Artificial intelligence is rapidly opening new possibilities for agriculture. From predicting crop yields and detecting pests to providing customized agronomic advice and improving access to markets and finance, AI could help farmers make better decisions and governments design more responsive agricultural policies. For agriculture in Africa, where smallholder farmers often operate with limited access to information, extension services, and climate-risk tools, these possibilities are particularly compelling.
But there is a catch: AI is only as useful as the data and digital ecosystem behind it.
This is where investments already being made in agricultural statistics could provide an important head start.
Across 10 African countries (Burkina Faso, Liberia, Malawi, Mali, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, and Uganda) recent agricultural surveys undertaken with support from World Bank-financed statistical operations and the 50×2030 Initiative to close the agricultural data gap have generated increasingly rich data on farms, crops, production, inputs, livestock, agricultural practices, and agricultural households. These surveys have strengthened the evidence-base available to governments and other stakeholders to inform agricultural and food security policies.
In addition, seven of these countries (Burkina Faso, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda) have also benefited from the World Bank Group’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) program, which has supported nationally representative longitudinal household surveys with a strong focus on agriculture.
These investments were primarily intended to strengthen agricultural statistics and support evidence-based policymaking. But they may now have a second important use: helping countries build the data foundations for digital and AI-enabled agriculture.
AI needs data from the farm
Agricultural AI depends on data. Satellite imagery, weather systems, and soil maps can reveal vegetation, climate, and agroecological conditions, but they cannot fully explain what is happening on farms. That requires ground-level observations.
Agricultural surveys provide this context by recording what farmers plant, the land and inputs they use, what they harvest, the losses they experience, and their access to irrigation, extension, finance, and markets. When appropriately georeferenced and linked with other sources, these data provide the “ground truth” needed to train and validate AI models.
For example, Eyes in the Sky, Boots on the Ground by Lobell et al. (2020) illustrates how satellite and survey data can be combined to improve crop-yield measurement. Using smallholder maize plots in Uganda, the researchers found that satellite imagery captured useful information on crop conditions and yield variation, while ground observations from a household survey provided the plot-level information needed to train the models and significantly reduce the error in yield estimates present in the satellite-only approach. Combining the two data sources allows for improved estimates of crop yields at scale, as well as validation of model performance against ground-based measures, demonstrating how agricultural surveys can provide the “boots on the ground” needed to calibrate and validate the scalable “eyes in the sky” perspective offered by satellites.
The opportunity, therefore, is not to choose between surveys and geospatial, weather, or environmental data, but to combine them as complementary inputs for AI-enabled agricultural decision-making.
From statistical data to digital agricultural infrastructure
Doing so requires a shift in how agricultural data investments are viewed.
Traditionally, the pathway has been relatively linear:
Collect data → produce statistics → publish reports → inform policy
That remains essential. But an AI-enabled agricultural data system could extend the pathway:
Collect data → produce statistics → integrate data → train & validate models → generate agricultural intelligence → deliver services to farmers & policymakers
Imagine combining a country’s agricultural survey observations with satellite imagery, rainfall and temperature data, soil maps, market information, and administrative agricultural records. Such an integrated system could potentially support models that identify crops, estimate yields, predict areas at risk of production loss, identify emerging pest or drought stress, classify farms according to their production constraints, target extension services, guide agricultural investments, and connect farmers with relevant market and agribusiness opportunities.
This is increasingly consistent with the direction of digital agriculture. The World Bank’s digital agriculture roadmap identifies farmer, plot, crop and product registries; soil maps; crop-health surveillance; agricultural data exchange standards; and AI assets for agronomic advisory services as important components of emerging digital public infrastructure for agriculture.
Agricultural survey investments can help countries build toward this architecture.
