DOI: 10.5281/zenodo.17618736
title: The Namibian Soil Profile Database (NSPD2025): An updated, expanded and harmonised compilation of national soil data

description: Introduction

The Namibian Soil Profile Database (NSPD2025) is a national compilation of 5,099 soil point observations and 13,425 horizons or layers, comprising site, profile, horizon, and analytical data from diverse legacy datasets. Data were verified, cleaned, and standardised to ensure compatibility with international systems. 

The spatial distribution of points shows denser sampling in agricultural and research areas, and sparser data in remote rural areas and the Namib Desert. The level of detail varies considerably, from basic site observations to full profile descriptions with analytical data.

The dataset is provided as a Microsoft Excel file with four worksheets – Reg&Site, Hor_lab, Hor_field, and Metadata – and a QGIS project with the profile locations and basic geographical information. 

NSPD2025 is intended for use in soil classification, conventional and digital soil mapping, environmental modelling and land management in Namibia and beyond. 

Data Sources

NSPD2025 integrates data from multiple legacy sources:

National Soil Survey (1998–2000) and MAWRD-AEZ Campaigns (2001–2009)

Systematic soil data collection in post-independence Namibia began with the National Soil Survey Phase I (NSS), conducted by the Ministry of Agriculture, Water and Rural Development[1] (MAWRD) in collaboration with the Cartographic Institute of Catalonia (ICC) from 1998 to 2000. It produced four soil maps and associated MS Access relational databases: NSD1000, NSD250, NSDkava, and NSDowa (ICC & MAWRD, 2000). Soil profiles were described according to the FAO Guidelines for Soil Profile Description (FAO, 1990). 

The data were reformatted into the Namibian Soil and Terrain Digital Database, NAMSOTER (Coetzee, 2001), based on the methodology of Van Engelen and Wen (1995), for inclusion in the Southern African SOTERSAF (FAO/ISRIC, 2003).

The Agro-Ecological Zoning (AEZ) Programme of MAWRD/MAWF continued soil survey campaigns in under-sampled regions between 2000 and 2009. These datasets were consolidated and maintained within the Namibian Agricultural Resources Information System (NARIS). By 2009, NSS NARIS contained 2,155 soil records of varying completeness.

All laboratory analyses for these profiles were performed by the ministerial Agriculture Laboratory (AgricLab) in Windhoek, which follows ISO-based quality management through standard operating procedures, internal standards, replicate analyses, and participation in the Agri-Laboratory Association of Southern Africa (AgriLASA) inter-laboratory proficiency testing.

During the present update, additional analytical and field data were recovered from original NSS records and integrated into the NSPD2025.

African Soil Microbial Genomics Project (AMP, AMP-NA, AMP-NDLS)

The African Soil Microbial Genomics Project (Wild, 2016; Cowan et al., 2022) contributed 179 topsoil samples (0–20 cm) across three datasets. Eleven physicochemical soil properties were analysed by the University of Pretoria using AgriLASA (2004) protocols.

BIOTA Project

The Biodiversity Monitoring Transect Analysis in Africa (BIOTA) Programme established 11 long-term observatories across Namibia (Petersen, 2008; https://biota-africa.org/). It contributed 638 samples from 257 profiles (0–10 cm, 10–30 cm, 30–60 cm), analysed for texture, pH (CaCl₂), electrical conductivity, total nitrogen, and organic carbon by the University of Hamburg’s Institute of Soil Science.

GIZ Bush-SOC Project

The Assessment of the Impact of Bush Encroachment and Bush Control on Soil Organic Carbon in Namibia project (Strohbach et al., 2024) contributed 162 topsoil samples (0–30 cm) analysed by the AgricLab for texture, pH (H₂O), bulk density, electrical conductivity, extractable cations, plant-available phosphorus, and organic carbon.

