CSIRO PUBLISHING
www.publish.csiro.au/journals/ajsr Australian Journal of Soil Research, 2006, 44, 233–244
Prediction and digital mapping of soil carbon storage
in the Lower Namoi Valley
Budiman Minasny
A,D
, Alex. B. McBratney
A
, M. L. Mendonc ¸a-Santos
B
,
I. O. A. Odeh
A
, and Brice Guyon
C
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 Botˆ anico 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
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 15–22kg/m
2
were predicted
for the forested area and 2–6 kg/m
2
in the cultivated area in the plains.
Additional keywords: soil information system, neural networks, carbon stock, carbon sequestration, organic carbon,
Vertosol, digital soil mapping.
Introduction
Estimation and mapping of carbon storage in the soil is
currently an important topic; carbon stored in the soil to
a given depth has been estimated for the whole world
(Batjes 1996), for the continent (Jones et al. 2005), countries
(Bellamy et al. 2005; Bernoux et al. 2002; Mikhailova and
Post 2006), and regionally (Knowles and Singh 2003). Soil
can hold more than twice as much carbon as held in vegetation
or the atmosphere (Batjes 1996). The amount of carbon stored
in the soil per unit of land area is highly variable depending
on the land use, annual input, soil type, and the degradation
rate. A global average of 16–20 kg/m
2
is estimated for carbon
stored up to 1 m in tropical forests and 11 kg/m
2
for cropping
area (Jobb´ agy and Jackson 2000).
The distribution of carbon content with depth is
essential information for estimating soil carbon storage.
Conventionally, carbon storage (also called the carbon stock,
carbon pool or carbon density), i.e. carbon mass per unit area
for a given depth, is calculated by summing the C density of
soil layers 1, 2, ... , N:
C
I
=
N
j=1
(C
m
× ρ
j
) × thick
j
(1)
where C
I
is carbon density (kg/m
2
), C
m
is carbon content in
mass basis (kg/kg), ρ is soil bulk density (kg/m
3
), and thick
is the thickness of the layer (m).
Alternatively, a profile depth function can be defined and
fitted to the soil carbon data, where carbon content at different
depths can be estimated, and the integral of the function
represents the carbon storage. This is useful where it is
necessary to estimate the carbon storage down to certain
depths. Expressing carbon content as a depth function is also
advantageous when dealing with soil databases where the
© CSIRO 2006 10.1071/SR05136 0004-9573/06/030233