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:

 Fields of the Hor_lab table:

Fields of the Hor_field table:

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