LandPKS Project

The Land Potential Knowledge System (LandPKS) (Herrick et al., 2016) developed a mobile app integrating user inputs with cloud-based geospatial data. Piloted in Namibia and Kenya, it provided 918 georeferenced observations, recording site, land use, vegetation cover, effective depth, stoniness, and field-estimated texture at seven standard depths (0–1 cm, 1–10 cm, 10–20 cm, 20–50 cm, 50–70 cm, 70–100 cm, 100–150 cm.). No laboratory data are included, and data quality varies due to contributions by non-specialists.

Land Degradation Neutrality (LDN, LDN-Omusati)

The Land Degradation Neutrality (LDN) Project (Nijbroek et al., 2018) contributed 319 topsoil (0–30 cm) and 277 subsoil (30–100 cm) samples, analysed by the AgricLab for organic carbon, bulk density, and particle size.

SASSCAL

The Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) Phase I subproject Monitoring agricultural ecosystems with regards to climate change effects (De Blécourt et al., 2018) added 181 samples from 30 profiles across four sites. These were analysed for texture, bulk density, pH (H₂O and CaCl₂), electrical conductivity, total nitrogen, organic carbon, cation exchange capacity, and porosity by the University of Hamburg’s Institute of Soil Science. Landscape and profile photographs are included.

Soils4Africa (S4A)

The EU Horizon 2020 Soils4Africa Project (https://www.soils4africa-h2020.eu/) contributed 139 site and descriptions, and laboratory data for 64 topsoil samples. Analyses were conducted by the South African Agricultural Research Council’s Institute for Soil, Climate and Water (ARC-ISCW) (Paterson, 2021). Bulk density measurements were carried out by the AgricLab.

Future Okavango (TFO)

The Future Okavango Project (Pröpper et al., 2015; https://www.future-okavango.org/) focused on knowledge-based land-use management in the Okavango catchment and contributed 484 samples from 141 profiles. These were analysed for texture, pH (H₂O and CaCl₂), bulk density, electrical conductivity, cation exchange capacity, total nitrogen, and organic carbon by the University of Hamburg and the University of Botswana’s Okavango Research Institute. Landscape and profile photographs are included.

Additional Contributions

Smaller datasets were obtained from Simmonds & Forbes (1995), Buch (1990), Trippner (1998), Kempf (2000), and Coetzee (in Strohbach et al., 2019).

Methods and Data Processing

All datasets were collated in Microsoft Excel, using both automated procedures and visual inspection – based on subject knowledge – for quality-control of data. Records lacking georeferencing or containing irreparable errors were removed. 

Key steps included:



Verification and correction of NSS and MAWF data against original field forms, and addition of previously undigitised information. Digital records could not be verified against the following missing field forms: AO, DOR, MAR, NDONGA, OMAH_A, OMAH_B, ONGO, OVI, SPERR, TSUM.

Standardisation of classes, codes, and measurement units following FAO Guidelines for Soil Profile Description (FAO, 2006) and SOTER/ISRIC standards (Van Engelen & Dijkshoorn, 2013).

Verification of profile identifications and horizon designations; assigning layer identifiers to non-pedogenic layers (e.g. LandPKS fixed depth intervals).

Conversion of coordinates to decimal degrees and addition of the coordinate reference system.

Confirmation of attribute definitions (e.g. organic matter vs organic carbon; minimum diameter of sand fraction 50 or 63 µm) and value ranges, some in combination with other attributes (e.g. the sum of particle size fractions being 100±1%; horizon thickness corresponding to the difference between upper and lower horizon limits).

Verification of conspicuous outliers.

Addition of registration and environmental information.

Where available, diagnostic elements (horizons, materials and properties) were used for classification according to the WRB (IUSS, 2015)


Limitations: variable data completeness, differences in analytical methods, and unverified records for some missing field forms. However, the harmonisation process ensures national consistency and compatibility with global frameworks.

Database Design

The NSPD2025 dataset is provided as an MS Excel workbook, NSPD2025.xlsx, with four worksheets (see all fields below): 



Reg&Site contains profile registration information and site descriptions.

Hor_field contains physical and morphological characteristics of the profiles and horizons. 

 Hor_lab contains laboratory measurements.

Metadata


The primary identifier of each point observation is the PRID (profile identification), with secondary identifiers for horizons/layers in the HONU field.

