Identification of areas within Australia with the
potential to enhance soil carbon content
Jeff Baldock1, Mike Grundy1, Peter Wilson1, David Jacquier1, Ted
Griffin2, Greg Chapman4, James Hall5, David Maschmet5,Doug
Crawford6, Jason Hill7, Darren Kidd8
(1CSIRO; 2DAFWA; 4NSW DNR, 5SA DWLBC, 6Vic DPI, 7NT Gov.,
8
TAS DPIW)
Enquiries should be addressed to:
Dr. Jeff Baldock, CSIRO Land and Water, PMB 2, Glen Osmond, SA, 5064
Mr. Mike Grundy, CSIRO Land and Water, 306 Carmody Road, St Lucia, QLD, 4067
Copyright and Disclaimer
© 2009 CSIRO To the extent permitted by law, all rights are reserved and no part of
this publication covered by copyright may be reproduced or copied in any form or by
any means except with the written permission of CSIRO.
Important Disclaimer
CSIRO advises that the information contained in this publication comprises general
statements based on scientific research. The reader is advised and needs to be aware that
such information may be incomplete or unable to be used in any specific situation. No
reliance or actions must therefore be made on that information without seeking prior
expert professional, scientific and technical advice. To the extent permitted by law,
CSIRO (including its employees and consultants) excludes all liability to any person for
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any other compensation, arising directly or indirectly from using this publication (in
part or in whole) and any information or material contained in it.
Table of Contents
1.
Introduction.............................................................................................. 6
2.
Objectives of this process ...................................................................... 7
3.
Components of the decision framework ............................................... 8
4.
Capacity Index ....................................................................................... 10
5.
6.
4.1
General comments ................................................................................................. 10
4.2
Clearing history....................................................................................................... 10
4.3
Vegetation: Crop versus pasture ............................................................................ 10
4.4
Clay Decline Index.................................................................................................. 11
4.5
Capacity Index ........................................................................................................ 12
Carbon Gains Index............................................................................... 14
5.1
General comments ................................................................................................. 14
5.2
Annual rain.............................................................................................................. 14
5.3
Rain Availability ...................................................................................................... 14
5.4
Effective rain ........................................................................................................... 15
5.5
Residue pressure ................................................................................................... 16
5.6
Carbon Gains Index................................................................................................ 17
5.7
Other factors to consider in future activities ........................................................... 17
Carbon Losses Index ............................................................................ 19
6.1
General statements ................................................................................................ 19
6.2
Previous landuse .................................................................................................... 19
6.3
Clay and mineral protection.................................................................................... 20
6.4
Microbial activity ..................................................................................................... 21
6.5
Carbon Losses Index.............................................................................................. 22
6.6
Other factors to consider in future activities ........................................................... 22
7.
Potential Capability Index ..................................................................... 24
8.
References ............................................................................................. 25
9.
Appendix 1 ............................................................................................. 26
i
List of Figures
Figure 1. Functions performed by organic matter present in soils. The black arrows identify the
links between soil organic matter (carbon and its associated nutrients) and the soil
properties that it contributes to. The grey arrows indicate the potential interactions and
dependencies between the various soil properties that organic matter (carbon) influences. 6
Figure 2: Parameters important to define the potential to build soil organic carbon content....... 7
Figure 3. Schematic representation of the decision framework used to derive the relative
Capability Index for enhancing soil organic carbon content. ................................................. 9
Figure 4. The two class Landuse (a) and four class Clearing date (b) data layers that were
combined to produce the four class Clearing History (c) data layer.................................. 11
Figure 5. The two class Present Vegetation (a) and two class Cropping (b) data layers were
combined to produce the three class Crop v Pasture (c) data layer. ................................. 11
Figure 6. Difference in the amount of carbon (t C/ha) found in the 0-30 cm soil layer at locations
in WA that were either uncleared or cleared and used for agricultural production (crops
and/or pastures) for different durations (Skjemstad and Spouncer 2003; Griffin et al. 2003).
Values given above each group of bars indicates the average clay content across the three
sample locations................................................................................................................... 12
Figure 7. Clay Decline Index data layer ................................................................................... 13
Figure 8. The Capacity Index describing the extent to which soil organic carbon has been run
down due to the initiation of agriculture. Values in blue represent the lowest run down and
thus the lowest capacity to increase soil carbon. Soils with the greatest capacity to build
soil carbon are identified in red. The Capacity Index increases in progressing from blue
through light blue, green, yellow and then red. .................................................................... 13
Figure 9. Annual rain data layer derived from MCAS. .............................................................. 15
Figure 10. Seasonality (a) and Erosivity (b) data layers used to construct the Rain
availability (c) data classified into two layers using the two way matrix defined at the
bottom of (c) with low Rain availability assigned the colour blue and high availability
assigned red......................................................................................................................... 16
Figure 11. The three class data layer for Effective rain produced by multiplying the eight class
Annual rain and two class Rain availability data layers together. The value for Effective
rain increases in progressing from blue to green to red...................................................... 16
Figure 12. The Landuse (a) and Stocking rate (b) data layers used to produce the three class
Residue pressure (c) data layer with the value increasing in progressing from blue to
green and then to red........................................................................................................... 17
Figure 13. Carbon Gains Index describing the potential to add additional carbon to the soil
and increase the soil carbon content. .................................................................................. 18
Figure 14. Previous landuse data layer defined from the MCAS Landuse data set. ............... 20
Figure 15. Clay protection (a) and Ferrosol (b) data layers used to derive the Clay and
mineral protection (c) data layer........................................................................................ 21
Figure 16. Availability of water (a) and Temperature (b) data layers used to derive the
Microbial Activity Index (c) data layer ............................................................................... 22
ii
Figure 17. Carbon Losses Index describing the potential for added carbon to be retained
within the soil........................................................................................................................ 23
Figure 18. The Potential Capability Index for increasing soil carbon as calculated according
to Equation [2]. ..................................................................................................................... 24
List of Tables
Table 1. Progression of colours from lowest to highest used to separate two, three, four and
five classes in the all data layers. .......................................................................................... 9
EXECUTIVE SUMMARY
Much interest exists in defining the content of organic carbon in Australian agricultural
soils and the capacity to increase soil carbon through altered agricultural management
strategies. This interest arises because of the important contributions that organic
matter, and thus organic carbon and its associated elements, make to both productivity
and the potential for mitigating greenhouse gas emissions. The objective of this report
was to develop an objective spatial assessment of the potential of Australian agricultural
soils to capture and retain additional organic carbon using existing environmental, soil
and land-use data.
