Perceived Potential Of AI-Enabled Climate Services For Enhancing Rural Women Farmers’ Participation In Climate Emergency Management In Gicumbi District, Rwanda

Abstract

Climate-induced flooding continues to threaten agricultural livelihoods, food security, and rural development across Sub-Saharan Africa, with women farmers disproportionately affected due to limited access to resources, Artificial Intelligence (AI), climate information, and adaptive technologies. However, limited research has examined the gendered dimensions of AI adoption among rural women farmers in Africa, particularly in climate-vulnerable regions. This study addresses these gaps by examining how AI can enhance the participation of rural women farmers in climate emergency management in Gicumbi District, Rwanda. Adopting the Capability Approach and Community-Based Disaster Risk Management (CBDRM) frameworks, the study employed a mixed-methods approach involving key informant interviews and focus group discussions with GIS-based spatial analysis using high-resolution satellite imagery and Digital Elevation Models (DEM) to identify flood-prone areas. Data were collected from women farmers across selected sectors of Gicumbi District to assess flood experiences, access to climate information, perceptions of AI technologies, and barriers to climate adaptation. Spatial analysis was used to identify communities exposed to varying levels of flood risk. The findings reveal that women farmers in highly vulnerable communities, particularly Tidgiri, Gaseke, Rwafandi, Mugina, and Rusumo, experience recurrent crop losses, livelihood disruptions, and reduced food security due to seasonal flooding. Although participants recognized the potential of AI-enabled weather forecasting, flood prediction, and early warning systems to improve preparedness and decision-making, adoption remains constrained by low digital literacy, limited internet access, financial barriers, and inadequate technological infrastructure. The GIS results further demonstrate that flood vulnerability is closely associated with low-lying terrain and poor accessibility. The study concludes that integrating AI-driven climate services with community-based adaptation strategies can strengthen women's participation in climate emergency management. Targeted investments in digital inclusion, climate information services, and spatially informed disaster planning are essential for building resilient and gender-responsive agricultural systems in Rwanda.