typologies

=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 __**

Protocol for statistical typology construction (CRP Humidtropics): []
 * Methods, protocols, procedures**


 * 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 ||  ||
 * 12:30 || Lunch ||  ||


 * Responsibilities and timeline**


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