Women’s Empowerment, Mixed Methods, And Long-Term Collaboration in Agricultural Research for Development

by Shelton Chinorumba

Published: March 24, 2026 • DOI: 10.47772/IJRISS.2026.100300020

Abstract

Women’s empowerment has become a central objective in agricultural research for development, yet its complex, context-specific nature poses conceptual, methodological, and operational challenges. This article reflects on a long-term, feminist-informed collaboration of more than a decade that used mixed methods to study women’s empowerment in agriculture across diverse projects, countries, and research teams. Drawing on quantitative indices, including adaptations of the Women’s Empowerment in Agriculture Index (WEAI and pro-WEAI), and complementary qualitative work, the collaboration interrogated how empowerment is defined, measured, and experienced, and how these understandings evolve over time. Mixed methods were applied within single projects, across related sub-projects, and cumulatively across projects to generate insights into joint asset ownership, intra-household decision-making, time use, and agency, revealing empowerment as multidimensional, relational, and dynamic rather than a static outcome. The article examines how sustained collaboration among researchers from the Global North and South, and across disciplines, challenged assumptions embedded in standardized measures, sharpened conceptual precision, and exposed tensions between instrumental and intrinsic, as well as individual and collective forms of agency. It also highlights the institutional conditions that enable meaningful collaboration—particularly long-term funding, leadership, and iterative learning processes—and how these conditions shape knowledge production and policy influence. The article concludes by proposing priorities for the next generation of agricultural research for development on women’s empowerment, including deeper integration of qualitative inquiry, attention to shifting norms and power relations, and the continued co-development of metrics that remain sensitive to context while enabling comparison across interventions and time.