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Land Degradation Neutrality (LDN) has been piloted in 14 countries and will be scaled up to over 120 countries. As a LDN pilot country, Namibia developed sub-national LDN baselines in Otjozondjupa Region. In addition to the three LDN indicators (soil organic carbon, land productivity and land cover change), Namibia also regards bush encroachment as an important form of land degradation. We collected 219 soil profiles and used Random Forest modelling to develop the soil organic carbon stock baseline. Values range between 0.53 and 4.27 kg/m2 in the sandy Otjozondjupa soils. LDN baselines were integrated into other national planning processes to add value to LDN products. Analyses of the relationship between soil carbon and land cover change, especially from grassland to bushland, increased the usefulness of soil carbon maps for the Integrated Regional Land Use Planning process. Local ownership of LDN baseline development, from data collection to digital soil mapping, was crucial for local stakeholders.

In a previous study we introduced structural equation modelling (SEM) for digital soil mapping in the Argentine Pampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi-layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C, for a 23 000-km2 region in the Argentine Pampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross-validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers.

This report describes the methodology that was used to compile the national Soil and Terrain (SOTER) database for the Republic of Malawi at scale 1:1,000,000. The GIS database provides generalized information on landform, parent material and soil conditions in digital format. The base information used to compile the database, including information on physiography, soils, agro-climate, land use and vegetation, was derived from the Land Resources Evaluation Project of the Food and Agriculture Organisation of the United Nations (Venema, 1990). Profile descriptions and soil analytical data were collected from Malawi’s Agricultural Development Divisions by the Maize Commodity Team; a digital database was created for the advisory and extension work of the Department of Agricultural Research in Chitedze, Central Region (Benson, Nambuzi, & Ligowe, 1999). This database was cleaned and incorporated into the Africa Soil Profiles Database (Leenaars, 2012). The soil map at scale 1:250,000 (Venema, 1990) provided the geographical basis for the SOTER map; representative soil profiles were mainly selected from the Africa Soil Profiles Database v1.0. All profiles are classified according to the World Reference Base for Soil Resources (IUSS Working Group WRB, 2007). For display purposes, all soil components are described according to the guidelines for small-scale map legends based on the World Reference Base for Soil Resources (IUSS Working Group WRB, 2010). To ensure consistency with earlier SOTER databases, the selected reference profiles are also characterised according to the Revised Legend of the Soil Map of the World (FAO, 1988). The database contains 231 unique SOTER units, comprising 298 terrain components and 534 soil components. The SOTER units are linked to 327 polygons in the geometric database. The soil components are characterized by 379 profiles. A total of 18 WRB Reference Soil Groups (RSG) were identified in Malawi. Lixisols are the dominant RSG, covering almost 26% of the country’s land surface, followed by Luvisols with 22% and Cambisols with 18%. The remaining RSGs cover less than 10% of the Malawi territory. Among these are Gleysols, Leptosols, Fluvisols, Ferralsols and Phaeozems that together cover some 35% of the land surface. The SOTER Malawi database can be used for broad-scale agro-environmental assessments such as estimation of soil organic carbon stocks, hydrological modelling, or crop modelling. The associated dataset is accessible via doi: 10.17027/isric-wdcsoils.20160002

Most practical applications of spatial interpolation ignore that some measurements may be more accurate than others. As a result all measurements are treated equally important, while it is intuitively clear that more accurate measurements should carry more weight than less accurate measurements. Geostatistics provides the tools to perform spatial interpolation using measurements with different accuracy levels. In this short paper we use these tools to explore the sensitivity of interpolated maps to differences in measurement accuracy for a case study on mapping topsoil clay content in Namibia using kriging with external drift (KED). We also compare the kriging variance maps and show how incorporation of different measurement accuracy levels influences estimation of the KED model parameters.

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