Identification of areas within Australia with the

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.

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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

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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.


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


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:


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.


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


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.


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;


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.

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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.


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


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.


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


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


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.


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


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


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.


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


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.


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].


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)

.

Documents

Identification of areas within Australia with the