scholarly journals Genetic Parameters for Age at First Calving and First Calving Interval of Beef Cattle

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2122
Author(s):  
Michaela Brzáková ◽  
Jindřich Čítek ◽  
Alena Svitáková ◽  
Zdeňka Veselá ◽  
Luboš Vostrý
Keyword(s):  
Beef Cattle ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Performance Testing ◽  
Breeding Strategy ◽  
Relationship Matrix ◽  
Calving Interval ◽  
Data Set ◽  
Age At First Calving ◽  
The Relationship

The objective of this study was to estimate genetic parameters for age at first calving (AFC) and first calving interval (FCI) for the entire beef cattle population and separately for the Charolais (CH) and Aberdeen Angus (AA) breeds in the Czech Republic. The database of performance testing between the years 1991 and 2019 was used. The total number of cows was 83,788 from 11 breeds. After editing, the data set contained 33,533 cows, including 9321 and 4419 CH and AA cows, respectively. The relationship matrix included 85,842 animals for the entire beef population and 24,248 and 11,406 animals for the CH and AA breeds, respectively. A multibreed multitrait animal model was applied. The estimated heritability was low to moderate. Genetic correlations between AFC and FCI varied depending on the breeds from positive to negative. Differences between variance components suggest that differences between breeds should be considered before selection and breeding strategy should be developed within a breed.

scholarly journals Selection of a suitable data set and model for the estimation of genetic parameters of the weaning weight in beef cattle

2007 ◽  
Vol 50 (6) ◽  
pp. 562-574
Author(s):  
L. Vostrý ◽  
J. Přibyl ◽  
Z. Veselá ◽  
V. Jakubec
Keyword(s):  
Beef Cattle ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Suitable Model ◽  
Data Set ◽  
Weaning Weight ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for weaning weight of beef cattle in the Czech Republic. Nine subsets were tested for the selection of a suitable subset. The subsets differed from each other in the limit of sampling criteria. The most suitable subset satisfied these conditions: at least 5 individuals per each sire, 5 individuals per HYS (herd, year, season), 2 sires per HYS, and individuals per dams that have at least one half-sister and two offspring (n = 4 806). The selection of a suitable model was carried out from 10 models. These models comprised some of the random effects: direct genetic effect, maternal genetic effect, permanent maternal environment effect, HYS, sire × herd or sire × year interaction, and some of the fixed effects: dam’s age, sex (young bull, heifer × single, twin born), HYS, year, herd. The direct heritability (h2a) ranged from 0.06 to 0.17, of maternal heritability (h2m) from 0.03 to 0.06. The genetic correlations between the direct and maternal effect (ram) were in the range of –0.15 –0.42.

scholarly journals Estimation of genetic parameters for female fertility traits in the Czech Holstein population

2019 ◽  
Vol 64 (No. 5) ◽  
pp. 199-206 ◽  
Author(s):  
Michaela Brzáková ◽  
Ludmila Zavadilová ◽  
Josef Přibyl ◽  
Petr Pešek ◽  
Eva Kašná ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Reproductive Traits ◽  
Additive Genetic Effect ◽  
Data Set ◽  
Age At First Calving ◽  
Fertility Traits ◽  
Holstein Population

Genetic parameters for fertility traits in Czech Holstein population were estimated. The database obtained from the Czech-Moravian Breeders Corporation with 6 414 486 insemination records between years 2005–2015 was used. Date of calving of the selected animals was taken from the database of milk records from 2005–2015. Fertility traits were age at first service (AFS), age at first calving (AFC), days open (DO), calving interval (CI) and first service to conception interval in cows (FSC-C) and heifers (FSC-H). The heritability of each trait was estimated using single-trait animal models. The model included fixed effects of herd-year-season of birth, herd-year-month of calving, lactation order, parity, last calving ease, linear and quadratic regressions on age at first insemination in heifers or on age at first calving in cows. Random effects were animal, permanent environmental effect and random residual error. After edits, the final data set included up to 599 901 observations from up to 448 037 animals dependent on traits. The range of heritability estimates was from 0.010 to 0.058. The lowest heritability was for first service to conception interval in heifers, and the highest heritability was for age at first service. Variances of random permanent effects were higher than variance of additive genetic effect in all traits manifested in mature cows. Repeatability ranged from 0.060 to 0.090. Genetic correlations between traits were estimated using a bivariate animal model. High positive genetic correlations were found between AFS–AFC, DO–CI, FSC-C–DO and FSC-C–CI. A moderate genetic correlation was found between AFS–FSC-H and between AFC. A negative correlation was found between AFS–FSC-C. Correlations between other traits were close to zero. The results suggest that the level of these reproductive traits can be improved by selection of animals with high genetic merit.

