes to acknowledge sincere gratitude to Dr. Timothy A. Olson, chairman of her Supervisory Committee, for his advice, guidance and suggestions throughout the graduate program and significant contributions to the planning and writing of the dissertation and also his friendly attitude. She wishes to express deep appreciation to the members of her doctoral committee, Dr. Ramon C. Littell, Dr. D. Owen Rae, and Dr. David G. Riley for always being willing to take time to answer questions with patience and to provide encouragement. Acknowledgements are extended to Dr. Dudley Huber, Dr. Javier Rosales-Alday and Mr. Salvador Gezan who helped in the data analysis; without their assistance this study could not have been completed. Recognition is also due to Dr. Brendemuhl and Dr. D. D. Johnson and Dr. A. Adesogan for their personal friendship, advice and encouragement. Appreciation is also due to fellow graduate students: Susan Malunga, Carlos Lucena, Paul Davis and Dervin Dean for their friendship and encouragements. She wishes to express her gratitude to the Participatory Development of Agricultural Technology Project (PAATP), and the Research Institute of Animal Production, Ministry of Agriculture of Republic of Indonesia for their financial support throughout her study. iv

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Finally, the author is particularly indebted to her husband, Chris and her children, Gloria, Imanuel and Gracia for all their patience, understanding and support in the attainment of this degree. v

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TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................iv LIST OF TABLES .............................................................................................................ix LIST OF FIGURES ...........................................................................................................xi ABSTRACT ......................................................................................................................xii CHAPTER 1 INTRODUCTION........................................................................................................1 2 LITERATURE REVIEW.............................................................................................6 Methods of Genetic Improvement................................................................................7 Genetic Evaluation................................................................................................7 Prediction of Breeding Value................................................................................7 Genetic Parameters................................................................................................8 Instruments for Estimation of Genetic Parameters..............................................10 Maternal Effects..................................................................................................15 Selection within Breed........................................................................................17 Crossbreeding Systems........................................................................................23 Genetic Parameters for Growth Traits in Bali Cattle..................................................28 History and Background of Bali cattle................................................................28 Characteristics of Bali Cattle...............................................................................30 Potential Advantages of Bali Cattle....................................................................31 Some Limitations of Bali Cattle..........................................................................33 Trend toward a reduced growth rate....................................................................34 Genetic Parameters for Growth Traits.................................................................36 Genetic and Phenotypic Correlations..................................................................37 Non-Genetic Factors............................................................................................38 Genetic Improvement of Meat Tenderness.................................................................43 Consumer Preferences in Meat Tenderness........................................................43 Measurement of Tenderness................................................................................44 Factors Affecting Meat Tenderness.....................................................................46 Genetic Factors in Meat Tenderness...................................................................47 Genetic Improvement of Reproductive Performance.................................................50 The Use of Brahman Breeding............................................................................52 vi

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Bovine Estrous Cycles.........................................................................................54 Estrus Detection...................................................................................................55 Estrous Synchronization......................................................................................57 Timed-Artificial Insemination.............................................................................61 Factors Influencing Reproductive Performance..................................................63 3 GENETIC PARAMETERS AND ENVIRONMENTAL FACTORS FOR GROWTH TRAITS IN BALI CATTLE......................................................................................71 Introduction.................................................................................................................71 Materials and Methods...............................................................................................73 Description of Location.......................................................................................73 Animal Management...........................................................................................74 Data Collection....................................................................................................75 Statistical Analysis..............................................................................................76 Description of Data..............................................................................................77 Estimation of Genetic Parameters.......................................................................79 Results and Discussion...............................................................................................83 Non-genetic Effects.............................................................................................83 Interactions..........................................................................................................89 Genetic Parameters..............................................................................................90 Implications................................................................................................................98 Summary.....................................................................................................................99 4 SELECTION FOR MEAT TENDERNESS IN ANGUS CATTLE.........................102 Introduction...............................................................................................................102 Materials and Methods.............................................................................................103 Formation of Selected Sire Groups...................................................................103 Animal Management.........................................................................................105 Warner-Bratzler Shear Force Evaluation..........................................................106 Data Analysis.....................................................................................................107 Estimation of Variance Components.................................................................108 Results and Discussion.............................................................................................110 Factors Influencing Response to Selection........................................................110 Phenotypic and Genetic Evaluations.................................................................115 Implications..............................................................................................................121 Summary...................................................................................................................121 5 REPRODUCTIVE PERFORMANCE FOLLOWING ESTROUS SYNCHRONIZATION OF ANGUS, BRAHMAN AND ANGUS X BRAHMAN CROSSBRED COWS..............................................................................................124 Introduction...............................................................................................................124 Materials and Methods.............................................................................................126 Source of Data...................................................................................................126 Animal Management.........................................................................................127 vii

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Data Collection..................................................................................................129 Description of Data............................................................................................130 Data Analyses....................................................................................................132 Results and Discussion.............................................................................................132 Response to Estrous Synchronization across Main Effects...............................132 Non-genetic Effects...........................................................................................135 Genetic Effects..................................................................................................140 Interactions........................................................................................................145 Gestation Length...............................................................................................146 Implications..............................................................................................................149 Summary...................................................................................................................150 6 GENERAL CONCLUSIONS...................................................................................152 LIST OF REFERENCES.................................................................................................157 BIOGRAPHICAL SKETCH...........................................................................................188 viii

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LIST OF TABLES Table page 2-1 Individual and maternal heterosis levels for crossbred cattle...................................25 2-2 Average heterosis levels for economically important traits in beef cattle 1 ..............26 2-3 Weaning and yearling weights of Bali cattle in different provinces in Indonesia...35 2-4 Mature weights of Bali bulls from several studies...................................................35 2-5 Genetic parameters for weaning and yearling weight of some cattle breed.............36 2-6 Genetic parameters for growth traits in Bali cattle...................................................38 2-7 Comparison of growth traits for Bali bulls and heifers from different regions........41 2-8 Least squares means for weaning weight and yearling weight in Bali cattle from different herds in BCIP............................................................................................42 2-9 Heritabilities of Warner-Bratzler shear force (WBSF)............................................50 2-10 Some estimates of estrous cycle length in cattle breeds...........................................54 2-11 Response of estrous synchronization using SMB from various studies...................61 3-1 Data structure before and after editing.....................................................................79 3-2 Least squares means ( S.E.), coefficients of variation, and regression coefficient for W-190d and W-350d........................................................................85 3-3 Estimates of variance components (kg 2 ), genetic parameters and standard errors of W-190d and W-350d.................................................................................93 4-1 Summary of average genetic parameters, selection differentials and generation intervals..................................................................................................................111 4-2 Least squares means and standard error for WBSF and the EBV for WBSF in the progeny of tough and tender sire and base population.....................................116 4-3 Summary of response to selection for WBSF........................................................119 ix

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5-1 Summary of number of cows per breed group by year of breeding, age of cow and body condition score........................................................................................131 5-2 Means of estrus, pregnancy and calving rate by year of cow and body condition.................................................................................................................135 5-3 Means of estrus rate, pregnancy rate, calving rate and by breed type of cow........143 5-4 Means and standard deviations of estrus rate by year of breeding and breed type of cow.............................................................................................................146 5-5 Least squares means of gestation length by year of breeding, age of cow, body condition scores and breed type.............................................................................147 x

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LIST OF FIGURES Figure page 3-1 Phenotypic trend for W-190d and W-350d..............................................................87 3-2 W-350d by cow age x calf sex.................................................................................90 3-3 Genetic trend for W-190d by year of birth...............................................................97 3-4 Genetic trend for W-350d by year of birth...............................................................98 4-1 Selection differential by year of birth.....................................................................112 4-2 Cumulative selection differential by year of birth..................................................113 4-3 Selection responses by CSD...................................................................................114 4-4 The changes of WBSF by year of birth..................................................................117 4-5 The changes of WBSF-EBV by year of birth........................................................120 4-6 Differences between the yearly means of the progeny of tough and tender sires..121 5-1 Estrus rate on day-1, day-2 and day-3 by cow age.................................................137 5-2 Estrous, pregnancy and calving rate by age of cow...............................................138 5-3 Estrus rate on day-1, day-2 and day-3 by body condition......................................139 5-4 Estrus, pregnancy and calving rate by body condition...........................................140 5-5 Estrus rates in different days by breed type of cows..............................................141 5-6 Reproductive performance by breed type of cows.................................................143 5-7 Gestation length by percentage of Brahman breeding...........................................149 xi

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy GENETIC EVALUATION FOR GROWTH TRAITS, REPRODUCTIVE PERFORMANCE AND MEAT TENDERNESS IN BEEF CATTLE By Lisa Praharani December 2004 Chair: T. A. Olson Major Department: Animal Sciences A genetic evaluation of Bali cattle (Bos javanicus) using data from the Bali Cattle Improvement Project on the island of Bali was conducted. Results showed that contemporary group, sex of calf, age of calf and dam affected growth traits. Genetic improvement through selection for W-350d (h 2 d = 0.5) might be achieved more quickly than selection for W-190d (h 2 d = 0.4) and their positive direct genetic correlation (r d = 0.74). The estimates of maternal effects in Bali cattle were not different from 0; however, the genetic correlation between direct and maternal was moderately negative. The decline in W-350d might be caused by factors other than genetic due to the observed genetic values of W-190d and W-350d. A second study of the response to divergent selection for meat tenderness using Warner-Bratzler Shear Force (WBSF) values of Angus bulls was conducted at the Santa Fe Beef Research Unit of the University of Florida. Results have shown that selection for meat tenderness decreased WBSF (0.32 kg/year) and its associated genetic values (0.03 xii

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kg per year) and the heritability of WBSF was found to be high (h d 2 =0.40.18), indicating that WBSF can be effective as a selection tool to genetically improve beef tenderness. A third study that evaluated the responses to estrous synchronization following timed-artificial insemination in heifers and cows of differing proportions of Angus and Brahman breeding was conducted at Beef Research Unit of the University of Florida. Results have shown that year of breeding, body condition, age and breed types of cows affected estrous, pregnancy and calving rate, and gestation length. The reproductive performance of cows subjected to estrous synchronization and timed-AI was not affected by the percentage of Brahman breeding. However, the tendency for high levels of Brahman breeding in cows to result in longer gestation lengths is of concern. xiii

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CHAPTER 1 INTRODUCTION The increasing human population significantly increases the demand for meat, milk and eggs which, in turn, should be followed by growth in livestock production. In addition, as economic growth brings about rapid changes in human life-style, consumption of livestock products increases. Nowadays, increasing income also causes consumers to demand higher quality livestock products and they are willing to pay more for such quality (Brooks et al., 2000), for instance for tender meat. To compete with other sources of protein the beef industry, therefore, has been challenged not only to increase quantity of their products, but also to produce meat products that meet the demands of worldwide consumers. Various ways of increasing the efficiency of beef production can be achieved. Genetic means are based primarily on two procedures: selection within and among breeds and appropriate combination of breeds. In purebred cattle, selection is utilized for genetic improvement of traits with moderate to high heritability and is an excellent tool, particularly under circumstances where crossbreeding is not possible such as with Bali cattle on the island of Bali (Pane, 1990). The knowledge of genetic parameters including heritability is needed in formulating efficient and practical selection programs to fit production goals. These attempts are aided by increased computing power and software capability available today that have facilitated the use of more appropriate models and more sophisticated statistical procedures to estimate variance components and to predict breeding values. 1

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2 Some indigenous cattle breeds, such as Bali cattle in Indonesia and other indigenous breeds in tropical countries, however, have been subjected to little genetic improvement due mostly to the lack of understanding of their genetic parameters and the infrastructure needed for genetic improvement. In fact, such breeds likely exhibit adaptability traits to their environments and other characteristics that make them very suitable for local producers (Martojo, 2003) in traditional sectors where the majority of farmers are smallholders which have greatly contribute to the local beef cattle production in most tropical countries, such as in Indonesia. Nowadays, improvement of meat quality is one of the top priorities of the beef industry. Tenderness has been identified as the most important palatability attribute of meat and, thus, the primary determinant of meat quality (Miller et al., 1995) and eating satisfaction (Miller et al., 2001). Meat tenderness, therefore, has become of more concern to beef retailers and restaurateurs (NCBA, 2000) as they seek to satisfy consumers. However, several studies have shown that a considerable proportion of steaks do not satisfy consumers (Roeber et al., 2001) and that tenderness is the major contributing factor to the inconsistency of eating quality of beef (Burrow et al., 2001). Regarding to the problem of lack of consistent tenderness of beef, many reports on the inheritance of meat tenderness traits and palatability traits have been reported (Marshall, 1994; Bertrand et al., 2001; Riley et al., 2003). Improving tenderness will require that the trait be accurately defined and consistently measured, exhibits genetic variation and, most importantly, is highly correlated with consumer perception of tenderness. Miller et al. (1997) suggested using Warner-Bratzler shear force as a means to measure and select for tenderness. However, such selection has not been conducted

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3 even though several authors have recommended the possibility of selection for tenderness traits as an alternative way to improve beef tenderness genetically due to the relatively high heritability of this trait (Bertrand et al., 2001; Burrow et al., 2001; Riley et al., 2002). Reproductive performance of females is the most important factor affecting the efficiency of beef production (Dickerson, 1970; Willham, 1973; Melton, 1995). Reproductive performance can be commonly measured as conception rate, estrus rate, pregnancy rate, and numbers of calves born per cow exposed. Several studies have shown that the failure to conceive at the end of the breeding season pregnancy rate is the largest factor affecting cow productivity (Wiltbank, 1994; Bellow and Short, 1994). Improving cow reproductive performance by selection is difficult due to its low heritability (Meyer et al., 1990) indicating that the possibility for genetic improvement through selection is limited, therefore, crossbreeding can be used to quickly improve reproductive performance via its resultant yield of heterosis (Cundiff and Gregory, 1999). Crossbreeding programs have been widely used to achieve improvement in beef production through taking advantage of the combination of the superior traits from two or more breeds and also taking advantage of both individual and maternal heterosis (Cundiff and Geary, 1994), particularly for traits with low heritability values such as reproductive performance. Crossbreeding programs were designed to exploit the genetic differences that exist among breeds, especially between Bos taurus and Bos indicus breeds under tropical and subtropical regions (Peacock et al., 1977; Olson et al., 1993; Cundiff et al., 1994; Elzo et al., 1998b). However, the percentage of Bos indicus breeding is of concern

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4 due to the impacts of high levels of Bos indicus in cows on productivity performance that may occur. A high reproductive rate facilitated by artificial insemination using bulls with high genetic merit can also influence the rate of genetic change in a population through increased selection pressure and has become one of the most important techniques devised in both the dairy and beef cattle industries. To make artificial insemination more practical and to allow more females to be bred at a predetermined time, estrous synchronization has been utilized (Odde and Holland, 1994). This method has been widely used to improve the efficiency of beef production and can be an important tool to improve cow reproductive performance, especially for cattle breeds that have low reproductive rates and unusual behavior patterns while in estrus, such as Brahman (Randel, 1994; Rae et al., 1999; Landaeta-Hernandez et al., 2002). Information of how estrous synchronization affects the reproductive performance of cows in different breeds is necessary. Therefore, a series of studies that have been conducted to attempt, to accomplish the following objectives: 1) to evaluate the potential for improving of a tropical breed of cattle through determining non-genetic factors affecting growth traits as well as their genetic parameters and evaluate genetic and phenotypic trends for growth traits (chapter 3); 2) to review the effectiveness of selection for meat tenderness in improving meat tenderness genetically through the evaluation of phenotypic and genetic change as a response to selection (chapter 4); and

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5 3) to evaluate breed differences resulting from crossbreeding on reproductive performance following estrous synchronization of purebred and crossbred cows (chapter 5).

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CHAPTER 2 LITERATURE REVIEW Genetic evaluation is a crucial preliminary step toward development of sound genetic improvement programs and will provide information to estimate the genetic merit of animals with respect to economically important traits. With the advances in computer technology, genetic evaluation has moved forward toward the evaluation of more traits and more animals with the use of more complex animal breeding models (Meyer, 1997). Traits which were thought not possible to be selected, such as maternal effects on growth traits, now have become possible to be estimated. Accurate, unbiased prediction of breeding values, and updated genetic parameter estimates for growth traits, for example, are needed to make effective breeding plans for the improvement of beef cattle. Genetic improvement for some economically importance traits through selection has been effective as has been reported in both livestock or laboratory animals (Moura et al., 1997; Holl and Robinson, 2003; Chen et al., 2003; Koch et al., 2004); the resulting genetic progress has been near expected rates. Therefore, there is a possibility that selection for meat tenderness will be effective in improving beef tenderness, since many studies have reported high genetic variation for this trait (Bertrand, 2001). Besides selection, crossbreeding programs of beef cattle have been widely used to achieve improvements in beef production by exploiting the genetic differences that exist among breeds. The amount of heterosis resulting from crossbreeding is largest for traits that have low heritabilities, such as reproductive traits (Willham, 1973; Meyer et al., 1990), especially in Bos taurus x Bos indicus crossbreds (Cundiff et al., 1994). 6

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7 Methods of Genetic Improvement Genetic Evaluation Genetic evaluation represents the synthesis of available information into a single value for each animal that can be used for purposes of ranking in selection. Over many years, genetic evaluation tools in the form of estimated breeding values have been used in the beef industry to provide more accurate predictions of the genetic merit of animals for economically important traits. Henderson (1953) developed a procedure for predicting breeding value which was later termed best linear unbiased prediction (BLUP) and this procedure was considered the standard for the estimation of breeding values at that time. The use of genetic evaluation tools such as breeding values in genetic improvement is a cumulative process which can generally be additive and permanent. Smith (1984) reported that rates of genetic progress in economically important traits have typically been to be in the range of 0.5 to 2.5% of the mean per year. Genetic change in growth traits, for instance has been significant and a positive genetic trend for these traits in most breeds has been shown to have been made through selection (Koch et al., 2004). Breeding values are necessary to know how to decide which animals are to be used as parents of the next generation. Prediction of Breeding Value Predicted breeding values are very important in breeding programs, particularly in selection. The accuracy of predicted breeding values is mostly dependent upon the availability of records (Mrode, 1996) which can include individual animal performance, and performance of its relatives, such as dams, sires and progeny. Every phenotypic observation on an animal is determined by environmental and genetic factors and may be defined using the following model

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8 y ij = i + g i +e ij where y ij is the j th record of the i th animal, i is the fixed environmental effect which usually include as herd, year of birth, season of birth, and sex of the i th animal g i is the sum of the additive (g a ), dominance (g d ) and epistatic (g e ) genetic values of the i th animal e ij is the sum of random environmental effects of the ith animal The additive genetic value represents the average additive effects of genes an individual receives from both parents as each parent contributes half of its genes to its progeny. Therefore, the breeding value of progeny is the sum of the transmitting abilities of both parents. Genetic Parameters Genetic parameters are characteristics of the population of animals in which they are measured and are important to know to make wise decisions regarding selection programs. Genetic parameters are functions of the covariance and variance components of the traits. E stimation of genetic parameters, therefore, is synonymous with the estimation of variance components. Effective breeding plans are based on the knowledge of genetic and phenotypic variation in a particular population. Genetic parameters may change due to selection or management over years (Koots et al., 1994). Genetic parameters estimates can vary among breeds (Trus and Wilton, 1988; Koots et al., 1994), methods of estimation (Mohiuddin, 1993), data origin, management (Tess et al., 1984), and over time (Koots et al., 1994). Also, they can differ depending upon the animal models used to estimate them. Fererra et al. (1999) stated that

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9 lower genetic estimates were obtained from sire models than from full animal models or sire-dam models, while full animal models and sire-dam models will provide similar estimates. Heritability is a measure of the strength of the relationship between the phenotypic and genetic values for a trait (Bourdon, 2000) i.e. the proportion of phenotypic variation accounted for by genetic variation. The magnitude of the heritability determines the expected response to selection in a population (Van Vleck et al., 1987). Knowledge of heritability values is important in selection for polygenic traits, and for prediction of breeding values and producing abilities. Heritability of a trait is not fixed and may vary from population to population and from environment to environment. Therefore, the determination of heritability estimates for traits of economic importance in a particular population would indicate the genetic progress expected from selection for improvement of that trait in that population. The higher the heritability of a trait, the better performance record is as an indicator of an animal’s true genetic values because when heritability is higher the prediction of breeding values will be more accurate. There are ways to increase heritability, such as making the environment more uniform, measuring the traits more accurately, adjustment for known environmental effects and forming contemporary groups (Bourdon, 2000). A genetic correlation is defined as a measure of the strength of the relationship between breeding values of two traits (Bourdon, 2000). A genetic correlation represents the correlation between the additive breeding values for two traits or between the sums of additive effects of the genes influencing both of the traits. Genetic correlations on traits can result from pleiotropy, that is, the phenomenon of a single gene affecting more than

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10 one trait, and can result from linkage effects, that is, the occurrence of two or more loci that affect the same trait on the same chromosome. Therefore, when two traits are genetically correlated, selection for one will cause genetic changes in the other. Furthermore, the breeding value of one trait can be predicted based on the observed performance of another trait that is genetically correlated with that trait. Knowledge of the magnitude of genetic and phenotypic correlations is important for multiple trait evaluation (Henderson & Quaas, 1976), particularly when predicting correlated responses to selection (Falconer & Mackay, 1996). Either substantial negative or positive correlations indicate that selection for or against a trait would influence other correlated traits. Instruments for Estimation of Genetic Parameters There are several methods for estimation of genetic parameters from simple analyses such as Analysis of Variances (ANOVA) to more complex analyses such as Maximum Likelihood (ML) or its modified version Restricted Maximum Likelihood (REML) (Meyer, 1989). In order to obtain estimates of (co)variance components with better accuracy for traits of interest, several procedures have been used practically in animal breeding. Best Linear Unbiased Prediction (BLUP). Henderson (1953) developed a procedure for predicting breeding value which was later termed best linear unbiased prediction (BLUP) (Henderson, 1975) and this procedure was considered the standard for the estimation of breeding values at that time. As a result, BLUP has found widespread usage in genetic evaluation of domestic animals because of its desirable statistical properties to estimate breeding values close to the true breeding values (BV) of animals by using a simple linear mixed model with matrix notation. The equation of BLUP is:

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11 y = Xb + Zu + e E[y] = Xb where y = vector of animal records, b = vector of unknown fixed effects, u = vector of unknown random BVs belonging to the animals making the records, e = vector of unknown random residual effects, X = known incidence matrix relating records to fixed effects in vector b, Z = known incidence matrix relating records to BV’s in vector u. This linear model has been developed into a model used in animal breeding called the Animal Model that can be modified based on certain assumptions and characteristics of the data. Animal model. The objective of this model is to estimate the breeding value for all animals based on their own records and/or records of their relatives based on assumptions that the animals are from a single population and may have one or more records, and that no selection has occurred in the population (Elzo, 1996). Data used for genetic evaluation have their own structure of records. For instance, some parents have no records, and some dams are related. Based on the structure of the data, some approximations to the animal model can be made. Quaas and Pollak (1980) developed the reduced animal model (RAM) which allowed equations to be set up only for parents. These modified versions include the Sire Model, the Sire Dam Model, and the Maternal Grandsire Model (Elzo, 1996).

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12 Sire model. The objective of sire model is to evaluate only sires using progeny records. This model is used when parents have no records and dams are unrelated. This model is simpler than the animal model and usually used in dairy cattle; however, the dam effect is not accounted for. Sire-dam model. The objective of this model is to evaluate maternal effects in addition to sires. This model can be used when parents have no records of their own. Sire-maternal grand sire model. The objectives of this model are to reduce computation time and to generate a better behaved set of mixed model equations with the assumption that the dams are related. Multivariate animal models. Single trait animal models have been more commonly used but multiple-trait applications are also possible (Benyshek et al., 1988). A multiple-trait analysis involves simultaneously evaluation of animals for two-or more traits and makes use of the phenotypic and genetic correlations between the traits. The first application of the BLUP for multiple trait evaluation was by Henderson and Quaas (1976). One of the main advantages of multivariate BLUP is that it increases the accuracy of evaluations (Mrode, 1996). The gain in accuracy is dependent on the genetic and residual correlations between the traits. Multiple-trait methods have been shown to be useful to reduce genetic prediction error variances (Schaeffer, 1984), selection biases (Pollak et al., 1984) and have enabled genetic effects to be partitioned into the animal and maternal contribution (Meyer et al., 1994). Moreover, an increase in the accuracy with multivariate analysis also results from better connections within the data, due to the residual covariance between traits (Thompson and Meyer, 1986).

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13 A bivariate analysis, which uses extra information, increases accuracy of estimation and improves data structure, as reported by Ducrocq (1994). Furthermore, with two-trait analysis, for example yearling weight and weaning weight, culling bias can be eliminated because a single trait analysis of yearling weight can include information on the weaning weight on which the selection was based. In spite of its advantages, a multiple analysis requires a high computing cost and reliable estimates of genetic and phenotypic correlations among the traits which may not be available. Relationship matrix. A relationship matrix describes the relationships among any number of animals. This matrix contains the additive relationship between two individuals explaining the probability that the two alleles at a random locus are identical by descendent in the two individuals (Elzo, 1996). The relationship matrix is created from the pedigree file. The information from relatives is important, especially for traits that have low heritability and are sex-limited (Wood, et al., 1991). The inclusion of the additive genetic relationship matrix (A) may increase the accuracy of genetic evaluation (Carlson et al., 1984) Connectedness. Connectedness can be defined as a measure of the genetic relationships among herds or contemporary groups that could affect the accuracy of covariance component estimates of a trait in one herd or group in relation to that of another. Mathur et al. (1998) stated that the higher the degree of connectedness, the more accurate the comparisons of estimated breeding values (EBV’s) across groups or herds will be.

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14 Data subsets that have good connectedness should result in better accuracy of genetic variance estimation (Schaeffer, 1975). Elzo (2002) has developed a connectedness program to construct connectedness sets of contemporary groups and to keep track of all connected sets using a link-list algorithm, renumbering contemporary groups and writing the largest connected sets of data to an output data file. Kennedy and Trus (1993) and Hanocq et al. (1996) reviewed the concepts of connectedness and implications for genetic evaluation and found important effects of connectedness on prediction error variances (PEV) of genetic differences between sub-populations. Connectedness could also affect the accuracy of covariance component estimates. Schaeffer (1975) suggested only well-connected data subsets should be used to estimate genetic variances. Eccleston (1978), however, suggested with a proof that all data should always be used. Computational software. As the complexity of models used in genetic evaluation increased, more advanced computer software was required to obtain this increased accuracy of estimation of the genetic parameters. Several computer software packages such as Least-Squares Analyses (Harvey, 1960), Variance and Covariance components (Henderson, 1977), Maximum Likelihood (Harville, 1977), Restricted Maximum Likelihood (Patterson and Thompson, 1971), Expectation-Maximization (Dempster et al., 1977), Derivative-Free Restricted Maximum Likelihood (Graser et al., 1987), and Multiple Trait Derivative-Free Restricted Maximum Likelihood (Boldman et al., 1995) and ASREML (Gilmour, 2000) have been used to compute genetic parameters. The advances in computer technology have led to the use of even more complex models in animal breeding.

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15 Maternal Effects The phenotypic expression of some traits in progeny, such as weaning weight in beef cattle, is influenced by the ability of the dam to provide a suitable environment in the form of better nourishment. The dam contributes to the performance of the progeny in two ways: firstly, through her direct genetic effects passed to the progeny and secondly through her ability to provide a suitable environment, for instance, in producing milk. The ability of the dam to provide a suitable environment for the expression of such traits in her progeny is partly genetic and partly environmental. Like the genetic component of an individual, the maternal genetic component can be partitioned into additive, dominance and epistatic effects (Willham, 1972). The environmental part may be partitioned into permanent and temporary environmental components. It is the maternal additive genetic component of the dam that is passed on to all her offspring, but it is expressed only when their female offspring have progeny of their own. In beef cattle, maternal effects are important for growth traits through weaning, although significant effects remain thereafter for later weights, and differences in the magnitude of maternal traits among breeds have also been found (Robinson and O’Rourke, 1992; Meyer et al., 1993; Eler et al., 1995). Maternal effects are currently included in almost all genetic evaluations of breeding values in beef cattle due to their influence on pre-weaning growth traits (Benyshek et al., 1988). Several reports have shown that there is substantial variation due to maternal effects on weaning weight in beef cattle (Albuquerque and Meyer, 2000; Lobo et al., 2000; Ferraz et al., 2000). Between 29 and 38 percent of the variance of the gain from birth to weaning is due to maternal effects (Meyer et al., 1994), with an average of 32 percent being reported by Koots et al. (1994).

