BrowseUploadSign inJoinBooksAudiobooksComicsSheet MusicWelcome to Scribd! Start your free trial and access books, documents and more.Find out moreGENETIC ANALYSES OF GROWTH TRAITS FOR THE SIMBRA COMPOSITE BREEDAlgina Maria Johanna Smith
(Animal Science) Faculty of Agricultural and Forestry Sciences Department of Animal Sciences University of Stellenbosch Republic of South Africa
Professor K. Dzama
I hereby declare that the work contained in this thesis is my original work and that it has not, as a whole or partially, been submitted for a degree at any other University.
Genetic analyses of growth traits for the Simbra composite breed Candidate Study leader Department Faculty Degree : : : : : Algina Maria Johanna Smith Professor K. Dzama Animal Sciences Agricultural and Forestry Sciences M.Sc Agric
The aim of this study was to evaluate the Simbra breed of cattle for certain non-genetic as well as genetic parameters influencing live weight traits in the breed. Live weight traits included birth weight (BW), weaning weight at 200 days of age (WW), yearling weight at 400 days of age (YW) and 600 day weight. The Simmental and Simbra Breeders’ Society of Southern Africa availed 148751 records for analysis from the year 1987 till 2009. Due to deficiencies of various kinds in the data and the restrictions imposed for the purposes of the analysis, 56.44% of the records were discarded for BW, 76.55% for WW, 91.54% for YW and 96.32% for 600-day weight. Non-genetic parameters affecting BW, WW, YW and 600-day weight were analysed using the General Linear Models procedure of the Statistical Analysis System (SAS, 2004) software. During this procedure sex of calf, breed composition of calf, breeder of calf, month of birth, year of birth and dam age were fitted in the models. BW, WW, YW and Mature Cow Weight (MCW) were fitted as covariates where possible. It was determined that the fixed effects of sex, dam age, breeder, year and month had a significant (P < 0.05) effect of BW and WW while dam age was not significant (P > 0.05) for YW or 600-day weight. Breed was found non significant for YW. Breeder of the calf accounted for the most variation in BW, WW, YW as well as 600-day weight with a contribution of 17.55%, 25.77%, 18.35% and 10.71% respectively. Tukey’s multiple range tests were performed for testing differences between least square means. Results indicated male calves to be significantly heavier than females for all four traits measured. Breed composition differences were found significant until WW. Calves with higher Brahman percentage weighted more at birth while calves with higher Simmental percentage weighed more at weaning. Middle-aged dams were found to account for heavier calves at both BW and WW while very young dams and very old dams produced lighter calves for the two live weight traits. A number of years showed a significant difference from each other for all the traits measured as well as month of birth. (Co) variance components and the resulting genetic parameters were estimated using single-traits and three-traits analysis by means of Restricted Maximum Likelihood procedures (Gilmour et al.,
2002). Appropriate models were selected by means of Log likelihood ratios tests and implemented to estimate genetic parameters for each of the traits studied. Direct additive heritabilities for BW, WW, YW and 600-day weight in the Simbra were respectively 0.56 ± 0.08, 0.67 ± 0.09, 0.70 ± 0.11 and 0.10 ± 0.03 when the most suitable animal model was fitted in single-trait analyses for each trait. Single traits analysis also included maternal additive as well as the correlation between direct additive and maternal additive for BW, WW and YW. Maternal additive heritability estimates of 0.24 ± 0.07, 0.33 ± 0.06 and 0.38 ± 0.07 was obtained for BW, WW and YW. Correlation estimates between direct additive and maternal additive were -0.75 ± 0.07, -0.93 ± 0.07 and -0.85 ± 0.08 for BW, WW and YW respectively. Furthermore, dam permanent environment was included as an additional random effect that increased the log likelihood value significantly. A value of 0.04 ± 0.05 was obtained for dam permanent environment estimate for WW. When a three traits analysis was done for the same traits, but using a significantly smaller data set, direct additive heritabilities of 0.24 ± 0.07 for BW, 0.33 ± 0.06 for WW and 0.38 ± 0.07 for YW were obtained. Genetic and environmental correlation estimates of 0.18 ± 0.16 and 0.09 ± 0.06 between BW and WW; 0.27 ± 0.16 and 0.07 ± 0.06 between BW and YW; as well as 0.52 ± 0.10 and 0.45 ± 0.05 between WW and YW were obtained during the three-trait analysis. The magnitude of the heritabilities obtained in this study indicates that the opportunity exists to make genetic progress through proper selection objectives.
55% vir WW. jaar van geboorte asook moeder ouderdom gepas in die modelle vir die onderskeie gewigte. Sommige jare waarin van die kalwers gebore is. Lewende gewigs eienskappe sluit in geboorte gewig (BW). maand van geboorte. het ook betekenisvol van mekaar verskil asook die maand waarin die kalf gebore is. 25. teler van die kalf. Ras samestelling vir BW en WW was betekenisvol verskillend vir die diere. Nie-genetiese parameters wat die onderskeie lewende gewigte beïnvloed het. speen gewig gemeet op 200 dae (WW).Opsomming
Genetiese analises van groei eienskappe vir die Simbra komposiete ras Kanditdaat Studieleier Departement Fakulteit Graad : : : : : Algina Maria Johanna Smith Professor K. jaaroue gewig gemeet op 400 dae (YW) en finale gewig gemeet op 600-dag gewig. Dzama Veekundige Wetenskappe Landbou en Bosbou Wetenskappe M. is ‘n goeie indikasie van groei potensiaal en is ‘n minimim vereiste vir meeste beesras telings genootskappe. ras samestelling. Middel-jarige moeders het swaarder kalwers geproduseer met geboorte en speen as baie jong en ou moeders. Gedurende die analise is geslag van die kalf.54% vir YW en 96. 18. Geboorte gewig. Kalwers met ‘n hoër Brahmaan persentasie het swaarder BW opgelewer as dié met ‘n hoër Simmentaler persentasie.35% en 10. Teler van die kalf was verantwoordelik vir die meeste variasie in BW.32% vir 600-dag gewig.44% van die rekords vir BW nie gebruik nie. YW asook 600-dag gewig met ‘n bydrae van 17. jaar.Sc Landbou
Die doel van hierdie studie was om die Simbra bees ras te evalueer op grond van sekere niegenetiese so wel as genetiese parameters wat lewende gewig beïnvloed. 2004) sagteware.05) vir YW of 600-dag gewig nie.77%. is geanaliseer deur Algemene Lineêre Modelle met behulp van die Statistiese Analitiese Sisteem (SAS. 76. Die moederouderdom was nie betekenisvol (P > 0.71% onderskeidelik. Tukey se veelvuldige vergelykings toets is gebruik om onderskeid te tref tussen “least square means”. Die Simmentaler en Simbra genootskap van Suid Afrika het 148751 rekords beskikbaar gestel vir evaluasie vanaf die jaar 1987 tot 2009. speen gewig. terwyl kalwers met ‘n hoër Simmentaler persentasie swaarder geweeg het met speen en dus ideal is vir speen kalwer produksie stelsels. Resultate het aangedui dat manlike diere swaarder weeg as vroulike diere tot en met finale gewig.05) invloed gehad het op BW en WW. teler. Volgens die resutate is daar vasgestel dat geslag van die kalf. Die ras samestelling was ook nie betekenisvol gevind vir YW. Daar was egter groot tekort komings aan die gewewe data en dus is daar 56.55%. jaaroue gewig asook volwasse koei gewig is gepas in elk van die modelle as ko-variate. moeder ouderdom. maand en volwasse koei gewig almal ‘n betekenisvolle (P < 0.
. 91. WW. Gereelde en akkurate opnames van lewende gewig.
Genetiese. Direkte genetiese oorerflikhede van 0.52 ± 0.27 ± 0.06 tussen BW en WW.38 ± 0. Tydens die veelvuldige-eienskap analise het die oorerflikhede merkwaardig verminder vir die betrokke gewigte en kan ook waargeneem word as die meer korrekte genetiese weergawe.07 vir BW.06 tussen BW en YW asook ‘n waarde van 0.85 ± 0. naamlik die permanente omgewing van die moeder. ‘n Adisionele faktor was ook ingelsuit vir WW. -0.07 vir BW.18 ± 0.11 vir YW en 0. Omgewings korrelasie waardes van 0. 0. ‘n Genetiese korrelasie waarde van 0.09 ± 0.16 tussen BW en YW en ‘n waarde van 0. 0.08 vir YW.03 vir 600-dag gewig. 0. Modelle is opgestel vir elk van die gewigte deur die geskikte genetiese terme toe te voeg en te toets met behulp van “Log likelihood tests” om sodoende die onderskeie genetiese parameters te bepaal.07 is bepaal vir BW. 0.24 ± 0.67 ± 0.10 tussen WW en YW. Die direkte maternale genetiese oorerflikhede tydens dieselfde enkel-eienskap analise vir die onderkeie gewigte was 0.07 vir WW en -0. Groot variasie asook negatiewe tendense vir WW en YW is ondervind van jaar tot jaar en dui daarop dat die seleksie doelwitte vir lewendige gewig nie in plek gestel is nie en is dit nodig om te her evalueer. 0.08 vir BW.93 ± 0.16 tussen BW en WW is bepaal asook ‘n waarde van 0.05.. Genetiese neigings is bepaal vir die onderskeie gewigte deur die gemiddelde voorspelde teelwaardes aan te teken teenoor elke jaar wat bereken was tydens die enkel-eienskap analises vir die onderskeie gewigte. Direkte genetiese oorerflikhede bepaal deur enkeleienskap analises vir die Simbra ras was as volg.07 ± 0.04 ± 0. ‘n Waarde van -0. Hierdie korrelasies dui daarop dat na-speengewigte vermeerder kan word deur te selekteer vir verhoogde WW sonder om BW dramties te vermeerder. Hierdie matig tot hoë parameters dui op genetiese vordering deur middel van korrekte seleksie prosedures.10 ± 0.05 tussen WW en YW is gevind.24 ± 0.75 ± 0.
.06 vir WW en 0.38 ± 0.07 vir YW.en omgewing korrelasies is ook bepaal tydens die analise en het positiewe waardes opgelewer.(Ko) variansie faktore en opeenvolgende genetiese parameters is bepaal met behulp van enkeleienskap analises asook meervuldige-eienskap analises deur middel van die “Restricted Maximum Likelihood” prosedure (Gilmour et al.33 ± 0.06 vir WW en 0. Korrelasies tussen direkt genetiese en direk maternale eienskappe was sterk negatief. 2002).70 ± 0. 0.56 ± 0.09 vir WW.33 ± 0.07 vir YW was bepaal. wat ‘n waarde opgelewer het van 0.45 ± 0.
for his valuable tuition. who helped me with the formatting and editing of this thesis. support. Frans Balzer. Dzama. supportive and sociable. Jacobus and Elize Smith. Mr. Oliver Zishiri. constant encouragement and constructive criticism. especially Ronel Basson. Mr. for his support. Pieter Stofberg. K. for his valuable guidance as well as the friendly supportive discussions. in Bloemfontein for the permission to use the data. love and financial help. My dearest friends that I met in Stellenbosch for always being friendly. Prof.
. who has enabled me to accomplish this study with the necessary courage and stamina to push through. Mr. allowing me time to study. My parents. love and patience. for their constant encouragement.ACKNOWLEDGEMENTS
The author wishes to express her profound appreciation and gratitude to the following persons (individually and collectively) and institutions: First and foremost I thank our Lord and saviour. Jesus Christ. My horse stud (Painted Appaloosa Stud) manager. My husband. who acted as supervisor. for caring for my horses as if his own. The Simmental and Simbra Breeders’ Society of Southern Africa.
......................................................... The effect of mature cow weight (MCW) on growth traits in the Simbra breed ................................................................................................ Year of birth ........................................................................ 22 NON-GENETIC FACTORS INFLUENCING GROWTH TRAITS IN SIMBRA CATTLE................................................ Genetic parameters ...4.... Statistical Analysis ................ 5 LITERATURE REVIEW ...................................................... 15
CHAPTER 3 ...............3...6...........2........3................................. 10 2.................................................1........................2........................................................ 2 1..... 8 2......................... 23 3..............................................2...............3............................. References ..1............ Justification .......................... 5 2.............. III OPSOMMING....................................................... Live weight traits .................................................................................... 11 2............................................................................................................................ II SUMMARY .........1...............4.................................... 10 2.................. Sex .......................... The effect of gender on growth traits in the Simbra breed ................. Dam age .....................................................4........1......................................3........................... 22
3. Breeder ....................................5..................................................................................................................................TABLE OF CONTENTS DECLARATION ..........................4............................3..3................................1....... 3
CHAPTER 2 ............ The effect of breeder on growth traits in the Simbra breed ................. Introduction ..................... 10 2..1.........4....................... Breed composition .........VII CHAPTER 1 ...4.. The advantage of composite breeds .....3.................. Records .........................................................................................1..............................................................................................................1........... 5
2............. 24 3..... 34
1................................................................................................................................................................................................................................5................................................................................... Materials and Methods ........................V ACKNOWLEDGEMENTS ...3............................................................... The effect of breed composition on growth traits in the Simbra breed ......3........................................................... Simbra breed ............................................ Month of birth .........4........................... 7 2.. 2 1............................. 1 GENERAL INTRODUCTION................... Objectives ...........................................2.................................................................................. Non-genetic factors affecting growth in beef cattle ................................. 27 3....................................................... Abstract ....................................................................................... 9 2....... 8 2................. 22 3.............................................................................................. 9 2..... Results and Discussion ..........................................4............................................3..........................................2.................3..................................... 30 3............3.......... 6 2................... 31 3........................... References .......................................3............ 23 3....................................... 22 3.......................................2.. 34 3.........................................................................................................................................................
.......................................................1.................... 60 5.................................. The effect of month on growth traits in the Simbra breed .....................6..5.......................4..................................................... The effect of year on growth traits in the Simbra breed ..........6.......... 55
CHAPTER 5 ..................................... 45 4........6...... Conclusion ...............2.................. Introduction ......................... Conclusion .... Three-trait analyses .. 46 4.......... Conclusion ...5.... The effect of dam age on growth traits in the Simbra breed ............................7.......... Unitrait analysis ............................................................................................................................ 53 4......1..............1.............................................................................................. 48 4..................................................................................................4.. Correlation among traits ..........................2...............................2. Abstract ....................................... Abstract . Records ........................................................................................................................................... Statistical Analysis .................. Introduction ...........................................2.........6...........4......... 60
5................ References ..........................3................ 61 5.................2.............................3........................................ Results and Discussion .......................... 68
................................................ 49 4...........4...................1....................... 37 3...................... 68 GENERAL CONCLUSIONS AND RECOMMENDATIONS ......1....................... Model Selection ........ 40
CHAPTER 4 ..................................................................................... 45 4.................... 45 GENETIC FACTORS INFLUENCING GROWTH TRAITS IN SIMBRA CATTLE.................................................................. 61 5............ Records ...............................3...........................3....3.... References ........ 36 3.................................................................................................................4.................... Materials and Methods ................3.................................................................................. 66
CHAPTER 6 ................................................................................................................ 60 5........................................................................................................................................................3..................................................3.................................... 54 4.4...................................4................................................................................... 61 5.......4...................................................................................................................... 38 3..................................... 61 5......5....... 65 5...........5...................................................................................... 48 4............................ 45
4. 54 4.......................................4............... Materials and Methods ......................................... 60 GENETIC TRENDS IN THE SIMBRA BREED ............................................. 46 4....... 34 3..................................... 46 4.......... Statistical Analysis ....... Results and Discussion ........................................................... References ..................
................................... Estimates (SE in brackets) of genetic (above diagonal).............5..... Mean squares (MS)......1.......... . .. Mean squares (MS)........... significance level and proportional contribution of fixed effects (FE %) to the overall variance for WW. ...........3.................. The effect of breed composition on final weight (600-day) for the Simbra breed................................ 5 Table 2........................ YW and 600-day weight................................ Mean squares (MS)..............) depicting the influence of fixed effects on growth traits in the Simbra breed.......... The regression of birth weight on year of birth for the Simbra breed..... significance level and proportional contribution of fixed effects (FE %) to the overall variance for BW.... YW and 600day weight in beef cattle..... ..........3.................. ......... Least square means ± Standard error (s..... 29 Table 3.... Vertical bars around the observed means denote standard errors......... The regression of weaning weight (200-day) on year of birth for the Simbra breed. Literature estimates for genetic parameters (h2a.. Vertical bars around the observed means denote standard errors........ Type III sum of squares (SS)............ 53 CHAPTER 3 Figure 3...............................1........................ 33 Figure 3............................. Vertical bars around the observed means denote standard errors.... 14 Table 3.................. The effect of breed composition on weaning weight (200-day) for the Simbra breed................................. 24 Table 3......... Type III sum of squares (SS). ....... significance level and proportional contribution of fixed effects (FE %) to the overall variance for 600-day weight.........................6.......... 49 Table 4......... 33 Figure 3.. Vertical bars around the observed means denote standard errors. Literature estimates for genetic correlations (ra) and environmental correlation (re) estimates for beef cattle breeds....4.............2. Type III sum of squares (SS)...... Type III sum of squares (SS)..........5............................ WW.... ..... Description of data set after editing for calf BW.... Vertical bars around the observed means denote standard errors.............2... Mean squares (MS). ...... 12 Table 2........... significance level and proportional contribution of fixed effects (FE %) to the overall variance for YW................... ....... 35 Figure 3....... 32 Figure 3.... The performance of the Simbra breed in comparison to the national average in the South African beef cattle performance and progeny-testing scheme (Mukuahima 2008).........2............... ................... Estimates of variance components and ratios from single-trait analysis of growth traits in the Simbra breed... Log likelihood ratios for the respective random effects models fitted to growth traits in the Simbra breed......3......... WW.................LIST OF TABLES
CHAPTER 2 Table 2............ 28 Table 3... Vertical bars around the observed means denote standard errors. 36
...................... 29 Table 3...........1....... h2m and ram) for BW...... 28 Table 3... 52 Table 4.............................. ............. 30 Table 4............... ................... The regression of yearling weight (400-day) on year of birth for the Simbra breed.3...........................e.....2........ .. ...... 35 Figure 3................................... The effect of breed composition on birth weight for the Simbra breed............... direct heritability estimates (h2) (on diagonal) and environmental correlation estimates (underneath diagonal) using a threetrait analysis in the Simbra breed........1...... ......... ........ ...................6.....4........
