Content for New Div Tag Goes Here
PREDICTION OF LIVE BODY WEIGHT USING BODY MEASUREMENTS
FOR JAWA BREBES (JABRES) CATTLE
M. S. Haq1, *I G. S. Budisatria1,
P. Panjono1 and D. Maharani1
Faculty of
Animal Science, Universitas Gadjah Mada, Jl. Fauna No 3, Bulaksumur,
Yogyakarta, 55281, Yogyakarta, Indonesia
ABSTRACT
Bodyweight estimation is
important for any aspect of livestock management. Jabres cattle farmers use
bodyweight estimation to determine the cattle price in the traditional market.
Nowadays, they only use eye-based assessment to predict cattle body weight with
a lot of inaccuracies. In this study, the cattle body weight was estimated
based on body size measurements data, namely body length, heart girth, withers
height, rump height, face length, and face width. The data were collected from
Jabres cattle reared in the district of Bantarkawung, Brebes, Central Java,
Indonesia. The data were taken from 521 Jabres cattle which were stratified by
sex and age. Generally, male Jabres cattle have a smaller body size than female
Jabres cattle and younger cattle have significant smaller body size than elder
cattle. The highest correlation coefficient came from body weight and heart
girth. The equation to predict body weight was obtained by multiple linear
regression and factorial analysis scores followed by multiple linear regression
methods. The result showed that multiple linear regression method was
preferable to be used to predict body weight of Jabres cattle because of its
better accuracy, better fitness, and more applicable for Jabres cattle farmers.
Key
words: Body weight
prediction, Jabres cattle, native cattle, Indonesia
https://doi.org/10.36899/JAPS.2020.3.0065 Published online March25, 2020
INTRODUCTION
Jabres
cattle are one of the Indonesian native cattle,
which is very potential to be used in meat production. Like its name, Jabres
(Jawa-Brebes) cattle came from Brebes Regency and has been stated by the Ministry of
Agriculture as one of the native cattle that should be protected and conserved.
The conservation of Jabres cattle was written in The Decree of Ministry of Agriculture No. 2842/Kpts/LB.430
/8/2012 on August 13th, 2012. This decree also stated that Jabres
cattle come from the crossbreeding of Ongole cattle, Madura cattle, and Bali
cattle. Jabres cattle have been cultured hereditary by the community in some places in Brebes
Regency (Adiwinarti et al. 2010; Lestari et al. 2014). Jabres cattle have uniform physical shape and good adaptation ability in
a various environment of Brebes Regency which composed of lowland and highland (Ministry of Agriculture of Indonesian
Republic 2012). Jabres cattle are
maintained by being released in natural pastures in the morning until late in
the evening and at night in the stall. The feed given is only grass that is in natural
pasture. Jabres cattle have similar
characteristics with Pasundan (Said et
al. 2017), Aceh (Putra et
al. 2014a),
and Madura cattle (Sutarno and Setyawan 2016).
Body size
is one of the phenotype characteristics (Abdullah et al. 2007; Adinata et
al. 2016; Said et al. 2017). The cattle
body size is composed of withers height, hip height, body length, heart girth,
head index, and body weight. Body size properties are used to characterize the
different breeds of livestock as they give an idea of body conformation (Pundir et
al., 2011)
and identify diversity within and between district breeds, based on their
observable attributes
(FAO 2011). The Jabres cattle body size measurement data can be used to make standardization
of Jabres cattle body size which had not been performed yet. It brought the development of Jabres cattle aimless and cross-breeding happened in many
places. If the cross-breeding was not prevented by making the standard, the germ plasm of Jabres cattle
would be lost in several years later. Therefore, the identification of Jabres cattle body
size is very important in order to conserve its germ plasm.
