Using genotype and feeding regime to analyse smallholder dairy systems
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Presentation by Mizeck Chagunda at the 5th All Africa conference on animal production, Addis Ababa, Ethiopia, 25-28 October 2010.
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Using genotype and feeding regime to analyse smallholder dairy systems
- 1. Using genotype and feeding regime to analyse smallholder dairy systems Mizeck Chagunda Scottish Agricultural College (SAC) Edinburgh Addis Ababa, Ethiopia, October 25-28 , 2010
- 2. • Co-authors – Victor Kasulo: Mzuzu University, Malawi – Susan Chikagwa-Malunga: Lunyangwa Agricultural Research Station, Malawi – Dave Roberts: SAC Dairy Research Centre, Scotland • Acknowledgements – DelPHE British Council – Scottish Government
- 3. Outline • Smallholder dairy in Malawi • Importance of smallholder dairying • Rationale • Data and Analysis • Discussion and Conclusions
- 4. Milk Production in Malawi Smallholder Farmers HFxMZ ≤4 Cows Large-scale Farms HF ≅60 Dairy Processor 9,000 t/yr Consumer (Including Home Consumption) Informal Market Formal market 60%
- 5. Smallholder Dairy in Malawi
- 7. Importance of Smallholder •Income •Food security •Employment •Business catalyst
- 8. Milk Consumption • Average milk consumption = 4.5 – 6.0 kg/capita • Africa = 15 kg/capita • Recommended (FAO) = 200 kg per capita
- 9. Breeds and Breeding HF Jrsy Ayrs MZebu Pure 7/8s 3/4r 1/2 N/A 0 20 40 60 80 100 120 140 160 Pure 7/8s 3/4r 1/2 N/A
- 10. Feeding Fodder Percentage Crop residues 49 Standing Hay 20 Fodder banks 20 Silage 11 Supplementaion • Maize bran • Dairy mash • Mineral
- 11. Rationale • Input- output driven classification – Assumes predetermined level • Land holding size – Input driven • Formal vs informal – Product driven Biologically driven
- 12. 40 50 60 70 80 90 100 Malawi Zebu 1/2FriesianXMalawi Zebu 3/4FriesianXMalawi Zebu Pure Holstein Friesian Genotype Performanceas%ofmaximum Productivity index Average test day milk yield Revesai and Chagunda 2003 Productivity inefficiency
- 13. Breeding inefficiency 0 200 400 600 800 1000 1200 1400 2004 2005 2006 2007 Year AI Cows on heat Cows inseminated Chindime, 2007 Central and Northern Malawi
- 14. Aim of Current Study • To explore the application of a biologically- oriented approach to classify smallholder dairy systems • Using major drivers of dairy production, genotype and feeding regime.
- 15. The study • Based on a survey • Northern Malawi • April 2009 • n = 654 cows from 284 farms 40% of households • Detail in Kasulo et al. (2010)
- 16. Data Analysis • 4 production systems – upgrade on stall feeding system (UGS) – upgrade on grazing system (UGG) – base genetics on stall feeding system (BGS) – base genetics on grazing system (BGG) • Production levels were reflected using milk yield (MY) and calving interval (CI).
- 17. Results 0 10 20 30 40 50 60 1-3kg 4-6kg 7-9kg 10-12kg 13-15kg 16-18kg 19-21kg 22-24gk 28-30kg Milk yield per cow (kg) Frequency During study •Of the Holstein Friesian, Jersey and Aryshire , 48% dry •Malawi Zebu, 59% dry.
- 18. Results: Milk Yield 0 1 2 3 4 5 UGS UGG BGS BGG Dairy System Ranking Ranking MY Expected ranking MY
- 19. Results: Calving Interval 0 1 2 3 4 5 UGS UGG BGS BGG Production System Ranking Ranking CI Expected ranking CI
- 20. Conclusion • The biologically-oriented approach to classify smallholder dairy systems has the potential to categorise smallholder farms in a meaningful way. • The approach offers an opportunity to study long-term specific effects and a wide range of management strategies for smallholder dairy farming.