Project Duration: 09 Dec 2022 - 25 Jan 2023
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 percent of staple foods (maize, millets, and groundnuts), is particularly vulnerable as it has access to less than 5 percent 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.
Coupled with satellite imagery and geofencing, farms can be tracked on the amount of forest coverage for afforestation: were any trees planted? Were any buildings built? Are fields being irrigated during a period? And the potential carbon that will be offset. This data can promote investment decisions based on potential tonnage of carbon that will be reduced and credits gained, track and evaluate the carbon or resilience credits. Resilience evolution projection for climate change could be added to the use case to track vulnerability traits.
We encourage applications from teams that can identify, access, and use suitable data to build feasible solutions for any portion of this proposal.