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RESEARCH ARTICLE
Contents Vol 44(3)

Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley

Budiman Minasny A D , Alex. B. McBratney A , M. L. Mendonça-Santos B , I. O. A. Odeh A and Brice Guyon C
+ Author Affiliations
- Author Affiliations

A Faculty of Agriculture, Food & Natural Resources, The University of Sydney, JRA McMillan Building A05, NSW 2006, Australia.

B EMBRAPA-Centro Nacional de Pesquisa de Solos, Rua Jardim Botânico 1024, 22.460-000 Rio de Janeiro-RJ, Brazil.

C Ecole Nationale d’Ingenieurs des Travaux Agricoles de Bordeaux, 1 cours du general de Gaulle, B.P. 201, 33175 Gradignan, Cedex, France.

D Corresponding author. Email: b.minasny@usyd.edu.au

Australian Journal of Soil Research 44(3) 233-244 https://doi.org/10.1071/SR05136
Submitted: 12 September 2005  Accepted: 17 February 2006   Published: 5 May 2006

Abstract

Estimation and mapping carbon storage in the soil is currently an important topic; thus, the knowledge of the distribution of carbon content with depth is essential. This paper examines the use of a negative exponential profile depth function to describe the soil carbon data at different depths, and its integral to represent the carbon storage. A novel method is then proposed for mapping the soil carbon storage in the Lower Namoi Valley, NSW. This involves deriving pedotransfer functions to predict soil organic carbon and bulk density, fitting the exponential depth function to the carbon profile data, deriving a neural network model to predict parameters of the exponential function from environmental data, and mapping the organic carbon storage. The exponential depth function is shown to fit the soil carbon data adequately, and the parameters also reflect the influence of soil order. The parameters of the exponential depth function were predicted from land use, radiometric K, and terrain attributes. Using the estimated parameters we map the carbon storage of the area from surface to a depth of 1 m. The organic carbon storage map shows the high influence of land use on the predicted storage. Values of 15–22 kg/m2 were predicted for the forested area and 2–6 kg/m2 in the cultivated area in the plains.

Additional keywords: soil information system, neural networks, carbon stock, carbon sequestration, organic carbon, Vertosol, digital soil mapping.


Acknowledgment

This work is supported by funding from ARC Discovery Project on Digital Soil Mapping and ARC Linkage Project on Soil Inference Systems. We thank Miss Charlotte Moore for classifying the soil profiles and Dr Damien Field for assistance in the pedological interpretation.


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