Australian Soil Classification Research Articles

Digital soil mapping of coarse fragments in southwest Australia: Targeting simple features yields detailed maps

The spatial distribution of soil coarse fragments (CF) is important for a variety of agricultural and environmental applications because it directly impacts soil processes including hydrology and nutrient cycling. However, there is often insufficient measured data to reliably model and map CF using a digital soil mapping approach. By targeting CF layer occurrence rather than CF as a continuous soil property, we increased the number of data sites available for spatial modelling which improved predicted patterns. We define CF layers as hard CF and segregations size > 2 mm, > 20% by volume, and > 10 cm thick, the definition of a ferric diagnostic horizon in the Australian Soil Classification system. Highly variable legacy data yielded nearly 40,000 georeferenced sites over the 1 M square kilometre study area. The binary classification models evaluated were random forest using regression trees (probability machines) and classification trees, with and without class balancing. The best performing models were regression forests, followed by classification using the threshold that maximised the Kappa coefficient. Prediction accuracy was determined by validating with a subset of legacy data randomly selected on an unaligned grid and withheld from model training; validation was performed on all modelling methods, plus other available CF digital soil maps. Incorporating low quality legacy observations improved CF predictions significantly. The final maps depict CF layer presence or absence in four depth slices (0–5, 5–15, 15–30, and 30–80 cm) and anywhere within the top 80 cm, for both CF of any composition and specifically for ironstone gravel (sesquioxide nodules). These new maps have high predictive power: ironstone gravel layers had AUC ranging from 0.86 to 0.89, Kappa between 0.48 and 0.52, and overall accuracy from 0.82 to 0.92; for CF layers of mixed composition AUC ranged from 0.79 to 0.84, with Kappa 0.43 to 0.46, and overall accuracy from 0.74 to 0.88. The maps are plausible representations of local variation in soil properties across the landscape. They reflect the expert knowledge encoded in conventional soil maps, and are more locally credible than other available modelled CF maps. Modelling CF as layers rather than continuous properties led to high accuracy spatial representation of simple but still useful soil features for our study area. These soil feature maps complement the quantitative soil property surfaces common to digital soil mapping studies that are frequently constrained by data availability.

A multivariate method for matching soil classification systems, with an Australian example

Differences between local systems of soil classification hinder the communication between pedologists from different countries. The FAO–UNESCO Soil Map of the World, as a fruit of world-wide collaboration between innumerable soil scientists, is recognised internationally. Ideally, pedologists should be able to match whole classes in their local systems to those in an international soil classification system. The Australian Soil Classification (ASC) system, created specifically for Australian soil, is widely used in Australia, and Australian pedologists wish to translate the orders they recognise into the FAO soil units when writing for readers elsewhere. We explored the feasibility of matching soil orders in the ASC to units in the FAO legend using a multivariate analysis. Twenty soil properties, variates, of 4927 profiles were estimated from their visible–near infrared reflectance (vis–NIR) spectra. We arranged the profiles in a Euclidean 20-dimensional orthogonal vector space defined by standardised variates. Class centroids were computed in that space, and the Euclidean distances between the centroids of the ASC orders and units in the FAO scheme were also computed. The shortest distance between a centroid of any ASC order and one of units in the FAO classification was treated as a best match. With only one exception the best matches were those that an experienced pedologist might expect. Second and third nearest neighbours in the vector space provided additional insight. We conclude that vis–NIR spectra represent sufficiently well the essential characters of the soil and so spectra could form the basis for the development of a universal soil classification system. In our case, we could assign with confidence the orders of the ASC to the units of the FAO scheme. A similar approach could be applied to link other national classification systems to one or other international systems of soil classification.

