Soil Research is an international journal of soil science publishing high quality research on: soil genesis, soil morphology and classification; soil physics and hydrology; soil chemistry and mineralogy; soil fertility and plant nutrition; soil biology and biochemistry; soil and water management and conservation; soil pollution and waste disposal
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80-metre Resolution 3D Soil Attribute Maps for Tasmania, Australia
Darren Kidd, Mathew Webb, Brendan Malone, Budiman Minasnay, Alex McBratney Abstract Until recently, Tasmanian environmental modelling and assessments requiring important soil inputs has had to rely on conventionally-derived soil polygons that were mapped up to 75 years ago. Following on from the âWealth from Waterâ project, where digital soil mapping (DSM) was used in a pilot project to map the suitability of twenty different agricultural enterprises over 70,000ha, the Tasmanian Department of Primary Industries Parks Water and Environment has applied DSM to existing soil datasets to develop enterprise suitability predictions across the whole state in response to further irrigation scheme expansion. The generated soil surfaces have conformed and contributed to the TERN (Terrestrial Ecosystem Research Network) Soil and Landscape Grid of Australia (www.csiro.au/soil-and-landscape-grid), a superset of GlobalSoilMap.net specifications. The surfaces were generated at 80m resolution for six standard depths and 13 soil properties (including pH, EC, organic carbon %, sand %, silt % and coarse fragments), in addition to several Tasmanian enterprise suitability soil attribute parameters. The modelling used soil site data with available explanatory state-wide spatial variables, including the SRTM-DEM and derivatives, gamma-radiometrics, surface geology, and multi-spectral satellite imagery. Regression trees were used to model the predicted spatial value, with upper and lower predictions estimated at the 90% confidence interval using a âleave-one-out-cross-validationâ within each âtreeâ or partition. A âten-fold-cross-validationâ was used to test overall model validation, and the final output derived by averaging each of the k-fold outputs to produce more-robust and less-biased outputs. The DSM has delivered realistic mapping for most attributes, with acceptable validation diagnostics and relatively low uncertainty ranges in âdata-richâ areas, but performed marginally in terms of uncertainty ranges in areas such as the world-heritage listed south-west of the state, with a low existing soil site density. The version 1.0 soil attribute maps form the foundations of a dynamic and evolving new infrastructure that will be improved and re-run with the future collection of new soil data. The Tasmanian mapping has provided a localised integration with the National Soil and Landscape Grid of Australia (www.csiro.au/soil-and-landscape-grid), and will help guide future investment in soil information capture by quantitatively targeting areas with both high uncertainties, and important ecological or agricultural value.
Modeling soil evolution is an important step towards understanding the complexity of the soil system and its interaction with the other systems. The major challenge confronted by pedologists until now is the ability to develop models capable of describing the complete complexity of the soil system. This paper presents the state of art overview of such a soil evolution model, SoilGen, its applications and limitations. In addition, the paper gives an overview of how the SoilGen model may be linked to landscape evolution models to model soilscape development. SoilGen is a mechanistic pedogenetic model in which soil forming processes such as clay migration, decalcification, carbon cycling, bioturbation, physical and chemical weathering coupled with water flow are simulated at multi-millennium time scale. The model has been calibrated and undergone extensive field testing, giving reasonable results at both pedon and landscape scales. However discrepancies between observed and simulated soil properties such as base saturation (BS), cation exchange capacity (CEC) and pH have been reported. These have been attributed partly to simplification of soil forming processes particularly in the weathering and chemical systems. There is therefore a need to extend the description of chemical and weathering systems in the SoilGen model. These extensions will not only improve model performance but will also enlarge its application range in simulating the genesis of typical features of more than half of the WRB-Reference Soil Groups. We also note here that although landscape evolution models have been successfully applied to model soil production and distribution, simplified and/or incomplete description of soil forming processes remain major limitations. We therefore add to the voices in scientific literature calling for integration of pedon and landscape scale models. In addition there is critical need for high quality chronosequence, climosequence, and toposequence profile datasets to enhance calibration and validation of soil evolution models.
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes.
Soil profile descriptions have largely relied on morphometrics by which soil profile properties are mechanically measured and visually observed. These observations are then combined with chemical, physical, and mineralogical data or thin sections from soil horizons. Official guidelines and handbook for describing soils include the Soil Survey Manual (Soil Survey Division Staff, 1993) and the Field Book for Describing and Sampling Soils (Schoeneberger et al., 2012). Detailed soil observations are made for a whole range of purposes (e.g., mapping, classification, land evaluation, and pedological investigation). Commonly, a soil pit is dug, but observations are also made using augers, samplers, push probes, slice shovels, trenches, road cuts, or in quarries. The overall purpose of describing a soil profile is to preserve the image of the soil, and a full soil profile description consists of reference and geographic location, profile environment (climate and geology), site and area description, and a description of the soil horizons and its attributes and properties. The traditional field toolbox for soil profile descriptions includes augers, pickaxe, spade, knife, spatula, rock hammer, Munsell charts, maps, notebook, water bottle, HCl, sample bags, tape measure, clinometer, compass, altimeter or GPS, and camera (Fig. 1). These are used to measure and observe soil properties and horizons.
