23 May 2022
 | 23 May 2022

Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0

Ashenafi Ali, Teklu Erkossa, Kiflu Gudeta, Wuletawu Abera, Ephrem Mesfin, Terefe Mekete, Mitiku Haile, Wondwosen Haile, Assefa Abegaz, Demeke Tafesse, Gebeyhu Belay, Mekonen Getahun, Sheleme Beyene, Mohamed Assen, Alemayehu Regassa, Yihenew G. Selassie, Solomon Tadesse, Dawit Abebe, Yitbarek Walde, Nesru Hussien, Abebe Yirdaw, Addisu Mera, Tesema Admas, Feyera Wakoya, Awgachew Legesse, Nigat Tessema, Ayele A. Abebe, Simret Gebremariam, Yismaw Aregaw, Bizuayehu Abebaw, Damtew Bekele, Eylachew Zewdie, Steffen Schulz, Lulseged Tamene, and Eyasu Elias

Abstract. Up-to-date digital soil resources information, and its comprehensive understanding, is crucial to support crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, which is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small-scale (1:2 M) which limits its practical applicability. Yet, a large legacy soil profile data accumulated over time and the emerging machine learning modelling approaches can help in generating a high-quality quantitative digital soil map that can provide accurate soil information. Thus, a group of researchers formed a coalition of the willing for soil and agronomy data sharing and collated about 20,000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and prepared 14,681 profile data for modelling. Random Forest was used to develop a continuous quantitative digital map of 18 WRB reference soil groups at 250 m resolution by integrating environmental variables-covariates representing major Ethiopian soil-forming factors. The validated map will have tremendous significance in soil management and other land-based development planning, given its improved spatial nature and quantitative digital representation.

Ashenafi Ali et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-301', Sileshi W Gudeta, 24 May 2022
  • AC1: 'Comment on egusphere-2022-301', Ashenafi Ali, 27 Jun 2022
    • CC3: 'Reply on AC1', Sileshi W Gudeta, 10 Jul 2022
      • AC2: 'Reply on CC3', Ashenafi Ali, 13 Jul 2022
  • CC2: 'Comment on egusphere-2022-301', Yitbarek Walde, 06 Jul 2022
    • AC3: 'Reply on CC2', Ashenafi Ali, 01 Sep 2022
  • CC4: 'Comment on egusphere-2022-301', Fuat Kaya, 10 Sep 2022
    • AC4: 'Reply on CC4', Ashenafi Ali, 30 Sep 2022
  • CC5: 'Comment on egusphere-2022-301', Skye Wills, 13 Sep 2022
    • AC5: 'Reply on CC5', Ashenafi Ali, 06 Oct 2022
  • RC1: 'Comment on egusphere-2022-301', Skye Wills, 06 Oct 2022
    • AC6: 'Reply on RC1', Ashenafi Ali, 06 Oct 2022
    • AC7: 'Reply on RC1', Ashenafi Ali, 06 Oct 2022
  • RC2: 'Comment on egusphere-2022-301', Skye Wills, 06 Oct 2022
    • AC8: 'Reply on RC2', Ashenafi Ali, 09 Oct 2022
  • RC3: 'Comment on egusphere-2022-301', Anonymous Referee #2, 12 May 2023
    • AC9: 'Reply on RC3', Ashenafi Ali, 29 May 2023

Ashenafi Ali et al.

Ashenafi Ali et al.


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Latest update: 01 Oct 2023
Short summary
This paper focuses on collating legacy soil profile data and the production of an updated national soil type map of Ethiopia-EthioSoilGrids version 1.0, using legacy data and a machine learning approach. Given its quantitative digital representation, the map and the associated data have tremendous contributions to agricultural development planning and digital agricultural solutions, as well as improving the accuracy of global predictive soil mapping efforts.