Skip to main content
SpringerLink
Log in

Effective Multivariate Description of Soil and Its Environment

  • Chapter
  • First Online:
Pedometrics

Part of the book series: Progress in Soil Science ((PROSOIL))

  • 1445 Accesses

Abstract

When we wish to characterize soil, it soon becomes very clear that one or two properties of soil materials, horizons, profiles or pedons will not suffice to give an adequate description. Soil classification, land capability, soil quality, condition and health assessments often involve the observation of tens or scores of soil properties on a single soil entity; e.g. the new soil microbial DNA descriptions involve hundreds or thousands of attributes. For analysis of such high-dimensional data, multivariate statistical techniques are most appropriate, particularly ordination techniques which help to reduce the dimensionality down to a few (typically 2 or 3) which can be graphed simply and the relationships between soil entities, and between observed soil attributes on those entities, displayed.

“… nature’s laws are causal; they reveal themselves bycomparison and difference, and they operate at every multivariate space/time point”.

Edward Tufte

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Aitchison J (1982) The statistical analysis of compositional data. J R Stat Soc.Series B (Methodological) 44(2):139–177

    Google Scholar 

  • Aitchison J (1986) The statistical analysis of compositional data. Chapman & Hall, London

    Book  Google Scholar 

  • Arya LM, Leij FJ, Shouse PJ, van Genuchten MT (1999) Relationship between the hydraulic conductivity function and the particle-size distribution. Soil Sci Soc Am J 63(5):1063–1070

    Article  Google Scholar 

  • Braimoh AK (2004) Seasonal migration and land-use change in Ghana. Land Degrad Dev 15:37–47

    Article  Google Scholar 

  • Chayes F (1960) On correlation between variables of constant sum. J Geophys Res 65(12): 4185–4193

    Article  Google Scholar 

  • Crave A, Gascuel-Odoux C (1997) The influence of topography on time and space distribution of soil surface water content. Hydrol Process 11:203–210

    Article  Google Scholar 

  • De Gruijter JJ, Walvoort DJJ, van Gams PFM (1997) Continuous soil maps – a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models. Geoderma 77(2):169–195

    Article  Google Scholar 

  • Dray S, Saïd S, Débias F (2008) Spatial ordination of vegetation data using a generalization of Wartenberg’s multivariate spatial correlation. J Veg Sci 19:45–56

    Article  Google Scholar 

  • Eckart C, Young G (1936) The approximation of one matrix by another of lower rank. Psychometrika 1:211–218

    Article  Google Scholar 

  • Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barceló-Vidal C (2003) Isometric logratio transformations for compositional data analysis. Math Geol 35(3):279–300

    Article  Google Scholar 

  • Gabriel KR (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58:453–467

    Article  Google Scholar 

  • Gower JC, Hand DJ (1996) Biplots. Chapman & Hall, London

    Google Scholar 

  • Gower JC, Harding SA (1988) Nonlinear biplots. Biometrika 75:445–455

    Article  Google Scholar 

  • Gower JC, Legendre P (1986) Metric and Euclidean properties of dissimilarity coefficients. J Classif 3:5–48

    Article  Google Scholar 

  • Graffelman J, Tuft R (2004) Site scores and conditional biplots in canonical correspondence analysis. Environmetrics 15:67–80

    Article  Google Scholar 

  • Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3/4):321–377

    Article  Google Scholar 

  • Huang J, Subasinghe R, Triantafilis J (2014) Mapping particle-size fractions as a composition using additive log-ratio transformation and ancillary data. Soil Sci Soc Am J 78(6):1967–1976

    Article  Google Scholar 

  • Islam KR, Weil RR (2000) Soil quality indicator properties in mid-Atlantic soils as influenced by conservation management. J Soil Water Conserv 55:69–78

    Google Scholar 

  • Jombart T, Devillard S, Dufour A-B, Pontier D (2008) Revealing cryptic patterns in genetic variability by a new multivariate method. Heredity 101:92–103

    Article  Google Scholar 

  • Karunaratne SB, Bishop TFA, Odeh IOA, Baldock JA, Marchant BP (2014) Estimating change in soil organic carbon using legacy data as the baseline: issues, approaches and lessons to learn. Soil Res 52(4):349–365

    Article  Google Scholar 

  • Kendall MG, Stuart A (1977) The advanced theory of statistics. C. Griffin, London

    Google Scholar 

  • Kenkel NC (2006) On selecting an appropriate multivariate analysis. Can J Plant Sci 86:663–676

    Article  Google Scholar 

  • Kenkel NC, Derksen DA, Thomas AG, Watson PR (2002) Multivariate analysis in weed science research. Weed Sci 50:281–292

    Article  Google Scholar 

  • Lark RM, Bishop TFA (2007) Cokriging particle size fractions of the soil. Eur J Soil Sci 58(3):763–774