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16 Heritability estimates for maternal effects increase from birth up to weaning and then decrease slowly to one year of age and then diminish rapidly at later ages (Albuquerque and Meyer, 2001). This can be explained because milk production is considered to be the main cause of the maternal effects and a high phenotypic correlation exists between the milk production of the dam and the performance of its offspring from birth to weaning in cattle (Robison, 1981). Another study has shown that there is high correlation between the direct effects of milk yield and maternal effects for weaning weight (Meyer et al., 1994). Most of the literature suggests that maternal effects in addition to direct genetic effects must be considered when carrying out estimation of genetic parameters of early growth traits in Bos taurus and Bos indicus cattle (Robinson, 1996; Tosh et al., 1999) to obtain better estimation of genetic variance components. Estimation of maternal effects and the corresponding genetic parameters has always been considered inherently problematic. Difficulties arise because direct and maternal effects are generally confounded. Moreover, the expression of maternal effects also is sex-limited, occurs relatively late in the life of the female, and lags by one generation (Willham, 1980). The restricted maximum likelihood (REML) algorithms for analyses fitting an animal model including maternal effects, genetic or environmental as an additional random effect has made the estimation of (co)variance components due to maternal effects less complicated (Meyer et al., 1993), although Meyer (1997) reported that the estimates are imprecise and that there are high sampling correlations among parameters.

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17 The application of BLUP to models with maternal effects was first presented by Quaas and Pollack (1980). The model for maternally influenced traits, in matrix notation, is: y = Xb + Zu + Wm + Spe + e where m = vector of random maternal (indirect) genetic effects pe = vector of permanent environment effects e = vector of random residual effects and W and S are incidence matrices relating records to maternal genetic and permanent environmental effects, respectively. Selection within Breed Genetic improvement can be accomplished through two techniques, selection and crossbreeding. Selection in a population makes it possible to increase the average genetic value of one or several characteristics to improve the genetic potential of animals of the population. Many studies using livestock or laboratory animals have reported that genetic improvement can be achieved through selection. Direct selection for litter size has been successful in increasing litter size (Holl and Robison, 2003) in swine. Selection for growth traits has succeeded using two-way selection in rabbit (Moura et al., 1997) and using indirect selection for yearling weight in cattle (Koch et al., 2004). Selection for carcass traits has been reported to improve carcass traits in swine (Chen et al., 2003). Selection for meat tenderness in beef cattle has not been reported, however, there have been studies that reported that significant improvement in shear force measurement might

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18 be achieved (Koohmarie et al., 1995; Wheeler et al., 1995) through selection due to its high heritability. Measuring responses to selection. Response to selection is defined as the change of the population mean resulting from selection defined as the deviation of phenotypic value of selected parents from the overall population from which they were selected. The response to selection occurs from generation to the next generation; however, it may not progress in a regular fashion (Santiago and Cabellero, 1995). In other words, the response to selection may fluctuate between generations or years. The variability of the means of response from one generation to the next generation was reported to be due mostly to random genetic drift and random environmental effects (Hill, 1972; Wei et al., 1996). Genetic change. The effectiveness of selection can be measured by the rate of genetic response and is influenced by four factors: selection intensity (i) or selection differential (S d ), heritability (h 2 ), phenotypic variation and the generation interval (G i ) (Falconer and Mackay, 1996; Bourdon, 2000). The rate of genetic change refers to the change of the mean breeding value of a population over time caused by selection with the unit of time usually being years ( BV/t ). The annual response to selection can be calculated using the equation below: BV/t = h 2 S d / G i Therefore, to increase the genetic change or response to selection, heritability and selection differential need to be increased, while generation interval needs to be kept as low as possible. Selection differential. Selection differential is defined as the average superiority of the selected parents over their population average. In truncation selection based on

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19 phenotypic values, the selection differential is predicted before selection and depends only on the means and standard deviations of the trait and/or the proportion of the population that is selected. Increased selection pressure brings about greater selection response (Sanchez et al., 1999) within the selected line. However, the selection differential can be different between selected lines due to natural selection that may aid response in one line but may impede it in another line; changes of variance resulting from changes in the mean in one line can increase or decrease selection differential in that line, but not in another line. Heritability. Heritability is usually measured within an unselected population. However, it can be measured from response to selection, so-called realized heritability. This heritability can be estimated through regression of the values of response against the cumulative selection differential within a selected line (Hill, 1972). The slope of this regression line is an estimate of realized heritability. Therefore, realized heritability can be derived from the equation h 2 = R/C, where R is the response to selection and C is cumulative selection differential (Falconer and Mackay, 1996). This estimate of realized heritability, however, may not provide a valid estimate of the heritability in the foundation population. Generation interval. In quantitative genetics, generation intervals are generally defined as the average age of both parents at birth of their offspring destined to replace them (Bourdon, 2000). Because the progress per unit of time is usually more important than the progress per generation, generation interval is quite important. The interval of time between generations is one of the most important factors in estimating the response to selection (Falconer and Mackay, 1996).

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20 Genetic trends. Genetic trends reflect the changes in a livestock population or a herd and are the best indicators of genetic progress (Magnabosco et al., 2002). Also genetic trend can provide information on changes of the genetic values of the sires and dams used or calves born each year in a population. Baco et al. (1998) defined genetic trend as an indicator of differences in average breeding value over years. It reflects the effectiveness of selection within the population. Divergent selection. Divergent selection is two-way selection that is created to form two selected groups through selecting individuals having highest or lowest value of a trait, that is, selection in opposite directions. Divergent selection experiments with or without controls and one-directional selection experiments with controls has been conducted. For estimation of genetic response, divergent selection experiments are more efficient than one-directional experiments with a control, because the response can be examined in both lines at the same time (Cameron, 1994). In divergent selection, the response to selection can be measured as the divergence between the lines, which is the difference between upward and downward selected lines. In small populations, the response in the population selected for increased values of the trait may not be the same as that in the population selected for reduced values. In other words, the response in the upward direction may be bigger or smaller than that in the downward direction leading to an asymmetric trend, or the response may unexpectedly go in the opposite direction similar to a study by (Cameron, 1994). Asymmetrical response can be found in many two way selection experiments (Cameron, 1994). If there is only one selected line in each direction with no replication, the selection response is subject to being asymmetrical. Replication of selected lines is

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21 necessary to prove whether the response is asymmetrical through regressing selected lines. There are several possible causes of asymmetrical responses, however, the main causes besides random drift, are inbreeding depression and maternal effects (Falconer and Mackay, 1996). Variability of means of the responses. The variation of the means of response from one generation to generation is caused by several factors such as random drift due to restricted numbers of parents, variation of selection differential, sampling error, and environmental factor (Falconer and Mackay, 1996). Population size . Population size reflects the number of individuals that are involved in reproducing each line each generation. Population size can influence the sampling variation between generations in the population. Smaller effective population sizes result in larger sampling variation. In domestic animals fewer males than females are used for breeding, however, each sex contributes equally to the next generation. To obtain an acceptable genetic gain and to hold the accumulation of inbreeding at a low rate, the effective population size (Ne) must be relatively high. There have been many studies reporting selection response per generation and realized heritability with increasing population size, particularly studies with Drosophila and mice (Weber, 1990; Hill, 2000). In fact, selection for quantitative traits often is conducted with relatively small effective population sizes. However, most studies do not include any statement about the minimum size necessary to achieve maximum response. Houle et al. (1996) stated that in any population the actual effective population size was typically in the range of 30 to 300 and making N e range from 15 to 150.

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22 Random genetic drift. Random genetic drift can be defined as the random changes of gene frequency in a population that can cause changes in the generation means. These changes are cumulative meaning any change in one generation becomes the starting point for the change in the next generation. In small populations, random drift can be seen in irregular random changes from one to next generation and there is no tendency to change back to original generation means (Falconer and Mackay, 1996). Sampling errors. Sampling errors are due to measurement error which depends on the numbers of individual measured. The error becomes larger when the response is measured in more than one selected line where it is the sum of the variances. However, sampling errors in estimating the generation means can be reduced by increasing the numbers of selected and measured individuals (Hill, 1972; Nicholas, 1980). Random environmental factors. Random changes of environmental factors or environmental trends include climatic and management effects that can be different from one season to another season, one generation to next and/or from one year to next. However, the fluctuation of random environmental changes can be eliminated from the response by creating control or unselected lines, but even this unselected line is subject to this effect as well (Falconer and Mackay, 1996). Inbreeding depression. Inbreeding is the mating of animals that have one or more ancestors in common such that at a particular locus their progeny may be homozygous for an allele, both alleles coming from the some common ancestor. The rate of inbreeding is the difference in the average inbreeding coefficient for the population from one generation to the next. Inbreeding rate can be calculated suing this equation: F = 1/2N,

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23 where F is the increment inbreeding rate N is the effective population size. Therefore, small population sizes lead to an increase in the rate of inbreeding (Cameron, 1997). Inbreeding depression is defined as a decrease in the performance of inbred animal relative to those which are not inbred. Inbreeding increases the incidence of expression of deleterious recessive alleles. Inbreeding depression can occur during the progress of selection due mostly to small population size of selection experiments. Inbreeding depression may cause selection progress to fluctuate randomly and may reduce the rate of response in both selected lines (downward and upward) and control lines leading to asymmetrical responses that can be predicted by knowing the rate of inbreeding (Falconer and Mackay, 1996). Crossbreeding Systems Crossbreeding systems in beef cattle have been widely used and have proven to be a quick and effective way to improve the productivity of beef cattle (Peacock et al., 1977). There are several reasons for practicing crossbreeding taking advantage of combination for the superior traits from two or more breeds and also taking advantage of both individual and maternal heterosis to improve traits that have low heritability such as reproductive traits (Bourdon, 2000). Moreover, crossbreeding also is practical when the performance of the purebred breeds is inferior due to inbreeding effects. The importance of crossbreeding in this matter is to remove the inbreeding depression and thus to increase performance. Heterosis. Heterosis or hybrid vigor measures the difference between the average performance of crossbreds and the average performance of the parent breeds used to make the crossbred animal. Generating hybrid vigor is one of the most important reasons for crossbreeding; therefore, every crossbreeding system should provide an adequate

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24 amount of hybrid vigor (Bourdon, 2000). Heterosis can be measured as individual, maternal and paternal. Individual heterosis is hybrid vigor for the direct component and is a function of the gene combinations present in the current generation. This is the heterosis that is expressed when the offspring perform better than their purebred parents; this increased performance is due to individual hybrid vigor. Table 2-1 shows individual and maternal heterosis for some economically important traits (Cundiff and Gregory, 1999). The percentage of heterosis for calving rate (a reproductive trait) has been shown to be higher than that of growth traits. Maternal or paternal hybrid vigor is hybrid vigor for maternal or paternal components of a trait and is the function of genes combinations present in the previous generation. Therefore, this hybrid vigor depends upon gene combinations in the dams and sires. Maternal heterosis is expressed for traits measured on crossbred cows, such as calving rate and for traits measured on their progeny, such as calf survival, birth weight, and weaning weight. Crossbred cows have higher reproductive performance traits such as calving and survival rates, and production performance, traits such as birth weights and growth traits than purebred cows. Maternal heterosis is always higher for Bos indicus x Bos taurus than Bos taurus x Bos taurus crossbred dams. The superiority of Bos indicus x Bos taurus crossbred cows would further increase expected weaning weight.

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25 Table 2-1. Individual and maternal heterosis levels for crossbred cattle a Individual Maternal Trait Observed improvement Heterosis, % Observed improvement Heterosis, % Calving rate (%) Survival rate (%) Birth weight, lb Weaning weight, lb ADG, lb Yearling weight, lb Longevity, yr Cow productivity: No. calves Cumulative WW, lb 3.2 1.4 1.7 16.3 0.08 29.1 4.4 1.9 2.4 3.9 2.6 3.8 3.5 0.8 1.6 18.0 1.36 0.97 600 3.7 1.5 1.8 3.9 16.2 17.0 25.3 a Adapted from Cundiff and Gregory, 1999 The superiority of Bos indicus x Bos taurus crosses can be explained because greater heterosis results from Bos indicus x Bos taurus crosses due to the greater genetic diversity between any given Bos indicus breed and any Bos taurus breed than between two Bos taurus breeds. Therefore, crosses of two Bos taurus breeds result in less heterosis because more loci remain homozygous for undesirable recessive genes. Table 2-2 shows a comparison of heterosis effect of Bos taurus x Bos taurus crosses and Bos indicus x Bos taurus crosses. Individual heterosis levels for Bos taurus x Bos taurus crosses are low relative to levels shown for Bos indicus x Bos taurus crosses for weaning weight and postweaning gain. Another study by Koch et al. (1994), however, reported the individual heterosis effect on calving rate averaged 1.8% and 1.1% for crosses among Bos taurus breeds and -0.1 and 1.9% for Brahman crosses.

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26 Table 2-2. Average heterosis levels for economically important traits in beef cattle 1 Type of crosses Trait Bos taurus x Bos taurus Bos indicus x Bos taurus Individual: (%) Birth weight Weaning weight Postweaning Maternal: %) Calving rate Calving survival Birth weight Weaning weight 2.4 3.9 2.6 3.7 1.5 1.8 3.9 11.1 12.6 16.2 13.4 5.1 5.8 16.0 1 Adapted from Cundiff et al. (1994) Other studies have shown that exploitation of hybrid vigor through the use of systematic crossbreeding has proven to be a quick, effective way to improve productivity of commercial beef herds, especially in economically important traits (Gregory and Cundiff, 1980; Long, 1980); and have shown that crossbred females had higher reproductive performances than straightbred females (Cartwright, 1973). Previous studies have also reported that heterosis in crosses among British breeds of beef cattle cause an increase in preweaning growth rate and survival of calves (Wiltbank et al., 1967) as well as better reproductive performance and milking ability of crossbred cows (Cundiff et al., 1974). Rohrer et al. (1988) and Nunez-Dominguez et al. (1991) have shown that longevity and cow survival are also enhanced in crossbred cows. These advantages of crossbred over straightbred cows in both cow and calf performance contribute to the heterosis for lifetime cow production reported by Sacco et al. (1989) and Cundiff et al. (1992). Breed complementarity. Breed complementarity refers to the production of a more desirable offspring by crossing breeds that are genetically different from each other, but have complementarity attributes. The classic form of complementarity is produced by

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27 crossing sires strong in paternal traits to dams that are strong in maternal traits to produce offspring that inherit superior economic characteristics from their sires and take advantage of good maternal environment provided by their dams. Complementarity can be explained as the effect of an appropriate combination of traits controlled by additive genetic genes which are contributed by superior parental breeds and passed consistently to the progeny (Bourdon, 2000). Complementarity, therefore, is dependent upon the superiority of the breeds chosen for traits of economic importance. Breed choices. Diverse breeds are required to exploit heterosis and complementarities through crossbreeding. Therefore, it is important to know the characteristics of each breed, especially their superior traits before crossbreeding. Moreover, it may be possible to produce superior crosses using exotic breeds excellent for productivity traits and crossing them with local breeds that are inferior in performance, but superior in adaptation as reported by Dickerson (1993). The choice of breeds for use in crossbreeding systems is very important and has to be considered because of large breed differences in productive and reproductive performance (Gregory et al., 1993). There can be greater variation in fertility between breeds than within breed. Besides considering those breeds that show superior production performance, the chosen breeds must also adapt to the local environmental conditions because under the same environmental conditions, breeds express their performance differently, that is, genotype x environment interactions as reported by Olson et al. (1991). Bos taurus and Bos indicus breeds are often selected because of their distinctive characteristics. For example, the Angus is noted for better fertility and growth (Williams

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28 et al., 1991), and the Brahman for adaptability to the subtropical environment and maternal ability (Koger et al., 1975; Peacock et al., 1978). Therefore, the combination of Brahman and Angus may result in animals with good fertility and growth, maternal ability and adaptation to the environment of the subtropics and tropics. Genetic Parameters for Growth Traits in Bali Cattle Growth traits are important economic traits in beef cattle production. These traits are influenced by both genetic and environmental (non-genetic) factors. These traits are recorded and routinely analyzed in genetic evaluation by beef cattle breed associations (e.g. AHA, 1998; AAA, 1999). Knowledge of genetic, phenotypic and environmental parameters for growth traits including heritability and variability of characters is needed to formulate efficient and practical breeding programs. Information on genetic, phenotypic and environmental correlations is imperative in planning selection priorities to fit production goals (Mohiuddin, 1993; Koots et al., 1994). In addition, consideration of specific non-genetic effects for this trait is needed in order to increase the accuracy of evaluation of individual breeding values for selection programs to achieve genetic improvement, particularly in a breed such as Bali cattle on the island of Bali where crossbreeding is prohibited. History and Background of Bali cattle Bali cattle were derived from the domestication of wild banteng (Hardjosubroto, 1994), although no official historical record exists. They are uncontaminated by crossbreeding with other domestic cattle since prehistoric times (Payne and Rollinson, 1973). Bali cattle belong to the family Bovidae, the most common members of which are the domesticated animals of the Bos taurus, Bos indicus and Bubaline groups (Payne and

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29 Hodges, 1997). They have been given several scientific names including Bibos banteng, Bos sondaicus, and Bos javanicus (Merkens, 1926). Bali cattle presumably originated on the island of Bali which has served as a center of distribution for them and they have been maintained as a pure breed there. In order to maintain the purity of Bali cattle, Indonesian government officials have enforced a law that prohibits crossbreeding and introduction or importation of any other breed of cattle onto the island of Bali (Anon., 1984). However, on some other islands of Indonesia, Bali cattle have been crossbred with Zebu cattle (Siregar et al., 1985; Wiryosuhanto, 1996). Bali cattle are found throughout nearly all of Indonesia. From Bali, they have been widely distributed throughout the eastern islands of Indonesia but they are concentrated in parts of East Java, Lombok, Timor, South Sulawesi, and South Sumatera where their numbers now exceed those in Bali (Siregar et al., 2001). Outside of Indonesia, small numbers of Bali cattle have been also introduced into Malaysia, Southeast Asia, Northern Australia and some experimental herds exist in Texas, USA (Davendra et al., 1973; Kirby, 1979; Payne, 1990; Maule, 1990). The total number of Bali cattle currently is about 3 million head or about 29 percent of the total beef cattle population of Indonesia. About 80 percent of them are found on the eastern Island and on South Sumatera (Talib et al., 2002). On the island of Bali itself, there are approximately 533,000 head of Bali cattle (Ditjennak, 2001), with the largest concentration being located in the dryland farming areas. There is, however some concern for the negative population growth trend due to extraction during years of the monetary crisis (Martojo, 2003), in addition to a high pre-weaning mortality rate, and a

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30 low calving percentage, particularly in areas within extensive production systems (Wirdahayati and Bamualim, 1990). Characteristics of Bali Cattle Martojo (2003) considered wild Banteng (Bali cattle) to be one of the most beautiful wild cattle species. Anon. (1984) describes Bali cattle as a bovine that resembles a small cow, while Kirby et al. (1979) said that Bali cattle have a deer-like appearance and temperament, with a mature size intermediate between that of red deer and Bos indicus cattle. Bali cattle are a sexually dimorphic species with mature males being dark bluish black and cows and juveniles, reddish brown. In bulls, the red hair on the body begins to darken at 12-18 months of age. Mature bulls are black, however, after castration, their black hair changes to red-brown within a few months (Payne and Rollinson, 1973). Bali cattle have an attractive appearance compared to other beef cattle breeds (Hardjosubroto, 1994). Their hindquarters areas are white, extending along the belly, and white socks reach from the hooves to above the hocks on both sexes. A thin black line along the middle of the back also distinguishes them from other cattle. Bali cattle are remarkably uniform in type and have changed little from their wild ancestors (Banteng), differing only in size and temperament. Domestication has brought about a smaller, easier to handle and more docile animal (Martojo, 2003). Bali cattle are smaller than either Bos indicus or Bos taurus cattle. Bulls stand from 1.3 to 1.5 m high at the shoulders with a mature weight of 300-400 kg (Talib et al., 2002), whereas cows are about 1.2 m height with a mature body weight between 110-300 kg. The body is relatively well muscled and has the general conformation of beef cattle, particularly in that of bulls. Their skin is tight, the neck is short and the dewlap is

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31 inconspicuous. The head is quite long and the ears are of medium size and erect. Bali cattle are a humpless cattle breed. Horns are present in both sexes, but are larger in males. Their average lifespan in the wild is 11 years, although domesticated animals can live up to 20 – 25 years of age. It is very common for captive Banteng to live into their late teens to mid-twenties (Byers et al., 1995). The chromosomes of Bali cattle are morphologically completely different from those of either European cattle or of zebu cattle (Anon., 1984; Kikkawa et al., 1995). A study that examined the sequences of mitochondrial genes for cytocrome b (Kikkawa et al., 1995) showed that Bos javanicus (Bos sondaicus) had different ancestors from those of European and Zebu cattle. Bali cattle have the same diploid number (60) as Bos indicus and Bos taurus cattle which are made up of 29 accrocentric chromosomes pairs and 2 submetatric sex chromosomes (Payne, 1990). However, crossing Bali cattle with either Bos taurus or with Bos indicus results in an infertile F 1 male, but the F 1 female is fertile (Pulungan and Ma’sum, 1978). Another study found that backcrosses of F 1 cows (Bali cattle x Brahman or Shorthorn) to either parental type (Bali cattle or Brahman or Shorthorn), which produced Bali: Brahman or Shorthorn or Bali: Brahman or Shorthorn also resulted in males that were infertile even though some semen was present in the Bali bulls (Payne and Rollinson, 1973; Kirby, 1979). Potential Advantages of Bali Cattle Several articles in the literature have suggested that Bali cattle have numerous advantages over other breeds found in Indonesia, such as Madura cattle and Ongole cattle (Masudana, 1990; Maule, 1990; Copland, 1996; Payne and Hodges, 1997). Adaptation. Bamualim and Wirdahayati (2002) indicated that Bali cattle are better adapted to poor nutrition and low levels of management compared to Ongole cattle,

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32 another Indonesian breed cattle, and that Bali cattle also showed a better response to improved nutrition. Also, they survive better in harsh environmental conditions, particularly in tropical environments (McCool, 1992). Suitability to hot environments has been shown by their reflective, short, sleek hair coat and black-pigmented skin (Kirby, 1979). Heat tolerance of Bali cattle is better than that of buffalo and other cattle (Brahman crossbred and Shorthorn steers) as reported by Moran (1973). Animals of all ages appear to have an ability to maintain weight and body condition even in poor quality pastures (Payne, 1990). In Australia, Bali cattle lost less weight during lactation than did Brahman-Shorthorn cows due to their lower milk production (Anon., 1984). In Australia, under similar management, Bali cattle were affected to be lesser degree by external parasites due to their short hair and also had a lower incidence of tick-borne diseases, and showed better tolerance to several internal parasites as compared with European breeds of cattle (Anon., 1984). Fertility and calving percentage. McCool (1992) summarized the reproductive performance of Bali cattle from different regions (Australia, Malaysia, Bali and Timor) and concluded that Bali cattle have a high conception rate. They are very fertile with an average fertility rate of 80% to 83% on Bali island using traditional management. Their calving percentage ranged between 69% and 86% (Payne and Rollinson, 1973; Wiryosuhanto, 1996). The calving percentage for Bali cows in South Sulawesi and Malaysia was reported to be 82% (Wardoyo, 1950 and Davendra et al., 1973). In Northern Australia and Sulawesi, Bali cattle regularly achieve an 80% to 90% conception rate (Kirby, 1979) and a moderate annual calving rate of from 52% to 67% (Talib et al., 2002).

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33 Carcass quality. Meat from Bali cattle has outstanding tenderness, leanness and a low total fat content, usually less than 4%, when the animals are kept under traditional village management (Subandryo, et al., 1979). The meat is dark red in color, darker than ordinary beef. The carcass dressing percentage is between 55-58% (Payne and Rollinson, 1973; Talib et al., 2002). Good work capability . As work animals, Bali cattle are easier to train than any other breed and recover their body condition faster after being worked than any other breed. They are also reported to be able to do this without influencing reproductive capacity. Bali cattle are preferred over Ongole cattle because of their better work capacity (Anon., 1984; Copland, 1996). Some Limitations of Bali Cattle Growth rate . Bali calves grow slower than calves from introduced beef cattle breeds, with average daily gains ranging from 200-400 grams/day (Davendra et al., 1973; Talib et al., 1998). One study showed Bali cattle given a high quality feed produced an average daily gain of only 660 grams/day (Moran, 1978). One factor contributing to the poor calf growth of Bali calves is the low milk production of Bali cows (Wirdahayati and Bamualim, 1990). Calf mortality . Several studies have reported that the average mortality of Bali calves up to 6 months of age ranged between 7-33 percent (Darmadja, 1980) and varied from 4 to 48 percent in another study (Talib et al., 2002). In the eastern islands of Indonesia with extensive systems of production, calf mortality ranged from 20% to 50% (Wirdahayati and Bamualim, 1990) due to low birth weights. This high calf mortality may be due to the low milk production for the cows and the resultant slow growth rate of their calves; however, calf mortality can be reduced under good nutrition and

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34 management. Talib et al. (2002) reported that in the extensive systems, especially in the eastern island of Indonesia, calves with light birth weights (<10 kg) frequently die soon after birth. Disease susceptibility . Bali cattle have a high susceptibility to certain diseases, such as Jembrana, a disease similar to rinderpest, Bali ziekte, and Malignant Catarrhal Fever (Perangin-angin, 1988; Soeharsono et al., 1995). Productivity of Bali cattle. Data on the productivity of Bali cattle collected from different provinces of Indonesia are summarized in Table 2-3. Some of the variation in weaning and yearling weight between provinces is due to different management and environment. These data are very useful as the basis of the development of future strategies for improvement of productivity of Bali cattle. Trend toward a reduced growth rate There have been some concerns expressed about decreasing growth rates in Bali cattle and the genetic erosion that may be occurring due to slaughter of the best animals. Talib et al. (2002) reported that the average mature weight of Bali bulls is decreasing and this, combined with increasing slaughter of females as well as fattened bulls due to a high demands for meat, may result in a loss average genetic merit for growth . The bulls and heifers slaughtered are usually the largest and best animals from the producers’ herds as the best prices are received for such animals.

