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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... Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry † † ‡ Robson S. Barducci,* Ziyu Y. Zhou,* Lisa Wormsbecher, Colleen Roehrig, Dan Tulpan, and ,1, Benjamin M. Bohrer* *Department of Food Science, University of Guelph, Guelph, Ontario, Canada N1G2W1; Conestoga Meat Packers Ltd., Breslau, Ontario, Canada N0B1M0; and Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada N1G2W1 ABSTRACT: This study aimed to examine the cor- weight and muscle depth (r  =  0.176; P  <  0.0001). relation of carcass weight, fat depth, muscle depth, Weak negative correlations were observed be- and predicted lean yield in commercial pigs. Data tween hot carcass weight and predicted lean yield were collected on 850,819 pork carcasses from the (r  =  −0.235; P  <  0.0001), and between fat depth same pork processing facility between October and muscle depth (r = −0.148; P < 0.0001). Upon 2017 and September 2018. Hot carcass weight was investigation of relationships between fat depth reported following slaughter as a head-on weight; and predicted lean yield, and between muscle depth while fat and muscle depth were measured with a and predicted lean yield using scatter plots, it was Destron PG-100 probe and used for the calcula- determined that these relationships were not linear tion of predicted lean yield based on the Canadian and therefore the assumptions of Pearson product Lean Yield (CLY) equation [CLY (%) = 68.1863 − moment correlation were not met. Thus, these re- (0.7833 × fat depth) + (0.0689 × muscle depth) + lationships were expressed as nonlinear functions 2 2 (0.0080 × fat depth ) − (0.0002 × muscle depth ) + and Spearman’s rank-order correlation coefficients (0.0006 × fat depth × muscle depth)]. Descriptive were used. A  strong negative correlation was ob- statistics, regression equations including coef-fi served between fat depth and predicted lean yield cients of determination, and Pearson product mo- (r = −0.960; P < 0.0001), and a moderate positive ment correlation coefficients (when assumptions correlation was observed between muscle depth for linearity were met) and Spearman’s rank-order and predicted lean yield (r  =  0.406; P  <  0.0001). correlation coefficients (when assumptions for lin- Results from this dataset revealed that hot carcass earity were not met) were calculated for attributes weight was generally weakly correlated (r < |0.35|) using SigmaPlot, version 11 (Systat Software, Inc., with fat depth, muscle depth, and predicted lean San Jose, CA). Weak positive correlation was ob- yield. Therefore, it was concluded that there were served between hot carcass weight and fat depth no consistent weight thresholds where pigs were (r  =  0.289; P  <  0.0001), and between hot carcass fatter or heavier muscled. Key words: commercial pork, correlation, fat depth, muscle depth, pork carcass weight © The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Transl. Anim. Sci. 2019.XX:0-0 doi: 10.1093/tas/txz169 Corresponding author: bbohrer@uoguelph.ca Received August 2, 2019. Accepted October 21, 2019. 1 Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 2 Barducci et al. INTRODUCTION 2019). However, there still exist many misunder- standings among the relationship of pork carcass In upcoming years, continued population weight and leanness parameters, particularly in growth and increased wealth in developing nations commercially representative pigs marketed under will likely increase the global demand for pork current times and conditions. Therefore, the ob- (Szymańska, 2018). To increase the quantity of jective of this study was to examine the correl- pork produced, while meeting industry (and con- ation of carcass weight, fat depth, muscle depth, sumer) demands for more sustainable production and predicted lean yield in commercial pigs mar- systems, utilization of modern technologies is re- keted at commercially representative weights and quired to lead to the development of a more ef- conditions. ficient and profitable global industry. With both efficiency and profitability in mind, pork proces- sors aspire to ensure that pig producers are mar- MATERIALS AND METHODS keting a consistent product that meets industry Pigs were slaughtered under the supervision of standards for weight and yield. At the same time, the Canadian Food Inspection Agency (CFIA) at it is critical that investigations of commonly used a federally inspected processing facility. Carcass technologies be conducted frequently as industry data from that facility were then shared with the re- standards can change very quickly. For instance, search team that conducted this study. Therefore, the weight of pork carcasses has steadily increased Animal Care and Use Committee approval was not over time. In fact, there has been a consistent linear required for this study as no live animal data were increase in pork carcass weight over the past three used by the university research team. decades. This is evident when evaluating histor- ical averages—the average hot carcass weight in 1989 was 81.2  kg, the average hot carcass weight Data Collection in 1999 was 86.8 kg, the average hot carcass weight in 2009 was 92.2  kg, and the average hot carcass Data were collected at a commercial pig weight to-date in 2019 (through August) was slaughter facility located in southwestern Ontario 96.8  kg (USDA ERS, 2019). The changes in car- between October 1, 2017 and September 30, 2018. cass weight over time justifies an evaluation of the Information from a total of 850,819 pork carcasses relationships between carcass weight and predicted were used for this study. Hot carcass weight, fat leanness of pork carcasses in a commercially rep- depth, and muscle depth of each individual car- resentative population of pigs. cass were measured and recorded on the day of Regarding predicted leanness of pork car- slaughter before the animal was chilled. Hot car- casses, many different technologies have been used