    Article  Google Scholar 

  • Latty EF, Canham CD, Marks PL (2004) The effects of land-use history on soil properties and nutrient dynamics in northern hardwood forests of the Adirondack Mountains. Ecosystems 7:193–207

    Article  Google Scholar 

  • Legendre P, Legendre L (2012) Chapter 11 – Canonical analysis. In: Legendre P, Legendre L (eds) Numerical ecology. Developments in Environmental Modelling. Elsevier, Amsterdam, pp 625–710

    Google Scholar 

  • McBratney AB, De Gruijter JJ, Brus DJ (1992) Spatial prediction and mapping of continuous soil classes. Geoderma 54(1):39–64

    Article  Google Scholar 

  • McGarry D, Ward WT, McBratney AB (1989) Soil studies in the lower Namoi Valley: methods and data, The Edgeroi Dataset. CSI4RO Division of Soils, Adelaide. 2 vols

    Google Scholar 

  • McGrath SP, Zhao FJ, Lombi E (2001) Plant and rhizosphere processes involved in phytoremediation of metal-contaminated soils. Plant Soil 232:207–214

    Article  Google Scholar 

  • Odeh IOA, Chittleborough DJ, McBratney AB (1991) Elucidation of soil-landform interrelationships by canonical ordination analysis. Geoderma 49(1–2):1–32

    Article  Google Scholar 

  • Odeh IOA, Todd AJ, Triantafilis J (2003) Spatial prediction of soil particle-size fractions as compositional data. Soil Sci 168:501–515

    Google Scholar 

  • Palmer MW (1993) Putting things in even better order: the advantages of canonical correspondence analysis. Ecology 74:2215–2230

    Article  Google Scholar 

  • Pawlowsky-Glahn V, Egozcue JJ (2001) Geometric approach to statistical analysis on the simplex. Stoch Env Res Risk A 15(5):384–398

    Article  Google Scholar 

  • Pawlowsky-Glahn V, Olea RA (2004) Geostatistical analysis of compositional data. Oxford University Press, New York

    Google Scholar 

  • Pearson K (1897) Mathematical contributions to the theory of evolution. On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc Royal Soc 60:489–498

    Article  Google Scholar 

  • Rayner JH (1966) Classification of soils by numerical methods. J Soil Sci 17:79–92

    Article  Google Scholar 

  • Shaw PJA (2003) Multivariate statistics for the environmental sciences. Hodder Arnold, London

    Google Scholar 

  • Sinowski W, Scheinost AC, Auerswald K (1997) Regionalization of soil water retention curves in a highly variable soilscape, II. Comparison of regionalization procedures using a pedotransfer function. Geoderma 78(3):145–159

    Article  Google Scholar 

  • Tanner JM (1949) Fallacy of per-weight and per-surface area standards, and their relation to spurious correlation. J Appl Physiol 2(1):1–15

    Article  Google Scholar 

  • Ter Braak CJF (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67:1167–1179

    Article  Google Scholar 

  • Ter Braak CJF (1987) The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetation 69:69–77

    Article  Google Scholar 

  • Ter Braak CJF (1995) Non-linear methods for multivariate statistical calibration and their use in palaeoecology: a comparison of inverse (k-nearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches. Chemom Intell Lab Syst 28:165–180

    Article  Google Scholar 

  • Ter Braak CJF, Prentice IC (1988) A theory of gradient analysis. Adv Ecol Res:271–317

    Google Scholar 

  • Webster R (1977) Canonical correlation in pedology: how useful? J Soil Sci 28(1):196–221

    Article  Google Scholar 

  • Woronow A (1990) Methods for quantifying, statistically testing, and graphically displaying shifts in compositional abundances across data suites. Comput Geosci 16(8):1209–1233

    Article  Google Scholar 

  • Zhang J-T, Oxley ERB (1994) A comparison of three methods of multivariate analysis of upland grasslands in North Wales. J Veg Sci 5:71–76

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia

    Alex. B. McBratney, Mario Fajardo, Brendan P. Malone, Thomas F. A. Bishop, Uta Stockmann & Inakwu O. A. Odeh

Authors
  1. Alex. B. McBratney
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario Fajardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brendan P. Malone
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas F. A. Bishop
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Uta Stockmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Inakwu O. A. Odeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex. B. McBratney .

Editor information

Editors and Affiliations

  1. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Alex. B. McBratney

  2. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Budiman Minasny

  3. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Uta Stockmann

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

McBratney, A.B., Fajardo, M., Malone, B.P., Bishop, T.F.A., Stockmann, U., Odeh, I.O.A. (2018). Effective Multivariate Description of Soil and Its Environment. In: McBratney, A., Minasny, B., Stockmann, U. (eds) Pedometrics. Progress in Soil Science. Springer, Cham. https://doi.org/10.1007/978-3-319-63439-5_4

Download citation

Publish with us

Policies and ethics

Search

Navigation