.... ..... 38 CHAPTER 4 Figure 4...... Figure 5.... Mean annual predicted breeding values (PBV) for weaning weight for the Simbra breed.........3................................... 53 CHAPTER 5 Figure 5.Figure 3...4. Figure 5..... .. The number of observations per year used for analyses for the Simbra breed..........2.7.......... The regression of weaning weight (200-day) on dam age for the Simbra breed.....................8..... Vertical bars around the observed means denote standard errors. Annual means are accompanied by the relevant standard error........ The regression of birth weight on dam age for the Simbra breed............. Trends in h2 and m2 respectively for the Simbra breed with an increase in the age at which weights were recorded........... ........ Mean annual predicted breeding values (PBV) for yearling weight for the Simbra breed. 62 Figure 5... Mean annual predicted breeding values (PBV) for birth weight (BW) for the Simbra breed.......... Annual means are accompanied by the relevant standard error................................... Annual means are accompanied by the relevant standard error....... 65 64 63
........... 38 Figure 3.... .........................................1... Vertical bars around the observed means denote standard errors..............1... Vertical bars around the observed means denote standard errors......
muscle expression. et al. providing the opportunity to evaluate the breed for growth potential. The breed exhibits large differences in growth potential and this variability provides the potential for genetic improvement for growth. It has been found that the majority of all the sectors in the beef cattle industry are interested in the animal’s growth potential throughout its life span as well as the traits associated with it (Denise & Brink. Schoeman & Jordaan. cut ability and marbling. This is where the Simbra has been established as the breed of choice for most production systems throughout Southern Africa. 1985). Higher Brahman composition has been found to be appropriate for effective and integrated beef production systems in both tropical and sub-tropical environment (Amen et al. however. namely female production and reproduction as well as growth of the calf from birth to the specific slaughter weight (Meyer et al. On the other hand. depending on production and marketing systems.CHAPTER 1
In beef cattle production there is no single breed that can be considered superior. Live weights include birth weight (BW). In order to be flourishing the Simbra. The breed is composed out of two of the most popular breeds in the world namely the Simmental relating to the Bos taurus specie and the Brahman relating to the Bos indicus specie. growth potential is still increasingly important to meat output from the production system (Eler et al. needs highly productive purebreds as foundation. Measuring live weights at regular intervals has been found to be good indicators of growth potential. A higher incidence of Simmental composition will be found in less extreme environments where weaner production is the main focus.. age at puberty. weaning weight (WW) at 200 days of age. possessing complementary characteristics to produce desirable offspring (Denise & Brink. more elaborate recordings. 1991.. 1995). in more extreme environments a higher incidence of Brahman composition will be utilized (De la Rey. However. production situation and market goals to be economical. environmental adaptability. 1999). milk production. The latter is evident in the South African Simbra due to the attention on weaner calf production and consumer preference for leaner beef (Mukuahima 2008). Live weights have been recorded for the Simbra in Southern Africa since 1987. for example more 1
. Synthetic breeds have been shown to be the most successful in commercial production systems. 2007) Some of the most important characteristics associated with beef production include body weight and size at different stages. The ideal breed combination will depend on the specific environment as well as market goal. rate and efficiency of gain. Although maintaining reproductive efficiency in the herd should be of particular concern. yearling weight (YW) at 400 days of age and 600-day weight. The reason for this is that the efficiency of beef production depends on three basic elements. 2004). A breed must be selected according to climatic conditions... Each beef breed has its advantages and limitations for certain traits. It is therefore important to obtain knowledge regarding breed characteristics to choose the most suitable breed. 1985).
.traits and more animals. like the Simbra. genetic. Increased computing power as well as highly advanced software has given the ability to facilitate more detailed models and more sophisticated statistical procedures to estimate the latter (Ferreira et al. Thus. Non-genetic factors influencing BW. dam permanent environmental effects and genetic as well as environmental correlations between BW. However. phenotypic and environmental) as well as repeatability. environmental and phenotypic correlations among various growth traits for different beef cattle breeds. there is a paucity of literature on genetic parameters of the Simbra breed in the major beef producing regions of the world.
The objectives of this study were to use recorded performance data of the Simbra breed obtained from the Simmental and Simbra Breeders’ Society of Southern Africa for the Simbra breed to estimate the following: 1. To obtain breeding values for BW. may differ from their parental breeds. 1964). 3. correlations (genetic. YW and 600-day weight using different animal models and construct genetic trends over the years to assess genetic progress over time or the lack thereof for the breed.e.
1.1. YW and 600-day weight. WW at 200 days of age.. Genetic parameters i. 1999). WW. additive maternal effects. Mohd-Yusuff & Dickerson (1991) found that the genetic variance in a composite population. This is accomplished by the estimation of accurate genetic. excluding repeatability. YW at 400 days of age and 600day weight. 2. are required for future accurate evaluation (Pico. phenotypic and environmental parameters for the breed and growth traits under investigation (Brinks et al. WW. heritabilities. The resemblance between genetically related animals can be used in estimating trait heritability. 2004). maternal additive and permanent maternal environment). The latter. This is where the computation of variance components within and between family members plays a fundamental part. are computed as functions of their (co)variance components.
. the quest to predict new and more accurate genetic parameters for the Simbra breed is a priority to improve the breed. These genetic parameters that are of interest include estimates for heritability (direct additive.
1. including South Africa. Although estimates for both the Simmental and Brahman breeds are readily available.2.
improvement of growth traits provides potential to improve the profitability of beef cattle farming.
Several recent studies have been published containing information on heritabilities.
F. 32533258.J. Champion and Supreme Beef Animal http://www.D. M. Mukuahima. Genetic variation in composite and parental Journal of Animal 2004. South Africa. Multitrait estimation of direct and maternal (co) variances for growth and efficiency traits in a multibreed beef cattle herd. J. Eler. carcass.A.. M. Variance components and breeding values for growth traits from different statistical models.embryoplus. Journal of Animal Science. Estimates of genetic. and meat traits.T. L... &Treadwell. C.J. S..S. K. Journal of Animal Science. De la Rey.. J.A.. K.K. G. & Dickerson. & Lobo. Keiffer.. Journal of Animal Science.S. 69.. N.. South African Journal of Animal Science. Clark. Bloemfontein. The use of genetic algorithms for optimising age structure in breeding population when inbreeding depresses genetic gain through effects on reproduction. Magister Thesis. Pico. 77. 124-136.B. 2641-2650. & Brinks. Genetic and Environmental aspects of the growth curve parameters in beef cows. 1991.D.S.J. Mohd-Yusuff. Sanders.. Schoeman. & Parnell. R. L. 1999. B.. Brinks. Journal of Animal Science. Ferraz. G. environmental and Phenotypic parameters in range Hereford females. 2008. [Online].. Van Vleck.O. & Urick. 1991.. 23.. G.K.B.. 1964. J. Magister Thesis.S. 1985. 449-457.html Denise. Meyer.F. 1431-1440. Journal of Animal Science.S. De la Rey. J. Mackinnon. 68. 2004. MacNeil. Evaluation of reciprocal differences in Bos indicus x Bos taurus backcross calves produced through embryo transfer: II. M. (of Nguni.P. P. Estimation of genetic parameters for growth traits in South African Brahman cattle. Journal of Animal Science. The performance of beef cattle bulls in the Vrede district of Mpumalanga. & Van Vleck.D. South African National all breeds). Hammond.1. 2007.. 29. & Jordaan. Post weaning.3. South Africa. R. University of Pretoria. R...B.. South Africa. Herring. 711-716. 1995.
Amen. M.com/index. G. Ferreira.. T.D. A. & Gill. 85. University of the Free State. Estimation of variances due to direct and maternal effects for growth traits of Nelore Cattle. R. 3983-3988. J.. 373-379. 73. Science. J. R. 61. Available:
populations: expectation for levels of dominance and gene frequency.
2.87 345 National average 35 203 35 438 83. feminine cows..CHAPTER 2
The literature review offers an exploration of the Simbra breed origin as well as its previous performance record studies in order to obtain an overall view of the breed. (Massmann. defined as a hardy. namely bull Nestua 851 and heifer Lichtenstein L135.1. 2003) Table 2. well-adapted breed and characterized by heavy muscled bulls and fertile. Environmental and genetic factors influencing these traits are investigated to obtain comparable estimates. The American Simmental Association registered the first Simbra in 1977. Genetic trends obtained from other composite breeds are also reviewed. The Simbra currently occupies an estimated 15% share within the group of nine synthetic breeds and is growing each year. The first South-African F1 Simbra’s were registered in 1986. Simbra breed During the late 1960’s the Simbra(h) breed was established by a few cattlemen acting upon the idea of a breed that could thrive in the sub-tropical climate of the Gulf Coast region of the United States and still meet the demands of the industry.68 347
The Simbra is classified as a synthetic breed.51 346 Simbra National average 36 215 34 423 455 1653 6. The performance of the Simbra breed in comparison to the national average in the South African beef cattle performance and progeny-testing scheme (Mukuahima 2008).1.3 36 232 34 420 462 1594 6. smooth-coated. 2004). This led to the development of a practical cattle breed with economic advantages (De la Rey et al. The Southern Africa Simmental Society soon afterwards amended their constitution to accommodate this exceptional breed and formed the Simmental and Simbra Breeders’ Society.9 489 1904 5. with the highest percentage increase in females of all breeds. Trait 1980-1992 1993-1998 Simbra Birth weight (kg) Weaning weight (kg) Age at first calving (months) Inter calving period (days) Calving percentage (%) Final weight (Standardised growth test) ADG (Standardised growth test) (g/d) FCR (Standardized growth test) Scrotum circumference (mm) 35 231 32 406 89. The subject of live weight traits at different ages as an indicator of growth in composite breeds is also explored. It has been described as “The
having a higher Simmental component than that of the Brahman. Heterosis values reported for Brahman-European crosses averaged more than three times that of European crosses (Koger. Gregory et al. The advantage of composite breeds The practice of combining breeds. 2004). In addition to increased heterosis and exploitation of breed
. like the Simmental. 1980). where weaner production is of high importance.all purpose American breed”. This is evident in the South African Simbra.. maternal ability and reproductive performance. (1989) have proposed a 25% Brahman inheritance to keep the favourable characteristics of both breeds. Breed additive differences enable the breed to achieve and maintain the performance level for a variety of economically important traits that are most favourable for specified production and market situations. 2005). Crouse et al.1. Lower production costs are associated with composite cows because they out perform the purebred cows by producing 32% more calves with 6% heavier weights (Mukuahima 2008). 1991). Schoeman & Jordaan (1999) found that crossbreeding improves cow/calf efficiency when measured as an energy requirement (14%) or input costs (20%) per kilogram of steer equivalent weight. 1991.. The latter represents the recovery of accumulated inbreeding depression within populations that have been genetically isolated from each other for many generations (Roso et al. 2006).. 2006). where the environment determines the ideal breed composition. Gosey. 1995a. due to consumer preference for leaner beef and more importantly increased weaning weights (Mukuahima 2008). A too high Brahman percentage could lead to less favourable meat characteristics. In less challenging conditions. The retention of heterosis in composite populations is generally proportional to retention of heterozygosity (Gregory et al. 2002).. The breed is composed out of the two most populous beef breeds in the world.1. 2003). It has been found that the unique feature of the Brahman breed is its excellent combining ability with European breeds. simultaneously to optimise additive genetic composition. rapid growth. which is an important factor due to increasing feed costs. in order to create a “new” breed offers so much more opportunities than the conventional purebreds. resulting in generally high levels of heterosis for growth. The Brahman adds the adaptability to harsh areas due to the breed’s tolerance to heat and disease including internal and external parasites. Heterosis observed for growth traits in large crossbreeding populations has been found to be mainly due to dominance effects (Gregory et al. like the Brahman and the Simmental. delayed age at puberty and decreased growth performance (Plasse et al. 2. Composite superiority is mostly due to the exploitation of breed complementarities that enables incorporating climatic adaptability and performance traits into one breed (Gosey.. early sexual maturity. milking ability. they also provide the advantage of longevity and calving ease (De la Rey et al. namely the Simmental and the Brahman. good beef characteristics and docile temperament can be attributed to the Simmental. This must be done to obtain an optimal adaptive breed for its specific environment. The Simbra’s fertility. a higher percentage Simmental is more evident while in a harsher environment a higher percentage Brahman will be included (Massmann. Furthermore it has been found that Heterosis provides the opportunity to composites also provide herds of any size the opportunity to use heterosis and breed differences increase production performance to a maximum.. In addition to its ability to consume low quality forage.
1992). unlimited increase in BW selection will lead to immense economic loss (Brown & Galvez. Patterson et al. It is easy to obtain with reasonable accuracy (Dawson et al. healthy calves have a greater capacity for milk consumption and tend to maintain lactation persistency of the dam. Live weight measurements usually include birth weight (BW).
2. resulting in heavier weaning weights.3. BW reflects the effects of several important factors influencing the economic value of the calf during its lifespan. BW has been shown to be associated with calf growth rate dystocia. The existence of a strong positive genetic correlation between BW and WW as well as the post weaning growth has been concurrent throughout the reported literature (see Table 2.. It is important to note that gut-fill can contribute up to 10-15% of the variation associated with live weight traits (Mukuahima 2008). Live weight traits
Performance assessment around the world for beef cattle has mainly been focusing on live weight measurement at regular intervals to evaluate growth potential.) BW has accordingly been established to be a valuable prediction trait for both pre-weaning and post-weaning growth. Thus the dam’s genotype affects the phenotype of her calf through a sample of her direct additive effect for growth as well as through her genotype for maternal effects on growth (Meyer. Care should be taken when selecting for increased BW due to detrimental effects of too large calves. (1992) found that cows that have experienced calving difficulty had a subsequent decrease in reproductive performance as well as reduced milk production. BW is not only determined by its own genetic potential. Large.1 the average BW for the Simbra breed is approximately 35kg. From Table 2.
. Thus.. This enhances the opportunity for growth rate selection at a very young age and thus the prospective value of the calf. 1947) and displays the vigour and size of the calf at birth. perinatal calf losses and losses of cows at calving (Holland et al. yearling weight (YW) and 600-day weight.complementarities. BW has also been associated with higher mature cow weights that lead to higher maintenance costs. For true evaluation of these traits regular and accurate measurement is a necessity. 1969). the development of composites also offers the opportunity of performance consistency and the ability to produce their own replacement heifers once the breed is stabilised and established (Skrypzeck et al. 1977). The maternal environment mainly represents the dam’s milk production and mothering ability through effects of the uterine environment and extra-chromosomal inheritance. weaning weight (WW).2.. but also by the maternal environment. 2000).
selection must not be based on these traits alone and must be brought in to perspective with other important traits like reproduction and carcass traits. sex of the calf. Non-genetic factors affecting growth in beef cattle The fixed effects include all the non-genetic circumstances that influence the phenotypic value of the calf. 1995). The latter indicates that improvement can be obtained through selection for this trait. 2005). dam age as well as cow parturition weight and previous parous state (Raphaka. region. Mature cow weight has mainly been studied as pertaining to reproduction rather than for growth traits in beef cattle (Raphaka.. 2008).11 and 0. Roberson et al. The main fixed effects that impact on growth traits include herd. breeding systems and market end points (Dhuyvetter. size and weight changes which are associated with ageing cows. 2005). Even though high growth rates contribute to the efficiency of most production systems. Sex It is generally recognized that the gender of an animal in most species of domestic animals has a definite influence at the different growth stages. Melka (2001) found that gender accounted for 4. Specification and potential qualification of the influence of these environmental variation (fixed effects) affecting growth traits are important in order to establish management and selection decisions (Krupa et al.57 ± 0. WW is also related to other economic important traits of post-weaning growth like 400. month and year of birth. Mature cow weight is also an important factor associated with dystocia.1.52 ± 0. Krupa et al. It is well documented that males
. 1996). It is important to minimize environmental variation to improve the estimation of genetic parameters.3. It is thus important that cows with an optimal frame size are used for the specific production situation according to feed resources. 2. Koch & Clark (1955) suggested that the physiological. It has been shown by Burfening (1981) that cows having an adequate body size and -weight at the time of calving had lower incidences of calving difficulties.WW is a trait of high importance to beef producers due to the relationship of WW and dam productivity (maternal traits) and to the genetic potential of the calf’s pre-weaning growth. might be expected to influence maternal environment and have a direct effect on BW and WW.3% and 3. Denise & Brink (1985) found mature weight to be highly inheritable and reported heritability estimates of 0.8% of the variation associated with BW and WW respectively. It is of great importance to establish these non-genetic characters to facilitate genetic parameters for the breed under investigation as well as breeding values for individuals in the population. 2. breeder of the calf. 2008. weaning age.11..3. (1986) indicated that large cows usually produce large calves and are capable of producing more milk due to genetic maternal effects as well as genetically transmitted effects. It is however important to note that heavier cows are associated with larger framed cows and tends to have higher maintenance requirements that can lead to increased expenses (Schoeman. breed of the calf. These heavy cows also have the advantage of increased reserves that could be converted into adequate milk production throughout lactation to produce heavy weighing calves.and 600 day weight.
. 2008) Similar to the year of birth effect. 1997) In a more recent study.. However. It has been found by Holloway et al. Raphaka.. 2006).. 2006. Koch & Clark. Other authors (Christian et al. on good grazing.. These dams will also have better quantity and quality milk to feed their young properly than those dams in poor condition. The contribution of year of birth can be extremely variable due to differences in climatic conditions. Koch.. 1955). carcass grade. Ahunu et al. 2.. Ahunu et al.of most species of domestic animals grow more rapidly and reach a greater mature weight than females. Christian et al. (2000) found male calves to be 6.. It has been shown that year of birth can also significantly influence other important traits like. 2. It has been found that testosterone exerts a direct This anabolic effect on protein synthesis in many non-reproductive organs and body tissues.
accounts mainly for the increased muscle mass associated with male calves and thus heavier body
.. produced calves with a heavier birth weight. area of eye muscle and fat thickness. which is depending on the available grazing for that specific year. 1965.. WW was found to be affected the most by the year of birth and showed significant variation between years. The dams on restricted planes of nutrition prior to calving Maternal produced lighter calves due to a decreased fetal growth rate (Villalba et al. weights (Raff & Widmaier.2.1kg heavier calf. colour of eye muscle. (Koger & Knox. Sushma et al. 1972) also reported a significant association between milk production of beef cows and gain of calves. Gregory et al. 0. shrinkage. This phenomenon is mainly due to the physiological effect of male endocrinology (Sushma et al. 1980. It is particularly evident in extensive grazing conditions. Villalba et al. 2000. dressing percentage. 2000). 1950. feed efficiency.4% heavier at birth than females. This is due to the fact that calves born from dams that were in a higher body condition score during their late pregnancy phase.3. average daily gain (ADG). Year of birth The year of birth has been found to significantly influence growth traits throughout the life of the calf (Sushma et al. slaughter grade. 2004). 1997. BW was only slightly affected by year of birth. (1985) that dams that were allowed to have high quality nutrients showed a 23% increase in fatness. Month of birth It was shown that month of birth had a significant effect on calf weight measured at different stages.3.. It is however important to calculate the contribution of the gender effect for the specific breed to formulate a proper conclusion as to how much gender contributes to the total phenotypic variance of the animal.. Villalba et al. feeding and management as well as the genetic composition of the herd. 2006). The outstanding effect of year of birth on WW is predominantly due to the quantity and quality of milk produced by the dam. 1995.90 kg/day increase in milk production and weaned a 20. 1955. Yearling weight as well as final weight varied considerably between years due to the availability of feed and the carry over effect from pre-weaning growth (Shelby et al. (Plasse et al.3. Dillard et al. 1945.. 1965. the plane of maternal nutrition in general reflects the effect of month of birth on calf weight.
(1965) also found that the amount of butterfat and non-fat solids produced by the dam is even more
.43 and 0. the consumed nutrients are not only used for maintenance.5. selection objectives as well as the choice of breeding animals accounts for the majority of the variation associated with growth traits.3. (1987) reported that this trend is a reflection of the greater ability of mature cows to provide the foetus with the necessary nutrients and environmental conditions for its development.4.6. (1959) found significant correlations of 0. lactation and gestation. presumably influence this environment and consequently have a direct effect on BW and WW. (1965) found that calves from mature cows were heavier at birth. Lower BW. (1978) found that milk yield estimates increased noticeably in middle-aged cows up till an age of 8 years with a remarkable decrease for cows older than 8 years. Pico. 2004). while younger dams are physically impaired by their immature body size. 1995). It has been found by Burfening (1981) that younger dams had a higher percentage of calving difficulties than older dams. 2. the higher the Bos taurus (Simmental) component will contribute to the breed composition.performance of beef cows has been found to be accountable for 40% of the variance in weaning weights (Robinson et al. 2004) 2.. This is probably due to the fact that the older dams are physically more mature with a larger body size. The effect of month of birth was found to be significant up to 18-months weight (Plasse et al. 1978). Changes in weight. received more milk from their dams. Very young dams that are not physically or biologically mature enough. the higher incidence of Brahman will be obtained in the breed composition when farmed in tropical or sub-tropical conditions where resources are limited. Due to the fact that the Bos indicus is well known for its survival and effective production in harsh conditions. Elzo et al. size and physiological function.3. but also towards their own growth (Rumph & Van Vleck. Drewry et al. In less challenging environments where resources are easily available. which are associated with ageing. Breeder Sushma et al. consumed more feed and were heavier at weaning than the calves of 2-year-old dams. 2005) 2.29 respectively between birth weight of the calf and milk production of the dam during the first and third months of lactation.. Day to day decisions made by the breeder mostly relating to management practises. Breed composition Numerous authors have found breed composition to account for the large amount of variation associated with live weight traits within cattle. The latter will add to higher WW and is thus ideal for weaner production systems. WW and YW were obtained for dams younger than two years old as well as dams older than 8 years (Krupa et al.. Interestingly enough Robinson et al. Christian et al. Dam age It was reported by numerous authors that BW and WW as well as YW increased along with increasing age of dam until the age of 5-7 years. (2006) found that calves bred by different breeders differed significantly from each other due to environmental conditions as well as human choice variation. (Krupa et al. 2005). 2003. (Massmann.. Christian et al.3...