cass weight was reported immediately following in the last 30  years for online measurement of slaughter as a head-on weight. Fat depth and muscle lean meat percentage in pork carcasses, with the depth were measured with a Destron PG-100 probe primary focus of identifying the true commercial (International Destron Technologies, Markham, value of the pork carcass to the processor and pro- Canada) inserted perpendicularly between the vide proper recommendations for pig producers third and fourth last rib and 7 cm off the mid-line (Swatland et al., 1994). The most common method according to Canadian grading standards (Pomar for the measurement of carcass lean yield in North and Marcoux, 2003). Fat depth and muscle depth America is the use of an optical probe to measure measurements were used to obtain the predicted fat depth and muscle depth of the loin, and a sub- lean yield of each individual carcass using the fol- sequent conversion of these measurements pre- lowing equation: sents the designated conversion equations used for the calculation of predicted lean yield (Busk CLY =(%) = 68.1863(0.7833 × fat depth) +(0.0689 × muscle depth)+(0.0080 × fat depth ) et  al., 1999; Zhou and Bohrer, 2019). Thus, fat depth, muscle depth, and predicted leanness are (0.0002 × muscle depth ) +(0.0006 × fat depth × muscle depth) often the only carcass leanness parameters evalu- ated in commercial pigs. Several research studies where CLY is the Canadian Lean Yield of the car- have characterized the correlation between pork casses and fat depth and muscle depth are the back- carcass weight and leanness parameters (Kure, fat thickness (mm) and muscle thickness (mm), 1997; Ohlmann and Jones, 2011; Plà-aragonés respectively (Pomar and Marcoux, 2003). et  al., 2013; Rodríguez et  al., 2014; Price et  al., Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 Relationship of pork carcass measurements 3 Statistical Analyses the purpose of clarity, hot carcass weight will be referred to as head-on or head-off when comparing Data were evaluated thoroughly, and the en- absolute values throughout the current study. In tire observation was removed in the case of missing general, head-on hot carcass weight and muscle data points and extreme outliers. Descriptive stat- depth were greater than historical observations in istics for carcass traits (mean, standard deviation, Canada, while fat depth was closer in value (CPC, minimum, and maximum) were calculated for all 1994; Pomar et  al., 2001; Pomar and Marcoux, parameters using SigmaPlot, version 11 (Systat 2003). Head-on hot carcass weight, fat depth, and Software, Inc., San Jose, CA). Scatter plots were muscle depth were similar to values reported in re- created with SigmaPlot to allow for better visual- cent studies conducted in Canada (Miar et al., 2014; ization of the relationship between all parameters. Zhang et  al., 2016). Likewise, hot carcass weight Further evaluation for linearity and homoscedas- (compared between head-on hot carcass weight ticity were evaluated using the scatter plots. When and head-off hot carcass weight), fat depth, and assumptions of linearity and homoscedasticity muscle depth were similar in value with the values were met, linear regression equations were created reported in recent studies conducted in Europe by SigmaPlot. All regression analysis included co- (Lisiak et  al., 2015; Knecht et  al., 2016) and the efficients of determination (R ). Coefficients of United States (Wilson et  al., 2016; Arkfeld et  al., determination were considered weak at R < 0.12, 2017). For example, Wilson et  al. (2016) reported 2 2 moderate at 0.13 ≤ R < 0.45, and strong at R ≥ 0.46 population statistics for 1,235 commercial pigs in (Bohrer and Boler, 2017). For relationships meeting the United States as: 103.6 kg for head-off hot car- assumptions for Pearson product moment correl- cass weight, 22  mm for fat depth, and 67  mm for ation, Pearson correlation coefficients were calcu- muscle depth. Furthermore, Arkfeld et  al. (2017) lated using SigmaPlot. For relationships failing to reported population statistics for 6,920 commer- meet assumptions for Pearson product moment cor- cial pigs in the United States as: 94.50 ± 9.39 kg for relation (nonlinear), Spearman’s rank-order correl- head-off hot carcass weight, 15.41  ± 4.00  mm for ation coefficients were calculated using SigmaPlot. fat depth, 68.00 ± 8.52 mm for muscle depth, and Correlation coefficients were considered signifi- 57.63% ± 2.76% for predicted lean yield. cantly different from 0 at P < 0.05. Correlation co- The observed differences in parameters over efficients were considered weak (in absolute value) time were likely due to changes in the genotype for r < 0.35, moderate for 0.36 ≤ r ≤ 0.67, and strong of pigs, the environment/rearing conditions, and for r ≥ 0.68 (Taylor, 1990). a combination of these things (genotype × envir- onment). The continuous genetic improvement of RESULTS AND DISCUSSION pigs has resulted in a 41% increase in head-off car- cass weight since 1970 (Wilson et  al., 2016) and a Population Mean and Variation significant reduction of fat thickness in the global Means and standard deviation for hot carcass pork population (Adebambo, 1986; Chimonyo and weight (head-on), fat depth, muscle depth, and pre- Dzama, 2007; Sellier et  al., 2010). As outlined by dicted lean yield were 106.30  ± 8.51  kg, 18.11  ± one highly cited report (Thornton, 2010), livestock 4.04 mm, 66.47 ± 8.88 mm, and 61.13 ± 1.90%, re- production has changed substantially over the past spectively (Table 1). It is worth mentioning that it is several decades, which has undoubtedly changed common practice to include the head in the weight the way pigs are raised and marketed. The observed of the carcass in Canada, while this is not gener- differences in parameters between the current study ally the case in Europe and the United States. For and recent studies from the United States were likely Table 1. Population summary statistics for carcass traits (N = 850,819 carcasses) Carcass traits Mean SD Minimum Maximum Hot carcass weight, kg 106.30 8.51 44.60 165.80 Fat depth, mm 18.11 4.04 4.00 46.00 Muscle depth, mm 66.47 8.88 25.00 85.00 Predicted lean yield, % 61.13 1.90 52.20 69.80 Measured at the third and fourth last rib, and 7 cm off the mid-line. Predicted lean yield was calculated using the following equation: CLY = (%) = 68.1863 − (0.7833 × fat depth) + (0.0689 × muscle depth) + 2 2 (0.0080 × fat depth ) − (0.0002 × muscle depth ) + (0.0006 × fat depth × muscle depth). Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 4 Barducci et al. due to subtle differences in the way that parameters Table 2. Pearson correlation coefficients (r) for car - were measured. As previously mentioned, hot car- cass traits (N = 850,819 carcasses) cass weight in Canada (the current study included) Carcass traits Fat depth Muscle depth Predicted lean yield is measured as a head-on weight, while studies con- Hot carcass weight 0.289** 0.176** −0.235** ducted in the United States (Wilson et  al., 2016; Fat depth −0.148* −0.960** Arkfeld et al., 2017) measured hot carcass weight as Muscle depth 0.406** a head-off weight. While the current study reported *P ≤ 0.0001. an average head-on hot carcass weight of 106.30 ± **P ≤ 0.0001. 8.51  kg, it must be noted that this carcass weight Assumptions for Pearson product moment correlation (linearity) included the head and data reported by United were not met for the relationship between fat depth and predicted lean States and most European studies would not have yield and the relationship between muscle depth and predicted lean yield. Therefore, Spearman’s rank-order correlation coefficients were included the head. Boler et al. (2014) reported head used for these two relationships. weights comprised of 4.47% of ending live weight, Predicted lean yield was calculated using the following equation: or 6.30  kg, for barrows and comprised of 4.50% CLY = (%) = 68.1863 − (0.7833 × fat depth) + (0.0689 × muscle depth) 2 2 of ending live weight, or 6.28  kg, for gilts. Using + (0.0080  × fat depth ) − (0.0002  × muscle depth ) + (0.0006  × fat depth × muscle depth). these figures, it can be calculated that the weight of the head was approximately 5.75% of the average carcass weight reported, or 6.11 kg of the reported depth (Miar et al., 2014). Nevertheless, correlation weight in the current study. Based on these calcula- coefficients between growth rate and fat depth have tions, the current study had an average calculated shown a wide range of unpredictability, which may head-off hot carcass weight of 100.19 kg (106.30 kg be due to the method of measurement, techni- − 6.11  kg). This value was less than the head-off cian effect, breed differences, and sampling errors hot carcass weight reported by Arkfeld et al. (2017), (Koots and Gibson, 1994). and greater than the head-off hot carcass weight Additionally, the weak negative correlation ob- reported by Wilson et  al. (2016). Therefore, the served between hot carcass weight and predicted average hot carcass weight for the commercially rep- lean yield (r  =  −0.235; P  <  0.0001; Figure 3) were resentative population of pigs in the current study similar to previous scientific observations (Fahey was intermediate to recent scientific studies (Wilson et al., 1977; Grisdale et al., 1984; Miar et al., 2014; et al., 2016; Arkfeld et al., 2017) and slightly greater Bertol et al., 2017). The weak correlation with hot than the 2019 head-off hot carcass weight figures carcass weight demonstrated the poor ability to reported by the United States Department of predict lean yield at both lighter and heavier than Agriculture of 96.8 kg (USDA ERS, 2019). average weights. Consequently, it may be possible to consider that less variation in hot carcass weight within a population of pigs (or the marketing of Correlation among Parameters narrower ranges for weights of pigs) would not lead Moderate to strong correlation of carcass to less variation in predicted lean yield. This would weight with fat depth, muscle depth, and predicted counter the perception that increasing marketing lean yield was hypothesized. However, few carcass groups by selecting targeted weights of pigs im- traits demonstrated the strong correlation coeffi- prove predicted lean yield variation as outlined by cients that were expected (Table 2). Zhou and Bohrer (2019). The weak positive correlation observed be- In addition, weak negative correlation be- tween hot carcass weight and fat depth (r = 0.289; tween fat depth and muscle depth was observed P  <  0.0001; Figure 1), and between hot carcass (r  =  −0.148; P  <  0.0001; Figure 4). Wilson et  al. weight and muscle depth (r  =  0.176; P  <  0.0001; (2016) reported a similar observation with a weak Figure 2) were actually similar to previous reported negative correlation of r = −0.13 between fat depth observations (Fix et  al., 2010; Miar et  al., 2014). and muscle depth for the 1,235 pigs in their study. Previous studies reported that the combination Other previous research studies reported that of hot carcass weight and fat depth accounted for fat depth and muscle depth had moderate nega- 77% and 83% of the variation in the total weight tive correlation (Newcom et  al., 2002; Miar et  al., of carcass lean, respectively (Edwards et al., 1981; 2014) with the implication that increased fat and Grisdale et  al., 1984). Increased growth rate was decreased muscling may be expected when selec- strongly correlated with greater hot carcass weight tion was directed toward increased marbling. The and selection for increased growth rate has been hy- growth curve is an important parameter for growth pothesized to increase both fat depth and muscle development and muscle biology, where the growth Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 Relationship of pork carcass measurements 5 Figure 1. Prediction of hot carcass weight using fat depth as the Figure 2. Prediction of hot carcass weight using muscle depth as the independent variable (N = 850,819 