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241
CHAPTER - VII
PRODUCTIVE AND REPRODUCTIVE FACTORS OF
MILK YIELD OF CATTLE
An attempt is made in this Chapter to study the Productive and
Reproductive factors of Milk Yield of milch cattle. The data for this
purpose is obtained from the cattle bred research station, Lam farm,
Guntur. Prior to the analysis of productive and reproductive factors
an attempt is made to highlight the problem of Milk Yield and to
present the brief review of the related studies on productive and
reproductive parameters.
India has become the world�s number one milk producing
country, with output in 1999-2000 at 78 million tonnes. The world
milk production in 1998 was 557 with tonnes which continued
steadily. Further more, the annual rate of growth in milk production
in India is between 5-6 per cent, against the World�s at 1 per cent.
The steep rise in the growth pattern has been attributed to a
sustained expansion in domestic demand, although per capita
consumption is modest. Despite the country�s large volume of
production, the average productivity of animals is quite low. Experts
agree that poor nutrition of the animals is the main underlying cause.
For years researchers have been working to develop an affordable
user- friendly animal feed product, which is widely used, could
increase milk production by 10-15 per cent per annum. To address to
this need Appropriate Technology (A.T.) was established in India in
id1231843 pdfMachine by Broadgun Software - a great PDF writer! - a great PDF creator! - http://www.pdfmachine.com http://www.broadgun.com
242
1994. They have innovated a molasses � urea product branded as
�PASUPOSHAK�- commercialization of this product has started in
1996. In the beginning, marketing of Pasuposhak was started in four
districts of Gujarat.
In productivity terms, India continues to record very low figures
with an average daily milk yield of 2.14 kg per animal (an indigenous
cow yields 1.89 kg, cross breed cow 6.46 kg and buffalo 3.19 kg). The
productivity of cows in India is 732 kg per lactation as against 1600
kg in China, 7200 kg in U.S. and over 1200 kg in Isreal. The Indian
Government identified this type of problem and started a genetic
improvement programme with a research project at Livestock
Research Station, at Lam Farm, Guntur, under the control of Sri
Venkateswara Veterinary University, Tirupathi. Vigorous
implementation of proven production technologies like artificial
insemination and cross breeding would be of great help in improving
the productivity of milch animals. It is in this direction that Ongole
breed of cattle was recognized as world famous for its drought power,
fast growing capability, resistant heat tolerance etc. Hence, efforts are
made to continue the research on various aspects of Ongole Breed
cattle.
Though Indian dairy industry is recognized as an important
activity suitable for increasing the income level of rural families
especially for small and marginal farmers and landless agricultural
labour, the Indian dairy farmers are facing lot of problems regarding
243
milk yield, feed and fodder, procurement and marketing and
profitability.
7.1 Problems of Milk Yield
7.1.1 Low Level of Milk Yield
There are several reasons for the low productivity of Indian
animals, out of which genetic potential and poor management
practices appear to be the most serious problems. Most animals
survive on crop residue such as wheat and rice straw, sugar cane tops
etc., that are deficient in Nitrogen, Carbohydrates and important
minerals. Eating low quality residue reduces that effectiveness of
digestive fermentation. As a consequence both cows and buffaloes
become malnourished and milk productivity suffers along with the
health of animals. Most experts deem poor nutrition as the main
cause underlying Indian Low level of milk productivity.
Feed supplementation has been recommended by Scientists to
improve productivity of Buffaloes and Cows that are being fed poor
quality, because conventional cattle feed can be relatively expensive.
Experts have favoured in the development of low cost feed supplement
based on urilaced molasses. If fed to animals in appropriate amount of
urea, (a low cost no protein source of Nitrogen) Molasses (a low cost
source of energy in sugar producing areas) and required minerals, can
improve both the milk quantity (overall production) and quality (butter
fat content). The fat content is important because it determines the
244
selling price of the milk � higher the percentage more it fetches in the
market.
7.1.2 Feed and Fodder
Feed and fodder form 60 per cent of Milk Production cost. The
low availability of feed and fodder is a major constraint to growth.