This is especially evident in situations with high selection intensities. 2004). 2006). (Co) variance components are unique to the population in which they were estimated and may change over years (Pico. This is due to the fact that the young calf’s consumption is limited by its abomasal capacity and is in need of a higher energy source at this stage. For the latter it would be for minimal variance. Genetic parameters The estimation of genetic parameters is an integral part of animal breeding since they estimate gene transmission from one generation to the next. the more the environmental variance decreases and consequently the heritability increases. can be used in the estimation of trait heritability. However with selection and management practices this is not always the result. similar to the Simbra. Mohd-Yusuff & Dickerson (1991) found that the genetic variance in a composite population. The more uniform the measuring conditions are. It is therefore imperative that a constant need for regular (co) variance component estimation exists in order to update genetic parameters as accurately as possible to improve the traits under selection. which is usually the preferred method. short generation intervals or a high degree of inbreeding. phenotypic and environmental) as well as repeatability. Based on particular biological rules we assume that (co) variances as well as the ratio between them does not change rapidly over time. Although the best method is not obvious. The mixed model is composed out of fixed effects and random effects. The resemblance between genetically related animals This is where the computation of variance components within and between family members plays a fundamental part (Van der Werf.important that the milk volume produced. correlation estimates (genetic. It is important to note that unbiased methods do not correct for selection in animal breeding data because
. 2000). variability occurring in a population is depicted by the variance components. There are various methods that can be used to estimate (co) variance components. Another important factor is the circumstances under which measurements are collected that can vary.4. The latter are computed as functions of their (co) variance components. Genetic parameters that are of interest include heritability (direct additive. The computation of genetic parameters will provide a better understanding of the genetic mechanisms involved in the breed under investigation. The genetic It is necessary to
estimate variance when we are interested in a new trait or breed that has no genetic parameters available in literature. might be more or less than that of the parental breeds.
2. These parameters also make it possible to select for superior breeding individuals to improve future populations (Bourdon. With a sufficient dataset we assume a mixed model that relates to the observations. The latter can also be associated with traits determined by only a few genes. it can be based on un-biasedness or in practice estimating the variance accuracy. The fixed effects determine the level (expected means) of observations and the random effects establish the variance. Thus for sound breed programs these parameters is of the essence and necessary to asses ongoing programs. maternal additive and permanent maternal environment).
YW and 600day weight in beef cattle. The heritability of growth traits (thus weight at different ages) at any stage during the calf’s life will differ from medium (0. 2000).55 0.38 0. Multibreed pop. Literature estimates for genetic parameters (h2a.45 0.32 0.25 -0. (2000) Australia New Zealand Australia South-Africa Ethiopia Venezuela Botswana Australia Australia New Zealand USA Canada 0. (1955) Tosh et al.17 0.4).09 0.31 0.14 0. 2 2 hm ram Reference Breed Country ha Birth weight (BW) Angus Angus Belmont red Bonsmara Boran Brahman Composite De los Valles Hereford Hereford Hereford Multibreed population Multibreed pop. (2003) South Africa 0.57 0.10 0. example of a program for parameter estimation is the ASREML package (Gilmour et al.14 0. 2002).16 0.27 0.23 0.72 0.13 0. Table 2.30 Meyer (1994) Waldron et al.05 0.2 and Table 2. it has been made the method of choice for most animal breeding applications. (1993) Burrow (2001) Maiwashe et al. making it thus impossible to eliminate. (1995) Ahunu et al. Maximum Likelihood (ML)-estimators maximizes the likelihood of the parameters.24 0.14 -0. (2002) Haile-Mariam & KassaMersha (1995) Martinez & Galindez (2006) Raphaka (2008) Gutierrez et al. h2m and ram) for BW.09 0..22 0.09 0. A summary of heritabilities and weight correlations at different stages of the calf’s life can be seen in Table 2.18 0.72 Eler et al.40 Skrypzeck et al.47 Demeke et al.14 0.31 0.55 0. Restricted ML (REML) on the other hand maximizes likelihood of parameters after correcting for fixed effects and does take the loss of DF in to account. Nelore Ndama and West African Brazil Ghana 0.26 -0.13 0.of least square equations utilization. WW.34 0.32 0. (1999) A
. (1997) Ethiopia 0.51 0.07 0.4) to high (> 0. Due to the fact that REML also accounts for selection. Such traits are easy to improve through selection and will lead to increased performance in future generations (Bourdon.3.12 -0.20 0. ML methods also do not consider the loss of degrees of freedom (DF) when correcting for fixed effects. (1993) Shelby et al.2.2-0.72 0. (1997) Meyer (1992) Waldron et al.08 0. Selection is an integral part of most animal data.09 0. but tends to be biased although they have smaller variances than unbiased estimators.
34 -0.13 0.73 -0. (1996) Schoeman et al.20 0.12 0. (2006) Weaning weight (WW ) measured at 200-days of age Colombia Canada USA South Africa 0. (1993) Burrow (2001) Maiwashe et al.32 -0.14 0.18 0. (2003) South Africa 0.15 0. (2000) Ghana 0.53 Eler et al.53 0. (1995) Sarmiento & Garcia (2007) Bennett & Gregory (1996) Schoeman et al. ( 2003) Botswana Australia New Zealand Australia South Africa Venezuela Botswana Australia New Zealand USA Brazil 0.29 Ahunu et al.41 0.66 0.64 0.30 0.60 0.13 Kenya 0.37 -0.33 0.22 -0.25 to 0. Ndama and West African Shorthorn Nelore Romosinuano Simmental Synthetic Brazil Colombia USA South Africa 0.17 0.03 0.38 0.22 -0. (2002) Martinez & Galindez (2006) Raphaka (2008) Gutierrez et al. (1997) Ethiopia 0.11 0.17 0. Multibreed pop.25 0. (1955) De los Reyes et al. (1993) Shelby et al.34 0.06 0.80 -0.11 Tosh et al. (2000)
.84 to 0.32 -0.19 0.53 0. Multibreed pop.33 0. (1999) Wasike et al.11 0.34 0.32 Sarmiento & Garcia (2007) Trus & Wilton (1988) Bennet et al.24 0.25 0.18 0.34 0.46 0.40 Raphaka (2008) Meyer (1994) Waldron et al. (1997) Waldron et al.07 0.27 -0.Shorthorn Romosinuano Simmental Simmental Synthetic breeds Tswana Angus Angus Belmot red Bonsmara Brahman Composite De los Valles Hereford Hereford Hereford x Brazilian Nellore Kenyan Boran Multibreed pop.65 Skrypzeck et al. (2000) Canada 0.19 0.19 0.21 0.13 0.12 0.61 to 0.28 0.07 Demeke et al.37 -0.21 -0.23 0.88 -0.53 0.17 0.36 -0.04 -0.20 0.31 0.
Breed Country Model ra re Reference Birth weight and Weaning weight Angus Zebu cross Nellore Canada Australia Brazil BAM BAM MAM 0.14 Meyer (1994) Meyer (1994) Eler et al. genetic covariance between direct and maternal genetic effects.79 0.33 0. (1998) Demeke et al.47 0.48 -0.78 0.. h2m.41 600-day weight Hereford Hereford Hereford Zebu cross USA USA Australia Australia 0. 1977)
Table 2. (2003) 0. BW will increase and will result in dystocia.15 0. Favourable as well as unfavourable correlation responses have been reported for growth traits.01 -0.23 0.07 Meyer (1992) Meyer (1992) Meyer (1992) Shehu et al.69 -0.76 0. (2008) Dodenhoff. (1955) Meyer (1992) Meyer (1992) Bennett & Gregory (1996) United Kingdom Australia Australia Australia Zamfara USA Ethiopia 0. heritability of maternal effects. If WW is increased dramatically through selection.49 -0. Simmental USA 0. (1995)
. Literature estimates for genetic correlations (ra) and environmental correlation (re) estimates for beef cattle breeds. ram.84 0.01 -0.3. 2004). heritability of direct additive effects.07 0.20 1.59 0.12 0.47 0.26 Bishop (1992) Botswana Australia 0.20 0.39 -0.03 0.25 0.14 0.16 0. phenotypic and environmental) is important to know how selection for one trait will influence another correlated trait that could be correlated (Zishiri. Knowledge about correlation estimates (genetic. 2009. For instance the strong positive correlation reported in literature between BW and WW.20 0.00 Swiger et al.22 0. (1961) Shelby et al. Pico.49 0.breeds Tswana Zebu Crosses Yearling weight (YW) measured at 400-days of age N/A Hereford Angus Zebu cross Bokoloji Hereford Multibreed pop. (Holland et al.74 Raphaka (2008) Meyer (1994)
h2a.04 0.09 0.38 0. et al.11 0. Adverse correlations could render improvement in a specific trait and lead to economic loss.
. (1995) Pico (2004)
BAM. Taking all of the above factors in consideration it is thus possible to construct different models that incorporate both non-genetic factors as well as genetic factors to calculate appropriate genetic parameters for the breed under investigation for the different live weight traits under investigation. P. 18. The genetic model for quantitative traits.L.12
Pico (2004) Meyer (1994) Meyer (1994) Eler et al.W. Bishop.Brahman Angus Zebu cross Nellore Brahman
South Africa Canada Australia Brazil South Africa
BAM BAM BAM MAM BAM
0. Phenotypic and genetic variation in body weight.12 0. MAM. Livestock Production Science. & Kissiedu.79 0.88 0. 1996. IN: Understanding Animal Breeding. K.5. 2nd Edition.60 0.. set breeding objectives and future goals.M. Stewart. Performance test results.95 0. Genetic and phenotypic parameters for birth and weaning weights of purebred and crossbred Ndama and West African Shorthorn cattle.. Ed. Jr. Charles E. 1992. bivariate animal model. Arthur. K.47 Meyer (1994) Meyer (1994) Eler et al. 1997. Inc.78 0. 200-day weight.62 0. Linsner & M. B.16 0.. and post-weaning gain in composites and parental breeds of beef cattle. P. Genetic (co)variances among birth weight.74 0. Prentive-Hall. Livestock Production Science.70 0.09 0. (1995) Pico (2004)
Birth weight and Yearling weight
Weaning weight and Yearling weight Angus Zebu cross Nellore Brahman Canada Australia Brazil South Africa BAM BAM MAM BAM 0.
2. 2000. 30.48 0.F. Bennett. Journal of Animal Science.79 0. food intake and energy
utilization in Hereford cattle. & Gregory. Carnis. The obtained results can then be compared to results obtained in previous studies for the same species in order to facilitate conclusions. G.E. 165-171.K.64 0. Bourdon. S. multivariate animal model..A.. R. References
0. 51.C. 2598-2611. H. 74.
1985.. additive and non-additive genetic effects for animal model analyses of weaning weights in a Nellore x Hereford mutibreed population in Brazil. South African National (of all breeds).html De los Reyes.. R.. Journal of Animal Science. Journal of Animal Science. 2001.. De la Rey.Brown. Available: Variances and co variances between productive and adaptive traits and
temperament in a composite breed of tropical beef cattle. 652-259. Burfening. Demeke. Non-genetic.embryoplus. 67. [Online]. J. D. & Robinson. Dillard.K.S. Denise. & Carvalheiro. M. 2006. 1980. Kress. 653-663. Bloemfontein.. 1431-1440. Koch.L. Cundiff.C. Koohmaraie. 1969.A. 213-233. M. M. Estimation of additive and non additive direct and maternal genetic effects from crossbreeding beef cattle.com/index.
. Journal of Animal Science. Livestock Production Science. O.. 162-167. 1210-1216. W.. Journal of Animal Science. Burrow.. L. 24. 84.R.D.C. L. Philips. 2003. Comparisons of Bos indicus and Bos tarus inheritance for carcass beef characteristics and meat palatability. Journal of Animal Science. 50.. 1947. Roso. 61. III.H. A. & Galvez. J. R.S.W.B.. Variance components and genetic parameters for early growth traits in a mixed population of purebred Bos indicus and crossbred cattle..R. 2661-2668.. & Schoeman.U. 8th World Congress on Genetics Applied to Livestock Production. S. R. & Treadwell. V... 6: 247-257.C.. Genetic and Environmental aspects of the growth curve
parameters in beef cows. R. F. Hauser. 28. Fries. & Brinks. Journal of Animal Science. Nguni. R. & Chapman.J. 1989. 70.W. & Black W.R. M.W. J. & Friedrich.D. L. 53.. A. Champion and Supreme Beef Animal 2004. Crouse. Christian. H. E.. M. Journal of Animal Science. South African Journal of Animal Science. Maternal and other effects on birth weight of beef calves. Rodriguez. Birth weight as criterion of selection in beef cattle. De la Rey. & Seiderman. Neser.. Dawson.. O. S. 1965. Elzo. Calving Ease and growth rate of Simmentalsired calves. P.. 1981.J.
http://www. E.A. Association of pre-weaning and post-weaning traits with weaning weight in cattle.M. S.M. R.M. 11-21.M....
1959. 76. R. 69. & Kassa-Mersha. J. 43-
. Breed effects heterosis in advanced generations of composite populations for birth weight.. 18. Mullaney. Parameter estimates for direct.D.. Journal of Animal Science. 1987. Estimation of direct and maternal genetic parameters for pre-weaning traits in the Australian de los Valles beef cattle breed through animal and sire models.. 114. Crossbreeding with composites. & Pollak.M. Available at:
www.edu/pubs/ansci/beef/as1091w.J. Gregory. Journal of Animal Science.M.J. Elzo. R. Journal of Animal Science. R. J.. H. & Hopkins. 73. Relationships among factors associated with
mothering ability in beef cattle. Estimation of variances due to direct and maternal effects for growth traits of Nelore cattle. 1995a. R.P. P.J. Blunn. Journal of Animal Breeding and Genetics. Journal of Animal Science.D. J. J. Gregory. J.Dodenhoff.J..S. 2521-2527.R. 17.V.. 261-266. Gregory.. A study of some of the factors influencing the birth and weaning weights of beef calves. 1977. Canon. 3574-3589. B. & Marvel. L.. & Goyache. Kachman. K. M.B. and Koch. dystocia and survival as traits of dam in beef cattle. J. L.D.A. 1997. 64.. I.B.L.T..V. C. K. M. Effects of age of dam on weight traits in the Simmental population.E. & Ferraz. 112. C. 22.. Journal of Animal Science.. Gosey. Gutierrez.M. E. Haile-Mariam. 42. 938-946. Journal of Animal Science. 73. Breed and environmental factors affecting birth weight in Victorian beef cattle.. Journal of Animal Breeding and Genetics.P..E. 1950. Dhuyvetter. L. R. & Honea. Minneapolis. F. 1998.E. Estimates of direct and maternal (co)variance
components of growth traits in Boran cattle. 1920-1926. maternal. J. K. Quaas. 1995.M... 5-9.ndsu. 52. 2006. Van Vleck.htm Drewry. & Kock.. Cundiff. S. Van Vleck. 1995. Cundiff.ag. L. 3253-3258. 1995... and grand maternal genetic effects for birth weight and weaning weight in Hereford cattle. Holland. Eler. K.. 9. Journal of Animal Science. Beef. 338-346. Beef cattle frame scores..D. Brown. & Kock. birth date. 1991. Journal of Animal Science.. L. 992-1001. Genetic and phenotypic (co) variances for growth and carcass traits of purebred and composite populations of beef cattle.
The role of maternal effects in animal breeding: VI. J. 14-21. Magister Thesis. 91-105. 75..N. Factors affecting growth traits of beef cattle breeds raised in Slovakia. 1215-1220. Butts. R. Peeler. 4. 2002.J. R. H. Variance components due to direct and maternal effects for growth traits of
Australian beef cattle.htm Melka. Krupa. 1316-1323. Koch.H.L..org/ENGLISH/Index. Brazil. R. History of the Simbra. Influence of sex... Appl.W.B. 2006.. The effect of sex on weaning weight of range valves. Theron. J.simbra. K. Martinez. Belo Milk
population. Breed x nutritional environment interactions for beef female weight and fatness: production and calf growth. Genetic and environmental trends for birth and weaning weights in registered Brahman cattle. H. 2003. 8th World. 2001.L. G. McCurley. 1955. 14.M. & Backus. P. & Galindez. Oravcova. 50. 50. & Krupova.. 1945. 61.. & Clark. R.. Z. Huba. J. Bradfield. University of Stellenbosch.. Czech Journal of Animal Science. Meyer. Estimates of direct and maternal correlations among growth traits in Australian beef cattle. season of birth and age of dam on economic traits in range beef cattle. A.P. Prod. E. Polak. 1972.H. Genetic parameter estimates for weaning traits in a multi-breed beef cattle In: Proc. M. J. W. South Africa. W. Livestock Production Science.. Effective crossbreeding systems utilizing zebu cattle.. Journal of Animal Science. 179-204. Koger.E.D. Massmann. Journal of Animal Science. & van Wyk. 1985.T. K. 31. Journal of Animal Science.T.E. Horizonte.H.Holloway.. 35. 15-19. Koch. & Knox.. Livest. M. 38.. Congr..
. Journal of Animal Science. Maiwashe. Genet. 386-397.. Livestock Production Science. 1992.M. 1354-1363. 1980.R. M. Livestock Production Science. 1994. Koger.. Available at: http://www. Beaver. Meyer. E. 2005. M. C.. Journal of Animal Science. The Simbra cattle breeders’ society of Southern Africa. 293-300. Maternal effects in beef cattle. Genetic parameter estimates for body measurements and growth traits in South African Bonsmara cattle..L. J.
O.L.A. D.Mohd-Yusuff. H. Plasse.. Verde.. 1978.U.
.T. LR. Patterson... & Dillard. Journal of Animal Science. Biometrika.. R. & Dickerson. R. Weights at birth. O. D. Pico. Roberson. 438-446. R. Ed.M. 1971. T. dominance and epistatic loss effects on pre-weaning weight gain of crossbred beef cattle from different Bos tarus breeds. Bellows. Strang. Yusuff... 2002. R. 69. University of Pretoria.W.. South Africa. 131-136. R. Rodriguez. 1986. (Co)variance components. Raff. University of the Free State. Bastidas. New York. & Thompson. 2008... & Cartwright. Schenkel. V. Journal of Animal Science. Romero. McGraw-Hill Companies Inc. R. Management considerations in heifer development and puberty. USA. I. & Corah. 63.C. 2005. Roso. Staigmiller.M.C... Magister Thesis. Means and correlation. H. IN Human physiology: The mechanisms of body function 9th Edition. weaning and 18 months.H.. Watnick. Robinson. K. Hoogensteijn. R... G. & Bastardo. Sanders.A. Hereford and Brahman-Hereford crossbred cattle. Journal of Animal Breeding and Genetics. 70. S. & Bastidas. South Africa. Kiracofe..O. Journal of Animal Science. I. 1780-1787. Milk production in Hereford cows... R. Fossi.
