Digital soil mapping and assessment for Australia and beyond: A propitious future

Abstract Digital Soil Mapping and Assessment (DSMA) has progressed from challenging traditional soil science paradigms, through small scale prototyping, to large-scale implementation capturing quantitative measures of soil attributes and functions. This paper considers the future for DSMA in the context of a highly uncertain world where high-quality knowledge of soil dynamics will be important for responding to the challenges of sustainability. Irrespective of whether the need is for survival, increased productivity or broadening the services provided from land management, or simply securing the soil itself, we see DSMA as a fundamental approach and essential tool. With a broadening need and a strong foundation in the practice of DSMA now in place, the theory, tools and technology of DSMA will grow significantly. We explore expected changes in covariate data, the modelling process, the nature of base data generation and product delivery that will lead to tracking and forecasting a much wider range of soil attributes and functions at finer spatial and temporal resolutions over larger areas, particularly globally. Equally importantly, we expect the application and impact of DSMA to broaden and be used, directly and collaterally, in the analysis of land management issues in coming decades. It has the capacity to provide the background to a soil and landscape ‘digital twin’ and the consequent transformation in monitoring and forecasting the impacts of land management practices. We envision the continued growth of DSMA skills amongst soil scientists and a much broader community of practice involved in developing and utilizing DSMA products and tools. Consequently, there will be a widening and deepening role of public-private partnerships in this development and application.

[1]  Philip Weinstein,et al.  Naturally-diverse airborne environmental microbial exposures modulate the gut microbiome and may provide anxiolytic benefits in mice. , 2019, The Science of the total environment.

[2]  David Clifford,et al.  Putting regional digital soil mapping into practice in Tropical Northern Australia , 2015 .

[3]  Ian J. Yule,et al.  Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling , 2013 .

[4]  D. R. Davis,et al.  Organic Farming, Soil Health, and Food Quality: Considering Possible Links , 2016 .

[5]  R. Lawson,et al.  Revealing the lifestyles of local food consumers , 2012 .

[6]  C. Walter,et al.  Is pH increasing in the noncalcareous topsoils of France under agricultural management? A statistical framework to overcome the limitations of a soil test database , 2017 .

[7]  T. Lenton,et al.  Shifts in national land use and food production in Great Britain after a climate tipping point , 2020, Nature Food.

[8]  Budiman Minasny,et al.  Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review , 2015 .

[9]  E. Kowalczyk,et al.  The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model , 2006 .

[10]  A. McBratney,et al.  The dimensions of soil security , 2014 .

[11]  J.G.B. Leenaars,et al.  WoSIS: providing standardised soil profile data for the world , 2016 .

[12]  K. Gruber Deep influence of soil microbes. , 2015, Nature plants.

[13]  Luca Montanarella,et al.  Digital soil assessments: Beyond DSM , 2007 .

[14]  D. J. Brus,et al.  Sampling for Natural Resource Monitoring , 2006 .

[15]  K. Paustian,et al.  Quantifying carbon for agricultural soil management: from the current status toward a global soil information system , 2019, Carbon Management.

[16]  R. Webster,et al.  Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change , 2014, Global Change Biology.

[17]  Johan Bouma,et al.  How to communicate soil expertise more effectively in the information age when aiming at the UN Sustainable Development Goals , 2019, Soil Use and Management.

[18]  Elisabeth N. Bui,et al.  Soil surveyor knowledge in digital soil mapping and assessment in Australia , 2020 .

[19]  K. Shepherd,et al.  The global Land-Potential Knowledge System (LandPKS): Supporting evidence-based, site-specific land use and management through cloud computing, mobile applications, and crowdsourcing , 2013, Journal of Soil and Water Conservation.

[20]  David Gobbett,et al.  Data rich yield gap analysis of wheat in Australia , 2016 .

[21]  David L. Bish,et al.  Field deployment of a portable X-ray diffraction/X-ray flourescence instrument on Mars analog terrain , 2005, Powder Diffraction.

