Brendan P. Malone

Digital Mapping of Soil Carbon

There is a global demand for soil data and information for food security and global environmental management. There is also great interest in recognizing the soil system as a significant terrestrial sink of carbon. The reliable assessment of soil carbon (C) stocks is of key importance for soil conservation and in mitigation strategies for increased atmospheric carbon. In this article, we review and discuss the recent advances in digital mapping of soil C. The challenge to map carbon is demonstrated with the large variation of soil C concentration at a field, continental, and global scale. This article reviews recent studies in mapping soil C using digital soil mapping approaches. The general activities in digital soil mapping involve collection of a database of soil carbon observations over the area of interest; compilation of relevant covariates (scorpan factors) for the area; calibration or training of a spatial prediction function based on the observed dataset; interpolation and/or extrapolation of the prediction function over the whole area; and finally validation using existing or independent datasets. We discuss several relevant aspects in digital mapping: carbon concentration and carbon density, source of data, sampling density and resolution, depth of investigation, map validation, map uncertainty, and environmental covariates. We demonstrate harmonization of soil depths using the equal-area spline and the use of a material coordinate system to take into consideration the varying bulk density due to management practices. Soil C mapping has evolved from 2-D mapping of soil C stock at particular depth ranges to a semi-3-D soil map allowing the estimation of continuous soil C concentration or density with depth. This review then discusses the dynamics of soil C and the consequences for prediction and mapping of soil C change. Finally, we illustrate the prediction of soil carbon change using a semidynamic scorpan approach.

Digital Soil Mapping

In recent times we have bared witness to the advancement of the computer and information technology ages. With such advances, there have come vast amounts of data and tools in all fields of endeavor. This has motivated numerous initiatives around the world to build spatial data infrastructures aiming to facilitate the collection, maintenance, dissemination and use of spatial information. Soil science potentially contributes to the development of such generic spatial data infrastructure through the ongoing creation of regional, continental and worldwide soil databases, and which are now operational for some uses e.g., land resource assessment and risk evaluation (Lagacherie and McBratney 2006).

Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia

An operational Digital Soil Assessment was developed to inform land suitability modelling in newly commissioned irrigation schemes in Tasmania, Australia. The Land Suitability model uses various soil parameters, along with other climate and terrain surfaces, to identify suitable areas for various agricultural enterprises for a combined 70 000-ha pilot project area in the Meander and Midlands Regions of Tasmania. An integral consideration for irrigable suitability is soil drainage. Quantitative measurement and mapping can be resource-intensive in time and associated costs, whereas more ‘traditional’ mapping approaches can be generalised, lacking the detail required for statistically validated products. The project was not sufficiently resourced to undertake replicated field-drainage measurements and relied on expert field drainage estimates at ~930 sites (260 of these for independent validation) to spatially predict soil drainage for both areas using various terrain-based and remotely sensed covariates, using three approaches: (a) decision tree spatial modelling of discrete drainage classes; (b) regression-tree spatial modelling of a continuous drainage index; (c) regression kriging (random-forests with residual-kriging) spatial modelling of a continuous drainage index. Method b was chosen as the best approach in terms of interpretation, and model training and validation, with a concordance coefficient of 0.86 and 0.57, respectively. A classified soil drainage map produced from the ‘index’ showed good agreement, with a linearly weighted kappa coefficient of 0.72 for training, and 0.37 for validation. The index mapping was incorporated into the overall land suitability model and proved an important consideration for the suitability of most enterprises.

Digital Soil Assessments

Digital soil assessment goes beyond the goals of digital soil mapping. Digital soil assessment (DSA) can be defined (from McBratney et al. (2012)) as the translation of digital soil mapping outputs into decision making aids that are framed by the particular, contextual human-value system which addresses the question/s at hand. The concept of DSA was first framed by Carre et al. (2007) as a mechanism for assessing soil threats, assessing soil functions and for soil mechanistic simulations to assess risk based scenarios to complement policy development. Very simply DSA can be likened to the quantitative modeling of difficult-to-measure soil attributes. An obvious candidate application for DSA is land suitability evaluation for a specified land use type, which van Diepen et al. (1991) define as all methods to explain or predict the use potential of land. The first part of this chapter will cover a simple digital approach for performing this type of analysis. The second part of the chapter will explore a different form of digital assessment by way of identifying soil homologues (Mallavan et al. 2010).

Eighty-metre resolution 3D soil-attribute maps for Tasmania, Australia

Until recently, Tasmanian environmental modelling and assessments requiring important soil inputs relied on conventionally derived soil polygons that were mapped up to 75 years ago. In the ‘Wealth from Water’ project, digital soil mapping (DSM) was used in a pilot project to map the suitability of 20 different agricultural enterprises over 70 000 ha. Following on from this, 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 expansion of irrigation schemes. The soil surfaces generated have conformed and contributed to the Terrestrial Ecosystem Research Network Soil and Landscape Grid of Australia, a superset of GlobalSoilMap.net specifications. The surfaces were generated at 80-m resolution for six standard depths and 13 soil properties (e.g. pH, EC, organic carbon, sand and silt percentages 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 Shuttle Radar Topography Mission digital elevation model and derivatives, gamma-radiometrics, surface geology, and multi-spectral satellite imagery. The DSM has delivered realistic mapping for most attributes, with acceptable validation diagnostics and relatively low uncertainty ranges in data-rich areas, but performed marginally in terms of uncertainty ranges in areas such as the World Heritage-listed Southwest of the state, with a low existing soil site density. 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, and it will guide future investment in soil information capture by quantitatively targeting areas with both high uncertainties and important ecological or agricultural value.

