As the main source of livelihood for the majority of the population, the performance of agriculture is a key determinant of rural livelihood resilience and poverty levels. General challenges facing smallholder farmers (SHF’s) include low and erratic rainfall, low and declining soil fertility, low investment, shortages of farm power – labour and draft animals, poor physical and institutional infrastructure, poverty and recurring food insecurity. Agricultural production is also vulnerable to periodic droughts. The peasant sector, which produces 70 per cent of staple foods (maize, millets, and groundnuts), is particularly vulnerable as it has access to less than 5 per cent of national irrigation facilities.
We have seen traction in demand for rural digital advisory services, however current systems for digital advisory are focused on the broad delivery of extension services based on a large number of farmers. AI can revolutionize extension services through the provision of individualized advisory based on several data elements (on-farm data, satellite imagery, remote sensing, and GIS) thereby increasing the value for extension services to the individual farmer. Although use cases are being built in other development agencies and countries, we have not seen greater traction on AI and other technology integration in IFAD-supported projects. This could be an opportunity to develop a Proof-of-Concept (POC) and develop a potential use case for scale.