• Asian-Australasian Journal of Animal Sciences
• /
• v.26 no.11
• /
• pp.1637-1643
• /
• 2013

The present work describes the effect of slaughtering age on horse carcass traits and on meat quality. Eighteen male Italian heavy draught horse (IHDH) breed foals were employed in the study. Soon after foaling they were randomly subdivided in 3 groups according to 3 age at slaughtering classes: 6 months old, 11 months old and 18 months old. Live weight, hot carcass weight and dressing percentage of each animal were recorded. After slaughtering, meat samples were collected from Longissimus Dorsi muscle between 13th and 18th thoracic vertebra of each animal and then analyzed. The right half carcass of each animal was then divided in cuts. Each one was subdivided into lean, fat and bones. Then, the classification of the lean meat in first and second quality cuts was performed according to the butchers' customs. Older animals were characterized by a lower incidence of first quality cuts (p<0.01) on carcass. Younger animals showed greater content in protein (p<0.01). Fatty acid profile showed an increasing trend of PUFA connected to the increasing of slaughtering age (p<0.05). The unsaturation index of intramuscular fatty acids was not affected by slaughtering age, confirming that horse meat, if compared to beef, is more suitable from a nutritional point of view. Season influenced reproduction, birth as well as production aspects of this species. The different slaughtering age could represent the way to produce meat of IHDH foals during the entire year without change in the qualitative standard expected by consumers.

• Animal Bioscience
• /
• v.36 no.12
• /
• pp.1785-1795
• /
• 2023

Objective: This study was to estimate heritabilities, additive genetic correlations, and phenotypic correlations between number of piglets born alive (NBA), litter birth weight (LTBW), number of piglets weaned (NPW) and litter weaning weight (LTWW) in different parities of Landrace (L), Yorkshire (Y), Landrace×Yorkshire (LY), and Yorkshire×Landrace (YL) sows in a commercial swine operation in Northern Thailand. Methods: Two models were utilized, a single trait repeatability model (RM) and a multiple trait animal model (MTM). The RM assumed reproductive records from different parities to be repeated values of the same trait, whereas the MTM assumed these records to be different traits. The two models accounted for the fixed effects of farrowing year-season, genetic group of the sow, heterosis, and age at first farrowing, and the random effects of sow, boar, and residual. Results: Heritability estimates from RM were 0.02±0.01 for NBA, 0.10±0.01 for LTBW, 0.04±0.01 for NPW, and 0.11±0.01 for LTWW. Heritability estimates from MTM fluctuated across parities, ranging from 0.04±0.01 in parity 2 to 0.09±0.02 in parity 4 for NBA, 0.07±0.02 in parity 2 to 0.16±0.02 in parity 3 for LTBW, 0.04±0.02 in parity 4 to 0.08±0.01 in parity 1 for NPW, and 0.16±0.02 in parity 1 to 0.20±0.02 in parity 2 for LTWW. Additive genetic correlation estimates from MTM were also variable, ranging from 0.29±0.24 between NBA in parity 1 and NBA in parity 2 to 0.99±0.05 between LTWW in parity 3 and LTWW in parity 4. Conclusion: The findings of this study highlight the advantage of using MTM for the genetic improvement of reproductive traits in swine and contribute to the development of sustainable swine breeding programs in Thailand.

• Journal of Animal Science and Technology
• /
• v.50 no.1
• /
• pp.33-44
• /
• 2008

The purpose of this study was to estimate the genetic parameters and trend of Landrace and Yorkshire pigs, which were raised on private farms from 1999 to 2005 and tested for their reproductive performance by the Korea Animal Improvement Association. Prior to analysis, records without pedigree or having value with larger than±3×standard deviation for the Total number of born were excluded. The effects of breed and environmental factors were estimated with least square method(Harvey, 1979), and estimation of breeding values and genetic parameters were performed on the data of 1’st litter only with GIBBSF90(Misztal, 2001) which was programmed according to Gibbs Sampling method based on Bayesian Inference by Gianola and Fernando(1986), Jensen(1994) and others. Gibbs sampling was performed 50,000 times for each parameter, and the first 5000 samples were regarded as those in burn-in period and thus, excluded for post hoc analysis. Total number of born and total number of accident were statistically significant(p<0.01) for the breed, farrowing year, farrowing season and parity effects, and the number born alive at birth was statistically significantp<(0.01) for the breed, farrowing year, farrowing season and parity effects. No particular trend was observed in the genetic and phenotypic improvement of the total number of born and number born alive at birth before 2001, when the piglet registration system started, but the tendencies of increasing for the total number of born and number born alive and decreasing for the total number of accident were observed since 2001. Somewhat higher heritability estimates of our study seems to be attributed to the situations that first parity records with poor farrowing performances were used in the analyses and it was impossible to obtain accurate reproductive performance due to the absence of criteria for record keeping at the level of individual farms.

