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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., 8TAS 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 any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.
i
Table of Contents 1. Introduction.............................................................................................. 6
2. Objectives of this process ...................................................................... 7
3. Components of the decision framework ............................................... 8
4. Capacity Index ....................................................................................... 10 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
5. 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
6. 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
ii
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
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
4 Identification of areas within Australia with the potential to enhance soil carbon content
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
Identification of areas within Australia with the potential to enhance soil carbon content 5
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.
6 Identification of areas within Australia with the potential to enhance soil carbon content
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 plant-available 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
Identification of areas within Australia with the potential to enhance soil carbon content 7
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:
8 Identification of areas within Australia with the potential to enhance soil carbon content
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.
Identification of areas within Australia with the potential to enhance soil carbon content 9
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
10 Identification of areas within Australia with the potential to enhance soil carbon content
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, non-native vegetation, buildings” were differentiated from all other lands to produce a 2 class layer.
Identification of areas within Australia with the potential to enhance soil carbon content 11
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;
12 Identification of areas within Australia with the potential to enhance soil carbon content
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.
0
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tham
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Identification of areas within Australia with the potential to enhance soil carbon content 13
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.
14 Identification of areas within Australia with the potential to enhance soil carbon content
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, 226-291 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
Identification of areas within Australia with the potential to enhance soil carbon content 15
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.
16 Identification of areas within Australia with the potential to enhance soil carbon content
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
Identification of areas within Australia with the potential to enhance soil carbon content 17
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
18 Identification of areas within Australia with the potential to enhance soil carbon content
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.
Identification of areas within Australia with the potential to enhance soil carbon content 19
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
20 Identification of areas within Australia with the potential to enhance soil carbon content
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
Identification of areas within Australia with the potential to enhance soil carbon content 21
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.
22 Identification of areas within Australia with the potential to enhance soil carbon content
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.
( ) ( )4Microbial activity Clay and mineral PreviousCarbon Losses Index protection landuse= + + [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
Identification of areas within Australia with the potential to enhance soil carbon content 23
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.
24 Identification of areas within Australia with the potential to enhance soil carbon content
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 Carbon Gains Carbon LossesCapability 2 Capacity Index Index IndexIndex
⎛ ⎞⎜ ⎟⎜ ⎟⎝
⎛ ⎞= × + +⎜ ⎟⎝ ⎠⎠
[2]
Figure 18. The Potential Capability Index for increasing soil carbon as calculated according to Equation [2].
Identification of areas within Australia with the potential to enhance soil carbon content 25
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).
26
Iden
tific
atio
n of
are
as w
ithin
Aus
tralia
with
the
pote
ntia
l to
enha
nce
soil
carb
on c
onte
nt
9.
APP
END
IX 1
Sour
ces o
f dat
a us
ed fo
r cre
atio
n of
the
vario
us c
ompo
nent
dat
a la
yers
use
d to
der
ive
each
of t
he in
dice
s.
Inde
x/da
ta la
yer
Des
crip
tion
and
leve
l of c
lass
ifica
tion
used
D
ata
sour
ce a
nd d
eriv
atio
n
Cap
acity
Inde
x
La
nd u
se (u
se la
nd w
as p
ut to
on
clea
ring)
Tw
o cl
ass d
ata
laye
r. M
CA
S A
LUM
land
use
dat
a la
yer w
ith
“Mod
ified
pas
ture
s” a
nd “
Cro
ppin
g”
diff
eren
tiate
d fr
om a
ll ot
her u
ses.
C
lear
ing
date
(dat
e la
nd w
as
clea
red)
Fo
ur c
lass
dat
a la
yer (
clas
s 1 p
ost 1
980
or
uncl
eare
d, c
lass
2 1
950-
1980
, cla
ss 3
192
0-19
50, c
lass
4 p
re 1
920)
.
Ave
rage
cle
arin
g da
te c
lass
es a
ssig
ned
to
entir
e N
RM
regi
ons.
C
lear
ing
inde
x Fo
ur c
lass
dat
a la
yer.
Gen
erat
ed in
MC
AS
usin
g a
two
way
ta
ble
betw
een
Land
use
and
Cle
arin
g da
te.
Pr
esen
t veg
etat
ion
Two
clas
s dat
a la
yer.
