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RESEARCH ARTICLE

Predictive mapping of soil organic carbon stocks in South Australia’s agricultural zone

Craig Liddicoat A B E , David Maschmedt A , David Clifford C , Ross Searle C , Tim Herrmann A , Lynne M. Macdonald D and Jeff Baldock D
+ Author Affiliations
- Author Affiliations

A Department of Environment, Water and Natural Resources, GPO Box 1047, Adelaide, SA 5001, Australia.

B The University of Adelaide, North Terrace, Adelaide, SA 5005, Australia.

C CSIRO, EcoSciences Precinct, 41 Boggo Road, Dutton Park, Qld 4102, Australia.

D CSIRO, Waite Campus, Waite Road, Glen Osmond, SA 5064, Australia.

E Corresponding author. Email: craig.liddicoat@sa.gov.au

Soil Research 53(8) 956-973 https://doi.org/10.1071/SR15100
Submitted: 13 August 2014  Accepted: 21 August 2015   Published: 12 October 2015

Abstract

Better understanding the spatial distribution of soil organic carbon (SOC) stocks is important for the management and enhancement of soils for production and environmental outcomes. We have applied digital soil mapping (DSM) techniques to combine soil-site datasets from legacy and recent sources, environmental covariates and expert pedological knowledge to predict and map SOC stocks in the top 0.3 m, and their uncertainty, across South Australia’s agricultural zone. In achieving this, we aimed to maximise the use of locally sourced datasets not previously considered in national soil C assessments. Practical considerations for operationalising DSM are also discussed in the context of working with problematic legacy datasets, handling large numbers of potentially correlated covariates, and meeting end-user needs for readily interpretable results and accurate maps. Spatial modelling was undertaken using open-source R statistical software over a study area of ~160 000 km2. Legacy-site SOC stock estimates were derived with inputs from an expert-derived bulk-density pedotransfer function to overcome critical gaps in the data. Site estimates of SOC were evaluated over a consistent depth range and then used in spatial predictions through an environmental-correlation regression-kriging DSM approach. This used the contemporary Least Absolute Shrinkage and Selection Operator penalised-regression method, which catered for a large number (63 numeric, four categorical, four legacy-soil mapping themes) of potentially correlated covariates. For efficient use of the available data, this was performed within a k-fold cross-validation (k = 10) modelling framework. Through this, we generated multiple predictions and variance information at every node of our prediction grid, which was used to evaluate and map the expected value (mean) of SOC stocks and their uncertainty. For the South Australian agricultural zone, expected value SOC stocks in the top 0.3 m summed to 0.589 Gt with a 90% prediction interval of 0.266–1.086 Gt.

Additional keywords: digital soil mapping, geostatistics, LASSO, soil organic carbon, pedotransfer function, spatial analysis.


References

Arrouays D, McKenzie N, Hempel J, Richer d Forges AC, McBratney A (Eds) (2014) ‘GlobalSoilMap: basis of the global spatial soil information system.’ (CRC Press: London)

Baldock JA, Skjemstad JO (1999) Soil organic carbon/soil organic matter. In ‘Soil analysis: an interpretation manual’. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 159–170. (CSIRO Publishing: Melbourne)

Baldock J, Sanderman J, Macdonald L, Allen D, Cowie A, Dalal R, Davy M, Doyle R, Herrmann T, Murphy D, Robertson F (2013) Australian Soil Carbon Research Program. v1. CSIRO. Data Collection. 10.4225/08/5101F31440A36

Bishop TFA, McBratney AB, Laslett GM (1999) Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma 91, 27–45.
Modelling soil attribute depth functions with equal-area quadratic smoothing splines.Crossref | GoogleScholarGoogle Scholar |

Bouma J (1989) Using soil survey data for quantitative land evaluation. Advances in Soil Science 9, 177–213.
Using soil survey data for quantitative land evaluation.Crossref | GoogleScholarGoogle Scholar |

Braunisch V, Suchant R (2010) Predicting species distributions based on incomplete survey data: the trade-off between precision and scale. Ecography 33, 826–840.
Predicting species distributions based on incomplete survey data: the trade-off between precision and scale.Crossref | GoogleScholarGoogle Scholar |

