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RESEARCH ARTICLE
Contents Vol 47(7)

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

John Triantafilis A C , Scott Mitchell Lesch B , Kevin La Lau A and Sam Mostyn Buchanan A
+ Author Affiliations
- Author Affiliations

A School of Biological Earth and Environmental Sciences, The University of New South Wales, NSW 2052, Sydney, Australia.

B Statistical Consulting Collaboratory, U.C. Riverside, 2683 Stat-Comp, 900 University Ave, Riverside, CA 92521, USA.

C Corresponding author. Email: j.triantafilis@unsw.edu.au

Australian Journal of Soil Research 47(7) 651-663 https://doi.org/10.1071/SR08240
Submitted: 26 October 2008  Accepted: 7 July 2009   Published: 6 November 2009

Abstract

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

Additional keywords: electromagnetic (EM) induction, EM38, EM31, digital soil mapping, multiple linear regression (MLR), hierarchical spatial regression (HSR), ordinary kriging (OK).


Acknowledgments

The Australian Cotton Research and Development Corporation and Australian Cotton Cooperative Research Centre provided the core funding (CRC11C). The MESS survey, soil coring and laboratory analysis were funded by the Australian Federal Government Natural Heritage Trust (NW0688.99). We acknowledge the support of the Coordinating Committee of lower Namoi valley Water Users Association in obtaining these funds. We thank the landholder who allowed access to the study field. We acknowledge Mr Andrew Huckel who carried out the MESS survey and Dr Mohammad Faruque Ahmed for collecting and preparing the soil samples for determination of CEC.


References


Ash HB (1941) ‘Columella: On agriculture, Vol. I, Books I–IV.’ Loeb Classical Library No. 361. English Translation. pp. xxix + 461. (Harvard University Press/William Heinemann Ltd: Cambridge, MA/London)

Bishop TFA, McBratney AB (2001) A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma 103, 149–160.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Buchanan SM, Triantafilis J (2009) Mapping water table depth using geophysical and environmental variables. Ground Water 47, 80–96.
| Crossref | GoogleScholarGoogle Scholar | PubMed | open url image1

Castrignanò A, Giugliarini L, Risaliti R, Martinelli N (2000) Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma 97, 39–60.
| Crossref | GoogleScholarGoogle Scholar | open url image1

©Department of Lands (1996) Wee Waa 1 : 50 000 Topographic Map Sheet, Run 4, Image No. 0004. NSW Department of Lands, Panorama Avenue Bathurst, NSW, Australia. www.lands.nsw.gov.au

ERDAS (2003) ‘ERDAS Imagine Version 8.7.’ (Leica Geosystems GIS and Mapping: Brisbane, Qld)

ESRI (2005) ‘ArcGIS Version 9.1.’ (Environmental Systems Research Institute: Redlands, CA)

Hedley CB, Yule IY, Eastwood CR, Shepherd TG, Arnold G (2004) Rapid identification of soil textural and management zones using electromagnetic induction sensing of soils. Australian Journal of Soil Research 42, 389–400.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Holmgren GGS, Juve RL, Geschwender RC (1977) A mechanically controlled variable rate leaching device. Soil Science Society of America Journal 41, 1207–1208. open url image1

Isbell R (2003) ‘The Australian soil classification (Revised edn).’ Australian Soil and Land Survey Handbooks Series Vol. 4. (CSIRO Publishing: Collingwood, Vic.)

Kuras O, Meldrum PI, Beamish D, Ogilvy RD, Lala D (2007) Capacitive resistivity imaging with towed arrays. Journal of Environmental & Engineering Geophysics 12, 267–279.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Lark RM, Webster R (1999) Analysis and elucidation of soil variation using wavelets. European Journal of Soil Science 50, 185–206.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Laslett GM, McBratney AB, Pahl PJ, Hutchinson MF (1987) Comparison of several spatial prediction methods for soil pH. Journal of Soil Science 38, 325–341.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Loveday J , Beatty HJ , Norris JM (1972) Comparison of current chemical methods for evaluating irrigation soils. CSIRO Australia, Division of Soils, Technical Paper No.14.