Allocating soil profile descriptions to a novel comprehensive soil classification system

Previous work has been put into the creation of a comprehensive soil classification system (CSCS) using a harmonised dataset of 23 soil properties at 18 depth intervals. The classification consists of selected soil taxa from the US Soil Taxonomy, World Reference Base for Soil Resources, the Australian Soil Classification, and the New Zealand Soil Classification. In this paper, the CSCS was tested for allocation using data for from 44 soil profiles collected in Iran. A distance-based algorithm was used to allocate and name the soil profiles according to the CSCS. It was found that 36 Iranian soil profiles are close to the existing taxa of the CSCS in the taxonomic space. Three Iranian profiles with distances between 25 and 30 taxonomic units to the closest CSCS taxa were added to the CSCS and assigned with new systematic names. Allocating the remaining 5 Iranian taxa would require regenerating the nomenclature system. The CSCS has shown advantages for allocating soil profiles from other regions of the world other than the USA, Australia and New Zealand. It also enables cross-referencing with existing soil classification systems. In the future, the CSCS can be further improved by adding taxa from other global or regional soil classification systems.

A nomenclature algorithm for a potentially global soil taxonomy

Previous work has been put into the creation of a global soil taxonomy using a harmonised dataset of 23 soil properties at 18 depth intervals. The taxonomy consisted of selected soil taxa from the US Soil Taxonomy, World Reference Base for Soil Resources, the Australian Soil Classification, and the New Zealand Soil Classification. In this paper, a nomenclature algorithm was proposed for this established comprehensive taxonomy. Firstly, a Ward dendrogram was calculated from a weighted distance matrix determined from principal components of the taxa. This dendrogram was then cut at three levels, creating 15 groups, 86 subgroups, and 493 sub-subgroups at tiers 1, 2 and 3, respectively. A sequence of consonants was used to name the taxa at each tier alphabetically with “A” and “E” inserted between the consonants of tiers 2 and 3 and “OZEM” appended after the consonants of tier 1. In addition, a distance-based algorithm was used to allocate and name 10 unknown soil profiles to the comprehensive soil taxonomy. It was concluded that the nomenclature algorithm can be easily disaggregated by computer and can be used to understand the inter-relationships between soil profiles from different classification systems. In the future, there is a need to include other soil classification systems to the comprehensive system and assign different weights to the soil properties and depths used to construct the comprehensive soil classification system.

Updating a national soil classification with spectroscopic predictions and digital soil mapping

Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1×1km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders.

Comparisons between USDA soil taxonomy and the Australian Soil Classification system II: Comparison of order, suborder and great group taxa

Soil taxonomies over the world are incongruent- based on different tiers and different sets of properties. This second paper is concerned with understanding the relationships of each tier (Order, Suborder and Great Group) in both Soil Taxonomy (ST) and the Australian Soil Classification system (ASC) using mean nearest neighbour distances and convex hull areas in two principal component dimensions. It is determined that in most instances, convex hull comparisons using only two principal components, representing 30% of the variation in the data describe much of the variability between and within the orders, suborders and great groups of each classification system. These are useful for visual comparisons of taxa at various levels. Mean nearest-neighbour distances can include all 414 variables if necessary, which is more rigorous but complex. Both distance calculations and convex hulls highlight the same associations between taxa from ASC and ST. Both these methods demonstrate the robust soil classification capability of the ST and the ASC, but with convex hull sizes and nearest-neighbour distances that are smaller, the ASC proves to be slightly more coherent. The two systems occupy somewhat different areas in PC space, and ST covers a larger overall area, demonstrating that ASC is a purpose-built classification for Australian conditions while ST Is a more general system that can cover a wider variety of soils and management issues. We also show that great groups in ST are at about the same level of taxonomic generalization as great groups of the ASC. Combining the best elements and taxa of both these systems would be a positive step in the creation of a comprehensive system.