scholarly journals First Lactation Genetic Parameters of Buffaloes Under Multiple Trait Animal Model

1997 ◽  
Vol 2 ◽  
pp. 27
Author(s):  
M.S. Khan ◽  
K. Z. Gondal ◽  
S. H. Raza ◽  
A. A. Asghar
Keyword(s):  
Animal Model ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Calving Interval ◽  
Economic Traits ◽  
Dry Period ◽  
Multiple Trait ◽  
Lactation Length ◽  
The Relationship

There are many computational algorithms available for estimating (co) variance components under multiple trait models. Paternal half-sib correlation is the most commonly used method for estimating genetic parameters of economic traits of buffaloes. The models used are Single trait, ignoring covariances with other traits. The relationship matrices are also ignored. This study was undertaken to estimate genetic parameters of first lactation traits of Nili Ravi buffaloes under a multiple trait animal model. First lactation milk yield was l7% heritable when traits such as age of calving interval were considered. Age at first calving was 18% heritable with very low genetic correlations with other traits. Lactation length, dry period and calving interval were lowly heritable traits. Estimates differed by inclusion or exclusion of traits due to the covariances present among all these traits. A high genetic correlation between mills yield and lactation length (0.74) does not warrant the selection of milk yield as the only trait.

scholarly journals Genetic parameters of test-day somatic cell scores for the first three lactations of Polish Holstein-Friesian cattle

2011 ◽  
Vol 56 (No. 9) ◽  
pp. 381-389 ◽  
Author(s):  
K. Rzewuska ◽  
J. Jamrozik ◽  
A. Żarnecki ◽  
T. Strabel
Keyword(s):  
Somatic Cell ◽  
Bayesian Methods ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Large Data ◽  
Somatic Cell Score ◽  
Data Set ◽  
Holstein Friesian ◽  
Covariance Components ◽  
Friesian Cattle

 Genetic parameters for somatic cell score in the first three lactations of Polish Holstein-Friesian cattle were estimated. A multiple-lactation model was applied with random herd-test-day effect, fixed regressions for herd-year and age-season of calving, and random regressions for the additive genetic and permanent environmental effects. The large data set was used that included over one million test-day records and more than 58 000 cows. Estimates of covariance components and genetic parameters were obtained by Bayesian methods using the Gibbs sampler. Average daily heritabilities of somatic cell score (SCS) in the first three lactations were 0.11, 0.12 and 0.14 for the first, second and third lactation, respectively. Estimates of daily heritabilities were rather independent of days in milk (DIM), with no serious abnormalities at the beginning or the end of lactation. Average genetic correlations between SCS on the same DIM were 0.68, 0.62 and 0.70 for first and second, first and third, and second and third parities, respectively, and did not exceed 0.77. The low level of heritability estimates and relatively low genetic correlations between lactations would suggest that selection based on the first lactation only could limit a response in mastitis resistance for later lactations.

Genetic correlations and trends for traits of economic importance in dairy buffalo

2020 ◽  
Vol 60 (4) ◽  
pp. 492 ◽  
Author(s):  
Ranielle Nogueira da Silva Vilela ◽  
Thomaz Marques Sena ◽  
Rusbel Raul Aspilcueta-Borquis ◽  
Leonardo de Oliveira Seno ◽  
Francisco Ribeiro de Araujo Neto ◽  
...  
Keyword(s):  
Milk Fat ◽  
Genetic Correlations ◽  
Reproductive Traits ◽  
Milk Quality ◽  
Calving Interval ◽  
Age At First Calving ◽  
Productive Traits ◽  
Dairy Buffalo ◽  
Genetic Trends ◽  
Low Magnitude

