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The intensification of human pressures on soil can reduce pedodiversity and decrease soil multifunctionality impacting soil security. Mapping genosoils (least modified soils within a soil class or soil map unit by contemporary drivers of soil change) and phenosoils (variants resulting from land use history and management) can be a preliminary step for quantifying soil security dimensions and prioritising areas for soil preservation and regeneration. Genosoil properties can be used as a baseline for assessing the effects of management on soil condition for a particular pedological, climatic and landscape context. In this study, we stratified Australia into 1370 pedogenons (i.e., groups with relatively homogeneous environmental covariates, proxies of soil-forming factors) that represent soil classes prior to the European settlement from 1788 onwards. We overlayed the maps of global Human Modification and the Habitat Condition Assessment System for Australia for identifying areas with minimum human influence on terrestrial ecosystems and soils. Areas with very low human influence were defined as genosoils at the continental level. The percentage of land mapped as genosoils accounted for 56% of the continent and had a median area of 2550 km2. There were 32 pedogenon classes that did not have any remaining genosoils while 218 pedogenon classes had less than 5% of their area as genosoils. The proportion of genosoils protected in conservation areas or managed resource protection varied widely, although almost 25% of the genosoils had at least half of their area under conservation. In addition to soil multifunctionality, the criteria for prioritising soil conservation areas could consider: 1) endangered genosoils and 2) genosoils closest (in the scorpan feature space) to the phenosoils without an existing reference soil.

Abstract We introduce a new dataset of high-resolution gridded total soil organic carbon content data produced at 30 m × 30 m and 90 m × 90 m resolutions across Australia. For each product resolution, the dataset consists of six maps of soil organic carbon content along with an estimate of the uncertainty represented by the 90% prediction interval. Soil organic carbon maps were produced up to a depth of 200 cm, for six intervals: 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The maps were obtained through interpolation of 90,025 depth-harmonized organic carbon measurements using quantile regression forest and a large set of environmental covariates. Validation with 10-fold cross-validation showed that all six maps had relatively small errors and that prediction uncertainty was adequately estimated. The soil carbon maps provide a new baseline from which change in future carbon stocks can be monitored and the influence of climate change, land management, and greenhouse gas offset can be assessed.

We set out in this work to create a suite of 3D-like digital soil attribute maps for a research farming enterprise situated in the South Eastern Agricultural Zone of NSW, Australia. These digital maps come about because of a reconnaissance soil survey to map out the farm's soil baseline conditions and bring to bear the tools and processes of the digital convergence. This entails on-the-go proximal soil sensing for rapid and granular farm landscape characterisation, which helped inform field-based soil survey and sampling, and later the soil mapping too. Soil infrared spectroscopy associated inference (vis-NIR) was then used to facilitate very detailed soil characterisations of key soil attributes that included soil pH, carbon, soil texture, bulk density, and cation exchange capacity amongst others. This work then enabled the ability to build bespoke spatial models to generate 3D-like inherent and dynamic digital soil attribute maps. We highlight some of the digital soil infrastructure features which include flexibility, customisation, and work pipelines that will ease the updating and improvement of maps. We also highlight some immediate goals which include a formal inclusion of measurement uncertainties brought about by soil spectral inference into the spatial modelling process. To date, some of the digital soil mapping has been used for applications such as lime requirement estimations to address subsoil acidity issues and therefore changing/improving the soil's capability at BARS. Overall, the intention of this work is to provide a detailed account of what is entailed in creating a comprehensive digital soils infrastructure for a farm including the likely costs to implement and the associated opportunities it provides.

Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff.