Research on high yield fodder and ways of upgrading crop residue is
to be encouraged. Waste lands are to be developed as fodder grounds
through gram panchayat participation. High yield fodder seeds are to
be made available in rural areas. Fodder cropping is to be encouraged
and quality standards for feed concentrates and mixes are to be setup.
7.1.3 Breeding Technology
The milk production and milk yield is also dependent upon the
Breed that is available. It is found that the indigenous Cows and
Buffaloes yield less quality of milk compared with breded animals.
NDDB and other organisations have made efforts in the yield of
breeding and dairy development. The major strategy adopted in this
context was cross breeding of indigenous cows and grading up of local
buffaloes. The new technology has resulted significant increase in milk
yield. The yield of cross breed cow is 1200 litres as compared to 300
litres for local cow. Likewise, the yield of breaded buffalo is 900 litres
against 500 litres for local buffalo. This higher yield of improved
breeds is not entirely due to higher level of breeding. The feed
conversion efficiency is high for the improved breeds than local
245
breeds. The milk yield per 10kg field dry matter raises the increase in
milk yield of breeded buffalo from 2- 2.9 litres.
Agro climatic conditions also influence the milk yield of diary
animals. Significantly the yield of local buffalo is lowest in the dry
zone at 1.5 litres per day and highest in wet zone at 2.9 litres per day.
This variation is also observed in the case of cross breed cows and
graded buffaloes. The minerals present in the blood of the lactating
animals are transferred into the milk causing in the process a
depletion of these salts in the animal body. If these minerals are not
recharged through the addition of mineral mixer the milk yield
naturally decline.
7.1.4 Seasonal Variations in Buffalo milk production
Seasonal variations influence milk production and consequently
the incomes of dairy farmers. Stabilizing milk yield in different
seasons is an urgent necessity. In the case of local buffalo the milk
yield would be higher in the summer season as compared with winter
and rainy seasons. This might be due to the fact that during winter
and rainy reasons farmers are busy with their crop production work
and they may not devote special attention to the maintenance of milch
animals. The price of milk would be higher in the summer season.
Moreover during summer the fields are barren with no crops. Hence
farmers in order to get advantage of higher price for milk, they have to
concentrate more on milk production during summer season.
246
The higher milk yield among the local and graded buffalo during
the summer might be due to the reason that the majority of these
buffaloes might have calved during the summer itself. So
interseasonal fluctuation in milk production can be minimized by
adjusting the calving dates of buffaloes. The yield can be stabilized
through advanced planning of calving dates to ensure continuous milk
production through adjustment of mating dates of the buffaloes. This
means that at a given time all the buffaloes would not go dry and at
least one or two animals would be giving milk to the dairy farmers.
7.2 Survey of Literature
An attempt is also made in this chapter to present the major
findings of earlier studies on productive and reproductive parameters
of milk yield of cattle in India. The production aspects are examined
in terms of persistency, peak yield, and lactation length, period of
lactation and dry period etc., while the reproductive parameters are
studies in terms of age at puberty, number of services per conception,
gestation length, age at first calving, service period and calving
internal.
Persistency for milk yield is studied by Dutt and Saksena1
(1966). They observed that a non-significant correlation between age
at first calving, calving interval and also reported that the service
period, seasons of calving and lactation numbers are statistically
highly significant.
247
Peak yield is the maximum daily production of milk in lactation.
According to Dutt2 (1966) and Singh3 (1967) study, peak yield had a
slightly significant phenotypic correlation between peak yield and first
lactation milk yield. Genetic correlations of peak yield with lactation
yield and lactation length were 0.57±0.18 and 0.07±0.32 respectively.
The study also observed positive and statistically significant
correlation between average lactation length and age at first calving.
