scholarly journals Estimation of Genetic Parameters for Peak Yield, Yield and Persistency Traits in Murciano-Granadina Goats Using Multi-Traits Models

Animals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 411
Author(s):  
Judith C. Miranda ◽  
José M. León ◽  
Camillo Pieramati ◽  
Mayra M. Gómez ◽  
Jesús Valdés ◽  
...  

This paper studies parameters of a lactation curve such as peak yield (PY) and persistency (P), which do not conform to the usual selection criteria in the Murciano-Granadina (MG) breed, but are considered to be an alternative to benefit animal welfare without reducing production. Using 315,663 production records (of 122,883 animals) over a period of 24 years (1990–2014), genetic parameters were estimated with uni-, bi- and multivariate analysis using multiple trait derivative free restricted maximum likelihood (MTDFREML). The heritability (h2)/repeatability (re) of PY, yield (Y) and P was estimated as 0.13/0.19, 0.16/0.25 and 0.08/0.09 with the uni-trait and h2 of bi- and multi-traits analysis ranging from 0.16 to 0.17 of Y, while that of PY and Y remained constant. Genetic correlations were high between PY–Y (0.94 ± 0.011) but low between PY–P (–0.16 ± 0.054 to –0.17 ± 0.054) and between Y–P (–0.06 ± 0.058 to –0.05 ± 0.058). Estimates of h2/re were low to intermediate. The selection for Y–PY or both can be implemented given the genetic correlation between these traits. PY–P and Y–P showed low to negligible correlation values indicating that if these traits are implemented in the early stages of evaluation, they would not be to the detriment of PY–Y. The combination of estimated breeding values (EBVs) for all traits would be a good criterion for selection.

2019 ◽  
Vol 32 (2) ◽  
pp. 100-106 ◽  
Author(s):  
Farzane Shokri-Sangari ◽  
Hadi Atashi ◽  
Mohammad Dadpasand ◽  
Fateme Saghanejad

