Farm typologies in Africa RISING




Introduction
Africa RISING is testing alternative technology options with heterogeneous populations of farmers that will likely respond to the technologies differently. The identification of different farmers' types within the program is therefore crucial to achieve the following goals:
  • Identify suitable farms to target innovations (ex-ante): we assume that not all innovations are appropriate for all farms, and that structuring into groups would support the identification of technology-specific suitable farming systems.
  • Scale out innovations: on the basis of the heterogeneity in a population we can formulate extension messages, policies and other incentive schemes to further spread the use of designed innovations.
  • Assess agro-economic effects (ex-post) Explaining trends and farmer ‘behavior’ (functional characteristics, including sustainable intensification indicators) and verification of the agro-economic effects of the interventions for different farm types.

IFPRI produced five typology reports, one for each AR country, that use the harmonized ARBES data (Africa RISING Baseline Evaluation Surveys) to produce statistical typologies of the farmers in each project area. Each report describes the methodology used to derive the groups as well as the main characteristic of each of the obtained types. These results await further validation and testing from the research teams.

Africa RISING typologies

Here we want to provide an overview of the different typologies used:






Links to relevant materials


https://cgspace.cgiar.org/handle/10568/67875
https://cgspace.cgiar.org/handle/10568/67869

Overview of farm/household datasets and typologies in Africa RISING

Methods, protocols, procedures
Protocol for statistical typology construction (CRP Humidtropics): http://humidtropics.cgiar.org/openaccess/?did=231

Available datasets
Country (region/district)
Teams
Size (n)
Type (techniques)
Objective/hypothesis
Tanzania
IITA

Participatory

Tanzania (Babati, Kongwa, Kiteto)
IFPRI
810
Statistical
To compare beneficiaries of AR agricultural technology innovations with randomly selected non-beneficiaries and control households
Tanzania (Babati, Kongwa, Kiteto)
WUR
160
Statistical (PCA, HC)
To provide a starting point for evaluation of agronomic interventions and tradeoff analysis as affected by farm endowment
Tanzania (Babati)
WUR/CIAT
120
Statistical (PCA, HC)
To provide a starting point for evaluation of animal feeding interventions and tradeoff analysis as affected by farm endowment
Malawi (Dedza, Ntcheu)
IFPRI
1149
Statistical
To compare beneficiaries of AR agricultural technology innovations with randomly selected non-beneficiaries and control households
Malawi
MSU



Malawi (Dedza, Ntcheu)
WUR
80
Statistical (PCA, HC)
To provide a starting point for evaluation of agronomic interventions and tradeoff analysis as affected by farm endowment
Ethiopia
ILRI
c.500
Participatory / Statistical based on livelihoods capital assets
Community characterisation and stratification.
Ethiopia
ILRI
c. 200
Statistical (study baseline)
Explaining experiences / uptake of tree lucerne.
Ghana (UE, UW, NR)
WUR
240
Statistical (PCA, HC)
To provide a starting point for evaluation of agronomic interventions and tradeoff analysis as affected by farm endowment
Ghana (Northern Region)
WUR
80
Participatory
To assess the community perspective on the diversity of farms and households; verification of statistical typology
Ghana (UE, UW, NR)
IFPRI
1284
Statistical
To compare beneficiaries of AR agricultural technology innovations with randomly selected non-beneficiaries and control households

Typologies

Agenda of typologies meeting
August 31
Topic
Presenter
9:30
Introduction -What are the typologies
General discussion
10:00
Why are typologies useful in Africa RISING
Chief Scientists
10:30
Break

11:00
Review of typologies for farming system analysis
Jeroen
11:30
Review of typologies for Ethiopian Highlands
Peter T.
12:00
ARBES data available for typologies
Carlo
12:30
How to make the best use of data for typology construction
General discussion
1:00
Lunch

2:00
Typologies for TZA
Mateete (lead)
3:00
Typologies for MWI
Mateete (lead)
4:00
Typologies for ETH
Peter T.
5:00
Typology consistency checks across countries
General discussion
6:00
Closing




September 1


9:00
Recap of day 1
Carlo
9:30
What typologies would be useful for GHA and MLI
Asamoah
10:00
Data available for GHA and MLI typologies
Carlo
10:30
Break

11:00
Typologies for GHA and MLI
Asamoah (lead)
12:00
Final considerations and wrap-up
General discussion
12:30
Lunch


Responsibilities and timeline


Draft Concept Note for typologies in Africa RISING (courtesy of Jeroen Groot)