The
studies of live body weight prediction in cattle have been performed
previously. The
prediction of live
body weight in female Bali cattle and Aceh cattle had been performed using hearth girth by correlation and
regression analysis (Ni’am et al. 2012; Putra et al. 2014b). The body weight prediction in
Ongole cattle had been performed based on body length and heart girth by using simple regression analysis (Paputungan et al. 2013). Suliani et al. (2017) used body length and chest circumference to predict body weight in male
Simmental Ongole Crossbred by simple regression analysis (Suliani et al. 2017). In addition, body weight, carcass weight, and live
weight of male Simmental Ongole Crossbreed bulls had been performed by multiple
linear regression using body length, abdominal girth, hump height, coxae width,
root tail width, neck width, and hearth depth (Prabowo et al. 2012).
Body
weight is one parameter that used as a reference for the farmer to evaluate
their husbandry fruitfulness. However, weighing is not always feasible and
therefore live weight is often estimated from easily accessible body size data (Mutua et
al. 2011).
Live body
weight prediction is an important factor associated with several management practice,
including selection for slaughter or breeding, determining feeding levels,
administration of veterinary product. Furthermore, body weight is also a good
indicator of animal condition (Ulutas et
al. 2002). For Jabres
cattle farmer, the live body weight is very important factor in determining the
cattle sell price in the market. They cannot weight the cattle by digital
weighing scale because most of the Jabres cattle farmers live in rural region.
Nowadays, they estimated the cattle live body weight only by eye-based
assessment or they called “Jogrog” method, without weighing or measuring the
cattle body size. More accurate method for live body weight estimation was
needed in order to help farmers reach the proper cattle price. The main purpose
of this study was to predict live body weight of Jabres cattle based on their body size measurements result, especially in district of Bantarkawung, Brebes,
Central Java, Indonesia.
The validation of the
body weight estimation equation was also provided.
MATERIALS AND METHODS
This
research was conducted in Bantarkawung District (Brebes Regency, Central Java)
from January 7th to May 20th, 2017. The number of cattle measured in this study
was 521 heads which detailed below:
Table 1.
Number of Sample Size.
Number of Sample
Size |
< 1 years old |
≥ 1 years old |
Total |
Female |
79 |
322 |
401 |
Male |
44 |
76 |
120 |
Total |
123 |
398 |
521 |
Jabres cattle pictures can be
seen in Fig. 1. The cattle body size was measured
using stationery,
weight scale, measuring tape and ruler. when animals were standing as described
in Ozkaya and Bozkurt (2008). The measurement parameter are Body Length (BL), Heart Girth (HG),
Withers Height (WH), Rump Height (RH), Face Length (FL), Face Width (FW), and
Body Weight (BW) Tolenkhomba et al. (2012). The gained data was analyzed statistically by IBM SPSS
19 Software. Normality
test was performed for the collected data using Kolmogoro-Smirnov and Q-Q Plot
methods.
Fig. 1. Female (left) and male (right) Jabres cattle.
The
descriptive analysis was performed by calculating the mean, standard deviation
(SD), and coefficient of variance (CV%). The data were stratified by sex and age (under
1 years old and same or above 1 years old). Multivariate Anova was performed in order to explore the significance of difference for measurement
result between groups. The Pearson’s correlation
coefficients among various body measurements were also calculated.
