Presented by Adugna Tolera (Hawassa University) at the Training Workshop on Feed Assessment Tools, ILRI, Addis Ababa, 18-21 November 2013

1. TechFit : A Tool for Prioritizing Feed
Technologies
Adugna Tolera (ICARDA)
Training on Feed Assessment Tools, ILRI, Addis
Ababa, 18-21 November 2013

2. Objectives
 To have a common understanding, interpretation and application of
the tool
 To learn how to score and match technology attributes and context
attributes of farmers
 To customize the application of the tool to the local context

3. Background
 Reality No. 1 (Reality of farmers)
 Livestock production is important
 Feed is a major constraints (FEAST & Other reports)
 Farmers are looking for a remedy to the problem
 Reality No. 2 (Reality of research & development
efforts)
 Various feed technologies generated by the research
system
 Lack of systematic approach for prioritizing available
feed technologies
 Poor adoption rate of available technologies
 Wastage of substantial efforts and resources

4. Feed interventions often do not work – why?
 Failure to place feed in broader livelihood
context
 Lack of farmer design and ownership
 Neglect of how interventions fit the
context: land, labour, cash, knowledge
etc
FEAST
Techfit

5. What is TechFit?
 A discussion tool for prioritizing feed technologies
 Helps to identify suitable technologies for evaluation and screening
 Designed to filter best bet technologies from a basket of
technologies available to farmers
 Provides better understanding of why and why not technologies
work or do not work

6. How does it work?
Technology options to address feed
problem (list of potentially available
technologies)
Technologies are filtered at different
levels
Only technologies with high total scores
carried forward to the main filter

7. How does it work? (Cont …)
 Main filter – involves combining scores of technology and context
attributes to arrive at total score
 Technology attributes – requirement of a given technology for
land, labor, cash/credit, inputs and knowledge
 High score => low likelihood of adoption
 Context attributes – availability of land, labor, cash/credit, inputs
and knowledge
 High score => high likelihood of adoption

8. Match farmers’ context to technology
Score (1-5) for
technology attribute
Land (1-5)
Labor (1-5)
Credit (1-5)
Input (1-5)
Knowledge (1-5)
Total score
Rank
Score (1-5) for
context attribute
X
X
X
X
X
Land (1-5)
Labor (1-5)
Credit (1-5)
Input (1-5)
Knowledge (1-5)
=
=
=
=
=
=
?
If technology demands land => low score for land
If farmers do not have or have very small land holding => Low score for land

9. Technology filter
III.
TECHNOLOGY
FILTER
(Technology
options to
address
quantity,
quality,
seasonality
issues)
Urea treatment
of straw
Supplement with
UMMB
By-pass protein
feed
Feed
conservation
(surplus)
(HAY)
etc
etc
Pre-select the obvious
(5-6)
based
on context relevance
and impact potential
Score the pre-selected technologies based on the requirement, availability and scope for
improvement of five technology attributes
Attribute 1:
Land
Attribute 2:
Labour
Attribute 3:
Cash /credit
Attribute 4:
Input delivery
Attribute 5:
Knowledge
/skill
Scope for
improve
ment of
attribute
s
Context Impact
Total
Requ
Avail
Requ
Avail
Requ
Avail
Requ
Avail
Requ
Avail Score 1-5
relevanc potential score Score 1-3 Score 1-3 Score 1-3 Score 1-3 Score 1-3 Score 1-3 Score 1-3 Score 1-3 Score 1-3 Score 1-3 (1 for
e (score 1- (score 1- (context (1 for
(1 for
(1 for
(1 for
(1 for
(1 for
(1 for
(1 for
(1 for
(1 for less and
6; low- 6; low- X impact) more;
less;
more;
less;
high;
less;
high;
less;
high;
less;
5 for
high))
high)
3 for
3 for
3 for
3 for 3 for low) 3 for 3 for low) 3 for 3 for low) 3 for
more)
less)
more)
less)
more)
more)
more)
more)
2
3
6
3
2
5
10
3
1
3
3
3
4
3
12
3
2
3
3
2
2
3
3
2
1
2
1
1
2
3
1
2
1
1
3
3
3
0
1
2
3
3
3
Total
Score
22
0
3
1
41

10. How to do scoring and ranking?
• List of potential technologies obtained from the research
system
• Context relevance and impact potential – by experts at each
specific location
• Technology attributes (requirement of the technology for
land, labor, etc. ) – by experts
• Context of farmers (availability of land, labor etc.) – by farmers
(interview a group of representative farmers and ask them to
score)

11. Cost benefit analysis
• Short list the best 3-4 technologies for cost-benefit analysis
• What does the technology cost?
(type of feed, amount used, % of total feed, cost, % of total feed cost)
• What does the technology deliver?
(animal performance measure, % contribution to the performance
change, % contribution to income gain)
• Is it worthwhile?

