International Journal of
Recent Innovations in Academic Research
Open Access, Peer Reviewed, Abstracted and Indexed Journal
E-ISSN: 2635-3040; P-ISSN: 2659-1561
Homepage: https://www.ijriar.com/
Volume-5, Issue-4, April-2021: 1-10
Research Article
Descriptive Statistics, Correlation and Regression Coefficient of
Growth Traits in the Nigerian Heavy Local Chicken Ecotype
Udeh, F.U., Ndofor-Foleng, H.M., Nwosu, C.C. and Ozor, D.C.
Department of Animal Science, University of Nigeria, Nsukka
Correspondence email: fredrickudeh11@mail.com
Received: March 15, 2021
Accepted: March 29, 2021
Published: April 3, 2021
Abstract: Body weight is a very important characteristic in animal husbandry due to selection criteria
and economic profit, and linear body measurement is used as an indirect method of estimating body
weight. The aim of the study was to predict body weight from the linear body measurement and establish
regression equations for the prediction of body weight and determine the growth trait that most predicts
body weight for selection and improvement purposes. A total of 240 grower chicks were used for the
study. The birds were generated from the existing 7th Generation of the Nigeria heavy local chicken
ecotype (NHLCE) parent stock in the Department of Animal Science, University of Nigeria, Nsukka
farm. Ten cocks and sixty hens were selected from the existing flock to establish 10 sire families. Each
cock was randomly assigned to 6 hens to form a sire family at a mating ratio of 1:6. Each sire family was
housed in a particular pen with a demarcation separating the cock from the hens. Artificial insemination
was used as the mating method. Artificial insemination was done at an interval of two days. Fertile eggs
produced were marked according to sire families. Fertile eggs were hatched with the aid of an electric
automated incubator. The chicks were brooded for eight weeks. At the end of the brooding period, the
grower-chicks were randomly chosen for the study and given similar treatments. Body weight and linear
body measurements were taken bi-weekly. The results showed that at 20 weeks of age, the birds had an
average body weight of 1627.78g and, an average body length, shank length, chest circumference, thigh
circumference and thigh length of 24.14cm, 8.32cm, 37.19cm, 11.01cm and 14.50cm, respectively. The
correlation coefficients between body weight and linear body measurements were significant (p<0.01),
strong and positive. Chest circumference had highest coefficient of 0.91, followed by thigh length 0.86
and shank length 0.75, respectively, on the body weight. The regression result showed that chest
circumference is the best predictor of body with highest value of R 2 (0.72) and lowest standard error
(1.49). It was therefore concluded that linear body measurements can be used to predict body weight and
the best predictor of body weight is the chest circumference.
Keywords: Descriptive statistics, correlation, regression, growth traits and NHLCE.
Introduction
The Nigerian heavy local chicken ecotype is a fast growing strain of local chickens developed in the
Department of Animal Science, Teaching and Research farm, University of Nigeria, Nsukka. The
birds were bred as dual purpose chickens to produce meat and eggs (Udeh et al., 2018). Growth is
fundamental to all living things and it can be defined as an increase in number of body cells and
elongation in size per unit of time (Schulze et al., 2001). Indigenous chickens, like improved breeds
have a sigmoid growth pattern with differences in growth rate and feed efficiency (Nwosu, 1979).
Singh and Singh (1983) had earlier reported that growth is affected by genetic and non-genetic
factors. Hence, Cam et al., (2010) affirmed that live weight could be affected by differences in
management, environmental and enterprise feeding conditions. Body weight is a very important
characteristic in animal husbandry due to selection criteria and economic profit. Body weight is used
in determining several other economic traits in farm animals (Pesman and Yardimci, 2008). Due to
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International Journal of Recent Innovations in Academic Research
the fact that body weight forms the basis for assessing growth, feed efficiency and also in making
economic and market decisions in farm animals, it is an important attribute (Momoh et al., 2010).
However, Maciejowski et al., (1982), in agreement, had earlier reported that there is positive
correlation between body weight and a number of linear body measurements. Linear body
measurements are kind of growth indicators in animal life, and are also helpful in predicting body
weight and carcass trait (Atta and El–Khidir, 2004).
