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Tanzania dairy genetics: Matching dairy genetics to smallholder farmers’ input systems

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Presented by Morris Agaba (Nelson Mandela African Institute of Science and Technology) at the Inception workshop of the AgriTT project: Evaluation of breed composition, productivity and fitness for …

Presented by Morris Agaba (Nelson Mandela African Institute of Science and Technology) at the Inception workshop of the AgriTT project: Evaluation of breed composition, productivity and fitness for smallholder dairy cattle in Tanzania, Dar es Salaam, 10-11 June 2014

Published in: Technology

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  • 1. Inception workshop of the AgriTT project: Evaluation of breed composition, productivity and fitness for smallholder dairy cattle in Tanzania, Dar es Salaam, 10-11 June 2014 Morris Agaba The Nelson Mandela African Institute of Science and Technology
  • 2. Farmer Targeting and Feedback Aims: Identify a diverse representation of farmers needed to capture the range of genotypes, and on farm practices in the study sites. Develop or refine the most optimal mechanisms to encourage continued participation in recording and uptake of research outputs and of improved management practices.
  • 3. Farmer Targeting and Feedback
  • 4. Criteria for Selection  Randomized in all the wards of Rungwe and Lushoto surveyed by MilkIT.  One at least one cow with either – • a calf born in last 3 months • in third trimester.  Willingness of farmer to participate.  Where farmers are closely clustered – e.g. Lushoto should we target the entire cluster?  If farmers are in groups – do we target entire group?
  • 5. Data collection  Reproductive performance and health  Heat expression and detection.  Mating strategy –AI/Natural service  Efficiency of service  Genital infections/other infections/diseases  Production data • Growth rate of calves • Milk off take. • Milk sales/home consumption • Milk quality? • Enterprise characteristics?
  • 6. Data collection  Animal Health and welfare  Incidences of disease: clinical signs/mastitis  Interventions and outcomes thereof  Housing - and hygiene and waste management  Inputs Data  Source and type of feeds .– especially forages  Amounts of feeds and supplements  Type of vegetation? Bioactive compounds? Nutrient analysis? e.g. minerals?  What other Data should be collected?
  • 7. Data collection • Enterprise characteristics: the farmer and farm  Gender, Role in house hold, Role in Animal Management.  Education Characteristics, Social Network, Membership to “trade union”, Access to market  Size of enterprise - number of animal, land size, type of infrastructure – animal housing.  Disposition to adopting new methods, experience with dairy farming.  Value of cattle compared to there revenue streams
  • 8. Innovations on Feed back  Characterization of information needs • Type of information • Quality – including timeliness • Regularity of feed back  Assessment of feedback mechanisms • Peer to peer (farmer visits and exchange) • Existing extensions system mechanism. • Possible role of ITC – especially mobile phones  How do we evaluate the effects of feedback? What outcomes to target?
  • 9. Innovations on Feed back (chickenApp) • Data is Data, no value if not mined. • Paper data expensive. • Paper data is vulnerable. • IT may be one tool to transform recording. Example of Farm Records
  • 10. Innovations on Feed back (chickenApp) • Front line workers armed with client specific Data. • Aggregate data available to higher level actors. • Correlations can be made with other events, deaths, illness, change of feed. INPUTS (monthly) EGG OUT (Daily)
  • 11. Information “Value Chain” • How best to model information flows? • What best intervention points? • What types of analyses? • What media of communication?