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. 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. 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. 3. Outline • Smallholder dairy in Malawi • Importance of smallholder dairying • Rationale • Data and Analysis • Discussion and Conclusions
  4. 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. 5. Smallholder Dairy in Malawi
  6. 6. Milk Bulking Groups
  7. 7. Importance of Smallholder •Income •Food security •Employment •Business catalyst
  8. 8. Milk Consumption •      Average milk consumption = 4.5 – 6.0 kg/capita •      Africa = 15 kg/capita •      Recommended (FAO) = 200 kg per capita
  9. 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. 10. Feeding Fodder Percentage Crop residues 49 Standing Hay 20 Fodder banks 20 Silage 11 Supplementaion • Maize bran • Dairy mash • Mineral
  11. 11. Rationale • Input- output driven classification – Assumes predetermined level • Land holding size – Input driven • Formal vs informal – Product driven Biologically driven
  12. 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. 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. 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. 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. 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. 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. 18. Results: Milk Yield 0 1 2 3 4 5 UGS UGG BGS BGG Dairy System Ranking Ranking MY Expected ranking MY
  19. 19. Results: Calving Interval 0 1 2 3 4 5 UGS UGG BGS BGG Production System Ranking Ranking CI Expected ranking CI
  20. 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.

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