A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

A methodology for digital soil mapping in poorly accessible areas

Effective soil management requires knowledge of the spatial patterns of soil variation within the landscape to enable wise land use decisions. This is typically obtained through time-consuming and costly surveys. The aim of this study was to develop a cost-efficient methodology for digital soil mapping in poorly-accessible areas. The methodology uses a spatial model calibrated on the basis of limited soil sampling and explanatory covariables related to soil-forming factors, developed from readily available secondary information from accessible areas. The model is subsequently applied in the poorly-accessible areas. This can only be done if the environmental conditions in the poorly-accessible areas are also found in the accessible areas in which the model is developed. This study illustrates the methodology in an exercise to predict soil organic carbon (SOC) concentration in the Limpopo National Park, Mozambique. Readily-available secondary data was used as explanatory variables representing the soil-forming factors. Conditions in the accessible and poorly-accessible areas corresponded sufficiently to allow the extrapolation of the spatial model into the latter. The spatial variation of SOC in the accessible area was mostly described by the sampling cluster (71.5%) and the landscape unit (46.3%). Therefore ordinary (punctual) kriging (OK) and kriging with external drift (KED) based on the landscape unit were used to predict SOC. A linear regression (LM) model using only landscape stratification was used as control. All models were independently validated with test sets collected in both accessible and poorly-accessible areas. In the former the root mean squared error of prediction (RMSEP) was 0.42–0.50% SOC. The ratio between the RMSEP in the poorly-accessible and accessible areas was 0.67–0.72, showing that the methodology can be applied to predict SOC in poorly-accessible areas as successful as in accessible areas. The methodology is thus recommended for areas with similar access problems, especially for baseline studies and for sample design in two-stage survey

Soil organic carbon stocks in the Limpopo National Park, Mozambique: Amount, spatial distribution and uncertainty.

Many areas in sub-Saharan African are data-poor and poorly accessible. The estimation of soil organic carbon (SOC) stocks in these areas will have to rely on the limited available secondary data coupled with restricted field sampling. We assessed the total SOC stock, its spatial variation and the causes of this variation in Limpopo National Park (LNP), a data-poor and poorly accessible area in southwestern Mozambique. During a field survey, A-horizon thickness was measured and soil samples were taken for the determination of SOC concentrations. SOC concentrations were multiplied by soil bulk density and A-horizon thickness to estimate SOC stocks. Spatial distribution was assessed through: i) a measure-and-multiply approach to assess average SOC stocks by landscape unit, and ii) a soil-landscape model that used soil forming factors to interpolate SOC stocks from observations to a grid covering the area by ordinary (OK) and universal (UK) kriging. Predictions were validated by both independent and leave-one-out cross validations. The total SOC stock of the LNPwas obtained by i) calculating an area-weighted average from the means of the landscape units and by ii) summing the cells of the interpolated grid. Uncertainty was evaluated by the mean standard error for the measure-and-multiply approach and by the mean kriging prediction standard deviation for the soil-landscape model approach. The reliability of the estimates of total stockswas assessed by the uncertainty of the input data and its effect on estimates. The mean SOC stock from all sample points is 1.59 kg m-2; landscape unit averages are 1.13–2.46 kg m-2. Covariables explained 45% (soil) and 17% (coordinates) of SOC stock variation. Predictions from spatial models averaged 1.65 kg m-2 and are within the ranges reported for similar soils in southern Africa. The validation root mean square error of prediction (RMSEP) was about 30% of the mean predictions for both OK and UK. Uncertainty is high (coefficient of variation of about 40%) due to short-range spatial structure combined with sparse sampling. The range of total SOC stock of the 10,410 km-2 study area was estimated at 15,579–17,908 Gg. However, 90% confidence limits of the total stocks estimated are narrower (5–15%) for the measure-and-multiply model and wider (66–70%) for the soil-landscape model. The spatial distribution is rather homogenous, suggesting levels are mainly determined by regional climate

Rescue and renewal of legacy soil resource inventories: A case study of the Limpopo National Park, Mozambique.