The organic carbon content of a soil is defined by the balance of inputs and losses.
Soils with the greatest potential to capture additional organic carbon will be those that
meet the following criteria:
•
a significant loss of carbon occurred on initiation of agriculture,
•
the capacity exists to support additional plant biomass production and thus
enhance inputs of carbon to the soil, and
•
a capability exists to protect added carbon against decomposition.
Three separate indices, one for each of the identified criteria, were created. The indices
were labelled respectively as: a Capacity Index, a Carbon Gains Index and a Carbon
Losses Index. The three indices were combined to provide an overall Potential
Capability Index that defined the relative potential of a soil to capture additional
organic carbon beyond that which it currently contains. The assessment was completed
by combining spatial data layers of key variables within the Multi-Criteria Analysis
Shell (MCAS) produced by the Bureau of Rural Sciences
(http://adl.brs.gov.au/mcass/index.html).
The Capacity Index provided an assessment of how much organic carbon had been lost
from the soil since agriculture was initiated. The index incorporated time since
clearing, the nature of the agricultural system employed (cropping versus pasture), and
the soil clay content.
The Carbon Gains Index assessed the potential for increasing the input of organic
carbon to the soil. It used data for the amount and distribution of annual rainfall, soil
erosivity to provide an indicator of the usefulness of rain in creating plant biomass, and
grazing intensity to assess the potential return of plant residues to soils.
The Carbon Losses Index was used to assess the ability of a soil to protect added carbon
against loss principally through biological decomposition. The Carbon Losses Index
used data layers for soil clay content, previous land use, annual temperature and the
Prescott Index.
All data were input into MCAS as classified spatial data layers and then added or
multiplied together to produce the three initial indices. The final Potential Capability
Index was created by multiplying the Capacity Index by 2 (to reflect its greater
4 Identification of areas within Australia with the potential to enhance soil carbon content
importance than the other two indices) and adding it to the sum of the Carbon Gains and
Carbon Losses Indices.
In general, soils with a long history of agricultural production (particularly crop
production), significant clay contents and high amounts of low intensity rainfall have
received higher rankings for their potential to increase soil organic carbon content.
Sandy soils cleared recently and used predominantly for pasture production in regions
with low rainfall have received lower rankings.
Identification of areas within Australia with the potential to enhance soil carbon content
5
1. INTRODUCTION
An interest in enhancing soil organic carbon content (SOC) exists because of the
combined effects that this would have on soil productivity and the mitigation of
greenhouse gas emissions. Soil organic carbon (SOC) and its associated elements in
soil organic matter (e.g. O, H, N, P and S) have a beneficial effect on a number of soil
biological, physical and chemical properties important to defining soil productivity
(Figure 1). Of particular importance, under the forecasted changes to a warmer and
drier climate, is the ability of increased soil organic carbon contents to enhance the
plant-available water holding capacity of a soil. An increased ability to store plantavailable water will help Australian agricultural systems maintain productivity under
drier conditions. From a greenhouse gas emissions point of view, an increase in the
amount of organic carbon stored in soil will offset emissions of greenhouse gases and
could provide a means for farmers to enter a carbon trading scheme.
Figure 1. Functions performed by organic matter present in soils. The black arrows identify the
links between soil organic matter (carbon and its associated nutrients) and the soil properties
that it contributes to. The grey arrows indicate the potential interactions and dependencies
between the various soil properties that organic matter (carbon) influences.
The potential to build soil organic carbon is a function of three parameters (Figure 2):
1) the capacity for a soil to hold additional organic carbon,
2) the ability to deliver more organic carbon to the soil, and
3) the rate of loss of organic carbon through decomposition.
To describe these parameters, an analogy of a bucket that is simultaneously filled with
organic carbon and emptied will be used (Figure 2). The capacity for a soil to hold
additional organic carbon is defined by the size of the bucket and how full the bucket is.
Soil properties that define the amount of soil present (bulk density and depth) and
provide mechanisms to stabilise carbon against decomposition (mineralogy) will affect
the size of the bucket. The magnitude of organic carbon input to a soil is defined by the
net primary productivity (NPP) of the vegetation present (the ability of the vegetation to
capture carbon via photosynthesis and add captured carbon to the soil). Losses of
carbon are controlled by the ability of the soil to protect added organic materials against
6 Identification of areas within Australia with the potential to enhance soil carbon content
decomposition and mineralisation. Due to variations in the magnitude of these three
parameters across Australian climate/soil/plant systems, significant differences in
current soil organic carbon content and the capacity to increase soil organic carbon
content exist.