Taking account of uncertainties in digital land suitability assessment.

Simulations are used to generate plausible realisations of soil and climatic variables for input into an enterprise land suitability assessment (LSA). Subsequently we present a case study demonstrating a LSA (for hazelnuts) which takes into account the quantified uncertainties of the biophysical model input variables. This study is carried out in the Meander Valley Irrigation District, Tasmania, Australia. It is found that when comparing to a LSA that assumes inputs to be error free, there is a significant difference in the assessment of suitability. Using an approach that assumes inputs to be error free, 56% of the study area was predicted to be suitable for hazelnuts. Using the simulation approach it is revealed that there is considerable uncertainty about the ‘error free’ assessment, where a prediction of ‘unsuitable’ was made 66% of the time (on average) at each grid cell of the study area. The cause of this difference is that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in this study area. Despite differences between the comparative methods, taking account of the prediction uncertainties provide a realistic appraisal of enterprise suitability. It is advantageous also because suitability assessments are provided as continuous variables as opposed to discrete classifications. We would recommend for other studies that consider similar FAO (Food and Agriculture Organisation of the United Nations) land evaluation framework type suitability assessments, that parameter membership functions (as opposed to discrete threshold cutoffs) together with the simulation approach are used in concert.

Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental mapping

Understanding the uncertainty in spatial modelling of environmental variables is important because it provides the end-users with the reliability of the maps. Over the past decades, Bayesian statistics has been successfully used. However, the conventional simulation-based Markov Chain Monte Carlo (MCMC) approaches are often computationally intensive. In this study, the performance of a novel Bayesian inference approach called Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation (INLA-SPDE) was evaluated using independent calibration and validation datasets of various skewed and non-skewed soil properties and was compared with a linear mixed model estimated by residual maximum likelihood (REML-LMM). It was found that INLA-SPDE was equivalent to REML-LMM in terms of the model performance and was similarly robust with sparse datasets (i.e. 40-60 samples). In comparison, INLA-SPDE was able to estimate the posterior marginal distributions of the model parameters without extensive simulations. It was concluded that INLA-SPDE had the potential to map the spatial distribution of environmental variables along with their posterior marginal distributions for environmental management. Some drawbacks were identified with INLA-SPDE, including artefacts of model response due to the use of triangle meshes and a longer computational time when dealing with non-Gaussian likelihood families.

Is Percent 'Projected Natural Vegetation Soil Carbon' a Useful Indicator of Soil Condition?

The concentration of Soil Organic Carbon (SOC) is often used as an indicator of soil condition and soil health. To be useful as an indicator, SOC must be considered in context, with soil type, climatic region, local rainfall, slope, and land use history influencing measured amounts of SOC. The concept of Percent Projected Natural Vegetation Soil Carbon (PNVSC) implicitly incorporates these context variables. Percent PNVSC is defined as a simple percentage comparing the contemporary measured soil carbon against a hypothetical amount of soil carbon that would be observed in the local landscape today, if the areas under managed agroecosystems remained under natural vegetation (which in this study is dry sclerophyll forest). The term ‘natural’ used in the PNVSC concept approximates with the natural system prior to agrarian settlement. Percent PNVSC was calculated for a 22,000 ha sub-catchment of the Hunter Valley, Australia and it showed a spatially weighted average of 73, indicating substantial soil carbon loss as a result of cumulative land use change over more than 100 years. The Percent PNVSC map highlights changes in soil carbon distribution across the landscape with mid-slope positions lower in the catchment showing the greatest loss of soil carbon. Viticulture has resulted in half of the original SOC being lost, compared to a 75 % PNVSC for unimproved pasture and an 83 % PNVSC for improved pasture. Average soil carbon loss due to mixed land-use change in this sub-catchment is 13,331 kg C/ha.

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

ABSTRACT Spatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECav and horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted ECe over the study area accurately (R2 = 0.87, ME = −1.04 and RMSE = 16.36 dSm−1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.

Quantitatively Predicting Soil Carbon Across Landscapes

Quantitative prediction of soil carbon (C) in the landscape can be achieved by empirical or mechanistic models, or a combination of both. The empirical approach called digital soil mapping, usually involves: collection of a database of soil carbon observations over an area of interest; compilation of relevant covariates for the area; calibration or training of a spatial prediction function based on the observed dataset; interpolation and/or- extrapolation of the prediction function over the whole area; and finally validation using existing or independent datasets. The resulting digital maps of C can be used in landscape mechanistic models simulating soil organic C evolution laterally and vertically (within the profile). Here we demonstrate the two approaches in predicting C stock evolution in a landscape in Northwest of France. We introduce the pedogeomorphometry approach which can combine the two approaches to map soil carbon dynamics at the landscape scale.