Der
ived
from
“C
urre
nt m
ajor
veg
etat
ion
grou
p (c
lass
)” in
MC
AS
with
“cl
eare
d”,
“non
-nat
ive
vege
tatio
n” a
nd “
build
ings
” di
ffer
entia
ted
from
all
othe
r lan
ds u
ses.
C
ropp
ing
Two
clas
s dat
a la
yer.
Cre
ated
from
the
“Cat
chm
ent s
cale
land
us
e (A
LUM
seco
ndar
y cl
ass)
” M
CA
S da
ta la
yer.
Lan
ds c
lass
ified
as “
crop
ped”
w
ere
diff
eren
tiate
d fr
om a
ll ot
her l
ands
.
Iden
tific
atio
n of
are
as w
ithin
Aus
tralia
with
the
pote
ntia
l to
enha
nce
soil
carb
on c
onte
nt
27
C
rop
vers
us p
astu
re
Thre
e cl
ass d
ata
laye
r. C
alcu
late
d w
ithin
MC
AS
as (P
rese
nt
vege
tatio
n +
Cro
ppin
g).
C
lay
decl
ine
inde
x (s
oil c
lay
cont
ent e
xpre
ssed
as a
% o
f tot
al
soil
mas
s)
Four
cla
ss d
ata
laye
r bas
ed o
n va
riatio
ns in
su
rfac
e so
il cl
ay c
onte
nt (C
lass
1: <
10%
cla
y,
Cla
ss 2
: 10-
25%
cla
y, C
lass
3: 2
5-40
% c
lay,
an
d C
lass
4: >
45%
cla
y)
The
clay
dec
line
inde
x w
as g
ener
ated
fr
om a
spat
ial l
ayer
cre
ated
by
com
bini
ng
the
ASR
IS a
nd A
tlas d
ata
sets
for s
oil c
lay
cont
ent a
nd c
lass
ifyin
g in
to 4
gro
ups.
C
apac
ity in
dex
Five
cla
ss d
ata
laye
r def
inin
g th
e ca
paci
ty to
ca
ptur
e ad
ditio
nal c
arbo
n.
Cal
cula
ted
with
in M
CA
S as
(Cle
arin
g hi
stor
y *
Cro
p v
Past
ure
* C
lay
decl
ine)
.
Car
bon
Gai
ns In
dex
A
nnua
l Rai
nfal
l (m
m)
Eigh
t equ
al a
reas
cla
sses
M
CA
S an
nual
rain
fall
data
laye
r
Se
ason
ality
of r
ainf
all (
prop
ortio
n of
yea
r whe
n m
ost r
ain
falls
) Th
ree
clas
ses b
ased
on
dom
inan
t rai
nfal
l se
ason
(sum
mer
or w
inte
r dom
inan
t or
equi
seas
onal
)
MC
AS
clim
ate
zone
s dat
a la
yer
Er
osiv
ity (m
m)
Thre
e cl
asse
s of e
rosi
vity
M
CA
S er
osiv
ity d
ata
laye
r
R
ain
avai
labi
lity
Two
clas
s ass
essm
ent o
f the
ava
ilabi
lity
of ra
in
to p
lant
s thr
ough
out t
he y
ear
Gen
erat
ed w
ithin
MC
AS
usin
g a
two
way
ta
ble
betw
een
Seas
onal
ity a
nd E
rosi
vity
Ef
fect
ive
rain
(mm
) Th
ree
clas
ses o
f equ
al a
rea
that
def
ine
the
effe
ctiv
enes
s of r
ainf
all f
or p
rodu
cing
pla
nt d
ry
mat
ter
Cal
cula
ted
with
in M
CA
S as
(Ann
ual r
ain
* R
ain
avai
labi
lity)
28
Iden
tific
atio
n of
are
as w
ithin
Aus
tralia
with
the
pote
ntia
l to
enha
nce
soil
carb
on c
onte
nt
La
nd u
se fo
r res
idue
pre
ssur
e Tw
o cl
ass m
ap to
show
are
as w
ith si
gnifi
cant
bi
omas
s rem
oval
. M
CA
S A
LUM
land
use
dat
a la
yer l
and
uses
that
rem
ove
sign
ifica
nt b
iom
ass
sele
cted
St
ocki
ng ra
te
Thre
e cl
ass d
ata
laye
r bas
ed o
n st
ocki
ng ra
te
Dat
a la
yer w
as p
rovi
ded
by T
ed G
riffin
.
