World Bank: Agricultural Surveys Can Drive AI-Powered Farming In Nigeria, Nine African Countries

Toyyibat Qasim

The World Bank Group has identified investments in agricultural surveys and data collection as a critical foundation for artificial intelligence-driven farming in Nigeria and nine other African countries, saying the data could help farmers predict crop yields, detect pests and manage production risks.

The global financial institution said integrating agricultural survey data with satellite imagery, weather information, soil maps and other digital resources could transform agricultural decision-making, improve productivity and provide farmers with more targeted advisory services.

According to a report on agricultural statistics and artificial intelligence readiness, recent surveys conducted in 10 African countries, including Nigeria, have generated valuable information that could support the development of digital agricultural systems and locally relevant AI applications.

The countries covered by the surveys are Burkina Faso, Liberia, Malawi, Mali, Niger, Nigeria, Senegal, Sierra Leone, Tanzania and Uganda.

The surveys were conducted with support from World Bank-financed statistical operations and the 50×2030 Initiative, generating data on farms, crops, agricultural production, livestock, farming practices and the socioeconomic conditions of farming households.

The World Bank explained that although the investments were initially designed to strengthen agricultural statistics and support evidence-based policymaking, they could now serve a broader purpose by providing the ground-level information required to develop artificial intelligence applications for agriculture.

It noted that satellite imagery, weather systems and soil maps could reveal important information about environmental and agricultural conditions but were not sufficient on their own to establish what was happening on individual farms.

According to the report, agricultural surveys provide critical details about crops cultivated, land sizes, inputs used, harvests, production losses and farmers’ access to irrigation, extension services, finance and markets.

It said combining such information with other digital data sources could help train and validate AI models for estimating crop yields, identifying production challenges and assessing agricultural risks.

The institution cited research involving smallholder maize farms in Uganda, which demonstrated that combining satellite imagery with household survey data produced more accurate crop-yield estimates than relying on satellite imagery alone.

The report further explained that integrating agricultural survey observations with rainfall patterns, temperature records, soil maps, market information and administrative records could support the development of systems capable of identifying crops, forecasting harvests, detecting drought stress and directing agricultural extension services to farmers who need them.

However, the World Bank cautioned that collecting agricultural data alone would not automatically make Nigeria and other African countries ready to adopt artificial intelligence in farming.

It identified reliable digital infrastructure, standardised and interoperable data systems, secure access to information, appropriate governance frameworks and skilled personnel as essential requirements for translating agricultural data into practical AI applications.

The report said the World Bank Group’s AI readiness framework was built around four critical requirements: connectivity, computing capacity, context and competency.

It explained that while agricultural surveys could provide the local information needed to develop relevant AI systems, countries must also strengthen their digital infrastructure, invest in technical expertise and establish safeguards for responsible deployment of the technology.

To achieve this, the World Bank recommended sustained investment in digital agricultural surveys, stronger integration of survey information with geospatial data and the modernisation of survey methods to accommodate emerging digital applications.

It also called for the development of national agricultural data platforms and the testing of practical AI applications before wider deployment.

Among the potential applications identified are crop mapping, yield forecasting, drought monitoring, crop-loss assessment, pest surveillance and targeted agricultural advisory services.

The report also highlighted opportunities for regional cooperation among African countries, noting that comparable agricultural datasets across national borders could help develop more reliable AI models while allowing individual countries to retain control over their agricultural information.