80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management ...practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global ...predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.
Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil ...information systems already exist, these tend to suffer from inconsistencies and limited spatial detail.
We present SoilGrids1km--a global 3D soil information system at 1 km resolution--containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg-1), soil pH, sand, silt and clay fractions (%), bulk density (kg m-3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha-1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5-fold cross-validation were between 23-51%.
SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.
Wet chemistry: Organo‐SOMO activation is an intricate process. The catalyst is deactivated in the absence of H2O and its concentration is maintained with 2 equivalents of H2O. The kinetic role of ...ceric ammonium nitrate (CAN) is masked by phase transfer and its limited solubility is enhanced by added H2O. Mechanistic studies show that careful addition of H2O to dried reagents greatly enhances reaction. TMS=trimethylsilyl.
Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and ...availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (Formula: see text) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)-silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images-SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature-however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.
We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for ...multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce 'mixed scaling' a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4-7% more accurate compared to modelling with Random Forests.
The Ebola virus (EBOV) genome only encodes a single viral polypeptide with enzymatic activity, the viral large (L) RNA-dependent RNA polymerase protein. However, currently, there is limited ...information about the L protein, which has hampered the development of antivirals. Therefore, antifiloviral therapeutic efforts must include additional targets such as protein–protein interfaces. Viral protein 35 (VP35) is multifunctional and plays important roles in viral pathogenesis, including viral mRNA synthesis and replication of the negative-sense RNA viral genome. Previous studies revealed that mutation of key basic residues within the VP35 interferon inhibitory domain (IID) results in significant EBOV attenuation, both in vitro and in vivo. In the current study, we use an experimental pipeline that includes structure-based in silico screening and biochemical and structural characterization, along with medicinal chemistry, to identify and characterize small molecules that target a binding pocket within VP35. NMR mapping experiments and high-resolution x-ray crystal structures show that select small molecules bind to a region of VP35 IID that is important for replication complex formation through interactions with the viral nucleoprotein (NP). We also tested select compounds for their ability to inhibit VP35 IID–NP interactions in vitro as well as VP35 function in a minigenome assay and EBOV replication. These results confirm the ability of compounds identified in this study to inhibit VP35–NP interactions in vitro and to impair viral replication in cell-based assays. These studies provide an initial framework to guide development of antifiloviral compounds against filoviral VP35 proteins.
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•Ebola VP35 is a multifunctional viral protein critical for viral replication.•VP35 IID structure revealed several drugable pockets.•In silico screen identified potential binders near functionally important patches.•Binding was validated by NMR and complex x-ray crystal structures.•Select small molecules inhibit VP35 functions in vitro and in cell-based studies.
Procedures for automated predictive thematic mapping were developed and applied to project areas totaling more than 3 million ha of forested land in British Columbia, Canada. The effective scale of ...mapping was 1:20,000 using data at a grid resolution of 25 m. The methods can be described as a form of automated feature extraction or object recognition where the objects of interest consist of ecological site types. The methods implement a hybrid of automated, semi-automated and manual procedures that develop and apply heuristic, rule-based conceptual models of ecological-landform relationships. The methods rely heavily upon terrain derivatives extracted from available digital elevation models (DEMs) in addition to satellite imagery and manually digitized maps of ancillary environmental conditions. The primary input has been the BC provincial Terrain Resource Information Management (TRIM) digital elevation model (DEM) surfaced to a regular grid of 25 m. Other input layers include manually interpreted maps of parent material texture, depth and ecological exception classes, manually prepared maps of the spatial distribution of ecological zones of the BC Biogeoclimatic Ecosystem Classification (BEC) system and, to a limited extent, LandSat7 digital satellite imagery. The procedures do not use any field sampling to develop or train classification rules. A knowledge-based approach is used to establish classification rules which are defined and implemented using a Semantic Import (SI) Model implementation of fuzzy logic. All rules are constructed by examining and deconstructing published field guides that define the required ecological output classes and that document the current expert understanding of the conditions and criteria that control the spatial distribution of these desired output classes. An iterative, trial and error, process is used to develop, apply, evaluate and revise object recognition rules that relate ranges of values of key input data layers to an expert-assigned likelihood of occurrence for each ecological class of interest. Local expert knowledge is used at each stage to evaluate each new set of output results and to guide refinement of the fuzzy SI model classification rules. Field sample observations obtained along randomly selected closed traverses were collected following a line intercept approach and used to assess the accuracy of the final predictions of ecological classification. Application of the procedures has progressed from an initial pilot project through projects to evaluate operational scale-up to full-scale commercial application to millions of hectares. Costs have been reduced from a high of $3.50/ha to less than $0.20/ha. Rates of progress increased from 150,000 ha per person year to more than 2.0 million ha per person year. Independent assessments of map accuracy produced results superior to the highest accuracies reported for all alternatives, including traditional manual mapping methods. We conclude that we have formalized and automated many of the concepts and techniques previously used to create thematic maps of ecosystems using manual interpretation of stereo air photos and ancillary data combined with field observations. We have shown that automated feature extraction is able to capture and apply the concepts of landform control referenced by typical landform-based ecological models and classification systems. We have demonstrated that it is possible to produce accurate and cost-effective ecological-landform maps by applying fuzzy and Boolean logic and automated landform analysis procedures to widely available spatial data.
Teleconnections in spatial modelling Behrens, Thorsten; MacMillan, Robert A.; Viscarra Rossel, Raphael A. ...
Geoderma,
11/2019, Letnik:
354
Journal Article
Recenzirano
In pedology, spatial context is relevant to soil-landscape systems on at least three different scales: i) the scale of quasi-local processes, which are independent of influence from the direct or ...wider neighborhood, ii) the scale of short-range processes for example on the local hillslope or catena, and iii) the scale of long-range processes, or teleconnected systems. We can represent the effects of teleconnections using existing tools and covariates, but we cannot easily infer or identify their controls, landscape processes or landscape units. We consider that an ability to identify the relevant controls in teleconnected systems would greatly improve pedological interpretation and understanding. Such understanding relates to the interaction of environmental factors and processes in the spatial context, which is relevant for environmental mapping generally. Here we show that teleconnected systems can be disassembled and interpreted using contextual modelling in such a way that the controls, i.e. the cause, can be localized in space. We present examples of how teleconnected systems can be deciphered. The methodology is based on the previously described ConMap approach in combination with Random Forest's measures of local feature importance. ConMap uses elevation differences, computed along multiple rays radiating out from a center grid cell, as predictors instead of complex surface derivatives or decomposed scales of a digital elevation model (DEM) or terrain attribute. Using synthetic and real-world data sets, we show how to identify and interpret teleconnections in soil environmental systems. In the synthetic example, elevation peaks are shown to produce larger values of soil properties, while, in contrast, a valley-mountain system is the main control of soil texture in the real-world example. Our analyses of teleconnected soil environmental systems illustrate that the stochastic component of the universal model of spatial variation is an integral but typically unresolved part of the deterministic component.
•The three most important contextual environmental process systems are defined.•The term teleconnection is introduced for digital soil mapping and pedology.•A method is presented that makes it possible to decode teleconnected systems.•The control of a teleconnected system can be determined and spatially localized.
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