Figure 2: Parameters important to define the potential to build soil organic carbon content.
2. OBJECTIVES OF THIS PROCESS
The objectives of the process described in this report were:
1) To design and implement a system capable of defining the relative potential for
enhancing the organic carbon content of soils across Australia for benefits to
soil productivity and to offset greenhouse gas emissions through carbon
sequestration in soil.
2) To use the designed system to identify areas within Australia with the greatest
potential to increase soil carbon levels as a guide to targeting investments made
through the Caring for our Country program.
It is important to note that this exercise was conducted to identify areas where the
biggest potential returns from a limited investment may occur. It is acknowledged that
in any one region variations will occur in the potential to accumulate soil carbon. The
exercise used average values for climatic conditions, edaphic properties and
management practices within the various regions to derive a relative quantification of
the potential for soil carbon enhancement. Soils under agriculture, rangelands and
managed forest stands were included. Soils under native forests were excluded
because, under native unmanaged conditions, soil carbon was assumed to be in balance
with environmental and edaphic properties and no potential exists to alter soil carbon
through the application of management practices.
The methodology developed to complete this assessment consisted of three steps:
Identification of areas within Australia with the potential to enhance soil carbon content
7
1) Construction of a decision framework capable of defining the relative potential
to increase the amount of organic carbon stored in soils across Australia,
2) Identification of spatial data layers that could be used to provide indications of
the potential to increase carbon inputs based on the framework, and
3) Aggregation of the spatial data to provide a relative classification of areas with
the greatest potential to build soil carbon.
3. COMPONENTS OF THE DECISION FRAMEWORK
A decision framework was developed to derive an index of the capability for enhancing
soil organic carbon content (Figure 3). After developing this framework, spatial layers
were acquired for the required input data from the Australian Soil Resource Information
System (ASRIS), the Digital Atlas of Australian Soils
(http://www.asris.csiro.au/index_other.html) and from Bureau of Rural Sciences
collated data within the Multi-Criteria Analysis Shell (MCAS)
(http://adl.brs.gov.au/mcass/index.html). MCAS was used for spatial decision support;
it allowed the derivation, classification and manipulation of data layers to provide
estimates for the Capacity, Gains, Losses and Capability Indices identified in Figure 3.
As importantly, it provided a capacity to display and interact with the spatial data and
the combinations used in capturing and applying the logic of Figure 3. Thus, an expert
overview was possible over each stage of the process.
The compilation processes used for deriving each index are described in the subsequent
sections of this report. Spatial data layers were typically divided into classes that were
assigned a relative score. To derive the indices the scores were either added (where
layers provided independent evidence) or multiplied (where layers provided a
modification of a value). The three indices (capacity, gains and losses) were used to
calculate an index of the potential of enhancing soil organic carbon content. It needs to
be acknowledged that all soils would benefit from additional carbon; however, the final
Capability Index was designed to highlight where the potential to increase soil organic
carbon content would likely be highest.
In all Figures presented in the subsequent portion of this report, the changes in colour
used to separate classes are given in Table 1.
8 Identification of areas within Australia with the potential to enhance soil carbon content
Figure 3. Schematic representation of the decision framework used to derive the
relative Capability Index for enhancing soil organic carbon content.
Table 1. Progression of colours from lowest to highest used to separate two, three, four and
five classes in the all data layers.
Number of classes
Progression of colours from low to high
2 classes
Blue, Red
3 classes
Blue, Green, Red
4 classes
Blue, Green, Yellow, Red
5 classes
Blue, Aqua, Green, Yellow, Red
Identification of areas within Australia with the potential to enhance soil carbon content
9
4.
CAPACITY INDEX
4.1 General comments
Using the analogy presented in Figure 2 the Capacity Index was developed to provide
an indication of how full the soil organic carbon bucket currently is. Where the bucket
is empty (low carbon storage) the index is high indicating a high capacity to add
additional carbon to the soil. Where the bucket is full (high carbon storage), the
capacity to add additional carbon is reduced. The spatial layers used to derive this
index included:
1) Clearing history – date on which soils were cleared of native vegetation and
brought into agricultural production.
2) Crop versus pasture – on clearing were the cleared lands brought into cropping
or pasture production and have they remained in this land management?
3) Clay content – the potential decline in soil organic carbon on initiation of
agriculture required modification due to clay content.
4.2 Clearing history
Soil organic carbon content can be significantly altered by clearing native vegetation
and initiating agricultural production. The introduction of agriculture typically results
in a net decrease in the amount of soil carbon; however, in some circumstances (low
fertility sand soils) an increase in soil organic carbon may occur when agricultural
production is implemented. Increases occur when a deficiency present under native
condition (e.g. low availability of phosphorus) can be overcome in an agricultural
production system (e.g. by application of phosphorus fertiliser). Irrespective of whether
the direction of change is positive or negative, an increasing length of time since
clearing will allow the magnitude of the effect to be increased. A four class Clearing
History (Figure 4c) layer was created as a two-way combination in MCAS using the
MCAS Landuse data layer (Figure 4a) with “Modified pastures” and “Cropping”
differentiated from other landuses and a Clearing Date data layer (Figure 4b) with four
class values assigned to NRM regions.
