Effects of pork differentiation strategies in Canada on pig performance and carcass characteristics

Zhang, Hongbo; Aalhus, J. L.; Gariépy, C.; Uttaro, B.; López-Campos, Ó.; Prieto, Nuria; Dugan, M. E. R.; Jin, Yufeng; Juárez, M.

doi:10.1139/cjas-2015-0197

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…In general, head-on hot carcass weight and muscle depth were greater than historical observations in Canada, while fat depth was closer in value ( CPC, 1994 ; Pomar et al, 2001 ; Pomar and Marcoux, 2003 ). Head-on hot carcass weight, fat depth, and muscle depth were similar to values reported in recent studies conducted in Canada ( Miar et al, 2014 ; Zhang et al, 2016 ). Likewise, hot carcass weight (compared between head-on hot carcass weight and head-off hot carcass weight), fat depth, and muscle depth were similar in value with the values reported in recent studies conducted in Europe ( Lisiak et al, 2015 ; Knecht et al, 2016 ) and the United States ( Wilson et al, 2016 ; Arkfeld et al, 2017 ).…”

Section: Resultssupporting

confidence: 87%

The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

Barducci

Zhou

Wormsbecher

et al. 2019

Translational Animal Science

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This study aimed to examine the correlation of carcass weight, fat depth, muscle depth, and predicted lean yield in commercial pigs. Data were collected on 850,819 pork carcasses from the same pork processing facility between October 2017 and September 2018. Hot carcass weight was reported following slaughter as a head-on weight; while fat and muscle depth were measured with a Destron PG-100 probe and used for the calculation of predicted lean yield based on the Canadian Lean Yield (CLY) equation [CLY (%) = 68.1863 − (0.7833 × fat depth) + (0.0689 × muscle depth) + (0.0080 × fat depth2) − (0.0002 × muscle depth2) + (0.0006 × fat depth × muscle depth)]. Descriptive statistics, regression equations including coefficients of determination, and Pearson product moment correlation coefficients (when assumptions for linearity were met) and Spearman’s rank-order correlation coefficients (when assumptions for linearity were not met) were calculated for attributes using SigmaPlot, version 11 (Systat Software, Inc., San Jose, CA). Weak positive correlation was observed between hot carcass weight and fat depth (r = 0.289; P < 0.0001), and between hot carcass weight and muscle depth (r = 0.176; P < 0.0001). Weak negative correlations were observed between hot carcass weight and predicted lean yield (r = −0.235; P < 0.0001), and between fat depth and muscle depth (r = −0.148; P < 0.0001). Upon investigation of relationships between fat depth and predicted lean yield, and between muscle depth and predicted lean yield using scatter plots, it was determined that these relationships were not linear and therefore the assumptions of Pearson product moment correlation were not met. Thus, these relationships were expressed as nonlinear functions and Spearman’s rank-order correlation coefficients were used. A strong negative correlation was observed between fat depth and predicted lean yield (r = −0.960; P < 0.0001), and a moderate positive correlation was observed between muscle depth and predicted lean yield (r = 0.406; P < 0.0001). Results from this dataset revealed that hot carcass weight was generally weakly correlated (r < |0.35|) with fat depth, muscle depth, and predicted lean yield. Therefore, it was concluded that there were no consistent weight thresholds where pigs were fatter or heavier muscled.

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Section: Resultssupporting

confidence: 87%

The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

Barducci

Zhou

Wormsbecher

et al. 2019

Translational Animal Science

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“…Comparatively small litters are characteristic of the Duroc breed (Johnson, 1981; Sonderman and Luebbe, 2008). Yet, use of Duroc as a terminal sire is a mainstay of swine production due to favorable direct effects on growth and carcass traits (Johnson, 1981; Zhang et al, 2016). Early embryonic mortality, affecting litter size, is recognized as a significant issue in swine (Bolet, 1986; Christianson, 1992).…”

Section: Introductionmentioning

confidence: 99%

Putative Loci Causing Early Embryonic Mortality in Duroc Swine

MacNeil

Kemp²,

Dyck

et al. 2018

Front. Genet.