The assessment of changes in soil condition and capability requires the identification of a reference state specific to each soil class. This study develops a framework for mapping soil classes that can be used as a reference state. It identifies soil classes that should have undergone similar historic anthropedogenesis, and differentiate, within each class, zones that have been less affected by human activities. This approach could be used as a baseline for assessing contemporary soil change, as demonstrated in the state of New South Wales in Australia. First, we established soil classes with similar multimillennial natural pedogenesis and historic anthropedogenesis, called pedogenons. This was achieved by applying unsupervised classification (k-means) to a set of quantitative state variables, proxies of the soil-forming factors at the time of the European settlement in New South Wales (climate, relief, parent material, and estimated pre-1750s vegetation). Pedogenon classes were then stratified into subclasses (ranging from remnant pedogenons to different pedophenons) by combining information on native vegetation extent, status (remnant or cleared) and current land use (i.e., land use history). The stratification of 1000 pedogenon classes resulted in 5448 subclasses, ranging from remnant pedogenons (located in protected areas of intact native vegetation), quasi-remnant pedogenons (production with low intervention on remnant native vegetation), cleared, grazing, and cropping pedophenons. The median of the area proportion of the pedogenon that was still preserved as remnant vegetation was 5.3%. This quasi-remnant pedogenon or the less affected pedophenon could be used as reference state. Pedophenon grazing and cropping occupied larger areas, with mean values of 73 km2 and 153 km2, respectively. The application of this framework for assessing soil change is illustrated using legacy data of topsoil pH (5 – 15 cm) as one indicator of soil condition. The ability of the pedogenon and pedophenon subclasses for explaining the variation of three stable (total Si, total Al, clay) and three dynamic (bulk density, particulate organic carbon, pH) soil properties from agricultural soils. A generalised least squares model indicated that the effects of pedogenon, land use history and their interaction on topsoil pH were statistically significant (p

It is widely acknowledged that the global stock of soil and environmental resources are diminishing and under threat. This issue stems from current and historical unsustainable management practices, leading to degraded landscapes, which is further compounded by increased pressures upon them from ever-increasing anthropogenic activities. To curb the trajectory toward a collapse of our ecosystems, systematic ways are needed to assess the condition of our natural resources, how much they might have changed, and to what extent this might impact on the life sustaining functions we derive from our environment and the extent of our food producing systems. Some solutions to these issues come in the form of measurement, mapping and monitoring technology, which facilitates powerful ways in which to be informed about and to understand and assess the condition of our landscapes so that they can be managed strategically or simply improved. This Special Issue showcases from several locations across the globe, detailed examples of what is achievable at the convergence of big data brought about by remote and proximal sensing platforms, advanced statistical modelling and computing infrastructure to understand and monitor our ecosystems better. These utilities not only provide high-resolution abilities to map the extent and changes to our food producing systems, they also have yielded new ways to determine land-use and climate effects on the fate of soil carbon across living generations and to identify hydrological risk strategies in otherwise data-poor urban environments. Leveraging the availability of remote sensing data is telling, but the papers in this Special Issue also highlight the sophistication of modelling capabilities to deliver not only highly detailed maps of temporal dynamic soil phenomena but ways to draw new inferences from sparse and disparate model input data. The challenges of restoring our ecosystems are immense and sobering. However, we are well equipped and capable of confronting these pervasive issues in objective and data-informed ways that have previously never been possible.

Background The use of visible-near infrared (vis-NIR) spectroscopy for rapid soil characterisation has gained a lot of interest in recent times. Soil spectra absorbance from the visible-infrared range can be calibrated using regression models to predict a set of soil properties. The accuracy of these regression models relies heavily on the calibration set. The optimum sample size and the overall sample representativeness of the dataset could further improve the model performance. However, there is no guideline on which sampling method should be used under different size of datasets. Methods Here, we show different sampling algorithms performed differently under different data size and different regression models (Cubist regression tree and Partial Least Square Regression (PLSR)). We analysed the effect of three sampling algorithms: Kennard-Stone (KS), conditioned Latin Hypercube Sampling (cLHS) and k-means clustering (KM) against random sampling on the prediction of up to five different soil properties (sand, clay, carbon content, cation exchange capacity and pH) on three datasets. These datasets have different coverages: a European continental dataset (LUCAS, n = 5,639), a regional dataset from Australia (Geeves, n = 379), and a local dataset from New South Wales, Australia (Hillston, n = 384). Calibration sample sizes ranging from 50 to 3,000 were derived and tested for the continental dataset; and from 50 to 200 samples for the regional and local datasets. Results Overall, the PLSR gives a better prediction in comparison to the Cubist model for the prediction of various soil properties. It is also less prone to the choice of sampling algorithm. The KM algorithm is more representative in the larger dataset up to a certain calibration sample size. The KS algorithm appears to be more efficient (as compared to random sampling) in small datasets; however, the prediction performance varied a lot between soil properties. The cLHS sampling algorithm is the most robust sampling method for multiple soil properties regardless of the sample size. Discussion Our results suggested that the optimum calibration sample size relied on how much generalization the model had to create. The use of the sampling algorithm is beneficial for larger datasets than smaller datasets where only small improvements can be made. KM is suitable for large datasets, KS is efficient in small datasets but results can be variable, while cLHS is less affected by sample size.