Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms

Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon

Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0–15 cm, 15–30 cm, 30–100 cm, 0–30 cm and 0–100 cm) in Cameroon at 100 m spatial resolution, using a national harmonized legacy soil database (Camsodat 0.1) with 1432 georeferenced soil profiles. We assessed the prediction performances of random forest (RF) and generalized boosted regression (GBR), combined with two hybridization approaches of spatial interpolation of the residuals using ordinary kriging (OK) and inverse distance weighting (IDW). The estimates were compared to two global estimates derived from the Harmonized World Soil Database (HWSD) and SoilGrids250m. The SOCS distribution across the country showed a moderate spatial heterogeneity at all depth layers with coefficients of variation between 35% and 47%, and values ranging from 6 to 108 Mg C ha−1 at 0–15 cm, from 4 to 107 Mg C ha−1 at 15–30 cm, from 10 to 276 Mg C ha−1 at 30–100 cm, from 11 to 210 Mg C ha−1 at 0–30 cm and from 21 to 468 Mg C ha−1 at the 0–100 cm layer. Of the selected environmental covariates, terrain and climate attributes were the most relevant to predict the SOCS spatial distribution at country level. The RF model outperformed the GBR model, with about 10% improvement on prediction performance (R2) for most soil depths. The hybridization further slightly improved performance. However, OK was only slightly better than IDW in the overall assessment. Compared to national estimates, SoilGrids overestimated the SOCS by 15% at 0–30 cm depth, while HWSD underestimated SOCS by 26% at the same depth. Overall, about 5.7 Pg C are stored in the top 1 m of soils in Cameroon, with about 50% of that in the top 30 cm. The national distribution of SOCS is consistent with the pattern of agro-ecological zones. Our assessment provides baseline information for sustainable land management and climate change mitigation, as well as for improving the understanding of the spatial distribution of SOCS in Cameroon.</p

Uncertainty quantification of interpolated maps derived from observations with different accuracy levels

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.</p

Linking diagnostic features to soil microbial biomass and respiration in agricultural grassland soil : A large-scale study in Ireland

The functional potential of soil ecosystems can be predicted from the activity and abundance of the microbial community in relation to key soil properties. When describing microbial community dynamics, soil physicochemical properties have traditionally been used. The extent of correlations between properties, however, differs between studies, especially across larger spatial scales. In this research we analysed soil microbial biomass and substrate-induced respiration of 156 samples from Irish grasslands. In addition to the standard physicochemical, soil type and land management variables, soil diagnostic properties were included to identify if these important soil-landscape genesis classes affected microbial biomass and respiration dynamics in Irish soil. Apart from physicochemical properties, soil drainage class was identified as having an important effect on microbial properties. In particular, biomass-specific basal (qCO2) and substrate-induced respiration (SIR:CFE) were explained best by the soil drainage. Poorly drained soil had smaller values of these respiration measures than well-drained soil. We concluded that this resulted from different groups within the microbial community that could use readily available carbon sources, which suggests a change in microbial community dynamics associated with soil texture and periods of water stress. Overall, our results indicate that soil quality assessments should include both physicochemical properties and diagnostic classes, to provide a better understanding of the behaviour of soil microbial communities. Highlights: Assessing the effect of soil diagnostic features and properties on microbial biomass and respiration A soil biological survey from 156 grassland sites in Ireland Soil drainage class has an important effect on microbial properties Soil quality assessments should include both physicochemical properties and diagnostic classe

Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa

In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled geo-referenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km2. The mean RZ-PAWHC for SSA is 74 mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km3) due to soil conditions restricting root zone depth. Of these, 4800 km3 are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km3 due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km3, aluminium toxicity by 600 km3, porosity by 120 km3 and alkalinity by 20 km3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available

GlobalSoilMap for Soil Organic Carbon Mapping and as a Basis for Global Modeling

The demand for information on functional soil properties is high and has increased over time. This is especially true for soil organic carbon (SOC) in the framework of food security and climate change. The<em> GlobalSoilMap</em> consortium was established in response to such a soaring demand for up-to-date and relevant soil information. The majority of the data needed to produce <em>GlobalSoilMap</em> soil property maps will, at least for the first generation, come mainly from archived soil legacy data, which could include polygon soil maps and point pedon data, and from available co-variates such as climatic data, remote sensing information, geological data, and other forms of environmental information.<br/>Several countries have already released products according to the <em>GlobalSoilMap</em> specifications and the project is rejuvenating soil survey and mapping in many parts of the world. Functional soil property maps have been produced using digital soil mapping techniques and existing legacy information and made available to the user community for application. In addition, uncertainty has been provided as a 90% prediction interval based on estimated upper and lower class limits. We believe that <em>GlobalSoilMap</em> constitutes the best available framework and methodology to address global issues about SOC mapping. Main scientific challenges include time related and uncertainties issues