Creating a novel comprehensive soil classification system by sequentially adding taxa from existing systems

Many different soil classification systems exist around the world and soil profile descriptions have always been difficult to successfully translate between taxonomic systems. In order to do this, there is the requirement for a comprehensive soil classification. This database needs to be comprehensive, non-duplicated, have the capacity for other suitable taxa to be added to, and at the same time to be small and simple. In this paper, we proposed a nearest-neighbour distance calculated based on the principal component space of the soil variables to remove redundancy of the existing soil classification and evaluate the equivalence between the different soil classification system. After removing the redundancy, Great Groups of the New Zealand Classification and Australian Soil Classification and Soil Groups of the World Reference Base for Soil Resources were sequentially added to the Great Groups of the US Soil Taxonomy. This resulted in a comprehensive soil classification. The comprehensive soil classification allowed for the further addition of taxa from other soil classification and was capable of robustly allocating unknown soil profiles via either hard or fuzzy classification methods. The possibility merging and simplifying of such different systems demonstrates the possibility of similar mergers with taxonomies of other nations, creating a fully populated taxonomic space – a truly global or universal soil classification system.

Comparisons between USDA Soil Taxonomy and the Australian Soil Classification System I: Data harmonization, calculation of taxonomic distance and inter-taxa variation

Soil classification as a world exercise consists of predominantly individual organizations, creating locally meaningful categories for regional soils. This process has inevitably created a recognized disconnect between classification systems, and a push for a universal classification has been proposed. In this paper, as a way of standardization between systems, soil taxa at the great group level from two separate regions and soil classification systems, Australia and the United States of America were represented by separate databases of soil profile descriptions (SPDs) comprising the same 23 properties at 18 depth intervals. Taxa centroids from Soil Taxonomy (ST) and the Australian Soil Classification System (ASC) were calculated via principal component analysis. Convex hulls of each soil order of both systems were created and the associations each taxon had with other individuals in the same taxon discussed, as well as the variance. We determined that ASC orders have smaller overall dispersion compared with the ST. The influence of each property to the overall taxonomic distances was also explored. It was concluded that this analysis opened the way for the possibility of comparing differing taxonomies and could pave the way for a more comprehensive classification method.

Surface lime and silicate application and crop production system effects on physical characteristics of a Brazilian Oxisol

This work aimed to evaluate the effects of crop rotations and soil acidity amelioration on soil physical properties of an Oxisol (Rhodic Ferralsol or Red Ferrosol in the Australian Soil Classification) from October 2006 to September 2011 in Botucatu, SP, Brazil. Treatments consisted of four soybean (Glycine max)–maize (Zea mays)–rice (Oryza sativa) rotations that differed in their off-season crop, either a signal grass (Urochloa ruziziensis) forage crop, a second crop, a cover crop, or fallow. Two acid-neutralising materials, dolomitic lime (effective calcium carbonate equivalent (ECCE) = 90%) and calcium-magnesium silicate (ECCE = 80%), were surface applied to raise the soil’s base saturation to 70%. Selected soil physical characteristics were evaluated at three depths (0–0.1, 0.1–0.2, and 0.2–0.4 m). In the top 0.1 m, soil bulk density was lowest (P < 0.05) and macroporosity and aggregate stability index were greatest (P < 0.05) in the forage crop compared with all other production systems. Also, bulk density was lower (P < 0.05) and macroporosity was greater (P < 0.05) in the acid-neutralising-amended than the unamended control soil. In the 0.1–0.2-m interval, mean weight diameter and mean geometric diameter were greater (P < 0.05) in the forage crop compared with all other production systems. All soil properties evaluated in this study in the 0.2–0.4-m interval were unaffected by production system or soil amendment after five complete cropping cycles. Results of this study demonstrated that certain soil physical properties can be improved in a no-tillage soybean–maize–rice rotation using a forage crop in the off-season and with the addition of acid-neutralising soil amendments. Any soil and crop management practices that improve soil physical properties will likely contribute to sustaining long-term soil and crop productivity in areas with highly weathered, organic matter-depleted, acidic Oxisols.