4.3 Vegetation: Crop versus pasture
The extent of decline in soil organic carbon content on clearing will be influenced by
the use to which the land was put. Organic carbon declines on land converted to pasture
will be less than on land converted to cropping. A Crop v Pasture (Figure 5c) data
layer was created from a combination of two layers:
1) Present Vegetation (Figure 5a) – created from the “Current major vegetation
group (class)” MCAS primary data layer. Lands classified as “cleared, nonnative vegetation, buildings” were differentiated from all other lands to produce
a 2 class layer.
10 Identification of areas within Australia with the potential to enhance soil carbon content
2) Cropping (Figure 5b) – created from the “Catchment scale land use (ALUM
secondary class)” MCAS primary data layer. Lands that were classified as
“cropped” were differentiated from all other lands to produce a 2 class layer.
Figure 4. The two class Landuse (a) and four class Clearing date (b) data layers that were
combined to produce the four class Clearing History (c) data layer.
Figure 5. The two class Present Vegetation (a) and two class Cropping (b) data layers were
combined to produce the three class Crop v Pasture (c) data layer.
4.4 Clay Decline Index
Although declines in soil carbon on clearing are evident for most soils, it was
recognised that the extent of this decline will vary with clay content. On sandy soils,
examples exist where soil carbon content either remained constant or even increased on
clearing and implementation of agricultural production systems. In Figure 6 the carbon
content of the 0-30 cm soil layer obtained under native vegetation is compared to that
under paired agricultural systems that had been cleared for varying amounts of time. At
low clay contents, clearing resulted in either little change or an increase in soil carbon;
Identification of areas within Australia with the potential to enhance soil carbon content
11
however, at higher clay contents, decreases in soil carbon were noted subsequent to
clearing and initiating agricultural production.
The Clay Decline Index was generated from a spatial layer created by combining the
ASRIS and Atlas data sets for soil clay content. The spatial layer obtained was divided
into four classes (<10% clay, 10-25% clay, 25-40% clay, and >45% clay) and assigned
values from 1 to 4, respectively, to produce the Clay decline layer (Figure 7). The
observations supporting this were:
• soils with higher clay content would have a higher initial carbon content when
agriculture was initiated than soils with a low clay content
• the magnitude of soil carbon loss on initiating agricultural production will be
greater for clay soils than sands
Using the bucket analogy (Figure 2), the size of the bucket and the degree to which the
bucket will be emptied on initiating agricultural production will both increase with
increasing clay content. This will lead to a greater potential to capture additional soil
carbon compared to the current soil carbon condition.
4.5 Capacity Index
The Capacity Index was created by multiplying the Clearing history, Crop v Pasture
and Clay decline data layers together and then classifying the resultant Capacity Index
product into 5 classes (Figure 8). Low values (blue) indicate little depletion and thus a
low potential to capture additional organic carbon in the soil while areas with a high
potential to capture additional soil carbon are shown in red.
Figure 6. Difference in the amount of carbon (t C/ha) found in the 0-30 cm soil layer at locations
in WA that were either uncleared or cleared and used for agricultural production (crops and/or
pastures) for different durations (Skjemstad and Spouncer 2003; Griffin et al. 2003). Values
given above each group of bars indicates the average clay content across the three sample
locations.
25
20
Uncleared
18
Short clearing history
19
Long clearing history
7
2
Badgingarra
30
Northampton
1
4
1
15
10
5
Location
12 Identification of areas within Australia with the potential to enhance soil carbon content
Condingup
Newdegate2
Newdegate1
Mullewa
0
Brookton
0-30 cm Soil organic carbon
(t C/ha)
35
Figure 7. Clay Decline Index data layer
Figure 8. The Capacity Index describing the extent to which soil organic carbon has been run
down due to the initiation of agriculture. Values in blue represent the lowest run down and thus
the lowest capacity to increase soil carbon. Soils with the greatest capacity to build soil carbon
are identified in red. The Capacity Index increases in progressing from blue through light blue,
green, yellow and then red.
Identification of areas within Australia with the potential to enhance soil carbon content
13
5. CARBON GAINS INDEX
5.1 General comments
The Carbon Gains Index was constructed to provide an assessment of the potential to
increase the inputs of organic carbon to the soil. Over the majority of Australia under
rain fed agriculture, the availability of water places a constraint on productivity which
translates to a constraint on organic carbon addition to the soil. The greatest additions
of organic carbon in the form of residues will occur where transpirational losses of
water are maximised and direct evaporation, leaching and runoff losses of water are
minimised. Potential increases in plant residue inputs under current
cropping/pasture/agroforestry systems are possible if one or more of the following
conditions exist:
1) Agricultural systems are not using water efficiently and the potential exists to
utilise additional water to grow bigger crops.
2) Existing constraints to efficient use of plant-available water can be overcome
by management (e.g. addition of fertility, deep ripping, and mitigating subsoil
constraints).
3) Extension of the growing season by inclusion of perenniality which will allow
carbon to be captured by plants for a greater duration of the year.
4) Crops are grown when the soil contains enough water irrespective of time of
year to maximise water use and reduce water lost via evaporation or leaching
(e.g. opportunity cropping initiated when the soil profile fills in a manner
typical of the northern cereal producing region).
The parameters considered to be most influential on defining the potential for
increasing the amount of organic carbon added to soils include annual rainfall, the
distribution of rainfall over the year, the intensity of rain and extent of residue removal.