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35 Table 2-3. Weaning and yearling weights of Bali cattle in different provinces in Indonesia Production traits (kg) Province Weaning weight Yearling weight Reference Eastern islands Western islands Bali South Sulawesi BCIP (Bali) 79.2 .2 83.9 25.9 82.9 8.2 64.4 12.5 100.2 7.3 1 95.7 1.8 2 100.3 12.4 129.7 15.1 127.5 5.7 99.2 10.4 149.7 2.4 1 145.2 2.4 2 Talib et al., 2002 Talib et al., 2002 Talib et al., 2002 Talib et al., 2002 Ardika (1995) Ardika (1995) 1 bulls 2 heifers Table 2-4 shows that mature weights of Bali cattle have declined from 1922 to 2002. Martojo (1988) has indicated that the genetic potential of Bali cattle may be declining because of inbreeding depression in this population. Furthermore, a survey by Siregar et al. (2001) found very light cows (110 kg) in South Sulawesi that were rearing very light calves. A study by Garantjang (1993) showed that the availability of land for forage production was not an issue. Therefore, this decline of mature weight could be due to factors other than the availability of forages and/or overgrazing and the resultant under nutrition. An explanation of this decline in weights is important to be verified; and one of doing this is by evaluating both the phenotypic and genetic potential of Bali cattle. Table 2-4. Mature weights of Bali bulls from several studie s . References Mature Bull Weight (kg) Angelino, 1922* Aalfs, 1934 Sutedja et al., 1976 Martojo, 1988 Garantjang, 1993 Talib et al, 2002 432 413 336 310 303 287 *in Garantjang (1993)

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36 Genetic Parameters for Growth Traits Many studies and reviews of genetic parameter estimates for growth traits in different breeds including Bali cattle, in different countries, and using various models for the analyses, have been reported. Genetic parameters that have been estimated include heritabilities for direct and maternal, correlations of genetic, phenotypic and environment effects among traits. Table 2-5. Genetic parameters for weaning and yearling weight of some cattle breed Breed h 2 d h 2 m r dm r d r e r p References Brahman a Angus b Zebu-crossb Hereford b Pooled a Pinzgauer a Bos indicus a Crossbreed a 0.52 0.33 0.24 0.23 0.31 0.34 0.07 0.12 0.38 0.07 0.04 0.09 0.13 0.11 0.04 0.03 0.01 0.32 0.49 -0.35 -0.15 -0.26 0.40 0.07 -0.29 0.16 0.89 0.83 0.70 0.57 Robinson & O’Rourke et al, 1992 Meyer, 1992 Meyer et al, 1994 Blackwell et al., 1962 Mohiuddin, 1993 Dodenhoff et al., 1999 Demeke et al., 2003 Ahunu et al., 1997 a Weaning weight; b Yearling weight; h 2 d : direct heritability; h 2 m : maternal heritability; r dm : direct-maternal correlation; r d : genetic correlation; r e : environmental correlation Heritability estimates for growth traits in beef cattle are abundant in the literature, mainly for Bos taurus cattle from temperate regions as reviewed by Mohuiddin (1993) and Koots et al. (1994). Lobo et al. (2000) presented reviews of parameter estimates for beef and dairy cattle in the tropics. However, only few estimates of direct and maternal effects on growth traits of Bali cattle have been reported. The heritability estimates for weaning weight of various breeds are given in Table 2-5. Heritability estimates for weaning weight have ranged from 0 to 0.90 with an average of 0.30. Table 2-6 presents genetic parameters of Bali cattle from several studies. All of the studies in Bali cattle were analyzed using data from the Bali Cattle Improvement Project

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37 (BCIP), but from different periods of time. Some of them adjusted weaning weight to 205 days of age and yearling weight to 365 days of age. Most of the analyses used sire nested within villages and assumed that there were no linkages among villages; therefore, no study examined connectedness between contemporary groups. Most estimates of genetic parameters in previous study were only for additive genetic effects with no separation into direct and maternal effects. Genetic and Phenotypic Correlations All of the estimates of genetic and phenotypic correlations between weaning weight and yearling weight reviewed by Koots et al. (1994) and Mohiuddin (1993) were found positive indicating that selection for weaning weight should increase yearling weight through indirect selection. The estimates of phenotypic, genetic and environmental correlation between weaning and yearling weight ranged from 0.57 to 0.85, 0.16 to 0.92 and 0.43 to 0.83, respectively. The correlations between direct additive genetic and maternal effects on weaning weight of beef cattle vary widely, ranging from -0.72 to 0.53 (Robinson, 1996; Kriese et al., 1999) and averaging -0.15 as reviewed by Mohuidin (1993). A study by Van Vleck et al. (1996) has shown that direct-maternal genetic covariances for weaning weight may be negative, close to zero or slightly positive for some pure breeds. Meyer (1992) reported that the negative relationship become larger as calves matured from 150 to 180 and to 210 days of age. Meyer (1997) reported that the large negative genetic estimates of the correlation between direct and maternal genetic effects are associated with overestimates of additive direct and maternal genetic variances. In some breeds, the negative relationship may be influenced by a negative dam-offspring environmental correlation, known as “fatty udder syndrome”. The negative genetic correlation between direct

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38 growth and the maternal portions of variation must be considered in breeding program, because this antagonistic correlation can be detrimental to selection for either trait (Robison, 1981). Table 2-6. Genetic parameters for growth traits in Bali cattle References Year covered Models Software package Traits Parameters Sudrana (1988) 1983-1986 Sire Harvey W-205d* W-365d* h 2 g = 0.19 h 2 g = 0.32 Djegho et al (1992) 1983-1987 Sire SAS W-205d W-365d W-205d W-365d h 2 g = 0.11 h 2 g = 0.13 h 2 g = 0.13 h 2 g = 0.17 r g = 0.64 r p = 0.32 Packard et al (1990) 1983-1987 Sire Harvey W-205d* W-365d* h 2 g = 0.15 h 2 g = 0.31 Talib et al (1998) 1983-1990 Sire Harvey W-120d W-205d h 2 g = 0.38 h 2 g = 0.33 r g = 0.64 r e = -0.21 h 2 g = heritability, r g = genetic correlation, r p = phenotypic correlation r e = environmental correlation *adjusted weight Non-Genetic Factors Some of the major non-genetic factors that can affect calf weight from birth through 12 months of age are herd (contemporary group), year and season of birth, sex of calf, age of dam, sire, feeding management, and their interactions (Tess et al., 1984; Lasley, 1987). These factors need to be considered and their effects removed before selection for growth traits is done. It has been suggested that weight adjusted for non-genetic factors allows for approximate comparison among animals in selection. In Bali cattle studies have suggested that the growth traits were influenced by non-genetic factors such as year, parity and the effects of linear dam and calf age, sex of calf,

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39 season of birth, villages and, as such, these effects should be considered in analyzing 205 day weaning weight (Packard, et al., 1990; Djegho et al., 1992; Talib et al., 1998). They suggested that all weaning and yearling weights be adjusted for non-genetic effects in attempting to rank animals according to their genotype in order to increase selection accuracy and, therefore, rate of genetic change. Year effects. Year effects reflect climatic conditions, managerial changes, genetic merit changes and other variables. Most studies agree that the great variation among years results from the influence of year on weight traits that may be due to total rainfall that can affect quantity and quality of forages as well as yearly management changes. Studies of beef cattle data show highly significant effects of year of birth on calf weights (Ahunu et al., 1997; Abreu et al., 2002). Season of birth. Season has a great influence on the weight of beef cattle. In tropical areas calves born in the wet season are often heavier than those born in the dry season. Sudrana (1988) reported that the 205-day adjusted mean of weaning weight and yearling weights of Bali calves showed a significant season of birth effect. Calves born in the wet season averaged 91.2 kg and 141.4 kg for weaning and yearling weight while those born in the dry season averaged 88.7 kg and 140.4 kg for weaning and yearling weight, respectively. Similar results were found by Djegho et al. (1992) and Talib et al. (1998) who reported that both weaning and yearling weight of Bali cattle were significantly affected by season of birth. However, in some years, Talib et al. (1998) found that calves born in the dry season were heavier. Some studies have reported a non-significant effect of season on calf growth (Abreu et al., 2002) but only if supplementary feeding was provided in dry season (Ahunu and Grieve, 1980).

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40 Sex of calves . Nearly all studies have agreed that sex of calf has a highly significant effect on birth weight, weaning and yearling weight (Tess et al., 1984; Burfening et al., 1987; Ahunu et al., 1997; Baker and Boyd, 2003). Males are heavier than females, because bulls apparently consume more milk from their dams and have the ability to stimulate milk production of their dams more than the heifers (Burfening et al., 1987) in addition to hormonal factors that influence calf growth. Smaller differences between bull and heifer weaning weights were reported by Abreu et al. (2002). This study with Pantaneiro calves observed that adjusted 205-days weaning weights were 116.12 kg and 112.24 kg for males and females, respectively. Ahunu et al. (1997) found that in purebred and crossbred N’dama and Shorthorn cattle in West Africa that bulls were 4.6 kg heavier than heifers at weaning. Summarized data on growth traits of Bali cattle by sex from various studies are presented in Table 2-7. Some studies in Bali cattle found smaller differences between males and females in Bali cattle than did others. Djegho et al. (1992) found that bulls have significantly higher weaning weight than heifers when weaning occurred after 182 days of age. Sex differences have been shown to increase as growth rate increases indicating the males are more responsive to their environment (Hopkins, 1977). Age of dam. Age of dam at calving contributes the most important source of variation in the weaning weight of a calf (Baker and Boyd, 2003). Most studies have reported the lightest weight calves at weaning were those produced by the youngest cows, and that the weight of calves tended to increase as age of dam increases from 2 years of age up to 5-8 years of age and then declined after 10-11 years (Hearnshaw et al., 1994; Ahunu et al., 1997).

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41 Table 2-7. Comparison of growth traits for Bali bulls and heifers from different regions Production traits (kg) Birth weight Weaning weight Yearling weight Reference Bulls Heifers Bulls Heifers Bulls Heifers Talib et al., 1998 Sumbung et al., 1978 Davendra et al., 1973 Djegho et al., 1992 12.6 16.5 11.9 16.0 94.06 72.0 89.8 85.67 70.7 80.5 122.3 113.3 The age of dam effects are related to milk yield that will affects calf growth (Freetly and Cundiff, 1998). It is generally assumed that as cows mature, their milk yield increases (Van Oijen et al., 1993). Age of dam effects were significant for weaning weight and increased as the cows became older with the average weaning weight of calves from cows 5-6 years old exceeding those of the 2 year old dams by 35 to 37 kg (Baker and Boyd, 2003). There is a linear increase in weights up to weaning as milk yield increases (Fiss and Wilton, 1993). In Bali cattle, Djegho et al. (1992) and Talib et al. (1998) reported that age of dam has a significant effect on both weaning and yearling weight. Haryana (1989) stated that weaning weight of Bali calves increased as age of dam increased, with peak weaning weight occurring at third parity, or at 5 to 6 years of age. Age of calves. Most studies have indicated that age of calf has a significant effect on weaning and post weaning weights (Buvanendran 1990) and is responsible for the largest proportion of variation in both weaning and yearling weight in Bali cattle (Djegho et al., 1992; Talib et al., 1998). Most studies of calf growth to weaning have shown a linear growth, but some have shown a quadratic growth pattern from birth to weaning; these results suggest the use of regression of weaning weight on age or adjustment of the records to 205 days, a common average age. Sudrana (1988) found linear effects of calf

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42 age on weaning weight and both linear and quadratic effects for yearling weight in Bali cattle. Herd effects. Herd effects are mostly due to the combined effects of differences in location, management and nutrition, and genetic merit. Several studies have found that herd effects were found to have highly significant effects on growth traits of the calf (Pacho, 1978; Bertran, 1981). In Bali cattle, Talib et al. (1998), Djegho et al. (1992) and Sudrana (1988) studied location effects and found highly significant effects on weaning weight and yearling weight as shown in Table 2-8. Haryana (1989) found that 205-day weaning weight for Bali calves raised in highland areas were lower than those raised in lowland areas. Table 2-8. Least squares means for weaning weight and yearling weight in Bali cattle from different herds in BCIP Weaning weight Yearling weight Location (herds) Sudrana (1988) Talib et al. (1998) Sudrana (1988) Penebel Marga Baturiti Selemadeg 94.44.60 91.30.58 90.50.56 83.64.75 95.52.34 90.65.31 88.62.64 84.66.21 153.90.91 139.68.87 136.57.87 133.40.31 Interaction effects. Interactions among environmental effects that have had an influence on growth traits have been reported (Buvanendran et al., 1990). In Bali cattle, Sudrana (1988) and Djegho et al (1992) found both weaning and yearling weight were influenced by various interactions between non-genetic factors. Significant interactions on weaning weight were village by year, year by season, year by calf sex, and dam age by season, and dam age by calf sex. Village by year, village by season, and village by age of dam, village by sex of calf, year by calf sex, dam age by season, dam age by sex of the

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43 calf were all significant sources of variation on yearling weight. The interaction effects demonstrated that the non-genetic factors can be dependent upon each other. Genetic Improvement of Meat Tenderness Improvement of meat quality is one of the top priorities of the beef industry. Tenderness has been identified as the most important palatability attribute of meat and, thus, the primary determinant of meat quality (Morgan et al, 1991; Miller et al., 1995) and eating satisfaction (Savell et al., 1989, Miller et al., 2001). Regarding the to the problem of lack of consistent tenderness of beef, many reports on the inheritance of meat tenderness traits and palatability traits have been reported (Marshall, 1994; Bertrand et al., 2001; Riley et al., 2003). Genetic improvement through selection is an alternative solution to produce consistently tender meat due to the high genetic variation for meat tenderness such as in Warner-Bratzler shear force that has been summarized by Burrow et al. (2001). Consumer Preferences in Meat Tenderness Consumers consider three beef characteristics consisting of flavor, juiciness and tenderness as they evaluate “palatability” and/or eating satisfaction. These three characteristics are what consumers desire and what the beef industry is trying to supply to them on a consistent basis. Of these three characteristics, tenderness is the most important factor in determining eating satisfaction of beef (Savell et al., 1989; Morgan et al., 1991; Brooks et al., 2000; Miller et al., 2001) and is the major contributing factor to the inconsistency of eating quality of beef (Burrow et al., 2001), although some have argued that palatability is a combination of these three components (Wheeler et al., 1995). The importance of tenderness on beef palatability (Savell et al., 1989; Miller et al., 1995) has been documented. They have shown that there is a relationship between

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44 tenderness and consumer satisfaction. Jeremiah et al. (1993) showed that 64% percent of participants of a study chose tenderness as the primary criterion of satisfaction versus 20% for flavor and 11% for leanness. Miller et al. (2001) reported that tenderness accounts for more than 50% of consumers’ overall acceptability of beef steaks. Meat tenderness has become of more concern to beef retailers and restaurateurs (NCBA, 2000) as they seek to satisfy consumers. However, several studies have shown that there are a considerable proportion of steaks that do not satisfy consumers (Roeber et al., 2001). Only around 10% of fed cattle processed meet the requirements for upper 2/3 USDA choice or prime grade carcasses (McKenna et al., 2002). A meat tenderness study involving taste panels (Wheeler et al., 2000) showed that in their sample of 310 carcasses, only 20% were considered tender, 68% were intermediate, and 11% were tough. Earlier consumer studies have shown that 15 to 20% of steaks were deficient in tenderness and that certainly this fact will negatively affect beef consumption. Other studies have shown that 10 to 25% of the beef steaks sold at retail stores were classified as tough (Savell et al., 1989; Morgan et al., 1991). Measurement of Tenderness There are several ways of measuring meat tenderness, basically using taste panels and instruments such as computer, ultrasound and shear force evaluation. Taste panel tenderness by a human panel is a subjective evaluation of meat tenderness based on a 1 to 8 scale with 1 being essentially inedible because of its toughness to 8 being extremely tender. This kind of evaluation needs to have properly trained individuals to avoid bias and to become more precise and sensitive in detecting tenderness differences. Attempting to develop an accurate instrument for the measurement of meat tenderness has been done (Culioli, 1995), including using ultrasound values for carcass

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45 traits that are correlated to tenderness. Recently, computer vision has been developed to explain differences in cooked beef palatability (Belk et al., 2001). Beef tenderness can also be measured objectively using mechanical shear force, which is a measure mainly of myofibril toughness (Bouton and Harris, 1972). Warner-Bratzler shear force (Warner, 1952; Bratzler, 1954) is the most commonly used and the most popular method of measuring meat tenderness (Culioli, 1995) and has been established as a standard for prediction of beef tenderness (Smith et al., 1969). Its units of measurement are kilograms of force needed to shear a 1 cubic centimeter muscle sample. The more force needed, the tougher is the meat. Shackelford et al. (1995) and Van Oeckel et al. (1999) stated that tenderness of cooked meat samples can be assessed much more easily via WBSF than by trained sensory panel analysis, in addition to being less expensive and less time consuming as more samples can be evaluated in the same of amount of time and it also avoids the difficulty of maintaining a well trained sensory panel. Compared to shear force, the WBS machine for objective estimates of tenderness reflects tenderness accurately (Harris and Shorthose, 1988) with high repeatability (Wheeler et al., 1994). National Cattlemen’s Beef Association recently developed a standardized procedure for conducting Warner-Bratzler shear force procedure for genetic evaluation with new revised by AMSA (1995), while Savell et al. (1994) has developed a standard protocol for determining Warner-Bratzler shear force value on cooked beef to maximize the repeatability of the measurement. There are no exact standards for connecting shear force to consumer acceptability. However, Devitt et al. (2002) assumed that tender meat would be classified as 4.05 kg or less, and tough meat would be 5.64 kg and greater, while steaks with values between

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46 these two levels would be considered intermediate in tenderness. Miller et al. (2001) indicated that Warner-Bratzler shear tenderness values of < 3.0, 3.4, 4.0, 4.3, and > 4.9 kg would result in 100, 99, 94, 86, and 25% consumer satisfaction for beef tenderness, respectively. Huffman (1996) suggested that the beef industry should target production of beef steaks that have a WBSF value of 4.1 kg or less to ensure high levels (98%) of consumer acceptability. Factors Affecting Meat Tenderness Several studies have shown that the non-genetic factors affecting tenderness include age of the animal at slaughter (Wulf et al., 1996), castration (Martinez-Peraza et al., 1999), health status of the animal (Gardner et al., 1999), energy content of the diet (Van Koevering et al., 1995), temperament and ante-mortem stress (Voisinet et al., 1997), and fatness (Dikeman, 1996). Also, chemical differences created during post-mortem aging can affect tenderness (Koohmaraie, 1995). In summary, tenderness generally is affected by aging, proteolytic activity, and chemical and physiological factors occurring during postmortem and rigor mortis (Pearson, 1987). Tenderization is currently believed to be the result of weakening of the myofibrils caused by proteolysis of proteins (Wheeler and Koohmaraie, 1994) and it is well known that meat tenderness increases gradually during postmortem storage. Koohmaraie (1992) have reported that tenderization begins either at slaughter or shortly after slaughter. Jiang (1998) reported that tenderness is caused by the enzymatic break-down of the muscle contractile proteins at the time of slaughter. Aging is a method for tenderization of meat that results in a more acceptable product. During postmortem aging, myofibrillar protein is degraded by enzyme endogenous proteases (Smith et al., 1978). Generally, there are two different methods of

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47 aging; wet and dry (Parrish et al., 1991). Meat can go through the tenderization process and may be consumed after 1, 3, 7, or 14 days (Wheeler et al., 1994). However, Miller et al. (1997) found that aging beef for 14 days may improve the consistency of beef tenderness and should be recommended as a processing control point for the beef industry. The National Cattlemen Beef Association standardized Warner-Bratzler shear force using 14 days of aging for genetic evaluation (Savell et al., 1994) . Calpain is an endogenous, Ca 2+ -dependent proteinase that functions to initiate in-vivo muscle protein degradation, while calpastatin is one of the endogenous inhibitors that plays a key role in regulating calpains. This system appears to be related to meat tenderness through the regulation of postmortem proteolysis (Page et al., 2002). There is a positive relationship between calpastatin levels and meat toughness (Johnson et al., 1990; Wheeler et al., 1990; Shackelford et al., 1991). The role of calpastatin in tenderness, as well as the genetic components of calpastatin, has been reported (Riley et al., 2003). Genetic Factors in Meat Tenderness It is well documented that genetics is responsible for a significant contribution to the total variation in carcass traits as tenderness varies both within and among breeds (Bertrand et al., 2001; Burrow et al., 2001). Approximately 65% of the variation in tenderness among cattle of all breeds was due to genetic and 35% to environmental (Shackelford et al., 1994). Between breeds differences. Tenderness differences between breeds, especially between Bos indicus and Bos taurus breeds, have been identified (Crouse et al., 1989; Wheeler et al., 1995). Tenderness differences between Bos taurus and Bos indicus breeds apparently associated with several factors. These factors include degree of marbling,

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48 amount of heat-resistant connective tissue, differences in enzymatic degradation of myofibril proteins during postmortem carcass storage, and biochemical differences (Wheeler et al., 1994). Johnson et al. (1990) also reported that differences in meat tenderness among breed types, especially between Angus and Brahman, may be due to differences in postmortem proteolytic enzyme activity. British cattle breeds. Tenderness as evaluated by shear force was slightly more favorable (lower) for British breeds than for the European breeds (Burrow et al., 2001). Considerable data are available that show breed differences in tenderness among purebred Bos taurus cattle (Koch et al., 1976; Gregory et al., 1994; Wulf et al., 1996). British breeds, Angus, Hereford, Shorthorn, for example, produce meat that generally is very acceptable in average tenderness, and produced progeny which were 88.8% Choice or higher, and 22.3% Yield Grades 1 & 2 (Cundiff et al., 2001), although variability still exists among animals. Among the British breeds, Angus and Red Angus sires were superior to Hereford in marbling score and percent Choice. Angus and Red Angus also had higher marbling scores than the Continental breeds (Cundiff et al., 2001). The Angus breed is known throughout the world among the British breeds for its ability to consistently produce the finest high quality beef with lower shear force values (Bidner et al., 2002) and acceptable ribeye areas (Cundiff et al., 2001). The USDA recognizes over 50 certification programs which have as their goal the assurance of some level of guaranteed palatability. The majority of these certification programs refer to Angus. Bos indicus influences in tenderness. There is evidence that beef becomes less tender as the percentage of Brahman inheritance increases (Crouse et al., 1989; Johnson

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49 et al., 1990). Studies reported that the estimates of heritability, additive genetic correlations, and phenotypic correlations for meat tenderness traits within Brahman steers have been variable (Crews and Franke, 1998; Riley et al., 2003). A study by O’Connor et al. (1997) recommended that composite cattle should not be more than 3/8 Brahman breeding, as long as the other 5/8 consists of a breed(s) with high genetic potential for tender meat. Another study reported that several of the composite breeds include at least 25% British breeding, and that mean tenderness generally is very acceptable (Burrow et al., 2001). However, other research data suggest that the percentage of Bos taurus British breeding should be at least 62.5% when crossed to Bos indicus in order to provide an acceptable average level of tenderness, because of the strong negative influence on tenderness of the Bos indicus content (Crouse et al., 1989; Johnson et al., 1990; Van Vleck et al., 1992; Wheeler et al., 1994; Barkhouse et al., 1996.). Within breed differences. Table 2-9 presents heritability values for meat tenderness reported from several studies in beef cattle. Differences in tenderness among sires within breeds are greater than mean tenderness differences among breeds (Wulf et al., 1996; O’Connor et al., 1997). Studies showed that within breed genetics control about 30 to 50% of the variation in beef tenderness (Crews and Franke, 1998; Elzo et al., 1998). Some authors, however, have reported lower estimates for the heritability of meat tenderness (Van Vleck et al., 1992; Wulf et al., 1996). Robinson et al. (1998) found that British breeds had a low heritability estimate for shear force of 0.04. Overall, the literature suggests that there is adequate genetic control of meat tenderness within and between breeds to allow producers to produce consistently tender beef through proper selection.

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50 Table 2-9. Heritabilities of Warner-Bratzler shear force (WBSF). Source Breed WBSF Van Vleck et al., 1992 Shackelford et al., 1994 Barkhouse et al., 1996 Wheeler et al., 1996 Robinson et al., 2001 Bertrand et al., 2001 Marshall, 1994 Koots et al., 1994 Burrow et al., 2001 Wulf et al., 1996 O’Connor et al., 1997 Elzo et al., 1998 B.taurus and indicus B.taurus and indicus B.taurus and indicus B.taurus and indicus B.taurus and indicus B.taurus and indicus B.taurus and indicus B.taurus and indicus B.taurus and indicus Crossbred B.taurus and indicus Crossbred 0.31 0.53 0.17-0.58 0.37 0.38 av. 0.22 av. 0.37 av. 0.43 0.05-0.92 0.12-0.41 0.17-0.47 0.17-58 Genetic Improvement of Reproductive Performance Reproductive performance of females is the most important factor affecting the efficiency of beef production (Dickerson, 1970). Studies emphasize the importance of reproduction on productivity and have shown that reproduction of females is ten times as important as growth and twenty times greater than product attributes (Willham, 1973). A more recent study (Melton, 1995) reported that reproduction was 3.24 times more important economically than consumption attributes. Wiltbank (1994) emphasized that an economically viable beef cow operation must have good reproductive performance. Reproductive performance can be commonly measured as conception rate, estrus rate, pregnancy rate, and numbers of calves born per cow exposed. Cow productivity largely depends on their reproductive performance. Poor reproductive performance is caused by failure to become pregnant due primarily to anestrus, failure to maintain the pregnancy and calf death losses. Several studies have shown that the failure to conceive at the end of the breeding season pregnancy rate is the largest factor affecting cow productivity (Wiltbank, 1994; Bellow and Short, 1994). They concluded that the limiting factors affecting the failure of females to become pregnant included the absence of estrus

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51 expression, lower fertilization rates, and anestrous postpartum in addition to diseases and reproductive tract abnormalities. Reproductive improvement in beef cow-herds can be accomplished through genetic improvement using selection or crossbreeding and also through changing environment or management (Lasley, 1987). However, studies have shown that the heritabilities of reproductive traits are low, ranging between 0.03 and 0.2, compared to those of production or growth traits (Koots et al., 1994). For instance, pooled heritability estimates for calving rate were 0.07 for Herefords, 0.11 for Angus and 0.17 for tropical crossbreds (Meyer et al., 1990). However, Koots et al. (1994) indicated that the mean heritabilities of reproductive traits in beef cattle are higher than those in of dairy cattle which may mean that this greater genetic variability could be used to improve the genetic potential for fertility of beef cattle. It is obvious that much less than 10% of the variation in reproduction traits is due to genetic effects and, thus, the selection response is likely to be small. In contrast to slow changes associated with selection within breed due this low heritability of reproductive performance, crossbreeding can be used to quickly improve reproductive performance and establish new breeds by using superior breeds which utilize both heterosis and blend the distinctive characteristics of each breed through use of complementarity. Moreover, if both additive and non-additive variations are important, then improvement will be maximized by combining crossbreeding with selection among and within breeds (Cundiff, 1970). Besides crossbreeding, the fastest improvement in reproduction will come as a result of changes in environment or management practices. Currently, there are many

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52 management tools and practices that may reduce the failure to conceive and lead to increases in reproductive efficiency, such as estrous synchronization which facilitates artificial insemination. However, the response to estrous synchronization may differ among cattle breeds due to breed differences. The Use of Brahman Breeding Bos indicus breeds originated from what is now India and Pakistan (Yturria, 1973) and today are widely used throughout the tropics. Most beef cattle raised in these areas contain at least some Bos indicus breeding. Positive impact (Turner, 1980) of the Brahman breed on beef production under subtropical and tropical conditions and negative impacts on reproductive efficiency due to Brahman breeding have been reported as compared to Bos taurus breeds (Martin et al., 1992). The adaptive traits of Bos indicus crossbred cattle include tolerance of internal and external parasites as well as high ambient temperature and humidity, and the ability to utilize high fiber forages (Koger, 1963; Turner, 1980; Byers, 1996), and also to retain minerals to prevent deficiencies (Jamarun et al., 1993). Studies have also reported that the muscular hide of Bos indicus cattle aids in resisting external parasites and the high vascularity of their hide makes it well adapted to high temperatures (Turner, 1980). The higher heat tolerance of Bos indicus cattle also is due to its lower basal heat production (Koger, 1963; Cartwright, 1980). The influences of the Brahman and Brahman-based breeds on productivity traits have been documented (Koger, 1975). One of the most appropriate of uses of Brahman breeding is to generate maternal heterosis and its associated effects on production and reproduction when crossed to Bos taurus breeds (Cundiff et al., 1994) and also to increase longevity (Cartwright, 1980). In addition, the Bos indicus crossbred female has

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53 shown the ability of restrict the birth weight of her calf and has a greater pelvic area due to higher pelvic height (McElhenney et al., 1985) both of which lead to less calving difficulty (Olson et al., 1990; Bellow et al., 1994). Moreover, compared to Bos taurus x Bos taurus females, Bos taurus x Bos indicus crossbreds produce more milk resulting in increased calf weaning weight (Paschal et al., 1991). One of the negative effects of Brahman genetics, however, is lower reproductive rates as compared with European breeds (Randel, 1994). Moreover, when Brahman bulls are bred to Bos taurus females, their crossbred calves have been characterized as having higher birth weights and greater calving difficulty levels than would be expected from the size of the Bos indicus sire breed (Roberson et al., 1986). Brahman heifers were older at puberty than heifers of Bos taurus breeds (Plasse et al., 1968) and cows of Brahman-based breed had longer intervals from calving to estrus than those of Bos taurus breeds (Reynolds, 1967). In addition, they have also been shown to be more sensitive to cold weather and to have higher mortality levels when born in cold weather (Josey et al., 1987) and to be less docile, especially when raised under extensive conditions. The Brahman and Brahman-based cow is a long day breeder meaning that their reproductive function increases as day length increases and is higher during the summer months and lower in the winter months (Neuendorff et al., 1984) and that they also exhibit greater seasonal fluctuation than European breeds (Randel, 1994). Brahman and Brahman-influenced cows have a shorter duration of standing estrus, with lower intensity of their heats, and ovulate earlier after the onset of estrus than European breeds (Plasse et al., 1970; Stevenson et al., 1996). Plasse et al. (1970) found 26% of Brahman in Florida showed silent heats or estrus without visible signs. Their reduced estrus behavior and

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54 length of standing estrus as compared to European breed cows might be due to their earlier estrogen surge prior to estrus and lower pre-ovulatory LH production (Randel, 1980). Bovine Estrous Cycles The bovine estrous cycle usually averages from 18 to 24 days, that is, the length of time between two periods of sexual activity. It consists of a sequence stage of reproductive events starting from estrus, metestrus, diestrus and proestrus. There are differences in estrous cycle length between Bos indicus and Bos Taurus females. The length of the estrous cycle of Bos taurus is normally 21 days (Hansel et al., 1973), whereas those of Bos indicus breeds were more variable as shown in Table 2-10. Table 2-10. Some estimates of estrous cycle length in cattle breeds Breeds Length (days) References Brahman Boran Gir Senepol Angus Sahiwal Zebu 19.7 23 21.7 20.4 19.5 22.5 24.2 Plasse et al., 1970 Llewelyn et al., 1987 Moreira-Vianna et al., 2000 Alvarez et al., 2000 Alvarez et al., 2000 Aria and Cristofori, 1980 Martinez et al., 1984 Estrus is the most identifiable stage of the estrous cycle and can be characterized by sexual receptivity and mating. Estrogen has been shown to be the hormone responsible for the induction of estrus behavior in the bovine (Short et al., 1973; Randel, 1980) and also the induction of the LH (Luteinizing Hormone) preovulatory surge in the bovine (Randel, 1990). The signals of estrus include mounting other cows and standing to receive mounting (Allrich, 1993). Other secondary estrous signs include activity, nervousness, and restlessness. After estrus, females show clear vaginal mucus hanging from the swollen, moist, reddened vulva (Allrich, 1993).

