Typologies for farming systems analysis in Africa RISING

Typologies for farming systems analysis in Africa RISING

Jeroen Groot

(Wageningen University)

with IITA, ICRISAT, MSU, CIMMYT

Africa RISING Monitoring and Evaluation MeetingArusha, Tanzania, 13-14 November 2014

Use of typologies

TargetingSuitable interventions

Scaling outAdoption, dissemination

Scaling upImpact assessment

SelectionFarms to work with

Functional typology

Different approaches

• Ghana: statistical, expert-based, community-based• Mali: existing typology (Gatien Falconnier)• Malawi and Tanzania: statistical• Zambia: repeated statistical, using SIMLEZA baseline

Can be linked back to the structural IFPRI types

Typology methodsDiverse farming systems

Data collection

Selection of key variables

Grouping techniques

Farm types

Experts

Community

Alvaréz et al. (2014) Typology Guideline for Humidtropics

Typology II Zambia

Typology III Zambia

Lundazi Chipata

Typology III Zambia – Lundazi

Typology III Zambia – Lundazi

Comparison of districts

Ghana: expert based classification

Arable landLivestockHH sizeAssets

Wealthindicators

Type 1

Resourceendowment

Productionorientation

Source ofincome

Type 2

Type 3

Type 4

Type 5

LRE

MRE

HRE

Subsistence

Consumption > market

Consumption = market

Consumption < market

On-farm only

On-farm> off-farm

On-farm= off-farm

On-farm< off-farm

Ghana: community-based

Conclusions

• Different approaches due to contrasts in development stage of projects, and in availability of data and typologies

• Comparison of approaches and classifications can help validation and provides insights in different perceptions of farms and their opportunities and constraints

• Good basis for further evaluation of the entry points, exploration of tradeoffs, to inform decisions on improvements in farm performance

Africa Research in Sustainable Intensification for the Next Generation

africa-rising.net

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