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Exploring feminization of agriculture through gender dynamics across scales

Alessandra Galiè. 1. , . Stephen Oloo. 1. , Catherine Pfeifer. 2. 1. International Livestock Research Institute (ILRI). 2 . Research Institute of Organic Agriculture (. FiBL. ). Writeshop at KIT, November 2019.

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Exploring feminization of agriculture through gender dynamics across scales

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Exploring feminization of agriculture through gender dynamics across scales

Alessandra Galiè


, Stephen Oloo1, Catherine Pfeifer21International Livestock Research Institute (ILRI)2 Research Institute of Organic Agriculture (FiBL)

Writeshop at KIT, November 2019


This study in a nutshell

Methodological inter-disciplinary study

to understand how gender dynamics

at household and community level

influence Feminization of Agriculture (


Research question:How do intra-household gender dynamics scale to shape the regional/global trend ‘FoA’?


What we do in this study

Develop a methodology to study how gender dynamics at hh and community level scale to influence FoA.Pilot methodology with existing datasets on FoA in selected countries of sub-Saharan Africa (focus on livestock) and qual findings.

Validate/challenge persistent

assumptions about FoA.


How we do it: 6 main steps

Develop a conceptual framework on drivers affecting FoA

Identify key drivers and dynamics behind FoA

Use existing DHS national-level data to identify countries with FoA or MoA Use DHS, WB and SIGI to analyse how 3 drivers interact to form clusters of countriesUse existing data sets at hh

level (i.e. DHS) to identify interaction among drivers in the countries identifiedIdentify patterns of interactions

Conducts qualitative fieldwork to explore how gender dynamics have played out in realityAdjust the drivers and modelPossibly: adapt ‘model’ for exploring future emerging social landscapes in agriculture


Key findings: 3 drivers identified

Individual capabilitiesMarket opportunities


(policies and gender norms)

Individual and household level

Local, national, global levels


, community, national levels


Key Findings

cont…Identified 6 clusters of countries with similar patterns: Cluster 1 : Uganda

Cluster 2 : Congo-BrazavilleCluster 3 : Kenya, Liberia, Rwanda, Tanzania

Cluster 4 : Burundi, Benin, Lesotho, Nigeria Cluster 5 : Democratic Congo, Namibia, Zimbabwe, ZambiaCluster 6 : Egypt, Ghana, Sierra LeoneOverall: Only in Ug: FoA; Proportion of women in ag is decreasing (Y2-Y1) in 3 clusters and increasing in 3 clusters. The proportion of men in ag (Y2-Y1) is increasing in all clusters. First 5 clusters show a move from agriculture to non-agriculture, these are also the countries with economic growth.There is a return to agriculture (mostly by men but also some women) in cluster 6 - countries which show economic recession.


Questions and concernsOur aim is to scale gender dynamics (not gender-disaggregated data). Are we getting there? Is qual the best approach? Or how can we scale unpredictable, gendered, human behaviour?