• Asian-Australasian Journal of Animal Sciences
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• v.15 no.1
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• pp.11-15
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• 2002

The present investigation was undertaken to study the effects of non-genetic factors and association among replacement and lifetime production traits. The data on 542 Holstein Friesian cows maintained during 1975-98 at State Cattle Breeding Project, Sector III, Hisar, were utilized. The average sex-ratio, abnormal births, mortality, culling and replacement rates on total calf born and total female calf born basis were 51.62, 8.50, 17.52, 31.05, 22.78 and 51.41 per cent, respectively. The study revealed that a minimum of 4 to 5 progenies are required per cow over its lifetime to replace itself. It indicated that each cow should produce a minimum of 2 female calves during its life so as to replace herself before being lost. The least-squares means for productive herd life, longevity and lifetime production were $1439.32{\pm}87.64$ and $2419.18{\pm}8.25$ days and $11317.95{\pm}913.15kg$, respectively. The heritability estimates for all replacement traits were very low indicating that sire selection may bring no desirable change in these traits. Heritability estimates were $0.178{\pm}0.157$, $0.288{\pm}0.184$ and $0.096{\pm}0.195$ for corresponding lifetime production traits. Breeding values and ranking of sires were generated for replacement and lifetime production traits to estimate the rank correlations between these traits. Moderate desirable rank correlations were obtained between replacement rate and lifetime production traits indicating that sires proven on the basis of milk production are also expected to have better replacement rate.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.9
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• pp.1222-1228
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• 2016

The objective of this research was to estimate genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive (LBA), lifetime number of piglets weaned (LPW), lifetime litter birth weight (LBW), and lifetime litter weaning weight (LWW) in a commercial swine farm in Northern Thailand. Data were gathered during a 24-year period from July 1989 to August 2013. A total of 3,109 phenotypic records from 2,271 Landrace (L) and 838 Yorkshire sows (Y) were analyzed. Variance and covariance components, heritabilities and correlations were estimated using an Average Information Restricted Maximum Likelihood (AIREML) procedure. The 5-trait animal model contained the fixed effects of first farrowing year-season, breed group, and age at first farrowing. Random effects were sow and residual. Estimates of heritabilities were medium for all five traits ($0.17{\pm}0.04$ for LPL and LBA to $0.20{\pm}0.04$ for LPW). Genetic correlations among these traits were high, positive, and favorable (p<0.05), ranging from $0.93{\pm}0.02$ (LPL-LWW) to $0.99{\pm}0.02$ (LPL-LPW). Sow genetic trends were non-significant for LPL and all lifetime production traits. Sire genetic trends were negative and significant for LPL ($-2.54{\pm}0.65d/yr$; p = 0.0007), LBA ($-0.12{\pm}0.04piglets/yr$; p = 0.0073), LPW ($-0.14{\pm}0.04piglets/yr$; p = 0.0037), LBW ($-0.13{\pm}0.06kg/yr$; p = 0.0487), and LWW ($-0.69{\pm}0.31kg/yr$; p = 0.0365). Dam genetic trends were positive, small and significant for all traits ($1.04{\pm}0.42d/yr$ for LPL, p = 0.0217; $0.16{\pm}0.03piglets/yr$ for LBA, p<0.0001; $0.12{\pm}0.03piglets/yr$ for LPW, p = 0.0002; $0.29{\pm}0.04kg/yr$ for LBW, p<0.0001 and $1.23{\pm}0.19kg/yr$ for LWW, p<0.0001). Thus, the selection program in this commercial herd managed to improve both LPL and lifetime productive traits in sires and dams. It was ineffective to improve LPL and lifetime productive traits in sows.