Bui EN, McKenzie NJ, Jacquier DW, Gregory LJ (2008) Synthesis studies: making the most of existing data. In ‘Guidelines for surveying soil and land resources’. 2nd edn (Eds NJ McKenzie, MJ Grundy, R Webster, AJ Ringrose-Voase) pp. 407–417. (CSIRO Publishing: Melbourne)

Bui E, Henderson B, Viergever K (2009) Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia. Global Biogeochemical Cycles 23, GB4033
Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia.Crossref | GoogleScholarGoogle Scholar |

Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in Central Iowa soils. Soil Science Society of America Journal 58, 1501–1511.
Field-scale variability of soil properties in Central Iowa soils.Crossref | GoogleScholarGoogle Scholar |

Carré F, McBratney AB, Minasny B (2007) Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping. Geoderma 141, 1–14.
Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping.Crossref | GoogleScholarGoogle Scholar |

Clifford D, Cressie N, England JR, Roxburgh SH, Paul KI (2013) Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models. Forest Ecology and Management 310, 375–381.
Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models.Crossref | GoogleScholarGoogle Scholar |

Clifford D, Payne JE, Pringle MJ, Searle R, Butler N (2014) Pragmatic soil survey design using flexible Latin hypercube sampling. Computers & Geosciences 67, 62–68.
Pragmatic soil survey design using flexible Latin hypercube sampling.Crossref | GoogleScholarGoogle Scholar |

DeCarlo LT (1997) On the meaning and use of kurtosis. Psychological Methods 2, 292–307.
On the meaning and use of kurtosis.Crossref | GoogleScholarGoogle Scholar |

Department of the Environment (2014) ‘Carbon Farming Initiative: soil sampling and analysis method and guidelines.’ Version 1.0. (Australian Government, Department of the Environment: Canberra, ACT)

DEWNR (2014) Soil and land information. Department of Environment Water and Natural Resources, South Australia. Available at: www.environment.sa.gov.au/Science/Information_data/soil-and-land (accessed 1 February 2014)

Friedman JH, Popescu BE (2008) Predictive learning via rule ensembles. The Annals of Applied Statistics 2, 916–954.
Predictive learning via rule ensembles.Crossref | GoogleScholarGoogle Scholar |

Friedman J, Hastie T, Simon N, Tibshirani R (2015) Package ‘glmnet’. Lasso and elastic-net regularized generalized linear models. The R Foundation for Statistical Computing, Vienna. Available at: http://cran.r-project.org/web/packages/glmnet/glmnet.pdf (accessed 19 February 2015)

Gallant JC, Austin JM (2015) Derivation of terrain covariates for digital soil mapping in Australia. Soil Research 53, 895–906.

Gärdenäs AI, Agren GI, Bird JA, Clarholm M, Hallin S, Ineson P, Kätterer T, Knicker H, Ingvar Nilsson SI, Näsholm T, Ogle S, Paustian K, Persson T, Stendahl J (2011) Knowledge gaps in soil carbon and nitrogen interactions—from molecular to global scale. Soil Biology & Biochemistry 43, 702–717.
Knowledge gaps in soil carbon and nitrogen interactions—from molecular to global scale.Crossref | GoogleScholarGoogle Scholar |

GlobalSoilMap Science Committee (2012) ‘Specifications. Tiered GlobalSoilMap.net products. Release 2.3 [21/9/2012].’ (GlobalSoilMap.net) Available at: www.ozdsm.com.au/resources/GlobalSoilMap%20specs%20version%202point3.pdf

Gray JM, Bishop TFA, Wilford JR (2014) Lithology as a powerful covariate in digital soil mapping. In ‘GlobalSoilMap: basis of the global spatial soil information system’. (Eds D Arrouays, N McKenzie, J Hempel, AC Richer de Forges, A McBratney) pp. 433–439. (CRC Press: London)

Grundy MJ, Viscarra Rossel RA, Searle RD, Wilson PL, Chen C, Gregory LJ (2015) Soil and Landscape Grid of Australia. Soil Research 53, 835–844.

Hall JAS, Maschmedt DJ, Billing NB (2009) ‘The soils of southern South Australia.’ (Department of Water, Land and Biodiversity Conservation, Government of South Australia: Adelaide, S. Aust.)