Linear body measurement is used as an indirect method of estimating body weight, and it has been
reported to be practical, faster, easier, and cheaper approach, especially in the rural areas where the
resources are insufficient and the acquisition of expensive sensitive weighing scale is unaffordable
(Semakula et al., 2011). The shank length and diameter are important indicators of leg development,
while body girth and length are good indicators of breast development. Estimates of the relationship
between body weight and these linear measurements is not only important in developing predictive
equations, it could also be employed in genetic improvement strategies to achieve an optimum
combination of body weight and good conformation (Chineke et al., 2002). Thus, this study was
designed to predict body weight through linear body measurements and also determine the growth
trait that most predicts body weight for selection and improvement purposes.
Materials and Methods
Study Location
The study was carried out at the Local Chicken Breeding section of the Poultry Unit, Department of
Animal Science Teaching and Research Farm, University of Nigeria, Nsukka. Nsukka. Nsukka is in
Enugu state, Nigeria and lies between latitude 06o 52' 24"N, Longitude 07o 39' 23" E and 550 meter
elevation above the sea level. Nsukka covered land area of 17.52 sq mi (45.38 km2) with a
population of 309,633 people (Federal Republic of Nigeria Official Gazette, 2007). The climate in
Nsukka is humid tropical with average annual rainfall of 1680–1700mm. The mean ambient
temperature is 26.6oC (Breinholt et al., 1981).
Experimental Animals and Management
A total of 240 grower chicks were used for the study. The birds were generated from the existing 7 th
Generation of the Nigeria heavy local chicken ecotype (NHLCE) parent stock in the Departmental
farm. Ten cocks and sixty hens were selected from the existing flock to establish 10 sire families.
Each cock was randomly assigned 6 hens to form a family at a mating ratio of 1:6. Each sire family
was assigned to a particular pen with a demarcation separating the cock from the hens. Artificial
insemination was used as the mating method. Artificial insemination was done on interval of two
days. Fertile eggs produced were marked according to sire families. Fertile eggs were hatched with
the aid of an electric automated incubator. The chicks were brooded for eight weeks. At the end of
the brooding period, 240 grower-chicks were randomly chosen for the study. The birds were given
feed and water ad libitum throughout the experimental period. Vaccinations and medications were
provided as at when due and other routine management practices were provided accordingly. Body
weight and other linear body measurements were taken bi-weekly throughout the experimental
period.
Experimental design and models
The experimental design used for this study was the complete randomized design (CRD) with model
Xij = µ + Ti
+
Ʃ ij
Where
Xij = Individual observation; µ
random error
= Overall population mean; Ti = Hatch effect; Ʃ ij = Experimental
Correlation: The correlation model is given as: r sire =
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International Journal of Recent Innovations in Academic Research
𝐶𝑜𝑣𝑠
√𝜎 2 𝑠(𝑥). 𝜎 2 (𝑦)
Regression: The regression models are given as:
Y = B + BX ------------------------------------------- simple regression model
Y = B + B1X1 + B2X2 +--------+ BXZXZ------------------------ multiple regression model
Standard Partial Regression Coefficient
Y= µ+b1x1+b2x2+---+bnxn+Ʃ ijklm
Where
Y= Body weight; µ= population mean; b1= Regression coefficient; x1= Individual trait; Ʃ ijklm =Error
term
Data analysis
Data generated were subjected to descriptive statistics to obtain the means and standard errors of the
mean. Correlation and the linear regression coefficients were obtained using SPSS (2013) version 19.
Results and Discussions
Descriptive statistics
The result of the descriptive statistics of the body weight and linear body measurements of the
Nigerian heavy local chicken ecotype are presented in table 1.
Table 1. Descriptive statistics of the body weight and linear body measurements of the Nigerian
heavy local chicken ecotype
Age
Parameters
Mean
Std. Dev.
