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Research Article
 

Prediction of Body Weight and other Linear Body Measurement of Two Commercial Layer Strain Chickens



L.O. Ojedapo, S.R. Amao, S.A. Ameen, T.A. Adedeji, R.I. Ogundipe and A.O. Ige
 
ABSTRACT

A total of 509 birds comprising 249 Nera Black (NB) and 260 Brown Shaver (BR) were used for this study. Data were collected on the birds from week one to twenty. Prediction of Body Weight (BW), Chest Girth (CG), Keel Length (KL), Body Length (BL) and Shank Length (SL) were highly significant (p<0.001). The coefficient of determination (R2) varied from 85 to 99% for CG and KL. In both genotypes, the relationship between BW and other body measurements were higher in CG and KL traits and best described by cubic model. Cubic function (R2 = 99%) predicted BW more accurately than quadratic and linear functions. The phenotypic correlation coefficients at day old in NB between BW and BL were positive, medium and highly significant (p<0.01), the phenotypic correlation coefficients for BW and BL in BR was low, negative and highly significant (p<0.01). Lower correlation values were obtained between BW and BL and significant (p<0.05) in NB strain at 4 weeks old. Also, negative highly significant was achieved between BW and SL. At 8 weeks old, low to medium correlation coefficients were observed between BW and SL, BW and KL, BW and CG in NB strain. Significant correlations were achieved between BW and SL, BW and CG traits in BR strain. At 12 weeks old, high, positive and significant (p<0.01) values were observed between BW and other traits in NB. The phenotypic correlation coefficients were of medium to high in NB between BW and KL, BW and BL at 16 weeks old. There were highly significant differences (p<0.01) between BW and other traits measured in BR strain. At 20 weeks old, the correlation values obtained were low to high in NB, lower values were also obtained for BR at the same age. As a result of these observations, it was considered possible to use the body weight in determining BL, SL, KL and CG.

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L.O. Ojedapo, S.R. Amao, S.A. Ameen, T.A. Adedeji, R.I. Ogundipe and A.O. Ige, 2012. Prediction of Body Weight and other Linear Body Measurement of Two Commercial Layer Strain Chickens. Asian Journal of Animal Sciences, 6: 13-22.

DOI: 10.3923/ajas.2012.13.22

URL: https://scialert.net/abstract/?doi=ajas.2012.13.22
 
Received: August 05, 2011; Accepted: October 30, 2011; Published: December 29, 2011

INTRODUCTION

Poultry population was put at 114.3 million comprising of 82.4 million chickens (11% of which was commercially raised) and 31.9 million other poultry which included pigeons, ducks, guinea fowls and turkeys (RIM, 1992). Poultry outnumbers all other forms of livestock in Nigeria and not surprisingly is found throughout the country wherever, there is human settlement. Although, pigeons, ducks, guinea fowl and some turkeys are also widely kept, chickens are by far the most common. Typically, they are maintained under traditional, low input, free range systems of management but substantial numbers are also raised intensively on commercial bases, particularly in the southern states. Commercial holdings account for some 10 million chickens or 11.2% of the total estimated population of 82.4 million (Anaeto and Chioma, 2007).

Protein supply from livestock products was estimated to be only 3.0 g/caput/day in 1993 and a projection of 5.32 g/caput/day has been made for the year 2010 (Shaib et al., 1997). This is dismally short of the 35 g recommended by FAO (1993).

Poultry farming in Nigeria was mostly a backyard venture up to about 1960. Some indigenous poultry used for the production of eggs and meat were hardy but poor in productivity. Lack of interest in poultry was due to low productivity of indigenous birds (poor genetic make up), low prices for eggs and meat, inadequate knowledge of poultry diseases and scientific methods of feeding and management (including housing/nutritional requirements).

Growth is a fundamental property of biological systems and it can be defined as an increase in the number of cells in the body size per unit of time (Schulze et al., 2001; Lawrence and Fowler, 2002). Growth of fowl is analogous to growth of mammalian; consisting of three or four cycles, which however, occurred after hatching. Indigenous chickens, like improved breeds has a sigmoid growth pattern with differences in growth rate and feed efficiency (Nwosu, 1979), although the indigenous fowl seemed to complete the rapid growth phase earlier than improved breeds (Oluyemi, 1980). Growth is affected by genetic and non-genetic factors (Singh and Singh, 1983; Gupta et al., 1988; Pinchasov, 1991). The assessment of a growth model is of particular importance in animal production, because of its practical implications (possibility of verifying the adherence of a feeding schedule or a rearing system to a reference condition, as it is calculated by a regression equation (Sabbioni et al., 1999).