In order to gain accurate live body weight prediction equation based on body size
measurements, the regression analysis was performed by multiple linear regression and
factorial analysis scores followed by multiple linear regression methods. In the multiple linear
regression methods, the cattle body weight was predicted by using measured body
size parameter as independent variables using the following multiple regression
model:
Y = α + β1.Χ1 + β2.Χ2 + β3.Χ3 + … + βn.Χn + En
Where Y : Body weight (in kg unit)
α : Regression constant
β1, β2, ..., βn : Regression coefficient for nth body size parameter
X1, X2,
..., Xn : The observed variables
E : Residual error
In the factorial analysis, the
factor was extracted by principle components with factor number based on
Eigenvalue greater than 1. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy
coefficient, Barlett’s Test of Sphericity significance, and anti-image
correlation was evaluated in order to make sure the data appropriateness for
factorial analysis. In this method, the variables may be expressed as linear
functions of the factors with equation below,
X1 =
a11F1 + a12F2 + ... + a1mFm + a1U1
X2 =
a21F2 + a22F2 + ... + a2mFm + a2U2
Xn =
an1F1 + an2F2 + ... + anmFm + anUn
Where X1,
X2, ..., Xn : The observed variables
a11,
a12, ..., anm : Coefficients which best
reproduce the observed variable n from the factors m
F1,
F2, ..., Fm : The extracted factors
U1,
U2, ..., Un : The unique factors
The cattle body weight was
predicted by using factor scores as independent variables using the following
multiple regression model:
Y = a + b1FS1 + b2FS2 + ... + bkFSk + e
Where: Y = Cattle body weight (in kg
unit)
a =
Regression constant
bk =
Regression coefficient for factor score of k
FS =
Factor score
e =
Random error term
Normality test of the residual
from each equation was evaluated by Kolmogoro-Smirnov and Q-Q Plot methods. The
fitness of the body weight prediction equations from both methods were
evaluated by calculating the adjusted R2, RMSE, and standard
deviation ratio values. The best equations were chosen based on the highest
adjusted R2 value and the lowest RMSE and standard deviation ratio
values.
RESULTS AND DISCUSSION
The
Identification of Jabres Cattle Body Size Properties: The descriptive statistics for
body measurement and body weight of Jabres cattle was presented in Table 2. The
data were stratified by sex and age (< 1 years old and ≥ 1 years old
) because the multivariate test result showed that there are significant
differences among these data sets (P<0.05). The data obey normal
distribution which indicated by Kolmogoro-Sminov significance value greater
than 0.50 or the Q-Q plot results where the data located around the normal
line. From these data, it can be seen that age really matters to most of the
body size parameter of Jabres cattle because the younger cattle have
significant smaller body size than elder cattle. Generally, male Jabres cattle
under 1 years old have significant smaller body size than female Jabres cattle
under 1 years old, except for face length and face width which are not
significantly different. However, for cattle same or above 1 years old, there
are no significant difference between male and female cattle although female
cattle have bigger body length, heart girth, face length, and body weight than
male cattle. This result was a little bit different with previous research due
to the different sample size (Adinata et al. 2016; Panjono et al. 2017).
Compared to Aceh cattle, adult
female Jabres cattle had bigger body size than adult female Aceh cattle but
adult male Jabres cattle had smaller body size than adult male aceh cattle (Putra et al. 2014a). Compared to adult female Ongole grade cattle, adult
female Jabres cattle had smaller body size (Paputungan et al. 2013)
Table 2. Mean, standard deviation (SD) and coefficient of
variation (CV in %) for body size and body weight of
Jabres cattle.