Australian Soil Classification: an Review

As a means of improving Korean Soil Classification System, we have reviewed Australian Soil Classification System by comparing Soil Taxonomy and FAO/WRB Classification System. Australian Soil Classification System is composed of 14 of Order, 87 of Sub-order, 556 of Great-group, 2,451 of Sub-group, and 7,276 of Family. Interestingly, soil order has the Anthroposols which is not classified with Soil Taxonomy, and the classification for some of soils is based on soil texture abruption horizon and soil structure. Seven of 14 soil orders are classified with an old version based on soil color rather than morphological characteristics. The distribution scale of Australian soil order is the largest in Tenosols, and followed by Kandosols, Rudosols, Sodosols and Vertisols in Australia.Key words: Soil Classification, Australia, Soil, Soil Orders

Assessing the Australian Soil Classification using cladistic analysis

The Australian Soil Classification (ASC) has its roots in both the Handbook of Australian Soils and the Factual Key. The scheme’s use of mutually exclusive characteristics has led to Soil Orders containing a diverse range of soils, such as the Dermosols. The extent of these groupings has resulted in classes of soils sharing greater relationships with soils from other classes than they do with soils in the same class. Situations such as this arise from artificial classifications and highlight the need for natural classifications. Natural classifications accurately represent what is occurring in nature and are desirable because they represent evidence of a common history, process or mechanism. This study uses cladistics, a robust biological method that uncovers natural classifications, to assess the naturalness of the ASC. The analysis has the secondary aims of identifying natural soil orders and establishing which characters and tiers require revision. Two measures commonly used in cladistics, consistency index (CI) and retention index (RI), are used along with confidence levels generated by bootstrapping. The cladistic analysis undertaken consisted of coding 113 morphological and non-morphological characters used to identify 13 of the 14 Soil Orders in ASC into binary and multi-state matrices and analysis using a parsimony cladistic algorithm. The results suggest that, because of its low CI (0.196), the ASC is not a natural classification. However, certain Soil Orders of Organosols, Podosls and Vertosols, which all registered high CI, are natural. The analysis also indicated which soil morphological characters and Soil Orders require revision in order to make the ASC more natural, namely, soil colour and characters located in the Great Groups as well as Soil Orders such as Chromosols, Ferrosols and Dermosols. We conclude that cladistics offers a new avenue in discerning relationships between soils and in assessing the accuracy of, and identifying where improvements can be made in, the classifications used to identify them.

Corrigendum to: Soil phosphorus–crop response calibration relationships and criteria for winter cereal crops grown in Australia

Soil testing is the most widely used tool to predict the need for fertiliser phosphorus (P) application to crops. This study examined factors affecting critical soil P concentrations and confidence intervals for wheat and barley grown in Australian soils by interrogating validated data from 1777 wheat and 150 barley field treatment series now held in the BFDC National Database. To narrow confidence intervals associated with estimated critical P concentrations, filters for yield, crop stress, or low pH were applied. Once treatment series with low yield (CaCl2 1 t/ha). There was a clear increase in critical P concentration from early trials when full tillage was common compared with those conducted in 1995–2011, which corresponds to a period of rapid shift towards adoption of minimum tillage. For wheat, critical Colwell-P concentrations associated with 90 or 95% of maximum yield varied among Australian Soil Classification (ASC) Orders and Sub-orders: Calcarosol, Chromosol, Kandosol, Sodosol, Tenosol and Vertosol. Soil type, based on ASC Orders and Sub-orders, produced critical Colwell-P concentrations at 90% of maximum relative yield from 15 mg/kg (Grey Vertosol) to 47 mg/kg (Supracalcic Calcarosols), with other soils having values in the range 19–27 mg/kg. Distinctive differences in critical P concentrations were evident among Sub-orders of Calcarosols, Chromosols, Sodosols, Tenosols, and Vertosols, possibly due to differences in soil properties related to P sorption. However, insufficient data were available to develop a relationship between P buffering index (PBI) and critical P concentration. In general, there was no evidence that critical concentrations for barley would be different from those for wheat on the same soils. Significant knowledge gaps to fill to improve the relevance and reliability of soil P testing for winter cereals were: lack of data for oats; the paucity of treatment series reflecting current cropping practices, especially minimum tillage; and inadequate metadata on soil texture, pH, growing season rainfall, gravel content, and PBI. The critical concentrations determined illustrate the importance of recent experimental data and of soil type, but also provide examples of interrogation pathways into the BFDC National Database to extract locally relevant critical P concentrations for guiding P fertiliser decision-making in wheat and barley.