Std. error
Variance
Coefficient
No of
of mean
of variation
Animal
8
BWT (g)
400.21
149.91
15.59
22472.36
37.48
weeks
BL (cm)
17.08
3.23
0.32
10.40
18.89
180
SL (cm)
5.54
0.97
0.10
0.95
17.55
CC (cm)
21.82
3.29
0.35
10.80
15.06
TC (cm)
5.81
0.99
0.10
0.98
17.02
TL (cm)
8.48
1.22
0.13
1.49
14.39
14
BWT (g)
1186.14
193.01
20.58
37253.87
24.55
weeks
BL (cm)
21.86
1.77
0.19
3.14
8.10
175
SL (cm)
6.22
0.61
0.06
0.37
9.74
CC (cm)
30.29
3.24
0.35
10.52
10.71
TC (cm)
9.53
1.23
0.13
1.50
14.37
TL (cm)
11.93
1.36
0.14
1.84
11.37
20
BWT (g)
1627.78
354.88
39.43
125937.50
24.85
weeks
BL (cm)
24.14
1.84
0.21
3.41
8.34
SL (cm)
8.32
0.83
0.09
0.68
11.28
173
CC (cm)
37.19
4.01
0.45
16.11
11.41
TC (cm)
11.01
2.14
0.24
4.56
12.33
TL (cm)
14.50
1.37
0.15
1.88
9.47
BWT = Body weight, B.L = Body length, S.L = Shank length, C.C = Chest circumference, T.C = Thigh
circumference, T.L = Thigh length.
The shank length, chest circumference, body length and wing length increased with age. The body
length varied from 17.08 to 24.14 with a mean of (17.08, 21.86 and 24.14) at week eight, fourteen
and week twenty, respectively. The shank length varied from 5.54 to 8.32 with a mean of (5.54, 6.22,
and 8.32) at week eight, fourteen and twenty, respectively. The chest circumference had similar trend
and varied from 21.82 to 37.19 with a mean of (21.82, 30.29, and 37.19) for weeks eight, fourteen
and twenty, respectively. The thigh circumference and thigh length followed the same pattern of
increment. Body weight increased from 400.21g at week eight to 1.6kg at week twenty.
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International Journal of Recent Innovations in Academic Research
Ojo et al., (2014) who agreed with this pattern of growth had reported that there is a gradual increase
of body weight with age. The observed mean body weight in this study 1.627kg at week twenty was
slightly higher than 1.45kg reported by Ukwu et al., (2014). Similarly, it is higher than the values
(1.19kg) and (1.06kg) reported by Yakubu (2009) and Momoh and Karishima (2008), respectively.
These differences could be attributed to the different breeds/strains of chicken used and different
management practices employed. Semakula et al., (2011) on another study, reported that all linear
body measurements increased with increasing age and stagnated above eight months of age. He
noted that linear measurements reflect structural growth, thus, are not expected to change much after
maturity is attained but, however, that body weight and other linear measurements such as girth
depend on changes in muscle and fat deposition. The coefficient of variation (CV) is a measure of
dispersion of the variable or the degree of variation to the population mean. It is important as an
indicator of how well you have taken representative samples and how your analytical method
performs. It is a ratio of standard deviation to the mean. A lower C.V implies low degree of variation
while a higher C.V connotes a higher variation. The CVs were higher at week 8 than weeks 14 and
20. This is in agreement with the findings of Semakula et al., (2011) who reported that all linear
body measurements increased with increasing age and stagnated above eight weeks of age. He noted
that linear measurements reflect structural growth, thus are not expected to change much after
maturity is attained. At week eight, B.L had the highest coefficient of variation (18.89%) whereas
TC was highest in week 14 and twenty, respectively. This agreed with the result obtained by Momoh
and Karishima (2008) who reported that reported body length and chest circumference were the traits
with highest variation when he utilized standard error of the mean to estimate variation. On the other
hand, Gambo et al., (2014) in their work observed the shank length to be the trait with the highest
variability occurring highest in week 2 but gradually decreasing towards week six. Egene et al.,
(2014) observed that highest coefficient of variation was found in body length, shank length, and
shank thickness. Above all, Daikwo (2011) and Gambo et al., (2014) asserted that age has highly
significant effect on all the linear body parameters.
Correlation Coefficient
The results of the correlation coefficients between body weight and linear body measurements of the
Nigerian heavy local chicken ecotype are presented in table 2. It could be observed from the table
that the correlations were significant (p<0.01) and positive. At week 8, the correlation between body
weight and linear body measurements were low to moderate except shank length.