McKenzie DC (1998) ‘SOILpak for cotton growers.’ 3rd edn (NSW Agriculture: Orange, NSW)

McNeill JD (1990) ‘Geonics EM38 Ground Conductivity Meter: EM38 Operating Manual.’ (Geonics Ltd: Mississauga, ON, Canada)

McNeill JD (1998) ‘Electromagnetic terrain conductivity measurement at low induction numbers.’ Technical Note TN-6. pp. 1–15. (Geonics Ltd: Mississauga, ON, Canada)

Minasny B , McBratney AB , Whelan BM (1999) ‘Variogram Estimation and Spatial Prediction with ERror (VESPER).’ (Australian Centre for Precision Agriculture: Sydney, NSW)

Monteiro Santos FA (2004) 1-D laterally constrained inversion of EM34 profiling data. Journal of Applied Geophysics 156, 123–124.
| Crossref |
open url image1

Mueller TG, Pierce FJ, Schabenberger O, Warncke DD (2001) Map quality for site-specific fertility management. Soil Science Society of America Journal 65, 1547–1558. open url image1

Odeh IOA, Todd AJ, Triantafilis J (2003) Spatial prediction of particle size fractions as compositional data. Soil Science 168, 501–515.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Odeh IOA, Todd AJ, Triantafilis J, McBratney AB (1998) Status and trends of soil salinity at different scales: the case for the irrigated cotton growing region of eastern Australia. Nutrient Cycling in Agroecosystems 50, 99–107.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Royle JA, Berliner LM (1999) A hierarchical approach to multivariate spatial modelling and prediction. Journal of Agricultural Biological & Environmental Statistics 4, 29–56.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Royle JA , Berliner LM , Wikle CK , Milliff R (1998) A hierarchical spatial model for constructing wind fields from scatterometer data in the Labrador Sea. In ‘Case studies in Bayesian statistics’. pp. 51−75. (Springer-Verlag: New York)

SAS Institute (2002) ‘JMP Version 5.’ (SAS Institute Inc.: Cary, NC)

Siri-Prieto G, Reeves DW, Shaw JN, Mitchell CC (2006) World’s oldest cotton experiment: relationships between soil chemical and physical properties and apparent electrical conductivity. Communications in Plant and Soil Analysis 37, 767–786.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Stannard ME , Kelly ID (1977) ‘The irrigation potential of the lower Namoi valley.’ (Water Resources Commission, NSW: Sydney, NSW)

Triantafilis J, Ahmed MF, Odeh IOA (2002) Application of a mobile electromagnetic sensing system (MESS) to assess cause and management of soil salinisation in an irrigated cotton-growing field. Soil Use and Management 18, 330–339.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Triantafilis J, Huckel AI, Odeh IOA (2001b) Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables. Soil Science 166, 415–427.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Triantafilis J, Huckel AI, Odeh IOA (2003) Field-scale assessment of deep drainage risk. Irrigation Science 21, 183–192. open url image1

Triantafilis J, Kerridge B, Buchanan SM (2009) Digital soil-class mapping from proximal and remotely sensed data at the field level. Agronomy Journal 101, 841–853.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Triantafilis J, Laslett GM, McBratney AB (2000) Calibrating an electromagnetic induction instrument to measure salinity in soil under irrigated cotton. Soil Science Society of America Journal 64, 1009–1017. open url image1

Triantafilis J, Lesch SM (2005) Mapping clay content variation using electromagnetic induction techniques. Computers and Electronics in Agriculture 46, 203–237.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Triantafilis J, Odeh IOA, McBratney AB (2001a) Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Science Society of America Journal 65, 869–878. open url image1

Triantafilis J, Odeh IOA, Short M, Kokkoris E (2004b) Estimating and mapping deep drainage risk at the district level in the lower Gwydir and Macquarie valleys, Australia. Australian Journal of Experimental Agriculture 44, 893–912.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Triantafilis J, Odeh IOA, Warr B, Ahmed MF (2004a) Mapping of salinity risk in the lower Namoi valley using non-linear kriging methods. Agricultural Water Management 69, 203–231.
| Crossref | GoogleScholarGoogle Scholar | open url image1

Tucker BM (1974) Laboratory procedure for cation exchange measurements in soils. CSIRO Division of Soils, Technical Paper No. 23, CSIRO, Australia.