World Bank pushes digital data platforms for agriculture

The World Bank has proposed developing national agricultural data platforms that could help Nigeria and other African countries build the data infrastructure needed to deploy artificial intelligence in farming.

The proposal is part of a broader push by the bank to turn investments in agricultural statistics into digital infrastructure capable of supporting AI-powered services, including crop mapping, yield forecasting, pest surveillance and targeted agricultural advice.

“Nigeria is among 10 African countries where recent agricultural surveys supported by World Bank-financed statistical operations and the 50×2030 Initiative have generated data covering farms, crops, production, inputs, livestock, farming practices and agricultural households,” the lender revealed in a recent blog post.

The World Bank said these investments, originally designed to strengthen agricultural statistics and evidence-based policymaking, could also serve a second purpose by providing the data foundations for digital and AI-enabled agriculture.

“AI is only as useful as the data and digital ecosystem behind it,” the bank said. It said agricultural AI requires more than satellite imagery, weather information and soil maps because these sources cannot fully explain conditions at individual farms.

Agricultural surveys can provide that ground-level information by capturing what farmers plant, the land and inputs they use, what they harvest, production losses and their access to irrigation, extension services, finance and markets.

When appropriately georeferenced and combined with other sources, the data could provide the ground-level information needed to train and validate AI models, according to the bank.

The World Bank pointed to research in Uganda showing how satellite imagery and household survey data could be combined to improve crop-yield estimates, with ground observations helping to reduce errors associated with relying on satellite information alone.

For Nigeria, the proposed architecture could bring together agricultural survey data with satellite imagery, rainfall and temperature records, soil maps, market information and administrative agricultural records.

Such an integrated system could support models designed to identify crops, estimate yields, predict areas exposed to production losses, detect emerging pest or drought stress and help governments target extension services and agricultural investments.

• NITDA advocates inclusive AI framework for Nigeria
• Teachers’ council develops AI-powered lesson tool for Nigerian schools
• Trump renames AI as ‘Super Intelligence’

The bank said national agricultural data platforms would also make well-documented data more accessible to ministries, researchers and policymakers, while governance arrangements would be needed to protect farmers’ privacy.

However, it cautioned that collecting high-quality agricultural statistics alone would not make a country ready for agricultural AI. It said digital readiness would require agricultural data to be standardised, documented, appropriately georeferenced, interoperable, securely accessible and capable of being linked with other sources.

The World Bank identified four key requirements for broader AI readiness: connectivity, compute, context and competency, alongside effective governance and cybersecurity.

Agricultural surveys, it said, contribute particularly to the “context” component by providing reliable information on local farmers, crops, production systems and conditions.

The bank recommended that countries begin with practical applications rather than immediately pursuing large-scale national AI platforms. Potential early use cases include crop mapping, yield forecasting, drought and crop-loss monitoring, pest surveillance and targeted advisory services.

It also proposed stronger regional cooperation as more African countries generate comparable agricultural datasets.

A federated African agricultural data architecture could allow countries to retain control of their data while sharing standards, methodologies, model components and lessons, the bank said.

The proposed approach reflects a shift in how agricultural data investments are viewed. Rather than ending with the publication of statistics for policymaking, the bank said countries could integrate those datasets with other digital sources, train and validate models, generate agricultural intelligence and eventually deliver services to farmers and policymakers.

The World Bank said the next agricultural data gap may therefore not simply be whether countries have statistics, but whether their systems are sufficiently integrated, accessible, interoperable, locally grounded and AI-ready.

It said African countries that have already invested in agricultural surveys have an opportunity to build on those systems and develop AI applications capable of understanding local farms, crops and production conditions.