Writing from inside the problem — not above it. Our insights draw on what we see from satellite passes, soil sensors, market price feeds, and the daily reality of running a farm in Northern Nigeria.
We write about precision agriculture, African market dynamics, climate risk, supply chain traceability, and the policy landscape shaping African agribusiness.
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A single harvest. Three markets. A ₦39,000 per metric tonne price gap between Osun aggregation points and Lagos Mile 12. We map the spread, explain why it persists, and show how price intelligence changes the sell decision.
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EUDR is not a distant policy concern. It is a documentation requirement that takes effect at the farm-polygon level. Nigerian soybean and cocoa exporters targeting the EU need GPS-tagged field records — or they lose the market. Here is what compliance actually requires.
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Most precision agriculture platforms assume cellular connectivity. That assumption breaks in many rural farming communities across southwest Nigeria and most of the continent. We explain how LoRaWAN changes the infrastructure calculus — and why SoilPulse was built around it from day one.
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The FAO puts post-harvest loss in Nigeria at 30–50% of crop value. That figure is cited often and interrogated rarely. We dig into the actual loss points — grading, handling, storage, sell timing — and quantify which interventions move the needle most.
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Calendar-based irrigation is the default on most Nigerian commercial farms. It is also wrong — systematically, seasonally, and expensively. We document what IrrigateAI generated for our Zone B tomato block over one season and compare it to the calendar schedule it replaced.
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Parametric crop insurance exists in theory across Africa. In practice it fails because the trigger data does not exist at the farm level. CropSentinel was designed to be that infrastructure — a satellite-plus-IoT signal that can activate a policy without a loss adjuster in the field.
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Mucuna and cowpea between maize cycles was not a sustainability decision — it was a cost decision. SoilPulse NPK readings before and after rotation made the economics visible. We publish the numbers from two seasons of data on the Ede farm.
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A non-technical explainer of the model architecture behind CropSentinel — what LSTM networks are, why we chose an ensemble approach, what data the model trains on, and why African crop stress patterns require a different model than the ones built for temperate agriculture.
If you're a researcher, agency, or operator with a specific intelligence need — talk to us. Our products are built to answer questions that currently have no answer.