5.2 Annual rain
The annual amount of rainfall provides a strong control over potential capture of carbon
by growing plants through photosynthesis. As rainfall increase so too does the potential
for capturing carbon. An Annual rain data layer divided into eight equal area classes
was created from the MCAS mean annual rainfall layer (Figure 9). The classes increase
in progressing from blue to red in the following order: <194 mm, 194-226 mm, 226291 mm, 291-324 mm, 324-421 mm, 421-582 mm, 582-809 mm, >809 mm.
5.3 Rain Availability
In addition to the total amount of rain that falls, the ability of plants to use rain to
capture carbon is also defined by the duration of the year over which the rain falls and
how much of the rain remains available. The issue being examined here is whether or
not the growing season can be extended beyond that of annual plants by bringing
perennials into the agricultural production system in an effort to have green leaves
displayed throughout the year. The introduction of perennial species will allow carbon
capture, whenever it rains, to be maximised. If green leaves are absent for part of the
year (such as under annual systems in winter dominant rainfall zones), carbon capture
per mm of water received will be reduced and total dry matter production will be
14 Identification of areas within Australia with the potential to enhance soil carbon content
reduced. Under equiseasonal rainfall conditions, a potential exists to incorporate
perennials into the management system; however, under summer or winter dominant
systems the potential is more limited. The effectiveness of rainfall will be a function of
the episodic nature of rainfall events and how much rain remains in the soil versus that
which drains or runs off over the surface.
The Rain availability data layer (Figure 10c) was created from a MCAS two way table
created using Seasonality (Figure 10a) and Erosivity (Figure 10b) data layers. The
Seasonality data layer was created by classifying the MCAS climate zones data layer
into summer dominant (>60% of annual rain received in the summer), winter dominant
(>60% of annual rain received in the winter) or equiseasonal. The Erosivity layer was
created by classifying the MCAS erosivity layer into high (red), medium (green) and
low (blue) values. Lower values for erosivity will provide a greater potential for rain to
be used to produce vegetative material and thereby increase inputs of organic carbon to
the soil. The two way classification matrix is shown in Figure 10c. Areas coded red
will have a higher Rain availability and therefore a greater potential to capture carbon
and return more residues to the soil.
5.4
Effective rain
An Effective rain data layer was created by multiplying the classified layers of Annual
rain and Rain availability and classifying the product into three equal area classes
representing low through to high effective rain classes (Figure 11).
Figure 9. Annual rain data layer derived from MCAS.
Identification of areas within Australia with the potential to enhance soil carbon content
15
Figure 10. Seasonality (a) and Erosivity (b) data layers used to construct the Rain
availability (c) data classified into two layers using the two way matrix defined at the bottom of
(c) with low Rain availability assigned the colour blue and high availability assigned red.
Figure 11. The three class data layer for Effective rain produced by multiplying the eight class
Annual rain and two class Rain availability data layers together. The value for Effective rain
increases in progressing from blue to green to red.
5.5 Residue pressure
It is important to assess the fate of agricultural residues. Are the residues retained,
baled and removed, grazed or burnt? Residue handling will significantly alter the
amount of the carbon that is captured in a given region that is returned to the soil. The
land uses, in the MCAS Landuse data layer, that remove significant biomass were
identified (Forestry, Plantations, Modified pastures, Cropping, Horticulture, Irrigated
pastures and cropping, Irrigated horticulture and Intensive animal and plant production)
and given a higher classification value than the remaining lands (Figure 12a). A higher
value was assigned because residue removal will have accentuated previous losses of
carbon and therefore enhanced the potential for carbon gains. In addition to land use,
average stocking rates in the regions have been used to provide an indication of the
pressure to remove or graze residues rather than retain them in the system. The
Stocking rates data layer (Figure 12b) expressed as the number of animals per unit of
productive land were obtained from Ted Griffin (personal communication). The
16 Identification of areas within Australia with the potential to enhance soil carbon content
Stocking rates data layer was classified into three equal area classes. A Residue
pressure (Figure 12c) data layer was then created by multiplying the Stocking rates
data layer by 2 and adding the result to the Landuse data layer. The resultant Residue
pressure data layer was divided into three classes.
Figure 12. The Landuse (a) and Stocking rate (b) data layers used to produce the three class
Residue pressure (c) data layer with the value increasing in progressing from blue to green
and then to red.
5.6 Carbon Gains Index
The final Carbon Gains Index (Figure 13) data layer was created by summing the
Effective rain and Residue pressure layers. The Carbon Gains Index layer was
classified into five groups separated by the same magnitude. This layer provides an
indication of potential to increase the input of carbon to the soil with values increasing
in the order of dark blue, light blue, green, yellow and red.
5.7 Other factors to consider in future activities
Additional variables that may be included in future activities are subsequently
described.
1) The provision of energy in the form of heat will also have an impact on plant
productivity. There is little indication, for the majority of Australian agricultural
systems that they are energy or heat limited. However, having heat available
when the soils are also wet may be important. It was considered that this was
being accounted for through the rainfall distribution variable. It may be useful
to consider plant active degree days in a future exercise where monthly rainfall
and heat available (average temperature) are combined to give an indication of
the influence of the combined availability of water and heat on plant growth and
potential carbon capture.
2) An indication of the potential capacity to capture carbon is required. The
usefulness of potential primary productivity, net primary productivity (NPP) or a
measure of actual productivity was discussed. Net primary productivity values
are examined but appeared to be constrained by light, water and nutrients.