[22]  John Triantafilis,et al.  Estimating and mapping deep drainage risk at the district level in the lower Gwydir and Macquarie valleys, Australia , 2004 .

[23]  Nathan P. Odgers,et al.  Spatial disaggregation of conventional soil mapping across Western Australia using DSMART , 2014 .

[24]  Dominique Arrouays,et al.  Digital soil mapping and GlobalSoilMap. Main advances and ways forward , 2020 .

[25]  Luis Guanter,et al.  From HYSOMA to ENSOMAP – A new open source tool for quantitative soil properties mapping based on hyperspectral imagery from airborne to spaceborne applications , 2016 .

[26]  Alex B. McBratney,et al.  A preliminary spatial quantification of the soil security dimensions for Tasmania , 2018, Geoderma.

[27]  Philip Weinstein,et al.  Ambient soil cation exchange capacity inversely associates with infectious and parasitic disease risk in regional Australia. , 2018, The Science of the total environment.

[28]  Chris Sharman,et al.  Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition. , 2017, Environmental science & technology.

[29]  Raja Majid Mehmood,et al.  A Low-Cost Information Monitoring System for Smart Farming Applications , 2020, Sensors.

[30]  A. H. Phulpoto,et al.  Bioprospecting actinobacterial diversity antagonistic to multidrug-resistant bacteria from untapped soil resources of Kotdiji, Pakistan , 2019, Biologia.

[31]  Catherine Linard,et al.  Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling , 2019, Geocarto International.

[32]  A. McBratney,et al.  A new model for intra- and inter-institutional soil data sharing , 2019 .

[33]  B. Iticha,et al.  Digital soil mapping for site-specific management of soils , 2019, Geoderma.

[34]  Constantinos A. Georgiou,et al.  Multi-element and multi-isotope-ratio analysis to determine the geographical origin of foods in the European Union , 2012 .

[35]  Alex B. McBratney,et al.  Pedometric Treatment of Soil Attributes , 2018 .

[36]  David C. Weindorf,et al.  Chapter One – Advances in Portable X-ray Fluorescence (PXRF) for Environmental, Pedological, and Agronomic Applications , 2014 .

[37]  B. Ellert,et al.  Calculation of organic matter and nutrients stored in soils under contrasting management regimes , 1995 .

[38]  L. Fleskens,et al.  Investigating farmers’ preferences for alternative PES schemes for carbon sequestration in UK agroecosystems , 2017 .

[39]  Dick J. Brus,et al.  Sampling for digital soil mapping: A tutorial supported by R scripts , 2019, Geoderma.

[40]  What we learned from the Dust Bowl: lessons in science, policy, and adaptation , 2013, Population and environment.

[41]  Valerie O. Snow,et al.  APSIM Next Generation: Overcoming challenges in modernising a farming systems model , 2018, Environ. Model. Softw..

[42]  Afshin Ghahramani,et al.  A tool for monitoring soil water using modelling, on-farm data, and mobile technology , 2018, Environ. Model. Softw..

[43]  Thomas Nauss,et al.  Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction , 2019, Ecological Modelling.

[44]  Sunil R. Das,et al.  A compact multispectral image capture unit for deployment on drones , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[45]  Patrick Hostert,et al.  The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..

[46]  Paul Box,et al.  A Data Specification Framework for the Foundation Spatial Data Framework , 2015 .

[47]  Neville D. Crossman,et al.  Supply of carbon sequestration and biodiversity services from Australia's agricultural land under global change , 2014 .

[48]  L. Montanarella,et al.  A linkage between the biophysical and the economic: Assessing the global market impacts of soil erosion , 2019, Land Use Policy.

[49]  Budiman Minasny,et al.  Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia , 2015 .

[50]  Prabhu L Pingali,et al.  Green Revolution: Impacts, limits, and the path ahead , 2012, Proceedings of the National Academy of Sciences.