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55 Bovine estrus results in sexual receptivity for a short time range of between 15 to 18 hours on the average of every 21 days with ovulation occurring between 10 to 14 hours later (Allrich 1993). However, breed variation in estrus duration exists. Behavioral estrus is shorter in duration and less evident in Bos indicus breeds (Plasse et al., 1970; Galina et al., 1994; Landaeta-Hernandez et al., 2002) compared with Bos taurus breeds (Stevenson et al., 1996). Schams et al. (1977) reported that the duration of estrus in Bos taurus breeds ranges from 3 to 26 h with an average of 14 h, while the duration of estrus of Bos indicus breeds ranges from 2 to 22 h with an average of 7 h (Plasse et al., 1970; Rae et al., 1999). Another study reported the mean estrus duration in Bos indicus to be around 10 hours while in Bos taurus cattle in the tropics and subtropics it was about 15 hours (Mukasa-Murgewa, 1989). The timing of onset of estrus in cattle varies from day to night and differs among breeds. One study reported that 60% of heats of Bos indicus females began at night (Orihuela et al., 1983), but Mattoni et al. (1988) observed that 63% of heats started during the day. However, Landaeta-Hernandez et al. (2002) reported that the timing of onset of estrus was not different between Angus and Brahman. Esslemont et al. (1980) recommended that in order to improve heat detection rates to 80% it is, therefore, useful to observe heat three or four times a day. Estrus Detection Detection of estrus is defined as the process of monitoring the behavior and physiological changes following ovulation of a cow and can be observed by visual, non-visual, hormonal and laparoscopic means. Initiation of estrus is the best external sign to estimate time of ovulation and to determine the appropriate time of insemination (Rae, 2002).

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56 Accurate detection of estrus and understanding of the factors that influence expression is essential for effective reproductive management. In other words, the ability to recognize behavioral estrus likely determines the success or failure of an estrus detection program (Van Vliet and Van Eerdenburg, 1996). Failure to detect estrus or misdiagnosis of estrus leading to untimely insemination results in a loss of income of over $300 million per year in the dairy industry (Senger, 1994). Moreover, insufficient time devoted to estrus detection is another factor that lowers efficiency and missed estrus cycles, especially in some breed types such as Brahman and Brahman crossbred dams that have estrous cycle with lesser intensity and shorter duration (Pinheiro et al., 1998; Rae et al., 1999). The estrus detection method used should identify estrus simply and accurately. Many methods have been developed for estrus detection including tail paint or chalk, chin-ball markers on androgenized females or teaser bulls, heat mount patches, video cameras, dogs trained to detect the odors of estrus, pedometers and pressure-sensitive electronic mount detection systems such as HeatWatch (Mortimer et al., 1990, Lehrer et al., 1992; Senger, 1994). Each method has its own advantages and disadvantages. Some of these aids are more efficient when combined with human observation to establish the appropriate insemination time (Senger, 1994). Tail-paint-mark methods. In this method, a strip of a crayon-like paint is applied to the rump of the cow, so that when cow is mounted the paint is removed. The animals are examined at least once daily to determine whether they have been mounted. Kerr and McCaughey (1984) found the tail painting method to be 88% accurate in detection of estrous animals; they also crosschecked estrous animals by regular

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57 progesterone assays. Kerr et al. (1991) reported 99% accuracy, 33% higher than the rate of detection through behavioral observation alone. Estrous Synchronization Synchronization of estrus can be defined as the manipulation of the estrus cycle or induction of estrus which is basically used to control the timing of estrus or ovulation by altering the length of estrous cycle through manipulation of follicular growth. Moreover, estrus synchronization can decrease the number of days required for estrus detection, instead of 21 days in cows without synchronization to approximately 5 to 7 days with synchronization. Therefore, all females have an opportunity to be artificially inseminated and this makes AI more practical. Several studies investigating the advantages of synchronization of estrus were reviewed by Odde (1990). Estrus synchronization is one of the most important tools to increase the rate of implementation of artificial insemination in beef cattle in many areas of the world. Through the use of estrous synchronization, a high percentage of a group of females can come into estrus at predetermined time, perhaps resulting in more heifers and cows becoming pregnant during the subsequent breeding season. Moreover, it may provide the opportunity for a shorter breeding season or more opportunities for breeding, resulting in earlier calving cows during the calving season and heavier calves at weaning than non-synchronized females (Dunn and Kaltenbach, 1980). In management contexts, since females can be bred at a predicted time, it may provide more uniform management of cows, heifers and calves for better scheduling of labor. Estrous synchronization programs generally use one or a combination of two basic synthetic hormones such as prostaglandin and progestogen. Prostaglandin injections cause corpus luteum (CL) regression and standing heat in 2 to 3 days. The utilization of

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58 exogenous progestogens in estrus synchronization protocols mimics the action of the CL by suppressing estrus and ovulation (McDowell et al., 1998). Progestogen. Several forms of progestogens have been used in estrus synchronization systems including norgestomet, melengestrol acetate (MGA), injections of progesterone, progesterone-releasing intravaginal devices (PRID) and controlled intravaginal progesterone-release devices (CIDR). They have been successfully used for synchronization of estrous cycles in cows and have enhanced the conception rates of heifers and cows following AI (Odde, 1990; Perry et al., 2004; Patterson et al., 2003). The utilization of both PRID and CIDR results in progesterone being absorbed via the vagina for duration of 7 to 12 d and then, combined with PGF 2 , luteolysis, in achieved. Several studies have reported that these regimens increase the synchronized estrous response (Lammoglia et al., 1998; Lemaster et al., 1999; Lucy et al., 2001; Yelich, 2002) in postpartum beef cows, beef heifers, and dairy heifers. Melengestrol acetate (MGA), an orally active progestogen, is popular with beef cattle producers because of the advantages that it is both inexpensive and practical. Moreover, MGA can induce estrous cycles in both postpartum cows (McDowell et al., 1998) and peripubertal heifers (Fike et al., 1999). The disadvantage of this treatment, particularly in extensive range systems, however, is assurance that the animals consume enough MGA during treatment. Low fertility rates of cow herds at the synchronized estrus following long term administration of progestin have been reported (Savio et al., 1993). Exogenous progestins used via long-term feeding can cause a dominant persistent follicle that can extend the estrous cycle through an estrogen-secreting follicle (Lucy et al., 1990; Cupp et al., 1992).

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59 The persistent follicle is caused by increased pulsatile secretion of gonadotropin, however, corpus luteum has been regressed during the time that exogenous progestins was given (Ahmad et al., 1995; Revah and Butler, 1996). Therefore, the persistent follicle need to be removed to initiate a new wave of follicular development through shortening the period that progestin is administered in order to improve the fertility of cattle (Schmitt et al., 1996). Synchromate-B. Synchro-mate-B (SMB) is a progestagen-estrogen combination that consists of an ear implant (in situ 9 days) and an intramuscular injection of 3 mg of norgestomet (NOR) and 5 mg of estradiol valerat (EV) at the time of the implant insertion. The injection of EV acts as a luteolysin for cattle treated beginning early in the estrous cycle through regressing the existing dominant follicles, and acts to initiate growth of a new wave of follicular development (Beal, 2002). Spitzer et al. (1978) stated that the NOR implant functions as an artificial CL and the injection EV presumably inhibits CL formation or stimulates CL regression. The ear implant is removed after 9 days and cows can be inseminated based on observed estrus behavior beginning 36 hours after removal of the implants or simply by breeding all of them 48 hours and 54 hours after removal of the implants. Usually, the cow comes into estrus in 2 to 3 days after removal of implants. Synchromate-B is effective both in synchronizing estrus of cyclic cows and in inducing estrus of non-cyclic cows with satisfactory results (Brown et al., 1988; Johnson and Spitzer, 2001) and provides a high degree of synchrony, thus allowing for a single, timed insemination (Darling, 1993; Kesler and Favero, 1996). Solano et al. (2000) reported that SMB has also been shown to be effective in producing synchronized estrus in suckled beef cows. The expected estrus synchronization rate of beef females

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60 with the use of SMB alone is approximately 85% (Kesler and Favero, 1996). Kesler et al. (1997) demonstrated studies of estrus synchronization using SMB with rates of 86% to 87%, respectively. In general, the range of females showing estrus after SMB treatment was 77-100% with the first service conception rate ranging from 33 to 68%; the variability being mostly due to the stage of estrus cycle of the cow at synchronization. This method is only effective in beef females which are not in metestrous, approximately 85% of a herd at a given time (Kesler and Favero, 1996). Cyclic and heifers. Administering exogenous progestin and estradiol combinations to cows and heifers showed a high incidence (more than 90%) of estrus within 5 days after removal of the progestin (Beal, 2002). Miksch et al. (1978) has shown about 65% of either heifers or postpartum cows were observed in estrus between 24 and 48 hours after implant removal. Odde (1990) working with 1032 heifers treated by SMB showed that observed estrus was detected in 92.5% within 5 days after removal of SMB and conception rates of animals treated with SMB and bred after 12 hours of estrous detection were the same as those of control (untreated) animals. In 736 cows or heifers, a majority (65%) of the animals were observed in estrus between 24 and 48 hours after implant removal (Miksh et al., 1978; Spitzer et al., 1978). Mares et al. (1978) reported that pregnancy rates following timed breeding 48 to 54 hours after implant removal were higher (51%) than when SMB-treated heifers were inseminated 12 hours after estrous detection (39%). Galina et al. (1996), in his review of data from Zebu cattle, reported that 60% of cows were observed in heat following treatment with SMB. However, ultrasound examination of the ovaries of some cows showed no evidence of follicular development

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61 associated with the overt estrous signs observed. Therefore, animals sometime showed riding activity in the absence of active follicles for ovulation. Table 2-11 shows synchronized estrous responses using SMB from several studies. Although there is variability, the response to SMB treatment, in general, shows a high degree of synchronization. This degree of synchrony of estrus becomes one of the advantages of these treatments that facilitate the use of timedartificial insemination, especially when estrous detection is difficult and also that increase the effectiveness of estrous synchronization systems in cattle of Bos indicus breeding. Table 2-11. Response of estrous synchronization using SMB from various studies References Breed Type of female evaluation (%) Odde* (1990) B. indicus, B. taurus heifers, cows Estrus Pregnancy 77-98 32-86 Kerr et al (1991) B. indicus cross B. taurus heifers Pregnancy 53 Tagegne et al (1989) B. indicus cross cows heifers pregnancy 46.2 55.6 Johnson and Spitzer (2001) Bos taurus heifers, cows Estrus pregnancy 66 51 Seguin, 1999 Hereford cows Estrus pregnancy 65 57 *Review of several studies. Anestrous postpartum cows (suckling). Synchro-mate B can induce estrus in non-cyclic postpartum cows. Smith et al (1979) evaluated the effects of SMB with 48 hour calf removal resulting in synchronized of pregnancy rates 44, 46 and 35% in three herds. However, body condition scores of cows should be higher than 4. Timed-Artificial Insemination Artificial insemination (AI) has become one of the most important techniques devised for genetic improvement by facilitating greater usage of bulls of high genetic merit in both dairy and beef cattle. This practice of breeding more cows to the very most

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62 superior sires may result in increased intensity of selection. However, it has been shown that less than 5% of the national (U.S) beef cow herd is inseminated artificially per year because most beef cows are maintained in range environments and AI programs require superior management skills and considerable extra labor (Corah and Kiracofe, 1989; NAHMS, 1994). Perhaps the most serious problem limiting usage of AI is estrus detection. Estrus detection can be both a time and labor consuming experience that makes artificial insemination programs unfeasible for many beef producers. Many efforts have been directed at insemination based on factors other than observed estrus. One of the efforts is breeding at a determined time, so called timed-artificial insemination after estrus induction, to avoid the problem of estrus detection. Females are usually inseminated based on the time of observed estrus which means that cows are observed for signs of estrus and inseminated approximately 12 hours after estrus detection. Instead of being bred based on observed signs of estrus, animals may be inseminated at a fixed time following synchronization. This is possible because cows in a synchronized estrus condition express a longer, more intense estrus than do cows in a spontaneous natural estrus (Anderson et al., 1982; Landaeta-Hernandez et al., 2002) thus providing a high degree of synchrony to allow a timed insemination. Practicing timed insemination might help alleviate the problems of AI in Brahman and Brahman crossbred cows due to their shorter duration of estrus and less intense estrus (Howard and Cranfield, 1995; Geary and Whittier., 1998). While the advantages of the use of timed-AI in reducing labor and management for detection for estrus and cattle handling are substantial, some studies have shown lower pregnancy rates based on timed-AI as compared to breeding cattle based on observed

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63 estrus. Cavalieri and Fitzpatrick (1995) reported that pregnancy rates of heifers following synchronized estrus and breeding at a fixed time insemination as opposed to breeding based on estrus observation were 34.5% and 57.1%, respectively. Another study has shown the conception rate of heifers with timed-AI treatment was lower (55% vs 73%) while a third study reported the pregnancy rate was not different between heifers treated by timed-AI vs estrus detection (61% vs 55%) (Seguin, 1999). Several studies with various combinations of treatments of synchronization drugs followed by timed-AI in Bos taurus females have showed acceptable pregnancy rates (Twagiramungu et al., 1995; Geary and Whittier., 1998; Thompson et al., 1999). Mares et al. (1977) observed that the pregnancy rates after at least 5 days into their breeding of cyclic heifers treated with SMB followed by timed insemination 48 to 54 hours after implant removal were higher (51%) compared to insemination 12 hours after estrous detection (39%). Fullenwider et al. (2001) reported acceptable (40-60%) pregnancy rates resulted from synchronized estrus using CIDR followed timed-AI in Bos indicus breeding. Yelich et al. (2001) studied cows of Bos indicus breeding and reported that pregnancy rates were not different for estrus detection (56.5%) or timed-insemination (51.7%). Factors Influencing Reproductive Performance The use of various estrus synchronizing agents have been reported for heifers and both cyclic and non-cyclic cows in temperate and tropical regions. Variation in estrus expression and achieving pregnancy is known to be due to factors which include breed, season and year, age of cows, parity, nutrition, body condition, suckling status, stage of cycle and the synchronizing agent used (Osoro and Wright, 1992).

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64 Breed effects. Breed differences in the expression of estrus due to physiological and behavioral differences have been identified between Bos indicus and Bos taurus breeds of cattle which may influence their responses to estrus synchronization regimens (Cavaliere et al., 1997). Landaeta-Hernandez et al. (2002) reported that Angus cows expressed estrus earlier and for a longer duration of a synchronized estrus than did Brahman. However, Mattoni and Ouedraogo (2000) showed the estrous response was not significantly different between Bos taurus (Baoule) and Bos indicus cows; however, the duration of estrus shorter was in Bos indicus cows. Using the same treatments, synchronized pregnancy rates of GnRH + PGF 2 protocols appear to be greater in Bos taurus cows (Geary and Whittier., 1998; Stevenson et al., 2000) compared with Bos indicus x Bos taurus cows (Lemaster et al., 2001). This may be due to a less responsive corpus luteum in the Bos indicus x Bos taurus female as opposed to the CL of the Bos taurus female. Body condition effects. Body condition can be used to estimate the body fat reserves which are an indicator of energy stores in cattle that are mostly influenced by nutrition intake. Body energy reserves are very important in reproductive performance of females, particularly in regulation of gonadotropin secreted by hypothalamic and pituitary tissues in controlling ovarian function and follicular growth (Senger, 2000). Studies have demonstrated that the estrous cycle may pause following reduction of gonadotropin secretion (Imakawa et al., 1986; Wettemann et al., 2003) due to decreasing follicular growth. Furthermore, Wettemann et al. (2003) also reported that decreased energy intakes reduced the secretion of gonadotropin-releasing hormone from the hypothalamus, that might result in decreased gonadotropin hormone (Senger, 2000).

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65 Therefore, when body energy reserves are not adequate or available because of inadequate energy intake, the hypothalamus and pituitary will not secrete hormones and thus the estrous cycle may be disrupted. Several studies have reported that nutrient intake and the level of body condition are important for initiation of postpartum estrous cycle (Osoro and Wright, 1992; Spitzer et al., 1995; Morrison et al., 1999). Relationships between body energy reserves and weight loss (before and after parturition) with the duration of the postpartum anestrous period have been established (Selk et al., 1988). The most important factor that influences pregnancy rate is body energy reserves at calving. When beef cows had a BCS (Body Condition Score) (Wagner et al., 1988) of five or greater at calving, the number of days from calving to first estrus and ovulation was 15 to 35% fewer than if cows calved with a BCS of less than 5 (Lents et al., 2000). The minimal body condition required for cows to resume estrous cycle has been documented. Cows in moderate body condition resume ovarian activity more quickly after calving than thin cows (Osoro and Wright, 1992; Vizcarra et al., 1998). Other studies have reported cows that are in poor body condition, less than 4, during the late gestation and at calving, tend to have an extended post-partum anestrous interval and do not begin cycling until 110 to 140 days postpartum compared to cows with body condition scores greater or equal to four, that begin cycling by 80 to 90 days postpartum (Seguin, 1999). Wettemann (1994) determined that minimal body condition scores of 5 for cows at calving in the spring and 5.5 for those the previous fall are necessary for cows to beginning cycling quickly postpartum.

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66 A minimum body condition of 5 is required to maintain a yearly calving interval (Kunkle et al, 1994). In commercial beef herds in Florida, Rae et al. (1993) reported that cows having body condition scores at pregnancy examination of 3, 4, and 5 had pregnancy rates of 31, 60, and 89%, respectively. This BCS of 5 at breeding seems to be the critical level affecting subsequent reproductive performance (Richards et al., 1986). Since parity also affected pregnancy rates, maintaining body condition 5 (moderate) or higher in young cows was found to be more important than in mature cows. Their results indicate that the likelihood of a range beef cow of becoming pregnant when calving with a body condition score 5 is greater than that of a cow calving with a body condition score of 4. The relationship between body condition score and fertility is quadratic in nature meaning that fertility increases up to a certain body condition and then further increases in body condition will decrease fertility. A study in South Africa found that Simmental cows with poor, moderate, and good condition scores had pregnancy rates of 8%, 43%, 70%, respectively, however, excess body condition score decreased reproductive efficiency (Van Niekerk, 1982). Suckling effects. Cows have a connection to their calf (Stevenson et al., 1997). The effect of suckling on neuroendocrine control of postpartum ovarian function has been reported (Williams, 1990). After calving, suckled beef cows are capable of ovulating and initiate estrous cycle normally within 2 weeks (Short et al., 1990). However, suckling can inhibit cows from initiating an estrous cycle for 45 (mature cows) to 65 days (first-calf heifers) after calving (Williams, 1990; Beal, 2002). Short et al. (1990) stated that suckling prolongs postpartum anovulation, and the effect is of greatest magnitude in primiparous

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67 and thin cows. Lamb et al. (1999) and Stagg et al. (1998) reported the effect of suckling by a cow’s calf is greater than suckling by a foster calf and compared once-daily suckling and ad libitum suckling by 80 d postpartum on ovulation resulting in 90% and 43%, respectively. Body energy reserves at calving influence the effect of suckling on ovarian function. If cows had a BCS greater than or equal to 5 at calving, and calves were weaned at 35 d postpartum, all cows ovulated by 25 d after weaning (Bishop et al., 1994). In contrast, only 40% of cows with a BCS less than 5 had ovulated by 25 d after weaning. Generally, most studies agreed that estrus synchronization treatments can be used to induce estrus in some non-cyclic postpartum cows. This effect is enhanced with calf removal or when the calves temporarily weaned from the cows (Beal, 2002). Age of dam effects. Age of dam also affects pregnancy rates; older cows tend to express higher pregnancy rates (Newman et al., 1993). However, several studies have shown that yearling heifers tend to have higher first service conception rates than cows (Seguin, 1999) because of the lack of the stress of nursing a calf in heifers. Some studies have shown that reproductive rate increases up to a certain age and then decreases. Tagegne et al. (1989) studied cows with both Brahman and Hereford breeding and found that pregnancy rates increased from parity 1 to parity 2 and 3 and then decreased at parity 4. Buck et al. (1976) found that pregnancy rate increased from 69% in 2.5-year-old cows to a maximum of 82% in 6to 7 year-old cows and then declined. Plasse (1973) also recorded an increase in pregnancy rate from 50% in 3-year-old purebred Criollo and Criollo x zebu crossbreds to 75% in 7-year-olds. Fertility then declined to 50% among 12-year-olds. A study by Osoro and Wright (1992) with

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68 crossbred Holstein Friesian x Hereford cows showed that pregnancy rate decreased after 7 years of age. A similar study reported by Rae et al. (1993) showed a decreased pregnancy rate after parity 7. The differences in reproductive rate of cows of different ages may be caused by lactation stress in young growing animals and the inability of older cows to regain bodyweight and condition quickly after calving. Lactation has a negative effect on cow bodyweight and, thus, may affect animal reproduction. Trail et al. (1971) and Topps (1977) observed that when cows were given by mediumor low-quality of forage, they needed to use body reserves to maintain milk yield for suckling. Therefore, animals should be supplemented during lactation to increase their conception rates if forage quality or quantity is lacking (Topps, 1977). Year and season effects. The influence of year and season effects, particularly changes of environmental temperature within and between years on reproductive behavior and fertility, can be applied to both males and females. In beef cows, the effects of season on estrous behavior and time of ovulation have been reported by White et al. (2002). Galina and Arthur (1990) reviewed studies in Mexico and found there is a significant difference in conception rates by seasons over years. Environmental temperature, either hot or cold, may affect conception rates, and also influences differences in estrous behavior between Bos taurus and Bos indicus cows (Landaeta-Hernandez et al., 2002). Wilson (1998) reported seasonal and thermal stresses can be detrimental to reproductive efficiency, especially in dairy cows, through altering endocrine function and follicular dynamics.

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69 Thorpe and Cruickshank (1980) reported that significant effects of year on calving rate were due to differences between years due to the quantity and quality of forage available. Bishop (1978) found that calving percentage of Africander-cross cows in South Africa was positively correlated with rainfall in the previous year. Similarly Butterworth (1983) studied Nguni cattle in Swaziland and reported that monthly calving frequency was correlated with previous monthly rainfall records. Jochle (1972) also found direct linear correlations between conception rate in Brahman cows and precipitation and temperature. Gestation length. Gestation is defined as the period from conception to parturition. Gestation length is one of the components of the reproductive cycle and its role in influencing reproductive performance particularly calving rate is through its effect on the calving and breeding seasons. Larsen et al. (1994) considered gestation length to be an important component in modeling beef cattle reproduction. Amer et al. (2001) explained that gestation length must be taken into account when evaluating the potential economic success of a production system. A shorter gestation length results in a longer effective breeding season and less barren cows (Amer et al., 1996) in spite of heavier weaning weights due to earlier calving season. Longer gestation length may cause cows to have their first postpartum estrus later during the breeding season, thus reducing the number of estrus periods of exposure to the bull. Gestation length is affected by breed (Egbunike and Togun, 1980), breed of dam (Liboriussen, 1977), number of fetuses (Bazer and First, 1983), year and season of calving (Lobo et al., 1981), and sex of calf (both Taylor et al. (1984) and Jainudeen and

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70 Hafez (1980) showed bulls were carried longer), biological type of sire (Reynolds et al., 1997), age of dam (Newman et al., 1993), and calf size (Nadarajah et al., 1989). Estimates of the gestation length among Bos indicus cows including Brahman cows from various countries were summarized by Mukasa-Mugerwa (1989) and ranged from 275 to 297 days. Differences in gestation length occur between Bos indicus and Bos taurus cows. A review reported by Thrift (1997) showed Brahman and Brahman based cows have a longer gestation length than Bos taurus cows, between 289 and 294 days with average of 291 days. Other studies showed that Brahman and Brahman crossbred cows had longer gestation length than Bos taurus cows, with an average of 292 days (Reynolds, 1967) and 293 days (Plasse et al., 1968), while Angus have relatively short gestations of 278 d (Plasse et. al., 1968) and 282 d (Cundiff et al., 2001).

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CHAPTER 3 GENETIC PARAMETERS AND ENVIRONMENTAL FACTORS FOR GROWTH TRAITS IN BALI CATTLE Introduction Bali cattle (Bos javanicus) are the most populous of the four breeds of indigenous cattle in Indonesia (Ditjenak, 2001), the others being Aceh, Pesisir, and Madura cattle. Their desirable characteristics include their adaptability to poor environmental conditions and minimal nutrition, and their ability to be used as draught animals without influencing their reproductive capacity (Payne and Rollinson, 1973; Kirby, 1979; McCool, 1992; Bamualim and Wirdahayati, 2002). The primary function of this breed is for beef production. Other functions include draught power, capital investment, the utilization of crop residues and savings, and the production of manure to improve soil fertility (Wiryosuhanto, 1996). Bali cattle are the most important cattle breed in Indonesia because of their great contribution the Indonesian beef industry. They are also very popular with the smallholder Indonesian farmers because of their functionality. Martojo (2003) considered Bali cattle to be the best suited cattle breed for sustainable small farms. The Indonesian government has made the decision to develop and increase Bali cattle as a valued national resource by improving their productivity through selection (Pane, 1990). Therefore, in 1981, the Bali Cattle Improvement Project (BCIP) was established. In the future, Bali cattle are expected not only to be an important beef cattle breed for Indonesia, but also to be an export commodity. Thus, some effort is required to 71

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72 improve their productivity and to make them better able to be a potential resource for the Indonesian beef industry. There has been some concern expressed about decreased growth rates of Bali cattle over the years (Siregar et al., 2001) and also the genetic erosion that may be occurring due to the slaughter of the phenotypically best animals (Talib et al., 2002; Martojo, 2003). Furthermore, there is also concern that inbreeding depression might be causing a decline in the growth rates and in the genetic potential of this breed (Martojo, 1988). Therefore, both a genetic and phenotypic evaluation of this breed is needed to validate these concerns. In addition the genetic evaluation is expected to be useful in predicting the response to ongoing selection in the breeding program for Bali cattle of the BCIP. To predict selection response, it is important to know the genetic parameters for growth within this breed. Meyer (1994) stated that effective breeding plans are based on the knowledge of the composition of genetic and phenotypic variation of a trait in the population. Several studies reporting genetic parameters for a number of traits in various breed cattle from various countries have been reviewed (Mohiuddin, 1993; Koots et al., 1994 and Lobo et al., 2000). In Bali cattle, few studies have reported genetic parameters for growth traits (Packard et al., 1983, 1990; Djegho et al., 1992; Talib et al., 1998). Updated, accurate information of genetic parameters for growth traits using appropriate methods of analyses are necessary in order to obtain better results for genetic improvement. The growth traits of mammals are affected not only by their own genetic makeup (direct additive) but also that of their dams (maternal) as they provide much of the environment, both preand postnatal. Most literature studied with both Bos taurus and

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73 Bos indicus cattle has suggested that maternal effects must be considered when carrying out estimation of genetic parameters of early growth traits to obtain better estimation of genetic variance components for growth (Meyer et al., 1993, 1994; Koots et al., 1994, Robinson, 1996; Tosh et al., 1999). Moreover, under traditional management in many tropical countries, calves especially depend on their dams for growth until weaning showing the importance of maternal components in these cattle. However, there is no information on maternal effect estimates in Bali cattle. There has been little research conducted on genetic evaluation of Bali cattle, and few comprehensive analyses of data with the goal of obtaining greater accuracy of genetic parameters or of evaluating the genetic progress of this breed have ever been attempted. Therefore, the objectives of this research are 1) to determine the magnitude of non-genetic factors influencing growth traits, 2) to estimate genetic parameters including direct and maternal additive heritability for growth traits in Bali cattle in three different analyses, 3) to estimate genetic, phenotypic and environmental correlations between growth traits and 4) to evaluate genetic and phenotypic trends for growth traits in Bali cattle. Materials and Methods Description of Location The geographical coordinates of the island of Bali are between 7’ and 8’ south latitude, and between 114’ and 115’ east longitude (Annonymous, 1985). The island of Bali is included in the province of Bali, which consists of eight districts. The present study includes data from the Tabanan districts of Baturiti, Marga, Penebel and Selemadeg. The topography of locations Marga, Penebel and Selemadeg are similar and they are between 400-500 m above sea level, however, Baturiti is between 500-700

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74 m above sea level. The region has two distinct seasons, wet and dry. The wet season is from April through September during which approximately two-thirds of the annual rainfall occurs. The average annual rainfall for locations 2, 3 and 4 is between 1,500-2,000 mm, but is above 3,000 mm for location 1 (Monographic of Bali, 1995). The average minimum and maximum temperatures are approximately 25C and 33C, respectively. Animal Management In Bali, cattle are generally managed in very small groups, usually only 2-4 head per holding (small-holder farm). Animals are typically housed in a simple shed, usually open-sided buildings with a soil floor and a roof of dry grass/straw/coconut leaves or iron sheets. Calves, heifers and cows are usually housed together, while the bulls or steers are kept separately for fattening. Bali cattle are usually kept in confinement. Traditional feeding systems are based on cut-and-carry systems. Once or twice a day, cattle are fed cut forages collected from roadsides, drains, crop borders or grass fields containing Setaria splendida and elephant grass, often with some legumes such as Centrocemas pubecens, stylosanthes and Leucaena leucocephala. Also, legumes are often cut from trees such as Sesbania grandiflora and Glyricidia sepium. Animals were fed 20-30 kilograms of forage per day with no concentrate or other supplementation; however, during the rice harvest animals are often fed rice bran. Some farmers also tie their animals under fruit or coconut trees for part of the day to graze on weeds and native grasses. All farmers have access to a full animal health program provided by BCIP. Animals were regularly treated to remove internal and external parasites, and were vaccinated against various tropical diseases.