12. Cost-benefit analysis
 Method not yet well developed and refined
 Mostly based on a number of assumptions using partial budget
analysis
 Compare additional costs and additional benefits i.e. marginal benefits

13. Intervention name
Clear description focusing processes and actions with pictures and
glossary for specific terms
Technical Information
Key technology attributes
• Land area required
• Labour, including gender
• Skills/Knowledge
• Cash/Credit
• External inputs
• Capital / infrastructure
Benefits
• Primary (including
time dimension, etc.)
• Secondary
• …
Applicability
• Purpose / Addresses constraints – opportunities
• Which animal?
• Agroecological, farming system suitability including socio-cultural
issues (e.g., taboos) if applicable
• Scale
• History of use
• Potential to integrate with …
Adoptability characteristics
• (=conclusion: simplicity, observability, use, etc.
• …

14. Adoptability Protocol - Process
• Past experiences regarding introduction of technologies,
including uptake, community feeling, etc.
• Ranking of livelihood ambitions/aspirations in general and
for agriculture and livestock in particular
After becoming more and more reductionist and
analytical, bring it back into the broader
perspective Objective  Subjective
 FGD on options
• Give info on options
• Ask community to rank
• Discuss ranking, ‘why’, etc.
(guiding points/questions)
 Link to CBA data
 Select trial farmers for AR
(model or pioneer farmers)

15. Factor
Relative
advantage
superiority
Compatibility
Complexity
Guiding points/questions to keep in
mind in FGD
CBA analysis, but subjective points may be raised
in group
• Quality of labour (drudgery), etc.
• Riskiness - technology, risk aversion
• Social acceptability &/or taboos
• Effect on gender aspects or child labour
• Possibility of adapting to or in local situation
Relatable to something simple, familiar, routine,
etc.
Trialability
Resources present for implementation
Observability
(Should perhaps be made as Techfit filtre)
Delivery
process
• Competence, capacity & buy-in of local
extension staff
• Enabling environment

16. Data we need to derive from FEAST to feed into Techfit
 Main constraint
 Seasonality
 Quantity
 Quality
 Dominant commodity
 Beef
 Dairy
 Sheep/Goats
 Pigs/poultry
 Farming system
 Pastoral
 Agro-pastoral/mixed
 Intensive/mixed (crop-livestock)
 Landless
 Core context attributes
 Requirement for land
 Requirement for labour
 Requirement for cash credit
 Requirement for inputs
 Requirement for knowledge/skills

17. Seasonality
 Consult seasonal calendar – estimate proportion of minimum
availability to maximum availability





1.0 = 0
>0.75 = 1
>0.5 = 2
>0.25 = 3
>0.0 = 4
 Is minimum in the dry/winter season? – Winter season scarcity
 Is minimum in the growing season? – Growing season scarcity

18. Quantity
 If you place more basal feed in front of your animals would they
consume it?





With extreme enthusiasm = 4
With considerable interest = 3
With some interest = 2
Yes but not immediately = 1
No = 0
 Something also about interest in supplemental/high quality feed?

19. Quality
 If you placed more basal feed in front of your animals would they
consume it?





With extreme enthusiasm = 0
With considerable interest = 1
With some interest = 2
Yes but not immediately = 3
No = 4

20. Commodity focus
 On a scale from 1 to 10 how important are the following enterprises to cash
income:
 Beef
 Fattening
 Breeding stock
 Dairy
 Sheep/Goats
 Fattening
 Breeding stock
 Pigs/poultry





0-2 = 0
2-4 = 1
4-6 = 2
6-8 = 3
8-10 = 4

21. Farming system
 Which of the following best describes the target group:




Pastoral
Agro-pastoral/mixed
Intensive/mixed (crop-livestock)
Landless

23. Experiences in testing and application of the tool
 Tested to prioritize feed technologies for 3 different
commodities (dairy, beef, sheep) in different parts of
Ethiopia
 Preceded by assessment of livestock production and
feeding systems using Feed Assessment Tool (FEAST)
 Enabled rapid prioritization and short listing of potential
feed technologies
 The pre-filter (context relevance score) helped a great
deal to focus attention on those technologies that are
relevant in the area.

24. Strengths of the tool
 Enables rapid location specific prioritization and
short listing of feed technologies in different agroecologies and production systems
 Puts feed in a broader context and filters
technologies for specific contexts (agro-ecology,
production system, farmers’ contexts etc.)
• It is robust in screening out technologies that are not
relevant in a given context
• Gives good indication why some technologies are not
easily adopted

25. Limitations of the tool
 All scores are based on subjective judgments. Thus
one has to be well versed with the subject matter
and the local conditions to give a realistic score.
 Cost benefit analysis is based on a number of
assumptions and the validity depends on the
soundness of each assumption.
 Most feed technologies make only partial
contribution to the total diet  a challenge of
partitioning the contribution of the feed in question
to the performance of the animal

26. Project partners in Ethiopia

27. Thank You
Africa Research in Sustainable Intensification for the Next
Generation
africa-rising.net
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