Mukuahima.. M.C. J. O. F.K. Estimation of genetic parameters for growth traits in South African Brahman cattle..B.D..E. Blood and hemostasis. Miller.K. Journal of Animal Breeding and Genetics. P. Verde. 1992.. Magister Thesis. B. 119. 47. E.J. 1995. Rodriguez.. 83..P. M. 141-153. 4018-4035. E. Hoogesteijn. Perry. Direct and maternal genetic effects on preweaning characters of Brahman.
Genetic variation in composite and parental Journal of Animal
populations: expectation for levels of dominance and gene frequency. G. R... Recovery of inter-block information when block sizes are unequal. 117132. 2004.P. R. 3983-3988. H... & Widmaier. Journal of Animal Science.S. Fossi. M. 1991.. Science.. J. Patterson. South Africa. The performance of beef cattle bulls in the Vrede district of Mpumalanga. genetic parametrs and annual trends for calf weights in a pedigree Brahman herd under selection for three decades. 2004. 545-554. & Wilton. 112. M. 58. H. Growth of F1 Bos taurus x Bos indicus vs Bos indicus beef cattle in Venezuela. Additive. G. Plasse. D. J.W.
Pre-weaning growth traits of the Hereford breed in a multi breed composite beef cattle population. Genetic and phenotypic parameters for body weight of Sokoto Gudali (Bkoloji) cattle. Sushma. Jordaan. Shelby C. Shehu... B.R. 1996. & Garcia. G.A. Skrypzeck. Estimation of genetic parameters and variance components for growth traits in Romosinuano cattle in the Colombian humid tropics.. 1961.A. Oni.Rumph. Sarmiento. 2006. S. Characterization of beef cattle breeds by virtue of their performances in the National Beef Cattle Performance and Progeny testing Scheme. Multitrait estimation of direct and maternal (co) variances for growth and efficiency traits in a multibreed beef cattle herd. 1955. 124-136.. Journal of Animal Science. R.. The heritability of some economic characteristics of beef cattle. South African Journal of Animal Science.S. G. S.A.V. 2. Ramesh Gupta. International Journal of Applied Sciences. L. 2000. South Africa. Swiger. & Skrypzeck.M. Journal of Animal Science. O. G. 228-235. 2008. 15-19.J. K... Influence of genetic and non-genetic factors of body weights and body measurements of Ongole cattle. & Neser. 2004..... Schoeman. 1-17.J. South African Journal of Animal Science. Reddy. N. & Van Vleck. Genetic and environmental influences on gain of beef cattle during various periods of life.. & Woodward. 183-188. & Adeyinka. 372-385.C. 30. 30. J.. S. G. H. South African Journal of Animal Science. S. 6. 26... Genetic Molecular. University of Stellenbosch.F. 29.P.. Magister Thesis.M. 2000. Raphaka.. Schoeman. Olorunju.T. G. I...F. The influence of proportion of Simmental breeding in a multi breed synthetic beef cattle population on pre-weaning growth traits. 14. R.D. 220-229. Estimation of genetic and non-genetic parameters for growth traits in two beef cattle breeds in Botswana.
.S. Clark. S. J. 482-491.. 20. Genetic and Molecular Research. 75. Jordaan. South Africa Journal of Animal Science. Schoeman.F.J. H.. 2008...M. Schoeman. 2007. R.W. 98-109. L. 64-67.J. Age-of-dam adjustment factors for birth and weaning weight records: A review. 3. & Reddy . & Jordaan. F. Indian Journal of Animal Science. Research.A. D. Vinoo.O. 1999.
C. R. Trus. Casasus. D. R. J. D.A. 2000. Kemp..A.. J. Estany. D. 433-439. D. & W. Zishiri. A.. 1999..
. Maternal effects for growth traits in beef cattle.L. I. 79. Magister Thesis. 1993. Morris. 34.. & Johnson.. University of New England. Sanz. 1988. NSW Australia. 68. Pre-weaning growth curves in Brown Swiss and Pirenaica calves with emphasis on individual variability. & Ward. 1132-1140. University of Stellenbosch. 110. 119-128. D.. Waldron. Baker. R.L..R. Estimation of genetic parameters.Tosh. Canadian Journal of Animal Science.. 2006. J. Armidale. Canadian Journal of Animal Science. P. Journal of Animal Science. Genetic analyses of South African terminal sire sheep breeds.F. 78. 57-70.T. Van der Werf. Livestock Production Science. O. Villalba. Estimates of direct and maternal genetic parameters for weight traits and back fat thickness in a multi breed population of beef cattle. J. 2009... & Revilla. South Africa.W. Genetic parameters for maternal traits in beef cattle.J.
weaning weight. WW. Some years showed a significant difference from each other for all the traits measured as well as month of birth.1. BW. Introduction
An animal’s phenotype is dependant largely on factors other than genotype. YW as well as 600-day weight with contributions of 25. dam age. Gender of the calf accounted for most of the variation (2. yearling weight at 400 days (YW) and 600-day weight were analysed using the General Linear Models procedure of the Statistical Analysis System.71% respectively.2. year of birth and age of dam were fitted in the models analysed because of their significant (P < 0.05) effects of BW and WW while dam age was not significant (P > 0. 600-day weight. month of birth. month and MCW had significant (P < 0.05) up till weaning weight. It is thus evident that certain non-genetic factors play an important part in the respective live weight traits and should be considered during the formulation of breeding objectives. Breed was found not significant for YW. Calves with a higher Brahman percentage weighted more at birth while calves with higher Simmental percentage weighted more at weaning. dam age. Tukey’s multiple range tests were performed for testing differences between least square means. genotype of calf.25%) associated with BW. yearling weight. sex. year. 18. Breed composition differences were significant (P < 0.05) influence on variation in the respective weights.35% and 10. weaning weight at 200 days (WW). Breeder of the calf accounted for the most variation in WW. Keywords: birth weight. During this procedure sex of calf. Abstract
The effect of different fixed effects on birth weight (BW). breeder. Results indicated that male calves were significantly heavier than females for all four traits measured.CHAPTER 3
NON-GENETIC FACTORS INFLUENCING GROWTH TRAITS IN SIMBRA CATTLE
3. The latter can be seen from the following equation: P=G+E Where P = phenotype G = genotype E = environment
. It was determined that the fixed effects of sex.05) for YW or 600-day weight. YW and Mature cow weight (MCW) where fitted as covariates where appropriate. breed
3. Middle-aged dams were found to account for heavier calves at both BW and WW while very young and very old dams produces lighter calves for the two live weight traits.77%. breeders of calf.
weaning weight at 200 days (WW). weaning age. it is essential to determine these factors that act as environmental variation as well as their contribution to the animal’s phenotype. The information contained in the data set included pedigree information. to be accountable for the large variability in beef growth traits. To improve the performance of the Simbra breed. However. The animals were composed out of Brahman and Simmental with differing percentages of each parental breed.3. calving number. yearling weight (400-day weight). Calves were born all year round in different parts of South Africa. breed composition of calf. Heritabilities are normally smaller than 0. gender of calf. birth date. The animals were also not assigned to contemporary groups with the prevalence of some single sires and dams with few progeny.3. especially management practices. weaning weight (200-dayweight). The following main factors have been found in literature to effect growth weight in cattle: Herd. whereas a smaller number have weaning weights. region.
3. there were some important traits lacking from the obtained data. breed type. birth weight. (2000) has found that these environmental factors. Some sires and dams also had no performance records recorded. dam’s body conformation. Materials and Methods
3. muscularity of calf. The available records ranged from the year 1987 to 2009. birth weight. Most of the calves have birth weight records. 600-day weight and mature cow weight. The objective of this study was therefore to gain information on the environmental factors that influence birth weight (BW). Pribyl et al.5 and indicate the importance of the environmental proportion. it was possible to calculate a dam-age for most of the dams in the remainder of the dataset. A smaller h² means that the environmental influence will be more and less progress will be made through selection and management should receive more attention.1. intercalving period. month of calving.The greater proportioned E. condition and age. yearling weights and 600-day weights available. Although this was a rather large and complex data set. breeder. Records The Simmental and Simbra Breeders Society of South Africa availed a total of 148751 performance records for the Simbra breed. However if enough genetic variation exists for exploitation between individuals for the respective trait genetic improvement is inevitable. yearling weight at 400 days (YW) and 600-day weight while determining the value of each character in the Simbra breed. There was also the incidence of traits that were measured but not documented correctly. year. the less P will be as an indication of G and thus the smaller heritability (h²) will be for that trait. The latter was evident for the number of calvings as well as birth status. For instance the date of weighing that was not documented for either one of the weight traits and made it impossible to calculate other important traits like average daily gain.
3. genotype of the calf (3 levels). When treated in this manner. The uni-variate procedure in SAS was then utilized to determine the ranges of all fourweight traits. year of birth (23 levels) and age of dam (8 levels). 95% confidence intervals were constructed.220. 91.The unedited data was opened in GenStat version 7. weaning weight from 133-332kg. Statistical Analysis Data were analyzed using the General Linear Models (GLM) procedure of the Statistical Analysis System (SAS.
Summary statistics and one-way
frequencies were done to get an overall view of the data.0 57.2 65. Description of data set after editing for calf BW.1. The models fitted included the fixed effects of sex of the calf (2 levels). Number Trait Birth weight (kg) Weaning weight (kg) Yearling weight (kg) 600-day weight (kg) of records 64794 34880 12585 5477 Minimum 25 133 171 234 Maximum 45 332 448 547 Mean 35 233 303 384 Standard deviation 4.2. Animals with a dam age equal to one were removed and the animals with a dam age of more than 9 were pooled together. 25% Simmental and 75% Brahman (SI25%BB75%). Table 3.1.1
3.44% of the records were discarded for modelling birth weight. Records of animals with unknown identity and unknown pedigree. The total number of records and means for all the traits available for analysis after editing are presented in Table 3.
. 75% Simmental and 25% Brahman (SI75%BB25%).2. birth weight ranged from 2545kg.0. Because of deficiencies of various kinds in the data and the restrictions imposed for the purposes of the analysis. leaving eight classes of dam-age. 76. yearling weight from 171-448kg and 600-day weight from 234547kg. month of birth (12 levels). as well as false weight traits for all of the weights measured were discarded. Animals were considered Simbra if they were of the following composition: 50% Simmental and 50% Brahman (SI50%BB50%). breeder of the calf (265 levels). After calculating the means and standard deviations.2 43. WW.54% for modelling yearling weight and 96. with the exclusion of base animals. 2004) to study the influence of factors affecting the different growth traits.55% for modelling weaning weight. Dam age was calculated and grouped together according to observed trends in BW and WW. YW and 600-day weight. The remainder of the data was then transferred to Microsoft Excel in order to obtain a proper set of data that could be transferred to the Statistical Analysis System (SAS. 56. 2009). Only calves with weight records within the 95% confidence interval for that specific weight were then retained in the analysis.32% for modelling 600-day weight.
3) SI50%BB50%. 3) SI50%BB50%. Least square means and standard errors were calculated. SI75%. 2) male / female Gj = fixed effect of the jth genotype (j = 1.09 Dm = fixed effect of the mth age of dam (m = 8) Bn = fixed effect of the nth breeder (n = 265) eijklmn = random error Weaning weight Yijklmn = μ + Si + Gj + Mk + Yl + Dm + Bn + b0 (MCW) ijklmn + b1 (BW) ijklmn + eijklmn Where: Yijklmn = Weaning weight μ = population mean Si = fixed effect of the ith sex (i = 1. All fixed effects and their interactions that had no influence (P > 0. February…. 89…. February…. SI25%BB75%.December
. 2. SI25%BB75%.December Yl = fixed effect of the l year of birth (I = 23) 87. YW and 600-day weight were excluded from the final analyses according to a step down procedure. 2. 2) male / female Gj = fixed effect of the jth genotype (j = 1.. WW. YW and Mature Cow Weight (MCW) were fitted as linear covariates where appropriate. The following models were fitted: Birth weight Yijklmn = μ + Si + Gj + Mk + Yl + Dm + Bn + eijklmn Where: Yijklmn = Birth weight (kg) μ = population mean Si = fixed effect of the ith sex (i = 1. BB25% Mk = fixed effect of the kth month of birth (k = 12) January. WW.BW. SI75%BB25% Mk = fixed effect of the kth month of birth (k = 12) January.05) on BW.. Tukey’s multiple range tests were performed for testing differences between least square means.
Yl = fixed effect of the lth year of birth (I = 23) 87. b2 = linear regression coefficients of Yijkl on birth weight (BW) and weaning weight (WW).09 Dm = fixed effect of the mth age of dam (m = 8) Bn = fixed effect of the nth breeder (n = 265) MCW ijklmn = mature cow weight fitted as a linear covariate b0.09 Bl = fixed effect of the lth breeder (l = 265) MCW ijkl = mature cow weight fitted as a linear covariate b0. b1 = linear regression coefficients of Yijklmn on mature cow weight (MCW) and birth weight (BW). b1.December Yk = fixed effect of the kth year of birth (k = 23) 87. eijkl = random error 600-day weight Yijklm = μ + Si + Gj + Mk + Yl + Bm + b0 (MCW) ijklm+ b1 (BW) ijklm + b2 (WW) ijklm + b3 (YW) ijklm + eijklm Where: Yijklm = Yearling weight μ = population mean
. eijklmn = random error Yearling weight Yijkl = μ + Si + Mj + Yk + Bl + b0 (MCW) ijkl+ b1 (BW) ijkl + b2 (WW) ijkl+ eijkl Where: Yijkl = Yearling weight μ = population mean Si = fixed effect of the ith sex (i = 1. 2) male / female Mj = fixed effect of the jth month of birth (j = 12) January. 89…. 89….. February….
3. 2) male / female Gj = fixed effect of the jth genotype (j = 1. 0. 3. b3. SI75%. SI25%BB75%.
. February…. = linear regression coefficients of Yijklm on birth weight (BW). 2008).65 respectively.4.44. 2.39. 1982. BB25% Mk = fixed effect of the kth month of birth (k = 12) January. 0. b1. This indicates that the fixed effects fitted for the respective models are explaining 23%. 54% and 65% of the variation. The R-square values for the models fitted for BW.23.4.. eijklm = random error
3. weaning weight
(WW) and yearling weight (YW).09 Bm = fixed effect of the nth breeder (n = 265)
MCW ijklm = mature cow weight fitted as a linear covariate b0. 54% and 65% of the variation. 39%.6.5. Raphaka. This indicates that the fixed effects fitted for the respective models are explaining 23%.. 39%. 89….3. but not with that of Melka (2001) who found an R-square value of 0. The contribution of each fixed effect for that particular growth trait can be seen in Tables 3.Si = fixed effect of the ith sex (i = 1. The R-square value obtained in this study for BW is consistent with the findings of Raphaka (2008) and that of Nephawe (1998). b2.2. WW. The remainder of the variation is due to other factors (random effects as well as other fixed effects) not included. The R-square value obtained from the model fitted for each trait measured gives us a good idea of the variation within the data associated with each fixed effect component. The least squares means for these growth traits are represented in Table 3.December Yl = fixed effect of the l year of birth (I = 23) 87. 3. 3) SI50%BB50%.54 and 0. Results and Discussion
The contribution of each fixed effect (FE%) on every particular growth trait was depicted by expressing the sum of squares for such an effect as a percentage of the total corrected sum of squares (TCSS) (Leighton et al. YW and 600-day weight were 0.
29 10.05 P< 0. Source of variation DF Type II SS MS F Value FE (%) Mature cow weight Dam age Month Year Sex Breed Breeder Birth weight Corrected total Sum of Squares Error mean square R-square Coefficient of variation (CV%)
1 7 11 21 1 2 168 1 27893 27681
148969.74* 79216.24 2. Mean squares (MS).Table 3.89 618546. Type III sum of squares (SS).77 0.76 1.49*** 19296.55
13.97 0.74 14.04 17.68 77.39 14.51 196.75*** 223634.25 0.001
2084.18 32.35 3.71 0.93 855905.19 0.05 P< 0.88 484345.48 13308324.37*** 1043465.10*** 93. significance level and proportional contribution of fixed effects (FE %) to the overall variance for WW.95*** 4383.49 424.29 1.63 150196.92***
153.46 404.20 4.13*** 37609.60 0.3.85 69.88
130.53 2.46*** 202. Source of variation DF Type III SS MS F Value FE (%) Dam age Month Year Sex Breed Breeder Corrected total sum of squares Error mean square R-square Coefficient of variation (CV%)
7 11 22 1 2 264 48953 48645
1.23 10. significance level and proportional contribution of fixed effects (FE %) to the overall variance for BW.99 915.89*** 88363. Type III sum of squares (SS).22*** 484345.94
P< 0.08*** 1139.24
Table 3.87 19296.02 0.07 2056.08 51652321.87 41.87 1418.01
P< 0.02 25.95 8767.23 2459975.71 1043465.89*** 146.01
P< 0. Mean squares (MS).32*** 568.49
P< 0.74 9.
Source of variation DF Type II SS MS F Value FE (%) Mature cow weight Month Year Sex Breeder Birth weight Weaning weight Corrected total Sum of Squares Error mean square R-square Coefficient of variation (CV%)
1 11 20 1 127 1 1 12584 12422
15304.02 1704751.95 1949.16
Table 3.62** 229241.34
P< 0.05 P< 0.21*** 59507.5.09 0.45*** 1512.45 23233791.16* 28597.74 4. Source of variation DF Type II SS MS F-value FE (%) Mature cow weight Month Year Sex Breed Breeder Birth weight Weaning weight Yearling weight Corrected total Sum of Squares Error mean square R-square Coefficient of variation (CV%)
1 11 18 1 2 87 1 1 1 5476 5353
0.78 16.97*** 148956.65 10.02 0.92 1.69 379122.57*** 14221.25*** 3981761.85
10.01 P< 0.99 7.Table 3. Mean squares (MS).06 148956.45 2631.35 39220.04 0.15*** 6179.67
P< 0.35 431426.25 255990. Type III sum of squares (SS).13
40. significance level and proportional contribution of fixed effects (FE %) to the overall variance for YW.4.91 13.55 1126.02*** 1704751.82 21134.10 3.25 7.65 813433.99 39. significance level and proportional contribution of fixed effects (FE %) to the overall variance for 600-day weight.85 25.50 0.40 512928.69 34465.25 3981761.67*** 25646.70*** 813433.12 22.62 229241.01 P< 0.39
15304. Mean squares (MS).54 12.93 41175258.40 537.93*** 1513.23*** 21134.35 0.21 7557512.001
.86 1.36 9.93 9.71 0.27 1.68 0.32 2487958. Type III sum of squares (SS).17 18.29 2950370.05 10.97 151.41*** 2950370.33 98.05 P< 0.15 12358.98
) depicting the influence of fixed effects on growth traits in the Simbra breed.12 37.E.1.07 *** *** *** *** na na
P< 0. WW.15 ± 1.66 ± 0.12 35.07 *** 229.64 ± 1.12 ***
23150 590 11100
231.3.12 35.29 ± 1.05 P< 0. 3. The variance contribution of gender was lower than the findings of Melka (2001) who found a 4.07 241. Least square means ± Standard error (s.72 ± 1. The effect of gender on growth traits in the Simbra breed Sex was shown to be significant (P< 0.2.07 242.6.8% contribution for BW and WW respectively.39 ± 1.34 ± 0.07 ***
31671 33123 4078 11433 8453 6873 5429 3972 2968 5748
36.12 35.09 ± 0. The difference between male and female calves was highly significant for BW.4 and 3. It can be seen from Table 3.07 235.47 ± 1.07 238.96 ± 0.12 *** *** *** na na na
16710 18170 1996 6629 5007 3987 3126 2344 1648 3178
237. Birth weight Weaning weight Fixed effect Overall mean Breed SI50%BB50% SI25%BB75% SI75%BB25% Sex Male Female Dam age (year) 2 3 4 5 6 7 8 9+ Mature cow weight Year Month Breeder Birth weight Weaning weight Yearling weight
kg ± S.29 ± 0.12 36.29 ± 0.02 ***
kg ± S.e.47 ± 0.07 240.E.87 ± 0. 7. 3.02%.12 36.47 ± 1.50% respectively to BW.12 *** 35.001) for all four traits measured.07 240.17% and 3. 35.46 ± 1.01 P< 0.07 237.07 231.12 37. YW and 600-day weight total variance.