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75 Cows were bred by natural service throughout the year; no defined breeding season was used. However, during some years, some cows were bred by artificial insemination. Heifers were selected and first mated between 18 and 24 months of age. Sires were selected based on their growth rate from weaning through two years of age and usually were used for two years. Calving occurred year around. Cows were not milked and calves were not weaned at any specific time. Many of the cows may continue suckling their calves until the next calf is born. Even though the exact weaning ages of the calves were not known, calves generally remained with dams until about 8-9 months of age on average. Within each location, there were two groups of selected sires available. Sires used for breeding were bulls owned in common by the village community, BCIP project bulls or AB (Artificial Breeding) bulls owned by BCIP used via artificial insemination. Project bulls and AB bulls were recommended to the farmers for mating their cows; however, if such bulls were unavailable, cows were mated by the village bulls. Since they lacked identification numbers, progeny of these village bulls were not included in genetic analysis. Data Collection Data were collected from 1985 through 2000 on purebred Bali cattle owned by the Bali Cattle Improvement Project (BCIP). Animals were kept on smallholder farms who received two 18 months-old-heifers through a credit system. They were located in four different locations on the island of Bali, where each location consisted of many villages. Animals were regularly brought to an installation within each location for animal health and breeding programs. Between 1,000 and 1,200 cattle in each of these villages were identified, regularly weighed, and had information recorded. The following calf data

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76 were recorded on each calf: calf number, calf date of birth, sire number, dam number, and sex of calf. There were two weighing periods per year during which all calves were recorded for weight, the data being recorded by technicians employed by the BCIP. Calf data were grouped by year of calf’s birth, village, season of birth, sex, sire group, age of dam, and age of calf. Data were available for 16 years from calves born from 1985 through 2000 that were obtained from four different locations. Season of birth was recorded as wet season between October and March or dry season, between April and September, which were identified as season 1 and 2, respectively. Age of dam ranged from 2 to 7 years old which was estimated by examining the teeth. Since castration of males was not practiced, there were only two sex groups, bulls and heifers which were represented as sex 1 and 2, respectively. Animals were arranged in contemporary groups (CG) based on being born the same village, year and season of birth. Traits of interests were weight of calves at 190 days and weight at 350 days of age which would be roughly representative of weaning weight and yearling weight, respectively, since exact ages at weaning were unknown. Statistical Analysis The first step was to investigate non-genetic effects through a preliminary analysis using SAS mixed models procedures (Littell et al., 1996). Pair-wise comparisons were generated using the PDIFF option of the Least Squaress Means statement of PROC MIXED in all analyses (Littell et al., 1996). Data were analyzed to determine which fixed effects have significant influence on data. The model included CG, sex and age of dam as fixed effects and age of calf as a covariate. Sire was fitted as a random effect. Age of dam was grouped in three groups, which were dams of 2-3, 4-5 and older than 5 years of age as groups 1, 2 and 3, respectively. Age of calf was fitted as linear, quadratic and cubic

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77 covariates. All two-factor interactions were investigated, and were removed from the model if they failed to show significant (P > 0.05) effects on the traits of interest. In the preliminary analysis, the mathematical model included the following effects: Y klop = + C k +S l + A o + G p + I s + a 1 x + a 2 x 2 + a 3 x 3 + e klop Y klop was the dependent variable (unadjusted W-190d and W-350d) was the overall mean C k was the fixed effect of the k th contemporary group S l was the fixed effect of the l th sex of calf A o was the fixed effects of the o th age of dam Gp was the random effect of the pth sire of calf which was normally and independently distributed with the mean equal to zero and a variance of 2 I s represents all possible two factor interactions a was the partial linear, quadratic and cubic regression of W-190d and W-350d x was age of calf e klop was the random error which was normally and independently distributed and with mean equal to zero and a variance of 2 . Description of Data The original data utilized in this study included weight records from 8,260 calves consisting 4,281 bulls (51.82 %) and 3,979 (48.18%) heifers born from 1985 to 2000. The pedigree file included 13,386 animals born from 1979 to 2000 and consisted of 4,873 dams, 294 sires and 8,219 calves. There was no additional information about the pedigree of sires and dams; therefore, only paternal or maternal half sib information was available. Data were edited on the basis of the range of calf age and ranged from 85 days to 415

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78 days for W-190d with an average age of 191 days as shown in Table 3-1. The range of calf age at W-350d was actually recorded at 194 days to 546 days of age, with an average age of 351 days. Age of calf at W-190d and W-350d was limited to minimize bias from non-linear growth of outside of the age range and to reduce variation of distribution of calf age. There was variation in number of calves collected from the 4 locations consisting of 1,865 calves in Baturiti, 2,380 calves in Penebel, 2,683 calves in Marga and 1,332 calves in Selemadeg due to the number of farmers participating in the credit systems. There were 2,517 (30.47%) calves born in the dry season and 5,743 (69.54%) calves born in the wet season. There were 2,386; 4,495 and 1,379 calves born from 2-3 year old cows, 4-5 year old cows and cows older than 5 years, respectively. Records of animals aged 110 days up to 270 days from first weighing data were used in the analysis of W-190d. Weights from second weighing data were obtained from animals more than 271 days up to 450 days of age were considered as W-350d. In addition, the data were edited such that only animals with complete identification of sire or dam and all fixed effects were used. Animals with no sire identification were not included in analyses because sire misidentification can bias estimation of genetic parameters (Lee et al., 1997; Israel and Weller, 2000; Banos et al., 2001; Senneke et al, 2004). As a result, numerous observations were eliminated from the data set. The number of animals in the final data set used in the analyses is presented in Table 3-1. Differences in the number of records per year and decreased numbers of observations at W-350d relative to those at W-190d were due to deaths, calves that were missing or sold and a few records of calves excluded from the analysis of W-350d because their weights were beyond three standard deviations from the mean. Little data were collected in 1991

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79 due mostly to administrative problems. Preliminary analyses were obtained using SAS mixed models procedures (Littell et al., 1996) for both W-190d and W-350d data. Table 3-1. Data structure before and after editing Number of Observations Items Original data Edited data Calves (#) Dams Sires Progeny/sire Progeny/dam Calf age for W-190d (days) MeanSD Range W-190d (kg) MeanSD Range Calf age for W-350d (days) MeanSD Range W-350d (kg) MeanSD Range 8,219 4,873 294 27.95 1.69 7,973 191.65.85 85-419 7,973 86.46.32 40-203 7,193 351.81.21 194-546 7,193 135.39.80 78-268 7,979 4,716 281 28.39 1.69 7,570 189.79.51 110-270 7,570 85.43.16 40-130 6,955 352.31.45 271-450 6,955 135.34.83 80-219 Estimation of Genetic Parameters Animals were arranged in contemporary groups (CG) based on being born the same location, year and season of birth. All contemporary groups used for genetic analyses had more than nine animal records. The contemporary groups with less than nine animals and with only one sire were deleted. There were a total of 118 contemporary groups. A connectedness program verified with a FORTRAN program (Elzo, 2002) was used to determine the genetic linkages between CG through common sires. A pedigree file was constructed that included calf, sire and dam. Pedigrees were checked to ensure that all parents were born at appropriate length of time before their offspring and, therefore, the identification numbers of all animals were renumbered such that the lower number

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80 identification numbers were used for sires, the highest numbers were for the calves and dams’ identification numbers were in between. Best Linear Unbiased Prediction (BLUP) for direct additive and maternal and breeding values were obtained for each animal using a single and two-trait animal model including the fixed effects of contemporary groups, sex of calf, age of dam, two-way interactions resulting from SAS analysis that had significant effects (P<0.05) and calf age as a covariate. Sire and dam were included as random effects of animal direct and maternal effects, and the random effects of permanent environmental effects of dams were also included. Sequential single-trait animal model analyses were performed to examine the effects of an additional maternal component on (co)variance components for growth traits. The first analysis included sire as a random effect to compute the direct genetic effect, the second analysis included sire and dam as random effects ignoring covariance between sire and dam to compute direct and maternal additive variances and the third analysis included sire and dam as random effects with covariance between sire and dam to compute direct, maternal and direct-maternal covariance. Models were compared using likelihood-ratio tests (LRT) with an error probability of 5%. All models contained the same fixed effects. The difference between the function values for pairs of models can be tested against the chi-squares distribution with degrees of freedom being the difference in number of variance or covariance components in the models (Dobson, 1990). A set of analyses was performed to estimate variance and covariance components for genetic analyses using restricted maximum likelihood (REML). Computation was performed using the ASREML software package (Gilmour et al., 2000). Convergence

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81 was assumed to have been achieved when the log-likelihood changed less than 0.002 in two consecutive iterations. All known pedigree information was included in the analyses through a relationship matrix. The two-trait model with maternal effect that was used for this analysis can be represented as follows: y = Xb + Z i u i + Z d u d + Z pe d pe + e E[y] = Xb IRIRAGAGAGAGeduuepedddiidiipedi*0000*0000**00**var000000 where y = vector of W-190d and W-350d records, b = vector of contemporary groups, sex of calf, age of dam, the significant two-way interactions and calf age at weighing covariates (linear, quadratic and cubic) fixed effects u i = vector of random calf additive direct genetic effects, u d = vector of random dam additive maternal genetic effects ( dam additive direct plus maternal), d pe = vector of random dam permanent environment maternal effects, X = matrix of 1’s, 0’s, and linear, quadratic, and cubic factors that relates calf records to elements of b, Z i = matrix of 1’s and 0’s that relates calf records to elements of u i , Z d = matrix of 1’s and 0’s that relates calf records to elements of u d , and Z pe = matrix of 1’s and 0’s that relates animal records to elements of d pe . e = vector of residuals

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82 For the definitions of covariance matrices from two-trait analyses, * = direct product, A = additive relationship matrix, and the subscripts i = calf, d = dam, 1 = W-190d, and 2 = W-350d. Thus, 2,21,22,11,10iiiiiiiiiiG = 2 2 matrix of covariances between calf additive direct genetic effects for trait j and calf additive direct genetic effects for trait j’ (j, j’ = 1, 2), 2,21,22,11,10didididiidG = 2 2 matrix of covariances between calf additive direct genetic effects for trait j and dam additive maternal genetic effects for trait j’ (j, j’ = 1, 2), 2,21,22,11,10ididididdiG = 2 2 matrix of covariances between dam additive maternal genetic effects for trait j and calf additive direct genetic effects for trait j’ (j, j’ = 1, 2), 2,21,22,11,10ddddddddddG = 2 2 matrix of covariances between dam additive maternal genetic effects for trait j and dam additive maternal genetic effects for trait j’ (j, j’ = 1, 2), 2,21,1000pepepepepeR = 2 2 matrix of covariance between dam permanent environment maternal effects for trait j and dam permanent environment maternal effects for trait j’ (j, j’ = 1, 2), 2,21,1000eeeeeR = 2 2 matrix of covariances between temporary environmental effects for trait j and temporary environmental effects for trait j’ (j, j’=1,2).

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83 Estimate of heritabilities and genetic and environmental correlations and their standard error estimates were computed using ASREML. Heritabilities for direct additive and maternal additive genetic were calculated using the equation: h 2 d = 2 d / 2 p , where 2 d is direct additive variance and 2 p is the sum of all variance phenotypic components estimated by the model, h 2 m = 2 m / 2 p , where 2 m is maternal additive variance. Heritabilities and genetic and environmental correlations were then tested for significance with a 95% confidence interval utilizing their standard errors. Expected breeding values for direct effects of W-190d, W-350d were computed for all animals using ASREML from the two-trait analysis. Genetic trends were plotted as the average of breeding values estimated by the solution of the model equations by year of birth and overall trend was estimated as the linear regression of all breeding values on year of birth. Results and Discussion Non-genetic Effects Mean W-190d and W-350d were 85.4 and 135.3 which were 3 kg higher and 7 kg higher than those reported for 205-d and 365-d weight by Talib et al. (2002) for weaning and yearling weight, respectively (Table 3-2). Talib et al. (2002) analyzed data from all Bali cattle raised on the island of Bali, whereas the present study utilized data only from the BCIP. Most reported results for Bali cattle include pre-adjusted weights. Contemporary group effects. A significant contemporary group effect on W-190d and W-350d was found (P<0.01). These results were in general agreement with most reports in the literature, where most of the studies including those with Bali cattle found evidence of significant year, season and herd effects on weaning and yearling

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84 weight (Sudrana, 1988; Talib et al., 1998; Djegho et al., 1992; Ahunu et al., 1997; Abreu et al., 2002). The contemporary group effects in this study might be due to variation in rainfall and management changes among years, season and herds. The variation of annual rainfall may have affected the quantity and quality of forages available allowed great quantities of high quality forage to be produced throughout the year in all location. The W-190d reflects the nutrition available for the dam and calf during pre-weaning and weaning stage mediated through the quality and quantity of forage available to calves and their dams, especially for animals with no supplementary feed. The Bali cows and their calves were housed together until birth of the subsequent calf. Therefore, calf was dependent largely upon its dam and indirectly depends upon the availability of forages throughout the year. This might be the reason of highly significant effects of CG on W-190d. The Bali cattle are raised in intensive farming systems with cut-carry feeding system. After weaning, calves were dependent upon directly to the availability of forges throughout the year. This may also be the reason for high variation in W-350d among contemporary groups.

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85 Table 3-2. Least squares means ( S.E.), coefficients of variation, and regression coefficient for W-190d and W-350d Effect N W-190d (kg) N W-350d (kg) Total 7,184 5,361 85.438.05 4,728 135.3311.82 C.V (%) 9.42 8.77 CG 118 118 *** 118 *** Sex n.s * Bulls 3,900 87.92 a 0.19 3636 141.02 a .29 Heifers 3,670 81.99 b 0.20 3319 128.88 b .31 Cow age n.s n.s 2-3 2,198 83.66 a 0.33 2001 134.64 a .61 4-5 4,160 85.44 b 0.27 3923 135.75 b ..52 6-7 1,212 85.76 b 0.37 1031 135.67 ab .66 Calf age Linear *** n.s Quadratic *** n.s Cubic *** n.s Interaction: Calf age x sex linear *** n.s Bull 4145 0.31*0.004 Heifer 3833 0.27*0.008 Calf age x cowage linear *** n.s 2-3 2339 0.27*0.007 4-5 4390 0.30*0.004 6-7 1249 0.28*0.008 Cowage x sex Calf sex n.s * 2-3 Bull Heifer 1025 976 141.05 a .67 128.25 c .67 4-5 Bull Heifer 2054 1869 142.18 b .56 129.36 d .57 6-7 Bull Heifers 557 474 140.80 a .75 130.84 e .80 Regres. Coeff. phenotypic 0.32 n.s 0.24 -1.3*0.22 genetic 0.01 n.s 0.02 0.07 n.s .03 abc Means with the same superscript in the same column are not significant different at P<0.05 *P<0.05. **P<0.01 ***P<0.001 n.s: non significant (P>0.05) C.V: coefficient variations Phenotypic trend. Yearly least squares means for W-190d and W-350d are shown in Table 3-2. The changes from year to year between 1985 through 2000 are presented in Figure 3-1.The regression coefficient shown in Table 3-2 for W-190d was positive (0.32 0.24) but that for W-350d was negative (-1.3 0.22). However, the regression coefficient for W-190d was not different from zero (P>0.05), but the regression

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86 coefficient for W-350d was different from zero (P<0.05). Weight at 190d tended to increase 320 grams per year but weight at 350d decreased 1,300 grams per year during the time period included in this study. The positive trend of W-190d indicates that there may have been some genetic improvement due to selection; however, this may be due to the other confounded environmental influences. The explanation for the dissimilar trend between W-190d and W-350d may be due to the management of the calves. Bali calves remain stayed with their dams until the subsequent calf was born; therefore, the W-190d must have been influenced by maternal effects which, in turn, might be expressed mostly by the availability of forages. However, weights taken at older ages, after weaning, (W-350d) may have been more dependent environmental management procedures. This finding also indicated that ongoing selection may have led to a lack of response to selection. The decline in W-350d indicates that, in general, the weights of calves have declined in recent years. This might be due to several factors changes such as reduced genetic merit, poorer nutritional management and other environmental effects that may have affected the quantity and quality of feed consumed after weaning. In the study area, similar to many tropical regions, annual rainfall and its distribution varies widely between years. The negative trend in W-350d of Bali cattle is of concern to both for decision makers and farmers as their goal is to improve the productivity of these cattle. These decreasing W-350d over time warrant further observation to determine which environment effects are responsible.

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87 70115160198519881991199419972000Year of birthWeight-190d (kg) weight-190d weight-350d Figure 3-1. Phenotypic trend for W-190d and W-350d Sex of calf. Sex of calf was not found as a source of variation in W-190d (P=0.37) but was in W-350d (P<0.05). Bulls were heavier at each weight. In addition to the male hormonal influence, the cows nursed by males had milk yields higher than those nursed by heifers (Baker and Boyd, 2003). The average difference between bull and heifer calves at W-190d and W-350d were 5.75 kg and 12.14 kg respectively. These differences were lower than most values found in the literature for Bali cattle (Djegho et al., 1992; Talib et al., 1998). They, however, were close to those reported by Davendra (1973) in Bali cattle from different populations (Malaysia) where the difference between bull and heifer calves for weaning weight was only 2-3 kg. Smaller differences of 5 kg between bull and heifer weaning weights were also reported by Abreu et al. (2002) for Pantaneiro calves and Ahunu et al. (1997) for purebred and crossbred N’dama and Shorthorn cattle in West Africa. The degree of advantage of bulls recorded in this study is similar to that from other studies with Bali cattle but lower than values estimated from other breeds. The significant difference between male and female calves for both W-190d and W-350d was expected but the

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88 level of the male weight advantage was quite low. This could be related to the low level of nutrition available. Age of dam. Age of dam had a significant (P<0.01) effect on W-190d, but not on W-350d (P=0.12). This study showed the effects of cow age only in pre-weaning weight and small effects of age of dam on post-weaning weight, similar to a study by Cruz (1972). These findings agree with the reported results in the literatures in Bali cattle (Djegho et al., 1992; Talib et al., 1998) and are similar to those of Koger et al. (1962) studying Brahman cattle and Vernon et al. (1964) studying Brahman-Angus crossbred cows who have reported that weaning weight increased as dam age increased, even though the differences of W-190d among age of dam group in the present study were small. Age of dam significantly affects milk yield and, therefore, influences the weaning weight of calves (Baker and Boyd, 2003). Two-year old cows tended to produce lighter calves and the 5 to 7 year old cows tended to produce heavier calves similar to studies with other breeds in tropics (Hearnshaw et al., 1994; Ahunu et al., 1997) that reported 5-8 year old cows had the heaviest calves at weaning. That peak production occurred in cows between 5 and 7 years of age is similar to a study by Haryana (1989). The highest (P<0.05) W-190d and W-350d were produced by 4-5 year old dams. However, in the present study, the oldest cows only were 8 years old and there were only 5 records of these; therefore, they were included in group 3. It should be remembered, however, that the age of dam in the present study was only estimated from teeth evaluation. Age of calf. The linear, quadratic and cubic effects of age of calf on W-190d were significant, but the age of calf was not found significant on W-350d. These findings

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89 agree with Talib et al. (1998) and Djegho et al. (1992) who found linear effects of calf age on weaning weight in Bali cattle and Martojo (1972) who found similar results in Angus, Hereford, Santa Gertrudis and Brahman calves. The fact that W-350d was not affected by age of calf is surprising but might be due to the limited nutrition available to calves post-weaning Interactions The two-way interactions between the main effects that were significant (P<0.05) for W-190d were sex of calf x calf age and calf age (linear) x cow age (Table 3-2). The regression coefficient estimates of calf age by calf sex were 0.31 0.004 and 0.27 0.01 for bull and heifer calves, respectively. This interaction indicated that W-190d would increase 310 grams and 270 grams as calf age increased one day for bull and heifer, respectively. Bulls have showed greater increases in W-190d than heifers as calf age increased one day. The regression coefficient estimates of calf age by cow age were 0.27 0.01, 0.30 0.004 and 0.28 0.01 for cows aged 3 years old and younger, cows aged 4 and 5, and cows older than 5 years, respectively. This finding indicated that W-190d would increase 270 grams, 300 grams and 280 grams as calf age increased one day in cows of the three age groups, respectively. The increase in W-190d as calf age increased was greater in cows aged 4 to 5 years old than those of other cow age groups. The two-way interactions that were significant (P < 0.05) for W-350d included cow age x sex of calf (Table 3-2). Figure 3-2 showed that the pattern of increases in W-350d as cow age increased differed by calf sex. The W-350d of heifers increased linearly as cow age increased (P < 0.05); however, the W-350d of bulls increased from cow ages of 2 and 3 years to cow ages of 4 and 5 years and then decreased in older cows. This figure revealed that the trend of increase in W-350d as cow age increased was dependent upon

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90 calf sex. The W-350d depended not only on cow age and calf sex, but also the interaction between age of dam and calf sex. These all two-way interactions were included in the animal model for estimation variance components. These findings were in agreement with several studies in Bali cattle (Djegho et al., 1992; Talib et al., 1998) and in other breeds. 1251452 to 34 to 5>5Cow ageW-350d (kg) bull heifers Figure 3-2. W-350d by cow age x calf sex Genetic Parameters Direct effects. The estimates of variance and covariance components and genetic parameters for weights are shown in Table 3-3. All (co)variances for both W-190 and W-350d resulting from single-trait analysis of three different random effect analyses (sire, sire-dam and sire-dam with covariance) were similar. Overall, the direct additive heritability for W-190d (h 2 d =0.4.05) were lower than those of W-350d (h 2 d =0.5.05), however, they are quite similar. The direct heritability for W-190d compared to W-350d was similar to a study with Nellore cattle reported by Albuquerque and Meyer (2001) found that while initially low, the heritability estimates increased after 300 days off calf age. All direct heritabilites in this study were different from zero (P<0.05). The estimates of heritability for both W-190d and W-350d resulting from two-trait analysis

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91 were quite similar to those resulting from single-trait analysis due to similar direct and maternal variances. The estimate of direct heritability for W-190d was lower than those of W-350d. The estimated direct heritabilities in present study were in agreement with those of reviews by Koots et al. (1994) and Mohiuddin (1993). However, the estimated heritabilities in this study were higher than those of previous studies with Bali cattle (Talib et al., 1998; Djegho et al., 1992). The previous estimates used a nested model with sire nested within herds and ignored maternal effects. Tess et al (1979) reported that the sire variance component could be inflated by sire x herd interaction in the models with sires nested within herds. However, Meyer (1992) showed that models ignoring maternal effects could result in higher estimates of heritability. The fact that heritability from previous study was lower than those from the current study might be due to different numbers of records used. In addition this might be due to different methods of analysis, different numbers of observations or different criteria for editing the data. The estimated heritabilities of the present study were computed by partitioning additive variance components into direct and maternal effects which might have affected the variance component for direct additive and may be expected to be lower than those of previous studies in which heritabilities were not separated into direct and maternal. Mohiuddin (1993) stated that estimates of heritability from different procedures may not be the same due to the fact that different methods of estimation result in a different proportioning of variation due to non-additive genetic and environmental effects. Moreover, the estimated of the current study were computed using data set that was converted to connectedness among CG. Eccleston (1978) suggested that all data should always be used. However,

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92 several studies have found important effects of connectedness on prediction error variances of genetic differences between sub-populations and suggested only well-connected data subsets should be used to estimate genetic variances (Schaeffer, 1975; Kennedy and Truss, 1993; Hanocq et al., 1996; Mathur et al., 1998). Overall, the estimated direct heritabilities obtained in this study indicate that both W-190d and W-350d are at least moderately heritable, therefore, they provide evidence to that selection for either W-190d or W-350d might be effective. This indicates that there is considerable opportunity for the improvement of both traits. Direct selection for W-190d, of course, would be less likely to be successful than for W-350d due to lower heritability, although they are quite similar. Since there is no exact weaning age and calves stay with their dams, it would be reasonable to recommend selection for W-350 as oppose to W-190d in this population. Maternal effects. Since there were only a small number of dams in this study with more than one progeny with records, the data did not allow permanent environmental and maternal genetic effects to be separated. Willham (1980) and Meyer (1992) both previously reported that difficulties in separating additive genetic (both direct and maternal) and environmental maternal effects using field data sometimes exist. The maternal heritability estimates resulting from single-trait and two-trait analyses of two different random effect analysis both for W-190d (h 2 m =0.01.01) and W-350d (h 2 m =0.01.01) were low and not different from zero (P>0.05) due to low maternal variances in all three different analyses. The low maternal effects in this study were surprising because calves often remained with their dams until the next calves were born. This low estimate of maternal effects deserves more investigation to verify it. However,

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93 the low maternal heritabilites in this study might be due to the low milk production of the Bali dams as reported by Bamualim and Wirdahayati (2002) which has been considered the main cause of maternal effects. In addition, there were few dams that had more than one calf. There might be inadequate data structure to permit computation of maternal effects. Table 3-3. Estimates of variance components (kg 2 ), genetic parameters and standard errors of W-190d and W-350d Analysis Connectedness Program Parameters A-1 A-2 A-3 Two-trait Variance D-W190 M-W190 P-W190 DM-W190 D-W350 M-W350 DM-W350 P-W350 39.8.2 104.6.3 144.3.3 280.0.3 39.8.2 0.7.2 104.6.2 144.3.3 2.1.8 280.0.3 39.8.2 0.7.2 103.4.75 -0.6.5 143.7.2 2.1.8 -5.3.3 268.9.0 41.6.4 1.2.0 135.9.6 145.1.5 3.4.0 309.7.3 Heritability D-W190 M-W190 D-W350 M-W350 0.4.05 0.5.04 0.4.05 0.01.01 0.5.04 0.01.01 0.4.05 0.01.01 0.5*.06 0.01.01 0.3.03 0.01.001 0.5.04 0.01.001 correlation DM-W190 DM-W350 DD MM EE -0.20.1 -0.6.2 0.74.08 0.99.00 0.19.02 Log-likelihood* W-190 W-350 -12305.2 -13141.9 -12305.0 -13141.6 -12304.9 -13141.4 -25315.6 D: direct; M: maternal; P: phenotypic; E: residual; A-1 (sire as random effect); A-2 (sire and dam ignoring covariance); A-3 (sire and dam with covariance) *Log-likelihood resulting from ASREML Low maternal heritabilities of 0.04 and 0.06 also were reported by Demeke et al. (2003), Robinson and O’Rourke (1992) and Meyer (1992) for Brahman and Wokalup (a

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94 synthetic breed) cattle, respectively. The maternal heritabilites increased up to 180-210 days of calf age and then decreased at 300 days of age and were greatly diminished after 300 days of calf age. Albuquerque and Meyer (2001) reported that maternal effects were important for weights until 540 days of age and might be important to consider for genetic evaluations for weights after weaning. Genetic direct-maternal correlations. Estimates of genetic correlations between direct and maternal effects (r dm ) resulting from a single trait analysis shown in Table 3-3 were -0.2.1 and -0.6.2 for W-190d and W-350d, respectively. Although the maternal effects from this study were low, the direct-maternal covariance had showed high indicating that maternal variance might be higher than those resulting from this study. Meyer (1997) stated that most studies concerned with largely negative estimates of direct-maternal covariance reported a marked increase of direct and maternal effects. Moreover, she stated that the exclusion of environmental effect that are not taken into account would bias the estimates of direct, maternal and direct-maternal (co)variances. Overall, the direct-maternal correlation of this study were in agreement with those of Mohiuddin (1993) and Koots et al. (1994) which ranged from -0.91 to 0.25 and -0.91 to 0.49 for weaning and yearling weight, with average direct maternal correlations of -0.30 and -0.16 for weaning and yearling weight, respectively. The correlation between direct and maternal effects was also in the range of 17 values (averaging -0.25) from Bos taurus and Bos taurus x Bos indicus studies reviewed by Meyer (1992). The genetic correlations between direct and maternal effects in this study were moderately to highly negative for both traits indicating that some sort of antagonism exists; therefore, selection for both the direct and maternal components of growth traits

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95 might be advisable as suggested by Robinson (1981). A possible explanation for the negative genetic correlation between the direct and maternal effects might be the harsh environmental conditions; even some studies argued that harsh environment may impose a negative correlation (Meyer, 1993; Swalve, 1993). Genetic, phenotypic and environmental correlations. Estimates of genetic, phenotypic and environmental (temporary/residual) correlations between traits, are shown in Table 3-3. The direct genetic correlation between W-190d and W-350d was moderately positive (r d =0.74.05) and higher than those reported in Bali cattle by Djegho et al. (1992). These estimates agree with a report by E ler et al. (1995) and are in the same range of those reported by Mohiuddin (1993) and Lobo et al. (2000). The high correlation between weights indicates that selection for or against one trait would result in associated genetic changes in the other trait. The high additive genetic correlation between the weights together with the high direct heritability for W-350d indicates that direct selection for W-350d would be more favorable than selecting for W-190d due to the higher heritability for W-350d. Maternal genetic correlations between W-190d and W-350d were close to one, similar to those published by Meyer et al. (1993, 1994) for weaning and final (16-23 months) weight. The high maternal-genetic correlation between these weights indicates that there might be important maternal genetic effects from the W-190d period that persist at W-350d, similar to observations made by Tosh et al. (1999). The phenotypic correlation between W-190d and W-350d (r p =0.33.02) was moderate, similar to those in a study of Bali cattle reported by Djegho et al. (1992) and higher than those of the reviews of Mohiuddin (1993) and Koots et al. (1994). The

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96 environmental and phenotypic correlations obtained in this study were smaller than the genetic correlations. The residual (temporary environmental) correlation between W-190d and W-350d (r e =0.19.02) was low. These results agreed with results from Australia (Meyer , 1994 ) and those reviewed by Mohiuddin (1993) and those reported by Talib et al. (1998) did found in Bali cattle found environmental correlation between weight-120d and weight-205d. Comparing models. Table 3-3 presents the log-likelihood of three different random effect analyses. The log likelihood resulting from A-1 with only sire as random effect for estimating direct additive; A-2 with sire and dam as random effect without covariance between sire and dam for estimating direct and maternal additive effect ignoring covariance between sire and dam; A-3 analysis with sire and dam as random effects and covariance between sire and dam for estimating direct and maternal and correlation between direct and maternal effects. The likelihood of three different analyses were similar for both W-190d and W-350d indicating that adding maternal effects had no significantly effect (P > 0.05) the variance components. This finding was surprising and disagrees with Meyer (1997) who found the estimate both direct and maternal heritability markedly increased when allowing a direct-maternal covariance in the model. Therefore, it is not easy to conclude whether or not the inclusion of maternal effect is important and to choose the best fit model for Bali cattle since all log likelihood of the models were similar. Since the results of this study failed to detect a significant maternal effect in Bali cattle, it is also not easy to decide whether this result represent biological truth.