Many areas of developing countries are covered by legacy soil surveys, which, however are hardly used, as they are not available in digital form, used outdated standards, and have unknown quality. There have been very few attempts to rescue and renew these surveys, nor are there established criteria for the evaluation of their quality. We therefore decided to test the applicability of the Cornell Adequacy Criteria (CAC) to assess the quality of several renewed soil surveys in or near the Limpopo National Park, Mozambique (centroid: 23° 18' 55.57¿ S, 31° 55' 16.24¿ E), using the concepts of digital soilmapping. The qualitywas assessed formapping andmonitoring soil organic carbon (SOC), in terms of geodetic control, positional accuracy, map scale, and texture and adequacy of map legend. Metadata was attached to the renewed maps. SOC stocks were estimated qualitatively based on the description of themap units and quantitatively by themeasure-and-multiply approach fromlegacy laboratory measurements. The positional accuracy of georegistrationwas 13 to 45% of the square root of aMinimumLegible Area (MLA). Point and area-class layers could be created with high positional accuracy. However the index of maximumreductionwas high, indicating that the original publication scale could be reduced.Map unit definitions and overall information content of the surveyswere adequate. Integration of remotely sensed optical imagery and digital elevation models could be used to derive accurate contours, against which the positional accuracy of contour-basedmap borderswas assessed. Less than 30% of their lengths were within a distance equal to the square root of MLA. These sources could not be used to evaluate internal map borders, due to the subdued topography and major land-use changes since the original survey. Qualitative estimates of SOC are between lowand medium, consistent with other studies in this area. The CAC proved to be a useful framework for determining the fitness for use of legacy surveys

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Soil organic carbon (SOC) is a key soil property and particularly important for ecosystem functioning and the sustainable management of agricultural systems. Conventional laboratory analyses for the determination of SOC are expensive and slow. Laboratory spectroscopy in combination with chemometrics is claimed to be a rapid, cost-effective and non-destructive method for measuring SOC. The present study was carried out in Limpopo National Park (LNP) in Mozambique, a data- and access-limited area, with no previous soil spectral library. The question was whether a useful calibration model could be built with a limited number of samples. Across the major landscape units of the LNP, 129 composite topsoil samples were collected and analyzed for SOC, pH and particle sizes of the fine earth fraction. Samples were also scanned in a near-infrared (NIR) spectrometer. Partial least square regression (PLSR) was used on 1037 bands in the wavelength range 1.25–2.5 μm to relate the spectra and SOC concentration. Several models were built and compared by cross-validation. The best model was on a filtered first derivative of the multiplicative scatter corrected (MSC) spectra. It explained 83% of SOC variation and had a root mean square error of prediction (RMSEP) of 0.32% SOC, about 2.5 times the laboratory RMSE from duplicate samples (0.13% SOC). This uncertainty is a substantial proportion of the typical SOC concentrations in LNP landscapes (0.45–2.00%). The model was slightly improved (RMSEP 0.28% SOC) by adding clay percentage as a co-variable. All models had poorer performance at SOC concentrations above 2.0%, indicating a saturation effect. Despite the limitations of sample size and no pre-existing library, a locally-useful, although somewhat imprecise, calibration model could be built. This model is suitable for estimating SOC in further mapping exercises in the LNP

Potential Use of Biochar in Pit Latrines as a Faecal Sludge Management Strategy to Reduce Water Resource Contaminations: A Review

Faecal sludge management (FSM) in most developing countries is still insufficient. Sanitation challenges within the sub-Saharan region have led to recurring epidemics of water- and sanitation-related diseases. The use of pit latrines has been recognised as an option for on-site sanitation purposes. However, there is also concern that pit latrine leachates may cause harm to human and ecological health. Integrated approaches for improved access to water and sanitation through proper faecal sludge management are needed to address these issues. Biochar a carbon-rich adsorbent produced from any organic biomass when integrated with soil can potentially reduce contamination. The incorporation of biochar in FSM studies has numerous benefits in the control of prospective contaminants (i.e., heavy metals and inorganic and organic pollutants). This review paper evaluated the potential use of biochar in FSM. It was shown from the reviewed articles that biochar is a viable option for faecal sludge management because of its ability to bind contaminants. Challenges and possible sustainable ways to incorporate biochar in pit latrine sludge management were also illustrated. Biochar use as a low-cost adsorbent in wastewater contaminant mitigation can improve the quality of water resources. Biochar-amended sludge can also be repurposed as a useful economical by-product