Period of lactation may be useful in improving annual milk
production. Singh�s study reported that heritability of milk yield in
15-75-135 and 305 days to be 0.22±0.19, 0.39±0.22, 0.63 ±0.27 and
0.37±0.21 respectively. This study also reported large phenotypic
and genetic correlations between part and whole lactation. Monthly
averages indicates a rise up to the third month and then a gradual
decline. The similar findings are also observed by Madden4 (1955),
and Van Vleck and Henderson5 (1961).
Dadlani and Prabhu (1968) found a highly significant intra-herd
correlation of 0.73 between dry period and calving interval. In the
herd under study Balaine6 (1971) reported the average first dry period
of 342±8 days. This is larger than the values reported in the
literature. The coefficient of variation in the percent herd was 46 per
cent when compared with 44 to 71 per cent in the studies on other
Hariyana herds. The study reported that heritability of 0.08±0.12 and
0.60±0.20 based on intra-sire daughter-dam.
248
In the light of the above variables considered under production
aspects, the following various reproductive parameters presents a
brief review of findings.
Generally the age at first oestrus is taken as the age of puberty.
Ahuja7 (1956) observed the first signs of oestrus in Hariyanan heifers
at the age of 997±42.9 days, and regular oestrus from 1067.3±66.1
days. Choudary et al (1965) reported heritability of age at puberty as
0.30. The heritability estimates for age at maturity in other breeds of
Indian Cattle range from 0.26±0.18 to 0.44±0.50 (Puri and Mullick,
1963).
In the herd under study, Singh et al.8 (1968) found the average
age at puberty as 1,403.70±10.50 date (n=610; CV=18 per cent). This
is higher than the figures reported by Luktuke and Subramaniam
(1961) and Choudhury et al (1965). Singh et al. (1968) compounded
heritability of age at puberty from half sub correlations as 0.76±0.16.
This is higher than the range of values for age at puberty and age at
maturity. This is higher than the range of values for age at puberty
and age at maturity. They reported phenotypic and genetic
correlations between ages at puberty with firstly lactation milk yield
and some other reproduction traits.
Large number of services per conception increase the age at
first calving and also the service period and this ultimately affects the
249
productive life of dairy animals. The correlation with first lactation
milk yield was similar to the one reported by Boyd et al9 (1954).
Age at first calving is an important parameter for milk
production. According to Balanine D.S. et al10 (1970) the average age
at first calving in different Hariana herd ranged from 40.9 to 53.0 ±
0.3 months. The study observed that the age at first calving of 46.7 ±
3 months in Hariana cow in the villages of Punjab. The heritability
estimates reported to this character in Hariana cows range from 0.15
± 0.3 to 0.40 ± 20. The phenotypic and genetic correlations of age at
first calving with other character are generally small, except the
genetic correlations with peak yield and first lactation length.
Service period is generally defined as the interval between
calving and next conception. Added to gestation length it makes the
calving interval. Gestation length is not only a variable within a
breed, variation in calving interval is almost wholly accounted for by
the service period. The genetic correlations were low and negative
with first lactation milk yield, but high and positive with first lactation
length and dry period. The average first service period of 305 ± 6
days in Hariyana cows range as reported by Kohli and Acharya (1961)
shows that the averages for service periods from first to sixth parity as
327 ± 8, 263 ± 9, 282 ± 15, 273 ± 16, 258 ± 20 and 286 ± 36 days with
an overall average of 289 ± 5 days. The average in the present herd
was higher than those reported from other Hariyana herds. Cows
calving during February to August had on an average a longer service
250
period than those calving during September to January. The service
period is shortest in cows calving in November and longest in April.
The month of calving had significant influence and accounted for 2.1
per cent of the total variability in this trait. Similar observations were
made on other breeds by Sikka11 (1933). The influence of the month
of calving on service period may be attributable to differences in
availability of pasture and green fodder in different months. Sex of
the calf had no significant effect on service period, though animals
giving birth to male calves did have a slightly longer service period
(274.5 days) than those giving birth to female calves (270.9). Similarly
results were reported by Singh et al12 (1968 and 1969).