Context The planning and execution of selection programs requires estimates of the genetic correlations between traits. As genetic change is achieved for a given trait, it is important to consider possible genetic changes for other traits. Understanding the magnitude and direction of genetic correlations can assist in selection decisions. Aims The aim of the present study was to estimate the genetic correlations of reproductive traits with productive traits and with percentages of fat and protein in the milk of dairy buffalo. Additionally, genetic trends were estimated for the traits under study over the years. Methods Data from 11530 complete lactations of 3431 female buffalo were used. The following traits were analysed: milk, fat and protein yields; percentages of fat and protein; age at first calving; and calving interval. The (co)variance components were estimated by Bayesian inference in multi-trait analyses, considering a linear animal model. To calculate the genetic trends, the average annual genetic values were regressed on the year of birth. Key results The means of genetic correlations estimated between reproductive (age at first calving and calving interval) and productive (milk, fat and protein yields) traits were positive, but of moderate to low magnitude. The association between the reproductive and milk quality (fat and protein percentages) traits were negative and of low magnitude. Genetic trends for the productive traits were positive (5.25 ± 0.63, 0.15 ± 0.034 and 0.09 ± 0.038 kg/year for milk, fat and protein yields respectively). Genetic trends for the reproductive traits of age at first calving and calving interval increased by 0.47 ± 0.09 and 0.48 ± 0.10 days/year respectively. In terms of milk quality, however, the percentages of fat and protein decreased by 0.016 ± 0.003 and 0.011 ± 0.001%/year respectively. Conclusions Genetic gains in productive traits may elevate the number of days at first calving and extend the calving interval, in addition to leading to the production of milk of lower quality. Implications The use of a multi-trait selection index is an alternative, as it combines information from different sources, such that an optimal selection criterion can be achieved over time by virtue of its emphasis on appropriate weighting for all traits.

scholarly journals Comparison of models for estimating genetic parameters and predicting breeding values for birth weight and calving ease in Czech Charolais cattle 

2014 ◽  
Vol 59 (No. 7) ◽  
pp. 302-309 ◽  
Author(s):  
L. Vostrý ◽  
Z. Veselá ◽  
A. Svitáková ◽  
H. Vostrá Vydrová
Keyword(s):  
Animal Model ◽  
Birth Weight ◽  
Beef Cattle ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Parameters Estimation ◽  
Calving Ease ◽  
Heritability Estimates

The most appropriate model for genetic parameters estimation for calving ease and birth weight in beef cattle was selected. A total of 27 402 field records were available from the Czech Charolais breed. For estimation of genetic parameters for calving ease and body weight, three bivariate models were tested: a linear-linear animal model (L-LM) with calving ease classified into four categories (1 – easy; 2–4 – most difficult), a linear-linear animal model (SC-LM) in which calving ease scores were transformed into Snell scores (Snell 1964) and expressed as percentage of assisted calving (ranging 0–100%), and a bivariate threshold-linear animal model (T-LM) with calving ease classified into four categories (1 – easy, 2–4 – most difficult). All tested models included fixed effects for contemporary group (herd × year × season), age of dam, sex and breed of a calf. Random effects included direct and maternal genetic effects, maternal permanent environmental effect, and residual error. Direct heritability estimates for calving ease and birth weight were, with the use of L-LM, SC-LM, and T-LM, from 0.096 ± 0.013 to 0.226 ± 0.024 and from 0.210 ± 0.024 to 0.225 ± 0.026, respectively. Maternal heritability estimates for calving ease and birth weight were, with the use of L-LM, SC-LM, and T-LM, from 0.060 ± 0.031 to 0.104 ± 0.125 and from 0.074 ± 0.041 to 0.075 ± 0.040, respectively. Genetic correlations of direct calving ease with direct birth weight ranged from 0.46 ± 0.06 to 0.50 ± 0.06 for all tested models; whereas maternal genetic correlations between these two traits ranged from 0.24 ± 0.17 to 0.25 ± 0.53. Correlations between direct and maternal genetic effects within-trait were negative and substantial for all tested models (ranging from –0.574 ± 0.125 to –0.680 ± 0.141 for calving ease and from –0.553 ± 0.122 to –0.558 ± 0.118 for birth weight, respectively), illustrating the importance of including this parameter in calving ease evaluations. Results indicate that any of the tested models could be used to reliably estimate genetic parameters for calving ease for beef cattle in the Czech Republic. However, because of advantages in computation time and practical considerations, genetic analysis using SC-LM (transformed data) is recommended.