Table 2. Correlation coefficients between body weight and linear body measurements of the
Nigerian heavy local chicken ecotype (NHLCE)
Age
Parameters
BWT
BWT
1
BL
0.54**
SL
0.72**
8
CC
0.45**
Weeks
TC
0.35**
TL
0.38**
BWT
1
BL
0.75**
SL
0.59**
14
CC
0.91**
Weeks
TC
0.58**
TL
0.64**
BWT
1
BL
0.57**
SL
0.69**
20
CC
0.69**
Weeks
TC
0.54**
TL
0.64**
BWT = Body weight, B.L = Body length,
circumference, T.L = Thigh length
BL
0.54**
1
0.66**
0.63**
0.41**
0.37**
0.75**
1
0.58**
0.77**
0.50**
0.71**
0.57**
1
0.57**
0.48**
0.46**
0.52**
S.L = Shank
SL
0.72**
0.66**
1
0.66**
0.53**
0.48**
0.59**
0.58**
1
0.49**
0.47**
0.58**
0.69**
0.57**
1
0.57**
0.52**
0.65**
length, C.C
CC
TC
TL
0.45**
0.35**
0.38**
0.63**
0.41**
0.37**
0.66**
0.53**
0.48**
1
0.47**
0.52**
0.47**
1
0.43**
0.52**
0.431**
1
0.91**
0.58**
0.86**
0.77**
0.50**
0.71**
0.49**
0.47**
0.51**
1
0.50**
0.83**
0.50**
1
0.52**
0.75**
0.48**
1
0.69**
0.54**
0.64**
0.48**
0.46**
0.52**
0.57**
0.52**
0.65**
1
0.49**
0.64**
0.49**
1
0.45**
0.64**
0.45**
1
= Chest circumference, T.C = Thigh
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International Journal of Recent Innovations in Academic Research
At week 14, the correlation between body weight and chest circumference became highest with
coefficient of 0.91, followed by thigh length 0.86 and shank length 0.75, respectively. However, at
week 20, the correlation coefficient between body weight and linear body measurements decreased
with highest value 0.69 on shank length and chest circumference, and 0.64 on thigh length. The
decrease in correlation coefficient is a proof that linear body measurements are not expected to
change much when maturity is attained (Semakula et al., 2011). The results in this study corroborate
the report of Ukwu and Okoro, (2014) who reported significant high and positive correlation
between body weight and the linear body traits. It also conforms to the findings of Yahaya et al.,
(2012).
All the correlations were high, positive and significant (p<0.01). This implies that there exist a strong
linear relationship between body weight and linear body parameters. This result also suggests that an
improvement in linear body parameters would lead to a corresponding improvement in the body
weight also, it implies that for every change in the linear body trait, there is a change in the body
weight measurements of the animal that shows growth is taking place. The values obtained in week 8
agreed with the values reported by Okon et al., (1996), where moderate to high correlation
coefficient on body weight and body linear measurement were observed. The result corroborates the
findings of Ige et al., (2013) who reported low correlation coefficients between body weight and
chest circumference at week 18 and 20.
Semankula et al., (2011) findings strongly agreed with the results of this study. He reported that the
highest correlation coefficient was C.C (0.88) closely followed by B.L (0.81) and thigh length (0.80).
It also corroborates the findings of Vincent et al., (2015) who recorded highest correlation (r= 0.88
and 0.79 for males and female) between B.W and C.C. Ojo et al., (2014) also reported that the best
correlation was obtained between body weight and chest girth at the 2nd week of age with r =0.70.
This could mean that chest circumference has the highest relationship with body weight while the
thigh circumference has the least relationship.
The high association of body weight with chest circumference was possible due to large contribution
to body weight by chest circumference consisting of bones, muscles and viscera. The decrease
correlation coefficient at week 20 indicates that no reasonable genetic progress can be made as the
chicken grow older and also, this could be partly explained from the fact that most of the linear body
traits have reached their terminal growing point and could no longer increase proportionately with
increase in weight.
Linear regression
The results of the linear regression of body on linear body measurement of the Nigerian heavy local
chicken ecotype are presented in table 3.
