Regression models for estimating breast, thigh and fat weight and yield of broilers from non invasive body measurements

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1 AGRICULTURE AND BIOLOGY JOURNAL OF NORTH AMERICA ISSN Print: , ISSN Online: , ScienceHuβ, Regression models for estimating breast, thigh and fat weight and yield of broilers from non invasive body measurements *Abdulrazaq Onimisi Raji, Joseph U. Igwebuike and Ibrahim Dankasa Kwari Dept of Animal Science, University of Maiduguri, Maiduguri, Borno State, Nigeria. ABSTRACT The possibility of using non invasive body measurements for in vivo estimation of carcass components weight and yield was investigated. The study which was carried out at the University of Maiduguri Livestock Teaching and Research farm involved 192 six weeks finished broilers. Sexual dimorphism in favour of the male for most carcass trait and body measurements was observed. Breast, thigh and fat weights were significantly (P<0.05) affected by sex while their yields were not. The live weights of male and female broilers were 2040g and 1910g and the carcass yield 74.5% and 74.34% respectively. For the carcass traits, the values of males compared to females for weight and corresponding yield of breast were g (20.40%) vs g (19.88%), thigh; 233.3g (22.85%) vs g (21.13%) and fat; 17.62g (0.85%) and 21.66g (1.13%). The result showed that male broilers weighed more, yielded more meat and had less fat compared to their female counterparts. In the two sexes, the relationship between breast weight and all the body measurements were significant (P<0.01) and moderate to high. Similarly, in females, thigh weight, had a significant correlation (r=0.224 to 0.885) with all body measurements while in males, the relationship (r=0.259 to 0.745) was positive and significant (P<0.01) for live weight, chest girth and drum length. R 2 values showed that body measurements described more variation in carcass component weights (55.2 to 88.6%) than in the yields (17.6 to 78.8%). Fat weight was predicted with slightly lower accuracy in females compared to males with F values (3.93* vs 21.06***), R (0.740 vs 0.917), R 2 (0.548 vs 0.840) and adjusted R 2 (0.462 vs 0.830). Finally, the slight differences observed between the actual and predicted values for absolute weight of the carcass traits demonstrates the ability of regression equations to predict these traits in the broiler industry. Key words: Breast, thigh, weights, non invasive measurements, broilers. INTRODUCTION The broiler industry has witnessed tremendous improvement in recent years viz: rapid early growth and high feed efficiency of broiler chickens. The time taken to finish broilers has reduced to 42days with a slaughter weight above 2 kg (Havenstein et al., 2003). Most of these improvements were achieved through selection for the traditional performance indicators of live weight gain and feed efficiency. However, increased demand for breast meat has made producers to look for ways to optimize breast muscle growth. This is in spite of low genetic correlations (0.12 to 0.15) with growth traits (Zerehdaran et al., 2004). The carcass dressing percent of broiler is about 72% and the breast and thigh are 32.7 and 29.2% respectively of the carcass weight (Goliomytis et al. 2003). Breast muscle is the most important carcass part from an economic stand point (Zuidof, 2005) and the yield of breast and thigh has a large influence on the efficiency of processing when portioned and further processed products are marketed. (Khosravania et al., 2006). Baeza and Leclercq (1998) observed that for many producers, breast yield was as important as growth rate and feed efficiency. In addition, its prediction is of primary importance in economic modeling in order to optimize production and processing decisions (Zuidof, 2005). Non invasive measurements provide an opportunity to collect slaughter value information from live birds and as a result, own information for carcass traits are available on candidates for selection while still alive. In addition, linear and non linear mathematical or statistical functions provide estimates for target variables using one or more easily measurable non invasive body traits. Khosravania et al. (2006) observed that regression models can be used to predict carcass, breast and leg weights utilizing data on body conformation traits and weight at different ages. Studies on the correlation between carcass parameters and indirect measurements showed a high correlation between live weight and carcass lean content in poultry (Bochno et al. 1997, 1999a and Michalik et al 2002), while breast weight in ducks (Rymkiewiez and Bochno, 1999) and geese (Bochno