Growth curves are used to describe the regular change generated by the live weight or some part of the animal with the age increasing which commonly is an S-type curve. Animal growth involves increase in size and changes in functional capabilities of the various tissues and organs of animals that occur from conception through maturity. The growth process includes increases in cell numbers (hyperplasia) and increases in cell size (hypertrophy). Growth performance of an animal is a phenotypic expression which is the outward expression of the animal genetic make up. Genetic factors have great influence on the performance of an animal. This explains the situation where within the same breed or strain, individual variations in performance are common observations. Individuals that show superior performance should be identified and used in the genetic improvement programme (Chineke, 2001).

The justification of the study is to characterize the commercial layer strains of chicken with respect to their growth traits.

The objective was to estimate the growth traits of two commercial layer strains Nera black and Black shaver, the correlation coefficients among body weight and other body parameters within each strain at various ages and to compare the growth pattern of Nera black and Black shaver strains using three regression models.

MATERIALS AND METHODS

Experimental site: The research was carried out at the Poultry Unit, Teaching and Research Farm of the Ladoke Akintola University of Technology, Ogbomoso, Oyo state, Nigeria. Ogbomoso lies on the longitude 4° 15’ East of the Greenwich Meridian and Latitude 8° 15’ North-East of the equator. It is about 145 km North-Eastwards from Ibadan, the capital of Oyo state. The altitude is between 300 and 600 meters above sea level. The mean annual temperature is about 27°C (Oguntoyinbo, 1978) while that of rainfall is 1247 mm. The vegetation of the area is derived savanna.

The experimental birds: Bovan Nera black (NB) and Black Shaver (BL) are egg laying, light strain birds and are tolerant to tropical climate. The population of chickens consisted of 249 Nera black and 254 Black shavers. They were obtained from a reputable poultry farm (Zartech, Ltd), Ibadan, Oyo state at day-old.

Housing and management of chicks: Before the arrival of the chicks, the floor of the house and the surroundings were kept clean of debris and cobwebs. The feeders and drinkers were thoroughly washed and disinfected. Chicks were wing-tagged according to the strain and housed in the brooding pen for five weeks and rearing of chicks continued until 20 weeks. The chicks were fed ad libitum with a commercial chicks starter diet that supplied 18.49% crude protein and 2522.9 kcal kg-1 metabolizable energy from 0 to 8 weeks. Thereafter, they were fed on a commercial growers ration that supplied 15.85% crude protein and 2400.8 kcal kg-1 metabolizable energy to 18 weeks. The birds were fed the same feed throughout the experimental periods. Clean water was supplied ad libitum throughout the experimental period.

Growth traits: Two hundred and forty birds randomly selected in all the two strains were individually weighed, to determine the initial body weight (BW) using a sensitive weighing balance of 0.05 g sensitivity. Other body measurements were also taken with the use of a measuring tape calibrated in centimeter which included Body Length (BL), Chest Girth (CG) and Keel Length (KL) from day-old to twenty weeks. The traits measured on weekly basis for 20 weeks include:

Body Weight (BW): Measured with the use of a sensitive weighing balance with a capacity of three decimal digits
Body Length (BL): Measured as the distance between the base of the neck and the cloaca
Chest Girth (CG): Taken as the circumference of the breast around the deepest region of the breast
Keel Length (KL): Taken as the length region of the sternum

Regression model: Traits studied were body weight, chest girth, body length, shank length and keel length from week one to twenty. Measurements of chest girth, keel length, body length and shank length were regressed against body weight using simple linear, Quadratic and Cubic regression analysis (SAS, 2003).

Model function:

Linear Y1 = a + bx
Quadratic Y2 = a + bx + cx2
Cubic Y3 = a + bx + cx2 + dx3

Y1, Y2 and Y3 are dependent variables (body weights) while x represents the independent variables (chest girth, keel length, shank length and body length), b, c, d are the regression coefficients associated with independent variables and a is the intercept represents the estimate of dependent variable when the independent variable is zero.

