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

The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.

Thank You