[51]  Gerard B. M. Heuvelink,et al.  Sampling design optimization for soil mapping with random forest , 2019 .

[52]  James E. Payne,et al.  Pragmatic soil survey design using flexible Latin hypercube sampling , 2014, Comput. Geosci..

[53]  B. Whelan,et al.  Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale , 2019, Agronomy.

[54]  P. Finke Modeling the genesis of luvisols as a function of topographic position in loess parent material , 2011 .

[55]  Reamonn Fealy,et al.  Are fine resolution digital elevation models always the best choice in digital soil mapping , 2013 .

[56]  Philippe C. Baveye,et al.  Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground , 2016, Front. Environ. Sci..

[57]  Budiman Minasny,et al.  Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping , 2007 .

[58]  M. R. Tabilio,et al.  Defining and Evaluating a Decision Support System (DSS) for the Precise Pest Management of the Mediterranean Fruit Fly, Ceratitis capitata, at the Farm Level , 2019, Agronomy.

[59]  Lubomír Gryc,et al.  Mapping of radiation anomalies using UAV mini-airborne gamma-ray spectrometry. , 2018, Journal of environmental radioactivity.

[60]  A. McBratney,et al.  Towards meaningful geographical indications: Validating terroirs on a 200 km2 scale in Australia's lower Hunter Valley , 2019, Geoderma Regional.

[61]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[62]  A. Thomson,et al.  Defining Sustainability as Measurable Improvement in the Environment: Lessons from a Supply Chain Program for Agriculture in the United States , 2019, Sustainability Perspectives: Science, Policy and Practice.

[63]  T. Behrens,et al.  Soil bacterial abundance and diversity better explained and predicted with spectro-transfer functions , 2019, Soil Biology and Biochemistry.

[64]  M. Leach,et al.  Integration: the key to implementing the Sustainable Development Goals , 2016, Sustainability Science.

[65]  Philippe Lagacherie,et al.  Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France) , 2015 .

[66]  A. McBratney,et al.  Soil Security for Australia , 2019, Sustainability.

[67]  J. Gardner,et al.  Food wedges: Framing the global food demand and supply challenge towards 2050 , 2014 .

[68]  Valentina Melini,et al.  Asian grain-based food products and the European scheme for food protected designations of origin: A critical analysis , 2019, Trends in Food Science & Technology.

[69]  Federica Camin,et al.  Food authentication: Techniques, trends & emerging approaches , 2016 .

[70]  Johan Bouma,et al.  Soil science contributions towards Sustainable Development Goals and their implementation: linking soil functions with ecosystem services , 2014 .

[71]  François Waldner,et al.  Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods , 2020 .

[72]  T. Bishop,et al.  Catchment-scale 3D mapping of depth to soil sodicity constraints through combining public and on-farm soil databases – A potential tool for on-farm management , 2020 .

[73]  Sébastien Lambot,et al.  A new drone-borne GPR for soil moisture mapping , 2019, Remote Sensing of Environment.

[74]  S. Grunwald,et al.  Current State of Digital Soil Mapping and What Is Next , 2010 .

[75]  Marvin N. Wright,et al.  SoilGrids250m: Global gridded soil information based on machine learning , 2017, PloS one.

[76]  Wei Sun,et al.  Dsmart: An algorithm to spatially disaggregate soil map units , 2014 .

[77]  Gerard B. M. Heuvelink,et al.  The GlobalSoilMap project specifications , 2014 .

[78]  Alex B. McBratney,et al.  Machine learning for digital soil mapping: Applications, challenges and suggested solutions , 2020 .

[79]  Investigating Consumer Preference for Organic, Local, or Sustainable Plants , 2011 .

[80]  Dominique Arrouays,et al.  GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth. , 2016, The Science of the total environment.

[81]  Himadri Nath Saha,et al.  IOT-based drone for improvement of crop quality in agricultural field , 2018, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).

[82]  P.A.L. Le Roux,et al.  Functional digital soil mapping: A case study from Namarroi, Mozambique☆ , 2014 .

