Articles | Volume 10, issue 1
https://doi.org/10.5194/soil-10-189-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-10-189-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
05 Mar 2024
Original research article |
| 05 Mar 2024
| 05 Mar 2024
Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0
Ashenafi Ali
CORRESPONDING AUTHOR
Department of Geography and Environmental Studies, Addis Ababa University (AAU), Addis Ababa, Ethiopia
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Addis Ababa, Ethiopia
International Center for Tropical Agriculture (CIAT), Addis Ababa, Ethiopia
Teklu Erkossa
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Addis Ababa, Ethiopia
Kiflu Gudeta
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Wuletawu Abera
International Center for Tropical Agriculture (CIAT), Addis Ababa, Ethiopia
Ephrem Mesfin
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Terefe Mekete
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Mitiku Haile
Land Resource Management and Environmental Protection, Mekelle University, Mekelle, Ethiopia
Wondwosen Haile
private consultant: Addis Ababa, Ethiopia
Assefa Abegaz
Department of Geography and Environmental Studies, Addis Ababa University (AAU), Addis Ababa, Ethiopia
Demeke Tafesse
Ethiopian Construction Design and Supervision Works Corporation (ECDSWCo), Addis Ababa, Ethiopia
Gebeyhu Belay
private consultant: Addis Ababa, Ethiopia
Mekonen Getahun
Amhara Design and Supervision Enterprise (ADSE), Bahir Dar, Ethiopia
Department of Natural Resources Management, BahirDar University (BDU), Bahir Dar, Ethiopia
Sheleme Beyene
School of Plant and Horticultural Science, Hawassa University (HU), Hawassa, Ethiopia
Mohamed Assen
Department of Geography and Environmental Studies, Addis Ababa University (AAU), Addis Ababa, Ethiopia
Alemayehu Regassa
Department of Natural Resource Management, Jimma University (JU), Jimma, Ethiopia
Yihenew G. Selassie
Department of Natural Resources Management, BahirDar University (BDU), Bahir Dar, Ethiopia
Solomon Tadesse
Ethiopian Construction Design and Supervision Works Corporation (ECDSWCo), Addis Ababa, Ethiopia
Dawit Abebe
Engineering Corporation of Oromia, Addis Ababa, Ethiopia
Yitbarek Wolde
Engineering Corporation of Oromia, Addis Ababa, Ethiopia
Nesru Hussien
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Abebe Yirdaw
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Addisu Mera
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Tesema Admas
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Feyera Wakoya
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Awgachew Legesse
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Nigat Tessema
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
School of Plant and Horticultural Science, Hawassa University (HU), Hawassa, Ethiopia
Ayele Abebe
National Soil Testing Center, MoA, Addis Ababa, Ethiopia
Simret Gebremariam
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Yismaw Aregaw
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Bizuayehu Abebaw
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Damtew Bekele
Ethiopian Construction Design and Supervision Works Corporation (ECDSWCo), Addis Ababa, Ethiopia
Eylachew Zewdie
private consultant: Addis Ababa, Ethiopia
Steffen Schulz
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Addis Ababa, Ethiopia
Lulseged Tamene
International Center for Tropical Agriculture (CIAT), Addis Ababa, Ethiopia
Eyasu Elias
Ministry of Agriculture (MoA), Addis Ababa, Ethiopia
Center for Environmental Science, Addis Ababa University, Addis Ababa, Ethiopia
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Jérôme Ebagnerin Tondoh, Issa Ouédraogo, Jules Bayala, Lulseged Tamene, Andrew Sila, Tor-Gunnar Vågen, and Antoine Kalinganiré
SOIL Discuss., https://doi.org/10.5194/soil-2016-45, https://doi.org/10.5194/soil-2016-45, 2016
Preprint withdrawn
Short summary
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The present paper is intended to fill in the gap of field data on soil carbon sequestration in west african dry areas. As increased carbon concentration in soils through improved agricultural management practices is one of the options to mitigate greenhouse gases and improved soil quality, the results of this study will help designing the most promising practices in the study sites.
Alemayehu Adugna and Assefa Abegaz
SOIL, 2, 63–70, https://doi.org/10.5194/soil-2-63-2016, https://doi.org/10.5194/soil-2-63-2016, 2016
Short summary
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The purpose of our study was to explore the effects of land use changes on the dynamics of soil properties and their implications for land degradation. The result indicates that cultivated land has a lower organic matter, total nitrogen, cation exchange capacity, pH, and exchangeable Ca2+ and Mg2+ contents than forestland and grazing land.
A. Adugna and A. Abegaz
Solid Earth Discuss., https://doi.org/10.5194/sed-7-2011-2015, https://doi.org/10.5194/sed-7-2011-2015, 2015
Revised manuscript not accepted
Short summary
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Most of selected soil properties showed highest variation with depth under forestland and lowest under cropland. There were no consistent spatial trends in CEC. The correlation matrix suggests OM, CEC and Ca are fundamental soil properties in both sampled depths. The Correlations among soil properties are determined by soil-forming processes than parent materials. However, it is difficult to explain on which sampled depths that correlation between soil properties would be stronger.
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Pedometrics
Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach
Accuracy of regional-to-global soil maps for on-farm decision-making: are soil maps “good enough”?
Shapley values reveal the drivers of soil organic carbon stock prediction
How well does digital soil mapping represent soil geography? An investigation from the USA
Jeremy Rohmer, Stephane Belbeze, and Dominique Guyonnet
EGUsphere, https://doi.org/10.5194/egusphere-2024-323, https://doi.org/10.5194/egusphere-2024-323, 2024
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Machine learning (ML) models have become key ingredients for digital soil mapping. To explain why the ML model is confident, we apply a popular method from the field of explainable artificial intelligence, i.e. based on the Shapley values, to the uncertainty prediction of hydrocarbon pollutants on an urban soil. To alleviate the implementation difficulties (number of factors, complex relationships between the factors, high resolution maps), a simple-but-efficient grouping approach is tested.
Jonathan J. Maynard, Edward Yeboah, Stephen Owusu, Michaela Buenemann, Jason C. Neff, and Jeffrey E. Herrick
SOIL, 9, 277–300, https://doi.org/10.5194/soil-9-277-2023, https://doi.org/10.5194/soil-9-277-2023, 2023
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Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields on smallholder farms. This study evaluated the accuracy of soil map information for agronomic decision-making. Based on four publicly available soil maps in Ghana, we found that soil map data significantly overestimated crop suitability, potentially leading to ineffective agronomic investments by smallholder farmers.
Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin
SOIL, 9, 21–38, https://doi.org/10.5194/soil-9-21-2023, https://doi.org/10.5194/soil-9-21-2023, 2023
Short summary
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We introduce Shapley values for machine learning model interpretation and reveal the local and global controlling factors of soil organic carbon (SOC) stocks. The method enables spatial analysis of the important variables. Vegetation and topography determine much of the SOC stock variation in mainland France. We conclude that SOC stock variation is complex and should be interpreted at multiple levels.
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
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Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
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Short summary
This paper focuses on collating legacy soil profile data and on 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 make tremendous contributions to agricultural development planning and digital agricultural solutions, as well as improving the accuracy of global predictive soil mapping efforts.
This paper focuses on collating legacy soil profile data and on the production of an updated...