Varia-ble |
Female |
Male |
< 1 years old |
≥ 1 years
old |
< 1 years old |
≥ 1 years
old |
Mean |
± |
SD |
CV (%) |
Mean |
± |
SD |
CV (%) |
Mean |
± |
SD |
CV (%) |
Mean |
± |
SD |
CV (%) |
n |
79 |
322 |
44 |
76 |
BL |
80,75 |
± |
13,58b |
16,82 |
101,52 |
± |
8,36c |
8,23 |
75,25 |
± |
12,60a |
16,74 |
99,81 |
± |
11,99c |
12,01 |
HG |
119,60 |
± |
20,11b |
16,81 |
147,41 |
± |
9,62c |
6,52 |
112,52 |
± |
17,28a |
15,36 |
144,84 |
± |
15,71c |
10,85 |
WH |
99,91 |
± |
11,41b |
11,42 |
113,07 |
± |
6,73c |
5,95 |
95,36 |
± |
12,24a |
12,84 |
113,09 |
± |
7,45c |
6,59 |
RH |
103,85 |
± |
11,53b |
11,10 |
116,94 |
± |
6,85c |
5,86 |
100,32 |
± |
11,66a |
11,62 |
117,36 |
± |
6,93c |
5,91 |
FL |
33,76 |
± |
5,97a |
17,70 |
40,19 |
± |
2,78b |
6,93 |
32,53 |
± |
6,37a |
19,59 |
39,89 |
± |
4,59b |
11,52 |
FW |
15,20 |
± |
3,16a |
20,77 |
17,22 |
± |
1,95b |
11,35 |
14,86 |
± |
2,91a |
19,55 |
17,83 |
± |
3,02b |
16,95 |
BW |
113,71 |
± |
47,24b |
41,54 |
205,72 |
± |
35,52c |
17,27 |
93,77 |
± |
38,89a |
41,47 |
199,74 |
± |
62,37c |
31,23 |
Note : a,b,c :
different superscript indicates the significant differen (P<0.05)
n : sample size, BL: Body length, HG: Heart girth, WH:
Withers height, RH: Rump height, FL: Face length, FW: Face width, BW: Body
weight.
Correlation
Analysis of Body Size of Jabres Cattle: The correlation analysis of body size of Jabres Cattle
is performed separately for each cattle group. The correlation analysis result for Jabres cattle body size was shown in Table 3. Morphology or body size expresses a
strong relationship with productive potential since it contains the structure
which supports the biological functionality of the animal.
Jabres cattle have strong
correlation among body size parameter in every group except female Jabres
cattle same or above 1 years old. It can be happen because the cattle growth
after 2 years old did not obey the linear curve (Gano et al. 2015). The same condition also happen for Bali cattle (Ni’am et al. 2012). In this study, there were large number of female
Jabres cattle with age above 2 years old meanwhile there were only a little male
Jabres cattle with age above 2 years old. In Brebes, male cattle with age nore
than 2 years old could be found rarely because community used 2-years old male
cattle to be slaughtered in religio(Ni’am et al. 2012; Putra et al. 2014a; Thiruvenkadan 2015)us events every year. So that, the population of
female Jabres cattle was bigger than male Jabres cattle.
Related to body weight, all
cattle have very strong correlation with heart girth as the highest corelation
coefficient. It followed by body length and withers height for female cattle in
all ages and male cattle same or above 1 years old. Meanwhile, for male cattle
under 1 years old, the heart girth was followed by rip height and withers
height. This result was consistent with Ni’am et al. (2012); Putra et
al. (2014b); Thiruvenkadan (2015) who stated that cattle body weight had the strongest
correlation with hearth girth compared to another body size parameter (Ni’am et
al. 2012; Thiruvenkadan 2015). Compared to another animal, heart girth also became
an important parameter to predict body weight of the pig (Mutua et
al. 2011) and goat (Adeyinka and Mohammed 2006).