carcasses). independent variable (N = 850,819 carcasses). Figure 3. Prediction of hot carcass weight using predicted lean yield Figure 4. Prediction of fat depth using muscle depth as the inde- as the independent variable (N = 850,819 carcasses). pendent variable (N = 850,819 carcasses). of muscle tissue during fattening was associated acceptable level of fat (Strzelecki et al., 1998; Price with protein accretion for muscle growth and the et al., 2019). deposition of fat in the pig (Schinckel et al., 2001). Consequently, during normal growth muscle mass Impact of Heavier Pork Carcasses efficiency decreased and maintenance require- Several recent studies and market assessments ments increased (Owens et  al., 1993). Once pigs have indicated that the weight of pork carcasses have reached homeostasis and energy is no longer is expected to increase in upcoming years (Morin required for skeletal and muscle growth, it is the- et  al., 2015; Harsh et  al., 2017; Rice et  al., 2018; orized that fat is first deposited in the form of sub- Gilleland et  al., 2019; Price et  al., 2019). Harsh cutaneous fat, followed by intermuscular fat, and et  al. (2017) goes as far as to state predictions for then intramuscular fat (De Smet et  al., 2004). carcass weights in the future, which were stated as Therefore, genetics companies have shifted their 104 kg in 2030, 111 kg in 2040, and 118 kg in 2050. efforts to pigs with a greater mature size and an These predictions were based on the 0.6 kg/year in- increasing number of pork processors have begun crease that the United States pork carcass weights to focus on the greater slaughter weight, which in are currently experiencing (USDA ERS, 2019). turn yields a leaner product while maintaining an Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 6 Barducci et al. There are several misunderstandings surrounding compared with optical probe measurements col- the topic of increased hot carcass weight of pigs, lected at the loin location. particularly that heavy weight pigs are fatter and/ Although definitive conclusions are not yet or heavier muscled. These data suggests that heavy plausible, the available data suggest that future in- pigs were not consistently fatter or heavier muscled creases in carcass weight should not change the compared with normal, or even light weight pigs assumptions of the predicted lean yield equation. for that matter. Notwithstanding, the fact that there The consideration of allometric growth and lean was a high level of variation in fat depth and muscle deposition of pigs at heavier live weights (and the depth for pigs of all carcass weights makes this inherent effect on carcass weight) does not seem study representative of many different situations to be influential when considering the parameters and may be useful for a variety of pork entities in evaluated in this study. This should continue to the future that are concerned with the variation in be explored by the pork industry in the future as leanness of heavy weight pigs. pigs continue to be marketed at heavier weights, While genetic selection, nutritional improve- as changes in allometric growth and lean depos- ments, and more efficient production systems have ition could have major impacts on all sectors of the allowed for improved growth rates of pigs over global pork industry. time, it is possible that allometric growth of pigs (i.e., rate of muscle and fat accretion throughout CONCLUSIONS the pig) has remained similar during this change in To begin, the data reported in this study were finishing weight and carcass weight. Beyond genetic one contribution to a much larger body of litera- improvement over time, it is also important to con- ture, yet this information should provide insight to sider other variables, such as production system, current grading systems and how heavier pork car- production strategy, sex, season, region, and the casses in the future may fit within current marketing method of grading when factoring in relationships systems. Results from this dataset revealed that hot of predicted lean yield and carcass weight. These carcass weight was generally weakly correlated with variables can overestimate and/or underestimate fat depth, muscle depth, or predicted lean yield. The the grading indexes and the calculated yields, which conclusion of this study based on the current dataset could result in significant carcass index differences was that pigs do not reach a weight threshold where among a population of pigs (Pomar and Marcoux, they consistently become fatter or heavier muscled, 2003; Latorre et  al., 2004; Straadt et  al., 2013). and that current predicted lean yield equations may Moreover, it is important to note that carcass com- still be adequate for heavy weight pigs in the same position traits are typically less affected by envir- capacity that they are adequate for light weight pigs. onmental variations when compared with other heritable traits like reproduction and growth per- ACKNOWLEDGMENT formance (Akanno et al., 2013). These data along with other recent heavy weight pork carcass data The authors would like to acknowledge (notably Price et al., 2019) indicate that the relation- Conestoga Meat Packers Ltd. for their assist- ships between carcass weight and leanness param- ance with the data used in this study. This project eters are highly variable at all weights of pigs, and was funded wholly or in part by Conestoga Meat that allometric growth (and composition) is likely Packers, Ontario Pork, and Natural Sciences and not being significantly altered in heavier pigs. Engineering Research Council of Canada. Conflict of interest statement. None declared Future Outlook LITERATURE CITED Insights into the mechanism that affects pre- Adebambo,  A. 1986. Selective breeding of the Nigerian indi- dicted lean yield were difficult to define with this genous pigs for the rural producer, vol. X. In: Proceedings dataset. 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The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