Regression equations were determined for each strain and tested for parallelism. The relationship between body weight and each of the measurements (chest girth, keel length, shank length and body length) were also assessed and the coefficient of determination (R2) was used to compare the accuracy of prediction.

Growth traits: Analysis was done using mixed model least square and maximum likelihood computer program (Harvey, 1990). The model was fitted for the effect of strains with age used as covariate

Model 1:

Yij = μ+Si+A+eij

Where:
Yij = The observation of the individual bird.
μ = The overall mean
Si = The fixed effect of strain (i = 1, 2)
A = Age, as covariate
eij = The uncontrolled environmental and genetic deviation attributable to individual within each strain, assumed to be normally and independently distributed (-NID) with Zero mean and Variance σ2 with the age in weeks as a covariate

This model was subjected to one-way analysis of variance using the general linear model of SAS (2003). Correlation analysis for the growth traits was done with Pearson moment correlation of SAS (2003).

RESULTS

The least square means of body weight, body length, shank length, keel length and chest girth of two different commercial layer strains at different ages are presented in Table 1-5. Strains had significant (p<0.05) effect on body weight, body length, shank length, keel length and chest girth at all ages except at day-old for keel length. Nera black strains had superior BW (30.31±0.02 g) and were significantly different (p<0.05) from Brown shaver strain (27.02±0.09 g). At 4 to 20th week, Nera black had the heavier body weight and was significantly (p<0.05) different from the Brown shaver strains (Table 2). This trend was consistent from day-old to 20 weeks of age for all the two strains studied. There were significant (p<0.05) differences in chest girth between the two strains.

Table 1: Least square means of body weight (g) of two genotypes from day-old to 20 weeks
Means with different superscripts along the same row are significantly (p<0.05) different

Table 2: Least square means of body length (cm) of two genotypes from day-old to 20 weeks
Means with different superscripts along the same row are significantly (p<0.05) different

Table 3: Least square means of shank length (cm) of three genotypes from day-old to 20 weeks
Means with different superscripts along the same row are significantly (p<0.05) different

Table 4: Least square means of keel length (cm) of two genotypes from day-old to 20 weeks
Means with different superscripts along the same row are significantly (p<0.05) different

Table 5: Least square means of chest girth (cm) of two genotypes from day-old to 20 weeks
Means with different superscripts along the same row are significantly (p<0.05) different

Phenotypic correlations among growth traits: The phenotypic correlation coefficients for body weight and linear measurements for 0, 4 and 8 weeks are presented in Table 6.

Table 6: Phenotypic correlation coefficients of growth traits at day old, 4 and 8 weeks of age
BW = Body weight, BL = Body length, SL = Shank length, KL = Keel length and CG = Chest girth, *p<0.05 and **p<0.01

Table 7: Phenotypic correlation coefficients of growth traits at 12, 16 and 20 weeks of age
BW = Body weight, BL = Body length, SL = Shank length, KL = Keel length and CG = Chest girth **p<0.01 and *p<0.05

At day old, result showed that phenotypic correlation coefficients of body weight and body length in Nera black were positive, medium and highly significant (p<0.01, 0.404), the phenotypic correlation coefficients for body weight and boy length in Brown shaver was negative and significant (p<0.01, -0.260). At 4 weeks of age, the phenotypic correlation coefficients for body weight and linear body measurement for each strain for body weight and body length, keel length which was positive and negative as in case of shank length in Nera black strain. They showed significant (p<0.01) differences. The phenotypic correlation coefficient is low-high in BR, low-medium in NB. The phenotypic correlation coefficients for body weight and linear body measurements are very low and negative though significant (p<0.01) in NB, low-high and significant (p<0.01) in BR at 8 weeks of age.

The phenotypic correlation estimates for body weight and linear body measurement at 12, 16 and 20 weeks of age for the strains are shown in Table 7. The phenotypic correlation for body weight and linear body measurement was high and positively significant (p<0.01) in NB. BR strains also observed the same trend except against KL. All showed significant differences (p<0.01).