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97 Genetic trends. Figures 3-2 and 3-3 show the changes of estimated breeding values (EBV) for direct effects of W-190d and W-350d, respectively were computed for all animals using ASREML from the two-trait analysis. The lowest EBV for both W-190d and W-350d were in 1991 but this might be due in part to the small number of observations for that year. The highest EBV for W-190d and W-350d were in 1999 and 1989. The EBV for W-190d fell dramatically from year 1999 to 2000, but the EBV for W-350d increased considerably. In general, the EBV for both traits after 1991 shows a similar pattern of increasing, with the larger magnitude being for W-190d. Before 1991, EBV for W-190d decreased, while those for W-350d increased. The genetic trends were expressed as regression of EBV on year of birth. The linear regression analysis of the average yearly EBV shows a yearly decrease of 10 grams and 70 grams for W-190d and W-350d, respectively. The trends were slightly negative, but not different from zero (P>0.05). Although the declining of genetic value of Bali cattle were not significant, the genetic value in Bali cattle was shown the concern raised regarding the genetic erosion in Bali cattle might be warranted. Moreover, it shows that the declines in W-350d over time might not have been due to genetic factors. -0.61.219851987198919911993199519971999Year of birthEBV (kg) Figure 3-3. Genetic trend for W-190d by year of birth

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98 The ongoing selection for growth traits for weights in this Bali cattle population may not have resulted in genetic gain. Therefore, more effective methods of ongoing selection for weights in Bali cattle need to be applied and might be due to low selection pressure caused by a lack of availability of information for sire evaluation. The inclusion of sire evaluation programs with the estimation of expected progeny differences in this breed may increase the level of genetic gain in this breed. Other factors, including possible inbreeding depression, should also be considered. -0.5219851987198919911993199519971999Year of birthEBV (kg) Figure 3-4. Genetic trend for W-350d by year of birth Implications The results suggested that at least the following fixed effects accounting for the age of animal, sex of calf, location and year-season of birth should be included in the statistical model for any analysis in order to obtain better estimation of genetic values. The apparent lack of improvement in weights over the period of the study suggests that changes need to be made in the selection process. Genetic improvement in Bali cattle through selection for post weaning weight might be achieved more quickly than selection for pre-weaning weight, since there is no exact weaning age and farmers keep calves with their dams for extended periods of time.

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99 Selection programs for weight at later ages in Bali cattle should also increase weights of earlier ages as a result of indirect selection. Selection for weight at later ages, however, has the possibility of increasing the generation interval. Due to the failure to detect a significant maternal effect in Bali cattle, it is also not easy to decide whether this result represent biological truth. This low maternal effect, especially for 190 d weight, deserves investigation and verification. The decrease of post-weaning (W-350d) weight over time needs more attention to determine which environmental effects need to be improved since its cause does not seem to be genetic. The genetic trend observed in this study indicated that more effective means of selection for weights in Bali cattle need to be utilized. The estimation of expected progeny differences through sire evaluation may be an alternative way to increase genetic gain if followed by increased usage of the sires identified as superior through artificial insemination. Summary A genetic evaluation of Bali cattle (Bos javanicus) using data collected from the Bali Cattle Improvement Project (BCIP) on the island of Bali was conducted to determine the non-genetic factors and genetic parameters influencing growth traits and to evaluate both their phenotypic and genetic trends. There were 7,980 calves born from 1985 through 2000 used to analyze non-genetic factors and genetic parameters affecting weight at 190 days (W-190d) and 350 days (W-350d). Contemporary group (CG) was defined as a location-year-season combination. The main effects in the mixed model used in the analysis were CG, sex of calf, cow age and the two-way interaction as fixed effects and also included age of calf as covariates. Sire of calf was included as a random effect. Data were analyzed using SAS mixed models procedures (Littell et al., 1996). A

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100 connectedness program (Elzo, 2002) was used to evaluate genetic linkages between contemporary groups. Variance components were computed by the ASREML package (Gilmour et al., 2000) using single-trait animal models that included CG, sex of calf, cow age, and the significant two-ways interaction as fixed effects and the significant effects of calf age as covariates. A sequential analysis was performed by including additional random effect. Analysis-1 (A-1) estimated direct genetic only including sire as a random effect; A-2 computed direct and maternal effect using sire and dam as random effects ignoring covariance between sire and dam; and A-3 computed direct, maternal and direct-maternal genetic correlation using sire and dam as random effects and covariance between sire and dam in the models containing the same fixed effects. All models were compared using likelihood-ratio tests (LRT) with an error probability of 5%. Genetic trends were plotted as average of estimated breeding values (EBV), estimated by the solution of animal model equations by year of birth, and overall trend was estimated as a regression of all EBV on year of birth. All non-genetic effects including CG, calf age (in W-190d), calf sex (in W-350d), calf age x calf sex (in W-190d), and cow age x calf sex (in W-350d) were found significant (P<0.05). Estimates of direct additive heritabiliies (h 2 d ) for W-190d (0.4 0.05) were lower than those for W-350d (0.5 0.04). The estimated maternal heritabilities (h 2 m ) were low and not different from zero (P>0.05). The estimated genetic correlations between direct and maternal effect (r dm ) were -0.2.1 and -0.6.2 for W-190d and W-350d, respectively. The estimated correlations between W-190d and W-350d were 0.74.08 (genetic), 0.19.02 (environmental), 0.99.00 (maternal) and 0.33.07 (phenotypic). The phenotypic trend for W-190d was positive, but negative for

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101 W-350d. However, the breeding values for both traits tended to decrease 10 grams and 70 grams per year for W-190d and W-350d, respectively. These findings showed that some non-genetic factors have significant effects on growth traits in Bali cattle. The estimates of maternal effects in Bali cattle were not different from 0; however, the genetic correlation between direct and maternal was moderate and negative. The low maternal effect was inexplicable. The genetic variance for W-190d was lower than that of W-350d, therefore, genetic progress for W-350d might be expected to be faster than at for W-190d and increasing W-190d can be achieved by selection for W-350d since their genetic correlation was strong and positive. The genetic trends for both traits were slightly negative but were not different from zero. The decline in W-350d might be caused by factors other than genetic due to the observed genetic values of W-190d and W-350d.

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CHAPTER 4 SELECTION FOR MEAT TENDERNESS IN ANGUS CATTLE Introduction The beef industry has been challenged to seek ways of producing products with consistently desirable quality that satisfy consumer preferences. The eating satisfaction of consumers can be determined by the palatability of beef which, in turn, is influenced by factors such as meat color, flavor, aroma, juiciness and tenderness. Of these factors, tenderness is considered the most economically important trait determining consumer eating satisfaction (Savell et al., 1989; Morgan et al., 1991; Brooks et al., 2000). In addition, consumers were found to be willing to pay more for meat that was tender (Miller et al., 2001). As consumers have become more concerned with the palatability of meat, the desire to produce tender meat has increased. Consequently, the movement of the beef industry has been toward a more value-based marketing system; providing the industry with high beef quality has become more essential. One way to increase product quality and consistency is through genetic methods that depend primarily on selection of breeds, taking advantage of breed differences in tenderness as well as genetic variation within breeds. Moreover, the advantages of genetic selection within breeds are cumulative and permanent. Genetic variation within the carcass traits, including marbling score, tenderness, cut ability, ribeye area, fat thickness, and longisimmus muscle area has been reported in both Bos taurus and Bos indicus cattle (Marshall, 1994; Koots, et al., 1994; Robinson et al., 102

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103 2001; Burrow et al., 2001). Also many reports on the inheritance of meat tenderness traits, such as calpastatin activity and Warner-Bratzler shear force (WBSF), have been published (Bertrand et al., 2001; Riley et al., 2003). Genetics have been shown to make up a significant contribution to the total variation in most carcass traits and genetic variation occurs both within and among breeds. The average heritability estimates for most carcass traits vary within the moderate-to-high range indicating that selection for these traits should be effective. However, a study of selection for meat tenderness in beef cattle has not been conducted. In 1994, the University of Florida began a study of selection for meat tenderness in Angus cattle evaluating whether direct selection for meat tenderness could be an effective tool to improve tenderness in beef cattle. Therefore, the objective of this study is to report the response to selection for meat tenderness, with specific objectives being: to examine phenotypic and genetic differences between the progeny of tough and tender sires, to observe phenotypic (WBSF) and genetic (estimated breeding values for WBSF) changes over the years of study and to evaluate the factors influencing response to selection. Materials and Methods Formation of Selected Sire Groups The experiment was conducted at the University of Florida Santa Fe and Beef Research Units, located north of Gainesville, Florida. This experiment was designed to examine the effect of direct selection program for meat tenderness. The experiment began with Angus calves born in 1994. The bulls born in 1994 were sired by bulls available via AI in 1993. The bulls born in 1994 were semen collected and slaughtered and then evaluated for WBSF evaluation. The bulls born with highest and lowest values of WBSF were selected for use to inseminate cows. The dams of these bulls were purebred and

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104 grade Angus cows. Starting in 1996, the calves born in this study were sired by the bulls selected based on their tenderness breeding values. The first generation resulting from selection for the lowest and highest WBSF values was born in 1996 and established the two selected groups of progeny of sires selected for tenderness and toughness. The type of selection utilized is referred to as divergent selection which is selection in both directions, for increased tenderness and increased toughness via WBSF evaluation. Selection decisions were based on phenotypic values (WBSF) within year. Selection pressure for meat tenderness was applied to the males only. The exception to this was that foundation cows that had produced a son evaluated as tender based on WBSF were assigned subsequently to be bred to “tender” sires and cows that produced a “tough” son to “tough” bulls. Daughters of bulls selected as tough were assigned to be bred to tough sires and daughters of tender sires, to tender bulls Females were exposed for breeding first as yearlings and were culled primarily on fertility. After the bull calves were expected to have reached puberty, at 13 to 14 months of age, semen was collected and frozen. It was not possible to collect freezable semen on all bulls each year. The bulls were then slaughtered and their tenderness was determined using WBSF evaluation. After evaluation, three or four of the bulls with the highest and also the lowest WBSF values were selected for use. The semen from selected bulls was used to inseminate purebred and grade Angus cows with about half the cowherd bred to tough and half to tender sires. About 30-35 bulls from both progeny of sires selected for tough and tender were evaluated annually from those born from 1994 through 2000.

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105 Animal Management Calves were born in early spring of each year and remained with their dams up to weaning. Bull calves were not castrated. Cows and their calves were maintained on pastures consisting mostly of Pensacola and Tifton-9 bahiagrass (Paspalum notatum) with mineral supplementation. In winter, they were supplemented with bermudagrass (Cynodon dactylon) hay, urea and molasses. The progeny of sires selected for tender and tough were grazed together in the same pastures and other treatments throughout the year were identical for both groups; therefore, any observed differences in the mean performance between progeny of sires selected for toughness and tenderness could be attributed to a response to genetic selection. Calves were weaned in the fall at approximately 7-9 mo of age. Following weaning, calves were grazed in within sex groups. Post-weaning bulls were fed silage, molasses-based supplements, concentrates with minerals and vitamins until they reached a specified subcutaneous fat thickness of about 10 mm. The heifers were mated at 14 months of age and bulls were used for only one or two breeding seasons. Within progeny of selected sires, matings were planned to avoid inbreeding. The cows were bred through artificial insemination. Clean-up bulls were used following the AI program; their progeny were not included in this study. Breeding females were usually culled from both groups when they failed to conceive diagnosed pregnant (as determined by rectal palpation) or when they first failed to calve. In general, all heifers were kept as replacements in the herd each year. Older, unsound cows were removed from the experiment regardless of their phenotype or progeny performance.

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106 Warner-Bratzler Shear Force Evaluation The bulls were slaughtered when they were 13 to 14 months of age and had about 10 mm of backfat estimated using real-time ultrasound. The bulls were transported to Central Packing Company, Center Hill, Florida for processing. After slaughter, approximately 18 h postmortem, carcasses were graded for USDA quality and yield factors (USDA, 2001) were measured or evaluated by trained personnel from the University of Florida. The 13th rib section of the shortloin was removed and transported to the University of Florida Meat Processing Lab, where it was vacuum-packaged and held for 5 d at 2C before freezing. The strip loin from the left side of each carcass was removed, and fabricated into 2.54-cm-thick steaks. Steaks were vacuum-packaged in oxygen barrier bags (Cryovac B620, OTR = 30 to 50 mL 23C1 [m2]124 h1 at 1 atm; Duncan, SC), and assigned randomly to aging periods of 7, 14, or 21 d at 2C. They were then frozen at 18C until Warner-Bratzler shear force analyses were conducted with steaks aged for 7, 14 and 21 days. The shear force evaluation of tenderness used in this study was based on Warner Bratzler shear force measurement following procedures from AMSA (1995). Prior to evaluation, steaks were thawed overnight at 4C and broiled (Farberware Open-Hearth Model 455N, Yonkers, NY) to an internal temperature of 71C (AMSA, 1995) monitored by copper-constanta thermocouples (Omega Engineering Inc, Stamford, CT) placed in the approximate geometric center of each steak. After the steaks were cooled to 21C (room temperature), a minimum of 6 to 8 cores (1.27 cm in diameter) were removed parallel to the longitudinal orientation of the fiber and sheared with an Instron Universal Testing Machine (Instron Corp., Canton, MA) equipped with a Warner-Bratzler

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107 attachment (crosshead speed = 200 mm/m) (G-R Electric Mfg Co., Manhattan, KS). The Warner-Bratzler shear force reported for the different aging periods was the average kilograms of force required to shear through 6 to 8 one centimeter cores from each steak. Data Analysis Warner-Bratzler shear force data after 14 days of aging from a total of 299 bulls consisting of 110 bulls for base population used before 1996; 92 bulls for tough sire group and 97 for tender sire groups. These bulls were from 50 sires and 220 dams were collected from the male calves born from 1994 through 2001. The pedigree data included 568 animals born from 1986 through 2001. The data were grouped by year of birth of calf and by the progeny of sires selected as tender and tough. The selection criteria analyzed were cumulative and selection differential, generation interval, heritability and realized heritability following an equation from Falconer and Mackay (1996). Heritability. Heritability and its standard error were computed from variance components resulting from ASREML solutions. The realized heritabilities were computed from each selected sire group as the regression coefficient of the annual response against the cumulative selection differential following the procedures of Hill (1972). Cumulative Selection Differential (CSD). Selection differential was calculated as the mean phenotypic value of individuals selected (*) as parents (only bulls) expressed as a deviation from the population mean (), therefore, the equation for selection differential is SD = * and CSD was calculated by adding the selection differentials successively. Generation Interval (GI). Generation interval was calculated as the average age of the parents when their progeny were born. The equation for GI is:

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108 GI = (G s + G d )/2, where G is generation interval; s and d refer to sires and dams, respectively. Variation of WBSF and its EBV . WBSF and its EBV for WBSF were analyzed using SAS mixed models procedures (Littell et al., 1996). Pair-wise comparisons were generated using the PDIFF option of the Least Squares Means statement of PROC MIXED in all analyses (Littell et al., 1996). The fixed effects were year of birth of calves and group of progeny of sires selected, TOUGH and TENDER, and their interaction. All annual trends including phenotypic and genetic trends expressed as regression coefficients were obtained using general linear model (GLM) procedures (SAS, 2001). Phenotypic trends. Annual realized responses as phenotypic trends were measured as the average WBSF computed using a least squares means procedure. Phenotypic trends were plotted as averages of WBSF values for year of birth. Overall trend was estimated as a regression of all WBSF on year of birth. Genetic trends. Annual genetic trend was estimated using each progeny of the selected sires. Genetic trends were plotted as averages of estimated breeding values (EBV) for WBSF obtained from the ASREML solution of animal model equations for year of birth by linear regression of the annual responses on year of birth of calf. Divergent response. The divergence of response was calculated from the difference between least squares means of WBSF and resulting EBV for WBSF of progeny of tough and tender sires each year. The annual divergence was computed by regressing of the differences in response on year of birth. Estimation of Variance Components Variance components were computed using BLUP with a single trait-animal model including year of birth, sire group (tender and tough) and their interactions as fixed

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109 effects and animal as a random effect. All pedigree information recorded from 1984 to 2001 was also included in the analysis to construct the relationship matrix (A). Analyses were performed to estimate variance components using the ASREML program (Gilmour et al., 2000). Estimates of variance components were obtained by maximizing the REML log-likelihood function using the Average Information algorithm. Convergence was assumed to have been achieved when the log-likelihood changed less than 0.002 in two consecutive iterations. The Mixed Model in matrix notation is: y = Xb + Zu + e E[y] = Xb 2200000vareAIAGeu Var(y) = ZGZ’ + R = ZAZ’ 2A + 2eI where y = vector of animal records of WBSF b = vector of fixed effects: year of calf birth and group of progeny of selected sire u = vector of unknown random BV’s belonging to the animals making records, e = vector of unknown random residual effects, X= known incidence matrix relating records to fixed effects in vector b, Z= known incidence matrix relating records to BV’s in vector u. 2 e = residual error variance, = additive variance 2A

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110 A = relationship matrix, R = V (e) = I 2 e , I = identity matrix, G= V(u) = A 2A Results and Discussion Factors Influencing Response to Selection Selection differential. Average selection differentials for progeny of both tender and tough sires by year of birth and annual cumulative selection differentials are shown in Table 4-1 and Figures 4-1 and 4-2 for selection differential and cumulative selection differential, respectively. Selection differentials over the years of study have fluctuated and that could affect the response to selection. The fluctuation of selection differentials was greater in the progeny of tough sires than in those of the tender sires. The average selection differentials were 1.32 kg and -0.93 kg for the progeny of sires selected for toughness and tenderness, respectively. After 7 years of observations, CSD were -6.14 kg and 8.78 kg in progeny of tender and tough sires, respectively. Figure 4-2 shows that the divergence of CSD between the progeny of tender and tough sires is increasing as year of birth increases, with the greater increase occurring in the progeny of tough sires. This indicated that the response to selection should be expected to be diverging between progeny of selected sires over years if selection was effective. The greater magnitude of the selection differentials and cumulative selection differential in the progeny of sires selected for toughness indicates that the response to selection in this group would be expected to be higher than in the progeny of sire selected for tenderness assuming heritability and generation interval for both groups are the same.

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111 Table 4-1. Summary of average genetic parameters, selection differentials and generation intervals Items Tender sires Tough sires Realized heritability Selection differential (SD)/year: Cumulative SD (kg): Generation interval 0.32*.27 -0.93 a .13 -6.14 3.88.53 0.21 n.s .2 1.32 b .17 8.78 3.85.29 Variance components: 2 d 2 e h 2 d 0.34.17 0.85.08 0.40.18 abc Means with the same superscript in the same row are not significantly different at P<0.05. *(P<0.01) n.s (P>0.05) h 2 d is the estimate of heritability from ASREML analysis Heritability. Table 4-1 shows that the estimate of heritability for WBSF analyzed using ASREML was moderate (h 2 d =0.40.18). This estimate of heritability for direct effects is within the range of those reported from reviews in the literature (Marshall, 1994; Koots et al., 1994; Bertrand et al., 2001; Burrow et al., 2001). Most of reported estimates of heritability of WBSF have been based upon data from unselected population of purebred or crossbred cattle. However, the estimate of heritability in the current study was based on this selected population. The high estimate from the present study relative to the literature estimates may be due in part to the fact that this estimate came from a selected population. Van Dyk (2001) stated that genetic variance estimates from selected individuals can be quite different from those in the unselected base population. His study found that selection led to a substantial underestimation of variance components. However, the high estimate of direct heritability from the present study may indicate that selection for meat tenderness in this manner might be effective.

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112 -1.50.52.519941995199619971998199920002001Year of birthWBSF (kg) tough tender Figure 4-1. Selection differential by year of birth The realized heritability resulting from regressing response to selection on cumulative differential for progeny of tender sires was higher than that of tough sires (0.32 vs 0.21). These values are somewhat lower than the heritability for the entire population resulting from ASREML (h 2 d =0.40.18). These findings were similar to those reported by Holl and Robinson (2003) studying numbers of pigs born who found the realized heritabilities of two selected lines were different. Heritabilities calculated from the progeny of selected sire groups are only valid for the selected group and not for their base population. The differences in realized heritability between the tough and tender selections lines may have been due to differences in selection pressure applied to the two lines (Falconer and MacKay, 1996; Cameron, 1997; Bourdon, 2000). The magnitude of selection differential and cumulative selection differential for the progeny of sires selected for greater toughness was higher than those of tender sires; however, the selection response observed was greater in the progeny of tender sires than in the progeny of tough sires due to a higher realized heritability. The response to selection was expected to be higher in the progeny of tough sires similar to that reported in a study of two-way

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113 selection experiments in drosophila by Sanchez et al. (1999). They reported that increased selection pressure brings about greater selection response within the selected line; however, the observed response to selection of the current study was different. The observed response to selection of the progeny of tough sires was lower unexpectedly than the progeny of tender sires. There must have been factors other than selection pressure that caused the variation in the response to selection. In other words, the dissimilarity between the expected response and observed response to selection might be due to factors other than selection pressure, such as random genetic or environmental drift, because of small population size or other factors. However, the difference in the realized heritability between progeny of tough and tender sires may indicate there would be an asymmetrical pattern of responses as shown in Figure 4-3. -7101995199619971998199920002001Year of birthCSD (kg) tender tough Figure 4-2. Cumulative selection differential by year of birth Selection limits. Figure 4-3 shows the response means plotted against the cumulated selection differential where the slope of the regression lines measures their realized heritabilities. This figure provides clear evidence that the responses to selection are being influenced by other factors. In addition, Figure 4-3 shows that the response in one direction (the progeny of tender sires) falls short of expectation. The magnitudes of

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114 response to selection per year as selection differential increased were different between the progeny of two types of selected sires. There may have been biological limits that affected the progeny of tender sires meaning that as the WBSF generation means became increasingly lower (more tender) as cumulative selection differentials increased each year, that the population failed to continue to respond. Diminishing responses to selection have also been found in several other studies (Bunger and Herrendorfer, 1994; Bunger et al, 1994) who suggested that selection limits due to a reduction in genetic variance remaining in the population may have been responsible. 25024681CSD (kg)WBSF (kg) 0 tender tough Figure 4-3. Selection responses by CSD Generation interval. The average generation interval for sires and dams over the years of study are shown in Table 4-1. Generation intervals for the progeny of both tough and tender sires were similar, averaging 3.8 years for both. Therefore, the generation interval does not appear to be responsible for the difference in response to selection between the progeny of tender and tough sires.