The World Reference Base for Soils (WRB) and Soil Taxonomy: an appraisal of their application to the soils of the Northern Rivers of New South Wales

Few soil surveys in New South Wales have utilised international soil classifications. Extensive morphological and laboratory data collected during soil surveys in the Northern Rivers region provided a strong basis for correlation with the World Reference Base for Soil Resources (WRB), Soil Taxonomy (ST), and the Australian Soil Classification (ASC). Of the 32 reference soil groups comprising the WRB, 20 were present locally; nine of the 12 ST orders were present. After re-classification of soils, correlation of the ASC with the WRB and ST was undertaken. Soils not requiring extensive laboratory analysis for classification and sharing similar central concepts were the more straightforward to correlate. Several ASC orders have unique central concepts and were therefore difficult to correlate with any one WRB reference soil group or ST order/suborder. Other soils were difficult to correlate due to differences in definitions of similar diagnostic criteria. This is most applicable to soils with strong texture-contrast and those with natric conditions. Such soils are not adequately differentiated to suit the Northern Rivers conditions. Of the two international schemes, the WRB was easier to apply locally due to the relative simplicity of the scheme. Considering certain aspects of Australian soils would improve the applicability of the WRB as a truly international framework for soil classification and correlation. Amendments to both the ASC and WRB are suggested.

Mean Residence Time of Soil Organic Carbon in Aggregates Under Contrasting Land Uses Based on Radiocarbon Measurements

Radiocarbon is a useful tool for studying carbon dynamics in soil aggregates. The objective of the current study was to determine the mean residence time (MRT) of soil organic carbon (SOC) in macroaggregates and microaggregates under contrasting land uses. Contrasting land uses investigated at Alfisol (equivalent to Dermosol in Australian Soil Classification) sites were native pasture (NP), crop-pasture rotation (CP), and Eucalypt woodland (WL), whereas in Oxisol (Ferrosol in Australian Soil Classification) sites, land uses comprised improved pasture (IP), cropping (CR), and forest (FR). Soil aggregates were separated into macroaggregates (250–2000 μm) and microaggregates (53–250 μm) by wet-sieving, and their14C signatures were determined by accelerator mass spectrometry (AMS). The14C activity in both macro- and microaggregates was >100 pMC, indicating the presence of post-bomb carbon in the soil. The mean residence time (MRT) of SOC in macro- and microaggregates (MRTagg) was on average 68 yr longer in the Oxisol compared with that in the Alfisol. The MRTaggin microaggregates was 10 yr longer than that of macroaggregates in the Alfisol. However, the MRTaggin microaggregates was 50 yr shorter compared to macroaggregates in the Oxisol.The MRT of macro- and microaggregates can be separated into active, slow, and stable SOC pools. Among the 3 SOC pools, the MRT of the stable pool is of higher significance in terms of SOC stabilization in soil aggregates because of its longer MRT. However, isolation and direct MRT estimation of the stable SOC pool is difficult. The MRT of active and slow SOC pools associated with macro- and microaggregates was measured using a SOC mineralization experiment to estimate the MRT of the stable SOC pool under contrasting land uses by applying a mass balance criterion. The MRT of active (MRTA) and slow (MRTS) SOC pools in macro- and microaggregates varied between 1–50 days and 13–38 yr, respectively. The estimated MRT of the stable pool carbon (MRTP) in microaggregates was 897 yr longer compared to that of macroaggregates in the Alfisol. However, in the Oxisol, MRTPin microaggregates was 568 yr shorter than that of macroaggregates. Among the land uses, WL in Alfisol and CR in Oxisol had longer MRTaggand MRTPcompared to other land uses.