• Animal Bioscience
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• v.35 no.8
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• pp.1151-1161
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• 2022

Objective: This study was conducted to estimate the association of age at first calving (AFC) with first lactation traits as well as lifetime performance traits in Murrah buffaloes. Methods: Data on first lactation and life time performance of Murrah buffaloes (n = 679), maintained at Indian Council of Agricultural Research-Central Institute for Research on Buffaloes, Hisar, India during the period 1983 through 2017, were deduced to calculate heritability estimates, genetic and phenotypic correlation of different first lactation and lifetime traits. The univariate animal model was fitted to estimate variance components and heritability separately for each trait, while bivariate animal models were set to estimate genetic and phenotypic correlations between traits under study. Results: The heritability was high for first peak milk yield (FPY, 0.64±0.08), moderate for AFC (0.48±0.07) and breeding efficiency (BE 0.39±0.09). High genetic correlations of first lactation total milk yield (FLTMY) with first lactation standard milk yield (FLSMY, 305 days or less), FPY, and first lactation length (FLL) was seen. Likewise, genetic correlation of AFC was positive with FLTMY, FLL, first dry period (FDP), first service period (FSP), first calving interval (FCI), herd life (HL) and productive days (PD). Significant phenotypic correlation of FLTMY was observed with HL, productive life (PL), PD, total lifetime milk yield (LTMY), standard lifetime milk yield (standard LTMY). Moreover, positive genetic and phenotypic correlation of FPY was observed with HL, PL, PD, total LTMY and standard LTMY. Conclusion: This study reports that AFC had positive genetic correlation with FDP, FSP, FCI, and unproductive days while, negative association of AFC was observed with FLSMY, PL, total LTMY, standard LTMY, and BE. This suggests that reduction of AFC would results in improvement of lifetime performance traits.

• Animal Bioscience
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• v.36 no.12
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• pp.1806-1820
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• 2023

Objective: The present study was aimed primarily for estimating various genetic parameters (heritability, genetic correlations) of reproduction (age at first calving [AFC], first service period [FSP]); production (first lactation milk, solid-not fat, and fat yield) and lifetime traits (lifetime milk yield, productive life [PL], herd life [HL]) in Tharparkar cattle to check the association of reproduction traits with lifetime traits through two different methods (Frequentist and Bayesian) for comparative purpose. Methods: Animal breeding data of Tharparkar cattle (n = 964) collected from Livestock farm unit of ICAR-NDRI Karnal for the period 1990 through 2019 were analyzed using a Frequentist least squares maximum likelihood method (LSML; Harvey, 1990) and a multi-trait Bayesian-Gibbs sampler approach (MTGSAM) for genetic correlations estimation of all the traits. Estimated breeding values of sires was obtained by BLUP and Bayesian analysis for the production traits. Results: Heritability estimates of most of the traits were medium to high with the LSML (0.20±0.44 to 0.49±0.71) and Bayesian approach (0.24±0.009 to 0.61±0.017), respectively. However, more reliable estimates were obtained using the Bayesian technique. A higher heritability estimate was obtained for AFC (0.61±0.017) followed by first lactation fat yield, first lactation solid-not fat yield, FSP, first lactation milk yield (FLMY), PL (0.60±0.013, 0.60±0.006, 0.57±0.024, 0.57±0.020, 0.42±0.025); while a lower estimate for HL (0.38±0.034) by MTGSAM approach. Genetic and phenotypic correlations were negative for AFC-PL, AFC-HL, FSP-PL, and FSP-HL (-0.59±0.19, -0.59±0.24, -0.38±0.101 and -0.34±0.076) by the multi-trait Bayesian analysis. Conclusion: Breed and traits of economic importance are important for selection decisions to ensure genetic gain in cattle breeding programs. Favourable genetic and phenotypic correlations of AFC with production and lifetime traits compared to that of FSP indicated better scope of AFC for indirect selection of life-time traits at an early age. This also indicated that the present Tharparkar cattle herd had sufficient genetic diversity through the selection of AFC for the improvement of first lactation production and lifetime traits.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.11
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• pp.1511-1517
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• 2013