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Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry † † ‡ Robson S. Barducci,* Ziyu Y. Zhou,* Lisa Wormsbecher, Colleen Roehrig, Dan Tulpan, and ,1, Benjamin M. Bohrer* *Department of Food Science, University of Guelph, Guelph, Ontario, Canada N1G2W1; Conestoga Meat Packers Ltd., Breslau, Ontario, Canada N0B1M0; and Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada N1G2W1 ABSTRACT: This study aimed to examine the cor- weight and muscle depth (r  =  0.176; P  <  0.0001). relation of carcass weight, fat depth, muscle depth, Weak negative correlations were observed be- and predicted lean yield in commercial pigs. Data tween hot carcass weight and predicted lean yield were collected on 850,819 pork carcasses from the (r  =  −0.235; P  <  0.0001), and between fat depth same pork processing facility between October and muscle depth (r = −0.148; P < 0.0001). Upon 2017 and September 2018. Hot carcass weight was investigation of relationships between fat depth reported following slaughter as a head-on weight; and predicted lean yield, and between muscle depth while fat and muscle depth were measured with a and predicted lean yield using scatter plots, it was Destron PG-100 probe and used for the calcula- determined that these relationships were not linear tion of predicted lean yield based on the Canadian and therefore the assumptions of Pearson product Lean Yield (CLY) equation [CLY (%) = 68.1863 − moment correlation were not met. Thus, these re- (0.7833 × fat depth) + (0.0689 × muscle depth) + lationships were expressed as nonlinear functions 2 2 (0.0080 × fat depth ) − (0.0002 × muscle depth ) + and Spearman’s rank-order correlation coefficients (0.0006 × fat depth × muscle depth)]. Descriptive were used. A  strong negative correlation was ob- statistics, regression equations including coef-fi served between fat depth and predicted lean yield cients of determination, and Pearson product mo- (r = −0.960; P < 0.0001), and a moderate positive ment correlation coefficients (when assumptions correlation was observed between muscle depth for linearity were met) and Spearman’s rank-order and predicted lean yield (r  =  0.406; P  <  0.0001). correlation coefficients (when assumptions for lin- Results from this dataset revealed that hot carcass earity were not met) were calculated for attributes weight was generally weakly correlated (r < |0.35|) using SigmaPlot, version 11 (Systat Software, Inc., with fat depth, muscle depth, and predicted lean San Jose, CA). Weak positive correlation was ob- yield. Therefore, it was concluded that there were served between hot carcass weight and fat depth no consistent weight thresholds where pigs were (r  =  0.289; P  <  0.0001), and between hot carcass fatter or heavier muscled. Key words: commercial pork, correlation, fat depth, muscle depth, pork carcass weight © The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Transl. Anim. Sci. 2019.XX:0-0 doi: 10.1093/tas/txz169 Corresponding author: bbohrer@uoguelph.ca Received August 2, 2019. Accepted October 21, 2019. 1 Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 2 Barducci et al. INTRODUCTION 2019). However, there still exist many misunder- standings among the relationship of pork carcass In upcoming years, continued population weight and leanness parameters, particularly in growth and increased wealth in developing nations commercially representative pigs marketed under will likely increase the global demand for pork current times and conditions. Therefore, the ob- (Szymańska, 2018). To increase the quantity of jective of this study was to examine the correl- pork produced, while meeting industry (and con- ation of carcass weight, fat depth, muscle depth, sumer) demands for more sustainable production and predicted lean yield in commercial pigs mar- systems, utilization of modern technologies is re- keted at commercially representative weights and quired to lead to the development of a more ef- conditions. ficient and profitable global industry. With both efficiency and profitability in mind, pork proces- sors aspire to ensure that pig producers are mar- MATERIALS AND METHODS keting a consistent product that meets industry Pigs were slaughtered under the supervision of standards for weight and yield. At the same time, the Canadian Food Inspection Agency (CFIA) at it is critical that investigations of commonly used a federally inspected processing facility. Carcass technologies be conducted frequently as industry data from that facility were then shared with the re- standards can change very quickly. For instance, search team that conducted this study. Therefore, the weight of pork carcasses has steadily increased Animal Care and Use Committee approval was not over time. In fact, there has been a consistent linear required for this study as no live animal data were increase in pork carcass weight over the past three used by the university research team. decades. This is evident when evaluating histor- ical averages—the average hot carcass weight in 1989 was 81.2  kg, the average hot carcass weight Data Collection in 1999 was 86.8 kg, the average hot carcass weight in 2009 was 92.2  kg, and the average hot carcass Data were collected at a commercial pig weight to-date in 2019 (through August) was slaughter facility located in southwestern Ontario 96.8  kg (USDA ERS, 2019). The changes in car- between October 1, 2017 and September 30, 2018. cass weight over time justifies an evaluation of the Information from a total of 850,819 pork carcasses relationships between carcass weight and predicted were used for this study. Hot carcass weight, fat leanness of pork carcasses in a commercially rep- depth, and muscle depth of each individual car- resentative population of pigs. cass were measured and recorded on the day of Regarding predicted leanness of pork car- slaughter before the animal was chilled. Hot car- casses, many different technologies have been used cass weight was reported immediately following in