Mean Residence Time of Soil Organic Carbon in Aggregates Under Contrasting Land Uses Based on Radiocarbon Measurements

Radiocarbon is a useful tool for studying carbon dynamics in soil aggregates. The objective of the current study was to determine the mean residence time (MRT) of soil organic carbon (SOC) in macroaggregates and microaggregates under contrasting land uses. Contrasting land uses investigated at Alfisol (equivalent to Dermosol in Australian Soil Classification) sites were native pasture (NP), crop-pasture rotation (CP), and Eucalypt woodland (WL), whereas in Oxisol (Ferrosol in Australian Soil Classification) sites, land uses comprised improved pasture (IP), cropping (CR), and forest (FR). Soil aggregates were separated into macroaggregates (250–2000 μm) and microaggregates (53–250 μm) by wet-sieving, and their 14C signatures were determined by accelerator mass spectrometry (AMS). The 14C activity in both macro- and microaggregates was >100 pMC, indicating the presence of post-bomb carbon in the soil. The mean residence time (MRT) of SOC in macro- and microaggregates (MRTagg) was on average 68 yr longer in the Oxisol compared with that in the Alfisol. The MRTagg in microaggregates was 10 yr longer than that of macroaggregates in the Alfisol. However, the MRTagg in microaggregates was 50 yr shorter compared to macroaggregates in the Oxisol.The MRT of macro- and microaggregates can be separated into active, slow, and stable SOC pools. Among the 3 SOC pools, the MRT of the stable pool is of higher significance in terms of SOC stabilization in soil aggregates because of its longer MRT. However, isolation and direct MRT estimation of the stable SOC pool is difficult. The MRT of active and slow SOC pools associated with macro- and microaggregates was measured using a SOC mineralization experiment to estimate the MRT of the stable SOC pool under contrasting land uses by applying a mass balance criterion. The MRT of active (MRTA) and slow (MRTS) SOC pools in macro- and microaggregates varied between 1–50 days and 13–38 yr, respectively. The estimated MRT of the stable pool carbon (MRTP) in microaggregates was 897 yr longer compared to that of macroaggregates in the Alfisol. However, in the Oxisol, MRTP in microaggregates was 568 yr shorter than that of macroaggregates. Among the land uses, WL in Alfisol and CR in Oxisol had longer MRTagg and MRTP compared to other land uses.

Soil phosphorus–crop response calibration relationships and criteria for winter cereal crops grown in Australia

Soil testing is the most widely used tool to predict the need for fertiliser phosphorus (P) application to crops. This study examined factors affecting critical soil P concentrations and confidence intervals for wheat and barley grown in Australian soils by interrogating validated data from 1777 wheat and 150 barley field treatment series now held in the BFDC National Database. To narrow confidence intervals associated with estimated critical P concentrations, filters for yield, crop stress, or low pH were applied. Once treatment series with low yield (<1 t/ha), severe crop stress, or pHCaCl2 <4.3 were screened out, critical concentrations were relatively insensitive to wheat yield (>1 t/ha). There was a clear increase in critical P concentration from early trials when full tillage was common compared with those conducted in 1995–2011, which corresponds to a period of rapid shift towards adoption of minimum tillage. For wheat, critical Colwell-P concentrations associated with 90 or 95% of maximum yield varied among Australian Soil Classification (ASC) Orders and Sub-orders: Calcarosol, Chromosol, Kandosol, Sodosol, Tenosol and Vertosol. Soil type, based on ASC Orders and Sub-orders, produced critical Colwell-P concentrations at 90% of maximum relative yield from 15 mg/kg (Grey Vertosol) to 47 mg/kg (Supracalcic Calcarosols), with other soils having values in the range 19–27 mg/kg. Distinctive differences in critical P concentrations were evident among Sub-orders of Calcarosols, Chromosols, Sodosols, Tenosols, and Vertosols, possibly due to differences in soil properties related to P sorption. However, insufficient data were available to develop a relationship between P buffering index (PBI) and critical P concentration. In general, there was no evidence that critical concentrations for barley would be different from those for wheat on the same soils. Significant knowledge gaps to fill to improve the relevance and reliability of soil P testing for winter cereals were: lack of data for oats; the paucity of treatment series reflecting current cropping practices, especially minimum tillage; and inadequate metadata on soil texture, pH, growing season rainfall, gravel content, and PBI. The critical concentrations determined illustrate the importance of recent experimental data and of soil type, but also provide examples of interrogation pathways into the BFDC National Database to extract locally relevant critical P concentrations for guiding P fertiliser decision-making in wheat and barley.