The design embeds some redundancy: repetition of data in different formats (e.g. individual values, class ranges, class codes and descriptive text) and measuring units. For example, dates of profile description are recorded in YYYY-MM-DD, DD-Mon-YYYY, DD/MM/YYYY formats as well as in separate Year, Month and Day fields, while Organic Carbon is recorded in both % and g/kg units. This was done to facilitate integration with international soil information systems, and to accommodate users who are unfamiliar with the FAO profile description codes. The same rationale is behind the use of descriptive field names rather than codes.




 

Fields of the Reg&Site table:






PRID



Degree Square



Grazer Type



Surface Sealing Hardness Desc





Dataset



Quarter Degree Square



Human Influence Code



Surface Cracks Width cm





PRID Let



Farm No



Human Influence Desc



Surface Cracks Width Code





PRID Dig



Farm Name



Rock Outcrops Abundance/Cover Code



Distance Between Surface Cracks (m)





ALT PRID



Location



Rock Outcrops Cover %



Salt (Surface Salinity)





Date (YYYY-MM-DD)



Diagnostic Horizon / Property / Material



Rock Outcrops Cover Desc



Bleached Sand Code





Date (DD-Mon-YY)



Present Weather Code



Rock Outcrops - m between



Bleached Sand %





Date (DD/MM/YYYY)



Present Weather Desc



Rock Outcrops Type



Bleached Sand Desc





Year



Past Weather Code



Coarse Surface Fragments Cover Code



Flooded >2 weeks/a





Month



Past Weather Desc



Coarse Surface Fragments Cover %



Major Landform Code (2nd level)





Day



Soil Temperature Regime Code



Coarse Surface Fragments Cover Desc



Major Landform Desc





Coordinate Reference System



Soil Temperature Regime Desc



Coarse Surface Fragments Size Code



Minor Landform desc





Latitude



Soil Moisture Regime Code



Coarse Surface Fragments Size cm



Position code





Longitude



Soil Moisture Regime Desc



Coarse Surface Fragments Size Desc



Position Desc





Elevation m GPS



Slope Gradient Class



Surface Coarse Surface Fragments Type



Vegetation





Elevation m SRTM



Slope Form Vert



Erosion Type Code



Land Cover





Country ID



Slope Form Hor



Erosion Type



Parent Material





Data Origin



Slope Gradient Measured %



Erosion Area Affected Code



Lithology





Surveyor(s)



Slope Gradient Desc



Erosion Area Affected %



Drainage Desc





Status



Gradient % (for landform) from SRTM



Erosion Degree Code



Drainage Code





Profile Classification Original



Slope % SRTM



Erosion Degree Desc



Geology





Profile Classification Original Code



Relief m/km



Erosion Status Code



Effective Depth





Classification System



Drainage density



Erosion Status Desc



Depth Class





WRB2015 Reclassification RSG



Present Land Use Code



Surface Sealing Thickness Code



Biological activity





WRB2015 Reclassification Principal Qualifiers



Present land Use Desc



Surface Sealing Thickness mm



Notes





WRB2015 Reclassification Supplementary Qualifiers



Past Land Use Code



Surface Sealing Thickness Desc

 




WRB Classification by S Nambambi



Past Land Use Desc



Surface Sealing Hardness Code

 




 Fields of the Hor_lab table:






PRID



Electrical Conductivity µS/cm [2 soil:5 water suspension] (EL25)



Gypsum %



coSa %





HONU



Electrical Conductivity dS/m [2 soil:5 water suspension] (EL25)



Gypsum g/kg (GYPS)



meSa %





Dataset



Electrical Conductivity µS/cm [Saturated Paste Extract] (ELCO)



Total Nitrogen mg/kg (TOTN)



fiSa %





Latitude



Electrical Conductivity dS/m [Saturated Paste Extract] (ELCO)



Total Nitrogen g/kg (TOTN) Kjeldahl



vfiSa %





Longitude



Ca % [Exchangeable & Soluble; Mehlich #3, ICP-AES]



