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An approach for assessing soil health: a practical guide for optimal ecological management

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Abstract

How to integrate environmental geographic information and biodiversity data combined with management measures to effectively assess soil health is still an unresolved problem. This paper suggests an approach for systematically estimating soil quality and guiding ecological management. First, canonical correspondence analysis is used to predict the distributions of plant species or microorganism communities, principle pollutants and environmental variables from which spatial and environmental data are extracted by the geographic information system (GIS). Secondly, geostatistical methodologies are then used to estimate and quantify the spatial distribution characteristic of the species and pollutants and to create maps of spatial uncertainty and hazard assessment through ArcGis technology. Finally, redundancy analysis provides a suggestion about better management strategy and environmental factor for improving soil health and biodiversity. The combination of these methods with “3S” techniques as an assessment approach effectively meets the challenges for estimation and management in different soil environments.

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References

  • Andreasen C, Skovgaard IM (2009) Crop and soil factors of importance for the distribution of plant species on arable fields in Denmark. Agric Ecosyst Environ 133:61–67

    Article  Google Scholar 

  • Armstrong M, Chetboun G, Hubert P (1993) Kriging the rainfall in Lesotho. In: Soares A (ed) Geostatistics Troia ’92, vol 2. Kluwer, Dordrecht, pp 661–672

    Chapter  Google Scholar 

  • Bogaert P, Christakos G (1997) Spatiotemporal analysis and processing of thermometric data over Belgium. J Geophys Res 102(D22):25831–25846

    Article  Google Scholar 

  • Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White JSS (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 24(3):127–135

    Article  Google Scholar 

  • Cao SX (2008) Why large-scale afforestation efforts in China have failed to solve the desertification problem. Environ Sci Technol 42(6):1826–1831

    Article  Google Scholar 

  • Cao SX, Chen L, David S, Wang CM, Wang XB, Zhang H (2010) Excessive reliance on afforestation in China’s arid and semi-arid regions: lessons in ecological restoration. Earth Sci Rev. doi:10.1016/j.earscirev.2010.11.002

  • Carroll RJ, Chen R, George EI, Li TH, Newton HJ, Schmiediche H, Wang N (1997) Ozone exposure and population density in Harris County, Texas (with discussion). J Am Stat Assoc 92(438):392–415

    Article  Google Scholar 

  • Castrignanò A, Maiorana M, Fornaro F, Lopez N (2002) 3D spatial variability of soil strength and its change over time in a durum wheat field in Southern-Italy. Soil Till Res 65(1):95–108

    Article  Google Scholar 

  • Christakos G, Hristopulos DT (1998) Spatiotemporal environmental health modelling: a tractatus stochasticus. Kluwer, Boston

    Google Scholar 

  • Franco C, Soares A, Delgado J (2006) Geostatistical modelling of heavy metal contamination in the topsoil of Guadiamar river margins (S Spain) using a stochastic simulation technique. Geoderma 136:852–864

    Article  Google Scholar 

  • Gao Y, Zhou P, Mao L, Zhi YE, Shi WJ (2010a) Assessment of effects of heavy metals combined pollution on soil enzyme activities and microbial community structure: modified ecological dose-response model and PCR-RAPD. Environ Earth Sci 60:603–612

    Article  Google Scholar 

  • Gao Y, Mao L, Miao CY, Zhou P, Cao JJ, Zhi YE, Shi WJ (2010b) Spatial characteristics of soil enzyme activities and microbial community structure under different land uses in Chongming Island, China: Geostatistical modelling and PCR-RAPD method. Sci Total Environ 408:3251–3260

    Article  Google Scholar 

  • Gao Y, Miao CY, Mao L, Zhou P, Jin ZG, Shi WJ (2010c) Improvement of phytoextraction and antioxidative defense of Solanum nigrum L. under cadmium stress by application of cadmium-resistant strain and citric acid synergy. J Hazard Mater 181:771–777

    Article  Google Scholar 

  • Goovaerts P, Sonnet P (1993) Study of spatial and temporal variations of hydrogeochemical variables using factorial kriging analysis. In: Soares A (ed) Geostatistics Troia ’92, vol 2. Kluwer, Dordrecht, pp 745–756