Spatial uncertainty of137Cs-derived net (1950s–1990) soil redistribution for Australia

[1] The caesium-137 (137Cs) technique has been used successfully in many parts of the world to estimate net (ca. 30–50 years) soil redistribution by wind and water erosion and tillage activities. The point-based technique has hitherto been confined largely to individual fields and hillslopes, particularly in Australia. Its application here to the Australian continent (≈5 km grid) was achieved using geostatistics and nationally coordinated measurements (early 1990s) from ≈200 locations at the ≈1 km scale. A map of the 137Cs reference inventory for Australia has been previously established. Sequential indicator co-simulation of the 137Cs inventory and the Australian Soil Classification was used to estimate net (between mid-1950s and early 1990s) soil redistribution using the Australian Empirical Model. This geostatistical approach showed that nearly five times more soil was lost from cultivated land (−4.29 to +0.17 t ha−1 yr−1) than from uncultivated (−0.91 to +0.05 t ha−1 yr−1) land in Australia. This information on spatial uncertainty is essential for regional soil management to assess the risk to soil conservation. Soil erosion exceeding a tolerable threshold value (e.g., 0.5 t ha−1 yr−1) occurred over 16% of Australia, mainly in cultivated regions (median = −1.26 t ha−1 yr−1). Soil erosion estimates are neglected in carbon balances for greenhouse gas abatement and carbon accounting models. Reliable quantitative data on the recent extent and rates of soil erosion are needed to underpin the selection of effective soil conservation measures, to inform carbon balances and to understand regional soil function for sustainable agricultural systems.

Discrimination of Australian soil horizons and classes from their visible-near infrared spectra

Summary A soil’s reflectance spectrum in the visible and near infrared is rich in information. It is an integrative property of the soil that measures its colour, the abundance of iron oxides, clay minerals and carbonates, the amount of water and organic matter and its particle size. We explored the merit of discriminating between soil horizons, of which we had 13 654 samples, and soil orders from the Australian Soil Classification, of which we had samples from 1697 profiles with designated horizons, by analysing quantitatively their diffuse reflectance spectra in the visible-near infrared (vis–NIR) range (350–2500 nm). We re-sampled the spectra to 10-nm intervals and converted them to logarithms of deviations from their convex hulls. We then transformed them to canonical variates, which we display as scatter diagrams in loworder canonical planes. The minerals, colour and organic constituents thought to be responsible for their discrimination are identified. Each spectrum was re-examined and allocated to the group whose centroid was nearest in the canonical space. Topsoil horizons (A, A2 and transitional AB and AC horizons) were distinguishable from subsoils (B, C and transitional BC horizons). Vertosols, Ferrosols, Podosols (Podzols), Organosols, Calcarosols, Rudosols, Sodosols, Hydrosols, Kandosols and Kurosols were in general well separated from other soil orders and were assigned to their own orders such that 80% or more were correctly allocated. These orders possess characteristics that are easily distinguished by vis–NIR spectroscopy. Re-allocations to other orders were interpretable and could be related to identifying features of the ASC classification. Our results show that spectra distinguish soil horizons and soil type. They suggest that vis–NIR spectroscopy could make an important contribution to the definition and identification of classes in an effective system of soil classification.