Table 3. Linear regression of body weight (dependent variable) on body measurement
(independent variables) of the Nigerian heavy local chicken ecotype using simple linear
regression
Independent variable
Intercept
Std error
Regression
R2
Significance
(a)
coefficient
Body length
-909.52
4.33
83.67
0.38
0.000**
Shank length
-1159.38
10.99
316.42
0.58
0.000**
Chest circumference
-904.80
1.49
59.68
0.72
0.000**
Thigh circumference
-317.43
6.26
143.38
0.46
0.000**
Thigh length
-620.55
3.17
125.38
0.71
0.000**
Shank length had the highest regression coefficient (316.42), followed by thigh circumference
(143.38), thigh length (125.38), body length (83.67) and chest circumference had the least value
(59.68). The coefficient of determination (R2) is a key output of regression analysis. It is interpreted
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International Journal of Recent Innovations in Academic Research
as the proportion of the variance in the dependent variable that is predictable from the independent
variable. It explains the extent to which the dependent variable is predictable. The standard error of
the estimate is a measure of the accuracy of prediction made with a regression line. It is simply the
difference between what a subjects actual score was (y) and what the predicted score is (y').
All the linear body measurements under study were statistically significant; and they also had high
coefficients of determination (R2) ranging from 0.38 to 0.72. This showed that they were all good
predictors of body weight. This result is in agreement with the findings of Adeleke et al., (2004) who
reported that body weight can be predicted from linear body measurements for cross bred egg-type
chickens and Adeniji and Ayorinde (1990) for Cob broilers strain.
The values of R2 obtained in this study were lower than the R2 values (73.91% to 97.91%) and (82%
to 92%) reported by Adeleke et al., (2004) and Amao et al., (2011), respectively. The differences in
R2 values obtained in this study and those of earlier researches could be attributed to the difference in
chicken strain/breed used. The chest circumference had the lowest standard error (1.49) and the
highest value of coefficient of determination of 72.0% and hence, it is adjudged to be the best
predictor of body weight. This was also observed by Ojedapo et al., (2012) who reported the chest
circumference as the best predictor of weight in two commercial layer strain chicken. Similarly,
Ajayi et al., (2008) and Momoh and Karishima (2008), also reported that chest circumference is the
best predictor of body weight.
Regression Equation
The linear regression equation that could be used to predict body weight from the body linear
measurement of the Nigerian local chicken ecotype is presented in table 4. The result showed that the
best linear regression equation is the one of chest circumference (BWT = -904.804 + 5.680CC)
because it has the lowest value of standard error and highest value of coefficient of determination.
Table 4. Linear regression equation of body weight on linear body measurement for local
chickens
Regression equation
R
R2
Std. error
Probability
BWT = -909.52 + 83.67BL
0.615
0.37
4.33
0.00
BWT = -1159.37 + 316.42SL
0.758
0.58
10.99
0.00
BWT = -904.80 + 59.68CC
0.849
0.72
1.49
0.00
BWT = -317.43 + 143.38TC
0.679
0.46
6.26
0.00
BWT = -620.55 + 125.38TL
0.847
0.71
3.17
0.00
BWT=Body weight, R2=coefficient of determination
The least predictive equation was the body length (BWT = -909.52+83.67BL) because it has the
lowest coefficient of determination (37.9%) and standard error (4.33). Vincent et al., (2015) reported
that regressing chest circumference on the body weight had the highest coefficient of determination
(R2= 0.65 and 0.78 for females and males).
The regression coefficient associated with the independent variable X and partially representing the
amount of change in Y for each unit change in X had a positive value in the relationship between
body weight and the linear body parameters under study. The implication of the positive value for
the regression coefficient is that body weight gain increases directly with linear body dimensions.
This was also observed by Ajayi et al., (2008) for Ross and Anak titan broiler strains.
Multiple Regressions
The results of the multiple regression of body weight on linear body measurement of the Nigerian
heavy local chicken ecotype are presented in table 5. The results showed that not all the linear
measurements were strong predictors when multiple regressions were applied.