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Lethal recessive alleles that act prenatally may be detected from the absence of homozygous individuals in a population. However, these alleles may be maintained at relatively low frequencies in populations as heterozygotes. In pigs, they may reduce litter size. This study aimed to detect putative lethal variants in the Duroc breed. Phenotypes for the numbers of piglets born (TNB), born live (BA), alive at 24 h (L24), stillborn (SB), and born as mummified fetuses (MM) were available from 5340 recorded litters which resulted from mating of 192 genotyped boars with sows of unknown genotype (dataset 1). An additional 50 litters were produced from parents that were both genotyped (dataset 2). Imputed genotypes of 650K SNPs for 1359 Duroc boars were used in this study. One significant SNP (Bonferroni corrected P = 5.5E-06) was located on SSC14 with 45.3 homozygous individuals expected but none observed. This SNP was significant for mummified fetuses. One hundred fifty two haplotypes were also found to potentially harbor recessive lethal mutations. Twenty-one haplotypes had a significant harmful effect on at least one trait. Two regions, located on SSC8 (144.9–145.5 Mb) and SSC9 (19–19.4 Mb) had significant effects on fertility traits in both datasets. Additionally, regions on SSC1 (82.0–82.8 Mb), SSC3 (73.3–73.7 and 87.1–87.5 Mb) and SSC12 (35.8–36.2 and 50.0–50.5 Mb) had significant deleterious effects on TNB or BA or L24 in dataset 1. Finally, a region on SSC17 (28.7–29.3 Mb) had significant effects on TNB, BA and L24 in dataset 2. A few candidate genes identified within these regions were described as being involved in spermatogenesis and male fertility (TEX14, SEP4, and HSF5), or displayed recessive lethality (CYP26B1, SCD5, and PCF11) in other species. The putative loci detected in this study provide valuable information to potentially increase Duroc litter size by avoiding carrier-by-carrier matings in breeding programs. Further study of the identified candidate genes responsible for such lethal effects may lead to new insights into functions regulating pig fertility.

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“…The Piau genetic group stood out in the meat quality factor when compared with the Pietrain genetic group, while Duroc genetic group did not differ from the other ones. Duroc pigs are often referred to as a breed of improved meat quality because they have tender meat and lower drip loss compared with other commercial genetic groups (Peloso et al, 2010;Zhang et al, 2016). Naturalized Brazilian pigs contribute positively for the meat quality (Silva et al, 2019), which may be justified by the higher fatness level in Piau pigs and for the fact that these animals are not under selection (Serão et al, 2011).…”

Section: Effects Of Genetic Group Nutritional Plan and Sex On Latent ...mentioning

confidence: 99%

“…Even though differences in carcass and meat quality traits across genetic groups have been observed, pigs usually show similar performance traits (Ventura et al, 2012;Veloso et al, 2019;Nakev and Popova, 2020). Differences in performance are more evident when comparing animals with outstanding genetics to not selected animals (Zhang et al, 2015(Zhang et al, , 2016.…”

Section: Introductionmentioning

confidence: 99%

Depicting latent variables considering different pig genetics and nutritional plans in crossbred pigs

Henriques¹,

Silva²,

Veroneze³

et al. 2022

Revista Brasileira De Zootecnia

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Breeding and geneticsFull-length research article Depicting latent variables considering different pig genetics and nutritional plans in crossbred pigsABSTRACT -We aimed to reduce the dimensionality of quantitative traits in pigs applying factor analysis. Quantitative variables were collected, and the factor analysis extracted five factors with biological meaning related to performance, carcass quality, carcass yield, meat quality, and initial pH. These factors were posteriorly used as dependent variable to evaluate the effects of genetic groups (Piau, Duroc, and Pietrain crossbreds), nutritional plans (low, medium and high lysine levels) and sex. An interaction effect between genetic group and sex was observed in performance, in which Duroc crossbred showed the greatest scores. The Pietrain genetic group showed greater values of carcass quality compared with Duroc and Piau, while Piau crossbred pigs had greater values for meat quality compared with Pietrain and Duroc. A greater carcass yield was observed in Pietrain crossbred compared with Duroc pigs. Pigs fed under low lysine level had the lowest performance values. Carcass yield was affected by nutritional plans, in which the medium lysine level showed the greatest values. Meat quality was improved by the nutritional plan with high lysine level compared with the low lysine level. Initial pH showed increased means when using nutritional plans with low and medium lysine levels in diet composition. With regard to sex effects, a greater carcass quality was found for gilts compared with barrows. The reduction of the data dimensionality allowed the joint evaluation of genetic group, nutritional plan, and sex based on new latent variables that represented the original dataset under easier framework based on factors' biological interpretation.

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Effects of pork differentiation strategies in Canada on pig performance and carcass characteristics

Cited by 9 publications

References 45 publications

The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

Putative Loci Causing Early Embryonic Mortality in Duroc Swine

Depicting latent variables considering different pig genetics and nutritional plans in crossbred pigs

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