Total elemental composition of soils in Sub-Saharan Africa and relationship with soil forming factors

AbstractA thorough understanding of the variation in total soil element concentrations is important especially in the Sub-Saharan Africa (SSA) soil contexts for agricultural and environmental management at large scale. Fingerprinting of soil elemental composition may form a useful basis for evaluating soils in a way that relates to soil-forming factors and inherent soil functional properties. The objectives of this paper are to quantify the proportion of variability in total elemental composition by total X-ray fluorescence (TXRF) method of 1074 soil samples from the Africa Soil Information Service (AfSIS) Project baseline and to determine the relationships with soil forming factors. The samples were from 34 sentinel sites measuring 10×10km, randomized within major climate zones in SSA. Within each sentinel site there were sixteen spatially stratified 1km2 clusters, within which there were ten 100m2 plots. The within and between site patterns of variation in total element composition of 17 elements; Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sr, Y, Ta, and Pb, were explored. Total element concentration values were within the range reported globally for soil Cr, Mn, Zn, Ni, V, Sr, and Y and higher than reported range for Al, Cu, Ta, Pb, and Ga. There were significant variations (P<0.05) in total element composition within and between the sites for all the elements analyzed with the greatest proportion of total variance and number of significant variance components occurring at the site (55–88%) followed by the cluster nested within site (10–40%) levels. The explorations of the relationships between element composition data and site factors using Random Forest regression demonstrated that soil-forming factors have important influence on total elemental composition in the soil. The fact that the soil-forming factors are related to the concentration of naturally occurring elements in the soil gives rise to the notion that they might be predicted from the soils' element composition. Results implied that >70% of variation in soil element composition patterns can be predicted using information in existing databases or readily observable features. Successful use of TXRF technique would open up possibilities for using total soil elemental composition fingerprints as a useful basis for characterizing soils in a way that relates to soil-forming factors and inherent soil functional properties

Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso

The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer’s accuracy and user’s accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond

Africa Soil Information Service (AfSIS) - Soil Health Mapping

N/

Microarthropod use as bioindicators of the environmental state: case of soil mites (Acari)from Côte d’Ivoire.

The aim of this study was to identify biological indicators of soil state under four agrosystem types. Therefore, Lamto savannah (SOM-poor sites), Oume primary forest (SOM-rich sites), Oume teak plantation (SOM-less sites) situated in Sudanese domain and Tai primary forest (SOM-moderate sites) localized in Guinean domain (Ivory Coast) were sampled twice during one year. The Indval software was used to identify the indicator species, through two analyses. The first analysis separated level 1- climatic zones (Guinean vs. Sudanese), level 2- localities (Oumé vs. Lamto vs. Taï), level 3-segregated sites depending on the level of disturbance: A second analysis opposes litter dwelling to mineral soil dwelling mites. The results revealed that only one species was dominant and ubiquitous, particularly Afrotrachytes sp.1 whereas three species, respectively Rhysoglyphus sp.1, Dendracarus sp.1 and Acaridae sp.4 were dominant and specialist. Chemical elements Corg (g/kg), Ctot (%), Ntot (%), and SOM (g/kg) was higher in forest than in savannah and teak plantation. Dwelling mite indicator species characterizing the Guinean domain (Taï primary forest / undisturbed site) were highly different to those observed in Sudanese domain (disturbed sites). If the four sites were considered and distinguished between microhabitats, the essential species indicators were found in Oume primary forest where a moderate disturbance was observed. However, a lower number of indicator species were found in Oume teak plantation, characterized by a high disturbance. The value of Oribatida-Actinedida ratio ranged from 3.95 in teak plantation to 52.28 in Oume primary fores