Total Nitrogen %



coSi %





Laboratory



Mg % [Exchangeable & Soluble; Mehlich #3, ICP-AES]



Total Phosphorus mg/kg [Mehlich #3, ICP-AES]



fiSi %





Soil Lab ID



K % [Exchangeable & Soluble; Mehlich #3, ICP-AES]



Total P unknown method & unit



EC (1:5) mS/m





Representative Profile



Na % [Exchangeable & Soluble; Mehlich #3, ICP-AES]



Phosphorus [Olsen] mg/kg



CaCO3 g/kg





Hor thickness cm [contains >]



Ca mg/kg [Extractable; 1M NH4acetate, AAS] (SOCA)



P2O5 mg/kg [Olsen; P x 2.291]



inorgC g/kg





Upper Depth cm [contains >]



Mg mg/kg [Extractable; 1M NH4acetate, AAS] (SOMG)



Organic Carbon % LOI



totC g/kg





Lower Depth cm [contains >]



K mg/kg [Extractable; 1M NH4acetate, AAS] (SOLK)



Organic Carbon % WB



orgC g/kg CNS analyser





Hor thickness cm



Na mg/kg [Extractable; 1M NH4acetate, AAS] (SONA)



Organic Carbon g/kg WB



exchH cmol(-)/kg





Upper Depth cm



Cl mg/kg [Extractable] (SOCL)



Organic Matter % [OC*1.74]



exchAl cmol(-)/kg





Lower Depth cm



Chloride % [Extractable]



Mn mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES]



BaseSat % ?





Horizon Code Short



SSO4 ppm



Fe mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES]



exchAcid cmol(+)/kg





Dominant Sand Grade Code



Sulfate %



Al mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES]



totMo EPA 6010C?:2007





Dominant Sand Grade Desc



Soluble carbonate meq/l (SCO3)



Porosity Measured



totCd mg/kg EPA 6010C:2007





Sand % < 53 µm (SDTO)



Total Carbonate Equivalent g/kg (TCEQ)



Fe_ox mg/g acid-oxalate extr



totPb mg/kg EPA 6010C:2007





Silt % [2-53µm] (STPC)



Carbonate Estimate %



Al_ox mg/g acid-oxalate extr



totV mg/kg EPA 6020A:2007





Clay % [< 2 µm] (CLPC)



Exchangeable Calcium cmol(+)/kg (EXCA)



Al mg/kg?



totHg mg/kg EPA 6020A:2007





Silt + Clay %



Exchangeable Magnesium cmol(+)/kg (EXMG)



B mg/kg?



totCr mg/kg EPA 6010C:2007





Texture Code (PSCL)



Exchangeable Potassium cmol(+)/kg (EXCK)



Cu mg/kg?



totCo mg/kg EPA 6010C:2007





Texture Desc



Exchangeable Sodium cmol(+)/kg (EXNA)



Fe mg/kg?



totNi mg/kg EPA 6010C:2007





Bulk Density kg/dm3



CEC Effective cmol(+)/kg [Sum of Exchangeable Bases]



Mn mg/kg?



totCu mg/kg EPA 6010C:2007





pH CaCl2 (PHCA) [2 soil:5 CaCl2]



CEC Measured cmol(+)/kg



P mg/kg?



totZn mg/kg EPA 6010C:2007





pH water (PHAQ)



Base Saturation Estimate % [from pHwater]



S mg/kg?



totAs mg/kg EPA 6020A:2007





pH water other lab



Base Saturation % [CECeffective/CECmeasured]



Zn mg/kg?



totSb mg/kg EPA 6020A:2007





pH KCl (PHKC)



 



 



 





 

Fields of the Hor_field table:






PRID



Coarse Fragments Abundance Desc



Vertic Properties



Coatings Form Desc





HONU



Coarse Fragments Abundance %



properties for classification



Coatings Location Code





Dataset



Coarse Fragments Size Code



CLAF



Coatings Location Desc





Latitude



Coarse Fragments Size Desc



LOWER LEVEL UNITS



Cementation /  Compaction Nature Code





Longitude



Coarse Fragments Size mm



REFERENCE GROUP



Cementation / Compaction Nature Desc





Hor thickness cm



Course Fragments Shape Code



CLAV



Cementation /  Compaction Continuity Code





Upper Depth cm



Coarse Fragments Shape Desc



ALT PRID



Cementation / Compaction Continuity Desc





Lower Depth cm



Coarse Fragments Degree of Weathering



Consistence Dry Code



Cementation /  Compaction Fabric Code





Diagnostic Horizon / Property / Material Code



Coarse Fragments Type



Consistence Dry Desc



Cementation / Compaction Fabric Desc





Diagnostic Horizon



Drainage Code



Consistence Moist Code



Mineral Concentrations Nature Code





Diagnostic Property



Drainage Desc



Consistence Moist Desc



Mineral Concentrations Nature Desc





Diagnostic Material



Field Texture Code



Consistence Wet - Stickiness Code



Mineral Concentrations Abundance Code





Qualifier



Field Texture Desc



Consistence Wet - Stickiness  Desc



Mineral Concentrations Abundance Desc





Horizon Code Short



Carbonate Estimate  %.1



Consistence Wet - Plasticity Code



Mineral Concentrations Abundance %





Horizon Code Original



Carbonate Reaction Code



Consistence Wet - Plasticity Desc



Mineral Concentrations Size Code





Subordinate Horizon Code



Carbonate Reaction Desc



Porosity Class Code



Mineral Concentrations Size Desc





Lithology of R/C



Secondary Carbonates Code



Porosity Class Desc



Mineral Concentrations Shape Code





Horizon Transition Distinctness Code



Secondary Carbonates Desc



Porosity Class %



Mineral Concentrations Shape Desc





Horizon Transition Distinctness Desc



Munsell_col_moist



Voids Type Code



Mineral Concentrations Hardness Code





Horizon Transition Distinctness cm



Hue_moist



Voids Type Desc



Mineral Concentrations Hardness Desc





Horizon Transition Topography Code



Value_moist



Voids Size Class



Roots Abundance Code





Horizon Transition Topography Desc



Chroma_moist



Voids Size Desc



Roots Abundance Desc





Structure Type Code



Colour Moist Desc [Munsell]



Voids Size mm



Roots Abundance No





Structure Type Desc



Munsell_col_dry



Voids Abundance Class



Roots Diameter Code





Structure Strength Code



Hue_dry



Voids Abundance Desc



Roots Diameter Desc





Structure Strength Desc



Value_dry



Voids Abundance Number



Roots Diameter mm





Structure Size Code



Chroma_dry



Coatings Nature Code



Biological Features Code





Structure Size Desc



Colour Dry Desc [Munsell]



Coatings Nature Desc



Biological Features Desc





Coarse Fragments Abundance Code



Mottling Desc



Coatings Form Code



Notes





Supporting Files and Documents

GIS_zipped: QGIS project file (Namibia_Soil_Profiles.qgz) & shape files (Namibia_Soil_Points, National boundary, Regional boundaries, Trunk Roads, Main Roads, District Roads, All settlements, Larger settlements, Rivers, Etosha National Park)

Lab & Field Methods: AgricLab soil analysis methods; Soils4Africa lab & field observation guidelines; Africa Micobiome Project methods; FAO Guidelines for Soil Profile Description

Images: Distribution map of soil point data; Datasets in the database


Usage Notes

The NSPD2025 dataset is intended for:



       Conventional soil mapping and classification

       Digital soil mapping (DSM) and predictive modelling

       Land evaluation and agro-ecological zoning

       Environmental and hydrological modelling

       Educational and research purposes


Users are requested to cite the dataset when using the data. Derived products should acknowledge the original sources. 

Acknowledgements

The author gratefully acknowledges the Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR), formerly MAWRD/MAWF/MAWLR, for providing data and documentation from the Agro-Ecological Zoning Programme. Appreciation is extended to Ms Eva Corral-Pazos-de-Provens (Universidad de Huelva, Spain) for designing and populating a prototype relational database, and to the numerous surveyors, laboratory analysts, and GIS specialists who contributed to the original fieldwork and data compilation efforts (see Metadata).