.12 35.07 232. 2. 232.77 ± 0.Table 3. WW.29 ± 1.23 *
43625 1251 19918
35.21 ± 0.91 ± 0.07 224.12 37.33 ± 1.41 ± 1.16 ± 0.4.39 ± 1.3% and 3.52 ± 0.001
3. YW and 600-day weight when a multiple comparison was done with Tukey’s test.5 that sex contributed 2.13 ± 0.30 ± 1.12 37.25%.54 ± 0.
47 kg.001) for BW and less significant (P < 0. This indicates that male calves are more responsive to improvements in the environment (Hopkins. It is evident from Table 3. 2006. (1989) found that male calves are heavier than females of the same age.05% of the variation.53 kg.87 kg). et al.. The additive component of the breed. the
. Male calves showed a birth weight of 36. Garrick. A significant difference between SI25%BB75% and SI75%BB25% was found for BW during multiple comparisons by means of Tukey’s test. Male calves weighed 1.3% and 39. Kocak. Male calves had a WW of 238. It has been found that testosterone exerts a direct anabolic effect on protein synthesis in many non-reproductive organs and tissues of the body. Gregory et al. This is apparent in Figure 3.04% of the variation associated with BW and 0.05) for WW and 600-day weight. Koch & Clark.21 kg.47 kg and a 600-day weight of 421. et al.02% for WW. 1980..33 kg heavier than female calves generating higher values compared to the obtained literature results.2. WW and average daily gain in a multi breed population.87 kg and a 600-day weight of 384.13 kg..1. The effect of breed composition on growth traits in the Simbra breed Breed composition of the calf was shown to be significant (P < 0.96 kg) than calves with a higher percentage Simmental (35. Krupa et al. Breed of the calf is composed out of SI25%BB75% or SI50%BB50% or SI75%BB25%.. This was also evident for the other weight traits measured indicating that sex differences increased as growth rate increased up till 600-day weight. An extended gestation period has been found to be responsible for the higher BW associated with bull calves. Calves with a lower percentage Simmental and higher percentage Brahman weighed more at birth (35.3 and 3. The reason for this could be due to several factors. The latter may be due to the direct additive effect of the Brahman breed. 3. (1993) found that breed composition accounted for 17. Breed composition was the most important in 600-day weight measured at 600 days of age and accounted for 0. 1997. 1980). an YW of 309. (2001) and Amen et al. However Martin & Blunn (1952) found that 10% of the variation associated with differences in BW between sexes is due to gestation length. which is consistent with the results obtained in this study. 1955. 2007).. 1965. Melka (2001) found that calf genotype was one of the most important factors influencing BW..54 kg compared to female calves that showed a birth weight of 35. 2005. Dillard et al. Ahunu et al. individual heterosis and maternal heterosis is thought to be responsible for the difference in performance (Dillard et al.2. This accounts mainly for the increased muscle mass associated with male calves and thus heavier body weights (Raff & Widmaier.. 1977). (2007) that reported higher BW associated with higher Brahman percentage in crossbred calves.9% for BW and WW respectively. This is in relationship with the results of Franke et al. YW of 346..51 kg compared to female calves with a WW of 225. Schoeman et al. 1945.5 that breed composition accounted for 0. 3.The effect of gender on pre-weaning and post-weaning weight in beef cattle has been extensively studied (Koger & Knox. 1950. 2004). et al.4. Christian et al. breed maternal. Sushma. However for YW breed composition was not significant at all. A possible explanation for the observed pattern is the physiological effect of male endocrinology and not so much the influence of the dam.
but was still significant for WW (Figure 3. This is consistent with the results obtained by Pitchford et al. However it is evident from Figure 3.8 35..5 35.heterosis of foetal growth and the possible longer gestation length associated with Brahmans (Amen et al.
36.7 35.. The difference between SI25%BB75% and SI75%BB25% diminished with age.1 36 35. This is probably due to the enormous potential of the Simmental for growth intensity traits like ADG compared to the Brahman (Krupa et al.4 35. Calves with a higher percentage Brahman and lower percentage Simmental reached a higher 600-day weight of 411. This is consistent with the results reported by Barkhouse et al. 2005) as well as the elevated quantity of milk produced by dams with a higher percentage Simmental genes. Vertical bars indicate standard errors. (1993) in a study done with Brahman cross Hereford cattle as well as the results reported by Amen et al.
.2 36. but not for YW or 600-day weight.2 SI25%BB75% SI50%BB50% SI75%BB25%
Figure 3.1.3 that the weight advantage from the high percentage Simmental composition diminished completely with age.9 35. Calves with a higher percentage of Simmental weighed more at weaning (232. This is not consistent with the results obtained by Trus & Wilton (1988) who found that BW increased with an increasing percentage of Simmental genes.22 kg.2).. 2007).6 35. (1998).3 35.46 kg). The effect of breed composition on birth weight for the Simbra breed.64 kg) than those with a lower percentage Simmental (231. (2007).
Vertical bars indicate standard errors. Dillard et al. with a specific breed combination.Weaning weight in kg
234 233 232 231 230 229 228 SI25%BB75% SI50%BB50% SI75%BB25%
Figure 3. It was also evident from the obtained data that the number of animals partaking in the analysis. Vertical bars indicate standard errors.2. varied widely from each other.
410 405 400 395 390 385 SI25%BB75% SI50%BB50% SI75%BB25%
. (1980) have proposed that the difference in average breeding value of sires used could also have a significant influence if the breeding population is small enough. The possible difference in performance between the different breed compositions can be due to the additive or maternal effects of the breed or heterosis.3. The latter could have had a noteworthy affect on the outcome of the results. The effect of breed composition on 600-day weight for the Simbra breed. The effect of breed composition on weaning weight (200-day) for the Simbra breed.
3. It is evident from Figures 3.4.4. Roberson et al. It has been found by numerous recent reports that the differences in environmental conditions are mainly responsible for the variability in growth traits from year to year (Kocak et al. 25.001) all the traits studied.27% of the total variation for WW.29%.2.10% to BW.4 and 3. (1993) reported breed effects to play an important part in MCW with breeds consisting out of a higher Brahman proportion to obtain higher MCW. 3. WW.5 that MCW contributed 0. 2007.5. 18.3. 2008. 2009). (1955) who showed that year made the largest contribution to WW compared to the other weights measured. From these recordings it evident that year had a large impact on the variation associated with WW and YW. 3.77% for WW.55% of the variation in BW. It must be kept in mind that heavier cows are often large framed animals with higher nutritional requirements for maintenance and physiological functions. 1.4. The large amount of variation associated with the earlier years (1987-1992) is mostly due to the small number of animals recorded in those years.5 that breeder accounted for most of the variation associated with each trait.35% for YW and 10.5 that year contributed 0.53%. 3. This had a significant effect on the outcome of the results. The effect of mature cow weight (MCW) on growth traits in the Simbra breed Mature cow weight (MCW) measured at the weaning of their calf at 200 days of age was significant (P < 0.3.. attributable to genetic maternal effects as well as genetically transmitted effects. faster gain rate and heavier WW. heavier BW associated with calving difficulty. This was consistent with the results of Shelby et al.4.6 that weights differed between years. Raphaka.4 and 3. 0. YW and 600-day weight respectively.4. YW and 600-day weight respectively.3. It is evident from Tables 3. and 3.3. The great influx and out flow of breeders during the evaluation period of 1987 up till 2009 accounted for a lot of the variation associated with the traits as well as a loss of valuable records of animals that participated at one stage in the recording scheme. The breeder of the calf represents the farm that it was bred by and grew up on. selection objectives and management regimes that will differ.5 and 3.25% and 1. 3. that are not available for this data set. The effect of breeder accounted for 17. When the number of animals evaluated increased the amount of variation was also reduced. The breeder effects are more in relationship with the variation associated with that of contemporary groups.4.24%. 3. Zishiri. (1986) indicated that large cows usually produce large calves with a better milk production.001) for all of the growth traits measured when fitted as a covariate. It can be seen from Tables 3. This could be due to climatic conditions with its direct effect on the quantity and quality of grazing
. Pitchford et al.2.2. The effect of breeder on growth traits in the Simbra breed Breeder effect has been shown to be significant (P < 0.001) when fitted for each live weight. The effect of year on growth traits in the Simbra breed Year of birth affected (P< 0.04% and 0. YW and 600-day weight.71% for 600-day weight. This is consistent with results obtained from other studies. and accounts thus for most of the variation associated with WW. 3. 1. It is evident from Tables 3. An increased MCW are often associated with heifers being older and/or heavier at puberty. 3. 3. Every farm is different from one another with the breeder’s choice of breeding animals.3.
8 1987 1989 1991 1993 1995 1997
38.3266x + 234.3 36.0081x + 36. The regression of birth weight on year of birth for the Simbra breed. Vertical bars around the observed means denote standard errors. Genetic composition of the herd has been found to be responsible for the variation on weights on a yearly basis.34 2 R = 0.4.available.3 35.8 35.
275 265 255 245 235 225 215 205 195 185
y = 0. Human choice variation.8
38. The regression of weaning weight (200-day) on year of birth for the Simbra breed.3 37.8 36. that includes selection decisions. can also play an important part from year to year (Sushma et al.0089
Figure 3.8 37..
.3 34. Other environmental conditions can also include the management of the herd as well as availability and cost of roughage. 2006).025
Figure 3. Vertical bars around the observed means denote standard errors.577 2 R = 0.5.
4. Cows calving late in the rainy season are exposed for a longer period to limited. resulting in a reduced milk yield (Carvalheira et al. (1995) and Sushma et al.2.4 and 3.6..79 2 R = 0.97 kg). 3.. It can be seen from Table 3.31 kg measured at 200 days of age for the heavier calves at birth.92 and 1. Maternal performance of beef cows has been found to be accountable for 40% of the variance in WW (Robinson et al.76. (2000) attributed these lighter calves to a decreased fetal growth rate. The regression of yearling weight (400-day) on year of birth for the Simbra breed.3. 1978). 4. 1995). born earlier in the rainy season.5 that month had a large impact on the variation associated with all four traits measured.
. The respective FE % values for month of birth were 0.19. Villalba et al. 3. This resulted in calves that grew more rapidly due to a higher proportion of milk early in life and the ability to consume more forage during late lactation (Holloway et al.Yearling weight in kg
390 370 350 330 310 290 270 250 1988 1990 1992 1994 1996
y = -0. This was consistent with results of Plasse et al.9646x + 318. The latter was also evident in the current study for calves weighing less at birth and heavier at 200days (250.1695
Figure 3. received a better nutritional environment from birth to weaning due to dams that produced more milk from an improved and prolonged forage resource. 0. YW and 600-day weight. mature and less digestible pastures than dams of calves born earlier. exposed to sufficient grazing later in the rainy season. (2006) who found the effect of month of birth to be significant up to 18-month weight.82 for BW. Dams that had a higher body condition score during the late gestation phase. These calves were also weaned at an older age and thus weighed even heavier at weaning. WW.31 kg) than those born earlier in the season (35.6. 1985). It is important to note that the lighter calves.80 kg). Vertical bars around the observed means denote standard errors. The reason for this significant difference in birth weight is due to the fact that cows with low tissue reserves in a poor condition after the nutritional stressful season will produce lighter calves. BW was significantly higher for those calves born later in the rainy season (36. 3. produced heavier calves. The effect of month on growth traits in the Simbra breed Month of birth was found to be highly significant for all growth traits measured. These calves resulted in lighter a WW of 215..
This trend is a reflection of the greater ability of mature cows to provide the foetus with the necessary nutrients and environmental conditions for its development (Elzo et al. et al.29 kg at eight years of dam age... However milk yield showed a sharp decrease after 8 years of age. Younger as well as older dams gave birth to lighter calves when compared to mature dams. (1965) also found that the amount of butterfat and non-fat solids produced by the dam is even more important that the milk volume produced. Drewry et al. Ahunu et al.71% and 1. Christian et al.7 and 3. 1969).. 2007).. 1997. 1987. WW showed the same tendency with a weight of 229.20% of the total variation associated with BW and WW respectively. It has been shown in numerous studies that BW and WW increase from 2-year-old dams and peak in 5-8 year old dams with a subsequent decline (Smith et al. 2005. 1978). with R2 values displayed.43 and 0.7. The effect of dam age on growth traits in the Simbra breed Dam age accounted for 1.30 kg at eight years of age and a decline with a dam age of nine years and older. Kurpa et al. The R2 value for BW and WW on dam age was equal to 0. between dam age and both BW and WW can be seen in Figure 3.3. Elzo et al. (1965) found that the calves of mature cows were heavier at birth. These values indicate a strong relationship between the two variables. Maternal performance of beef cows has been found to be accountable for 40% of the variance in WW (Robinson et al. Christian et al. The BW showed a steady increase of 35.. Dillard et al. 1987).
. but no significant effect on either YW or 600-day weight. size and physiological function. This is due to the fact that the young calf’s consumption is limited by the capacity of its stomach and is in need of a higher energy source at this stage. (1978) found that milk yield estimates increased noticeably in middle age cows with the remainder being fairly level.29 between BW of the calf and milk production of the dam during the first and third months of lactation. The reason for the latter is due to the fact that the pre-weaning growth of the calf is influenced by the genes transmitted from their dam as well as the maternal environment provided to weaning (Brown & Galvez. lactation and gestation. got more milk from their dams. 1989. changes in weight. consumed more feed and were heavier at weaning than the calves of two-year-old dams. 1980. Presumably. 1976.41 for 2-year-old dams to 242. but also towards their own growth (Rumph & Van Vleck. The age of dam had no significant effect on either YW or 600-day weight.001) on both BW and WW.34 kg at two years of dam age up to 37. The young dams that are not physically or biologically mature enough.98 and 0. Gregory et al. the consumed nutrients are not only used for maintenance.. Reynolds et al... This is evident in the recent study done for the Simbra breed where the age of the dam showed a significant effect (P < 0.. respectively. Young et al. Interestingly enough Robinson et al. which are associated with ageing. (1978) found age of dam not to be significant for post-weaning traits. 2004).8. 1980.4. (1959) found significant correlations of 0. Riley et al... BW then declined with the advancing dam age of nine years and older.96 respectively. Garrick. 1978. might be expected to influence this environment and consequently have a direct effect on BW and WW. The curvilinear regression.
383x2 + 4. Conclusion The non-genetic factors of MCW. These factors should be considered when selection procedures are developed. Vertical bars around the observed means denote standard errors.0673x2 + 0. The regression of weaning weight (200-day) on dam age for the Simbra breed. The calves with a higher percentage of Brahman composition reached a heavier 600-day weight than calves with higher Simmental origin. Thus these heavier calves would be perfect for a weaner production system where quick growing calves are of essence for the system to be effective and economical.9544
Figure 3.5 35 34. year of birth.
3. Vertical bars around the observed means denote standard errors.5 34 2 3 4 5
y = -0.8737x + 34.447 R2 = 0. breed composition of calf as well as the breeder of the calf have been shown to be significant sources of variation for live weight traits at different stages of the animal’s life.72 R2 = 0.
240 235 230 225 220 2 3 4 5 6 7 8 9
y = -0.7.9763
Figure 3.9213x + 225. month of birth. sex of calf. dam age.8.5 36 35.38 37. It has been shown that calves with higher Simmental percentages weigh heavier at weaning than that of higher Brahman. The regression of birth weight on dam age for the Simbra breed. These
.5 37 36. Breed composition can be most favorable when implemented in the correct production system.5.
YW and 600-day weight. It can also been seen from the results that the breeder of the calf played a notable role accounting for most of the variation associated with WW. Although adjustment factors are easily obtainable in literature it may be deficient for the Simbra breed.calves with higher Brahman would be better for a finishing production system where there is sufficient feed all year round to keep calves till 600 days and sell at a later stage at a heavier weight. Many breeds have their own set of adjustment factors. the opportunity exists for inferior breeders to buy superior genetics from the more advanced studs or even learn from them to improve the overall mean for the traits studied for the Simbra breed. BW and WW are well known traits that include the adjustment of dam age and sex of calf (Raphaka. The age of dam adjustment factor enables fair comparison within the Simbra breed during performance evaluations. Raphaka..
. 1965. Considering the impact of the non-genetic factors in growth of the Simbra breed it is important to adjust performance records for known environmental effects to increase expected genetic change through selection (Marlowe et al. Thus. 2008). Adjustment procedures and adjustment factors often vary for different populations within species. 2008).
P. 3519-3526. & Honea. C. 2287-2293.S. Journal of Animal Science. P. B.. T.. 1980..F. Cundiff.. Relationships among factors associated with Journal of Animal Science.B.. & Galvez..K. 213-233.S. Hauser. carcass. 1998. O..L. 1987.L.J.. Carvalheira.J. K. Genetic and phenotypic parameters for birth and weaning weights of purebred and crossbred Ndama and West African Shorthorn cattle. J.. L. Christian.. H. 18. Journal of Animal Science.U. Maternal and other effects on birth weight of beef calves. Post weaning. Journal of Animal Science.M. E. Arthur.S. Livestock Production Science.J. Association of preweaning and postweaning traits with weaning weight in cattle.. 2007.O. 652-659. Journal of Animal Variances and covariances between productive and adaptive traits and
temperament in a composite breed of tropical beef cattle.A. D. & Gill. Science. 165-171. M. Brown. & Pollak.
. 1965. & Kissiedu. A.. 73. 24. Amen. D.. & Marshall.. Comparison of sire breed solutions for growth traits adjusted by mean expected progeny differences to a 1993 base. & Chapman. H. & Van Soest.. R.G.C. Pollak. 1959. Journal of Animal Science. J. Comparison of Landim and Afrikaner cattle in southern Mozambique: Body weights and growth... 1969. E. References
Ahunu. Dillard.R.. Elzo. D.W.. Livestock Production Science. 938-946. A. 70. 653-663. E. E.6. Van Vleck. H.D. 1997. Blake. C. M. Estimation of additive and non-additive direct and maternal genetic effects from crossbreeding beef cattle. & Robinson. 76.A. 1995.. Burrow. Herring. and meat traits. J.J. R.W. 992-1001.3.M.: 373-379.. Journal of Animal Science. Effects of age of dam on weight traits in the Simmental population. Evaluation of reciprocal differences in Bos indicus x Bos taurus backcross calves produced through embryo transfer: II. Barkhouse. Sanders. 64.A. Journal of Animal Science. 50. O.D.W. 2001. 162-167..L.J.
mothering ability in beef cattle.S. Rodriquez. 28. Brown. 51. Quaas. 8. Buchanan. R. Drewry. L.