2 et al., 2000a) is best predicted based on live weight and chest girth. Fat weight was found to be correlated with live weight, chest girth and breast bone crest length in fowls (Bochno et al.,2000a). Bochno et al. (1999a) and Michalik et al. (2002) observed a significant (P<0.01) correlation (r=0.7) between live weight and fat weight in broilers and turkeys respectively. Since slaughter value parameters (breast, thigh, fat weights etc) are difficult to obtain in the live animal except after slaughter, simple, reliable and indirect methods for the estimation of these traits in vivo is a necessity. Thus, the aim of this study was to develop regression models for estimating breast, thigh and fat weight and yield based on sex in broilers. MATERIALS AND METHODS The study was carried out at the poultry unit of the University of Maiduguri Teaching and Research Farm, Maiduguri, Borno State. Maiduguri, the Borno State capital is situated on latitude N and longitude E (Encarta, 2007) and at an altitude of 345m above sea level. The area falls within the Sahelian region of West Africa, which is noted for its great climatic and seasonal variation. It has very short period (3 4 months) of rainfall giving 645.9mm/annum with a long dry season of about 8 9 months. The ambient temperature could be as low as 20 0 C during the dry cold season and as high as 44 0 C during the dry hot season. Relative humidity is 45% in August which usually lowers to about 5% in December and January. The experimental animals were 192 six weeks old broilers of both sexes randomly selected from an experimental flock of 500 birds. They were fed formulated broiler starter diets containing 3100kcal metabolizable energy and 23% crude protein from 1-3 weeks and finisher diet containing 3100kcal metabolizable energy and 20% crude protein from week 4-6. All the birds were housed in deep litter pens where feed and water were provided ad libitum. At 1 day of age, male and female chicks were randomly kept in groups of 25 birds in 16 floor pens with wood shavings as the litter material. Each pen was treated as a replicate experimental unit. Twelve birds (6 males and 6 females) were randomly selected from each pen for carcass analysis after six weeks. Birds for slaughter were fasted overnight (about 10 hours) but water was provided. Before slaughter, the measurements taken were as follows: Chest girth (in centimeters) as the circumference of the body behind the wings, through the anterior border of the breast bone crest and the central thoracic vertebra. Breast bone crest length: this is the distance between the anterior and posterior border of the breast bone crest. Chest depth: the vertical distance between the backbone and the beginning of the breast bone crest Chest width: the distance between the medial protuberances of the humeral bones. Drumstick length: this is the length from the knee joint to the hock. Shank length: length between the genu and the regiotarsalis. Body length: length between the base of the neck and the tip of the cauda (tail without feathers). Wing length: length between the base and the tip of the wing. All length measurements were obtained with a simple cloth tape graduated in centimeters while width and depth measurements were obtained with a vernier caliper. Birds were slaughtered by restraining them and severing the carotid arteries and jugular veins of the neck with a sharp knife. They were then allowed to bleed for 5 minutes before scalding and plucking manually. The head and shank were severed and the carcass eviscerated by cutting around the vent to remove all the viscera. The empty carcass was weighed to obtain carcass weight. Each carcass was then cut into component parts (breast, thigh, etc). Each component was weighed to obtain their weights. Statistical analysis: Data collected was subjected to one way analysis of variance with sex as the fixed factor and significant means separated by Least Significant Difference. Pearson s correlation sub routine was used to determine the coefficient of simple correlation between live weight, body measurements and the target carcass components (breast, thigh and fat) weight (g) and yield (%). Multiple regression equations were derived to estimate breast, thigh and fat weights and yields from non invasive body measurements (independent variables) of the broilers. The best subset sub routine of the regression procedure was used. The procedure produced the best regression model for each dependent variable based on the R 2, adjusted R 2 and Malones cp values. All analysis was done with Statistix 9.0 software. RESULTS Means and their corresponding standard errors for all the body measurements and carcass traits as affected by sex are presented in Table1. The mean 470

