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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

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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

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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

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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

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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.

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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.

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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


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


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)


Significant / Not

significant


Significant/ Not

significant



X3 5.45 3.94 Significant 6.9 Not significant

X4 0.08 3.94 Not significant

6.9 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


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)


Significant / Not

significant


Significant/ Not

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)


Significant / Not

significant

F(tabulated value)

(1,98) df á =1 %

Significant/ Not significant



X3 0.26. 3.94 Not

significant

6.9 Not significant


6.9 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.

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