At 16 weeks of age, the phenotypic correlation coefficients for body weight and linear body measurement were highly significant for all traits in BR. Lower values were obtained for CG and SL in NB. The values obtained for all the traits were generally low in BR and medium in NB, at 20 weeks of age.

Prediction of growth pattern among the strains: The equation, estimate of parameter and coefficient of determination for the fitted functions are presented in Table 8a, b demonstrated a strong inter-relationship (p<0.001) between body weight and linear body measurements.

Table 8a: Estimation of parameters in simple linear, quadratic and cubic functions fitted for body weights-linear body measurements of two different layer strains
Sig. = Significant, **p<0.01

Table 8b: Estimation of parameters in simple linear, quadratic and cubic functions fitted for body weights-linear body measurements of two different layer strains
SE = Standard error, R = coefficient of determination and Sig. = Significant

The coefficient of determination varied from 85 to 99% and the magnitude of these coefficients of determination for each parameter in the regression equation shows the relative contribution of each body measurement to the body weight of bird for that particular genotype.

In the two genotypes, the relationship between body weights and other body measurements, chest girth and keel length were best described by cubic model. The coefficient of determination (R2) varied from 98 to 99% and 97 to 99% for chest girth and keel length respectively. Body weight and keel length, chest girth and keel length only in Nera black, chest girth and keel length in Brown shaver. Linear function predicted the body weight in the same way as quadratic function. The regression coefficient associated with independent variables X and partially representing any amount of change in Y for each unit change in X had a positive value in the relationships between body weight and chest girth; body weight and keel length. The observation, therefore, of the positive value for the regression coefficient is an indication that body weight would increase with linear body dimensions (chest girth and keel length).

DISCUSSION

The results of growth traits showed increase in all body measurements of each strain as growth advances in this study. This result is in line with reports of Sonaiya et al. (1986) that age is a major determinant of growth and physiological development. Omeje and Nwosu (1986) opined that these relationships could be utilized in the genetic improvement of growth through selection. Giordani et al. (1993) also reported significant difference in the growth performance of different strains of birds.

The result of body weight and linear body measurements as affected by chick genotype suggests that the NB was superior to BR considered in body weight and linear body measurement. NB therefore possesses gene for faster growth than the BR strains used in this study, although they are also light strains and were bred specifically for increased egg production.

The phenotypic correlation estimates between body weight and linear body measurements as reported in this study agreed with the findings of Ezzeldin et al. (1994) who reported medium to high phenotypic correlation coefficients in body weight and body measurements in pure breed chickens and their crosses.

The growth pattern result from this study showed that it was in agreement with the conclusion of Adeniji and Ayorinde (1990) that body weight of birds can easily be predicted from any given value of six body measurements (body length, body girth, keel length, shank length, drumstick length and shank thickness) in the Cobb broiler strain using linear and stepwise regression equation. For the two genotypes, the relationship between body weight, chest girth, keel length, body length and shank length were best described by cubic function. This is in line with the findings of Adeniji and Ayorinde (1990), Monsi (1992) and Adeleke et al. (2004) who reported that increasing chest girth or keel length through selection will result in corresponding increase in body weight.

CONCLUSION

The growth traits increased with age in the two strains and NB strain was found more favoured in almost all the ages. The relationship that existed among the traits was of low-medium and negative in NB but of positive value in BR strain. Higher correlated values were obtained in BR strains at 16th week of age.

The results from the study demonstrated a positive relationship between body weight and body measurement components (chest girth and keel length) showing that increase in the growth rate of any of the components will correspondingly increase live weight gain. The study also indicated that with these strains of chicken, body weight of birds could easily be predicted from any given value of the two body measurements. Cubic growth model produced the best fit from chest girth in all the strains and age data.

REFERENCES
Adeleke, M.A., M.O. Ozoje, O.A. Adebambo, S.O. Peters and A.M. Bamgbose, 2004. Estimation of body weight from linear body measurements among crosbred egg-type chickens. Proceedings of the 29th Genetic Society of Nigeria Conference, October 11-14, 2004, Abeokuta, Nigeria, pp: 88-91.

Adeniji, F.O. and K.L. Ayorinde, 1990. Prediction of body weight from body measurements of chickens. Nig. J. Anim. Prod., 17: 42-47.