the last 30  years for online measurement of slaughter as a head-on weight. Fat depth and muscle lean meat percentage in pork carcasses, with the depth were measured with a Destron PG-100 probe primary focus of identifying the true commercial (International Destron Technologies, Markham, value of the pork carcass to the processor and pro- Canada) inserted perpendicularly between the vide proper recommendations for pig producers third and fourth last rib and 7 cm off the mid-line (Swatland et al., 1994). The most common method according to Canadian grading standards (Pomar for the measurement of carcass lean yield in North and Marcoux, 2003). Fat depth and muscle depth America is the use of an optical probe to measure measurements were used to obtain the predicted fat depth and muscle depth of the loin, and a sub- lean yield of each individual carcass using the fol- sequent conversion of these measurements pre- lowing equation: sents the designated conversion equations used for the calculation of predicted lean yield (Busk CLY =(%) = 68.1863(0.7833 × fat depth) +(0.0689 × muscle depth)+(0.0080 × fat depth ) et  al., 1999; Zhou and Bohrer, 2019). Thus, fat depth, muscle depth, and predicted leanness are (0.0002 × muscle depth ) +(0.0006 × fat depth × muscle depth) often the only carcass leanness parameters evalu- ated in commercial pigs. Several research studies where CLY is the Canadian Lean Yield of the car- have characterized the correlation between pork casses and fat depth and muscle depth are the back- carcass weight and leanness parameters (Kure, fat thickness (mm) and muscle thickness (mm), 1997; Ohlmann and Jones, 2011; Plà-aragonés respectively (Pomar and Marcoux, 2003). et  al., 2013; Rodríguez et  al., 2014; Price et  al., Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 Relationship of pork carcass measurements 3 Statistical Analyses the purpose of clarity, hot carcass weight will be referred to as head-on or head-off when comparing Data were evaluated thoroughly, and the en- absolute values throughout the current study. In tire observation was removed in the case of missing general, head-on hot carcass weight and muscle data points and extreme outliers. Descriptive stat- depth were greater than historical observations in istics for carcass traits (mean, standard deviation, Canada, while fat depth was closer in value (CPC, minimum, and maximum) were calculated for all 1994; Pomar et  al., 2001; Pomar and Marcoux, parameters using SigmaPlot, version 11 (Systat 2003). Head-on hot carcass weight, fat depth, and Software, Inc., San Jose, CA). Scatter plots were muscle depth were similar to values reported in re- created with SigmaPlot to allow for better visual- cent studies conducted in Canada (Miar et al., 2014; ization of the relationship between all parameters. Zhang et  al., 2016). Likewise, hot carcass weight Further evaluation for linearity and homoscedas- (compared between head-on hot carcass weight ticity were evaluated using the scatter plots. When and head-off hot carcass weight), fat depth, and assumptions of linearity and homoscedasticity muscle depth were similar in value with the values were met, linear regression equations were created reported in recent studies conducted in Europe by SigmaPlot. All regression analysis included co- (Lisiak et  al., 2015; Knecht et  al., 2016) and the efficients of determination (R ). Coefficients of United States (Wilson et  al., 2016; Arkfeld et  al., determination were considered weak at R < 0.12, 2017). For example, Wilson et  al. (2016) reported 2 2 moderate at 0.13 ≤ R < 0.45, and strong at R ≥ 0.46 population statistics for 1,235 commercial pigs in (Bohrer and Boler, 2017). For relationships meeting the United States as: 103.6 kg for head-off hot car- assumptions for Pearson product moment correl- cass weight, 22  mm for fat depth, and 67  mm for ation, Pearson correlation coefficients were calcu- muscle depth. Furthermore, Arkfeld et  al. (2017) lated using SigmaPlot. For relationships failing to reported population statistics for 6,920 commer- meet assumptions for Pearson product moment cor- cial pigs in the United States as: 94.50 ± 9.39 kg for relation (nonlinear), Spearman’s rank-order correl- head-off hot carcass weight, 15.41  ± 4.00  mm for ation coefficients were calculated using SigmaPlot. fat depth, 68.00 ± 8.52 mm for muscle depth, and Correlation coefficients were considered signifi- 57.63% ± 2.76% for predicted lean yield. cantly different from 0 at P < 0.05. Correlation co- The observed differences in parameters over efficients were considered weak (in absolute value) time were likely due to changes in the genotype for r < 0.35, moderate for 0.36 ≤ r ≤ 0.67, and strong of pigs, the environment/rearing conditions, and for r ≥ 0.68 (Taylor, 1990). a combination of these things (genotype × envir- onment). The continuous genetic improvement of RESULTS AND DISCUSSION pigs has resulted in a 41% increase in head-off car- cass weight since 1970 (Wilson et  al., 2016) and a Population Mean and Variation significant reduction of fat thickness in the global Means and standard deviation for hot carcass pork population (Adebambo, 1986; Chimonyo and weight (head-on), fat depth, muscle depth, and pre- Dzama, 2007; Sellier et  al., 2010). As outlined by dicted lean yield were 106.30  ± 8.51  kg, 18.11  ± one highly cited report (Thornton, 2010), livestock 4.04 mm, 66.47 ± 8.88 mm, and 61.13 ± 1.90%, re- production has changed substantially over the past spectively (Table 1). It is worth mentioning that it is several decades, which has undoubtedly changed common practice to include the head in the weight the way pigs are raised and marketed. The observed of the carcass in Canada, while this is not gener- differences in parameters between the current study ally the case in Europe and the United States. For and recent studies from the United States were likely Table 1. Population summary statistics for carcass traits (N = 850,819 carcasses) Carcass traits Mean SD Minimum Maximum Hot carcass weight, kg 106.30 8.51 44.60 165.80 Fat depth, mm 18.11 4.04 4.00 46.00 Muscle depth, mm 66.47 8.88 25.00 85.00 Predicted lean yield, % 61.13 1.90 52.20 69.80 Measured at the third and fourth last rib, and 7 cm off the mid-line. Predicted lean yield was calculated using the following equation: CLY = (%) = 68.1863 − (0.7833 × fat depth) + (0.0689 × muscle depth) + 2 2 (0.0080 × fat depth ) − (0.0002 × muscle depth ) + (0.0006 × fat depth × muscle depth). Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 4 Barducci et al. due to subtle differences in the way that parameters Table 2. Pearson correlation coefficients (r) for car - were measured. As previously mentioned, hot car- cass traits (N = 850,819 carcasses) cass weight in Canada (the current study included) Carcass traits Fat depth Muscle depth Predicted lean yield is measured as a head-on weight, while studies con- Hot carcass weight 0.289** 0.176** −0.235** ducted in the United States (Wilson et  al., 2016; Fat depth −0.148* −0.960** Arkfeld et al., 2017) measured hot carcass weight as Muscle depth 0.406** a head-off weight. While the current study reported *P ≤ 0.0001. an average head-on hot carcass weight of 106.30 ± **P ≤ 0.0001. 8.51  kg, it must be noted that this carcass weight Assumptions for Pearson product moment correlation (linearity) included the head and data reported by United were not met for the relationship between fat depth and predicted lean States and most European studies would not have yield and the relationship between muscle depth and predicted lean yield. Therefore, Spearman’s rank-order correlation coefficients were included the head. Boler et al. (2014) reported head used for these two relationships. weights comprised of 4.47% of ending live weight, Predicted lean yield was calculated using the following equation: or 6.30  kg, for barrows and comprised of 4.50% CLY = (%) = 68.1863 − (0.7833 × fat depth) + (0.0689 × muscle depth) 2 2 of ending live weight, or 6.28  kg, for gilts. Using + (0.0080  × fat depth ) − (0.0002  × muscle depth ) + (0.0006  × fat depth × muscle depth). these figures, it can be calculated that the weight of the head was approximately 5.75% of the average carcass weight reported, or 6.11 kg of the reported depth (Miar et al., 2014). Nevertheless, correlation weight in the current study. Based on these calcula- coefficients between growth rate and fat depth have tions, the current study had an average calculated shown a wide range of unpredictability, which may head-off hot carcass weight of 100.19 kg (106.30 kg be due to the method of measurement, techni- − 6.11  kg). This value was less than the head-off cian effect, breed differences, and sampling errors hot carcass weight reported by Arkfeld et al. (2017), (Koots and Gibson, 1994). and greater than the head-off hot carcass weight Additionally, the weak negative correlation ob- reported by Wilson et  al. (2016). Therefore, the served between hot carcass weight and predicted average hot carcass weight for the commercially rep- lean yield (r  =  −0.235; P  <  0.0001; Figure 3) were resentative population of pigs in the current study similar to previous scientific observations (Fahey was intermediate to recent scientific studies (Wilson et al., 1977; Grisdale et al., 1984; Miar et al., 2014; et al., 2016; Arkfeld et al., 2017) and slightly greater Bertol et al., 2017). The weak correlation with hot than the 2019 head-off hot carcass weight figures carcass weight demonstrated the poor ability to reported by the United States Department of predict lean yield at both lighter and heavier than Agriculture of 96.8 kg (USDA ERS, 2019). average weights. Consequently, it may be possible to consider that less variation in hot carcass weight within a population of pigs (or the marketing of Correlation among Parameters narrower ranges for weights of pigs) would not lead Moderate to strong correlation of carcass to less variation in predicted lean yield. This would weight with fat depth, muscle depth, and predicted counter the perception that increasing marketing lean yield was hypothesized. However, few carcass groups by selecting targeted weights of pigs im- traits demonstrated the strong correlation coeffi- prove predicted lean yield variation as outlined by cients that were expected (Table 2). Zhou and Bohrer (2019). The weak positive correlation observed be- In addition, weak negative correlation be- tween hot carcass weight and fat depth (r = 0.289; tween fat depth and muscle depth was observed P  <  0.0001; Figure 1), and between hot carcass (r  =  −0.148; P  <  0.0001; Figure 4). Wilson et  al. weight and muscle depth (r  =  0.176; P  <  0.0001; (2016) reported a similar observation with a weak Figure 2) were actually similar to previous reported negative correlation of r = −0.13 between fat depth observations (Fix et  al., 2010; Miar et  al., 2014). and muscle depth for the 1,235 pigs in their study. Previous studies reported that the combination Other previous research studies reported that of hot carcass weight and fat depth accounted for fat depth and muscle depth had moderate nega- 77% and 83% of the variation in the total weight tive correlation (Newcom et  al., 2002; Miar et  al., of carcass lean, respectively (Edwards et al., 1981; 2014) with the implication that increased fat and Grisdale et  al., 1984). Increased growth rate was decreased muscling may be expected when selec- strongly correlated with greater hot carcass weight tion was directed toward increased marbling. The and selection for increased growth rate has been hy- growth curve is an important parameter for growth pothesized to increase both fat depth and muscle development and muscle biology, where the growth Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 Relationship of pork carcass measurements 5 Figure 1. Prediction of hot carcass weight using fat depth as the Figure 2. Prediction of hot carcass weight using muscle depth as the independent variable (N = 850,819 carcasses). independent variable (N = 850,819 