3 values for all traits were higher in the males compared to the females. Among the non invasive measurements, live weight, chest width, breastbone crest length, drum length and shank length were significantly (P<0.05) influenced by sex of broiler chickens. This was also applicable to breast, thigh and fat weight though the yields were not. The live weights of male and female broilers respectively were 2040 and 1910g and their yields and 74.34%. For the carcass traits, the values of males compared to females for breast, thigh and fat weight were vs g, vs g and and 21.66g respectively. The corresponding yields were; vs 19.88%, vs 21.13% and 0.85 vs 1.13%. The result showed that male broilers weighed more, yielded more meat and had less fat compared to their female counterparts. Simple Pearson correlation coefficients between body measurements and carcass components and their yields are presented in Table 2. Live weight had positive and significant (P<0.01) correlations (r= ) with carcass component parts and fat weight in both male and female broilers but the yields were low, negative and non significant (P>0.05) except fat yield (r=0.549) in males and thigh yield (r=0.570) in females. In males, moderate to high and significant relationships (P<0.01) were found between breast weight and live weight, chest girth and shank length. However, for females, the relationship between breast weight and all body measurements were moderate to high and significant (P<0.01; r=0.233 to 0.805) except wing length which was low and non significant (r=0.066). Similarly, in females, thigh weight, had a significant correlation (r=0.224 to 0.885) with all body measurements while in males, the relationships (r=0.259 to 0.745) were positive and significant (P<0.01) for live weight, chest girth and drum length. For fat weight, the relationship with body measurements were generally low and non significant for both male and females except for live weight and chest depth (0.629 and respectively) in males and live weight and chest girth (0.347 and 0.382, respectively) in females. The coefficient of simple correlation between the percentages (yield) of carcass components and non invasive body measurements were generally low, negative and non significant (P>0.05) except for fat yield and live weight (0.549) and thigh yield and chest girth (0.503) in males and thigh yield and live weight, shank length and breast bone crest length (r= 0.570, and respectively) in females. Multiple regression equations for in vivo estimation of the weight and percentages of carcass components in live animals for both males and females are presented in Table 3. These equations were developed using the best subset routine of the regression procedure of Statistix 9.0. The best model for each dependent variable was determined base on high adjusted R 2. Breast muscle weight can be predicted in males with the use of chest girth and live weight as shown in equation 1 and in females using live weight, chest width and breast bone crest length as shown in equations 2. Thigh weight was predicted (equation 3 and 4) based on live weight, chest width and chest girth in males while for females the independent variables were chest girth, chest width, live weight and chest depth. The R, R 2 and adjusted R 2 for both equations were high. They were 0.91, 0.83 and 0.77 respectively for equations 3 and 0.94, and for equation 4. Both equations were highly significant (P<0.001) with F values of and respectively. The independent variables for prediction of fat weight in males and females (equations 5 and 6) were chest girth, chest depth, chest width, live weight and wing length and chest depth, breast bone crest length, live weight and wing length respectively. The R, R 2 and adjusted R 2 were high. The F value for the prediction of fat weight in males was highly significant (P<0.001, F=13.03) while for females it was significant (P<0.05, F=3.93). Similarly, R 2 were and respectively. Equations 7-12 for predicting yield of the carcass component parts in broilers generally had lower coefficients of multiple correlation (0.41 to 0.81) and R 2 (0.17 to 0.65). The independent variables (chest girth, chest depth, weight, chest width) for estimation of fat yield in males had a high coefficient of multiple correlation (0.916), R 2 (0.84) and adjusted R 2 (0.824). The F value (14.39) was highly significant (P<0.001). Breast yield estimation in females had a non significant F value (1.06) and adj R 2 (0.11) indicating a low goodness of fit. Estimation of fat yield in female though significant had a low adjusted R 2 (0.308). The coefficient of multiple correlation between the dependent and various independent variables (breast bone crest length, wing length, chest girth, chest depth, r=0.65) were also low compared to those of the males. The predicted weight, difference between the actual and predicted weight and the percentage difference are presented on table 4. The difference (g and %) for dependent variables Y 1, Y 2 and Y 3 for both male and female were generally small. They ranged from 0.12 to 18.65g for the absolute weight difference and the range for the percentage difference was 0.03 to 4.16%. In contrast, dependent variables Y 4, Y 5 and 471