[83]  M. Schloter,et al.  Identification of new microbial functional standards for soil quality assessment , 2020 .

[84]  Budiman Minasny,et al.  Methodologies for Global Soil Mapping , 2010 .

[85]  Wenze Yang,et al.  Drought and food security prediction from NOAA new generation of operational satellites , 2019, Geomatics, Natural Hazards and Risk.

[86]  Luca Montanarella,et al.  Global soil organic carbon assessment , 2015 .

[87]  Luk Peeters,et al.  Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models , 2018 .

[88]  Michael Schmidt,et al.  A Paddock to reef monitoring and modelling framework for the Great Barrier Reef: Paddock and catchment component. , 2012, Marine pollution bulletin.

[89]  David Clifford,et al.  Digital soil assessment for regional agricultural land evaluation , 2015 .

[90]  Budiman Minasny,et al.  Operational sampling challenges to digital soil mapping in Tasmania, Australia , 2015 .

[91]  Benjamin E. Barrowes,et al.  Assessing the Frozen State of Soils Using iFrost: An Electromagnetic Induction Sensor on a UAS Platform , 2019, Cold Regions Engineering 2019.

[92]  Mike Grundy,et al.  Soil and landscape grid of Australia. , 2015 .

[93]  B. Minasny,et al.  A quantitative model for integrating landscape evolution and soil formation , 2013 .

[94]  R. Bartley,et al.  Development of a time-stepping sediment budget model for assessing land use impacts in large river basins. , 2014, The Science of the total environment.

[95]  Budiman Minasny,et al.  Multi-source data integration for soil mapping using deep learning , 2018, SOIL.

[96]  Thomas F. A. Bishop,et al.  Change in Soil Organic Carbon Stocks under 12 Climate Change Projections over New South Wales, Australia , 2016 .

[97]  Indra Abeysekera A Template for Integrated Reporting , 2013 .

[98]  Budiman Minasny,et al.  Using deep learning for digital soil mapping , 2018, SOIL.

[99]  T. Bishop,et al.  A space-time observation system for soil moisture in agricultural landscapes , 2019, Geoderma.

[100]  Budiman Minasny,et al.  Farm-Scale Soil Carbon Auditing , 2016 .

[101]  Mike Grundy,et al.  Guidelines for Surveying Soil and Land Resources , 2008 .

[102]  Deon van der Merwe,et al.  Drones in agriculture , 2020, Advances in Agronomy.

[103]  Alex B. McBratney,et al.  Operationalising digital soil mapping – Lessons from Australia , 2020, Geoderma Regional.

[104]  Kilian Vos,et al.  Shoreline change mapping using crowd-sourced smartphone images , 2019, Coastal Engineering.

[105]  L. Montanarella,et al.  Soil natural capital in europe; a framework for state and change assessment , 2017, Scientific Reports.

[106]  Reiner Anderl,et al.  Digital twin – Proof of concept , 2018 .

[107]  Peter Bartelmus,et al.  SEEA-2003: Accounting for sustainable development? , 2007 .

[108]  Ross Searle The Australian site data collation to support the GlobalSoilMap , 2014 .

[109]  Budiman Minasny,et al.  Pedology and digital soil mapping (DSM) , 2019, European Journal of Soil Science.

[110]  D. Holzworth,et al.  Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate. , 2009 .

[111]  Christopher K. Wikle,et al.  Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting , 2018, Environmetrics.

[112]  E. Bui,et al.  Modelling the abundance of soil calcium carbonate across Australia using geochemical survey data and environmental predictors , 2015 .

[113]  Nathan E. Owen,et al.  Impact of land use on water resources via a Gaussian process emulator with dimension reduction , 2019 .

[114]  Alfred E. Hartemink,et al.  Linking soils to ecosystem services — A global review , 2016 .

[115]  W. J. Young,et al.  Large-scale patterns of erosion and sediment transport in river networks, with examples from Australia , 2001 .