Ideally a measure of NPP only constrained by water availability is desired
Identification of areas within Australia with the potential to enhance soil carbon content
17
because, for example, fertilisation would compensate for a lack of nutrients that
may be controlling actual productivity. It is desirable to define the
unconstrained amount of carbon capture and input to a soil that could occur
based only on energy and water availability. Comparison of a water only
constrained net primary productivity with actual productivity should give us an
indication of where it is possible to capture more carbon. However, we were not
able to source a NPP map constrained only by water, and total dry matter
production values along with harvest indices for each region would need to be
acquired. The potential exists to obtain the dry matter and harvest index data
from current and related work. Using the difference between water constrained
net primary productivity and actual dry matter production should be a future
target for this form of analysis.
3) The proportion of time that a plant is actually in the system will also be
important to define. A factor related to cropping frequency would be useful to
account for years where no input of residues occurs due to long fallows or
climatic issues (e.g. drought).
Figure 13. Carbon Gains Index describing the potential to add additional carbon to the soil
and increase the soil carbon content.
18 Identification of areas within Australia with the potential to enhance soil carbon content
6. CARBON LOSSES INDEX
6.1 General statements
The decomposition of organic materials represents the major loss mechanism of soil
organic carbon. Erosion events may result in catastrophic losses of carbon from a
particular location, but whether or not the eroded carbon is retained where deposited or
more rapidly mineralised than it would have been in its original position remains a
question to be addressed. Decomposition processes are facilitated by the combination
of warm and moist soil conditions under which microbial activity is promoted (Baldock
2007). Some evidence exists to suggest tillage may have an influence on
decomposition rates (Lal 2004), however, this is often confounded with the handling of
stubbles in reduced or zero tillage systems. At present, conclusive evidence for an
influence of passing a tillage implement through soil on soil carbon content does not
exist for Australian soils (Valzano et al. 2005). Additionally, a term to account for a
tillage effect has not been required to be added to carbon cycling models in an effort to
model carbon dynamics under different tillage regimes (Skjemstad et al. 2004).
Accounting for the influence that the tillage system has on stubble retention has been
sufficient to achieve successful modelling outcomes. Soil texture is also an important
variable required in most modelling systems to assess the potential degree of protection
that a soil may offer to organic carbon (Jenkinson et al. 1987). With increasing clay
content, the ability of the soil to protect carbon against loss increases.
Three parameters were developed to create the Carbon Losses Index:
1) The extent of cropping,
2) The ability of a soil to protect carbon from decomposition, and
3) The potential for soil microbial and faunal activity.
6.2 Previous landuse
It is recognised that the previous land use will have an influence on the magnitude of
carbon losses. Many regions will have had a mixture of cropping and pasture with the
exception of forestry or grazing in rangelands and other marginal cropping lands.
Carbon in soils can be protected against biological attack and decomposition by a
number of mechanisms involving some form of interaction with mineral particles (e.g.
adsorption onto exposed surfaces and burial within aggregations of soil particles)
(Baldock and Skjemstad 2000). As the amount of organic carbon present in a soil
increases, the number of sites available for protecting any additional added carbon
against microbial attack will decrease.
If a soil was previously under a cropping regime, it is likely that soil carbon will have
been run down. Under conditions of low soil carbon, many of the potential sites that
can protect organic carbon against biological decomposition will be available and
unfilled. Under such circumstances the capability of protecting additional carbon will
be high. If a soil was previously under pasture, due to the higher carbon contents
typically achieved under pastures relative to grain crops, a greater proportion of the
Identification of areas within Australia with the potential to enhance soil carbon content
19
protection sites for the soil carbon will be occupied. Thus the ability of the soil to
protect additional carbon will be low. The MCAS Landuse data set with “Cropping”
area differentiated from all other landuses was used to produce a two class Previous
landuse data layer (Figure 14).
Figure 14. Previous landuse data layer defined from the MCAS Landuse data set.
6.3 Clay and mineral protection
Soil texture and mineralogy are important parameters defining the ability of a soil to
slow decomposition. Organic carbon can be protected against decomposition by
interaction with the minerals present in a soil (Baldock and Skjemstad 2000). These
interactions can result in a decreased solubility, adsorption onto mineral surfaces and/or
burial within assemblages of mineral particles. Each of these protection mechanisms
reduces the accessibility of organic materials to enzymatic attack and the extent of
potential protection increases with increasing clay content. The presence of oxides or
hydroxides of iron and aluminium offer an enhanced level of protection of soil organic
matter against decomposition relative to other soil minerals. Thus Ferrosols will tend to
offer a greater protective effect than other Australian soil types.
Debate exists as to whether it is soil clay content itself, soil particle surface area or
reactivity of soil surfaces as defined by mineralogy that is most critical. A clay content
map derived from ASRIS and Atlas data was developed (as described in Section 4.4).
Clay protection (Figure 15a) classes were defined as follows:
1) Class 1 – clay content <10%
2) Class 2 – clay content 10-25% and >45%
3) Class 3 – clay content 25-45%
Soils with a clay content >45% often exhibit vertic (significant shrink/swell on
exposure to wetting/drying cycles) properties that reduce the protective capability of
clay. Therefore high clay content soils were placed into Class 2 along with the 10-25%
clay soils. Given the strong capacity of Ferrosols to protect soil organic carbon, a map
of the distribution of Australian Ferrosols was created from an Atlas ASC order layer.