Hastie T, Tibshirani R, Friedman J (2009) ‘The elements of statistical learning: data mining, inference and prediction.’ 2nd edn (Springer: New York)

Heanes DL (1984) Determination of total organic-c in soils by an improved chromic acid digestion and spectrophotometric procedure. Communications in Soil Science and Plant Analysis 15, 1191–1213.
Determination of total organic-c in soils by an improved chromic acid digestion and spectrophotometric procedure.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL2cXmt12itL8%3D&md5=713ec9490fb99b2de59cfcb57528a029CAS |

Hengl T, Heuvelink GBM, Rossiter DG (2007) About regression-kriging: from equations to case studies. Computers & Geosciences 33, 1301–1315.
About regression-kriging: from equations to case studies.Crossref | GoogleScholarGoogle Scholar |

Heuvelink GBM (2014) Uncertainty quantification of GlobalSoilMap products. In ‘GlobalSoilMap: basis of the global spatial soil information system. Proceedings 1st GlobalSoilMap Conference’. Orleans, France, 7–9 October 2013. (Eds D Arrouays, N McKenzie, J Hempel, A Richer de Forges, A McBratney) pp. 335–340. (CRC Press: London)

Hijmans RJ, van Etten J, Mattiuzzi M, Sumner M, Greenberg JA, Lamigueriro OP, Bevan A, Racine EB, Shortridge A (2015) Package ‘raster’. Geographic data analysis and modelling. The R Foundation for Statistical Computing, Vienna. Available at: http://cran.r-project.org/web/packages/raster/raster.pdf (accessed 20 February 2015)

Isbell RF (2002) ‘The Australian Soil Classification.’ Revised edn (CSIRO Publishing: Melbourne)

Kidd D, Webb M, Malone B, Minasny B, McBratney A (2015) Eighty-metre resolution 3D soil-attribute maps for Tasmania, Australia. Soil Research 53, 932–955.

Ku HH (1966) Notes on the use of propagation of error formulas. Journal of Research of the National Bureau of Standards. C, Engineering and Instrumentation 70C, 263–273.
Notes on the use of propagation of error formulas.Crossref | GoogleScholarGoogle Scholar |

Lal R (2004) Soil carbon sequestration to mitigate climate change. Geoderma 123, 1–22.
Soil carbon sequestration to mitigate climate change.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXoslSmsLY%3D&md5=818b6b969fd91a7d0904a6ae860ad0ceCAS |

Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.
A concordance correlation coefficient to evaluate reproducibility.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL1M3kslKrtg%3D%3D&md5=2fd39f8449b2a4d74b7d3b7a787153a2CAS | 2720055PubMed |

Macdonald LM, Herrmann T, Baldock JA (2013) Combining management based indices with environmental parameters to explain regional variation in soil carbon under dryland cropping in South Australia. Soil Research 51, 738–747.
Combining management based indices with environmental parameters to explain regional variation in soil carbon under dryland cropping in South Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhvF2ktbjI&md5=8035d4fadbab349b10d2a548aa199f81CAS |

Malone B (2013) ‘Use R for digital soil mapping.’ (Soil Security Laboratory, The University of Sydney: Sydney) Available at: www.clw.csiro.au/aclep/documents/DSM_R_manual_2013.pdf (accessed: 1 November 2013)

Martin MP, Wattenbach M, Smith P, Meersmans J, Jolivet C, Boulonne L, Arrouays D (2011) Spatial distribution of soil organic carbon stocks in France. Biogeosciences 8, 1053–1065.
Spatial distribution of soil organic carbon stocks in France.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtVKltLrF&md5=937956374b77a040f12ecfe93459d945CAS |

Martin MP, Orton TG, Lacarce E, Meersmans J, Saby NPA, Paroissien JB, Jolivet C, Boulonne L, Arrouays D (2014) Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma 223–225, 97–107.
Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale.Crossref | GoogleScholarGoogle Scholar |

McBratney AB (1992) On variation, uncertainty and informatics in environmental soil management. Australian Journal of Soil Research 30, 913–935.
On variation, uncertainty and informatics in environmental soil management.Crossref | GoogleScholarGoogle Scholar |

McBratney AB, Mendonca-Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117, 3–52.
On digital soil mapping.Crossref | GoogleScholarGoogle Scholar |

