Why Africa’s Next Decade in Agriculture Will Be Won or Lost on Data

Africa’s agricultural productivity has stagnated, even falling in some parts of the continent. In response, the agricultural community has spent the last two years building the continent’s agricultural policy architecture to spur productivity through the Comprehensive Africa Agriculture Development Programme (CAADP) Strategy and Action Plan (2026–2035), and its related Kampala Implementation Guidelines. Together, these two documents were endorsed by African Union member states to give the continent’s third CAADP cycle something the previous two lacked: a four-part implementation cycle spanning governance, diagnostics, investment readiness, and mutual accountability, anchored by 22 core targets. It is, by any measure, a framework with depth. What will make this cycle different from the last? We think investment (or lack thereof) in the data.

The CAADP frameworks call for reporting every two years, known as the Biennial Review. After four Biennial Review cycles between 2017 and 2023, analysis showed a steadily declining share of benchmarks met – from 48% to 31% – reflecting a growing gap between progress towards ending food insecurity and the benchmarks set in the CAADP frameworks. A Global Policy Journal analysis published after the Kampala Summit in February 2025 put it plainly, noting that “the data indicates that African countries [were] not on track to achieve the Malabo targets,” the targets originally set under the second CAADP cycle. 

Breaking the Cycle Means Deepening Data Investments

Part of the target shortfall is a cadence problem: the Biennial Review only occurs every two years, which is out of sync with a sector that must adapt with every planting season. More timely data could help make Biennial Reviews a more fruitful check-in point, and help to better prioritize activities in between review milestones. Initiatives such as 50×2030, a joint effort of FAO and the World Bank to plug data timeliness gaps by funding annual agricultural surveys in partner countries, can help, but more still needs to be done. As pointed out by the Regional Strategic Analysis and Knowledge Support System (ReSAKSS), this annual survey data “could be leveraged in the CAADP process,” yet that connection remains more aspiration than routine practice. How we invest in data amid the push to rebuild the Kampala Results Framework and Biennial Review indicators matters. In addition, it’s not just about monitoring frameworks. Better data foundations are also mission-critical during this AI moment that is sweeping across African agriculture.

Data for Monitoring = Data for AI

Currently, there is real enthusiasm for AI-driven agriculture tools. However, we are pointing increasingly sophisticated AI and satellite tools at agricultural systems whose ground-truth data is still shaky. A peer-reviewed paper in Nature’s Scientific Data notes that after assembling over 535,000 georeferenced yield observations across the continent, researchers found that government statistics overestimate yields by roughly 32% relative to crop-cut measurements, and concluded that “the largest constraint to satellite-based model performance is now training data rather than imagery.” That gap will not close through better algorithms alone – it closes through the same unglamorous work of data collection, harmonization, and governance that CAADP’s own accountability architecture depends on.

Encouragingly, some of the needed infrastructure already exists, but it needs to be treated as core to CAADP’s accountability system rather than as an adjacent project. One example is Development Gateway’s Soil Nutrient Roadmap (SNR) which uses geospatial soil, crop, and nutrient data to model outcome scenarios in an interactive country dashboard with an AI interface, helping governments move from diagnosis to investment choices. It allows ministries to explore production ambitions, nutrient needs, environmental trade-offs, and costs – essentially the diagnostics-to-investment pipeline the Kampala Guidelines ask every country to run. Elsewhere, AKADEMIYA2063 has built a comparable analytical layer: its ReSAKSS Country eAtlases turn satellite imagery, household surveys, and census data into public, interactive dashboards on production, food security, nutrition, and livestock trends. Neither tool is hypothetical; both are already in use, and both depend on exactly the kind of steady, annual agriculture survey data that 50×2030 and other initiatives are building the pipeline for. The open question is whether tools like these, fed by timely data collection, become the backbone of the Kampala Results Framework, or keep running alongside it.

Looking Ahead to the Africa Food System Forum (and Beyond)

None of this argues against the Kampala Guidelines, or against AI’s genuine promise for African agriculture. It argues, instead, against treating these as separate tracks. As ministers, the private sector, and many organizations convene in Kigali at the 2026 edition of the Africa Food Systems Forum (AFSF), data and digital investments need to be at the top of the agenda for driving the CAADP’s ambitions forward. Investments in data for CAADP and for the growing body of AI models are the same infrastructure problem viewed from two angles. Making this infrastructure functional – for citizens, investors, and the AI tools everyone is racing to deploy – depends on treating digital tools, and the data underneath them, not as side projects but as the foundation the entire agriculture ecosystem must stand on. African productivity can’t keep stalling while farmers wait on decision makers to invest in the data and digital tools that will target investments more effectively. AFSF marks the right time to double down on investing in data for the continent’s increased agricultural productivity for the long term.