Planning sustainable soil exploitation and land resource evaluation require up-to-date and accurate maps of soil properties. In that respect, geophysical techniques present particular interests given their non-invasiveness and their fast data acquisition capacity, which permit to characterize large areas with fine spatial and/or temporal resolutions. We investigated the relevancy of combining data from airborne hyperspectral (Hs), electromagnetic induction (EMI) and far-field ground-penetrating radar (GPR) for mapping soil properties, in particular soil clay content, at the field scale. Data from the three techniques were acquired at a test site in Mugello (Italy) characterized by relatively strong spatial variations of soil texture. Soil samples were collected for determining ground truth clay content. For the frequencies used in this study (200–650 MHz), the GPR surface reflection is mainly determined by soil dielectric permittivity, itself primarily influenced by soil moisture. In contrast, EMI is mostly sensitive to soil electrical conductivity, which integrates several soil properties including in particular soil moisture and clay content. Taking advantage of the complementary information provided by the two instruments, the GPR and EMI data were combined and correlated to local ground-truth clay content data to provide high-resolution clay content maps over the entire field area. Besides, a relationship was also observed between Hs data and clay content measurements, which permitted to produce a Hs-derived clay content map. EMI–GPR and Hs maps showed close spatial patterns and a relatively high correlation was observed between both clay content estimates, as well as between clay content estimates and ground-truth clay content measurements. Moreover, data fusion allowed constraining the EMI–GPR and Hs information and reduced the uncertainty of mapped clay content estimates. These results demonstrated great promise for integrated, digital soil mapping applications.
Spatial Disaggregation – A Primer. Tom D’Avello – NRCS-NSSC-GRU c ontact: email@example.com Travis Nauman – NRCS-NSSC-GRU, WVU c ontact: firstname.lastname@example.org. Overview. Define ‘Disaggregation’ Approaches and Tools West Virginia Illinois Arizona Summary
The western Kenyan highlands are among the most highly populated and productive areas in Kenya's "breadbasket" regions. It is important, therefore, to make optimal use of available land to enhance food security. The objective of this project was to develop a first generation digital soil map of a portion of the Uasin Gishu Plateau to be used for both teaching and extension. To support digital map production, we sampled five representative pedons and analyzed them for organic matter, pH, extractable K+, Ca 2+, Mg2+, Al+3, and P, effective cation exchange capacity (ECEC), base saturation, soil texture, and clay mineralogy. Pedon KN12 is a poorly drained Vertisol (Typic Endoaquert) in a depression at ~2280 m elevation; pedons KN13 and KN14 are well-drained Oxisols at ~2230 m elevation with a petroferric contact within ~80 cm of the soil surface (Petroferric Eutrudox), and pedons KN15 and KN16 are well-drained Oxisols (Humic Eutrudox) at ~2780 m. All 5 pedons had clay textures throughout. There were no statistically significant differences (p>0.05) in extractable P and K+ levels, but the remaining parameters showed significant differences (p<0.05) among the sites. The Vertisol (KN12) had significantly higher ECEC, Mg2+ , Ca2+, base saturation, and pH, and lower Al +3 saturation than the Oxisols. As expected, base saturation was positively correlated with Ca2+, Mg2+, ECEC, and pH, and negatively correlated with Al+3. X-ray diffraction showed that the clay fraction of the Oxisols was predominately kaolinite with smaller amounts of mica. Goethite and rutile were also identified in KN13, KN15and KN16. The clay fraction of the Vertisol contained interstratified kaolinite-smectite and discrete kaolinite. One Oxisol (KN16) contained hydroxyl-interlayered vermiculite in addition to kaolinite. A Digital Elevation Model (DEM) was used to generate covariates such as topographic wetness index (WTI), percent slope, geomorphons, and altitude above channel network. These covariates were used to create a soil class map of a portion of the Usain Gishu Plateau that is significantly more detailed than the currently available soil maps of the area. A major constraint limiting digital map production in Kenya at this time is the poor spatial resolution (90 m) of the available DEM data.^ Keywords: XRD, x-ray diffraction; DSM, digital soil mapping, DEM, digital elevation model, geomorphons.^
This is the second of two events organized by the Global Soil Partnership (GSP), with the support of the European Commission, to follow up on priorities identified during launch workshops for the African Soil Partnership held in Accra, Ghana and...
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