This study was performed to estimate the effect of age at first calving and first two calving intervals on productive life and life time profit in Korean Holsteins. Reproduction data of Korean Holsteins born from 1998 to 2004 and lactation data from 276,573 cows with birth and last dry date that calved between 2000 and 2010 were used for the analysis. Lifetime profit increased with the days of life span. Regression of Life Span on Lifetime profit indicated that there was an increase of 3,800 Won (approximately $3.45) of lifetime profit per day increase in life span. This is evidence that care of each cow is necessary to improve net return and important for farms maintaining profitable cows. The estimates of heritability of age at first calving, first two calving intervals, days in milk for lifetime, lifespan, milk income and lifetime profit were 0.111, 0.088, 0.142, 0.140, 0.143, 0.123, and 0.102, respectively. The low heritabilities indicated that the productive life and economical traits include reproductive and productive characteristics. Age at first calving and interval between first and second calving had negative genetic correlation with lifetime profit (-0.080 and -0.265, respectively). Reducing age at first calving and first calving interval had a positive effect on lifetime profit. Lifetime profit increased to approximately 2,600,000 (2,363.6) from 800,000 Won ($727.3) when age at first calving decreased to (22.3 month) from (32.8 month). Results suggested that reproductive traits such as age at first calving and calving interval might affect various economical traits and consequently influenced productive life and profitability of cows. In conclusion, regard of the age at first calving must be taken with the optimum age at first calving for maximum lifetime profit being 22.5 to 23.5 months. Moreover, considering the negative genetic correlation of first calving interval with lifetime profit, it should be reduced against the present trend of increase.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.5
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• pp.661-668
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• 2007

Data on 1,037 Nili-Ravi buffaloes from four institutional herds were used to study lifetime milk yield, herd life, productive life and breeding efficiency. A general linear model was used to study the environmental effects while an animal model having herd, year of birth and age at first calving (as covariate) along with random animal effect was used to estimate breeding values. The lifetime milk yield, herd life, productive life and breeding efficiency averaged $7,723{\pm}164$ kg, $3,990{\pm}41$ days, $1,061{\pm}19$ days and 64 percent, respectively. All the traits were significantly (p<0.01) affected by the year of birth and herd of calving, while the herd life was also affected (p<0.01) by the age at first calving. The heritabilities for lifetime milk yield, herd life, productive life and breeding efficiency were $0.093{\pm}0.056$, $0.001{\pm}0.055$, $0.144{\pm}0.079$ and 0.001, respectively. The definition for productive life, where each lactation gets credit upto 10 months had slightly better heritability and may be preferred over the definition where no limit is placed on lactation length. The genetic correlation between productive life and lifetime milk yield was low but high between productive life and herd life. The selection for productive life will increase herd life while lifetime milk yield will also improve. The overall phenotypic trend during the period under the study was negative for lifetime milk yield (-280 kg/year), herd life (-93 days), productive life (-42 days/year) and breeding efficiency (-0.36 percent/year), whereas the genetic trend was positive for lifetime milk yield (+15 kg/year) and productive life (+4 days/year).

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.5
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• pp.499-503
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• 1995

The performance records of 410 Holstein Friesian crossbred cows belonging to seven genetic groups (Fl, 3/4, 1/4, 5/8, 3/8, triple cross and miscellaneous cross) maintained at Livestock Production Research Institute, Bahadurnagar, Okara were analyzed for various parameters of lifetime traits. For the analysis 2 data sets were made. Data set I included all the cows disposed off from the herd which have completed at least one lactation while for data set II performance traits for only first five lactations were considered. The data was analyzed by Mixed Model Least squares and Maximum Likelihood computer programme PC-I version. The least squares means ${\times}$ standard errors for data set I (periods are in days and milk yield is in litres) were $994.5{\pm}15.5$, $1,877.0{\pm}70.9$, $1,651.9{\pm}19.3$, $2,533.7{\pm}36.5$, $3,530.0{\pm}40.5$, $15,785.2{\pm}320.0$, $8.46{\pm}0.19$, $5.66{\pm}0.16$ and $3.79{\pm}0.08$, respectively for age at first calving (APC), Ist lactation milk yield (FLMY), productive life (PL), herd life (HL), total life (TL), lifetime milk yield (LTMY), milk yield per day of productive life (MY/PL), milk yield per day of herd life (MY/HL) and milk yield per day of total life (MY/TL). For data set II these values were $1,004.2{\pm}21.2$, $2,220.5{\pm}113.1$, $1,429.1{\pm}40.8$, $2,302.1{\pm}73.3$, $3,307.2{\pm}77.3$, $13,189.7{\pm}667.4$, $9.10{\pm}0.34$, $5.66{\pm}0.25$ and $4.02{\pm}0.18$ in the same order. For data set I the effect of year of first calving was significant for AFC, FLMY, PL, HL, LTMY and MY/PL. The season of Ist calving was significant only for MY/PL. The effect of genetic group was significant for AFC, FLMY, MY/PL and MY/TL while the effect of parity was significant for all the traits. For data set II the effect of year of Ist calving was significant only for AFC, FLMY and PL while the season of Ist calving was significant for FLMY and PL while the effect of genetic groups was significant for MY/HL only. The lifetime production performance is in general close to the various estimates reported in the literature.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1075-1079
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• 2005