Background: Lactation persistency influences cow health and reproduction and has an impact on the feed costs of dairy farms. Objective: To estimate (co)variance components and genetic parameters of 100- and 305-d milk yield, and lactation persistency in Holstein cows in Iran. Methods: Records collected from January 2000 to December 2012 by the Animal Breeding Center of Iran (Karaj, Iran) were used. The following four measures of lactation persistency were used: P21: Ratio of milk yield in the second 100-d in milk (DIM) divided by that of the first 100-d. P31: Ratios of milk yield in the third100-d divided by that of the first 100-d. PW: The persistency measure derived from the incomplete gamma function. PJ: The difference between milk yield in day 60th and 280th of lactation. Results: The estimated heritability of lactation persistency for the three first parities (first, second, and third lactation) ranged from 0.01 to 0.06, 0.02 to 0.10, and 0.01 to 0.12, respectively. Genetic correlations among lactation persistency measures for the three first parities ranged from 0.77 to 0.98, 0.65 to 0.98, and 0.58 to 0.98, respectively; while corresponding values for genetic correlations among lactation persistency with 305-d milk production ranged from 0.18 to 0.63, 0.32 to 0.75, and 0.41 to 0.71, respectively. The estimated repeatability for lactation persistency measures ranged from 0.06 to 0.20. Conclusion: The moderate positive genetic correlation between lactation persistency and 305-d milk yield indicates that selection for increasing milk yield can slightly improve lactation persistency.Key words: dairy cattle, heritability, lactation curve, milk yield, persistency, repeatability. ResumenAntecedentes: La persistencia de la lactancia tiene una gran influencia en la salud, la reproducción y los costos de alimentación de las granjas lecheras. Objetivo: Estimar los componentes de (co)varianza y los parámetros genéticos de la producción de leche a 100 y 305 d, asi como la persistencia de la lactancia en vacas Holstein en Irán. Métodos: Se utilizaron registros recopilados entre enero de 2000 y diciembre de 2012 por el Centro de cría de animales de Irán (Karaj, Irán). Se utilizaron las siguientes cuatro medidas de persistencia de la lactancia: P21: Proporción de producción de leche en los segundos 100-d en leche (DIM) dividida por la de los primeros 100-d. P31: Proporcion de producción de leche en los terceros 100-d dividido por el de los primeros 100-d. PW: medida de persistencia derivada de la función gamma incompleta. PJ: diferencia entre el rendimiento de leche en el 60 y el 280 día de lactancia. Resultados: La heredabilidad estimada de la persistencia de la lactancia para los tres primeros partos (primera, segunda y tercera lactancia) varió de 0,01 a 0,06; 0,02 a 0,10; y 0,01 a 0,12, respectivamente. Las correlaciones genéticas entre las medidas de persistencia de lactancia para los tres primeros partos variaron de 0,77 a 0,98; 0,65 a 0,98; y 0,58 a 0,98, respectivamente; mientras que los valores correspondientes para las correlaciones genéticas entre la persistencia de la lactancia con la producción de leche a 305 d variaron de 0,18 a 0,63; 0,32 a 0,75; y 0,41 a 0,71, respectivamente. La repetibilidad estimada para las medidas de persistencia de la lactancia varió de 0,06 a 0,20. Conclusión: La correlación genética positiva moderada entre la persistencia de la lactancia y la producción de leche a 305-d indica que la selección para aumentar la producción de leche puede mejorar ligeramente la persistencia de la lactancia.Palabras clave: curva de lactancia, ganado lechero, heredabilidad, persistencia, producción de leche, repetibilidad. ResumoAntecedentes: A persistência da lactação tem grande influência nos custos de saúde, reprodução e alimentação em fazendas leiteiras. Objetivo: Estimar os componentes da variância (co)variância e os parâmetros genéticos da produção de leite de 100 e 305 d e a persistência da lactação em vacas Holandesas no Irã. Métodos: Os dados utilizados foram registros coletados de janeiro de 2000 a dezembro de 2012 pelo Centro de Criação de Animais do Irã (Karaj, Irã). As seguintes quatro medidas de persistência de lactação foram utilizadas: P21: Razão da produção de leite no segundo 100-d em leite (DIM) dividido pelo primeiro 100-d. P31: Razões da produção de leite na terceira 100d dividida pela da primeira 100-d. PW: A medida de persistência derivada da função gama incompleta. PJ: A diferença entre a produção de leite no 60º e 280º dia de lactação. Resultados: A hereditariedade estimada da persistência da lactação para as três primeiras paridades (primeira, segunda e terceira lactação) variou de 0,01 a 0,06; 0,02 a 0,10; e 0,01 a 0,12, respectivamente. As correlações genéticas entre as medidas de persistência da lactação para as três primeiras paridades variaram de 0,77 a 0,98; 0,65 a 0,98; e 0,58 a 0,98, respectivamente; enquanto os valores correspondentes para correlações genéticas entre a persistência da lactação com produção de leite de 305d variaram de 0,18 a 0,63; 0,32 a 0,75; e 0,41 a 0,71, respectivamente. A repetibilidade estimada para medidas de persistência de lactação variou de 0,06 a 0,20. Conclusão: A correlação genética positiva moderada entre a persistência da lactação e a produção de leite de 305d indicou que a seleção para aumentar a produção de leite melhoraria ligeiramente a persistência da lactação.Palavras-chave: curva de lactação, gado de leite, hereditariedade, persistência, produção de leite, repetibilidade.


2021 ◽  
Vol 73 (6) ◽  
pp. 1371-1380
Author(s):  
O. Ermetin ◽  
B. Dağ

ABSTRACT In this study, milk yield, reproductive yield, and type traits of 533 Holstein cows in the first lactation raised in 54 farms were examined. In the three-year study, phenotypic (rP) and genetic (rG) correlations between type traits and milk yield were estimated based on the variance elements and heritability of the type traits of Holstein cows in the first lactation. Linear identification and scoring systems have been applied to classify the cows according to type traits. Heritability and correlations were estimated with ASREML models. The type traits included stature, angularity, rump width, hocks, rear udder height, central ligament, teat length, body capacity, feet and legs, udder composite and final score for genetic correlations with 305-day milk yield were estimated as -0.49, -0.14, -0.93, 0.35, 0.40, 0.11, -0.65, 0.70, 0.31, 0.54, and 0.70, for phenotypic correlations were estimated as 0.28, 0.28, 0.30, 0.21, 0.35, 0.39, -0.06, 0.46, 0.48, 0.56, and 0.58 respectively. Among the phenotypic correlations between the type traits, especially the phenotypic correlations between the final score and various type traits were found to be high and significant. The fact that these traits are in high correlation with other traits and milk yield may enable these to be used as indirect selection criteria in the selection for milk yield.