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

• Journal of Animal Science and Technology
• /
• v.54 no.4
• /
• pp.247-253
• /
• 2012

Ultrasound measures of back fat thickness (UBF), eye muscle area (UEMA) and marbling score (UMS) and carcass measures of carcass weight (CW), backfat thickness (BF), eye muscle area (EMA) and marbling score (MS) were available on 26,129 Hanwoo steers. Statistically significant differences by regions of the farms location and birth years-seasons for the steers taken ultrasound measures and their carcass measures (p<0.01) were found. Steers in Gyeonggi province showed the highest values in ultrasound measures and carcass traits except in BF compared to steers in the other provinces. Comparing between ultrasound and carcass measures, UBF was thicker in general than BF in all regions except in Daejeon city. UEMA was higher than EMA in all regions except in Gyeonggior in Jeju provinces. Especially, the difference in Jeonnam province between UEMA and EMA was $7cm^2$ while the differences between UMS and MS ranged from 0.9 to 2.26 depending on the regions of steers located. Steers born in spring showed greater ultrasound or carcass values than those born in autumn. However, carcass measures of steers born in autumn were greater than those born in spring, 2009 except MS. The pearson and residual correlations were 0.63 and 0.65 between UBF and BF, 0.31 and 0.32 between UEMA and EMA and 0.56 and 0.56 between UMS and MS, respectively.

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

• Clinical and Experimental Pediatrics
• /
• v.48 no.10
• /
• pp.1061-1067
• /
• 2005

Purpose : We studied a clinical analysis of pediatric patients who visited the emergency medical center of Yeungnam University Hospital to compare the characteristics of pediatric emergency patients after year 2000 with the previous studies. Methods : We reviewed 7,034 children under the age of 15 years who visited the emergency medical center of Yeungnam University Hospital during the 2 year period from January 2001 to December 2002, and then we performed a clinical and statistical analysis. We analyzed the pediatric patients according to gender, age, season, day of the week, time of the visit, the disease classification and the final disposition of the patients. Results : Among the patients who visited the emergency room, 15.6% of the total emergency patients were under the age of 15. The male to female ratio was 1.6 : 1. Among the 7,034 pediatric patients, the most common age group was between 1 year and under 3 years of age(26.9%). The peak seasonal incidence was early summer and spring, especially during June(11.2%) and May(10.6 %). The peak incidence day of the week was Sunday(24.8%) and the peak time when the emergency pediatric patients visited the emergency room was between 20 and 24 o'clock(28.8%). The distribution of diseases, according to ICD-10 system, were injury and poisoning(30.4%), diseases of the respiratory system(22.8%), and diseases of the digestive system(14.6%). 30% of total pediatric patients were admitted to the hospital. Conclusion : After year 2000, as compared with the previous studies, the proportions of emergency pediatric patients has decreased. The distribution of diseases was not much different from the previous studies and the proportions of non-urgent diseases, such as acute nasopharyngitis or acute gastroenteritis, were still high. These result have come about due to the declining birth rate and changes of the medical system in Korea.

• Journal of Animal Science and Technology
• /
• v.49 no.2
• /
• pp.177-182
• /
• 2007

The objective of this study was to evaluate the genetic relationship between weaning weight (WW) and carcass traits. Carcass traits were eye muscle area(EMA), back fat thickness(BFT), marbling score 1(MS1) in 21 grade scales, marbling score 2(MS2) in 7 grade scales and meat color scores(Mcolor). Parameters were estimated by REML procedure with MTDFREML package. Models included contemporary group as defined by the same year-season-sex at birth, linear covariates of age(days) at weaning, age of dam(days) and age at slaughter(days) as fixed effects and animal random effects for all the traits. Heritability estimates of WW, EMA, BFT, MS1, MS2 and Mcolor were 0.25, 0.20, 0.20, 0.32, 0.32 and 0.22, respectively. Genetic(phenotypic) correlation coefficients of WW with EMA, BFT, MS1, MS2 and Mcolor were 0.75(0.16), 0.18(0.05), －0.41(－0.09), －0.40(0.11) and －0.07(0.05), respectively. Results from this study suggest that single trait selection for weaning weight would lead to progeny population having carcass with large EMA, thick BFT and decreased MS.

