Typologies

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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: File:Typology Characterization Ethiopia_Final.pdf File:Typology Characterization Ghana_Final.pdf File:Typology Characterization Malawi_Final.pdf File:Typology Characterization Mali_Final.pdf File:Typology Characterization Tanzania_Final.pdf

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): [[1]]

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 File:Typology timeline.xlsx

Draft Concept Note for typologies in Africa RISING (courtesy of Jeroen Groot) File:Typology_construction_use_AR.docx