Animal Breeding & Genomics Centre

Nazir A Ganai

Guest Researcher

May 15, 2007

Livestock Husbandry in India:

Challenges and Opportunities

INDIA

India the land of infinite

variety.

8O NL

37O NL

Old civilization : 5,000 year Languages spoken 325 dialects 1,652 official 18 States + union territories 29 + 5 Area 3,287,590 sq. km (97 times Holland, 33, 883 sq km

(7th Largest 2.2 percent of the total land area of the planet)

Coastline 7,516 km Land Boundaries 14103 km People 1,000,000,000 in 2000

INDIA: An Overview

National Emblem National Animal

National Animal

Diversity

• People

– ethnic Aryan 72, Dravidian 25, Mongoloid and other 3

– Religious Hindu 81.3, Muslim 12, Christian 2.3, Sikh 2.0

– Languages 18 official, 325 spoken

• Land forms Mighty Himalayas, Gangetic Plains, Deccan Plateau

• Agro-climatic 15 macro Zones

• Biodiversity

– plant species - 49,219 (12.5% of worlds flora)

– animal species - 81,251 (6.6% of fauna)

Diversity

Temperature + 51O C to - 40OC

Altitude 0 ft to 24000 ft above MSL

Rainfall 110 mm Ladakh to 11,430 mm Cheranpunji (one year)

We owe a lot to the Indians, who taught us how to count,

without which no worthwhile scientific discovery could have

been made.

-Albert Einstein

India is,the cradle of the human race,the birthplace of

human speech, the mother of history,the grandmother of

legend, and the great grand mother of tradition.Our most

valuable and most instructive materials in the history of

man are treasured up in India only.

-Mark Twain

Agricultural Research System in India

• Indian Council of Agricultural Research (ICAR)

– National policies on Agriculture

– Clearing House for all International partnerships

• 96 Institutes and 86 AICRP

• 6000 scientists

• 40 State Agriculture universities

• 10 Veterinary Universities

• 30,000 scientific/teaching staff

Important Animal Science Organizations

• ICAR - Indian Council of Agricultural Research

• VCI - Veterinary Council of India

• NDRI - National Dairy Research Institute

• NDDB - National dairy Development Board

• NBAGR - National Bureau of Animal Genetic Resources

• IVRI - Indian Veterinary Research Institute

• Others supporting animal science research

LIVESTOCK POPULATION

• India has largest population of livestock

in the world.

Cattle 206 million Ist 18 breeds

Buffaloes 91 million Ist 7 breeds

Sheep 62 million 3rd 40

Goats 123 million 2nd 20

Pigs 17 million -

Poultry 354 million 7th 7

Camel 900,000 4

Yak,

Mithun

-

Production figures

• Milk 96 million tonnes I

• Wool 58,000 tonnes

• Silk 15,000 tonnes II

• Egg 1.60 million tonnes V

• Meat 6032,000 tonnes

• Animals slaughtered 106 million • (cows/buffaloes) 24 million

Livestock Diversity • Cattle

– Milk yield 200 lts - 4500 lts

– Lact Leng 150 – 400 days

– AFC 27 months – 48 months

• Sheep – Fibre diameter 11 µ - 30 µ

– Growth 12 kg - 50 kg ( 1 year)

– Fecundity 0.8 - 3.0 lambs / year

• Commercial poultry

– Pure Grandparent lines: 15

– Hatcheries: Layer – 115 producing 1.3 million parents ----95 million hybrid layer chicks

Broiler- 280 producing 280 million parents --------275 million broiler day old chicks

Adaptability of Local Livestock Breeds

• Disease tolerant

• Thrive Well in Local Harsh Environment

• Supply Organic Manure

• Draught purposes

• Low Input Management System

Livestock Biodiversity

Black Gold

Livestock Husbandry in India

• Rural Population - 65-70%

• Dependence on Agriculture - 80-85%

• Rural Poverty - 25%

• Dairy farmers - 30 million

– Herd size is small _ one or three milch animals,

– small and marginal landholding farmers -own about 70 % milch animals

– animals' nutritional requirements - are largely met by agricultural waste and

byproducts

• successful Indian dairy development programme – Operation Flood

– In the early 1950s, India was commercially importing around 55000 tonnes of

My business here

Use of Molecular Data in Selection

Unknown genes

Identified or

marked

QTL

Phenotypic

data EBV

Genotypic

data

Selection

strategy Molec. genetics ?