This map was imported into MCAS from ArcGIS and used to define a two class
20 Identification of areas within Australia with the potential to enhance soil carbon content
Ferrosol data layer (Figure 15b). A Clay and mineral protection (Figure 15c) data
layer was then constructed as a two-way table in MCAS
Figure 15. Clay protection (a) and Ferrosol (b) data layers used to derive the Clay and
mineral protection (c) data layer
6.4 Microbial activity
Losses of soil organic carbon due to decomposition are dependent on the activity of the
microbial biomass present in the soil. Two key soil properties governing microbial
activity are the availability of water and heat. To develop a scoring system for
microbial activity an index using both temperature and water availability (as defined by
the Prescott Index) was created.
1) Availability of water (a) – The Prescott Index (PI) was used to define the
availability of water to combine the effects of rainfall and evapotranspiration. A
data layer was provided by Ted Griffin (personal communication). Four classes
were created (<0.34, 0.34-0.82, 0.82-1.4 and >1.4). Lower PI values reflect a
drier environment in which microbial activity will be constrained due to a lack
of water.
2) Temperature (b) – to characterise the effect of temperature, a mean annual
temperature data layer was provided by David Jacquier (personal
communication). The temperature data layer was divided into four classes (<10
°C, 10-15 °C, 15-20 °C, >20 °C). If the mean annual temperature was low, the
potential to build carbon is high because added carbon will decompose slowly.
The Temperature and Availability of water data layers were multiplied together and the
resultant data layer was divided into six equally classes separated by equal magnitudes
to produce a Microbial Activity Index data layer (c). The highest class in the Microbial
Activity Index represents the locations where microbial activity would be lowest and
thus the greatest opportunity to build soil carbon.
Identification of areas within Australia with the potential to enhance soil carbon content
21
Figure 16. Availability of water (a) and Temperature (b) data layers used to derive the
Microbial Activity Index (c) data layer
6.5 Carbon Losses Index
The Carbon Losses Index data layer (Figure 17) was calculated in MCAS according to
Equation [1] and then classified into 5 classes separated by equal magnitudes. High
values of the Carbon Losses Index indicate regions where added carbon is likely to be
retained within the soil.
Carbon Losses Index =
(
)(
Microbial activity Clay and mineral
+
+ Previous
protection
landuse
4
)
[1]
6.6 Other factors to consider in future activities
Additional factors exist that could be incorporated into a future version of this
approach.
1) The type of minerals present in a soil will play an important role in defining the
soil carbon holding capacity. For example protection via carbonate, allophane
and oxides of Al and Fe. As the application of infrared technology (mid and
near infrared) to soil analyses and our database of soils information grows, it
would be expected that a more rigorous assignment of the influence of
mineralogy on the potential for protecting soil carbon could be evolved.
2) The approach to derive a microbial index is considered crude given that it is
based on annual average values. It would be desirable to move to a system that
calculates weekly or monthly values that can be integrated over a year to give
assessment of relative impact. Of particular interest is to attempt to derive an
indication of microbially active degree days.
3) Although erosion events are not handled specifically in the approach taken, the
time since clearing (see below) does provide a broad estimate of potential losses.
It would be of interest to build a specific index to deal with erosion, not only
from a carbon point of view, but also from the potential impact it will have on
22 Identification of areas within Australia with the potential to enhance soil carbon content
productivity due to the removal of nutrients and associated reductions in plant
growth and residue returns.
Figure 17. Carbon Losses Index describing the potential for added carbon to be retained
within the soil.
Identification of areas within Australia with the potential to enhance soil carbon content
23
7. POTENTIAL CAPABILITY INDEX
The potential for enhancing soil carbon was then calculated using the Capacity Index
with a combined Carbon Gains Index and Carbon Losses Index according to Equation
[2]. The Potential Capability Index was then divided into three classes (Figure 18) to
identify where the greatest potential to increase soil carbon exists.
(
)(
)
⎛ Potential ⎞
⎜ Capability ⎟ = 2 × Capacity Index + ⎛⎜ Carbon Gains + Carbon Losses ⎞⎟ [2]
Index
Index
⎜ Index ⎟
⎝
⎠
⎝
⎠
Figure 18. The Potential Capability Index for increasing soil carbon as calculated according
to Equation [2].
24 Identification of areas within Australia with the potential to enhance soil carbon content
8.
REFERENCES
Baldock JA (2007) Composition and cycling of organic carbon in soil. In 'Soil Biology,
Volume 10. Nutrient Cycling in Terrestrial Ecosystems'. (Eds P Marschner, Z
Rengel) Springer-Verlag: Berlin. p. 1-36.
Baldock JA, Skjemstad JO (2000) Role of the soil matrix and minerals in protecting
natural organic materials against biological attack. Organic Geochemistry 31,
697-710.
Griffin EA, Verboom WH, Allen DG (2003) Paired site sampling for soil carbon
estimation - Western Australia. National Carbon Accounting System Technical
Report No. 38. (http://www.climatechange.gov.au/ncas/reports/tr38final.html).
Jenkinson DS, Hart PBS, Rayner JH, Parry LC (1987) Modelling the turnover of
organic matter in long-term experiments at Rothamsted. Intecol Bulletin 15, 1-8.
Lal R (2004) Agricultural activities and the global carbon cycle. Nutrient Cycling in
Agroecosystems 70, 103-116.
Skjemstad JO, Spouncer L (2003) Integrated soils modelling for the national carbon
accounting system. Estimating changes in soil carbon resulting from changes in
land use. National Carbon Accounting System Technical Report No. 36.
