techfit: a tool for prioritizing feed technologies
DESCRIPTION
Presented by Adugna Tolera (Hawassa University) at the Training Workshop on Feed Assessment Tools, ILRI, Addis Ababa, 18-21 November 2013TRANSCRIPT
TechFit : A Tool for Prioritizing Feed Technologies
Adugna Tolera (ICARDA)
Training on Feed Assessment Tools, ILRI, Addis Ababa, 18-21 November 2013
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
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
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
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
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
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
Match farmers’ context to technologyScore (1-5) for technology attribute
Score (1-5) for context attribute
Land (1-5) X Land (1-5) =Labor (1-5) X Labor (1-5) =Credit (1-5) X Credit (1-5) =Input (1-5) X Input (1-5) =Knowledge (1-5) X Knowledge (1-5) =Total score =Rank ?
If technology demands land => low score for landIf farmers do not have or have very small land holding => Low score for land
Technology filter
Scope for improvement of attribute
s
Context relevance (score 1-
6; low-high))
Impact potential (score 1-6; low-high)
Total score
(context X impact)
Requ Score 1-3
(1 for more; 3 for less)
Avail Score 1-3
(1 for less; 3 for
more)
Requ Score 1-3
(1 for more; 3 for less)
Avail Score 1-3
(1 for less; 3 for
more)
Requ Score 1-3
(1 for high;
3 for low)
Avail Score 1-3
(1 for less; 3 for
more)
Requ Score 1-3
(1 for high;
3 for low)
Avail Score 1-3
(1 for less; 3 for
more)
Requ Score 1-3
(1 for high;
3 for low)
Avail Score 1-3
(1 for less; 3 for
more)
Score 1-5 (1 for
less and 5 for
more)
Urea treatment of straw
2 3 6 3 2 2 2 2 0
Supplement with UMMB
2 5 10 3 3 3 2 1 1 1 1 3 1 2 22
By-pass protein feed
1 3 3 3 3 1 1 3 0
Feed conservation (surplus) (HAY)
4 3 12 3 3 2 2 3 3 3 3 3 3 1 41
etcetc
III.
TECHNOLOGY FILTER
(Technology options to
address quantity, quality,
seasonality issues)
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
Total Score
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)
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?
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
Intervention nameClear 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
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 …
Benefits
• Primary (including time dimension, etc.)
• Secondary• …
Adoptability characteristics• (=conclusion: simplicity, observability, use, etc.• …
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)
Factor Guiding points/questions to keep in mind in FGD
Relative advantage superiority
CBA analysis, but subjective points may be raised in group• Quality of labour (drudgery), etc.
Compatibility • Riskiness - technology, risk aversion • Social acceptability &/or taboos • Effect on gender aspects or child labour• Possibility of adapting to or in local situation
Complexity 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
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
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
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?
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
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
Farming system Which of the following best describes the target group:
Pastoral Agro-pastoral/mixed Intensive/mixed (crop-livestock) Landless
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.
Strengths of the tool
Enables rapid location specific prioritization and short listing of feed technologies in different agro-ecologies 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
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
Project partners in Ethiopia
Africa Research in Sustainable Intensification for the Next Generation
africa-rising.net
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