2014 ◽  
Vol 94 (2) ◽  
pp. 281-285 ◽  
Author(s):  
Gang Guo ◽  
Xiangyu Guo ◽  
Yachun Wang ◽  
Xu Zhang ◽  
Shengli Zhang ◽  
...  

Guo, G., Guo, X., Wang, Y., Zhang, X., Zhang, S., Li, X., Liu, L., Shi, W., Usman, T., Wang, X., Du, L. and Zhang, Q. 2014. Estimation of genetic parameters of fertility traits in Chinese Holstein cattle. Can. J. Anim. Sci. 94: 281–285. The objective of this study was to estimate genetic parameters for fertility traits in Chinese Holstein heifers and cows. Data of 20169 animals with 42106 records over a period of 10 yr (2001–2010) were collected from Sanyuan Lvhe Dairy Cattle Center in Beijing, China. Traits included age at first service (AFS), number of services (NS), days from calving to first service (CTFS), days open (DO), and calving interval (CI). Genetic parameters were estimated with multiple-trait animal model using the DMU software. Heritability estimates for AFS, NS, CTFS, DO and CI were 0.100±0.012, 0.040±0.017, 0.034±0.011, 0.053±0.019 and 0.056±0.014, respectively. Genetic correlations between traits observed ranged from −0.13 to 0.99. Genetic correlations between AFS with NS, CTFS, DO and CI were −0.31, 0.15, −0.13 and −0.15, respectively. Calving interval was strongly correlated with NS, CTFS and DO (0.49–0.99), and DO showed strong correlation with NS and CTFS (0.49 and 0.58, respectively). The genetic correlation between CTFS and NS was negative moderate (−0.25). Results were in range with previous literature estimates and can be used in Chinese Holstein genetic evaluation for fertility traits.


2000 ◽  
Vol 51 (7) ◽  
pp. 833 ◽  
Author(s):  
Nguyen Hong Nguyen ◽  
Cam P. McPhee ◽  
Claire M. Wade ◽  
Brian G. Luxford

Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was –0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and –0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.


1996 ◽  
Vol 5 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Anne Kettunen ◽  
Esa A. Mäntysaari

Genetic parameters for test-day milk production at different stages of lactation of Finnish Ayrshire heifers were estimated with the REML method using the AI algorithm and animal model. The data consisted of 38 679 first lactation test-day milk yields of 4205 cows from 231 herds in three geographical regions (North Savo, Central Ostrobothnia and Lapland). To identify different test days, records were numbered according to the days in milk after calving, and were further categorized into three part-lactations according to the test-day classification. Expressions in the three part-lactations were considered as separate traits, and tests were treated as repeated observations within the trait. Heritability estimates for test-day milk yield varied between 0.11 and 0.17, being lowest at the beginning of lactation. Genetic correlations between test-day milk yields at different trimesters ranged from 0.64 to 0.91, being highest between consecutive trimesters. Standard errors of the estimates of genetic parameters varied between 0.02 and 0.08. Genetic interrelationships differed from 1.0, supporting the assumption that genetic variation exists in the shape of the lactation curve. The necessity of considering deviations from the general lactation curve in the test-day model, e.g. fitting random regression coefficients, is discussed.