4 Y 6 ranged from 0.30 to 7.53 and the percentage deviation was 7.67 to 52.43%. Table 1. Effect of sex on carcass traits and non invasive body measurements of broilers. Traits Male Female Non invasive measurements Live weight (g) 2040±20.0 a 1910±30.0 b Body length (cm) 24.0±0.23 a 23.67±0.28 a Chest girth (cm) 30.03±0.27 a 28.67±0.34 a Chest width (cm) 79.35±1.53 a 75.47±1.39 b Chest depth (cm) 84.91±1.8 a 81.86±1.47 a Wing length (cm) 18.81±0.27 a 18.64±0.21 a Drum length (cm) 12.68±0.14 a 12.06±0.12 b Breast bone crest length (cm) 15.5±0.10 a 15.05±0.10 b Shank length (cm) 9.21±0.07 a 8.8±0.11 b Carcass traits Dressed weight (g) 1520±20.0 a 1420±20.0 b Breast weight (g) ±4.65 a ±7.65 b Fat weight (g) 17.62±2.04 b 21.66±2.06 a Thigh weight (g) ±12.18 a 405.9±12.95 b Breast yield (%) 20.40±0.23 a 19.88±0.24 a Thigh yield (%) 22.84±0.48 a 21.13±0.23 b Fat yield (%) 0.85±0.09 b 1.13±0.10 a Carcass yield (%) 74.5± 1.80 a 74.34±1.35 a a,b means within rows with different superscripts a,b are significantly (P<0.05) different from each other DISCUSSION Simple non invasive measurements obtained with inexpensive tools such as cloth tape and vernier caliper provide an accurate base of data for estimation of carcass components. Also, the existence of positive and significant correlation between these measurements and the carcass components indicates their suitability for developing regression models with a reasonable goodness of fit. In this study, sexual dimorphism in favour of males for almost all the traits was observed and this prompted the development of separate models based on sex. The superiority (P<0.05) of males over females for carcass and component weights at a particular age agrees with the results of Zerehdaran et al. (2004) and Khosravania et al. (2005). The observed differences between males and females for breast and thigh yields respectively (20.39 vs 19.9% and vs 21.25%) were within the range of the difference (0.5 1%) reported by Camblee et al. (2003) and Tuchenskaya et al. (2004). No clear explanation for such gender related difference could be proposed. However, Zuidof (2005) observed that growth of the pectoralis major and minor breast muscle increased proportionally with live weight of birds. This corroborates the assertion by Marks (1995) and Moran (1995) that heavier birds produced a greater portion of breast. The males in this study had higher breast and thigh weights and yield probably because they had significantly (P<0.05) higher live weight. Table 2. Correlation coefficients between carcass components (absolute weight and yield) and body measurements in broilers Independent Breast Thigh Fat yield (%) Variables sex Breast weight Thigh weight Fat weight yield (%) yield (%) Live weight 0.584** 0.664** 0.629* * 0.805** 0.885** ns ns 0.570** ns Chest girth 0.727** 0.745** ns ns 0.693** 0.738** ns ns ns ns Chest width ns ns ns 0.373* 0.476* ns ns ns ns Chest depth ns * * ns ns ns ns ns ns Wing length ns ns 0.107* ns ns ns ns ns 0.480* ns Drum length ns * ns 0.451** 0.621** ns ns 0.562* ns Shank length * 0.197* ns ns ns 0.372* ns ns 0.492* ns Breast bone ns ns 0.281* * Crest length 0.488* 0.515* ns ns 0.536* ns -male - female ns = not significant (P>0.05) ** = P<0.01 * = P<

5 Table 3. Regression output including fitted functions, F value and R coefficients for both male and female for each carcass component. Equation Coefficients No. Sex Equations F value R R 2 Adj. R 2 1 Y 1 = cg+9.262wt 7.99** Y 1 = wt+1.021cw bbl 8.27** Y 2 = cg ** 3.767cw+0.223wt * Y 2 = wt+11.55wg+1.315cw cg 30.99** * Y 3 = wt+0.221cd-6.287cg 21.06** * Y 3 = wg cd+5.696bbl+0.025wt 4.93** Y 4 = cg-0.021cd-0.010wt 5.81** Y 4 = bbl+0.029cw-0.363cg 1.06 ns Y 5 = cg-0.199cw-1.307skl bbl 8.34** Y 5 = wg+0.013wt cg+0.697bbl 6.76** Y 6 = cg+0.015cd+0.005wt ** 0.010cw * Y 6 = bbl-0.250wg-0.008cd 3.523* Y 1 - breast weight, Y 2 - thigh weight Y 3 - fat weight Y 4 - breast yield Y 5 - thigh yield Y 6 - fat yield -male - female, *** = P<0.001 ** = P<0.01 * = P<0.05 cd- chest depth, wg- wing length, bbl- breast bone crest length, cw- chest width, wt-live weight, cg-chest girth. The high and positive correlation between live weight and carcass components for male and female broilers observed in this study has been reported by several authors (Rrymkiewicz and Bochno (1999) in chickens; kleczek et al. (2005) in Muscovy ducks and Vali et al. (2005) in quails) is an indication that live weight can be a good indication of carcass composition in poultry. Musa et al. (2006) reported that live weight was significantly (P<0.01) correlated (0.759 to 0.840) to breast muscle weight. Zerehdaran et al. (2004) also reported correlations of 0.68 to Apart from live weight, chest width, drum length and breast bone length in females and chest girth in both males and females were significantly correlated with breast and thigh weight. Wawro and Szypulenska (1999) and kleczek et al (2006) reported that chest girth and breast bone crest length could be used as indicators of the content of breast muscle in Muscovy ducks. Fat is a key tissue component that affects the quality of poultry carcasses. Fat weight had significant (P<0.01) correlations with live weight, chest depth, chest width and breast bone length in males and significant correlations with wing length, chest width, chest girth and live weight in females. Musa et al. (2006) and Zerehdaran et al. (2004) reported significant correlations of 0.44 and 0.46 respectively between live weight and fat weight in chickens. Kleczek et al. (2006) observed that in Muscovy ducks, fat content of carcasses may be predicted not only based on live weight but also chest girth and