Calving interval is the period between two consecutive calvings,
the interval between calving and subsequent conception being the
service period. Since variation in gestation length within a breed is
small, variation in calving interval is almost wholly accounted for by
the service period and these two intervals are highly correlated. The
first calving interval was reported to average 430.20 ±19.50 days. The
averages ranged from 403 to 551 days for first calving interval in
different herds. For all calving intervals, the averages reported range
from 441.00±9.4 to 523.7±7.2 days. The Calving interval was reported
to decline after the first calving. The season of calving had an
influence on calving interval, and the shortest calving interval was in
cows calving from July to November. Heritability of calving interval
ranged from zero to 0.22. Repeatability of this character was 0.10 and
251
0.25±0.05. The phenotypic and genetic correlations with first
lactation milk yield ranged from 0.06 to 0.74 and -0.79 to 0.19, which
weight at first calving were -0.13 and 0.05, with first lactation butter �
fat percentage were -0.25 and -0.37, with first lactation length were
high and positive; and with dry period were -0.07 and 0.74±0.02
respectively.
In the light of above, an attempt is made to examine the effects
of productive and reproductive parameters on milk yield of sample
Ongole breed cattle. The factors examined under productive
parameters are: Persistency, peak yield and lactation length, part of
lactation and dry period etc., while the reproductive parameters are
age at puberty, number of services per conception, gestation length,
age at first calving, service period and calving interval. Also the study
attempts to develop suitable regression equation for milk yield, so that
one can project the milk yield from time to time for taking necessary
measures to improve the monthly milk yield. Further, a suitable
model is developed with a feasible solution at farmer�s level to improve
the performance of production and reproduction traits.
7.3 Data Base and Methodology
The data for the present study is collected from Livestock
Research Station Cattle Project, Lam Farm, Guntur, Andhra Pradesh,
India. The data collected by the Lam farm Cattle Research Project
during 2006-2008 has been used in this Chapter to examine the
252
productive and reproductive factors of Milk Yield. For this study 100
milch animals are considered for each of First, second, third and
fourth location of Ongole breed. A total of 400 milch cattle are
considered for the purpose of the study. Only the records including
lactation lengths longer than 90 days, calving intervals maximum of
550 days and service period minimum of 30 days and maximum of
350 days are considered.
In this study milk yield depends on lactation length, lactation
peak yield, calving interval, calf birth weight and service period. The
effects of milk yield depends on the above variables is determined by
the method of regression model.
7.4 The Model
A multiple linear regression model is used in order to test
whether population value of each multiple regression coefficient is
zero. Let X1, X2, X3, X4 and X5 be independent variables. We consider
the Model
Y = â0+ â1X1+ â2X2+ â3X3+ â4X4+ â5X5+e.
Where Y is the lactation milk yield (Kg)
â0 = over all mean
X1= Lactation length(days)
X2= Peak yield(Kg)
X3= Calving interval(days)
X4= Calf birth weight(Kg)
X5= Service period(days)
253
e = Random error,
â1, â2, â3, â4 and â5 are respective regression coefficients.
7.5 Specification of the Variables
a) Lactation milk yield: Due attention has been paid in collecting
the data on milk yield in kg.
b) Lactation length: The period of continuous milk production from
the time of calving until dry.
c) Peak yield: Peak yield is the maximum daily production of milk
in a lactation (Peak yield is highly significantly effected by the
period of calving and lactation number)
d) Calving interval: The period of time from one calving to the next
calving (including the lactation period and the dry period prior
the next calving).
e) Calf birth weight: Weight of the calf at birth.
f) Service Period: This is generally defined as an interval between
calving and next conception.
7.6 Results and Discussions
7.6.1 First Lactation milk yield using Regression Analysis The regression equation is
Y = - 386 + 3.14 X1 + 81.4 X2 - 0.119 X3 + 3.16 X4 - 0.177 X5
R2= 90.5%, R2 (adj) = 90.0%
The calculated R2 value is 90.5%. Hence, it is concluded that
the first lactation milk yield highly influenced on X1, X2, X3, X4 and X5.