The main objective of this study is to examine genetic changes in growth rate and carcass composition traits in group housed, ad libitum fed pigs, from lines of Large White divergently selected over four years for high and low post-weaning daily gain on a fixed but restricted ration. Genetic parameters for production and carcass traits were also estimated by using average information-restricted maximum likelihood applied to a multivariate individual animal model. All analyses were carried out on 1,728 records of group housed ad libitum fed pigs, and include a full pedigree of 5,324 animals. Estimates of heritability (standard errors in parentheses) were 0.11 (0.04) for lifetime daily liveweight gain (LDG), 0.13 (0.04) for daily carcass weight gain (CDG) and 0.28 (0.06) for carcass backfat (CFT). Genetic correlations between LDG and CDG were highly positive and between LDG and CFT negative, suggesting that selection for lifetime daily gain under commercial conditions of group housing with ad libitum feeding would result in favourable improvement in carcass traits. CFT showed negative genetic correlations with CDG. Correlated genetic responses evaluated as estimated breeding values (EBVs) were obtained from a multivariate animal model-best linear unbiased prediction analysis. After four years of divergent selection for 6 week post-weaning growth rate on restricted feeding, pigs performance tested on ad libitum feeding in groups exhibited changes in EBVs of 6.77 and -9.93 (g/d) for LDG, 4.25 and -7.08 (g/d) for CDG, and -1.42 and 1.55 (mm) for CFT, in the high and low lines, respectively. It is concluded that selection for growth rate on restricted feeding would significantly improve genetic performance and carcass composition of their descendants when group housed and ad libitum fed as is a common commercial practice.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.3
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• pp.320-327
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• 2017

Objective: The objective of this research was to estimate genetic correlations between number of piglets born alive in the first parity (NBA1), litter birth weight in the first parity (LTBW1), number of piglets weaned in the first parity (NPW1), litter weaning weight in the first parity (LTWW1), number of piglets born alive from second to last parity (NBA2+), litter birth weight from second to last parity (LTBW2+), number of piglets weaned from second to last parity (NPW2+) and litter weaning weight from second to last parity (LTWW2+), and to identify the percentages of animals (the top 10%, 25%, and 50%) for first parity and sums of second and later parity traits. Methods: The 9,830 records consisted of 2,124 Landrace (L), 724 Yorkshire (Y), 2,650 LY, and 4,332 YL that had their first farrowing between July 1989 and December 2013. The 8-trait animal model included the fixed effects of first farrowing year-season, additive genetic group, heterosis of the sow and the litter, age at first farrowing, and days to weaning (NPW1, LTWW1, NPW2+, and LTWW2+). Random effects were animal and residual. Results: Heritability estimates ranged from $0.08{\pm}0.02$ (NBA1 and NPW1) to $0.29{\pm}0.02$ (NPW2+). Genetic correlations between reproduction traits in the first parity and from second to last parity ranged from $0.17{\pm}0.08$ (LTBW1 and LTBW2+) to $0.67{\pm}0.06$ (LTWW1 and LTWW2+). Phenotypic correlations between reproduction traits in the first parity and from second to last parity were close to zero. Rank correlations between LTWW1 and LTWW2+ estimated breeding value tended to be higher than for other pairs of traits across all replacement percentages. Conclusion: These rank correlations indicated that selecting boars and sows using genetic predictions for first parity reproduction traits would help improve reproduction traits in the second and later parities as well as lifetime productivity in this swine population.