Photos from Zenvus Soil Nutrition Lab to Improve Crop Yield in Africa

Photos from Zenvus Soil Nutrition Lab to Improve Crop Yield in Africa

By next year, Zenvus will begin supporting individual farmers. We made the first leap on that when we released the Zenvus Boundary which helps farmers in mapping the boundaries of their farms. At the moment, our focus has been governments and cooperatives largely because we need scale to serve customers.

Enjoy photos from Zenvus Soil Nutrition Lab where we are researching and building models on soil chemistry, computational algorithms and crop growths.

I am asking African governments to commission my firm to map all the farmlands in respective countries, archive the nutritional values, tag them to maps (via Zenvus Boundary) and make the data FREELY available. Yes, with that, farmers using their phones will have clarity on the state of the farmlands. It is a unfortunate that government sells the same fertilizer in Sokoto, Owerri and Ibadan (all in Nigeria) when the soils have different nutritional needs. Why waste nitrogen (in a fertilizer) in a farm with lots of urea if that saving can help support  more lands?

I think we can fix African agriculture. We just need resources to scale this thing really fast! If you have linkage to a state government, we have Zenvus Fusion to improve agriculture.

Zenvus Fusion is a service for governments and development organizations which is designed to help build Soil Fertility Geography in constituencies. It could be a state Soil Fertility Geography or even a National Soil Fertility Geography. We also support local governments.

Our product provides the data that makes it possible for farmers to understand the natures of the farmlands before they begin planting their crops. We do this by collecting many data samples to appropriately represent the soil fertility equivalence of that region or area.

The aggregated soil fertility data is made accessible to all stakeholders. The data is also GIS-tagged so that the exact locations of the soil are known. The Computational algorithm will use this data to make location-based recommendations on fertilizer applications across farming zones, and this will be delivered through mobile devices, to stakeholders that subscribe. Also, farmers can get predictive recommendations on the most economically viable crop to grow by feeding present crop prices, fertilizer cost, and population into the algorithm.

With this soil fertilizer geography, fertilizer production will become personalized and specifically engineered to mitigate deficiencies with deployment regions known pre-production. We welcome inquiries from governments, development organizations etc.


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