254
Table � 7.1 Analysis of Variance of First Lactation Milk Yield
Source DF SS MS F
Regression 5 5073531 1014706 180.12
Error 94 529542 5633
Total 99 5603073
á = 5%, table value is 2.3, á = 1%, table value is 3.2.
Calculated value is greater than tabulated value for á = 5% and
1%. Hence, it is concluded that the first lactation milk yield is
significant on X1, X2, X3 , X4 and X5. The correlation matrix for various
lactations is presented in Annexure � I.
Table � 7.2 Factors affecting Milk Yield in First Lactation
Calculated values are greater than tabulated value for á = 5%
and 1%. Hence, it is concluded that the first lactation milk yield are
significant on X1 and X2 only. But calculated values are less than
tabulated values for á = 5% and 1%. Hence, it is found that the first
lactation milk yield are not significant on X3, X4 and X5.
Y on F (calculated value)
F(tabulated value) (1,98) df á = 5 %
Significant / Not significant
F(tabulated value) (1,98) df á =1 %
Significant/ Not
significant
X1 426.89 3.94 Significant 6.9 Significant
X2 64.47 3.94 Significant 6.9 Significant
X3 0.91. 3.94 Not significant 6.9 Not significant
X4 2.29 3.94 Not significant 6.9 Not significant
X5 0.34 3.94 Not significant 6.9 Not significant
255
7.6.2 Second Lactation milk yield using Regression Analysis The regression equation is Y = - 441 + 2.93 X1 + 96.2 X2 - 0.204 X3 + 4.50 X4 + 0.180 X5 R2 = 91.6%, R2 (adj) = 91.1% The calculated R2 value is 91.6%. So the first lactation milk
yield highly influenced on X1, X2, X3, X4 and X5.
Table � 7.3
Analysis of Variance of Second Lactation Milk Yield
Source DF SS MS F
Regression 5 5896433 1179287 203.73
Error 94 544121 5789
Total 99 6440554
á = 5%, table value is 2.3, á = 1%, table value is 3.2.
Calculated value is greater than tabulated value for á = 5% and
1%. Hence, it is concluded that the first lactation milk yield is
significant on X1, X2, X3, X4 and X5..
Table � 7.4
Factors affecting Milk Yield in Second Lactation
Y on F (calculated
value)
F(tabulated value) (1,98) df á = 5 %
Significant / Not
significant
F(tabulated value) (1,98) df á =1 %
Significant/ Not
significant
X1 509.29 3.94 Significant 6.9 Significant
X2 106.72 3.94 Significant 6.9 Significant
X3 5.45 3.94 Significant 6.9 Not significant
X4 0.08 3.94 Not significant
6.9 Not significant
X5 0.56 3.94 Not significant
6.9 Not significant
256
The Calculated F values are greater than tabulated value for
á = 5% and 1%. So it is concluded that the Second lactation milk yield
are significant on X1, X2 and X3 only. But calculated F values are less
than tabulated values for á = 5% and 1%. Hence, it is concluded that
the second lactation milk yield are not significant on X4 and X5.
7.6.3 Third Lactation Milk Yield using regression analysis The regression equation is Y = - 289 + 3.11 X1 + 78.0 X2 - 0.282 X3 + 1.70 X4 + 0.040 X5
R2 = 88.5% R2 (adj) = 87.9% The calculated R2 value is 88.5%. So the third lactation milk yield is
highly influenced on X1, X2, X3, X4 and X5.
Table � 7.5 Analysis of Variance of Third Lactation Milk Yield
Source DF SS MS F
Regression 5 5059604 1011921 145.22
Error 94 655012 6968
Total 99 5714616
á = 5%, table value is 2.3, á = 1%, table value is 3.2.
The calculated F value is greater than the tabulated value for
á = 5% and 1%. Hence, it is concluded that the third lactation milk
yield is significant on.
257
Table � 7.6
Factors affecting Milk Yield in Third Lactation
The Calculated F values are greater than tabulated value for
á = 5% and 1%. So it is concluded that the third lactation milk yield
are significant on X1 and X2 only. But calculated F values are less
than tabulated values for á = 5% and 1%. Hence, it is concluded that
the third lactation milk yield are not significant on X3, X4 and X5.
