[Nigerian Chapter] Improving Digital Advisory Services for Rural Farmers

There are several challenges that rural farmers in Nigeria may face with respect to accessing and utilizing digital advisory services. Some of these challenges could include:

• Limited access to technology: Many rural farmers in Nigeria may not have access to the necessary technology, such as smartphones or computers, to take advantage of digital advisory services.
• Limited access to reliable internet connectivity: Even if farmers do have access to the necessary technology, they may not have reliable internet connectivity, which can make it difficult to access and use digital advisory services.
• Limited digital literacy: Many rural farmers may not be familiar with or comfortable using technology, which can make it difficult for them to use digital advisory services.
• Limited access to financial resources: Many rural farmers may not have the financial resources to pay for digital advisory services, which can be a barrier to accessing them.
• Limited understanding of the benefits of digital advisory services: Some farmers may not be aware of the potential benefits of digital advisory services or may not understand how to use them effectively.
• Limited availability of language-specific services: Digital advisory services may not be available in the languages spoken by some rural farmers, which can make them difficult to use.
• Limited trust in digital advisory services: Some farmers may be skeptical of or lack trust in digital advisory services, which could be a barrier to their adoption.

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 technologies 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.

The goals of this project can be broken down into the following:

• Facilitate predictive analytics on production and expected output thereby allowing farmers to know expected output and potential markets based also predictive analysis of market trends based on publicly available market data.
• Make decisions on the potential expected outputs based on analytics of weather and climate and at the same time support decisions on the best input or crop series to produce based on expected quantity and quality vs Production costs.

Data Analysis, Data Visualization, project management and communication and Machine Learning