(http://www.climatechange.gov.au/ncas/reports/tr36final.html).
Skjemstad JO, Spouncer LR, Cowie B, Swift RS (2004) Calibration of the Rothamsted
organic carbon turnover model (RothC ver. 26.3), using measurable soil organic
carbon pools. Australian Journal of Soil Research 42, 79 - 88.
Valzano F, Murphy B, Loen T (2005) The Impact of Tillage on Changes in Soil Carbon
with Special Emphasis on Australian Conditions. National Carbon Accounting
System - Technical Report No. 43
(http://www.climatechange.gov.au/ncas/reports/tr43final.html).
Identification of areas within Australia with the potential to enhance soil carbon content
25
APPENDIX 1
Four class data layer (class 1 post 1980 or
uncleared, class 2 1950-1980, class 3 19201950, class 4 pre 1920).
Four class data layer.
Two class data layer.
Two class data layer.
Clearing date (date land was
cleared)
Clearing index
Present vegetation
Cropping
26 Identification of areas within Australia with the potential to enhance soil carbon content
Two class data layer.
Description and level of classification used
Land use (use land was put to on
clearing)
Capacity Index
Index/data layer
Created from the “Catchment scale land
use (ALUM secondary class)” MCAS
data layer. Lands classified as “cropped”
were differentiated from all other lands.
Derived from “Current major vegetation
group (class)” in MCAS with “cleared”,
“non-native vegetation” and “buildings”
differentiated from all other lands uses.
Generated in MCAS using a two way
table between Land use and Clearing date.
Average clearing date classes assigned to
entire NRM regions.
MCAS ALUM land use data layer with
“Modified pastures” and “Cropping”
differentiated from all other uses.
Data source and derivation
Sources of data used for creation of the various component data layers used to derive each of the indices.
9.
Four class data layer based on variations in
surface soil clay content (Class 1: <10% clay,
Class 2: 10-25% clay, Class 3: 25-40% clay,
and Class 4: >45% clay)
Five class data layer defining the capacity to
capture additional carbon.
Clay decline index (soil clay
content expressed as a % of total
soil mass)
Capacity index
Three classes based on dominant rainfall
season (summer or winter dominant or
equiseasonal)
Three classes of erosivity
Two class assessment of the availability of rain
to plants throughout the year
Calculated within MCAS as (Annual rain
Three classes of equal area that define the
effectiveness of rainfall for producing plant dry * Rain availability)
matter
Seasonality of rainfall (proportion
of year when most rain falls)
Erosivity (mm)
Rain availability
Effective rain (mm)
Identification of areas within Australia with the potential to enhance soil carbon content
Generated within MCAS using a two way
table between Seasonality and Erosivity
MCAS erosivity data layer
MCAS climate zones data layer
Eight equal areas classes
MCAS annual rainfall data layer
Calculated within MCAS as (Clearing
history * Crop v Pasture * Clay decline).
The clay decline index was generated
from a spatial layer created by combining
the ASRIS and Atlas data sets for soil clay
content and classifying into 4 groups.
Calculated within MCAS as (Present
vegetation + Cropping).
Annual Rainfall (mm)
Carbon Gains Index
Three class data layer.
Crop versus pasture
27
Three class data layer based on stocking rate
Three class data layer combining land use and
stocking rate.
Five class data layer with classes separated by
a constant magnitude..
Stocking rate
Residue pressure
Carbon gains index
Three class data layer.
Two class data layer identifying Ferrosols
separately from all other soil types.
Clay protection
Ferrosols
28 Identification of areas within Australia with the potential to enhance soil carbon content
Two class data layer.
Previous landuse
Carbon Losses Index
Two class map to show areas with significant
biomass removal.
Land use for residue pressure
MCAS ALUM land use data layer with
the “Cropping” area differentiated from
all other land uses.
Derived from ASRIS and Atlas data with
classes were defined as follows: Class 1 –
clay content <10%, Class 2 – clay content
10-25% and >45%, Class 3 – clay content
25-45%.
The distribution of Australian Ferrosols
was created from an Atlas ASC order
layer and imported into MCAS from
ArcGIS.
Calculated within MCAS as (Effective
rain + Residue pressure).
Calculated within MCAS by adding
Landuse removal to 2*(Stocking
rate){residue pressure}
Data layer was provided by Ted Griffin.
MCAS ALUM land use data layer land
uses that remove significant biomass
selected
The potential for enhancing soil carbon
was calculated in MCAS as (Capacity
Index *2) + (Carbon Gains Index +
Carbon Losses Index).
Six class data layer
Five class data layer
Three class data layer defining the locations
where the highest potential to enhance soil
carbon exists.
Microbial activity
Carbon losses index
Potential Capability Index
Calculated in MCAS as (microbial
acitivity/4 + Clay and mineral protection
+ Previous landuse).
Four class data layer
Temperature
Identification of areas within Australia with the potential to enhance soil carbon content
The Prescott index and temperature data
layers were multiplied together and the
resultant layer divided into four classes.
A mean annual temperature data layer was
provided by David Jacquier. Four classes
were created (<10 °C, 10-15 °C, 15-20
°C, >20 °C).
The Prescott index was used to define
water availabilty using a data layer from
Ted Griffin. Four classes were created
(<0.34, 0.34-0.82, 0.82-1.4 and >1.4).
Four class data layer
Availability of water
Created in MCAS as a two way table
between the Clay protection and Ferrosols
data layers.
Three class data layer.
Clay and mineral protection
29