{"id":81157,"date":"2021-12-05T01:30:00","date_gmt":"2021-12-05T01:30:00","guid":{"rendered":"https:\/\/www.bgs.ac.uk\/?p=81157"},"modified":"2024-02-21T12:45:30","modified_gmt":"2024-02-21T12:45:30","slug":"geochemical-predictive-mapping-in-western-kenya","status":"publish","type":"post","link":"https:\/\/www.bgs.ac.uk\/news\/geochemical-predictive-mapping-in-western-kenya\/","title":{"rendered":"Geochemical predictive mapping in western Kenya"},"content":{"rendered":"\n
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This BGS ArcGIS web application was created to develop a predictive soil geochemistry map of western Kenya. The interactive app provides baseline geochemistry data<\/a> to the agri-community using BGS’s measured data combined with machine learning. <\/p>\n\n\n\n

The original data, relating to land use, crops grown, drinking water source\/usage and any local health problems, was generated from field collections between 2016 and 2019 during the BGS ODA-I programme, as part of a geochemistry and health project to investigate the spatial incidences of diseases within the Rift Valley (e.g. oesophageal cancer; micronutrient deficiencies). You can read more about the project and our time in western Kenya in our previous blogs on geochemistry and health in the Kenyan Rift Valley<\/a> and inorganic geochemistry in Kenya<\/a>.<\/p>\n\n\n

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Processing samples at the University of Eldoret laboratories. BGS \u00a9 UKRI.<\/p>\n<\/div>\n\t\t\t\t\t

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On completing our fieldwork trips, we brought the collected samples back to the BGS headquarters at Keyworth, Nottingham, and analysed them at the Inorganic Geochemistry Laboratories<\/a>. We created a dataset that compiled soil prediction maps for 56 chemical elements (mg\/kg), pH and organic matter content (per cent) using machine learning (Random Forest) analysis. The predictive maps, displayed as raster files with a spatial resolution of 500 m, were based on the 452 soil samples collected from discrete sampling locations across western Kenya and relevant environmental covariate data, such as elevation and rainfall.<\/p>\n\n\n\n

Once all of the predictive layers were created, they were entered into an ArcGIS web app<\/a>, accessible \u2018free\u2019 online via a PC or mobile device. Stakeholders in Kenya from the academic and outreach sectors were consulted during development and tested the web tool, making useful suggestions for refinements to better communicate both the tool and the data itself directly to farmers. Future developments will enable us to continue to add more data and expand the area, subject to funding.<\/p>\n\n\n\t\t\t\t