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International Journal of Recent Innovations in Academic Research
Table 5. Multiple regression of body weight (dependent variable) on body measurements
(independent variable) of the Nigerian heavy local chicken ecotype
Independent Variable Intercept (a)
Std error
Regression coefficients
Sig
Body length
-1076.398
3.652
-5.78
0.102
Shank length
12.220
84.970
0.000
Chest circumference
3.076
21.662
0.000
Thigh circumference
5.592
24.098
0.000
Thigh length
5.443
57.587
0.000
The body length was found not significant (p> 0.01) with negative coefficient of regression. This
shows that it was not really a strong predictor as the linear regression showed. It had a non-linear
increase with body weight. However, other linear body measurements were highly significant (p<
0.01), and thus, showed that they are strong predictors.
Yakubu et al., (2009) earlier reported that a good prediction equation with R2 = 0.87 was obtained
when chest circumference, thigh length and shank length were combined. The positive value of
regression coefficient in the linear traits shows a direct linear relationship with body weight meaning
that an increase in the linear body measurement will bring about a proportionate increase in the body
weight.
Multiple Regression Equation
The multiple regression equation of body weight on linear body measurements of the Nigerian heavy
local chicken ecotype is presented in table 6.
Table 6. Multiple regression equation of body weight on linear body measurements for local
chicken
Independent variable
Regression equation
Std. error
R
R2
BWT = -1076.39
Body length
-5.98
3.65
0.89
0.79
Shank length
84.97
12.22
Chest circumference
21.66
3.08
Thigh circumference
24.09
5.59
Thigh length
57.98
5.44
BWT = Body weight, R2 = coefficient of determination
Multiple linear regression equation can be said to be a better regression equation for prediction of
body weight considering its low standard error compared to linear regression equations. Multiple
regression equation also, had higher value of coefficient of determination R2 (0.79).
The regression equation is given as:
BWT = -1076.39 -5.98BL + 84.97SL + 21.66CC + 24.09TC + 57.98TL.
The coefficient of determination was observed to have increased when B.L, S.H, C.C, T.C and T.L
were combined in a multiple regression equation compared to the values obtained when the linear
body parameters were used singly in a linear regression equation. Similar result was also observed by
Momoh and Karishima (2008) who reported coefficient of determination (R2) of B.L (0.47 and 0.13)
and C.C (0.52 and 0.62) for male and female but when B.L, and C.C were combined in a multiple
linear regression, R2 in both male and female increased to 0.67 and 0.68 respectively. Ojo et al.,
(2014) reported an increment in R2 value when all linear body measurements were combined in a
multiple regression model, in Japanese quail. The result also, corroborate with the findings of Raji et
al., (2009). This is also in agreement with the findings of Adeniji and Ayorinde (1990), who reported
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International Journal of Recent Innovations in Academic Research
high coefficient of determination when two body linear measurements were combined. The very high
R2 value obtained when the linear body measurements were combined in a multiple regression
suggest that the combination of two or more linear body measurement will be more appropriate since
there is existence of variation in the maturing pattern of the various body parts in chicken. Multiple
linear regression equation also gave better results i.e. precise value of body weight than the simple
linear regression equation. Gueye et al., (1998) stated in his work that when chest circumference and
body length were put together in a multiple regression for body weight, they gave better results than
when used singly.
Conclusion
From the findings in this study, it could therefore, be concluded that chest circumference had the
lowest standard error (1.49) and the highest value of coefficient of determination of 72.0% and
hence, it is adjudged to be the best predictor of body weight.
Acknowledgement
The authors wish to acknowledge and appreciate the Tertiary Education Trust Fund (TETFund) of
the Nigerian government through the University of Nigeria Institutional Based Research (IBR)
Intervention (TETFUND/DESS/UNI/NSUKKA/2018/RP/VOL.I), which funded the research.
Conflicts of interest
The authors declare no conflicts of interest.
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Citation: Udeh, F.U., Ndofor-Foleng, H.M., Nwosu, C.C. and Ozor, D.C. 2021. Descriptive
Statistics, Correlation and Regression Coefficient of Growth Traits in the Nigerian Heavy Local
Chicken Ecotype. International Journal of Recent Innovations in Academic Research, 5(4): 1-10.
Copyright: ©2021 Udeh, F.U., et al. This is an open-access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source are credited.
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