References

AgriLASA. (2004). AgriLASA soil handbook. Pretoria: Agri Laboratory Association of Southern Africa.

Buch, M. W. (1990). Soils, soil erosion and vegetation in the Etosha National Park / Northern Namibia - Field & laboratory results of the investigations of the year 1989. Part I – field results; Part II – laboratory results. University of Regensburg (unpublished).

Coetzee, M.E. (2001). NAMSOTER – A SOTER database for Namibia. Agro-Ecological Zoning Programme, MAWRD. Windhoek.

Cowan, D., Lebre, P., Amon, C., Becker, R.W., Boga, H.I., Boulangé, A., Chiyaka, T.L., Coetzee, T., De Jager, P.C., Dikinya, O., Eckardt, F., Greve, M., Harris, M.A., Hopkins, D.W., Houngnandan, H.B., Houngnandan, P., Jordaan, K., Kaimoyo, E., Kambura, A.K., Kamgan-Nkuekam, G., Makhalanyane, T.P., Maggs-Kölling, G., Marais, E., Mondlane, H., Nghalipo, E., Olivier, B.W., Ortiz, M., Pertierra, L.R., Ramond, J.-B., Seely, M., Sithole-Niang, I., Valverde, A., Varliero, G., Vikram, S., Wall D.H., & Zeze, A. (2022). Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes. Microbiome 10, 131. https://doi.org/10.1186/s40168-022-01297-w

De Blécourt, M., Röder, A., Gröngröft, A., Baumann, S., Frantz, D. & Eschenbach, A. (2018) Deforestation for agricultural expansion in SW Zambia and NE Namibia and the impacts on soil fertility, soil organic carbon- and nutrient levels In: Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions (ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens, N.), pp. 242-250, Biodiversity & Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek. https://doi.org/10.7809/b-e.00330

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Nijbroek, R., Piikki, K., Söderström, M., Kempen, B., Turner, K.G., Hengari, S., & Mutua, J. (2018). Soil organic carbon baselines for land degradation neutrality: Map accuracy and cost tradeoffs with respect to complexity in Otjozondjupa, Namibia. Sustainability, 2018(10), 1610. https://doi.org/10.3390/su10051610. 

Paterson, G. (2021). Guidance for the laboratory analysis - Soils4Africa Project. 

https://www.soils4africa-h2020.eu/serverspecific/soils4africa/images/Documents/GuidanceonLaboratoryAnalysis.pdf

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Pröpper, M., Gröngröft, A., Finckh, M., Stirn, S., De Cauwer, V., Lages, F., Masamba, W., Murray-Hudson, M., Schmidt, L., Strohbach, B., Jürgens, N. (2015). The Future Okavango – Findings, Scenarios and Recommendations for Action. Research Project Final Synthesis

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Van Engelen, V.W.P., & Wen, T.T. (eds.) (1995). Global and national soils and terrain digital databases – SOTER. Procedures manual. World Soil Resources Report 74. Rome: UNEP, ISSS, ISRIC, FAO. https://edepot.wur.nl/493802

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Corresponding author:

Marina E. Coetzee (mcoetzee@nust.na; marina.e.coetzee@gmail.com)

Affiliations: 



Faculty of Engineering and the Built Environment, Namibia University of Science and Technology, Private Bag 13388, Windhoek, Namibia.

Doctoral School of Environmental Sciences, Magyar Agrár- és Élettudományi Egyetem (MATE; Hungarian University of Agriculture and Life Sciences), Páter Károly u. 1. H-2100 Gödöllő, Hungary.






[1] Ministry's name changed over time: Ministry of Agriculture, Water and Rural Development (MAWRD; 1991-2000); Ministry of Agriculture, Water and Forestry (MAWF; 2000-2020); Ministry of Agriculture, Water and Land Reform (MAWLR; 2020-2025); Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR, since 2025).