The document discusses livestock husbandry in India, including: 1) India has the largest population of livestock in the world, including cattle, buffalo, sheep, goats, and poultry. Livestock provide milk, meat, fiber, and more to rural communities. 2) Livestock biodiversity in India is high, with large variations in breed characteristics like milk yield, fiber diameter, and growth rates. Local breeds are important for their disease tolerance and adaptability to local environments. 3) Challenges for the livestock sector include small landholdings and herd sizes for many farmers, as well as the need to greatly increase milk production to meet future demand. Molecular genetics and the use of major genes

1. Animal Breeding &
Genomics Centre
Nazir A Ganai
Guest Researcher
May 15, 2007
Livestock Husbandry in India:
Challenges and Opportunities

2. INDIA
India the land of infinite
variety.

3. 8O NL
37O NL

4.  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

5. National Emblem National Animal
National Animal

6. 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)

7. 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)

8. 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

9. 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

10. 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

11. 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
-

12. 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

15. 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

16. Adaptability of Local Livestock Breeds
• Disease tolerant
• Thrive Well in Local Harsh Environment
• Supply Organic Manure
• Draught purposes
• Low Input Management System

17. Livestock Biodiversity

19. Black Gold

21. 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

23. My business here

24. Use of Molecular Data in Selection
Unknown
genes
Identified or
marked
QTL
Phenotypic
data
EBV
Genotypic
data
Selection
strategy
Molec. genetics ?
Molecular
score (MS)

25. • 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

26. 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

27. 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

28. • 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

29. Genes for quantitative traits
Finding genes
• QTL mapping
– high probability of success
– hard to use
• Candidates genes
– low probability of success
– easy to use

30. 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

31. 0
1
2
3
4
5
6
7
8
9
10
NoofQTLs
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

32. 0
2
4
6
8
10
12
14
16
NoofQTLs
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

33. 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)
Frequency
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.

34. 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)

35. • 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