Nigeria’s federal government has signed an agreement with Morocco’s OCP Africa and geospatial analytics firm Ground Truth Analytics to build a National Agro-Productivity System, an AI-driven platform intended to monitor crop conditions across the country’s farmland. The system will combine satellite imagery refreshed every five days with artificial intelligence to identify crop types, estimate yields and flag production risks.
OCP Africa, the African arm of Morocco’s major fertiliser producer, brings agronomic expertise to the partnership, while Ground Truth Analytics supplies the geospatial analysis underpinning the system’s crop identification and yield estimates. Together the two firms are building a platform meant to give Nigerian authorities a continuously updated picture of farmland conditions rather than relying on periodic surveys or delayed harvest reporting.
The National Agro-Productivity System, referred to by officials as NAPS, will launch first as a pilot in one state before expanding to fifteen priority agricultural states identified by the federal government. That phased rollout gives Nigerian authorities a chance to test the platform’s accuracy against ground conditions before committing to the wider deployment across the country’s main farming regions.
What the system is designed to track
NAPS is built to flag production risks early, giving agricultural planners and policymakers warning of potential shortfalls before they affect food supply or farmer incomes. The five-day satellite refresh cycle is frequent enough to catch rapid changes in crop condition, such as those caused by pest outbreaks, drought stress or flooding, well ahead of when such damage would typically surface in conventional agricultural surveys.
Yield estimation is the system’s other core function, intended to give Nigerian authorities earlier and more granular visibility into expected harvests than traditional reporting channels provide. Combined with crop-type identification across large areas of farmland, the platform is designed to support decisions on everything from input distribution to market planning, based on data drawn directly from satellite observation rather than self-reported figures from farming communities.
Part of a wider African push on agritech
Nigeria’s rollout adds to a wider pattern of African governments turning to satellite-linked AI systems to manage agricultural risk, even as the approach carries governance challenges elsewhere on the continent. Namibia’s cancelled AI crop-monitoring contract shows that such deals can unravel when procurement rules are not followed. That is a risk Nigeria’s government will need to manage as NAPS moves from its single-state pilot toward the fifteen-state rollout.
How NAPS performs during its pilot phase will likely shape appetite for similar systems elsewhere in West Africa, where staple crop monitoring remains largely dependent on manual surveys and delayed reporting. A successful expansion to fifteen states would mark one of the more ambitious national-scale deployments of satellite-based agricultural monitoring on the continent to date, assuming the pilot demonstrates the accuracy officials are counting on.
Pairing a fertiliser producer with a geospatial analytics firm gives NAPS a combination of agronomic and technical expertise rarely bundled into a single agricultural monitoring contract. OCP Africa’s background supplying fertiliser across the continent gives it direct insight into soil and crop conditions on the ground, while Ground Truth Analytics contributes the remote-sensing capability needed to translate satellite imagery into usable yield and risk estimates at scale.
That combination distinguishes NAPS from monitoring systems built purely around satellite data without an agronomic partner informing how the output gets used. Nigerian officials overseeing the rollout will be watching closely for whether that pairing translates into monitoring results accurate enough to justify scaling the system beyond the fifteen states already identified as priorities for the programme’s second phase.