1998 ◽  
Vol 66 (3) ◽  
pp. 685-688 ◽  
Author(s):  
M. J. de Vries ◽  
E. H. van der Waaij ◽  
J. A. M. van Arendonk

AbstractGenetic parameters were estimated for litter size in two prolific sheep breeds, i.e. the Zwartbles and the synthetic breed Swifter. Genetic parameters and breeding values for litter size in different parities were estimated using both a repeatability and a multivariate animal model. The estimated heritability from the repeatability model was 0·10 for the Zwartbles and 0·12 for the Swifter. For the multivariate model, heritability of litter size in first, second and third parity was 0·05, 0·07 and 0·10 for the Zwartbles and 0·09, 0·12 and 0·09 for the Swifter. Genetic correlation for litter size in Swifter was 0·81 between parity 1 and 2 and 0·99 between parity 2 and 3. For the Zwartbles genetic correlations were all very close to unity. Environmental correlations between litter size in subsequent parities were not constant over parities. Phenotypic variance in litter size in both breeds was 0·309 in first parity and was almost 50% higher in later parities. Based on the results it is recommended to apply a multiple trait model.


2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 347-347
Author(s):  
Pourya Davoudi ◽  
Duy Ngoc Do ◽  
Guoyu Hu ◽  
Siavash Salek Ardestani ◽  
Younes Miar

Abstract Feed cost is the major input cost in the mink industry and thus improvement of feed efficiency through selection for high feed efficient mink is necessary for the mink farmers. The objective of this study was to estimate the heritability, phenotypic and genetic correlations for different feed efficiency measures, including final body weight (FBW), daily feed intake (DFI), average daily gain (ADG), feed conversion ratio (FCR) and residual feed intake (RFI). For this purpose, 1,088 American mink from the Canadian Center for Fur Animal Research at Dalhousie Faculty of Agriculture were recorded for daily feed intake and body weight from August 1 to November 14 in 2018 and 2019. The univariate models were used to test the significance of sex, birth year and color as fixed effects, and dam as a random effect. Genetic parameters were estimated via bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.41±0.10, 0.37±0.11, 0.33±0.14, 0.24±0.09 and 0.22±0.09 for FBW, DFI, ADG, FCR and RFI, respectively. The genetic correlation (±SE) was moderate to high between FCR and RFI (0.68±0.15) and between FCR and ADG (-0.86±0.06). In addition, RFI had low non-significant (P > 0.05) genetic correlations with ADG (0.04 ± 0.26) and BW (0.16 ± 0.24) but significant (P < 0.05) high genetic correlation with DFI (0.74 ± 0.11) indicating that selection for lower RFI will reduce feed intake without adverse effects on the animal size and growth rate. The results suggested that RFI can be implemented in genetic/genomic selection programs to reduce feed intake in the mink production system.


Author(s):  
I.J. Ohagenyi ◽  
F.C. Iregbu ◽  
V.C. Udeh

Background: This study was conducted to estimate the genetic parameters of body weight and some colour traits in seventh generation (G7) index selected Nigerian Heavy Local Chicken Ecotype (NHLCE) progenies at point of lay to 12 weeks. Methods: 5 sires and 12 hens were used to generate the progenies used for the experiment. Traits measured included weekly body weight, egg colour, beak colour and feather colour. Data collected were subjected to one way analysis of variance in a Paternal half sib analysis using Animal model of SAS (2003). Four weeks body weight measurements, egg colour, beak colour and feather colour for 5 sires ranged from 1.29±0.05 1.54±0.07; 2.55±0.02 to 4.00±0.02; 2.45±0.02 to 4.83±0.02 and 1.73±0.02 to 4.58±0.04 respectively. Result: The new Duncan’s multiple range test shows that sire families are similar (p greater than 0.05) in the body weight and beak colour, but significantly differed (p greater than 0.05) in the egg colour and feather colour. The heritability estimates of mature body weight for week 3 was medium, while estimates of heritability for weekly mature body weight for weeks 1, 2 and 4, egg colour, beak colour and feather colour of NHLCE were low heritability. Low h2 of traits suggest that progeny and pedigree selection could be employed for improvement of the egg colour, beak colour and feather colour of NHLCE. The study showed positive genetic correlations between beak colour and egg colour, negative genetic correlations between beak and feather colour. This means that no decision can be taken in isolation as the selection of one trait will have consequences on other traits.


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