Franke, D.E., Habet, O., Tawah, L.C., Williams, A.R. & DeRouen, S.M., 2001. Direct and maternal genetic effects on birth and weaning traits in mutibreed cattle data and predicted performance of breed crosses. Journal of Animal Science. 79, 1713-1722. Garrick, D.J., Pollak, E.J., Quaas, R.L. & Van Vleck, L.D., 1989. Variance heterogeneity in direct and maternal weight traits by sex and percent purebred for Simmental-sired calves. Journal of Animal Science. 67, 2515-2528. Gilmour, A.R., Gogel, B.J., Cullis, B.R., Welham, S.J. & Thompson, R., 2002. ASREML user guide release 1.0 VSN International Ltd. Hemel Hempstead. UK. Gregory, K.E., Blunn, C.T. & Marvel, L.B., 1950. A study of some of the factors influencing the birth and weaning weights of beef calves. Journal of Animal Science. 9, 338-346. Gregory, K.E., Cundiff, L.V., Smith, G.M., Laster, D.B. & Fitzhugh, H.A., 1978. Characterization of biological types of cattle. Cycle II. I. Birth and weaning traits. Journal of Animal Science. 47, 1022-1030. Holloway, J.W., Butts, W.T., Jr., J.R., McCurley, E.E., Beaver,H.L., Peeler,H.L. & Backus,W.L., 1985. Breed x nutritional environment interactions for beef female weight and fatness, Milk production and calf growth. Journal of Animal Science. 61, 1354-1363. Hopkins, I.R., 1977. The effect of level of environment on sex differences in preweaning growth rate in beef cattle. Animal Production. 25, 47-51. Kocak, S., Tekerli, M., Ozbeyaz, C. & Yuceer, B., 2007. Environmental and genetic effects on birth weight and survival rate in Holstein calves. Turkey’s Journal of Veterinary Animal Sciences. 31, 241-246. Koch, R.M. & Clark, R.T., 1955. Influence of sex, season of birth and age of dam on economic traits in range beef cattle. Journal of Animal Science. 14, 386-397. Koger, M. & Knox, J.H., 1945. The effect of sex on weaning weight of range valves. Journal of Animal Science. 4, 15-19. Krupa, E, Oravcova, M, Polak, P, Huba, J., & Krupova, Z., 2005. Factors affecting growth traits of beef cattle breeds raised in Slovakia. Czech Journal of Animal Science. 50, 14-21.
Leighton, E.A., Willham, R.L. & Berger, P.J., 1982. Factors influencing weaning weight in Hereford cattle and adjustment factors to correct records for these effects. Journal of Animal Science. 54, 957-963. Marlowe, T.J., Mast, C.C. & Schalles., 1965. Some non-genetic influences on calf performance. Journal of Animal Science. 24, 494-501. Martin, J.B. & Blunn, C.T., 1952. Some factors affecting gestation length and birth weight of beef cattle. Journal of Animal Science. 11, 34-41. Melka, H.D., 2001. Genetic parameter estimates for weaning traits in a multibreed beef cattle
population. Magister Thesis, University of Stellenbosch, South Africa. Nephawe, K.A., 1998. An investigation into sire x ecological region interactions for birth and weaning weight in Bonsmara cattle. Magister Thesis, University of the Orange Free State, South Africa. Pico, B.A., 2004. Estimation of genetic parameters for growth traits in South African Brahman cattle. Magister Thesis, University of the Free State, South Africa. Pitchford, W.S., Barlow, R. & Hearnshaw, H., 1993. Growth and calving performance of cows from crosses between the Brahman and Hereford. Livestock Production Science. 33, 141-150. Plasse, D., Fossi, H., Hoogensteijn, R., Verde, O., Rodriguez, R., Rodriguez, M.C. & Bastidas, P., 1995. Growth of F1 Bos taurus x Bos indicus vs Bos indicus beef cattle in Venezuela. I. Weights at birth, weaning and 18 months. Journal of Animal Breeding and Genetics. 112, 117-132. Pribyl, J., Sebo, K. & Pribylova, J., 2000. Breeding value and variance components estimation of birth and 120 days weight of Charolais cattle with respect to direct and maternal genetic effect. Czechoslovakian Journal of Animal Science. 45, 397-403. Raff, H., Strang, K.T. & Widmaier, E.P., 2004. Blood and homeostasis. IN Human physiology: The mechanisms of body function 9th Edition. Ed. M. Watnick. McGraw-Hill Companies Inc. New York, USA. Raphaka, K., 2008. Estimation of genetic and non-genetic parameters for growth traits in two beef cattle breeds in Botswana. Magister Thesis, University of Stellenbosch, South Africa.
Reynolds, W.L., DeRowen, T.M., Moin, S. & Koonce, K.L., 1980. Animal Science. 51, 860-867.
Factors influencing gestation
length, birth weight and calf survival of Angus, Zebu and Zebu cross beef cattle. Journal of
Riley, D.G., Chase, C.C., Coleman, S.W. & Olson, T.A., 2007. Evaluation of birth and weaning traits of Romosinuano calves as purebreds and crosses with Brahman and Angus. Animal Science. 85: 289-298. Roberson, R.L., Sanders, J.O. & Cartwright, T.C., 1986. Direct and maternal genetic effects on preweaning characters of Brahman, Hereford and Brahman-Hereford crossbred cattle. Journal of Animal Science. 63, 438-446. Robinson, O.W., Yusuff, M.K.M. & Dillard, E.U., 1978. Milk production in Hereford cows. I. Means and correlation. Journal of Animal Science. 47, 131-136. Rumph, J.M. & Van Vleck, L.D., 2004. Age-of-dam adjustment factors for birth and weaning weight records: A review. Genetics and Molecular Research. 3, 1-17. SAS, 2004. Statistical Analysis Systems user’s guide. (Version 9). SAS Institute Inc., Cary, North Carolina, USA. Schoeman, S.J., Van Zyl, A. & De Wet, R., 1993. Direct and maternal additive and heterotic effects in crossbreeding Hereford, Simmentaler and Afrikaner cattle. South Africa Journal of Animal Science. 23, 61-66. Shelby, C.A., Clark, R.T. & Woodward, R.R., 1955. The heritability of some economic characteristics of beef cattle. Journal of Animal Science. 14, 372-385. Smith, G.M., Laster, D.B. & Gregory, K.E., 1976. Characterization of biological types of cattle. I. Dystocia and preweaning growth. Journal of Animal Science. 43, 27-36. Sushma, G., Ramesh Gupta, B., Vinoo, G.V., Reddy, N. & Reddy,S., 2006. Influence of genetic and non-genetic factors of body weights and body measurements of Ongole cattle. Indian Journal of Animal Science. 75, 228-235. Trus, D. & Wilton, J.W., 1988. Genetic parameters for maternal traits in beef cattle. Canadian Journal of Animal Science. 68, 119-128. Journal of
Villalba, D., Casasus, I., Sanz, A., Estany, J. & Revilla, R., 2000. Pre-weaning growth curves in Brown Swiss and Pirenaica calves with emphasis on individual variability. Journal of Animal Science. 78, 1132-1140. Young, L.D., Cundiff, L.V., Crouse, J.D., Smith, G.M. & Gregory, K.E., 1978. Characterization of biological types of cattle. VIII. Post-weaning growth and carcass traits of tree-way cross steers. Journal of Animal Science. 46, 1178-1191. Zishiri, O.T., 2009. Genetic analyses of South African terminal sire sheep breeds. Magister Thesis. University of Stellenbosch, South Africa.
85 ± 0. yearling weight (YW) and 600-day weight measurements.10 ± 0.2. The model for BW included direct additive effects (h2).75 ± 0. The genetic correlations between the traits studied were favourable although low and indicated that selection for one trait will improve the other traits in a desired direction.56 ± 0. Mohd-Yusuff &
. weaning weight at 200 days (WW).03 was obtained. Keywords: heritabilities.67 ± 0..15 ± 0.1.. Even though literature results also included estimates for the Simmental (Trus & Wilton.07 was obtained for WW. 2006). Traits studied included birth weight (BW). Live weight traits include birth weight (BW).07 was obtained for BW. Genetic parameter estimates for growth traits were readily available for European and some prominent tropical beef cattle breeds (Demeke et al. Genetic parameter estimates enable beef breeders to genetically improve production. thus consistent information about the traits indicating growth potential is of great importance (Liu et al.07 and a ram value of -0. multi-trait. by means of well-constructed breeding plans and precise selection. 1988.08 was obtained for YW. Introduction Growth rate has been proved to be the main selection criterion for most beef breeders around the world. To implement sound breeding principles and evaluate ongoing breeding programs knowledge about these genetic parameters are important. growth
4.. m2 value of 0. A h2 value of 0. The model for WW included the same effects as for BW except for the effect of dam permanent environment (c ) that had to be added. A h value of 0. Martinez & Galindez. correlations.25 ± 0. maternal additive effects (m2). A h2 value of 0.70 ± 0. c2 value of 0. 1996) and Brahman (Pico 2004.08. m value of 0.18 ± 0. yearling weight at 400 days (YW) and 600-day weight. Co (variance) components were estimated for the Simbra breed by means of ASREML procedures. which form the genetic basis of the Simbra. 1991).
environmental correlations were estimated using multivariate analysis. single-trait. 2003). m
value of 0.05 and a ram value of -0.11. Live weight traits throughout the animals’ life have been shown to be an effective indicator of growth potential (Pico 2004). weaning weight (WW). The model for 600-day Genetic and
weight only included direct additive effects and an h2 value of 0. The latter will contribute to higher profit margins that are the main objective of any beef producer and for that matter any other livestock producer. Abstract
Genetic factors affecting growth in Simbra cattle are reported for the first time in South Africa in this study.CHAPTER 4
GENETIC FACTORS INFLUENCING GROWTH TRAITS IN SIMBRA CATTLE
4. and the correlation between the two (ram). The most suitable animal models were used to construct genetic parameters.09. Bennet et al.04 ± 0.93 ± 0. as well as reproduction traits. The model for YW included the exact same effects as that for BW.05 and a ram value of -0.09..
4. Statistical Analysis The ASREML program (Gilmour et al. The resemblance between genetically related animals can be used in the estimation of trait heritability. The models fitted included the fixed effects of breed composition. Materials and Methods
4. 2002) was used for the estimation of (co) variance components for each trait separately and all pairs of traits jointly.3. Records The full description of the data as well as the editing is given in Chapter 3.05) were incorporated into the operational models. BW. 2000.3. The genetic parameters that are of interest include heritability. Even if estimates were available for the Simbra breed the genetic parameter estimates only applies to a specific population and may change over time due to selection and management practises (Bourdon. The objective of this study was to estimate (co) variance components for BW.and phenotypic correlation as well as repeatability. WW (200-day). The model incorporated all pedigree information available to minimize bias due to selection and to increase the accuracy of estimation through additional ties between animals. 4. month and dam age. assuming that the fixed effects are identical for each model
. The fixed effects (see Chapter 3) that were found to be significant (P < 0. Six alternative models were implemented that included the direct genetic effect. 2000). The LRT is a statistical test of the goodness-of fit between hierarchically nested models. Alternatively. WW. This was done by means of restricted maximum likelihood procedures (ASREML) (Gilmour et al. 1992). LRT is based on principle of testing twice the increase in Log-likelihood from adding a random effect to the analytic model as a Chi2 statistic. Meyer. WW at 200 days. The contribution of each random effect was assessed by means of Likelihood Ratio tests (LRT) to establish the significance of each random term contribution.. breeder. This is where the computation of variance components within and between family members plays a fundamental part (Bourdon. maternal genetic and permanent environmental effects for the traits studied. genetic. Model six was the comprehensive model with another five reduced models. YW at 400 days and 600-day weight.3. 1994)..2. The random effects were then added to the analytic models subsequently. YW (400-day) and 600-day weight for the Simbra breed.Dickerson (1991) found that the genetic variance in a composite population may be more or less than for the parental breeds. The latter are computed as functions of their (co) variance components. The traits under investigation were BW. YW and MCW were included as covariates where appropriate. There is thus a constant need for regular (co) variance component estimation to keep the genetic parameters as accurate as possible to improve the traits under selection in the breed under investigation. which ignored one or two of the maternal effects and/ or assumed no covariance between the direct and maternal genetic effects.1. year. The animal model is considered the model of choice for a wide range of applications and was implemented in the analysis of the four traits measured (Meyer. 2002).
m) with cov (a. m and c a = a random vector for the calf’s own additive genetic effects m = a random vector of maternal additive genetic effects c = a random vector of permanent maternal environmental effects e = a vector of random residual errors Ram = the covariance between the additive and maternal additive genetic effects It is furthermore assumed that: V(a) = Aσ2a V(m) = Aσ2m V(c) = Iσ2c V(e) = Iσ2e Cov (a. 2. the model with the higher Log-likelihood value will fit the data best (Zishiri. The following single-trait animal models (in matrix notation) were fitted for each trait: 1.m) with cov (a. 4. Where: Y = the vector of the calf’s record for each growth trait X = a known incidence matrix that relates Y to b b = the vector of fixed effects Z1. 5.with the same number of random effects. 2009). 3. Z2 and Z3 = known incidence matrices relating the observation (Y) to the unknown random effects of a.m) = 0
= the numerator relationship matrix = additive genetic variance = maternal additive genetic variance = maternal permanent environmental variance = genetic covariance between direct and maternal effects = the residual error variance = an identity matrix
σ σ I
.m) = 0 with cov (a. 6.m) = Aσam Where: A σ σ σ
2 2 2 2 2 a m c am e
Y = Xb + Z1a + e Y = Xb + Z1a + Z2m + e Y = Xb + Z1a + Z2c + e Y = Xb + Z1a + Z2m + Z3c + e Y = Xb + Z1a + Z2m + Ram + e Y = Xb + Z1a + Z2m + Z3c + Ram + e with cov (a.
50. where σ2p is the phenotypic variance
4.58. Results and Discussion
4. Model 5 was most suitable for BW and YW analysis with log likelihood values of –6779. The number of records used for the analysis showed a significant decline when compared to that of the single trait analysis. Direct additive genetic effects h2a = σ2a /σ2p 2. Maternal genetic effects h2m = σ2m /σ2p 3. Models with the highest log likelihood values were chosen for the analysis of the traits respectively.
. the maternal permanent environmental variance and the variance of residual error. Genetic correlation between direct and maternal effects ram = σam / (σ2a σ2m) 0. Model 6 was most suitable for WW (200-day) with a log likelihood value of –11838. maternal additive genetic variance. weaning weight (WW) and yearling weight (YW).1. (2003) and Meyer (1992).4. In order to compute the genetic and phenotypic relationships of the traits a three-trait analysis was conducted for birth weight (BW). direct additive maternal effects.4.Heritabilities were estimated as follows: 1.5 With A being a numerator relationship matrix between animals and I being an identity matrix. This result was consistent with the study of Meyer (1992) where a compressive model that included dam permanent environment was used. dam permanent environmental effects and the correlation between direct additive and maternal additive effects resulted in significant improvements in log likelihood values. The results that were obtained during model construction are depicted in Table 4.49 and –8987. σ2m.2. Inclusion of random factors such as direct additive effects. σ2c and σ2e direct additive genetic variance.32 respectively. Model Selection Log likelihood values were obtained for single-trait analyses of the respective traits. This was consistent with the results of Demeke et al. Where σ2a. Model 1 was used for the 600-day weight analysis with a log likelihood value of –632.
03 was obtained for 600-day weight. WW measured at 200-days (0. Ferreira et al.14 -9001. The higher estimates for direct additive heritability. WW and YW implies high genetic variability in the Simbra breed and therefore offers the opportunity of improvement of these traits through selection.3 -11866. Single-trait analysis The direct additive heritability estimates for BW and WW were consistent with that of Gutierrez et al. Rodriquez-Almeida et al.). the low direct additive heritability obtained for 600-day weight was roughly in agreement with that reported by Pico (2004).8 -11838.1 -11865.1 -11866.11) was relatively high compared to the bulk of estimates in Table 2.5
Fixed + h + m Fixed + h + c
Fixed + h + m + c
-6827. (2008). (2006) respectively.32 -8987.19-8987. who obtained a value of 0. Schoeman et al.3 that the direct additive heritabilities (h2) for BW (0. It can be seen from Table 4.32 -8987. Bennett & Gregory (1996) and Shenu et al.47 and 0.58 -632.61 -632. (2000) found even a higher direct additive heritability for BW in a multibreed beef cattle population in South Africa with an estimate of 0.2. together with a high coefficients of variation for BW. (1999) suggested
4.56 ± 0. A much lower direct additive heritability estimate of 0. Skrypzeck et al. (2000) and Wasike et al.32
weight -651.71 -6828.49
Fixed + h + m + ram Fixed+ h + m + ram+ c
-6799.58 -632.84 respectively.09) and YW measured at 400-days (0.58
-6887.4.67 ± 0.18.70 ± 0. The reason for this might be the data structure and a lot of environmental variance compared to genetic variance. Log likelihood ratios for the respective random effects models fitted to growth traits in the Simbra breed. Meyer (1992).58 -632.33
-11909.33 -6779. Effects Birth weight Weaning Yearling 600-day weight (200-day) Fixed effects only Fixed + h
weight (400day) -9029.2 -11877. The environmental variance could be due to calf management after yearling weight since environmental stress markedly affects the magnitude of the additive genetic variance for growth traits (Demeke et al.19 -9001.58 -632.2. 2003). However.Table 4. The dominance effect is one of the factors associated with the higher and possibly biased estimates in crossbred populations and were not fitted in the current model. (1995) suggested that the variability in heritability estimates might be due to the inclusion of non-additive genetic variances. for South African Brahman cattle. Although present literature was scarce for this trait it was not consistent with the results of Swiger (1961) and Shelby et al..3 -11877.33 -6827. (1955) that obtained a value of 0. The h2 estimate for YW was also much higher when compared to that of Bishop (1992).10 ± 0.72 (see Table 2.08).28 -6827.2. Skrypzeck et al. (2000) and Bennett & Gregory (1996) found that these high direct heritabilities reported for composites are in fact fqairly common compared to that of purebreds. (1997).58 -632.65 -9001.
(2006) and Demeke et al. 0. The high additive maternal heritabilities for BW and WW indicate that. The inflation was of higher importance for maternal effects than the direct additive heritabilities (Demeke et al. Maternal additive heritability was lower than direct heritability for BW. The current findings were more similar to those of Wasike et al. 1955).25 ± 0. possibly due to uterine capacity. Waldron et al.11 for Hereford and 0. The reason for these low estimates was due to the diminishing ‘carryover’ effect of the maternal influence till weaning on YW (Meyer. (1997). (1999) and Eler et al. This is also the reason for the little information found in literature. Martinez & Galindez (2006)..13 for permanent maternal environment for WW. (1995) that obtained much lower values for the maternal additive heritability for both BW and WW. (2000) and Schoeman et al. Skrypzeck et al. There was not much stated in literature for YW maternal additive heritability estimates. (2006). for the maternal effects associated with post weaning traits. (1997) reported c2 estimates of 0.07 and 0. (1997). It must be kept in mind that the rather large direct and maternal genetic estimates are substantially inflated due to the exclusion of both heterosis and recombination effects. The latter implies that body weights were determined more by genetic characteristics of the calf than by those of the dam.04 ± 0. Many authors regard maternal effects as unnecessary for post-weaning traits and do not even include them in the genetic evaluation (Garrick et al. The c2 was smaller than the maternal additive heritability. 1989 and Koch & Clark. (1998) and Tosh et al.03 for South African Brahman and American Simmental respectively. This is also in agreement with the findings of De los Reyes et al. The dam permanent environment (c2) estimates were only obtained for WW with a value of 0. Dodenhoff et al. 0. This effect is coded as an effect of the dam e.dominance effects as well as the epistatic variance to be partly responsible for inflated estimates associated with animal model estimates of composite breeds. Tosh et al. Skrypzeck et al. 2003). Pico (2004) and Lee et al. Ahunu et al..24.