R
esid
ue p
ress
ure
Thre
e cl
ass d
ata
laye
r com
bini
ng la
nd u
se a
nd
stoc
king
rate
. C
alcu
late
d w
ithin
MC
AS
by a
ddin
g La
ndus
e re
mov
al to
2*(
Stoc
king
ra
te){
resi
due
pres
sure
}
C
arbo
n ga
ins i
ndex
Fi
ve c
lass
dat
a la
yer w
ith c
lass
es se
para
ted
by
a co
nsta
nt m
agni
tude
.. C
alcu
late
d w
ithin
MC
AS
as (E
ffec
tive
rain
+ R
esid
ue p
ress
ure)
.
Car
bon
Loss
es In
dex
Pr
evio
us la
ndus
e Tw
o cl
ass d
ata
laye
r. M
CA
S A
LUM
land
use
dat
a la
yer w
ith
the
“Cro
ppin
g” a
rea
diff
eren
tiate
d fr
om
all o
ther
land
use
s.
Cla
y pr
otec
tion
Thre
e cl
ass d
ata
laye
r. D
eriv
ed fr
om A
SRIS
and
Atla
s dat
a w
ith
clas
ses w
ere
defin
ed a
s fol
low
s: C
lass
1 –
cl
ay c
onte
nt <
10%
, Cla
ss 2
– c
lay
cont
ent
10-2
5% a
nd >
45%
, Cla
ss 3
– c
lay
cont
ent
25-4
5%.
Fe
rros
ols
Two
clas
s dat
a la
yer i
dent
ifyin
g Fe
rros
ols
sepa
rate
ly fr
om a
ll ot
her s
oil t
ypes
. Th
e di
strib
utio
n of
Aus
tralia
n Fe
rros
ols
was
cre
ated
from
an
Atla
s ASC
ord
er
laye
r and
impo
rted
into
MC
AS
from
A
rcG
IS.
Iden
tific
atio
n of
are
as w
ithin
Aus
tralia
with
the
pote
ntia
l to
enha
nce
soil
carb
on c
onte
nt
29
C
lay
and
min
eral
pro
tect
ion
Thre
e cl
ass d
ata
laye
r. C
reat
ed in
MC
AS
as a
two
way
tabl
e be
twee
n th
e C
lay
prot
ectio
n an
d Fe
rros
ols
data
laye
rs.
A
vaila
bilit
y of
wat
er
Four
cla
ss d
ata
laye
r Th
e Pr
esco
tt in
dex
was
use
d to
def
ine
wat
er a
vaila
bilty
usi
ng a
dat
a la
yer f
rom
Te
d G
riffin
. Fo
ur c
lass
es w
ere
crea
ted
(<0.
34, 0
.34-
0.82
, 0.8
2-1.
4 an
d >1
.4).
Te
mpe
ratu
re
Four
cla
ss d
ata
laye
r A
mea
n an
nual
tem
pera
ture
dat
a la
yer w
as
prov
ided
by
Dav
id Ja
cqui
er.
Four
cla
sses
w
ere
crea
ted
(<10
°C, 1
0-15
°C, 1
5-20
°C
, >20
°C).
M
icro
bial
act
ivity
Si
x cl
ass d
ata
laye
r Th
e Pr
esco
tt in
dex
and
tem
pera
ture
dat
a la
yers
wer
e m
ultip
lied
toge
ther
and
the
resu
ltant
laye
r div
ided
into
four
cla
sses
.
C
arbo
n lo
sses
inde
x Fi
ve c
lass
dat
a la
yer
Cal
cula
ted
in M
CA
S as
(mic
robi
al
aciti
vity
/4 +
Cla
y an
d m
iner
al p
rote
ctio
n +
Prev
ious
land
use)
.
Pote
ntia
l Cap
abili
ty In
dex
Thre
e cl
ass d
ata
laye
r def
inin
g th
e lo
catio
ns
whe
re th
e hi
ghes
t pot
entia
l to
enha
nce
soil
carb
on e
xist
s.
The
pote
ntia
l for
enh
anci
ng so
il ca
rbon
w
as c
alcu
late
d in
MC
AS
as (C
apac
ity
Inde
x *2
) + (C
arbo
n G
ains
Inde
x +
Car
bon
Loss
es In
dex)
.