Table
3. Correlation coefficient for body size of Jabres cattle.
|
BL |
HG |
WH |
RH |
FL |
FW |
BW |
Female < 1
years old |
BL |
1,000 |
|
|
|
|
|
|
HG |
0,716** |
1,000 |
|
|
|
|
|
WH |
0,707** |
0,867** |
1,000 |
|
|
|
|
RH |
0,698** |
0,853** |
0,956** |
1,000 |
|
|
|
FL |
0,718** |
0,831** |
0,783** |
0,777** |
1,000 |
|
|
FW |
0,508** |
0,734** |
0,620** |
0,608** |
0,729** |
1,000 |
|
BW |
0,864** |
0,946** |
0,836** |
0,817** |
0,823** |
0,688** |
1,000 |
Female ≥ 1
years old |
BL |
1,000 |
|
|
|
|
|
|
HG |
0,250** |
1,000 |
|
|
|
|
|
WH |
0,140* |
0,591** |
1,000 |
|
|
|
|
RH |
0,135* |
0,559** |
0,848** |
1,000 |
|
|
|
FL |
0,026 |
0,452** |
0,182** |
0,197** |
1,000 |
|
|
FW |
-0,069 |
0,241** |
-0,008 |
0,142* |
0,397** |
1,000 |
|
BW |
0,675** |
0,854** |
0,507** |
0,485** |
0,338** |
0,149** |
1,000 |
Male < 1
years old |
BL |
1,000 |
|
|
|
|
|
|
HG |
0,713** |
1,000 |
|
|
|
|
|
WH |
0,654** |
0,898** |
1,000 |
|
|
|
|
RH |
0,725** |
0,921** |
0,964** |
1,000 |
|
|
|
FL |
0,662** |
0,760** |
0,677** |
0,663** |
1,000 |
|
|
FW |
0,421** |
0,778** |
0,652** |
0,670** |
0,584** |
1,000 |
|
BW |
0,820** |
0,965** |
0,851** |
0,887** |
0,755** |
0,689** |
1,000 |
Male ≥ 1
years old |
BL |
1,000 |
|
|
|
|
|
|
HG |
0,711** |
1,000 |
|
|
|
|
|
WH |
0,616** |
0,744** |
1,000 |
|
|
|
|
RH |
0,551** |
0,780** |
0,876** |
1,000 |
|
|
|
FL |
0,433** |
0,614** |
0,642** |
0,718** |
1,000 |
|
|
FW |
0,376** |
0,539** |
0,446** |
0,344** |
0,525** |
1,000 |
|
BW |
0,882** |
0,948** |
0,707** |
0,697** |
0,547** |
0,505** |
1,000 |
Note : ** indicates
that correlation is significant at the 0.01 level (2-tailed)
* indicates that correlation is
significant at the 0.05 level (2-tailed)
Regression
Analysis of Jabres Cattle Body Size: The regression analysis was performed for each group
using multiple
linear regression and factorial analysis scores followed by multiple linear
regression methods. The multiple linear regression result can be seen in Table
4. The best regression equation of each group was chosen by the highest R2,
lowest standard error of estimate, lowest RMSE, lowest standard deviation
ratio, and the normality of the residual. Each group had different predictors
and thus different equation obtained. All of the equation had normal residual
distribution. The best fit equation was obtained for male cattle with age same
or above 1 years old. Contrarily, the worst fit equation was obtained for
female cattle with age same or above 1 years old. It can be happened because
the growth of adult cattle after 2 years old is non-linear so the body weight
prediction became harder (Gano et al. 2015). The male cattle with age above 1 years only
contained a few cattle with age above 2 years old, meanwhile the female cattle
with age above 1aspects old contained many cattle with age above 2 years old.
So it can be concluded that body weight if male Jabres cattle can be predicted
more easily than female Jabres cattle with multiple linear regression method.
Compared to body weight
prediction of adult Aceh cattle which use withers height, body length, and
heart girth as predictors using multiple linear regression (Putra et al. 2014b), body weight prediction which obtained in this study
gave higher determination coefficient for adult male Jabres cattle but lower
determination coefficient for adult female Jabres cattle. Compared to adult
female Ongole grade cattle, the body weight prediction equation from this study
was less accurate which indicated by the smaller determination coefficient
value (Paputungan et al. 2013).
Table
4. Linear regression model summary for body size of Jabres cattle with body
weight as dependent variable.