. considerable opportunity exists for the improvement of these traits. together with a high coefficient of genetic variation.15 ± 0.07 were obtained respectively for the weights measured. Burrow.. Pico (2004).05.05. (2000) and Bennett & Gregory (1996) that obtained values of respectively 0. (2001). (1999). Maternal additive heritability estimates of 0. (2000) that obtained slightly higher maternal additive heritabilities.12 and 0. 1992).14 for Zebu cross cattle in Australia. but not with that reported by Melka (2001).07 and 0.04 for Angus. feeding level in late gestation and maternal behaviour (Eler et al. The results were also fairly consistent with their parental breeds estimates.g. except for the report of Meyer (1992) that reported a value of 0. (1993) and Trus & Wilton (1988) that reported even higher maternal additive heritabilities for BW and WW.18 ± 0. This was consistent with most findings in literature and is a well-known feature. (2003). This was not consistent with the findings of Raphaka (2008). This is also in concurrence with the results reported by Wasike et al. 1995).. WW and YW. 0. This was consistent with the findings of Meyer (1992). Gutierrez et al. (2006).
Skrypzeck et al. (1999). Heydarpour et al. (2005). Wasike et al. Tosh et al. This environmental co-variance could have been the result of i. (1995) and Koch (1972) showed a negative correlation between direct additive and maternal additive effects.e. Roso et al. (1998) and Meyer (1997) proposed that if models were to be fitted with regression of offspring performance on maternal phenotype and sire by herdyear interaction affects the magnitude of the direct-maternal correlation would decline. (1995) for Nelore cattle in Brazil. (1993) and Meyer. suggesting a slow selection response for the four growth traits measured. Fatty udder syndrome is a well-known occurrence where dams with superior maternal ability overfeed their daughters. The high negative values obtained in this study indicate an antagonistic effect between direct additive and maternal additive. Martinez & Galindez (2006). (2008) found that the data structure can affect both the magnitude as well as the standard error of the large negative values obtained for ram estimates. (2006). This was also reported in a recent study done for South African bred sheep done by Zishiri (2009).07). WW (ram = -0.93 ± 0. Haile-Mariam & Kassa-Mersha. Both effects should be taken into account in selection processes to achieve optimum genetic progress (Dezfuli & Mashayekhi.07) and YW (ram = -0. (1992) that found positive genetic correlations between direct and maternal additive effects. The latter is primarily due to inconsistent recording and small herd sizes of the Simbra breed (Wasike et al. The reason for this strong negative ram estimates can be partially explained by the poor and small data/population structure and not the true antagonistic biological relationship between the direct and maternal genetic effects. De los Reyes et al. Waldron et al. (2000). The ram estimates reported throughout the literature had conflicting magnitude and direction.
.. Meyer (1994). (2006) reported the same results with Kenyan Boran cattle. Dodenhoff et al. Meyer (1992) also obtained relatively strong negative ram values (-0. 2000). Burrow (2001).78) for WW and YW (-0. the values was still not as strongly negative than those obtained in the current study. (2000).08).85 ± 0. Thus selection of genetically superior animals for growth traits will result in genetically inferior animals for maternal genetic components for these traits. The results were however in agreement with those reported by Melka (2001) for a muti breed population in South Africa and Eler et al. Meyer (1992) suggested further research is required into the modelling of maternal effects in beef cattle. The data used for the analysis resembles that of field data and tend to be highly unbalanced with missing records and poor linkage between generations and herds. daughters of dams with superior maternal abilities that provide an inferior maternal environment for their offspring.39) in Zebu cross cattle.75 ± 0. Meyer (1997) attributed the negative ram estimates to unaccounted sources of variation due to management practices. Although the estimates obtained by Sarmiento & Garcia (2007). which inhibits proper development of its daughter’s udder resulting in below-average maternal ability of the daughter (Meyer. Robinson (1996). Negative estimates are howeverommon in beef cattle for preweaning growth traits (Skrypzeck et al.The genetic correlation between direct and maternal effects (ram) was highly negative for BW (ram = 0. It was indicated that these values are most possibly inflated by environmental co-variance.. 2006). 2009). Schoeman et al. 1997). The ram estimates for birth weight were not in agreement to those summarized in the reviews of Raphaka (2008).
95 ± 9.2.05
1006 ± 38.39
-305.70 ± 0.03 na na
.40 ± 2.40 1.23
-398.52 ± 3.10 ± 0.07 -0.24 48.75 ± 0.31 na 690.72 ± 0.6 29366.79 ± 3.38 ± 6.68 0.02 0. WW and YW together with the large negative correlation between direct and maternal effects need further investigation.86 na na
-2.The inflated direct additive heritabilities for BW.93 ± 0.32 5.20 0.65 na
weight 3269.25 ± 0.69 ± -3.67 ± 0.18 ± 0.07 na
32635 ± 645.54 ± 6.57 ± -4.95
weight (400 day) 707.08 0.25 ± 3.93 ± 6. Variance Birth weight Weaning weight Yearling 600-day Components Direct additive (σa ) Maternal additive (σm ) Maternal permanent environment (σc2 ) Direct additive and maternal additive covariance (σ am) Total phenotypic (σp ) Residual (σe2) Direct additive (h ± SE) Maternal additive (m ± SE ) Dam permanent environment (c ± SE) Direct additive and maternal additive correlation (ram ± SE) -0.56 ± 0.07 0. Table 4.70 417.09 0.15 ± 0.7 ± 32.85 ± 0.65 ± 5.08 na
(200day) 6.07 -0.98 ± 5.38 183.05 na
1020 ± 32.33 0.31 260.11 0.02 ± -5.95 ± 0.93
11.04 ± 0.03 420. Estimates of variance components and ratios from single-trait analysis of growth traits in the Simbra breed.
4.24 ± 0.38 ± 0. 2009.05) 0.18 (0.6 0.06) 0. Birth weight Weaning weight Yearling weight Birth weight Weaning weight Yearling weight 0.3. Estimates (SE in brackets) of genetic (above diagonal).16) 0.33 (0.3 that the direct additive heritability estimates (h2) for BW (0.The effect of age on direct and maternal additive effcts at different growth stages
0.52 (0.06) and YW measured at 400-days (0. (1999).10) 0.09 (0. WW at 200-days and YW at 400-days.45 (0. direct heritability estimates (h2) (on diagonal) and environmental correlation estimates (underneath diagonal) using a three-trait analysis in the Simbra breed. Trends in h2 and m2 respectively for the Simbra breed with an increase in the age at which weights were recorded.5 0.3.06) 0. This is mainly due to missing records in different patterns that are handled internally during the analysis. Table 4.4 0. 4. Finding initial values to commence the iteration sequence further exacerbates this and in some cases ASREML is coded to work some values out from the data (Zishiri.1. It can be seen from Table 4.07 (0. Vertical bars around the observed means denote standard errors.27 (0.16) 0. A lot of the animals were thus removed due to incomplete records and the loss of genetic linkage was inevitable. When conducting a multiple trait analysis the error structure for the residual must be specified as two-dimensional with independent records and unstructured variance matrix across traits.07) were in comparison with the bulk of estimates in Table 2.
When analysing for more than one trait in ASREML it becomes very complicated to specify the variance structure.2 0. Gilmour et al.38 (0.07). It was obvious from the log likelihood ratio tests that the animal model only containing additive genetic effects was the most suitable for the three-traits analysis. Three-trait analyses A three trait analysis was done for BW.3 0.2.. WW measured at 200-days (0.24 (0. 2002).33 ± 0.9 0.1 0 Birth w eight Weaning w eight (200-day) Yearling w eight (400-day) Final w eight (600day)
Direct additive Maternal additive
Figure 4. After editing a total of 12585 animals with complete weight records were kept for analysis.7 0.06) 0.07) 0. The direct additive heritability estimates for BW and WW was also identical to those obtained by HaileMariam & Kasse-Messa (1995) and Tosh et al.
10 was obtained for the genetic correlation between WW and YW indicating a strong positive correlation between the two traits. The obtained direct 54 Meyer (1994) reported a value of 0. Although the correlation estimates were relatively low. estimate. Interesting enough Eler et al.88 rg
.47 for the South African Brahman. Plasse et al.79 for Angus and Zebu cross cattle in Australia. compared to that of literature (Eler et al. A value of 0.16 was obtained for BW and YW.4. Genetic correlations between the traits studied were favourable indicating that selection for one trait will improve others in a desired direction. 600-day weight however showed a remarkable low direct heritability and could have been due to large environmental variance associated with this trait. (1995) reported an even lower genetic correlation estimate (0. 2002. An rg estimate of 0. This is a stronger positive correlation than that of BW and WW.5. This correlation estimates is not consistent with other estimates reported in literature for BW and WW. However. 1994. A value of 0..27 ± 0. Genetic correlation estimates are found to be renowned for their inconsistency and large standard errors.06) respectively.06 and 0.05) between WW and YW was much higher and indicated a strong positive relationship between the two traits..70 and 0. they were still positive indicating that selection for high BW will result in higher WW as well as YW.4. Correlation among traits The genetic (above diagonal) and environmental (underneath diagonal) correlation estimates are presented in Table 4.23 to 0. Pico (2004) reported a 0. High BW selection is generally not recommended to avoid economic loss because of dystocia. However the latter is still not consistent with the estimates obtained in literature. The reason for the diversity of estimates reported could be due to the fact that all estimates of rg depend on the models that were utilized as well as the random factors included in the model development (Zishiri.. WW and YW. The environmental correlations between BW and WW as well as BW and YW were relatively low (0. Conclusion Heritability estimates (both direct additive and additive maternal) were high compared to literature results from purebreds but not that of composites during single trait analyses for BW.52 ± 0. Pico (2004) reported a value of 0. 1995). The re estimate (0.95 and 0.3.16 was obtained for the genetic correlation between BW and WW indicating a small but positive relationship between the two traits. Meyer (1994) reported estimates of 0.09 ± 0.16) between BW and YW. From the published literature available rg estimates ranged from 0. When a three-traits analysis was done for the same traits it was found that the heritability estimates dropped remarkably as well.18 ± 0. 1995. Pico 2004). 4. Eler et al. These results were consistent with that reported by Pico (2004). but still not as elevated as some of the estimates reported in literature. 2009). the present correlation estimates is uncharacteristically low and need to be further investigated. Compared to literature estimates the obtained estimates were much lower for all the traits studied.79 for Angus and Zebu cross cattle.45 ± 0.07 ± 0.79 (Meyer.
Ahunu. and postweaning gain in composites and parental breeds of beef cattle. 84. Genetic and phenotypic parameters for birth and weaning weights of purebred and crossbred Ndama and West African Shorthorn cattle.. Ed. literature. The genetic model for quantitative traits. 2000. Non-genetic.
Variances and co variances between productive and adaptive traits and
temperament in a composite breed of tropical beef cattle. and Carvalheiro. food intake and energy
utilization in Hereford cattle. H. Bourdon.
Bennett. Carnis.F. 165-171. 70. F. IN: Understanding Animal Breeding. Roso. Jr. & Gregory. WW and YW indicating a strong antagonistic effect between direct and maternal additive and must be kept in mind during selection procedures. 213-233. but positive relationships between the traits studied. I.A.M. 110-120.. Phenotypic and genetic variation in body weight. 1-
Burrow. V.. 11-21. Livestock Production Science. Genetic (co) variances among birth weight. Livestock Production Science.C.additive heritability estimates from both analyses indicates that genetic improvement is possible through selection and should be implemented in such a manner that growth rate is maximized while BW is kept at an optimal level to avoid dystocia.M. 200-day weight. G. Arthur. 74. Stewart. Bishop.K. Journal of Animal Science. B. However. 2003. 2598-2611. S. L. The low rg estimates obtained between BW and WW as well as YW makes it possible to select for increased WW and YW and still avoid dystocia. due to the data structure these estimates could have been inflated and does perhaps not reflect the true biological values for the traits. Correlations were obtained during the three-trait analysis and indicated weak.J. 1992.
additive and non-additive genetic effects for animal model analyses of weaning weights in a 55
.. P. 1996. 18.M.L. 2006. These correlation estimates were much smaller than that reported in most
4. R. Livestock.C.. Charles. De los Reyes. 2001. Linsner & M.A..6. Elzo. 30. S. The correlation estimates between direct additive and maternal additive was very strong negative for BW.W.. R.. K. P. & Kissiedu. E. M. 51.E. Performance test results. S. 2nd Edition.. A. Neser.W. South African Journal of Animal Science. K. 1997.A.. H. & Schoeman. Variance components and genetic parameters for early growth traits in a mixed population of purebred Bos indicus and crossbred cattle. Demeke.. Fries.. Prentice-Hall Inc.
. 1998. M.. J..R.. Journal of Animal Science. Variance components and breeding values for growth traits from different statistical models. B. 77. 1972. L. Hemel Hempstead. & Koch. R. R.. J. 786-791.D.R. 112. 114. 1989. 3253-3258. Kachman. Gilmour.Nellore x Hereford mutibreed population in Brazil. L. 125. J. ASREML user guide release 1.R.J. & Mashayekhi.D.P.T. Variance heterogeneity in direct and maternal weight traits by sex and percent purebred for Simmental-sired calves. Journal of Animal Science. R. 8.0 VSN International Ltd. J. Journal of Animal Science.D. A.M. Van Vleck. Correlations between offspring and dam and offspring and sire.J. M. 261-266. R. D. 1999. and Thompson. Koch.B. Gogel. 276-280.D. 35. 2641-2650.M.M. F. Gutierrez... & Kassa-Messa. Welham.. Heydarpour. Genetic and Environmental relationships among economic Estimates of direct and maternal (co)variance
components of growth traits in Boran cattle. 73... Dezfuli. Pollak. L. Ferreira. & Yazdi. 2515-2528. Dodenhoff. & Goyache. G. Garrick. Eler. 1997. 76. L.. S. 2009. 1995. & Clark. Journal of Animal Science.. & Van Vleck.
characters in beef cattle. The role of maternal effects in animal breeding: VI. Koch. 43-52. 1995.
. and grand maternal genetic effects for birth weight and weaning weight in Hereford cattle. 2008. Maternal effects in beef cattle. R. B. Haile-Mariam.. Journal of Animal Breeding and Genetics.. E. L. Schaeffer.B.. M.P. S. M.. T. & Van Vleck.B. J. maternal. 8th World Congress on Genetics Applied to Livestock Production. II. Quaas.M. & Ferraz.L. 2521-2527. Genetic study of birth weight and weaning weight in Najdi calves. Journal of Animal Breeding and Genetics. Cullis. Estimation of direct and maternal genetic parameters for pre-weaning traits in the Australian de los Valles beef cattle breed through animal and sire models. Canon. UK. R. 14. Journal of Animal and Veterinary Advances. 67. Journal of Animal Breeding and Genetics.J. Parameter estimates for direct.. Influence of population structure on estimates of direct and maternal parameters. Estimation of variances due to direct and maternal effects for growth traits of Nelore cattle.J. 2002. 1955.. H.D. Journal of Animal Science. 1316-1323... 89-99. R.H.. Journal of Animal Science. Mac Neil. Van Vleck.D.
.. 108.. K.. Melka. & Bastardo. 293-300. J. 1992. Variance components due to direct and maternal effects for growth traits of
Australian beef cattle. & Pollak.B. 1994.. H. Raphaka. Livestock Production Science. 141-153.K. G. Brazil. Relationships between measures of absolute growth rate and between measures of relative growth rate in growing meat animals. (Co)variance components.. Meyer. Verde.. Livestock Production Science. Magister Thesis. R. Journal of Animal Breeding and Genetics. & van Wyk.. E. 2008.A. Estimates of genetic parameters for weaning weight of beef cattle accounting for direct-maternal environmental co variances. R. 75. Liu.. 38. A. Estimation of genetic variance and covariance components for weaning weight in Simmental cattle.D. Science. Genetic and environmental trends for birth and weaning weights in registered Brahman cattle.. 2006.. Theron. & Galindez. 1997. M. Magister Thesis. Magister Thesis. University of the Free State.E.F. 1997. Journal of Animal Breeding Genetics. Romero. 1991. D. H. Mohd-Yusuff.
. Bastidas.E. K. 2004. Belo Horizonte. Meyer. Fossi.. M. Genetic parameter estimates for body measurements and growth traits in South African Bonsmara cattle. H. M. Van Tassell. University of Stellenbosch. South Africa. 31.. Makarechian. Production. B. Livestock Production Science. 179-204. C.W.. In: Procedures of the 8th World Congress for Genetic Applied Livestock. Estimation of genetic parameters for growth traits in South African Brahman cattle. & Dickerson.J. Estimates of direct and maternal correlations among growth traits in Australian beef cattle... J. Genetic parameter estimates for weaning traits in a multibreed beef cattle
population. Hoogesteijn. 187-191. K... Livestock Production Science. & Tong. 119. 52. South Africa. C. Plasse. 2002. genetic parameters and annual trends for calf weights in a pedigree Brahman herd under selection for three decades. 2002.P.J. Estimation of genetic and non-genetic parameters for growth traits in two beef cattle breeds in Botswana.. 75. Martinez.N. South Africa.Lee. 3983-3988. Bradfield.. University of Stellenbosch. 325-330. 69. K. 1991. Meyer.. 2001. 187-199.. Maiwashe. K. Pico. R. 91-105. M. Journal of Animal Science. Genetic variation in composite and parental Journal of Animal
populations: expectation for levels of dominance and gene frequency. O. R. G.
Estimation of genetic parameters and variance components for growth traits in Romosinuano cattle in the Colombian humid tropics.
. R. Van Vleck. 83. Skrypzeck.. Journal of Animal Science.. 2. Canadian Journal of Animal Science. R. L. Models which might explain negative correlation between direct and maternal genetic effects.. S.A.D. 30.. Estimation of nonadditive genetic variances in three synthetic lines of beef cattle using an animal model.J. Additive. J.S. R. Oni. & Garcia. Livestock Production Science.Robinson.C. & Neser. 1995. J. G. Estimates of direct and maternal genetic parameters for weight traits and back fat thickness in a multibreed population of beef cattle. 119-128. & Ward. S. International Journal of Applied Sciences. South African Journal of Animal Science. F. Clark. R.T.. 79. 2000. Journal of Animal Science. & Woodward.. Jordaan. Trus. & Skrypzeck.. Genetic and environmental influences of gain of beef cattle during various
periods of life..O. The heritability of some economic characteristics of beef cattle. Genetic parameters for maternal traits in beef cattle.M.W.A. F. Journal of Animal Science. 1996.P. Schoeman...A. 433-439. D. 111-122. William. G. J. 20. Can. Journal of Animal Science. D.W. I. & Wilton..R. Tosh. 1961.. Kemp... 1955. Swiger. Miller. S.A. Roso.R. 2007.. Journal of Animal Science. V. Schenkel.W. 2008. H.L. S.M.F. 14. 2000.A...S. 1780-1787...A. D. S. Pre-weaning growth traits of the Hereford breed in a multibreed composite beef cattle population.J. 1999.. 45. Shelby C.. D.J. Olorunju. L. dominance and epistatic loss effects on preweaning weight gain of crossbred beef cattle from different Bos tarus breeds. 64-67. Schoeman. 98-109. 220-229. & Northcutt. Shehu. 1002-1011.M. Sarmiento.P. 183-188. 372-385. Genetic Molecular Research. 1988. R. Jordaan. 2005. Journal of Animal Science. Rodriquez-Almeida. F.L. 482-491.F. Genetic and phenotypic parameters for body weight of Sokoto Gudali (Bkoloji) cattle. O. & Adeyinka.. & Wilton.. 73. 30. H. J.L. The influence of proportion of Simmental breeding in a multibreed synthetic beef cattle population on preweaning growth traits. 68. 6.
D.A. South Africa.K. C. 2009. South African Journal of Animal Science.L. 1993. 57-70. Baker. Maternal effects for growth traits in beef cattle.D.
.T. 34.K. Magister Thesis. Morris.Waldron. C. Genetic parameters for weaning weight of Kenyan Boran cattle accounting for direct-maternal genetic co variances. O. E. 36.M. University of Stellenbosch. Wasike. R. Ilatsia..F. A. & Kahi. 275-281. Livestock Production Science.. Zishiri.B. 2006. Genetic analyses of South African terminal sire sheep breeds. D... J. Ojango.. & Johnson..L..