Further it is found that calculated F value is greater than tabulated
value for á = 5% which is significant and calculated F value is less
than tabulated value for á = 1 % which is not significant.
7.6.4 Fourth Lactation Milk Yield Regression Analysis
The regression equation is Y = - 436 + 3.16 X1 + 80.2 X2 +0.026 X3 + 3.24 X4 - 0.266 X5 R2 = 90.7% R2(adj) = 90.2% The calculated R2 value is 90.7%. So that the fourth lactation
milk yield is highly influenced on X1, X2, X3, X4 and X5.
Y on
F (calculated value)
F(tabulated value) (1,98) df á = 5 %
Significant / Not
significant
F(tabulated value) (1,98) df á =1 %
Significant/ Not
significant
X1 494.27 3.94 Significant 6.9 Significant
X2 84.09 3.94 Significant 6.9 Significant
X3 0.06. 3.94 Not significant
6.9 Not significant
X4 0,08 3.94 Not significant
6.9 Not significant
X5 4.36 3.94 Significant 6.9 Not significant
258
Table � 7.7
Analysis of Variance of Fourth Lactation Milk Yield
Source DF SS MS F
Regression 5 5034586 1006917 182.74
Error 94 517955 5510
Total 99 5552541
á = 5% ,table value is 2.3, á = 1%, table value is 3.2 The Calculated F value is greater than the tabulated value for á = 5%
and 1%. So it is concluded that the fourth lactation milk yield is
significant on X1, X2, X3, X4 and X5.
Table � 7.8 Factors affecting Milk Yield in Fourth Lactation
Calculated values are greater than tabulated value for á = 5%
and 1%. Hence, it is concluded that the fourth lactation milk yield are
significant on X1 and X2 only. But calculated values are less than
tabulated values for á = 5% and 1%. Based on this it is found that the
fourth lactation milk yield is not significant on X3, X4 and X5.
Y on F (calculated value)
F(tabulated value) (1,98) df á = 5 %
Significant / Not
significant
F(tabulated value)
(1,98) df á =1 %
Significant/ Not significant
X1 429.69 3.94 Significant 6.9 Significant
X2 66.23 3.94 Significant 6.9 Significant
X3 0.26. 3.94 Not
significant
6.9 Not significant
X4 2.36 3.94 Not significant
6.9 Not significant
X5 0.18 3.94 Not significant
6.9 Not significant
259
7.7 Summary
The study finds that in first lactation, lactation length, Peak
yield, Calving interval, Calf birth weight and Service period are
positively correlated on Milk yield. Peak yield and Calf birth weight
and Service period are negatively correlated and all other variable
relationships are positively correlated. The calculated R square value
is 90.5 per cent. Hence it is concluded that the reproduction
parameters are highly influenced on production parameter i.e.,
Lactation length, Peak yield, Calving interval, Calf birth weight and
Service period are highly influenced on Milk yield.
In second lactation, Service period is negatively correlated on
Milk yield. Lactation length, Peak yield, Calving interval, Calf birth
weight are positively correlated on Milk yield. Lactation length and
Service period, Peak yield and Service period, Calving interval on Calf
birth weight and Calf birth weight and Service period are negatively
correlated and all other variable relationships are positively correlated.
The calculated R square value is 91.6 per cent. Hence it is concluded
that the Reproduction parameters are highly influenced on production
parameter i.e., Lactation Milk, Peak yield, Calving interval, Calf birth
weight and Service period are highly influenced on Milk yield in
second lactation.
In third lactation, Calving interval and Service period are
negatively correlated on Milk yield. Lactation length, Peak yield,
260
Calving interval, Calf birth weight are positively correlated on Milk
yield. Lactation length and Calf birth weight, Lactation length and
Service period, Peak yield and Calving interval, Peak yield and Service
period and Calf birth weight and Service period are negatively
correlated and all other variable relationships are positively correlated.