carcasses). Figure 3. Prediction of hot carcass weight using predicted lean yield Figure 4. Prediction of fat depth using muscle depth as the inde- as the independent variable (N = 850,819 carcasses). pendent variable (N = 850,819 carcasses). of muscle tissue during fattening was associated acceptable level of fat (Strzelecki et al., 1998; Price with protein accretion for muscle growth and the et al., 2019). deposition of fat in the pig (Schinckel et al., 2001). Consequently, during normal growth muscle mass Impact of Heavier Pork Carcasses efficiency decreased and maintenance require- Several recent studies and market assessments ments increased (Owens et  al., 1993). Once pigs have indicated that the weight of pork carcasses have reached homeostasis and energy is no longer is expected to increase in upcoming years (Morin required for skeletal and muscle growth, it is the- et  al., 2015; Harsh et  al., 2017; Rice et  al., 2018; orized that fat is first deposited in the form of sub- Gilleland et  al., 2019; Price et  al., 2019). Harsh cutaneous fat, followed by intermuscular fat, and et  al. (2017) goes as far as to state predictions for then intramuscular fat (De Smet et  al., 2004). carcass weights in the future, which were stated as Therefore, genetics companies have shifted their 104 kg in 2030, 111 kg in 2040, and 118 kg in 2050. efforts to pigs with a greater mature size and an These predictions were based on the 0.6 kg/year in- increasing number of pork processors have begun crease that the United States pork carcass weights to focus on the greater slaughter weight, which in are currently experiencing (USDA ERS, 2019). turn yields a leaner product while maintaining an Translate basic science to industry innovation Downloaded from https://academic.oup.com/tas/article-abstract/4/1/txz169/5602903 by guest on 18 February 2020 6 Barducci et al. There are several misunderstandings surrounding compared with optical probe measurements col- the topic of increased hot carcass weight of pigs, lected at the loin location. particularly that heavy weight pigs are fatter and/ Although definitive conclusions are not yet or heavier muscled. These data suggests that heavy plausible, the available data suggest that future in- pigs were not consistently fatter or heavier muscled creases in carcass weight should not change the compared with normal, or even light weight pigs assumptions of the predicted lean yield equation. for that matter. Notwithstanding, the fact that there The consideration of allometric growth and lean was a high level of variation in fat depth and muscle deposition of pigs at heavier live weights (and the depth for pigs of all carcass weights makes this inherent effect on carcass weight) does not seem study representative of many different situations to be influential when considering the parameters and may be useful for a variety of pork entities in evaluated in this study. This should continue to the future that are concerned with the variation in be explored by the pork industry in the future as leanness of heavy weight pigs. pigs continue to be marketed at heavier weights, While genetic selection, nutritional improve- as changes in allometric growth and lean depos- ments, and more efficient production systems have ition could have major impacts on all sectors of the allowed for improved growth rates of pigs over global pork industry. time, it is possible that allometric growth of pigs (i.e., rate of muscle and fat accretion throughout CONCLUSIONS the pig) has remained similar during this change in To begin, the data reported in this study were finishing weight and carcass weight. Beyond genetic one contribution to a much larger body of litera- improvement over time, it is also important to con- ture, yet this information should provide insight to sider other variables, such as production system, current grading systems and how heavier pork car- production strategy, sex, season, region, and the casses in the future may fit within current marketing method of grading when factoring in relationships systems. Results from this dataset revealed that hot of predicted lean yield and carcass weight. These carcass weight was generally weakly correlated with variables can overestimate and/or underestimate fat depth, muscle depth, or predicted lean yield. The the grading indexes and the calculated yields, which conclusion of this study based on the current dataset could result in significant carcass index differences was that pigs do not reach a weight threshold where among a population of pigs (Pomar and Marcoux, they consistently become fatter or heavier muscled, 2003; Latorre et  al., 2004; Straadt et  al., 2013). and that current predicted lean yield equations may Moreover, it is important to note that carcass com- still be adequate for heavy weight pigs in the same position traits are typically less affected by envir- capacity that they are adequate for light weight pigs. onmental variations when compared with other heritable traits like reproduction and growth per- ACKNOWLEDGMENT formance (Akanno et al., 2013). These data along with other recent heavy weight pork carcass data The authors would like to acknowledge (notably Price et al., 2019) indicate that the relation- Conestoga Meat Packers Ltd. for their assist- ships between carcass weight and leanness param- ance with the data used in this study. This project eters are highly variable at all weights of pigs, and was funded wholly or in part by Conestoga Meat that allometric growth (and composition) is likely Packers, Ontario Pork, and Natural Sciences and not being significantly altered in heavier pigs. Engineering Research Council of Canada. Conflict of interest statement. None declared Future Outlook LITERATURE CITED Insights into the mechanism that affects pre- Adebambo,  A. 1986. Selective breeding of the Nigerian indi- dicted lean yield were difficult to define with this genous pigs for the rural producer, vol. X. In: Proceedings dataset. 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