Genetic changes will accumulate in the population over time and. in a population for certain traits (Zishiri. Keywords: genetic trends. Introduction Genetic trends are the result of selection pressured on specific traits in a particular direction.
. Stronger genetic trends have been reported for lighter breeds and weaker genetic trends for heavier breeds (Sullivan et al. weaning weight (WW) and yearling weight (YW) to construct genetic trends for the genetic evaluation of the Simbra breed. weaning weight. An annual increment in BW of 0. A regression coefficient of 0.. The objective of this study was to evaluate and quantify the genetic progress achieved in the Southern African Simbra breed. as the breed moves towards higher levels of production. 2004).005 and an R square (R ) of 0. 1999). The estimated genetic merit within breeds enables the fair comparison of across-breed genetic comparisons when combined with appropriate breed difference estimates this is ideal for across-breed selection to improve the breed lacking genetic merit. However the magnitudes varied considerably among breeds for the traits. Annual genetic trends can be estimated by regressing the mean annual breeding value of the animals on the corresponding birth years (Plasse et al. Simbra. 1999). these trends are being routinely published and enable improvement programs should the breed lack sustained genetic improvement (Sullivan et al.2.01 % was established for the breed.003 ± 0..11 ± 0. improvements in environmental conditions would also be necessary.30.CHAPTER 5
GENETIC TRENDS IN THE SIMBRA BREED
5. owing to the evolution of the breed.03 were obtained for BW. The three traits under investigation was birth weight (BW). 2009). In general the genetic trends for BW are acceptable while those for WW and YW are not improving. Genetic trends indicate the amount of genetic improvement over time. or lack thereof. Abstract
Estimated breeding values were obtained through single trait analysis for birth weight (BW). It is also important to note that where poor genetic links (connectedness) exists the comparisons of predicted breeding values (PBV) might be biased. It is thus important that the breeding objectives for the latter be revised in able to improve genetic trends in future generations for the Simbra breed.. weaning weight at 200-days (WW) and yearling weight at 400-days (YW). Genetic trends for growth traits have been found to be almost always positive across breeds. During the evaluation period of 1987 to 2009 a general decline in annual predicted breeding values (PBV) for WW and YW was found together with a slight increase in BW PBV’s. Due to the fact that genetic trends change over time.1. yearling weight
5. A regression coefficient (b) of –0.06 was obtained for the linear regression of annual predicted breeding value on WW with a R2 equal to 0. The single trait analyses were conducted by means of ASREML. birth weight.
year.1 that the mean annual predicted breeding values (PBV) for BW display great variation between years ranging from 1987 to 2009. maternal genetic. The mean breeding value then declined from 1998 to reach a low of –0. (Zishiri. Genetic trends were calculated as the regression of average predicted breeding value on year of birth. assuming that the fixed effects are identical for each model with the same number of random effects. 2002) was used for the estimation of (co) variance components as well as the prediction of breeding values for each trait during single trait analyses utilizing all available pedigree information. breeder.2) probably accounted for the large amount of variation associated with the predicted breeding values (PBV) compared to the later years.4. YW and MCW were fitted as covariates where appropriate.03 for the linear regression of annual predicted breeding value on BW during the evaluation period of 1987 to 2009.and permanent environmental effects for the traits studied. Records
Refer to Chapter 3 for the full description.05) were incorporated into the operational models. The model incorporated all pedigree information available in order to minimize bias due to selection and to increase the accuracy of estimation through additional ties between animals.12 in 2008. The models fitted included the fixed effects of breed composition.19 kg in 1996. Model six was the comprehensive model with another five reduced models. The contribution of each random effect was assessed by means of Likelihood Ratio tests (LRT) in order to establish the significance of each random term contribution. BW.35 kg in the year 1987 to reach a peak of +0.005 and the R2 was 0. Alternatively. the model with the higher Log-likelihood value will fit the data best. LRT is based on principle of testing twice the increase in Log-likelihood from adding a random effect to the analytic model as a Chi2 statistic. The traits under investigation were BW. The animal model is considered the model of choice for a wide range of applications and was implemented in the analysis of the three traits measured (Meyer.3.1.3. Six alternative models (as described in Chapter 4) were implemented that included direct genetic-. Materials and Methods 5. 1992). which ignored one or two of the maternal effects and/or assumed no covariance between the direct and maternal genetic effects (Demeke et al. The little number of records available from 1987 to 1996 (Figure 5.3.003 ± 0. 2009)
5. The fixed effects that were found to be significant (P < 0.2. WW at 200 days and YW at 400 days. 2003). WW. of records obtained in this study. Results and Discussion It is evident from Figure 5. The LRT is a statistical test of the goodness-of fit between hierarchically nested models. BW showed an increase from –0.5. Statistical Analysis The ASREML program (Gilmour et al. The random effects were then added to the analytic models subsequently. The overall regression coefficient (b) of the predicted breeding value for BW was 0. as well as editing. month and dam age. Expression of b as a percentage
PBV for bw (kg)
0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 -0.004 kg increase per year and over a few years this could become problematic for the breed if not evaluated on a regular basis.4 -0. which was much higher compared to the current study results. Mean annual predicted breeding values (PBV) for birth weight (BW) for the Simbra breed. In a study done by Plasse et al. dystocia and substantial economic loss (Brown & Galvez.0032x + 0. Plasse et al. Annual means are accompanied by the relevant standard error.1 -0. BW has been shown to be an important factor also associated with perinatal calf losses.01% per annum in BW is not high. However.of the least square mean of BW (35.0269
Figure 5. It is thus important that the selection objectives need to be reviewed for the Simbra breed to avoid future loss.
0. (2002) obtained similar results for Brahman in Venezuela.3 -0. 1969).0029 2 R = 0.3 0.01%. but this is equal to a 0..1. A genetic improvement of 0. (2004) he found a 0. 1977). Unlimited increase in BW selection will lead to excessive large calves.08 kg increase per year.2 -0.
.16 kg) yielded an annual increment in BW of 0. Patterson & Thompson (1971) found that cows that experience calving difficulty also had a subsequent reduction in reproductive performance as well as a decreased milk production.5
y = 0. losses of cows at calving and on calf growth rate (Holland et al.2 0.
52 kg) yielded an annual decline in WW of 0. A regression coefficient (b) of –0.52 kg per annum fro Brahman cattle.09 kg decline per year. (Sullivan et al. This is in association with that of annual breeding values for BW. (2004) showed a highly significant annual increase of 0. This is of vast concern for the Simbra breed and needs to be addressed with immediate effect.. However the overall trend showed a decline for annual predicted breeding values for WW. Kaps et al.
. Expression of b as a percentage of the least square mean of WW (232. Although 0.2. (2002) reported annual increases of 0. The current findings were not in agreement with that reported in other evaluation studies done for beef cattle. 1999. Figure 5. The latter is equal to a 0.14 kg respectively for weaning weight.04 % does not account for a great deal it can easily lead to substantial economic loss if not handled carefully.10 ± 0.06 was obtained for the linear regression of annual predicted breeding value on weaning weight with a R2 of 0.04 %. This is not in agreement with most literature results that reported positive genetic trends for weaning weight across various breeds.43 kg and 0. 1999) Enns & Nicoll (2008) and Plasse et al.3 indicates a large amount of variation associated with annual predicted breeding values for WW for the Simbra breed.26.9000 8000
7000 6000 5000 4000 3000 2000 1000 0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Figure 5. Plasse et al. The number of observations per year used for analyses for the Simbra breed. The annual PBV’s showed fluctuations of increased and decreased estimates throughout the evaluation period of 1987 to 2008.
As with WW. This is not in agreement with other literature results that reported positive genetic trends throughout for YW (Sullivan et al.02%. but it can easily lead to great economic loss if not treated with concern. After 1996 PBV’S increased to 0.1049x + 2. The annual predicted breeding value for YW (Figure 5.84 kg in 2007.28 kg in 2008.33 kg in 1987 and an ultimate low value of –0. but the overall trend was still negative for the majority of YW PBV’s with another decline of 0.24 kg in 1996. Annual means are accompanied by the relevant standard error. PBV’s showed another decline from 2000 up till 2003. A regression coefficient (b) of –0.06 kg decline annually for YW.4) showed a decline from 1987 to 1996 with a value of 3. 1999). 0.57 kg in 2000. Enns & Nicoll (2008) reported an annual genetic trend of 0.3.. PBV’s improved considerably after 2003 to reach a peak of 2. Mean annual predicted breeding values (PBV) for weaning weight for the Simbra breed. Expression of b as a percentage of the least square mean of WW (302.7526 2 R = 0.72 ± 0.02% does not account for a great deal.05 ± 0.05 was obtained for the linear regression of annual predicted breeding value on YW with a R2 of 0.2618
Figure 5.60 kg) yielded an annual decline in YW of 0. This accounts for 0.7 6
PBV for WW (kg)
5 4 3 2 1 0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 -1
. Kaps et al.06 for YW in Angus cattle. The latter indicates improvement in YW PBV’s. 1999.10..
. The decline in WW and YW is detrimental for any slaughter breed due to the direct effect on profitability and will lead to a reduced competitiveness for the Simbra breed in future generations. It is thus important to determine selection objectives and to implement them with immediate effect. Larger and better recorded datasets as well as improved genetic evaluation procedures may improve the accuracy of genetic predictions and thus result in more accurate genetic trends.5.5 1 0.70 in the single trait analysis and 0.33 in the tree trait analysis) infers that live weight did not receive much attention.38 in the three trait analysis) as well as for WW (0.5 0 1987 1988 1989 1990 1991 1992 1993 1994 1995
y = -0.8895 2 R = 0.0537x + 1. 5. Annual means are accompanied by the relevant standard error. It is needed to urgently revise the selection objectives for these traits with immediate effect. Newman et al. Thus we can only attribute this variability in the genetic trends due to different selection objectives for other traits rather than for live weight.4. Conclusion
Genetic trends varied between years for the traits studied indicating either changing selection objectives or selection deficiencies.67 in the single trait analysis and 0. According to the Southern African Simbra association more value is given to reproduction traits rather than growth traits and this could have been the reason for the trend obtained. Mean annual predicted breeding values (PBV) for yearling weight for the Simbra breed.5 4 3.5 2 1.4.0951
-0. The high to very high heritability estimates obtained for YW (0.5 3
PBV for YW (kg)
Figure 5. (1973) found that the selection differential accumulates very slowly in a herd in the first few years of a breeding program and the expected genetic change can take many years before the effect is evident.
Enns. J. K. R.A.. Journal of Animal Science. 2008. Rahnefeld. R. P. Newman.. Holland. 16. Bastidas. Mullaney. H. Raphaka. Pierre.R. Selection intensity and response to
selection for yearling weight in beef cattle. & Nicoll. Herring. Kaps... Arango. J.. 1999. & Hopkins.. Journal of Animal Science. 28.W. S.R. W..O.. Cullis. A. and Thompson. R. & Bastardo. B..... R. Gilmour. 1969. & Lamberson. J.0 VSN International Ltd. Wilton.M...P. Camaripano.
. 569-574. Journal of Animal Breeding and Genetics. D. R.R. Gogel.. 141-153. D. W.5. 2019-2027. 1999.B. Plasse. 1977. R. Livestock Research for Rural Development.W. Fossi. B. Journal of Animal Science. Journal of Animal Science. & Romero. Recovery of inter-block information when block sizes are unequal.M... H. Genetic trends and breed overlap derived from multiple-breed genetic evaluations of beef cattle for growth traits. & Galvez. South Africa. & Thompson.. UK. H. 86. References
Brown. University of Stellenbosch. R. 2002.R. Genetic and environmental parameters for mature cow weight in Angus cattle. Fossi.6.. Genetic change results from selection on an economic breeding objective in beef cattle.J. 53. Miller. Patterson. ASREML user guide release 1. I. G.W. Genetic and non-genetic trends for calf weights in a Bos indicus herd upgraded to pedigree Brahman. 77. Llamozas. genetic parameters and annual trends for calf weights in a pedigree Brahman herd under selection for three decades. Hoogesteijn. Magister Thesis... 119. L.. O. 1971.. 2002. A.D. 162-167.. Maternal and other effects on birth weight of beef calves. J. 1973. & Fredeen. M... 17. Romero. Verde. 3348-3357. B. J. Biometrika.J. Canadian Journal of Animal Science. Estimation of genetic and non-genetic parameters for growth traits in two beef cattle breeds in Botswana. 545-554. Sullivan.. 2008. & Banks.G. 142-159. (Co)variance components.J. 77. L.. Breed and environmental factors affecting birth weight in Victorian beef cattle. Journal of Animal Science. Hemel Hempstead. P.. S. H. Welham.R. Plasse.D. M. G. 5-9. 2004. 58..C.1-12. G.
. Magister Thesis. Genetic analyses of South African terminal sire sheep breeds.. South Africa.Zishiri. O.T. University of Stellenbosch. 2009.
month of birth and dam age.05) affects all live weight traits studied. The genetic correlation estimates obtained during the multi-trait analyses were relatively small. However. breeder of calf. Some years and months also showed significant differences for the traits. a difference between the breed compositions were encountered for BW and WW. The correlation between direct and maternal effects obtained in the single trait analyses was highly negative for BW. year of birth. During single trait analyses for each of the live weight traits relatively high heritability estimates were obtained for BW. Non-genetic factors have been found to affect live weights measured throughout the animal’s life. care should be taken when interpreting results from a scientific analysis because the accuracy and validity could have been jeopardised by unforeseen factors and does not reflect the true biological principle. with very small direct maternal and maternal permanent environment effect estimates. should be incorporated during genetic evaluation of live weight traits. The fixed effects. weaning weight (WW). Calves with higher Brahman inheritance were heavier at birth. Calves with a higher Simmental component would probably be more suitable for less challenging areas where weaner production is of higher importance. WW and YW indicating an antagonistic effect. During multiple comparison of least square means. a higher Brahman component is recommended due to the higher 600-day weight. Breed was found to not be significant enough to be encountered during yearling trait analysis. yearling weight (YW) and 600-day weight.CHAPTER 6
Genetic and non-genetic factors affecting growth traits in Simbra cattle are reported in this study for the first time. Fitting mature cow weight as a covariate significantly (P < 0. During the 68
. However dam age did not affect YW or 600-day weight significantly. Live weight includes birth weight (BW). breed composition of calf. WW and YW. The latter indicates that live weight traits are mostly under direct genetic control and provides the potential of genetic improvement through selection. with some years and months revealing higher weights than others. while calves with higher Simmental inheritance were heavier at weaning. but positive for all traits studied. which included gender of calf. indicating a favourable response for YW when selecting for WW. Male calves have also been found to weigh heavier than female calves up till 600-day weight. resulting in economic loss. The Direct heritability estimates obtained during the three-trait analysis were lower than that of the single trait analyses and were more realistic. There is thus a need to adjust for some of these factors to enable a fair comparison between animals. Positive genetic correlation between BW and later weights may be a cause for concern as an increase in BW could be associated with an increase in the incidence of dystocia. except for 600-day weight. In areas where the environment is more challenging and extensive cattle farming is practised with more available space to keep calves longer. The data structure has been found unsatisfactory in many ways and was the possible reason for the loss of valuable genetic information and some inflated values.
the same genetic gain can be made. The emphasis should rather be on the genetic and environmental improvement of total cow efficiency as well as functional efficiency.multi trait analyses 91. Genetic trends were constructed for BW. Although selection for the latter would be difficult because most of the fitness. When these traits are combined in a suitable selection index together with other important traits like carcass and reproduction. but only when selection objectives are valid. Due to the large amount of variation within the Simbra breed the opportunity exists for genetic improvement for the various live weight traits. During the analysis it was found that the stability of cows was very low. It is important that a proper selection balance between traits of economic importance without an over emphasis on growth traits. Although some of the years indicated positive means for the predicted breeding values the overall trend was negative for both WW and YW with large variation between years. This could have been due to the in and out flow of breeders during the analysis or due to inadequate cow performance. This is surely a trait that needs investigation due to the fact that heifers are expensive to maintain till they can reproduce and should at least remain in the herd for 6-8 years to optimise the farming system.
. thereby improving the growth potential of the Simbra. it should still be investigated and implemented.54% of the records were discarded and not useful for genetic analysis. It is thus very important that breeders need to be informed about the accurate and regular measurements of traits to obtain proper results. This is of great concern due to the fact that Simbra’s are supposed to be used for their effective growth. adaptability and functional efficiency traits are hard to measure and low in heritability. WW and YW. Maximising the rate of genetic change can indeed be valuable.
Giaotrinhdongvathoc-Trần TốConnecting Places, Constructing Tet. Home, City and the Making of the Lunar New Year in Urban VietnamDevelopment Economic SyllabusEstimation of Geneticparameters for Growth Traits in South Africa Brahman CattleDoctoral - Genetic Aspect of Calving Growth and Carcass Traits in Beef CattleGenetic Analyses of Growth Traits for the Kimbra Composite BreedEvaluation of Performance in Receiving Heifers Fed Different Sources of Dietary LipidWeather and ClimateNutrient Fluxes in Production SystemsNutrient Fluxes in Production SystemsSonnenschmidt Chapter 04 PPTLời khuyên hay nhất về sự thay đổi
Genetic Analyses of Growth Traits for the Simbra Composite Breed by Tran Kahn2 viewsEmbedDownloadDescriptionpresentationpresentationInterests: Types, ReviewsRead on Scribd mobile: iPhone, iPad and Android.Copyright: Attribution Non-Commercial (BY-NC)List price: $0.00Download as PDF, TXT or read online from ScribdFlag for inappropriate contentShow moreShow less
RelatedOverview of European Fusion Welding Standardsby ansarALLAAH2 Hypothesis Testingby Karan Singh KathiarAbstract 803by Victor AtilolaTesting AND Writingby Mohd Shaffizi AsaariDashboard Analysisby Kusuma SaddalaAnova e Printby Emine Alaaddinoglu3.2 Materi Kuliah Mp. Jaringan IIby imnotactiveEmmiby Rudra Kumar MishraCompensation Managementby narensinhaCourse Project bby Ahmad NijimCountry Analysisby beely2607-Risk analysis_b.pdfby Nia KhoirunnisaEstimation for Amount in Electrical Works--06.12.12by Vinod VermaKM Chapter 5by Hafizur Rahman DhruboTypes of research design - experimentsby jivi_manHealth Insuranceby faith23dbagul2-6_ManufacturingProcess (1)by amitaggarwal78Models for Successby irinuca12AE Databy Tristan OsborneEval Qualityby Alekhya Guntupallilec17Testby mniyas_1Tsai Wuby prichindel_eu20121027 - Session 3by nishithathiBabylover's Starting Strength With Assistanceby jhnsnBabylover's Starting Strength With Assistanceby Philip Gutierrezlec13by PINNASISUNIL172188628-data-miningby Le PutraMeasurment uncertainty in analytical chemestryby Carlos SamaniegoSimilar to Genetic Analyses of Growth Traits for the Simbra Composite BreedOverview of European Fusion Welding Standards2 Hypothesis TestingAbstract 803Testing AND WritingDashboard AnalysisAnova e Print3.2 Materi Kuliah Mp. Jaringan IIEmmiCompensation ManagementCourse Project bCountry Analysis7-Risk analysis_b.pdfEstimation for Amount in Electrical Works--06.12.12KM Chapter 5Types of research design - experimentsHealth Insurance2-6_ManufacturingProcess (1)Models for SuccessAE DataEval Qualitylec17TestTsai Wu20121027 - Session 3Babylover's Starting Strength With AssistanceBabylover's Starting Strength With Assistancelec13172188628-data-miningMeasurment uncertainty in analytical chemestry