Hillslope runoff and erosion on duplex soils in grazing lands in semi-arid central Queensland. III. USLE erodibility (K factors) and cover - soil loss relationships

Measured Universal Soil Loss Equation (USLE) soil erodibility (K) values are not available for soils in grazing lands in northern Australia. The K values extrapolated from croplands are used in national and river-basin scale assessments of hillslope erosion, using an assumption that the cover factor (C) equals 0.45 for undisturbed (uncultivated) bare soil. Thus, the K needed for input into the models is the measured K for undisturbed soil (KU) divided by 0.45. Runoff and erosion data were available for 7 years on 12 hillslope plots with cover of 10–80%, with and without grazing, with and without tree canopy cover, on a variety of soils according to various soil classification systems. Soils were grouped into those derived from sandstone (SS), mudstone (MS), and eroded mudstone (MSe). These data were used to determine USLE KU, K, and C factor–cover relationships. Methods used to fit the parameters affected the results; minimising the sum of squares of errors in soil losses gave better results than fitting an exponential equation. The USLE LS (length–slope) factor explained the increase in measured average annual soil loss with slope, for plots with low cover. Erodibility (K) was 0.042 for SS and MS soils, irrespective of Australian Soil Classification (Chromosol, Kandosol, Rudosol, Sodosol, Tenosol); K was 0.062 for exposed, decomposing mudstone (MSe Leptic Rudosol). The measured K factor for SS and MS soils was close to that used in catchment-wide soil loss estimation for the site (0.039). This indicates that the method used for estimating K from soil properties (derived from cultivated soils) gave a reasonable estimate of K for the main duplex soils at the study site, as long as the correction for undisturbed soil is used in deriving K from measured data and in applying the USLE model. A 20% increase in K (0.050) for SS and MS soils may be warranted for heavy grazing by cattle. The C factor–cover relationship was different from the standard revised USLE (RUSLE) relationship, requiring a greater exponent (‘bcov’) of 0.075, rather than the default for cropland of 0.035. Increasing cover is therefore more effective at the site than suggested by the USLE. Parameters of USLE were also derived for bedload, allowing suspended load to be calculated by subtracting bedload from total soil loss.

Terra Preta Australis: Reassessing the carbon storage capacity of temperate soils

Soils developed on the sites of Australian Aboriginal oven mounds along the Murray River in SE Australia, classified as Cumulic Anthroposols under the Australian Soil Classification, are shown to have traits similar to the Terra Preta de Indio of the Amazon basin. Seven such sites were characterised and compared with adjacent soils. The Cumulic Anthroposols contained significantly (p<0.05) more soil carbon (C), compared to adjacent non-Anthroposols. Solid-state 13C NMR spectroscopy showed that the C in the Cumulic Anthroposols was predominantly aromatic, especially at depth, confirming the presence of charcoal. Radiocarbon analysis carried out on charcoal collected from two of these sites showed that it was deposited 650±30 years BP at one site and 1609±34 years BP at the other site, demonstrating its recalcitrance in soil. The charcoal originated from plant material, as shown by SEM, and had high levels of Ca agglomeration on its surfaces. The Cumulic Anthroposols were shown to have altered nutrient status, with total N, P, K and Ca being significantly greater than in the adjacent soils throughout the profile. This was also reflected in the higher mean CEC of 31.2cmol (+)kg−1 and higher pH by 1.3 units, compared to the adjacent soils. Based on the similarity of these Cumulic Anthroposols with the Terra Preta de Indio of the Amazon, we suggest that these Cumulic Anthroposols can be classified as Terra Preta Australis. The existence of these soils demonstrates that Australian soils, in temperate climates, are capable of storing C in much higher quantities than has been previously recognised, and that this capability is founded on the unique stability and properties of charred organic matter. Furthermore, the addition of charcoal appears to have improved the physical and chemical properties of these soils. Together, this provides important support for the concept of soil amendment with “biochar”, the charred residue produced by pyrolysis of biomass, as a means for sequestering C and enhancing agricultural productivity.