Group |
Female |
Male |
˂
1 year old |
≥
1 year old |
˂
1 year old |
≥
1 year old |
Constant |
-167.510 |
-263.129 |
-146.489 |
-326.336 |
Variable |
|
|
|
|
BL |
1.405 |
- |
0.873 |
2.178 |
HG |
1.760 |
3.149 |
2.324 |
2,943 |
WH |
0.088 |
-0.077 |
- |
- |
RH |
-0.435 |
0.115 |
-0.603 |
-0.955 |
FL |
-0.187 |
- |
-0.292 |
- |
FW |
- |
- |
-1.143 |
-0.307 |
Adjusted
R2 |
0,968 |
0,726 |
0,971 |
0,990 |
Std. Error of
Estimate |
8,500 |
18.649 |
6,664 |
6,330 |
RMSE |
8.171 |
18.533 |
6.193 |
6.118 |
Standard
Deviation Ratio |
0.174 |
0.521 |
0.161 |
0.099 |
Different from direct multiple
linear regression which discussed previously, factorial analysis scores
followed by multiple linear regression method did not use body size parameter
directly to predict the body weight, but this method convert the body size parameter
into several factors that will be used for body weight prediction. In the
factorial analysis, the factor was extracted by principle components with
factor number based on Eigenvalue greater than 1 as followed by Kaiser Rule criterion (Shah et
al. 2018). The anti-image correlation, Kaiser-Meyer-Olkin Measure of Sampling Adequacy
coefficient,
and Barlett’s
Test of Sphericity significance evaluated in order to make sure the data
appropriateness for factorial analysis. The anti-image correlation coefficient
was above 0.50 for all variables, except for face width in female cattle with
age same or above 1 years old which will be excluded in the factorial analysis. For the Kaiser-Meyer-Olkin Measure of Sampling
Adequacy coefficient, the value are greater than 0.50 for all datasets. These low anti-image correlation
coefficient value which supported by high Kaiser-Meyer-Olkin Measure of Sampling
Adequacy coefficient
indicate that partial correlations were low, true factors existed in the data,
and the proportion of the variance in various body size parameter were exposed
(Shah et
al. 2018).
Barlett’s
Test of Sphericity showed signifant result for all groups. After the requirements fulfilled, the
factorial analysis was performed for each groups.
The result of factorial analysis
was shown in Table 5. There was only 1 factor that can be extracted from each
group data because only 1 component that had eigenvalue greater than 1. Almost
all of the communalities of the variables had greater value than 0.50 which
indicates that variables could describe factor. The extracted factor can
explained the variance over than 50% which means that the factor can be used fo
further analysis. The communality ranged from 0.217 (face length in female ≥ 1 years old) to 0.969 (hearth girth in Male < 1 years old). Higher estimates of
communality (ranged from 0.79 to 0.93) were observed by Yakubu (2010) and approximate estimates of communality (0.42 to 0.87 and 0.32 to 0.83) were reported by Sadek et
al. (2006). In the
present
study, common variance explains approximately.
Table 5.
Factorial analysis scores result of Jabres cattle body size.
Variables |
Female |
Male |
<
1 years old |
≥
1 years old |
<
1 years old |
≥
1 years old |
Factor
1 |
Communalities |
Factor
1 |
Communalities |
Factor
1 |
Communalities |
Factor
1 |
Communalities |
BL |
0.814 |
0.663 |
0.299 |
0.090 |
0.791 |
0.626 |
0.565 |
0.751 |
HG |
0.944 |
0.890 |
0.837 |
0.701 |
0.969 |
0.940 |
0.816 |
0.903 |
WH |
0.934 |
0.872 |
0.881 |
0.777 |
0.931 |
0.867 |
0.806 |
0.898 |
RH |
0.926 |
0.858 |
0.872 |
0.761 |
0.949 |
0.901 |
0.797 |
0.893 |
FL |
0.911 |
0.829 |
0.466 |
0.217 |
0.823 |
0.677 |
0.645 |
0.803 |
FW |
0.782 |
0.611 |
0 |
0 |
0.778 |
0.605 |
0.394 |
0.628 |
Eigenvalue |
4.724 |
2.545 |
4.615 |
4.023 |
% of Variance |
78.728 |
50.901 |
76.917 |
67.043 |
The factorial analysis scores
were then used to predict body by multiple linear regression. The regression
result was shown in Table 6. The regression equation gave good fitness for
cattle under 1 years old which indicated by high adjusted R2 value,
low standard error of estimates and RMSE, and the standard deviation ratio were
less than 0.40. On the other hand, the regression equation which obtained for
cattle same or above 1 years old gave bad fitness because the adjusted R2 value
were low, standard error of estimates and RMSE were high, and the standard
deviation ratio were more than 0.40. It could be happen because the growth of
adult cattle was more difficult to predict than young cattle.