Wet chemistry data for a subset of AfSIS: Phase I archived soil samples

This dataset contains a subset of the samples collected during the AfSIS Phase I project and was a collaborative effort between World Agroforestry (ICRAF) and Rothamsted Research. The soil samples were retrieved from ICRAF Soil Archive: https://worldagroforestry.org/output/icraf-soil-archive-physical-archive-systematically-collected-soil-samples and subject to wet chemical analysis at Rothamsted Research in the UK under a Global Challenges Research Fund project, "BBS/OS/GC/000014B: Chemical and Biological Assessment of AfSIS soils" funded through the UK Biotechnology and Biological Sciences Research Council. This dataset includes the Site, Cluster, Plot as well as the GPS coordinates and wet chemistry data from 2002 samples collected from 18 countries and 51 LDSF sites. The original data collection was part of the AfSIS Phase I project, funded by the Bill and Melinda Gates Foundation (BMGF) and took place between 2009-2013. ICRAF and CIAT contributed the Site, Cluster, Plot and GPS coordinates for the soil samples, ICRAF organized the sub-sampling of the soil samples from the ICRAF physical archive in Nairobi and Rothamsted analysed the soil samples in the UK in 2017 and 2018. Visit our websites here: https://worldagroforestry.org/landhealth and https://www.rothamsted.ac.uk/. The AfSIS Phase I project funded by the Bill and Melinda Gates Foundation (BMGF) from 2009-2013, aimed to provide a consistent baseline of soil information across sub-Saharan Africa (SSA). Led by CIAT-TSBF, partners included: ISRIC, CIESIN, The Earth Institute at Columbia University and World Agroforestry (ICRAF). ICRAF led the systematic assessments of soil health using the Land Degradation Surveillance Framework (LDSF), which was developed at ICRAF, http://landscapeportal.org/blog/2015/03/25/the-land-degradation-surveillance-framework-ldsf/. LDSF sites were randomized using spatial stratification based on Koeppen-Geiger Climate zones across 19 countries in SSA. In total 60 LDSF sites were sampled. Soil samples were collected using the LDSF at two depths, 0-20 cm (labelled Topsoil) and 20-50 cm (labelled Subsoil). In each LDSF site, approximately 320 standard soil samples were collected. All of these were also scanned using MIR Spectroscopy and are available on Dataverse here: Vågen, Tor-Gunnar;Winowiecki, Leigh Ann;Desta, Luseged;Tondoh, Ebagnerin Jérôme;Weullow, Elvis;Shepherd, Keith;Sila, Andrew, 2020, "Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013", https://doi.org/10.34725/DVN/QXCWP1, World Agroforestry - Research Data Repository, V1

Large-scale controls of soil organic carbon in (sub)tropical soils

Soil organic carbon (SOC) is a key component of terrestrial ecosystems. Experimental studies have shown that soil texture and geochemistry have a strong effect on carbon stocks. However, those findings primarily rely on data from temperate regions or use model approaches that are often based on limited data from tropical and sub-tropical regions. Here, we evaluate the controls on soil carbon stocks in Africa, using a dataset of 1,580 samples. These were collected across Sub-Saharan Africa (SSA) within the framework of the Africa Soil Information Service (AfSIS) project, which was built on the well-established Land Degradation Surveillance Framework (LDSF). Samples were taken from two depths (0–20 cm and 20–50 cm) at 46 LDSF sites that were stratified according to Koeppen-Geiger climate zones. The different pH-values, clay content, exchangeable cations and extractable elements across various soils of the different climatic zones (i.e. from arid to humid (sub)tropical) allow us to identify different soil and climate parameters that best explain SOC variance across SSA. We tested if these SOC predictors differed across climatological conditions, using the ratio of potential evapotranspiration (PET) to mean annual precipitation (MAP) as indicator. For water-limited regions (PET/MAP > 1), the best predictors were climatic variables, likely because of their effect on the quantity of carbon inputs. Geochemistry dominated SOC storage in energy-limited systems (PET/MAP < 1), reflecting its effect on carbon protection. On a continental scale, climate (e.g. PET) is key to predicting SOC content in topsoil, whereas geochemistry, particularly iron-oxyhydroxides and aluminum-oxides, is more important in subsoil. Clay content had little influence on SOC at both depths. These findings contribute to an improved understanding of the controls on SOC stocks in tropical and sub-tropical regions