As a breed of dairy cattle, Brown Swiss
originating in the Swiss Alps has a good adaptability against harsh conditions,
high altitudes, and hot or cold climates, and has been reared with the aim of
obtaining milk and milk products for many years. Since 1925, this breed has
been imported different times in the Turkey as one of the most preferred breeds
among dairy cattle breeds .
Birth weight is a trait accentuated
not only for breeding purposes but also for economic causes in order to improve
the profitability in meat production. Principally, a good recording system in
dairy raising is required to assess genetic improvement in birth weight, a
reliable measurement of prenatal period which affects postnatal
growth-development  Afterwards, the recorded data must be subjected to the
truest-the most effective estimation methods on the purposes of estimating
reliable genetic parameters on the trait as an early selection criteria.
However, knowledge on environmental factors significantly influencing the trait
has played a vital role in the best estimation of genetic parameters on the
In the literature, General Linear
Model has been mostly preferred to decide environmental factors that could be
influential on the birth weight, which is influenced by farm, parity, calving
year, calving season, and fodder availability etc. [2, 6], but Regression Tree
Method, one of visual-non parametric methods, provides easier interpretation of
results of statistical analysis [7,8]. Because, in regression tree method
forming homogenous sub-groups for available data, no assumptions are required about the
underlying distribution of explanatory variables. In addition, Regression tree
method is not influenced by multicollinearity, outlier, and missing values .
In recent years, use of regression tree method is
popular in different fields, but there were only a few reports on determination
of noteworthy factors with use of Regression Tree Method for the birth weight
trait [7,10]. Hence, an attempt was made in the present study to determine
significant environmental factors affecting the birth weight in the Brown Swiss
cattle reared at Sultansuyu State Farm in Malatya province of Turkey.
MATERIALS AND METHODS
of 3168 Brown Swiss calves the birth weight in the Brown Swiss cattle kept at
Sultansuyu State Farm (lat. 380 E, long. 380 N, and 981 m
above sea level) of Malatya province in the Eastern Anatolia Region of the
Turkey were provided for the present study.
winter and spring months are rainy. The state farm has a continental climate
which is dry and hot in summers, and cold in winters. Therein, the highest and
lowest temperatures are 43 0C and – 23 0C, respectively
with an average precipitation of 255.3 mm in the last decade. In the present
study, birth weight, sex, calving year, calving season, and parity records were
taken from the Brown Swiss calves during 1984 and 2010 years.
present study, data regarding Brown Swiss cattle used previously by Kaygisiz
 were used with the aim of statistically evaluating performance of a
different statistical (regression tree) method and obtaining new information
different from previous studies. That is, the present study and Kaygisiz 
used completely different statistical techniques.
In the present study, Regression Tree Method was used to identify
the best cut-off values for explanatory variables significantly affecting
response variable and to generalize prediction rules with regard to a response
variable, depending upon the values of explanatory variables . In the
regression tree method, first node is called “root node”, and homogenous
subgroups formed as a result of reducing variation on response variable with
help of explanatory variables, are named “terminal nodes” . Regression tree method turned
response (continuous=quantitative) variables into discrete (categorical)
variables using suitable cut-off values [8, 14].
In the present study, explanatory variables such as calving
season, calving year, parity, calving interval, and dry period, and response
variable (birth weight) were exposed to Regression tree method on the basis of
F test that was used as the significance test for a continuous dependent
variable as recommended by .
All the statistical computations were
performed using SPSS (Exhaustive CHAID) package program.
In the General linear model with a R2
value of 0.323, sex (P<0.01), calving year (P<0.01), sex x calving year
interaction (P<0.05), and calving year x calving season interaction
(P<0.01) were statistically found, whereas calving season, sex x calving
season interaction, and sex x calving year x calving season interaction were
insignificant (data not shown).
Figure I presents regression tree diagram
drawn with the aim of determining factors significantly affecting birth weight
in the Brown-Swiss cattle. In the present paper, the most influential factor on
birth weight was year (P<0.01), followed by sex (P<0.01) and calving
season (P<0.01), secondarily.
Average birth weight for the Brown Swiss
cattle was found 43. 834 (S=4.970) kg from Node O, root node, at the top of regression
tree diagram. Node 0, a group of all the Brown Swiss calves in the present
paper, was divided into eight child nodes (Nodes 1-8), respectively with
respect to year factor. Among these eight nodes, Node 5 produced the heaviest
birth weight, whereas, Node 1 had the lightest birth weight with an average of
39.434 (S=4.245) kg as illustrated in the regression tree diagram.