The calculated R square value is 88.5 per cent. Hence it is concluded
that the Reproduction parameters are highly influenced on production
parameter i.e., Lactation length, Peak yield, Calving interval, Calf birth
weight and Service period are highly influenced on Milk yield in third
lactation.
In fourth lactation, Lactation length, Peak yield, Calving
interval, Calf birth weight and Service period are positively correlated
on Milk yield. Peak yield and Service period, Calving interval and Calf
birth weight and Service period are negatively correlated and all other
variable relationships are positively correlated. The calculated R
square value is 90.7 per cent. Hence it is concluded that the
Reproduction parameters are highly influenced on production
parameter i.e., Lactation length, Peak yield, Calving interval, Calf birth
weight and Service period are highly influenced on Milk yield in fourth
lactation.
To sum up, the study found that the production of Milk yield is
highly influenced in first lactation when compared to the second, third
and fourth lactation.
261
Annexure - I
Correlation Matrix Lactation1 Y X1 X2 X3 X4 X1 0.902 X2 0.630 0.401 X3 0.096 0.196 0.014 X4 0.151 0.064 0.151 -0.167 X5 0.059 0.157 -0.011 0.795 -0.071 Lactation 2
Y X1 X2 X3 X4 X1 0.916 X2 0.722 0.542 X3 0.230 0.253 0.154 X4 0.090 0.021 0.055 -0.039 X5 -0.076 -0.087 -0.097 0.650 -0.068 Lactation 3 Y X1 X2 X3 X4 X1 0.914 X2 0.680 0.543 X3 -0.025 0.038 -0.020 X4 0.029 -0.017 0.084 0.009 X5 -0.206 -0.181 -0.188 0.498 -0.076 Lactation 4 Y X1 X2 X3 X4 X1 0.902
X2 0.635 0.406 X3 0.051 0.113 0.000 X4 0.153 0.068 0.156 -0.281 X5 0.042 0.143 -0.029 0.509 -0.082
262
References
1. Dutt, M. and S.C. Saksena. (1966) Persistency of milk production in Haryana cattle, An estimate of its heritability and its relationship with breeding traits, Indian Journal of Veterinary Science, 36, pp 147.
2. Ibid, pp 148.
3. Singh, D. (1967) Construction of selected indices and their relative efficiency for genetic advancement in Haryana cattle.
Ph.D. dissertation, Punjab Agricultural University, Hissar, India.
4. Madden D. E., Lush J. L. and McGillard L. D. 1955 Relation between part of lactations and producing ability of Holstein. Journal of Dairy Science, 83:1, pp 264-271.
5. Van Vleck L. D. and Henderson C. R. (1961) Extending part lactation records by regression ignoring herd effects, Journal of Dairy Science, 44:1, pp 519-528.
6. Balaine D.S., Acharya R. M. and Aggarwal, S.C. (1971) Effect of weaning on production and reproduction efficiency in Haryana cows, Indian Journal of Dairy Science, 24, pp 81-84.
7. Ahuja, L.D. (1956) Studies on certain aspects of physiology of reproduction in Haryana females, M.Sc. Thesis, Bombay University, Bombay.
8. Singh D., Acharya R. M. and Sundaresan D (1968) Phenotypic and genetic parameters of birth weight at first calving and their
relationship with reproduction and production in Haryana Cattle, Punjab Agricultural University, Journal of Research. 5, pp 555-561.
9. Boyd L.J. Seath D.M. and Olds D. (1954) Relationship between level of milk production and breeding efficiency in Dairy Cattle, Journal of Animal Science, 13, pp 89-93.
10. Balaine D.S., op.cit, p 85.
11. Sikka L. C. (1933) Statistical studies of records of Indian dairy
cattle. 2. Reliability of different lactation yields as measures of a cow�s milking capability, Indian Journal of Veterinary Science, 3,
pp 240-253.
12. Singh M. and R.M. Acharya, (1969) Inheritance of part Lactation in Haryana Cattle, Journal of Dairy Science, 52, pp 775.