    Chapter  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, Oxford

    Google Scholar 

  • Guisan A, Weiss SB, Weiss AD (1999) GLM versus CCA spatial modeling of plant species distribution. Plant Ecol 143:107–122

    Article  Google Scholar 

  • Handcock MS, Wallis JR (1994) An approach to statistical spatial-temporal modeling of meteorological fields (with discussion). J Am Stat Assoc 89(426):368–390

    Article  Google Scholar 

  • Heuvelink GB, Musters P, Pebesma EJ (1997) Spatio-temporal kriging of soil water content. In: Baaffi E, Schofield N (eds) Geostatistics Wollongong ’96, vol 2. Kluwer, Dordrecht, pp 1020–1030

    Chapter  Google Scholar 

  • Hohn ME, Liebhold AM, Gribko LS (1993) Geostatistical model for forecasting spatial dynamics of defoliation caused by the gypsy moth (Lepidoptera: Lymantriidae). Environ Entomol 22(5):1066–1075

    Google Scholar 

  • Komnitsas K, Modis K (2006) Soil risk assessment of As and Zn contamination in a coal mining region using geostatisretics. Sci Total Environ 371:190–196

    Article  Google Scholar 

  • Kyriakidis C, Journel A (1999) Geostatistical space–time models: a review. Math Geol 31(6):651–684

    Article  Google Scholar 

  • Lepš J, Šmilauer P (2003) Multivariate analysis of ecological data using Canoco. Cambridge University Press, New York, pp 232–235

    Google Scholar 

  • Rodríguez L, Ruiz E, Alonso-Azcárate J, Rincón J (2009) Heavy metal distribution and chemical speciation in tailings and soils around a Pb–Zn mine in Spain. J Environ Manag 90:1106–1116

    Article  Google Scholar 

  • Rouhani S, Ebrahimpour RM, Yaqub I, Gianella E (1992) Multivariate geostatistical trend detection and network evaluation of space-time acid deposition data-I. Methodology. Atmos Environ 26(14):2603–2614

    Article  Google Scholar 

  • Rouhani S, Wackernagel H (1990) Multivariate geostatistical approach to space-time data analysis. Water Resour Res 26(4):585–591

    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, Šmilauer P (2002) CANOCO reference manual and CanoDraw for windows user’s guide: software for canonical community ordination (version 4.5). Microcomputer Power, Ithaca

  • Van-den-Brink PJ, Ter-Braak CJF (1999) Principal response curves: analysis of time-dependent multivariate responses of a biological community to stress. Environ Toxicol Chem 18:138–148

    Article  Google Scholar 

  • Vyas VM, Christakos G (1997) Spatiotemporal analysis and mapping of sulfate deposition data over eastern U.S.A. Atmos Environ 31(21):3623–3633

    Article  Google Scholar 

  • Wackernagel H (2003) Multivariate geostatistics: an introduction with applications. Springer, Berlin

    Google Scholar 

  • Webster R, Oliver MA (1990) Statistical methods in soil and land resource survey. Oxford University Press, Oxford

    Google Scholar 

  • Webster R, Oliver MA (2007) Geostatistics for environmental scientists, 2nd edn. Wiley, Chichester

    Book  Google Scholar 

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Acknowledgments

We are grateful to Dr. Jan Lepš and Petr Šmilauer’s work named “Multivariate Analysis of Ecological Data using Canoco” to give us references and enlightenment. Opinions in the paper only reflect the personal views of the authors. This research was supported by the National Natural Science Foundation of China (40901098 and 40871085) and the special program of Water Pollution Control (2009ZX07210-006). Also the authors would like to thank the anonymous reviewers for their remarks that have improved the paper in its present form.

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Correspondence to Yang Gao.

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Y. Wang and Y. Gao contributed equally to this work.

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Gao, Y., Wang, Y., Zhang, G. et al. An approach for assessing soil health: a practical guide for optimal ecological management. Environ Earth Sci 65, 153–159 (2012). https://doi.org/10.1007/s12665-011-1101-1

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  • DOI: https://doi.org/10.1007/s12665-011-1101-1

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