Table 6. Linear regression equation of Jabres cattle body weight
prediction calculated by factorial analysis scores followed by multiple linear
regression.
Group |
Female |
Male |
˂
1 year old |
≥
1 year old |
˂
1 year old |
≥
1 year old |
Constant |
113.709 |
205.925 |
93.773 |
199.737 |
Coefficient
of Factor 1 |
44.216 |
27.223 |
36.865 |
54.772 |
Adjusted
R2 |
0.875 |
0.582 |
0.896 |
0.768 |
Std. Error of
Estimate |
16.727 |
23.041 |
12.520 |
30.036 |
RMSE |
8.171 |
22.969 |
12.231 |
32.047 |
Standard
Deviation Ratio |
0.174 |
0.645 |
0.318 |
0.478 |
The comparison of multiple
regression or factorial analysis scores followed by multiple linear regression
methods has been performed. In all cattle groups in this study, equations which
were obtained from multiple linear regression method gave better fitness. It
was indicated by the higher adjusted R2 value, lower standard error
of estimates, lower RMSE, and lower standard deviation ratio. The graph between
observed body weight and predicted body weight (Fig. 2) also showed that
multiple linear regression method could predict the cattle body weight more
accurately than factorial analysis scores followed by multiple linear
regression method. It can be concluded that multiple linear regression method
was preferable to be used to predict body weight of Jabres cattle than
factorial analysis scores followed by multiple linear regression. The similar finding reported by Putra et
al. (2014b)
in Aceh cattle.
Moreover, the equation which obtained by multiple linear regression method was
more applicable for Jabres cattle farmers because they can use the cattle body
size measurement result directly without need to transform it first to another
form (i.e. factor) that should be done if they use factorial analysis scores
followed by multiple linear regression method.
Fig. 2. Graph between observed body weight and predicted body weight
which calculated by multiple linear regression (¨) and factorial analysis scores
followed by multiple linear regression (●) for female under 1 years old
(a), female same or above 1 years old (b), male under 1 years old (c), and male
same or above 1 years old (d) of Jabres cattle.
Conclusions:
The
Jabres cattle body size and body weight had been measured for 521 cattle which
classified by sex and age. Age really matters to most of the body size parameter of Jabres cattle because the younger cattle have
significant smaller body size than elder cattle. Generally, male Jabres cattle under 1 years old have
significant smaller body size than female Jabres cattle under 1 years old,
except for face length and face width which are not significantly different.
However, for cattle same or above 1 years old, there are no significant
difference between male and female cattle although female cattle have bigger
body length, heart girth, face length, and body weight than male cattle.
Correlation analysis showed that Jabres cattle have strong correlation among
body size parameter in every group except female Jabres cattle same or above 1
years old. The highest corelation coefficient came from body weight and heart
girth. The equation to predict body weight was obtained by multiple linear
regression and factorial analysis scores followed by multiple linear regression
methods. The result showed that multiple linear regression method was
preferable to be used to predict body weight of Jabres cattle than factorial
analysis scores followed by multiple linear regression because of its better
accuracy, better fitness, and more applicable for Jabres cattle farmers.
Acknowledgements:
The authors
expressed special thanks to Ministry of Research and Higher Education of
Indonesian Republic which has partly supported this research activities through
Universitas Gadjah Mada, Yogyakarta, by providing scholarship of Master
Education Program Leading to Doctoral Degree for Excellent Graduates (PMDSU) to
the first author with contract number of 2046/UN1/DITLIT/DIT-LIT/LT/2018.
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