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115 Phenotypic and Genetic Evaluations Least squares means for WBSF and its EBV for WBSF. The WBSF values ranged from 1.82 to 5.98 kg with a mean of 3.12 kg for the progeny of tender sires and from 1.91 to 7.41 kg with a mean of 3.54 kg for the progeny of tough sires. Coefficients of variation (c.v) for progeny of both types of sires were similar 28.81% and 24.04% for the progeny of tender and tough sires, respectively. The WBSF was affected by year of birth of calf (P<0.01). However, its EBV for WBSF was not affected by year of birth. Both WBSF and EBV for WBSF were affected by sire within selected group (P<0.01). There was no interaction between year of calf’s birth and sire group indicating that year effects were independent from sire group effects. This year effect occurred in spite of the fact that the same machine, and for the most part, the same individuals, conducted the evaluations each year. The year variability apparently might be due to other effects affecting tenderness, such as temperament stress of the animals similar to those of a study reported by Voisinet et al. (1997) and/or other uncontrolled factors, such as sampling error due to the small number of animal evaluated. In addition, Robinson et al. (2001) reported that fluctuation in WBSF values may reflect differences in handling preand post-slaughter procedures that caused difficulties in measuring tenderness consistently. Least squares means for annual WBSF and the EBV for WBSF for the progeny of sires selected each year are presented in Table 4-2. The least squares means for both WBSF values (3.55.12 vs 4.00.13 kg) and the EBV for WBSF (-0.25.05 vs 0.37.05 kg) across all years were lower (P<0.05) for the progeny of tender sires than those of the tough sires. There was approximately 41.17% improvement of the

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116 phenotypic means, respectively, resulting from selection after 7 years observation. These findings indicated that selection for tenderness can be effective. Table 4-2. Least squares means and standard error for WBSF and the EBV for WBSF in the progeny of tough and tender sire and base population WBSF (kg) EBV for WBSF (kg) Base-population Year Tough Tender Tough Tender WBSF EBV 1994 1995 1996 1997 1998 1999 2000 2001 4.86 a .23 3.60 c .27 3.47 d .30 2.85 e .21 3.54 f .19 3.10 h .22 4.00 b .23 3.22 c .30 3.42 d .19 2.69 e .21 2.71 g .22 2.80 h .20 0.42 i .09 0.51 k .10 0.41 m .11 0.34 p .08 0.37 r .07 0.22 t .08 -0.18 j .09 -0.42 l .12 -0.23 n .07 -0.22 q .08 -0.26 s .08 -0.25 u .08 4.760.12 4.820.13 0.040.04 -0.010.05 abc Means with the same superscript in the same row are not significant different at P<0.05. Phenotypic trend. Figure 4-4 presents the changes of phenotypic value (WBSF) within the progeny of selected sires by year among calves born from 1996 through 2001. At the beginning of selection experiment through 1998, the phenotypic trend showed the progeny of the tough sires increased whereas the progeny of tender sires decreased. However, after 1998, the WBSF values for the progeny of both tender and tough sires decreased. In 1998 and 1999 the WBSF values for the progeny in both selected groups were nearly the same. Overall, the WBSF values for progeny of both selected sire groups tended to decrease. Year effects contributed significantly to the means of WBSF. This year effect may also be due to environmental effects including management preand post-slaughter of the animals. The averages after seven years of the experiment indicate that there were differences in both WBSF and the direct breeding values between the progeny of tender and tough sires. Regressions of differences between the progeny of the tough and tender sire groups for WBSF on year of birth are presented in Table 4-3. The annual phenotypic changes of

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117 WBSF were -0.32 kg and -0.30 kg for progeny of the tender and tough selected sire groups, respectively. The phenotypic trend of WBSF values of both progeny of sired selected for tenderness and toughness were different from zero (P<0.01) indicating that the WBSF for progeny of both tough and tender sires were decreasing 0.30 kg and 0.32 kg per year, respectively. Therefore, selection for both meat tenderness and for toughness in this study resulted in a decreased WBSF. 2.5519941995199619971998199920002001Year of BirthWBSF (kg) tough tender Figure 4-4. The changes of WBSF by year of birth The means of response to selection per year of birth were confounded with any random drift which may have occurred each year. The response to selection and random drift are inseparable because there was only one group of progeny of within each selection line with no replication. The decline in response which occurred after 1998 was due also to a possible decline of selection differential or lower selection pressure as shown in figure 4-1. However, the fact that some daughters of selected sires were included as dams in each group in the later years should have increased the response. An explanation for the declining WBSF for progeny of tough sires, instead of the expected increase might be due to short duration of selection, random environmental effects, low selection pressure, sampling error and random genetic drift, since there were

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118 limited numbers of the bulls evaluated and it was not possible to collect freezable semen from all of the bulls prior to slaughter. Genetic trend. Figure 4-5 shows the genetic trends for the progeny of both selected sire groups. The EBV of WBSF began to diverge early in the experiment and continued to diverge at a steady rate. However, after 1998 the divergence decreased gradually. The genetic trend of sires selected for tenderness showed few genetic changes after 1998. The regression coefficients of annual changes of WBSF-EBV on year of birth presented in Table 4-3 were of similar magnitude for progeny of both selected sire groups. The estimates of genetic trend in the progeny of both tough and tender sires were not different from zero (P<0.05). However, these findings showed that the EBV of WBSF of progeny of tender sire tended to decrease 0.038 kg per year and those of tough sire tended to increase 0.033 kg per year. The declining EBV of WBSF in the progeny of tough sires after year 1997 was unexpected. However, the results of this study showed that genetic improvement of meat tenderness through selection using WBSF can be achieved. The genetic change in this study, however, was small. The declining breeding values (improved tenderness) of the progeny of sires selected for toughness and the lack of greater response to selection can be attributed to several effects such as genetic variance of WBSF, insufficient selection pressure, long generation intervals relative to the length of the study, and/or random drift due to small population size.

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119 Table 4-3. Summary of response to selection for WBSF Items Tender sires Tough sires Meanstd.deviation (kg) Coefficient variation (%) Range WBSF (kg) WBSF-EBV (kg) Year effects (MSE) WBSF (kg) WBSF-EBV (kg) Least square means: WBSF (kg) WBSF-EBV (kg) Annual trends: WBSF (kg/year) WBSF-EBV (kg/year) 3.12.75 24.04 1.82 5.98 -0.65 0.47 * n.s 3.55 a .12 -0.25 c .05 -0.32 f *.05 -0.038 n.s .018 3.54.02 28.81 1.91 7.41 0.41 1.51 * n.s 4.00 b .13 0.37 d .05 -0.30 f *.07 0.033 n.s .028 Divergence trend: WBSF (kg/year) WBSF-EBV (kg/year) 0.03 n.s .09 0.07 n.s .04 P-Values Year effects (Y) Selected sire group effects (S) Interactions Y*S WBSF ** * n.s WBSF-EBV n.s ** ** *(P<0.01) **(P<0.0001) n.s(P>0.05) n.s: non significant abc Means with the same superscript in the same row are not significantly different at P<0.05. Divergence of response. The divergence between the yearly means of progeny of sires selected for tenderness and toughness for WBSF is presented in Figure 4-6 and shows fluctuation in the divergence for WBSF between the selection groups. The annual divergence of selection response for WBSF was 0.03 kg per year. The regression coefficients of the trends were not different from zero (P>0.05). The divergence increased at the beginning of selection experimental until 1997 and then decreased dramatically.

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120 -0.60.619941995199619971998199920002001Year of birthEBV (kg) tough tender Figure 4-5. The changes of WBSF-EBV by year of birth The overall trends from these figures indicate that the divergence in meat tenderness between the progeny of selected tough and tender sires declined over the years of the study. The declining divergence between the two selected groups occurred mostly due to the unexpected decrease of WBSF for the progeny of the tough sires as showing in Figure 4-2. There was no control group in this study, therefore, each selected group can be served as a control for the other and the response is measured as the divergence between two selected groups. In the absence of environmental differences, the magnitude of the response to selection for both selected groups is expected to be the same; therefore, the divergence between the generation means of two selected groups is expected to increase. An explanation of the decline in the divergence between the upward selection group and downward selection group can not be explained.

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121 00.91.81995199619971998199920002001Year of birthWBSF (kg) Figure 4-6. Differences between the yearly means of the progeny of tough and tender sires Implications Selection for meat tenderness can be effective. The average for WBSF can be reduced through selection. Divergent selection for meat tenderness in Angus cattle demonstrated that the use of WBSF values in selection did result in lowered WBSF values, but unexpected results occurred possibly because of the low selection pressure that was possible to be applied. Therefore, the number of animals measured in a selection program should be greater than those used in the current study. Overall, these findings have demonstrated the potential effectiveness of direct selection using WBSF as a tool to genetically improve beef tenderness and selection for WBSF might be possible since the heritability of WBSF was estimated to be relatively high (h d 2 =0.40 0.18). Summary A study of direct response of divergent selection for meat tenderness using Warner-Bratzler Shear Force (WBSF) values of Angus bulls was conducted at the Santa Fe Beef and Research Unit of the University of Florida beginning in 1994. After reaching puberty at 13 and 14 month of age, about 30-35 bulls per year were semen-collected, then

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122 slaughtered and evaluated for their WBSF values. Semen from the three or four of the bulls with the lowest and highest WBSF values was selected for use each year. WBSF data from a total of 299 bulls born from 1994 through 2001 were analyzed. Analyses were performed to estimate variance and covariance components using ASREML with a single trait-animal model including year of birth and sire group (tender and tough) as fixed effects and animal as a random effect along with the use of pedigree information in the relationship matrix. Genetic trends were plotted as averages of breeding values, estimated by the solution of animal model equations for year of birth, and overall trend was estimated as a regression of all breeding values on year of birth. WBSF and EBV (estimated breeding values) for WBSF were analyzed using SAS mixed models procedures (Littell et al., 1996) including year of birth, selection group (tough vs tender) and their interactions. The average WBSF values (3.55 vs 4.00 kg) and the EBV for WBSF (-0.25 vs 0.37 kg) of progeny of tender sires across years of study were lower (P<0.01) than those of tough sires. The phenotypic (WBSF) trends decreased 0.32 kg and 0.30 kg per year, respectively for the progeny of tender and tough sires (P<0.01). The genetic trends (EBV for WBSF) tend to decrease 0.04 kg per year for the progeny of tender sires and increase 0.03 kg per year for the progeny of tough sires. The estimate of direct heritability analyzed using ASREML from this selected population was moderate to high (h d 2 =0.40.18). Realized heritabilities obtained from the regression of selection response (WBSF) on cumulative selection differentials were 0.32.27 and 0.21.20 for progeny of tender and tough sires, respectively. An unexpected result was that the phenotypic values for WBSF values declined in both the progeny of sires selected as tough and those selected as tender. An explanation for the declining WBSF for progeny

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123 of tough sires, instead of the expected increase might be due to short duration of selection, random environmental effects, low selection pressure, sampling error and random genetic drift, since there were limited numbers of the bulls evaluated and it was not possible to collect freezable semen from all of the bulls prior to slaughter.

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CHAPTER 5 REPRODUCTIVE PERFORMANCE FOLLOWING ESTROUS SYNCHRONIZATION OF ANGUS, BRAHMAN AND ANGUS X BRAHMAN CROSSBRED COWS Introduction It is widely accepted that reproductive performance of females is the most important factor affecting the efficiency of beef cow-herds (Dickerson, 1970; Willham, 1973; Wiltbank, 1994; Melton, 1995). Improvement in cow productivity, including reproductive performance, can come through both non-genetic and genetic means including crossbreeding (Lasley, 1987). Cow productivity measures the output of the beef production cycle and the most important output of a cow-calf system is weaned calves. Cow productivity is a complex trait, controlled by the composite effects of cow fertility, calf survival and calf weaning weight (Mwansa et al., 2002). Increasing overall cow productivity may be possible by shortening the breeding season and thus the calving season. Generally, a shorter calving season is recommended to allow for more effective management of the cowherd. In addition to a shorter calving season, a shorter gestation length may influence reproductive success and needs to be included in selection indices (Amer et al, 1996, 2001). Several studies have shown that the failure to conceive at the end of the breeding season (pregnancy rate) is the largest factor affecting cow productivity (Bellow and Short, 1994; Wiltbank, 1994). They concluded that the limiting factors affecting the failure of females to become pregnant included the absence of estrus expression, lower 124

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125 fertilization rates, and anestrous postpartum in addition to diseases and reproductive tract abnormalities. Currently there are many management tools and practices to increase reproductive efficiency, one of which is estrous synchronization. The main objective of estrous synchronization is to bring a high percentage of females into estrus at the same time and to optimize the use of artificial insemination (AI) which, in turn, facilitates the use of genetically superior sires (Odde, 1990). Moreover, estrous synchronization may also enhance reproductive efficiency by allowing for a shortened breeding and calving season which, in turn, may result in heavier calves at weaning (Dunn and Kaltenbach, 1980). Estrous synchronization and artificial insemination have been used by the beef cattle industry but, in spite of beneficial economic results, they are not used extensively (Odde and Holland, 1994). Estrus detection is a problem that limits the use of AI, especially in extensive range systems. Timed artificial insemination (timed-AI) has been recommended as a method to avoid estrus detection, particularly for females that have a shorter and less intense estrous expression, such as Brahman (Plasse et al., 1970; Randel, 1994, Rae et al., 1999). Bos indicus breeds of cattle are used widely in many subtropical areas and tropical areas of the world because of their adaptive traits that make them very useful in such areas (Koger, 1963; Cartwright, 1980; Turner, 1980). Some advantages of Bos indicus germplasm relative to Bos taurus are greater heat tolerance (Carvalho et al., 1995; Hammond et al., 1996), and greater resistance to parasites (Fourie and Kok, 1995; Bock et al., 1997) and viral infections (Jimenez et al., 1995; Soeharsono et al., 1995). Bos indicus x Bos taurus crosses express greater heterosis effects for direct and maternal traits

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126 (Peacock et al., 1982), improved maternal calving ease (Olson et al., 1993) and neonatal survival rate (Peacock and Koger, 1980) than do crosses among Bos taurus breeds. The Brahman breed, in particular, has been used to develop Bos indicus influenced breeds and the utilization of cattle with different percentages of Bos indicus breeding for beef production is widespread throughout the world. Despite their desirable characteristics, some concerns regarding the Brahman have been raised, especially their low reproductive rate while lactating (Randel, 1994). Numerous studies have been conducted to evaluate the effects of estrous synchronization on reproductive performance of beef cattle of various Bos taurus breeds, and there have been a few studies involving cows with Bos indicus breeding. In particular, at present, little data are available comparing the reproductive performance of Brahman, Brahman crossbred and British breed cows subjected to estrus synchronization and timed-artificial insemination under similar conditions. Therefore, the purpose of this study is to evaluate the effects of percentage of Brahman breeding on reproductive performance expressed as: incidence of synchronized estrus, pregnancy rate after first synchronization and timed-insemination, calving rate to synchronization and timed-insemination, and gestation length. This study is important in understanding whether the percentage of Brahman breeding in cows influences their response to estrous synchronization. Materials and Methods Source of Data The study was conducted from 1991 through 1997 at the Beef Research Unit of the University of Florida located 13 miles northeast of Gainesville, Florida. Data were collected from females of known percentage of Angus (A) and Brahman (B) breeding.

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127 There were 564 heifers and 1,257 cows consisting of 276 Angus, 250 75%:A 25% B, 422 50% A:50% B, 277 25% A:75% B, 387 Brahman and 209 Brangus (62.5% A:37.5% B) females which were assigned cow group numbers of 1, 2, 3, 4, 5, and 6, respectively. Data were pooled over years to create a larger data set that may allow a more accurate examination of the effect of percentage of Brahman breeding on reproductive performance. Animal Management Feeding system. Cows and heifers grazed on bahiagrass (Paspalum notatum) pastures throughout the year along with mineral supplementation. During the winter month (from December through March), cows were supplemented with bermudagrass (Cynodon dactylon) hay along with molasses and urea (Odenya et al., 1992). Synchronization of estrus. Before estrous synchroniziation, calves were removed from their cows for 48 hours. Cows were estrous synchronized in the end of February through April with Synchromate-B (Rhone Merieux, Inc. Athens, Georgia). Brahman cows that were estrous synchronized in May were excluded from analyses due to confounding of breed type with month of synchronization. Heifers were synchronized 2 weeks earlier than cows. All cows that had calved less than 45 days before the synchronization dates were assigned to a later synchronization group (Elzo and Wakeman, 1998). To produce a synchronized estrus, all females were treated with an estradiol/progestin injection with 2 ml of 3.0 mg norgestomet and 5.0 mg estradiol valerate followed immediately by insertion of a progestin implant containing 6.0 mg norgestomet; this implant (SMB) was placed under the skin of the ear and was removed after 9 days (Rae et al., 1999).

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128 Mating system. All females were inseminated between 8 and 16 hours after their first detected estrus. Timed-insemination was performed at 48 to 54 hours (2 days) after the implant was removed on the rest of females that had not shown estrus by 48 hours post-removal. The date and time of insemination were recorded. All females were monitored for signs of estrus for 25 days following insemination. All females showing estrus were then inseminated again. All females were bred to the same AI bulls from the same breed groups as used for clean up bulls (Elzo et al., 1998; Rae et al., 1999). It should be emphasized that sires of all breed groups that were used as AI sires, cleanup sires, or as AI and cleanup sires in a breeding season were mated to cows of all breed groups (A, AB crossbred, and B cows). All females were artificially inseminated twice and then assigned to one of six cleanup herds and exposed to bulls via natural service for the subsequent 35 d of the 60-d breeding period. The insemination dates and cleanup periods overlapped. Semen was either donated or purchased from AI organizations. Also, semen was collected and used from some of the cleanup bulls. The cleanup bulls was either donated or purchased from cooperating cattle producers, but some of them were produced from within this research herd. Sires were used for two years breeding seasons between two and five sires per breed group per year were used (Elzo et al., 1998). Calves were born from late December to March, and weaned in September and October of each year. Body condition. The body condition score (BCS) of all females was recorded at the end of May (approximately the end of the breeding season) and in August each year at the time of pregnancy testing and weaning of calves. The BCS was originally assessed

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129 on scale ranging from 1 (emaciated) to 9 (obese) using a system adapted from Herd and Sprott (1986). Data Collection Variables that were calculated based on observational data from heifers and cows included estrus rate, pregnancy rate, calving rate, and gestation length. Estrous detection. Signs of estrus behavior were expected within 12 to 36 hrs after implant removal. All females were observed visually for signs of standing estrus twice daily; morning and evening, for a minimum of 15 minutes per observation and also were evaluated for signs of estrus through use of using tail painting at the time of implant removal. Cows were considered to be in estrus when the tail paint was at least half removed, indicating they have been mounted by other cows. The absence of paint on a particular day indicated that a cow was either in estrus on day-1, by the time implant was removed; on day-2, the following day, or on day-3, when an inseminator did timed-AI on all cows that had not previously shown estrus. Cows with intact tail paint were considered not to have stood to be mounted. Some cows in 1995, 1996 and 1997 also were monitored with the HeatWatch (Ddx Inc., Boulder, Colorado) system, about 200 cows over the three years (Rae et al., 1999, 2002). The numbers of females which exhibited estrus via HeatWatch or tail paint detection were not differentiated. Estrous rate. Estrous rate is the percentage of females in estrus which was calculated as the number of females detected in estrus before or at timed-AI divided by the number females treated with SMB. The data for percentage of females in estrus were divided into four groups: females in estrus within 12-24 hours (day-1), at 24-48 hours (day-2), at 48-54 hours (timed-AI) and total of females in estrus less than 3 days after implant removal.

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130 Pregnancy determination. Cows and heifers were assumed to be pregnant if they did not show estrus within 25 days after insemination following their first synchronization and timed-AI. Pregnancy rate is percentage of those females that were considered pregnant divided by the total number of females given treatments. Calving rate. Calving rate to first timed-AI was defined as the percentage of females that calved as a consequence of conception to first synchronized estrus and (or) timed-AI and was calculated as number of females that calved divided by the total number females that were treated. Gestation length. Gestation length was available only on progeny that resulted from AI matings. Gestation length was calculated as the difference between the calving date and the breeding date that resulted in conception and was calculated as the number of days between calving date and the date of first AI. Description of Data Table 5-1 presents the number of females used by year and percentage Brahman. Group 3 (50% A:50% B) had the largest numbers and group 6 (Brangus) had the smallest numbers over the years of study. In year 1991, group 2 (75% A:25% B) and 4 (25% A:75% B) had the smallest number of females. The total numbers of animal within percentage Brahman group varied from 206 to 415 animals. Moreover, the variation of total numbers of animal within group per year ranged from 16 to 70 animals. The heifers percentage of the females used in this study that were in the younger age groups, 3 and 4 years old and 2 years old were 38.6% and 32.0% of the total cows, respectively. In group 4 (25% A:75% B) there were only a small number of cows aged 7 or more and group 3 (50% A:50% B) had a higher proportion of cows aged 5 and 6 years of age.

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131 Most females in the study had good body condition scores ranging between 5 and 6 (69.85%). About 77 of all cows had no body condition score available and were excluded from the analyses. There were only 6 cows with BCS greater than or equal to 3 among the Brangus females. Table 5-1. Summary of number of cows per breed group by year of breeding, age of cow and body condition score Variables Group1 Group2 Group3 Group4 Group5 Group6 total Year 1991 1992 1993 1994 1995 1996 1997 Total 35 32 27 43 35 43 55 270 16 28 27 45 44 45 40 245 49 54 54 57 63 70 68 415 16 33 35 40 49 58 40 271 54 38 37 33 45 52 43 302 34 27 26 35 28 22 34 206 204 212 206 253 264 290 280 1709 AOC 2 years 3-4 years 5-6 years 7 years 73 99 38 60 97 88 32 28 123 154 69 69 105 120 39 7 90 110 55 47 59 89 41 17 547 660 274 228 BCS 4 (thin) 5-6 7 (fat) 67 176 9 41 175 23 75 275 52 40 192 27 37 234 23 54 134 3 314 1186 137 Group 1 (Angus), 2 (25%A:75%B), 3 (50%A:50%B), 4 (25%A:75%B), 5 (Brahman), and 6 (37.5%A:62.5%B); BCS: Body condition score; AOC: Age of cow The variation in numbers of females between breed groups of cows in each year, in body condition score and in age of cow could have influenced their response to estrous synchronization as expressed in estrus rate, pregnancy rate and calving rate but it is hoped that the statistical procedures that were utilized served to minimize these effects.

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132 Data Analyses Data were analyzed with the PROC GENMOD procedure of SAS (SAS Inst., Inc., Cary, NC), a procedure specifically used to analyze proportional data. Pair-wise comparisons among the fixed effects were obtained using DIFF option from the Least Squares Means procedure in PROC GENMOD (SAS, 2000). The model included the fixed effects of year of breeding (), age of cow (), proportion of Brahman in the cow (), body conditions() and breed type of sire () with days postpartum () being included as a covariant as well as all two-way interactions (I) and random errors (e). Cows were grouped into 4 levels by age: 2 years of age (first exposure) heifers, 3-4 year old cows (first or second calving), 5-6 year old cows (mature), and cows 7 years old and older. Body condition scores were grouped into 3 level scores: less than or equal to 4 (thin) cows, 5-6 (medium) cows and 7 or more than 7 (fat). Days postpartum were calculated as the difference between calving date and implant date. The mathematical model used in analyses is: Y jklmns = + j + k + l + c m + n + s + I + e jklmns, where was the overall mean and Y was estrus rate, pregnancy rate, calving rate and gestation length. Gestation length was analyzed with PROC Mixed procedures (SAS, 2000) using a model which included year of breeding, proportion Brahman in the cow, age of cow, body condition score, and two-way interactions using the same mathematical model above. Results and Discussion Response to Estrous Synchronization across Main Effects Table 5-2 shows the average estrus, pregnancy and calving rates were 77.2%, 67.6% and 62.3%, respectively. Estrus rate on day-1 (20.3%) was lower than day-2

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133 (27.7%) and day-3 (29.2%). Clearly estrus rate increases (P<0.05) as days after implant removal increase. Some studies have reported that the percentage of cows exhibiting estrus on day-1 and day-2 was as low as 15% (Pursley et al., 1995; Schmitt et al., 1996; Moreira-Vianna et al., 2000). There were total of 28.8% cows and heifers that did not exhibit observed sign of observed by the timed-AI date, however, they were also inseminated. These heifers and cows may have been either anestrous or have had silent heat, therefore some of them may have become pregnant in spite of lack of observed estrus. The difference between percentage of cows and heifers showing estrus and the percentage determined to be pregnant, about 10%, might be due to the fact that some females showed signs of estrus in the absence of active follicles capable of ovulation as was reported by Galina et al (1996). The responses to SMB treatments in this study might have been affected by stage of the estrous cycle at the initiation of the regimen. Studies have reported that heifers implanted on different days of the estrous cycle resulted in different responses to synchronization in turn of estrus, pregnancy and calving rates (Pratt et al., 1991; Mathis et al., 2001). However, the stage of cycle of estrus in each animal was not included as a source of variation in the current study due to lack of knowledge of this information. Moreover, in this study, estrus was primarily determined using behavior observation and tail-paint evidence as evaluated by observers. Cows were assumed to be in estrus based on standing to be mounted or by the tail paint being rubbed off. Although Kerr and McCaughey (1984) found the tail paint method to be 88% accurate in detection of estrous animals, the cows assumed to be in estrus in this study were not crosschecked by progesterone assay or any other method.

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134 The percent calving was less than the percent pregnant which, in turn, was less than the percent showing estrus. The discrepancy between the pregnancy and calving rate might be due to the fact that cows that were actually open failed to show subsequent heats or to spontaneous abortions. Another subject of concern is the method of determination of pregnancy rate; in this study cows were only determined to be pregnant when they did not exhibit estrus within 25 days after insemination following their first synchronization and timed-AI. Although a pregnancy check was conducted, the pregnancy rate resulting from those observations were not included in this study. The average estrus rate of this study was similar to that reported in a study with Hereford cows treated with SMB and bred based on observed estrus with a resulting conception rate of 65% and a pregnancy rate of 57% (Seguin, 1999), while Odde (1990) reported that 92.5% were observed in estrus within 5 days after removal of SMB. The rates of responses of estrous synchronization in present study were higher than those from the literature using other regiments (i.e. CIDR, MGA, PMSG, PRID and PGF 2 ), with the average percentage of cows exhibiting estrus ranging from 57-65%, pregnancy rates between 40-60% following fixed time insemination, and calving rates averaging 38%-44% (Kerr et al., 1991; Stevenson et al, 2003; Perry et al., 2004). Overall, the current study has showed that responses of estrous synchronization using SMB were acceptable, even tough there was no control group to compare. Results were in general agreement with those reported by Rentfrow et al. (1987) and Brown et al. (1988), with a high percentage of cows showing estrus after treatment and exhibiting estrus during a short period following implant removal (Solano et al., 2000). In addition,

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135 this study has shown that tail paint-marker in addition to signs of standing estrus can be a practical means for estrous detection. Table 5-2. Means of estrus, pregnancy and calving rate by year of cow and body condition Estrus Item Day-1 Day-2 Day-3 Total Pregnancy Calving Year 1991 1992 1993 1994 1995 1996 1997 N 204 212 206 253 264 290 280 *** 18.6*2.7 31.1*3.2 7.8*1.9 25.7*2.7 24.2*2.6 20.3*2.3 13.9*2.0 *** 56.4*3.4 45.3*3.4 11.1*0.9 14.2*2.2 12.9*2.0 34.5* 2.8 24.6*2.5 *** 5.9*1.6 17.0*2.5 29.1*3.1 36.4*3.0 28.4*2.7 43.4*2.9 35.3*2.8 *** 80.9*2.7 93.4*1.7 48.0*3.5 76.3*2.7 65.5*2.9 98.2*0.7 73.9*2.6 *** 67.6*3.2 78.3*2.8 70.9*3.1 68.8*2.8 53.0*3.1 61.3*2.9 76.1*2.6 *** 57.7*3.6 72.9*3.0 68.9*3.2 64.0*2.9 50.0*3.2 52.5*2.9 73.3*2.6 AOC 2 3-4 5-6 7 N 547 660 274 228 ** 29.2 a .9 13.2 b .3 16.4 b .2 24.1 c .8 ** 26.5 ab .9 31.5 b .8 25.5 ab .6 21.9 a .7 n.s 25.8 a .8 31.8 a .8 29.9 a .8 29.4 a .0 n.s 81.5 a .7 76.5 ab .6 71.9 b .7 75.4 ab .8 * 61.8 a .1 68.5 b .8 76.2 c .5 68.4 b .1 n.s 58.4 a .1 61.9 a .9 71.3 b .7 62.8 a .2 BCS 3-4 5-6 7 N 314 1186 137 * 12.4 a .8 20.1 b .1 30.6 c .9 n.s 28.3 a .5 27.7 a .3 27.0 a .7 n.s 28.6 a .5 29.8 a .3 32.1 a .1 * 69.4 a .6 77.6 b .2 89.7 c .0 * 54.7 a .2 68.4 b .3 74.5 c .4 * 51.1 a .3 63.7 b .4 67.3 b .6 PPD (linear) n.s ** * n.s n.s n.s Y*BT * * * * * * *P<0.01 n.s P<0.05 AOC: age of cow; BCS: Body condition scores BT: Breed type of cow; PPD: post partum days; Y: years of breeding abc Means with the same superscript in the same column are not significant different at P<0.05. Non-genetic Effects Year effects. Table 5-2 shows the least squares means of estrous, pregnancy and calving rates and gestation length by year. Year affected the percentage of cows showing estrus across day-1, day-2 and day-3 (timed-AI), estrus rate, pregnancy rate and calving rate (P<0.01). In 1993, the estrous rate was the lowest (P<0.05); however, the pregnancy and calving rates were not the lowest across the years and were higher than the estrus rate for year. The higher pregnancy rate than estrus rate in 1993 might be due to the fact that some cows did not exhibit estrus prior to timed-AI date, however, they did become

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136 pregnant. In 1996, estrous rate was higher (P<0.05), however, pregnancy and calving rate in the same year were lower (P<0.05). Pregnancy rate and calving rates were the lowest in 1995, however, there is no available information to explain the causative factors. Since the data of present study were pooled over several years, nutritional and temperature variation and other source of variation among years may have caused random changes in the responses over the years. However, the year effects on the responses in the present study are in agreement with those of the literature where seasonal and climatic changes among years influenced gestation length and estrous behavior of Bos taurus and Bos indicus females (Galina and Arthur, 1990; Wilson et al., 1998; Landaeta-Hernandez et al., 2002; Goyache et al., 2002). Moreover, seasonal effects on estrus could have been due to climatic effects that changed over the years that may have affected the willingness of herdmates to mount other cows, rather than to the physiological effect of temperature on the cow in estrus as reported by White et al. (2002). Changes in seasonal rainfall among years may also have affected the availability of quantity and quality of forages that can also influence fertility in agreement with studies reported in the literature (Thorpe and Cruickshank, 1980). Significant year effects on calving rate of Africander cross and Nguni cows were reported to be due to differences between years in the quantity and quality of forage available which, in turn, was related to the previous month’s rainfall (Butterworth, 1983). Age of cow. Table 5-2 showed that age of cow affected the percentage of cows in estrus on day-1, day-2, and pregnancy rate (P<0.01) but on estrus rate (P=0.15) and calving rate (P=0.07). The 2 year-old heifers exhibited the highest percentage estrus on day-1 of all other age groups (P<0.05). The cows aged 7 and older showed the lowest

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137 estrus rate on day-2 in agreement with Lensts et al. (2000) and Ciccioli et al. (2001). The percentage of 5 and 6 year old cows determined to be pregnant was greater than that showing estrus indicating that some cows had silent heats. Figure 5-1 shows the effect of cow age on percentage of cows in estrus on day-1, day-2 and day-3 following implant removal. The estrous rates of 2-year old cows and cows 7 years and older on day-1 were higher (P<0.05) than those of the 3, 4, 5 and 6-year old cows. Estrus rate on day-2 increased from heifers to cow aged 3-4 years then decreased. However, the pattern estrous rate on day-2 and day-3 from heifers to older cow groups were not clear. 03523&45&67 Age of CowResponse (%) day-1 day-2 day-3 Figure 5-1.Estrus rate on day-1, day-2 and day-3 by cow age Figure 5-2 shows the percentage of all responses (estrus, pregnancy and calving) following synchronization by age of cow. The estrous rate tended to decrease as age of cow increased; heifers tended to have a higher estrous rate. The pregnancy rate and calving rate have the same trend in that they increased as age of cow increased from 2 years old to 3-4 years old, became stable at 6 years and then decreased in older cows. The 2 year-old heifers were more responsive to estrous synchronization than older cows might

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138 be due to no experience of lactation; however, they showed the lowest pregnancy rate (P<0.05) than older cows. The results of the current study were similar to those of Newman et al. (1993) who reported that age of cow affected pregnancy rates, and that older cows tended to express higher pregnancy rates (Anderson and Plum, 1965; Goyache et al., 2002). These results also are in agreement with several studies that reported that pregnancy rate increased from younger heifers up to 7 year old cows and then decreased which might be due to lactation stress on body condition as a cow ages (Tagegne et al., 1989; Osoro and Wright, 1992; Seguin, 1999). 408523&45&67 Age of Cow (years)Response (%) estrus pregnancy calving Figure 5-2. Estrous, pregnancy and calving rate by age of cow Body condition effects. Table 5-2 and Figure 5-3 illustrate the percentage of cows showing estrus on day-1 to day-3 by body condition score. Body condition affected estrus rate on day-1 (P<0.01), but not on day 2 (P=0.06) or day-3 (P=0.52). There was a positive trend on day-1 estrous rates as body condition increased (P<0.05), while the estrus rate on day-2 and day-3 were similar among the body condition score groups. Therefore, when a cow is in good body condition, she is expected to be more responsive to estrous synchronization and exhibit estrus signs earlier. In practice, cows in good body condition

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139 can be inseminated earlier which may shorten the breeding season. A decrease in estrous rate of beef cows in poor body condition at or near the start of an estrous synchronization protocol has also been well documented (Odde, 1990; Yelich et al., 1995; Stevenson et al., 2000). 035 45&6 7Body condition scoreResponse (%) day-1 day-2 day-3 Figure 5-3. Estrus rate on day-1, day-2 and day-3 by body condition Figure 5-4 presents a graph of estrus, pregnancy and calving rate by body condition score. Body condition scores affected estrus, pregnancy and calving rate (P<0.05). The estrus, pregnancy and calving rate have similar positive trends indicating reproductive performance increased as body condition increased. These findings were similar to most studies in literature (Rae et al., 1993; Kunkle et al., 1994; Vizcarra et al., 1998). The pattern of increased estrus rate was linear and dramatically. Calving rate increased dramatically from cows with poor body condition (BCS 4) to body condition score 5 and 6 and then increased gradually as cows become fatter still. In general, the trend of pregnancy rates by body condition score in this study is similar to that found in several studies where a positive correlation between body condition scores and pregnancy rates was observed (Osoro and Wright, 1992; Spitzer et al., 1995; Morrison et al., 1999).