Bayesian estimates of genetic parameters for pre-conception traits, gestation length and calving interval in beef cattle

2009 ◽  
Vol 114 (1-3) ◽  
pp. 72-80 ◽  
Author(s):  
G. Yagüe ◽  
F. Goyache ◽  
J. Becerra ◽  
C. Moreno ◽  
L. Sánchez ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genetic Parameters ◽  
Gestation Length ◽  
Calving Interval ◽  
Bayesian Estimates

Genetic parameters for growth and carcass traits of Japanese Black (Wagyu) cattle

2000 ◽  
Vol 71 (1) ◽  
pp. 59-64 ◽  
Author(s):  
T. Oikawa ◽  
T. Sanehira ◽  
K. Sato ◽  
Y. Mizoguchi ◽  
H. Yamamoto ◽  
...  
Keyword(s):  
Body Weight ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Carcass Traits ◽  
Genetic Correlations ◽  
Subcutaneous Fat ◽  
Marbling Score ◽  
Data Set ◽  
Pooled Data

AbstractRestricted maximum likelihood analyses fitting an animal model were conducted to estimate genetic parameters with a pooled-data set of performance tests (growth traits and food intake) on 661 bulls and progeny tests (growth traits and carcass traits) on 535 steers. Traits studied included concentrate intake (CONC), roughage intake (ROU), TDN conversion (TCNV), TDN intake (TINT) of bulls; rib eye area (REA), marbling score (MARB), dressing proportion (DRES) and subcutaneous fat depth (SCF) of steers. Body weight at start (BWS), body weight at finish (BWF) and average daily gain (ADG) of all animals were measured. Estimated heritabilities were 0·18 (CONC), 0·71 (ROU), 0·11 (TCNV) and 0·36 (TINT); 0·02 (REA), 0·49 (MARB), 0·15 (DRES), 0·15 (SCF), and from 0·20 to 0·38 for growth traits. Genetic correlations of ROU were different from those of CONC, probably due to inconsistent restrictions on concentrate intake; those of TINT with the weights, ADG and SCF were high. MARB showed positive genetic correlations with growth traits and low correlations with TINT and SCF. High potentiality for improvement of marbling score was suggested.

scholarly journals 69 A new frame score system for Nelore cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 47-47
Author(s):  
Roberto D Sainz ◽  
Nayanny Guimarães ◽  
Cláudio U Magnabosco ◽  
Fernando Lopes
Keyword(s):  
Body Composition ◽  
Beef Cattle ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Subcutaneous Fat ◽  
Heritability Estimate ◽  
Reproductive Traits ◽  
Carcass Weight ◽  
Muscle Area ◽  
Nelore Cattle

Abstract Frame score (FS) systems for beef cattle generally represent the relationships among growth, body composition, reproduction and mature size, in a simple and practical form. This study aimed to: 1) develop a FS system for Nelore cattle that is biologically sound, easy to interpret, and useful for producers; and 2) estimate the genetic parameters of the FS with productive and reproductive traits. An arbitrary scale (1 to 12) was devised so that each unit corresponds to 15 kg of carcass weight (1 @), as this is a common measure used for marketing beef cattle in Brazil. Therefore, ideal carcass weight, defined as having 6 mm of backfat, would be 18 @ (269 kg) and 15 @ (224 kg) for FS = 6 males and females, respectively. Data from 36,030 animals (22,405 males, 13,565 females) raised on pasture were obtained from participating herds of the National Association of Breeders and Researchers (ANCP). Genetic parameters were estimated in uni- and bicharacteristic analyses under an animal model, using the EM-REML algorithm (AIREMLF90) and Bayesian inference (GIBBS1F90). The heritability estimate for the new FS was 0.38, and its additive genetic correlations were 0.70, 0.72, 0.77, 0.33, -0.57, 0.27, and 0.28 with BW at 365 d, BW at 450 d, hip height, longissimus muscle area, subcutaneous fat thickness, scrotal circumference at 450 d, and age at first calving, respectively. The estimated heritability and genetic correlations indicate that there is enough additive genetic variability to allow for genetic response to selection. The estimates support the notion that larger frame animals are taller, heavier, leaner and later maturing, both in body composition as well as sexually. The new frame score may be a useful tool for genetic selection of animals that are best suited to their environment.

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