Tlm presentation final
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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
Tlm presentation final
- 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
- 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