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

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.

Farmer Targeting and Feedback

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?

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?

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?

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

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?

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

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)

Information “Value Chain”
• How best to model
information flows?
• What best
intervention
points?
• What types of
analyses?
• What media of
communication?