• Asian-Australasian Journal of Animal Sciences
• /
• v.24 no.3
• /
• pp.322-329
• /
• 2011

A whole genome association (WGA) study was conducted to identify quantitative trait loci (QTL) for body conformation traits in Hanwoo cattle. The phenotypes of 497 steers were recorded from the Hanwoo Improvement Center of National Agricultural Cooperative Federation, Seosan, Korea, and analyzed using the Illumina Bovine 50 k SNP chip. A set of 35,987 SNPs that were available in the Hanwoo population was selected from the chip. After adjustments for the effects of year-season of birth, region and sire, phenotypes were regressed on each SNP using a linear regression model. Three hundred nineteen SNPs were detected for the ten conformation traits (p<0.003). For the significant SNPs, stepwise regression procedures were applied to determine best sets of markers. A total of 72 SNPs were selected (p<0.001), for which the sets of 5, 9, 10, 9, 8, 11, 4, 6, 3 and 7 SNPs were determined for height at withers, rump height, body length, chest depth, chest width, rump length, hip width, thurl width, pinbone width and heart girth, respectively. About 7-26% of the total phenotypic variation was explained by the set of SNPs for each trait. QTL for the conformation traits were harbored on most bovine chromosomes (BTAs). Four SNPs with pleiotropic effects on height at withers and rump height were detected on BTAs 3, 4, 6 and 16. A SNP with pleiotropic effects on chest width and rump length was also detected on BTA10. Two QTL regions, i.e. between 87 and 97 Mb in BTA3 and between 41 and 44 Mb in BTA7, were found, in which SNPs were detected for the five and three conformation traits, respectively. The detected SNPs need to be validated in other Hanwoo populations for commercial application to the genetic improvement of conformation characteristics in Hanwoo via marker-assisted selection (MAS).

• Asian-Australasian Journal of Animal Sciences
• /
• v.23 no.5
• /
• pp.543-555
• /
• 2010

The objectives of this research were the estimation of genetic parameters and trends for weaning-to-first service interval (WSI), and litter traits in a commercial swine population composed of Landrace (L), Large White (T), LT, and TL animals in Chiang Mai, Northern Thailand. The dataset contained 4,399 records of WSI, number of piglets born alive (NBA), litter weight of live piglets at birth (LBW), number of piglets at weaning (NPW), and litter weight at weaning (LWW). Variance and covariance components were estimated with REML using 2-trait analyses. An animal model was used for WSI and a sire-dam model for litter traits. Fixed effects were farrowing year-season, breed group of sow, breed group of boar (litter traits), parity, heterosis (litter traits), sow age, and lactation length (NPW and LWW). Random effects were boar (litter traits), sow, permanent environment, and residual. Heritabilities for direct genetic effects were low for WSI (0.04${\pm}$0.02) and litter traits (0.05${\pm}$0.02 to 0.06${\pm}$0.02). Most heritabilities for maternal litter trait effects were 20% to 50% lower than their direct counterparts. Repeatability for WSI was similar to its heritability. Repeatabilities for litter traits ranged from 0.15${\pm}$0.02 to 0.18${\pm}$F0.02. Direct genetic, permanent environment, and phenotypic correlations between WSI and litter traits were near zero. Direct genetic correlations among litter traits ranged from 0.56${\pm}$0.20 to 0.95${\pm}$0.05, except for near zero estimates between NBA and LWW, and LBW and LWW. Maternal, permanent environment, and phenotypic correlations among litter traits had similar patterns of values to direct genetic correlations. Boar genetic trends were small and significant only for NBA (-0.015${\pm}$0.005 piglets/yr, p<0.004). Sow genetic trends were small, negative, and significant (-0.036${\pm}$0.013 d/yr, p<0.01 for WSI; -0.017${\pm}$0.005 piglets/yr, p<0.007, for NBA; -0.015${\pm}$0.005 kg/yr, p<0.01, for LBW; -0.019${\pm}$0.008 piglets/yr, p<0.02, for NPW; and -0.022${\pm}$0.006 kg/yr, p<0.003, for LWW). Permanent environmental correlations were small, negative, and significant only for WSI (-0.028${\pm}$0.011 d/yr, p<0.02). Environmental trends were positive and significant only for litter traits (p<0.01 to p<0.0003). Selection based on predicted genetic values rather than phenotypes could be advantageous in this population. A single trait analysis could be used for WSI and a multiple trait analysis could be implemented for litter traits.