Molecular

score (MS)

• Most important traits are quantitative controlled

by a number of genes and environmental

factors

• Progeny testing- to effect Genetic improvement

• Limitations:

– Expensive, time consuming, difficult to full fill all the

conditions of a reliable progeny test,

– Not possible to produce enough PT bulls to cove

300 million cattle / buffaloes

Target Milk

Production

Breedable Cows Breedable

Buffaloes

Required No. of Breeding Bulls

Cross

bred

Indigenous AI Cover Cow bulls Buffaloes bulls

India 12

million 52 million 51 million

100% 10,600 8500 for AI

75% 8000 for AI +

66000 for NS

6300 for AI +

53000 for NS

Year Human

Population

Per capita

availability

Milk production /

Targets for 2030

% increase

warranted

India 2006 1.15 billion 250 ml 96 million tones

2030 1.5 billion

(growth rate

1.74%)

560 ml ~320 MT ~ 400%

Requirement of

breeding bulls

Genes

Advantage of Molecular

Genetic data for selection

Molecular genetics

QTL • Heritability of genotypes = 1

• Expressed in both sexes

• Expressed at early age

• Requires less phenotypic data

Candidate

genes

• Optimism

– Faster progress possible if we could identify

bulls and cows carrying desirable genes

• Challenge / Approach

– What are those ‘big’ genes

– Finding genes for milk production

– Using them in selection

– Future Research

Genes for quantitative traits

Finding genes

• QTL mapping

– high probability of success

– hard to use

• Candidates genes

– low probability of success

– easy to use

Number of QTL in Cattle by Trait Types

Milk Fat 90

Milk Protein 130

Milk Yield 62

Mastitis 68

Meat Quality 59

Carcase Characteris 20

Disease Resistance 10

Fertility 44

General 145

Growth 190

Life History Traits 16

Lifetime Production 10

http://www.animalgenome.org

Total QTLs 846

Publications 55

Traits 112

0

1

2

3

4

5

6

7

8

9

10

No

of Q

TL

s

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Milk Yld

Protein yld

Protein %

fat yld

Fat %

SCS

Chromosome number

Chromosomes Containing QTLs with highly significant effect on milk production traits

0

2

4

6

8

10

12

14

16

No

of

QT

Ls

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Milk Yld

Protein yld

Protein %

fat yld

Fat %

SCS

Chromosome No

Chromosomes containing QTLs with moderately significant effect on milk traits

How big are the gene effects?

0

2

4

6

8

10

12

14

16

18

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2

Effect (phenotypic standard deviations)

Fre

qu

en

cy

Reported gene effects in cows

• To have a panel of

major candidate

genes with an effect

of mean ± 2 SD, we

need to grow by 15 to

20 times more in the

information on such

genes.

Outline

• Use of QTL detected in breed crosses

• Marker-assisted introgression

• Marker-assisted selection in crosses

• Marker-assisted selection within breeds

• Gene Assisted Selection (candidate genes)

• Direct use of a discovered QTL effect for selection across families is not possible.

• There are statistical estimation errors, causing both false positive and false

negative effects, particularly when the effect of the QTL is small.

• There is a lack of consistency of the effect of the same QTL between studies,

caused by QTL by genetic background (epistasis) of QTL and by environment

interactions.

• Advantage from within-family selection for a QTL over BLUP or phenotypic

selection alone is frequently low and the methodology to exploit this information for

selection is complex and relatively inefficient.

• The net economic effect of the QTL may be lower than the effect on single traits,

because unfavourable effects on other traits.

• Selection using QTL is more complex than phenotypic selection alone. QTLs add

to the list of traits used as selection criteria. Reduced selection intensity and

relative emphasis given to each trait, make optimal selection more difficult.

• Short-term gains due to MAS may be at the expense of medium to long-term

polygenic responses for important traits.

Problems related to use of QTLs in genetic improvement programs