chest width. Fat weight was found to be correlated with live weight, chest girth and breast bone length in fowls (Bochno et al., 2000b). Bochno et al. (1999b) also reported significant correlation (P<0.01; r = 0.7) between live weight and fat weight in broilers and Michalik et al. (2002) in Turkeys. The low and non significant correlation between body measurements and percentages of the carcass components indicates that these traits may not be considered reliable predictors of relative carcass composition and fattiness in broilers. Kleczek et al. (2006) made similar observations in Muscovy Ducks. R 2 and adjusted R 2 are useful statistics for comparing models as they measure the goodness of fit of a regression model. Though R 2 measures the proportion of variance in the dependent variable explained by the regression, it always increases as new independent variables are included in the model even if they don t possess any relationship with the dependent variable. Thus the adjusted R 2 which is adjusted for the number of independent variables in the model may be a better measure of the goodness of fit for a regression model. 473

6 Generally, all models for predicting absolute weight of carcass components had highly significant F values, high coefficient of multiple correlation between the dependent and independent variables and consequently, high R 2 and adjusted R 2 values. Khrosavaria et al. (2006) made similar observation in their study with broilers finished at six week of age. Their R 2 values ranged from to while F values were all highly significant (P<0.001). Wawro (1990) in turkeys, and Kleczek et al. (2006) in Muscovy ducks observed that in vivo estimation of relative carcass content of particular tissue components usually gave low coefficient of multiple correlation and determination compared to absolute weight of breast, leg and total meat. That was also observed in this study as models 1-4 (Table 3) had higher R, R 2 and adjusted R 2 values than models These values were R (0.74 to 0.94 vs 0.41 to 0.88), R 2 (0.54 to 0.88 vs 0.17 to 0.78) and adjusted R 2 (0.50 to 0.85 vs 0.11 to 0.69). The R 2 values obtained in this study showed that body measurements described more variation (55.2 to 88.6%) in carcass component weights than in the yields (17.6 to 78.8%). Also in this study, breast weight was best predicted by chest girth and weight in males and weight, chest width and breast bone crest length in females. Kleczek et al. (2006) used breast bone crest length, weight and chest girth in males and weight, breast bone crest length and breast thickness in females and the coefficients of determination were and 64.14% respectively. Zuidof (2006) observed that body conformation traits like chest width, length and thickness are good indicators for predicting breast weight. Fat weight was predicted with slightly lower accuracy in females compared to males with F values (3.93* vs 21.06***), R (0.740 vs 0.917), R 2 (0.548 vs 0.840) and Y 1 - breast weight, Y 2 - thigh weight Y 3 - fat weight Y 4 - breast yield Y 5 - thigh yield Y 6 - fat yield -male - female adjusted R 2 (0.462 vs 0.830). Kleczek et al. (2006) made similar finding in Muscovy ducks. The small variation (0.03 to 4.16%) between actual and predicted values for the dependent variables Y 1, Y 2 and Y 3 as shown by the % deviation observed in this study is consistent with the findings of Kleczek et al. (2006) who reported small differences between actual and predicted values for absolute weight of carcass components. In contrast, the percent deviations for the yield of carcass components Y 4, Y 5 and Y 6 were relatively high (5.88 to 52.43%) indicating that the predicted values either overestimated or underestimated the actual values by a wide margin. The absolute weight of carcass components was more accurately predicted due to their higher R 2 and adjusted R 2 as observed by Thiruvenkadan (2005) in goats. CONCLUSION Carcass traits had significant correlations with non invasive measurements in both male and female broilers though the accuracy of prediction was higher in carcass component weight than their yields. These non invasive measurements therefore, allow indirect selection for carcass traits without slaughtering the birds. In essence, indirect carcass measurements will provide the opportunity to collect information from live birds and as a result the performance for carcass traits will be available on the live birds to be selected for a particular carcass trait. Finally, the high R 2 and adjusted R 2 values which reflect small deviations between the actual and predicted values for absolute weight of the carcass traits demonstrates the ability of regression equations to predict these traits in the broiler industry. Table 4. Mean of actual and predicted values, difference and percent difference for the different models generated to predict the carcass component weight and yield of broilers. Dependent Value Difference ( Y- Ỹ) Variables Sex Actual (Y) Predicted (Ỹ) g % in relation to Y Y Y Y Y Y Y

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