Vital signs, (working with International Soil Reference and Information Center (ISRIC) has produced “high-resolution soil nutrient maps” for Uganda, Tanzania Rwanda and Ghana.
“Vital Signs has collected 5,969 soil samples from points scattered across the countries (Uganda, Rwanda, Tanzania and Ghana).Of these, 3,714 soil samples were analyzed by the World Agroforestry Center laboratory in Nairobi to produce data on soil properties including particle size, pH, nutrient availability and nutrient content. The data was then combined with other soil samples from the Africa Soil Information Service (AfSIS), One Acre Fund, Legacy soil data (and others) to generate high resolution soil nutrient maps for Uganda, Tanzania and Ghana. (NB: Soil samples from Rwanda soil samples are still being analysed and the maps will be produced later this month).
The soil maps fill critical information gaps on soil nutrients, crop suitability, land degradation and they also provide useful information for targeting agricultural investments/ interventions and estimating yield gaps .For example in Uganda, the maps will be especially beneficial for the “Operation Wealth Creation” which is providing seeds and other agricultural inputs to farmers in an effort to increase agricultural yields. The maps will guide these investments by showing where the soil is most fertile and for which crops. Similar information would also be useful to guide agricultural investments in the Southern Agricultural Growth corridor (SAGCOT) in Tanzania.
Secondly, in Rwanda and Uganda where large restoration efforts are planned as part of the AFRI100 Forest landscape restoration initiative, the maps will be very useful in providing information about where the restoration efforts should be focused.”
 Tomislav Hengl,Johan G. B. Leenaars,Keith D. Shepherd,Markus G. Walsh,Gerard B. M. Heuvelink,Tekalign Mamo,Helina Tilahun,Ezra Berkhout,Matthew Cooper,Eric Fegraus,Ichsani Wheeler,Nketia A. Kwabena."Soil Nutrient Maps of Sub-Saharan Africa:Assessment of Soil Nutrient Content at 250m Spatial Resolution using Machine Learning". Nutrient Cycling in Agroecosystems 108.1(2017): 1-27