Minasny B, McBratney AB (2010) Methodologies for global soil mapping, In ‘Digital soil mapping: bridging research, environmental application and operation. Progress in soil science’. Ch. 34 (Eds JL Boettinger, DW Howell, AC Moore, AE Hartemink, S Kienast-Brown) pp. 429–436. (Springer: New York)

Minasny B, McBratney AB, Malone BP, Wheeler I (2013) Digital mapping of soil carbon. Advances in Agronomy 118, 1–47.
Digital mapping of soil carbon.Crossref | GoogleScholarGoogle Scholar |

National Committee on Soil and Terrain (2009) ‘Australian soil and land survey field handbook.’ 3rd edn (CSIRO Publishing: Melbourne)

Pebesma E, Graeler B (2014) Package ‘gstat’. Spatial and spatio-temporal geostatistical modelling, prediction and simulation. The R Foundation for Statistical Computing, Vienna. Available at: http://cran.r-project.org/web/packages/gstat/gstat.pdf (accessed: 1 April 2014)

Preiss WV, Robertson RS, Cowley WM (2000) Geological mapping and GIS products of the Geological Survey Branch, PIRSA. MESA Journal 16, 34–36.

R Core Team (2014) R: A language and environment for statistical computing. The R Foundation for Statistical Computing, Vienna, Austria. Available at: www.r-project.org (accessed 1 May 2014)

Robinson NJ, Benke K, Hopley J, MacEwan RJ, Clark R, Rees DB, Kitching M, Imhof MP, Bardos D (2014) Multi-source data integration and identification of uncertainties affecting production of a digital soil map. In ‘GlobalSoilMap: basis of the global spatial soil information system, Proceedings 1st GlobalSoilMap Conference’. 7–9 October 2013, Orleans France. (Eds D Arrouays, N McKenzie, J Hempel, A Richer de Forges, A McBratney) pp. 353–358 (CRC Press: London)

SAGA Development Team (2014) SAGA Homepage. SAGA: System for Automated Geoscientific Analyses. Available at: www.saga-gis.org (accessed 1 May 2014)

Sanderman J, Farquharson R, Baldock J (2010) Soil carbon sequestration potential: a review for Australian agriculture. Report prepared for Department of Climate Change and Energy Efficiency. CSIRO, Urrbrae, S. Aust.

Schmidt A, Smernik RJ, McBeath TM (2012) Measuring organic carbon in calcarosols: understanding the pitfalls and complications. Soil Research 50, 397–405.
Measuring organic carbon in calcarosols: understanding the pitfalls and complications.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xht1Smu7nO&md5=51fa54126a681c160dcd0816e3268696CAS |

Stace HCT, Hubble GD, Brewer R, Northcote KH, Sleeman JR, Mulcahy MJ, Hallsworth EG (1968) ‘A handbook of Australian soils.’ (Rellim Technical Publications: Glenside, S. Aust.)

Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B, Statistical Methodology 58, 267–288.

Veregin H (2005) Data quality parameters. In ‘Geographical information systems: principles, techniques, management and applications’. 2nd edn. Abridged. (Eds PA Longley, MF Goodchild, DJ Maguire, DW Rhind) pp. 177–189. (John Wiley & Sons: Hoboken, NJ, USA)

Viscarra Rossel RA, Webster R, Bui EN, Baldock JA (2014) Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change. Global Change Biology
Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change.Crossref | GoogleScholarGoogle Scholar | 24599716PubMed |

Viscarra Rossel RA, Chen C, Grundy MJ, Searle R, Clifford D, Campbell PH (2015) The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research 53, 845–864.

Walkley A, Black I (1934) An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science 37, 29–38.
An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaA2cXitlGmug%3D%3D&md5=6d1431184c84f58bc8b971881a32f2c1CAS |

Wetherby K (1994) ‘Soil description book.’ Revised edn (Cleve Research Laboratories, SA Department of Agriculture: Cleve, S. Aust.)

Xu T, Hutchinson M (2011) ‘ANUCLIM Version 6.1 User Guide.’ (Fenner School of Environment and Society, The Australian National University: Canberra ACT) Available at: http://fennerschool.anu.edu.au/files/anuclim61.pdf (accessed 1 June 2014)

Young AR (1984) Vegetation clearance: the South Australian experience. B. Arch. Thesis, South Australian Institute of Technology, Adelaide, Australia.