Anaeto, M. and G. Chioma, 2007. Avian influenza in Nigeria: Suggestions for eradication. Int. J. Poult. Sci., 6: 367-371.
CrossRef  |  Direct Link  |  

Chineke, C.A., 2001. Interaction existing between body weight and egg production traits in Olympia Black Layers. Niger. J. Anim. Prod., 28: 1-8.

Ezzeldin, Z.A., M.S. Hanafi, M.M. Khal and Z.A. Sabra, 1994. Phenotypic correlation between body weight and body measurement of chicken. Anim. Breed. Abstr., 62: 475-475.

FAO, 1993. Production Year Book. Food and Agricultural Organization of the United Nations, Rome, Italy, Pages: 320.

Giordani, G., A. Meluzli, C. Cristofori and F. Calini, 1993. Study on the performance and adiposity of modern broiler: Comparison among strains. Anim. Breed. Abstr., 61: 581-596.

Gupta, R.D., S.K. Joshi, S.S. Alkare and K.K. Bagheel, 1988. Influence of divergent egg weight on performance of progenies from reciprocal cross of dwarf x normal population. Indian J. Anim. Sci., 58: 130-132.

Harvey, W.R., 1990. Mixed Model Least Squares and Maximum Likelihood Computer Program. Ohio State University, USA.

Lawrence, T.L.J. and V.R. Fowler, 2002. Growth of Farm Animals. 2nd Edn., CABI Publishing, Oxon, UK., ISBN-13: 9780851994840, Pages: 347.

Monsi, A., 1992. Appraisal of Interrelationships among live measurements at different ages in meat type chickens. Nig. J. Anim. Prod., 19: 15-24.

Nwosu, C.C., 1979. Characterization of the local chicken of Nigeria and its potential for egg and meat production. Poultry production in Nigeria. Proceedings of the 1st National Seminar on Poultry Production in Nigeria, December 11-13, 1979, Ahmadu Bello University, Zaria, pp: 87-210.

Oguntoyinbo, J.S., 1978. Ogbomoso Vital Statistics. In: Ogbomoso Community the Dawn of a New Era, Ajao, C.A., E.A. Oyegade and J.O. Gbadamosi (Eds.). Daybis Limited, Ibadan, Nigeria, pp: 2-6.

Oluyemi, J.A., 1980. A comparison of five commercial laying strains of fowl in tropical environment. Nig. J. Anim. Prod., 7: 91-96.

Omeje, S.S. and C.C. Nwosu, 1986. Growth and egg production evaluation of F2 and backcross progeny chicks from Nigerian chicken by gold-link crosses. Proc. World Cong. Genet. Applied Livestock Prod., 10: 304-310.
Direct Link  |  

Pinchasov, Y., 1991. Relationship between the weight of hatching eggs and subsequent early performance of broiler chicks. Br. Poult. Sci., 32: 109-115.
CrossRef  |  PubMed  |  Direct Link  |  

RIM, 1992. Nigerian livestock resources: State reports. Vol. 1-4, Resources Inventory and Management Limited, Nigeria.

SAS, 2003. Statistical Analysis System. SAS Institute Inc., Cary. North Carolina.

Sabbioni, A., P. Superchi, A. Bonomi, A. Summer and G. Boidi, 1999. Growth curves of ostriches (Struthio camelus) in Northern Italy. Proceedings of the 50th EAAP Congress, August 22-26, 1999, Zurich -.

Schulze, V., R. Rohe, H. Looft and E. Kalm, 2001. Genetic analysis of the course of individual growth and feed intake of group-penned performance tested boars (In German Language). Arch. Tierz. Dummerstorf, 44: 139-156.

Shaib B., A. Aliyu and J.S. Bakshi, 1997. Nigeria: National agricultural research strategy plan1996-2010. Department of Agricultural Science, Federal Ministry of Agriculture and NationalResources Abuja, Nigeria.

Singh, H.N. and B.P. Singh, 1983. Inheritance of eight week body weight in pure and crossbreds population of two broiler breeds. Indian Vet. J., 60: 560-563.

Sonaiya, E.B., A.R. Williams and S.A. Oni, 1986. A biological and economical appraisal of broiler production up to 16 weeks. J. Anim. Prod. Res., 691: 73-79.

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