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140 30100 45&6 7Body condition scoreResponse (%) estrus pregnancy calving Figure 5-4. Estrus, pregnancy and calving rate by body condition Post-partum date effects. The calving date to timed-AI date (days post partum) did not affect estrus, pregnancy and calving rate (P>0.05). The calving date to timed-AI may be related to the stage of estrous cycle in cows. Most studies in literature indicated that stage of estrous cycle affected estrus rate (Pratt et al., 1991; Mathis et al., 2001). Kesler and Favero (1996) reported that SMB is only effective in beef females which are not in metestrus, approximately 85% of a herd at a given time. However, it needs to be remembered that in this study stage of estrous cycle was not observed. Genetic Effects Estrus rates. Table 5-3 and Figure 5-5 present the average estrus, pregnancy and calving rates by breed groups of cows across years. Estrus rate was affected by breed type of cow (P<0.05). The estrus rates of Brahman and Brangus cows were lower (P<0.05) than those of all other breed groups, while 50% A:50% B and 25% A:75% B cows were higher (P<0.05) than all other groups, but similar to Angus cows. The lower estrous rates in Brahman cows and cows with a higher percentage Brahman breeding might be due to their shorter duration and less intensity of estrus as has been reported in the literature

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141 (Plasse et al., 1970; Galina et al., 1994; Landaeta-Hernandez et al., 2002). The lower percentage of Brahman cows exhibiting estrus might also have been due to the fact that some Brahman cows had silent heats, that is, without visible signs as reported by Plasse et al. (1970) who found 26% of Brahman in Florida showed silent heats. 0400%25%37.550%75%100%Percentage of Brahman breedingResponse (%) day-1 day-2 day-3 Figure 5-5. Estrus rates in different days by breed type of cows In general, the numbers of cows exhibiting estrus on day-1, day-2 or day-3 were similar across all breed types of cow. However, more females showed signs of estrus on day-3 than on day-1 and day-2. Few Brahman cows exhibited day-1 estrus (P<0.05). This supports Landaeta-Hernandez et al. (2002) who reported that Brahman cows expressed synchronized estrus later than Angus, and that the percentage of Brahman cows that exhibited estrus on day-2 and day-3 was larger than those of other breed groups of cows. Group 3 (50% A:50% B) and 4 (25% A:75% B) females tended to have higher estrous rate (P<0.05). This might be due to maternal heterosis effects as reported by Cundiff et al. (1994) and Cundiff and Gregory (1999) who found the higher maternal heterosis in Bos indicus x Bos taurus F 1 cows compared to their purebred parents.

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142 The influence on estrus rate due to increasing the percentage of Brahman relative to that of Angus breeding in this study was not clear. Although Brahman cows tended to have lower estrous rates, increasing the percentage of Brahman breeding did not lower estrous rates. Mattoni and Ouedraogo (2000) and Tagegne et al. (1989) reported that the estrus response rate was not different between Bos indicus and Bos taurus x Bos indicus heifers or cows using PGF2, PRID and SMB. In general, estrous rates of Brahman cows were shown to be lower than those of Angus and that of 50% A:50% B cows was higher than those of both the Angus and Brahman. The lower reproductive rate in Brahman cows in this study was supported by Randel (1994) who concluded that Brahman cows have lower reproductive rates as compared with Bos taurus cows. However, Brahman x Angus crossbred cows were shown to have a higher estrous rate compared to Angus and Brahman cows (purebred parents), indicating heterosis effects. Pregnancy rates. Breed type of cow had no effect on pregnancy rate (P>0.05) similar to those reported by Koch et al. (1994) and Rae et al. (2001) who found that pregnancy rate between breed groups did not differ. Breed type of sire did not affect pregnancy and calving rate (P>0.05). The pregnancy rates of 50% A:50% B and 25% A:75% B cows were higher (P<0.05) than those of all other groups which had similar pregnancy rates. Most studies found lower reproductive rates in Brahman females, however, the findings of the current study have shown that Brahman females had similar pregnancy rates to Angus and their crossbreds. Other studies have reported that synchronized pregnancy rates using GnRH and PGF 2 appear to be greater in Bos taurus cattle (Geary and Whittier, 1998; Stevenson et al., 2000) compared with those of Bos indicus x Bos taurus females due to the less responsive corpus luteum (CL) of the Bos

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143 indicus x Bos taurus female to PGF 2 of the Bos taurus female (Lemaster et al., 2001). The dissimilarity the current and previous study might be due primarily to different estrous synchronization regimens. Table 5-3. Means of estrus rate, pregnancy rate, calving rate and by breed type Estrus rate (%) Pregnancy (%) Calving Rate (%) Item Day-1 Day-2 Day-3 Total BT 100%A:0%B 75%A 25%B: 62%A:38%B 50%A:50%B 25%A:75%B 0%A:100%B N 270 245 206 415 271 302 n.s 20.7 a .4 23.7 a .7 21.3 a .9 20.7 a .9 22.5 a .5 13.9 b .0 n.s 30.7 b .8 24.9 a .7 25.2 ab .0 28.2 b .2 23.6 a .5 31.8 b .7 ** 27.8 b .7 27.3 b .8 21.3 a .9 32.3 bc .3 36.2 c .9 27.1 b .5 ** 79.2 a .4 75.9 ab .7 68.0 b .3 81.2 a .9 82.3 c .3 72.8 b .5 ** 65.5 a .9 66.5 a .3 62.4 a .0 71.8 b .2 72.5 b .6 63.1 a .9 ** 60.0 a .0 57.0 a .0 59.6 a .4 67.9 b .3 67.6 b .7 56.8 a .2 Sire n.s n.s *P<0.05 n.s P<0.05 BT: Breed Type of cows; PPD: post partum days Y: years abc Means with the same superscript in the same column are not significant different Figure 5-6 demonstrates pregnancy rate among breed types of cow. These findings indicated that the pregnancy rate resulting from estrous synchronization using SMB and following timed-insemination is similar among Angus, Brahman and Angus x Brahman crossbred cows under the same sub tropical conditions. However, the pattern of the percentage of Brahman breeding on pregnancy rate was not clear. 0900%25%37.50%50%75%100%Precentage of Brahman breedingResponse (%) estrus pregnancy calving Figure 5-6. Reproductive performance by breed type of cows Calving rates. Breed type of cows had a significant effect on calving rate (P<0.05) as shown in Table 5-3. The 50%A:50%B and 25%A:75%B cows had higher calving rates

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144 (P<0.05) than other breed types of cows indicating that heterosis effects might be present. These findings have shown that Brahman females had a similar calving rate to that of Angus and Angus x Brahman crossbred cows other than 50%A:50%B cows, dissimilar to other studies that found lower calving rates in Brahman cows. These results suggest that crossbreeding Angus with Brahman showed increase pregnancy rate via heterosis effects. Brahman cows in present study were synchronized and bred in the spring (March-April) this may have affected their fertility of as Brahman cows are long day breeders (Jochle, 1972; Neuendorff et al., 1984) and thus are more affected by season than Bos taurus females. In spite of this possible effect, the estrous synchronization using SMB following timed-AI resulted in comparable reproductive rate of Bos indicus (Brahman) to Bos taurus (Angus) and crossbred cows, similar to study reported by Rae et al. (1999). Figure 5-6 demonstrates that the pattern of calving rate as the percentage Brahman breeding increased was not clear. This finding is not in agreement with some studies in the literature that reported lower reproductive rate in Brahman cows (Randel, 1994) and indicated that the higher the percentage of Brahman breeding in cows, the lower the reproductive performance will be. This study used pooled data over 7 years under similar conditions collected from cows treated with SMB following estrous synchronization. Different procedures in other studies may have influenced their results. Overall, this finding shows that under good management, Brahman reproductive performance may be comparable to that of Bos taurus (Angus) and their crossbreds. Moreover, the importance of introducing Brahman breeding via crossbreeding in subtropical areas to improve adaptability and utilize heterosis to improve reproductive performance female is evident.

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145 Interactions All possible two-way interactions were included in the initial model. However, preliminary analyses showed that year x breed type of cow was the only two-way interaction that was significant (P<0.05). Therefore, only year x breed type of cow was included in the final model. The interaction year of breeding by breed type of cow had effects on estrus, pregnancy and calving rate (P<0.05). An explanation is difficult to offer but the breed types may have responded differently to differences in nutrition across the years. Table 5-4 demonstrates estrous rate by cow breed group and year of breeding. The estrus rate ranged from 26% (lowest) for 75%A:25%B cows in 1993 to more than 97% for all breed types of cows in 1996. The pregnancy rate ranged from 35.1% (lowest) for 75%A:25%B cows in 1995 to 92.6% (highest) for 50%A:50%B in 1992. Calving rates ranged from 33.3% for Brangus cows in 1995 to 88.9% for Angus cows in 1995. There was large variation within year of breeding on the numbers of each breed type of cow group. This and the other non-genetic factors as explained previously may help explain the variable results. However, this year of breeding x breed type of cows interaction indicated that the reproductive rate was affected not only by breed type of cow and year of breeding but also by their interaction. This interaction shows that the pattern of reproductive performance as the percentage of Brahman increases might be different in different years.

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146 Table 5-4. Means and standard deviations of estrus rate by year of breeding and breed type of cow Estrus rate (%) Year 100A:0%B 75A:25%B 62A:38%B 50A:50%B 25A:75%B 0A:100%B 1991 85.75.8 81.2 10 79.47.0 87.74.7 98.02.5 79.47.1 1992 90.65.3 92.84.9 88.96.1 92.53.5 97.50.2 94.73.6 1993 55.59.6 25.98.5 34.69.4 64.86.0 57.18.5 35.17.9 1994 74.46.7 82.25.6 71.47.9 78.95.4 72.57.1 75.87.5 1995 57.18.5 72.76.5 60.79.2 65.16.1 75.56.1 57.87.5 1996 97.70.1 98.50.1 97.50.1 98.50.1 94.80.2 97.30.1 1997 83.65.0 65.07.6 47.08.2 79.48.2 82.56.0 74.46.7 Pregnancy rate (%) 1991 60.08.1 68.711.9 58.88.6 73.56.3 75.011.0 58.88.6 1992 71.98.2 71.48.7 85.25.1 92.64.9 78.87.2 68.47.7 1993 62.99.5 70.38.8 65.49.4 74.16.1 57.18.5 89.25.1 1994 55.87.6 71.16.8 74.37.4 70.26.1 60.07.9 84.84.8 1995 65.78.4 43.27.6 46.49.4 58.76.2 53.17.1 48.97.5 1996 65.17.4 53.37.5 50.010.8 65.75.6 56.86.6 69.26.5 1997 74.55.9 70.07.4 73.57.5 77.98.2 75.06.2 83.75.9 Calving rate (%) 1991 50.08.5 64.312.5 46.78.7 71.16.6 53.812.8 46.78.8 1992 65.38.6 68.09.0 83.37.3 81.85.3 76.77.5 61.78.0 1993 61.59.5 68.08.8 65.49.4 72.56.2 50.08.5 88.95.2 1994 50.07.6 61.57.2 71.07.7 66.06.4 57.98.0 83.36.7 1995 64.78.4 41.87.6 33.39.0 56.76.3 48.97.2 47.87.5 1996 56.87.5 40.57.6 45.011.0 55.55.8 45.66.8 65.26.7 1997 70.26.1 67.57.5 71.87.6 75.48.1 72.26.4 81.56.0 Gestation Length Gestation length was affected by year of breeding, age of cow, breed type of cow and sire (P<0.05) as presented in Table 5-5. Gestation length ranged from 283.2 days to 286.8 days across the years, with an overall average of 285.6 days. The fluctuation of gestation length across year of study suggests that there were environmental factors that may have changed over the years and that influenced gestation length (Goyache, 2002).

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147 Table 5-5. Least squares means of gestation length by year of breeding, age of cow, body condition scores and breed type. Variables Gestation length (day) Year of breeding 1991 1992 1993 1994 1995 1996 1997 * 285.7*.8 286.2*.7 284.1*.7 286.5*.6 286.3*.6 286.8*.6 283.2*.6 Age of cow 2 3-4 5-6 7 * 284.5 c .6 285.5 a .6 286.8 b .5 285.4 a .6 Body condition scores 3-4 5-6 7 n.s 285.6 a .6 285.7 a .3 285.4 a .8 Breed type of cows Angus 75% A:25% B 50% A:50% B 25% A:75% B Brahman Brangus * 282.1 a .6 284.1 b .6 285.9 c .5 286.5 cdf .6 289.4 e .6 285.3 cbf .7 Days Post-Partum Year x breed type Breed type of sire n.s * * *P<0.01 ; n.s: P>0.05 abc Means with the same superscript in the same column are not significantly different at P<0.05. In general, gestation length was similar across age of cow. However, cows aged 5 and 6 years old had longer gestation lengths (P<0.05) and they were shorter in 2 years old heifers than all other age groups. The trend of gestation length on age of cow tends to be quadratic. The gestation length increased from 2 year old heifers up to 6 years old cows and then decreased for cows older than 7 years. Andersen and Plum (1965) have reported that age of cow affected gestation length, older cows tended to express longer gestation

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148 length, whereas Hafez (1980) have reported that heifers conceiving younger tended to have shorter gestation periods. There was no effect of body condition scores on gestation length, which was similar to the results of Goyache (2002). Gestation length was affected by breed type of cow (P<0.05) as presented in Table 5-5 similar to a study by Egbunike and Togun (1980) who found that gestation length was affected by breed of cow. Reynolds et al. (1997) and Thrift (1997) reported that gestation length is affected also by biological type of sire, similar to the result in the current study. The gestation length of this study was within the range of reports in the literature. Figure 5-7 presents the effect of percentage of Brahman.Brahman breeding in the cow increased. Angus cows had the shortest gestation length (P<0.05), Brahman cows have the longest gestation length (P<0.05) and Brahman crossbred cows were intermediate between the Angus and Brahman cows. The results of this study were similar to those of Rae (2002) who reported that breeds of cows differed in gestation length showing gestation lengths for Angus of 281 1.2 d, for Angus-Brahman crossbred of 286 1.1 d and for Brahman of 291 1.8 d. Other studies have reported that Brahman and Brahman based cows had longer gestation length than Bos taurus cows, with an average of 292 days (Reynolds, 1967) and 293 days (Plasse et al, 1968). Angus cows had relatively short gestations of 278d (Plasse et. al., 1968) and 282d (Cundiff et al., 2001). The review of Thrift (1997) reported that the gestation length of Brahman cows was between 289 days and 294 days with an average of 291 days.

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149 2802900%25%3850%75%100%Percentage of BrahmanGestation (days) Figure 5-7. Gestation length by percentage of Brahman breeding Implications This study suggests that non-genetic factors such as year of breeding, age of cow and body condition score should be included in analyzing reproductive performance of cows and heifers as they can have important influences on the differences between breed-types of cows, especially within year of breeding. Good body condition results in higher reproductive performance. It is recommended that cows be kept in good body condition score of 5 and 6 during the breeding season and to reduce the feed intake of cows with body condition score of more than 6. Effective management of body condition score of the cowherd is critical for efficient beef production. Estrus rate, pregnancy rate and calving rates resulting from using Synchromate-B following timed-insemination were acceptable; therefore, SMB and timed-insemination combination procedures could be used as a management tool to improve the efficiency of reproductive performance in beef cows. The reproductive performance of cows subjected to estrous synchronization and timed-AI was not affected by the percentage of Brahman breeding. The Brahman female reproductive performance was comparable to Angus and their crossbreds.

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150 The introduction of Brahman breeding, particularly in subtropical regions, is important in improving beef cattle productivity because of their adaptive traits. However, the tendency for high levels of Brahman breeding in cows to result in longer gestation lengths is of concern. Summary The study evaluated responses of estrous synchronization using SMB following timed-AI in heifers and cows of differing proportions of Angus and Brahman breeding. Data from a total 564 heifers and 1,257 cows consisting of Angus (A), Brahman (B), 75% A 25% B, 50% A 50% B, 25% A 75% B, and Brangus (62.5% A 37.5% B) from 1991 through 1997 were collected at the Beef Research Unit of the University of Florida. Data were analyzed using the PROC GENMOD procedure (SAS, 2000) including the fixed effects of year of breeding, age of cow, body condition score, breed composition of cow, breed type of sire and days from calving to synchronization date as a covariates plus all two-way interactions. Year of breeding, age of cow, body condition score, breed type of cow and breed of sire all affected estrus, pregnancy rate, and calving rate. Gestation length was affected by year of breeding, breed composition of cow, and age of cow. Body condition score tended to have linear effects on estrus, pregnancy and calving rate indicating a tendency for more favorable responses as body condition score increased. Age of cow tended to have quadratic effects on fertility indicating a tendency of first increased fertility and then decreased fertility as age of cow increased. Few Brahman cows exhibited estrus on first day after implant removal. Estrus, pregnancy and calving rates across the breed types of cows were similar. The rates of estrus, pregnancy and calving obtained from estrous synchronization using SMB and

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151 timed-insemination were moderate to high. Gestation length ranged from 282.1 days to 289.4 days, Angus cows had the shortest gestation length and Brahman, the longest. There was a positive trend between percentage of Brahman breeding in cows and gestation length.

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CHAPTER 6 GENERAL CONCLUSIONS A genetic evaluation of Bali cattle (Bos javanicus) using data collected from the Bali Cattle Improvement Project (BCIP) on the island of Bali was conducted to determine the non-genetic factors and genetic parameters influencing growth traits and to evaluate both their phenotypic and genetic trends. There were 7,980 calves born from 1985 through 2000 used to analyze non-genetic factors and genetic parameters affecting weight at 190 days (W-190d) and 350 days (W-350d). Contemporary group (CG) was defined as a location-year-season combination. The main effects in the mixed model used in the analysis were CG, sex of calf, cow age and the two-way interaction as fixed effects and also included age of calf as covariates. Sire of calf was included as a random effect. Data were analyzed using SAS mixed models procedures (Littell et al., 1996). A connectedness program (Elzo, 2002) was used to evaluate genetic linkages between contemporary groups. Variance components were computed by the ASREML package (Gilmour et al., 2000) using single-trait animal models that included CG, sex of calf, cow age, and the significant two-ways interaction as fixed effects and the significant effects of calf age as covariates. A sequential analysis was performed by including additional random effect. Analysis-1 (A-1) estimated direct genetic only including sire as a random effect; A-2 computed direct and maternal effect using sire and dam as random effects ignoring covariance between sire and dam; and A-3 computed direct, maternal and direct-maternal genetic correlation using sire and dam as random effects and covariance between sire and dam in the models containing the same fixed effects. All models were 152

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153 compared using likelihood-ratio tests (LRT) with an error probability of 5%. Genetic trends were plotted as average of estimated breeding values (EBV), estimated by the solution of animal model equations by year of birth, and overall trend was estimated as a regression of all EBV on year of birth. All non-genetic effects including CG, calf age (in W-190d), calf sex (in W-350d), calf age x calf sex (in W-190d), and cow age x calf sex (in W-350d) were found significant (P<0.05). Estimates of direct additive heritabiliies (h 2 d ) for W-190d (0.4 0.05) were lower than those for W-350d (0.5 0.04). The estimated maternal heritabilities (h 2 m ) were low and not different from zero (P>0.05). The estimated genetic correlations between direct and maternal effect (r dm ) were -0.2.1 and -0.6.2 for W-190d and W-350d, respectively. The estimated correlations between W-190d and W-350d were 0.74.08 (genetic), 0.19.02 (environmental), 0.99.00 (maternal) and 0.33.07 (phenotypic). The phenotypic trend for W-190d was positive, but negative for W-350d. However, the breeding values for both traits tended to decrease 10 grams and 70 grams per year for W-190d and W-350d, respectively. These findings showed that some non-genetic factors have significant effects on growth traits in Bali cattle. The estimates of maternal effects in Bali cattle were not different from 0; however, the genetic correlation between direct and maternal was moderate and negative. The low maternal effect was inexplicable. The genetic variance for W-190d was lower than that of W-350d, therefore, genetic progress for W-350d might be expected to be faster than at for W-190d and increasing W-190d can be achieved by selection for W-350d since their genetic correlation was strong and positive. The genetic trends for both traits were slightly negative but were not different from zero. The decline in W-350d might be

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154 caused by factors other than genetic due to the observed genetic values of W-190d and W-350d. A study of direct response of divergent selection for meat tenderness using Warner-Bratzler Shear Force (WBSF) values of Angus bulls was conducted at the Santa Fe Beef Research Unit of the University of Florida beginning in 1994. After reaching puberty at 13 and 14 month of age, about 30-35 bulls per year were semen-collected, then slaughtered and evaluated for their WBSF values. Three or four of the bulls with the lowest and highest WBSF values were selected for use each year. WBSF data from a total of 299 bulls born from 1994 through 2001 were analyzed. Analyses were performed to estimate variance and covariance components using ASREML with a single trait-animal model including year of birth and sire group (tender and tough) as fixed effects and animal as a random effect along with the use of pedigree information in the relationship matrix. Genetic trends were plotted as averages of breeding values, estimated by the solution of animal model equations for year of birth, and overall trend was estimated as a regression of all breeding values on year of birth. WBSF and EBV (estimated breeding values) for WBSF were analyzed using PROC MIXED procedures (SAS, 2000) including year of birth, group of progeny of sires selected and their interactions. The average WBSF values (3.55 vs 4.00 kg) and the EBV for WBSF (-0.25 vs 0.37 kg) of progeny of tender sires across years of study were lower (P<0.01) than those of tough sires. The phenotypic (WBSF) trends decreased 0.32 kg and 0.30 kg per year, respectively for the progeny of tender and tough sires (P<0.01). The genetic trends (EBV for WBSF) tend to decrease 0.04 kg per year for the progeny of tender sires and increase 0.03 kg per year for the progeny of tough sires. The estimate of direct heritability analyzed using ASREML

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155 from this selected population was moderate (h d 2 =0.40.18). Realized heritabilities obtained from the regression of selection response (WBSF) on cumulative selection differentials were 0.32.27 and 0.21.20 for progeny of tender and tough sires, respectively. Approximately 41.17% improvement of phenotypic and genetic value for WBSF was observed after 7 years of selection. An explanation for the declining WBSF for progeny of tough sires, instead of the expected increase might be due to short duration of selection, random environmental effects, low selection pressure, sampling error and random genetic drift, since there were limited numbers of the bulls evaluated and it was not possible to collect freezable semen from all of the bulls prior to slaughter. A final study evaluated responses of estrous synchronization using Synchromate-B (SMB) following timed-artificial insemination (timed-AI) in heifers and cows of differing proportions of Angus and Brahman breeding. Data from a total 564 heifers and 1,257 cows consisting of Angus (A), Brahman (B), 75% A 25% B, 50% A 50% B, 25% A 75% B, and Brangus (62.5% A 37.5% B) from 1991 through 1997 were collected at the Beef Research Unit of the University of Florida. Data were analyzed using the PROC GENMOD procedure (SAS, 2000) including the fixed effects of year of breeding, age of cow, body condition score, breed composition of cow, breed type of sire and days from calving to synchronization date as a covariates plus all two-way interactions. Year of breeding, age of cow, body condition score, breed type of cow and breed of sire all affected estrus, pregnancy rate, and calving rate. Gestation length was affected by year of breeding, breed composition of cow, and age of cow. Body condition score tended to have linear effects on estrus, pregnancy and calving rate indicating a tendency for more favorable responses as body condition score increased. Age of cow tended to have

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156 quadratic effects on fertility indicating a tendency of first increased fertility and then decreased fertility as age of cow increased. Few Brahman cows exhibited estrus on first day after implant removal. Estrus, pregnancy and calving rates across the breed types of cows were similar. The rates of estrus, pregnancy and calving obtained from estrous synchronization using SMB and timed-insemination were moderate to high. Gestation length ranged from 282.1 days to 289.4 days, Angus cows had the shortest gestation length and Brahman, the longest. There was a positive trend between percentage of Brahman breeding in cows and gestation length.

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BIOGRAPHICAL SKETCH Lisa Praharani was born April 19, 1965, in Jakarta, Indonesia. In September, 1983, she enrolled in the College of Animal Husbandry at Bogor Agricultural University where she received her degree of Bachelor of Animal Sciences in 1987. Following her graduation she joined the staff of Centre Research Institute of Animal Sciences. She had been married to Christian Rogahang since 1988 and their marriage was blessed with three children, Gloria, Imanuel and Gracia. In September, 1996, she enrolled in the graduate school of Montana State University at Bozeman. She received her Master of Science degree with a major in animal breeding in August 1998. After returning to her country, she worked as a research assistant to the Research Institute for Animal Production. In January, 2001, she was granted a fellowship for further research training and study, and she enrolled in the Graduate School of the University of Florida. Until the present she pursued her work toward the degree of Doctor of Philosophy in animal science. In the future, she plans to continue working in the same area as a member of the researcher at the Research Institute of Animal Production in Indonesia. She is a member of the Indonesian Animal Breeding Scientist Society and the American Society of Animal Science. 188