Birth average weight for Nodes 1-8 ranged
from 39.434 kg (S=4.245) to 46.350 kg (S= 4.385). The birth weight fluctuated
considerably from year to year. Node 1, a group of calves born in 1988 and
earlier years, was divided into two child nodes, Nodes 9 and 10 in terms of sex
factor, respectively. Sex factor had a significant impact on birth weight for a
group of calves in Node 1.
Average of male calves (Node 9) was
heavier in the birth weight than the average of female ones in Node 10 (40.541
vs. 38.375 kg).
Nodes 2, 4, 5, 7, and 8 were terminal
nodes in the advanced stage of the regression tree diagram. The five Nodes
reached to sufficient homogenous in regression tree diagram.
Node 2, a group of calves born in the
years 1989 and 1990, had an average of 42.323 (S=4.508) kg. Node 4, a group of
calves born between the years 1996 and 1998, was with the birth weight of
45.541 (S=4.417) kg.
The birth weight of 46.350 (S= 4.385) kg
was obtained from Node 5, a group of calves born between 1999 and 2001. Node 7
(a group of calves born between 2005 and 2006 among all the calves) and Node 8
(a group of calves born later than 2006) produced the birth weight averages of
42.378 (S=4.794) kg and 45.312 (S=5.184).Node 3, a group of calves between the
years 1991 and 1995, was statistically influenced by sex factor and was
re-branched into two child Nodes 11-12 by sex factor, respectively. Male birth
weight average (Node 11) was heavier than female average (Node 12).
In the regression tree diagram, Node 6, a
group of calves born between 2002 and 2004 years, was divided into the new
child nodes, Nodes 13 and 14, based on calving season, respectively. Node 13 (a
group of calves born in fall and summer between 2002 and 2004 calving years)
yielded lighter calf birth weight compared to Node 14, a group of calves born
in winter and spring seasons between same years.
result revealed that Nodes 9-14 as terminal nodes showed sufficient homogeneity
in the regression tree diagram. Thus, re-division for these terminal nodes was stopped
in the advanced stage of the regression tree analysis. In the regression tree
diagram, year, sex, and season factors had significant influence on birth
weight. In a study, carried out in private organic diary
cattle enterprise in Kelkit district of Gümüshane province, in Turkey, Birth
type, sex, season, and body condition scores at birth were reported by  using regression tree method.
Significant effect of year factor was also
reported by many authors [6, 15-18] similarly.
Present results were in agreement with
those reported by numerous authors, who found a significant influence of sex
factor on the birth weight [6, 15-18].
In previous reports, calving season was a
significant factor on the birth weight as observed in the present paper [6, 16, 18].
The dissimilarities of present results
with previous published results may be attributed to use of similar or
different breeds at various managerial, and ecological conditions, and use of
various statistical methods in statistically evaluating data. As a consequence
of using different statistical analysis, present results (regression tree
method) could not discuss logically with those of other papers (general linear
model) published in literature.
Figure 1. Regression Tree Diagram for Birth
In dairy science, use of regression tree
method, which is not affected by multicollinearity, outlier, and missing
values, is relatively limited. Due to these advantageous, the effects of some
environmental factors on birth weight were investigated by using regression
tree method. In
present paper, results of regression tree method reflected that the statistically
significant effects of calving year, sex, and season on birth weight of Brown
Swiss cattle were noted. The obtained results could be summarized as follows:
Ø The highly significant factor
which statistically affected birth weight of Brown Swiss cattle were calving
year, followed by sex and season factors in the regression tree diagram.
Ø The greatest birth weight average
was from the group of calves born among 1999-2001 years at Node 5.
Ø Birth weight of Node 1, which was
the group of calves born in 1988 and earlier years than 1988, was statistically
affected by sex factor (P<0.01).
Ø Birth weight of Node 3, which was
the group of calves born between 1991 and 1995 years, was also statistically
affected by sex factor (P<0.01).
Ø Calving season had a highly statistical
influence on birth weight of Node 6, which was the group of calves born between
2002 and 2004 years (P<0.01).
Ø Average birth weight of Node 13,
the group of calves born in summer and fall seasons of 2002-2004 years was
lighter than those born in winter and spring seasons of same years.
In conclusion, the most special
information about the interactions of environmental factors which were studied
was very different compared to the information of earlier studies.
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