SOIL CARBON RESEARCH PROGRAM: PROJECT 11
SOIL ORGANIC CARBON BALANCES IN TASMANIAN AGRICULTURAL SYSTEMS
A collaborative project supported by the Climate Change Reduction Program of the Australian Department of
Agriculture, Fisheries and Forestry and the Grains Research and Development Corporation involving staff and
contributions from CSIRO, University of Western Australia, Department of Agriculture and Food of Western
Australia, Victorian Department of Primary Industries, Murray Catchment Management Authority, Department
of Environment and Natural Resources of South Australia, Queensland Government, University of New
England, New South Wales Department of Primary Industries, The University of Tasmania, and The Tasmanian
Institute of Agricultural Research.
SECTION C – FINAL RESEARCH REPORT
Project title: Soil Organic Carbon Balances in Tasmanian Agricultural Systems
Lead organisation and partner organisations: Tasmanian Institute of Agriculture/School of Agricultural
Science (TIA) and University of Tasmania
Project team: Richard Doyle, Garth Oliver, Mark Downie, William Cotching, Ross Corkrey, Eve White and
Jocelyn Parry-Jones
Primary contact and contact details
Dr Richard Doyle
Tasmanian Institute of Agricultural/School of Agricultural Science (TIA)
University of Tasmania
Hobart
Tasmania
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Table of Contents
EXECUTIVE SUMMARY 4 BACKGROUND 6 Tasmania soil carbon balances 8 METHODOLOGY 11 Sample site selection 11 Sampling procedure 12 Temporal Ferrosol study 12 SCaRP sampling protocol 13 Laboratory methods 13 Statistical methods 14 RESULTS OF THE PROJECT 15 Background to dataset 15 Unadjusted means - carbon 17 Carbon Stocks 0 – 0.3 m 24 Results for adjusted data – land use affects 25 Adjusted soil carbon – land management effects 28 Soil bulk density 30 Total nitrogen 33 Carbon to nitrogen ratio 35 MIR predictions 36 Dermosols – A case study 37 Temporal Ferrosol results – 1997 to 2010 39 DISCUSSION OF THE RESULTS OF THE PROJECT Soil carbon (TOC and Stocks) 43 Total nitrogen (TN) 44 Soil bulk density (BD) 45 Temporal Ferrosol study – 1997 to 2010 45 LIST OF FINDINGS OF THE PROJECT 46 FUTURE RESEARCH NEEDS 47 PUBLICATIONS 48 PLAIN ENGLISH SUMMARY 49 REFERENCES 52 ACKNOWLEDGEMENTS 55 43 2
APPENDICES 56 Appendix 1: Explanation of land management variables 56 Appendix 2: Tables outlining explanatory models 58 Appendix 3: Significance of land use effects on carbon with adjusted P values 60 Appendix 4: Sample numbering key 61 Appendix 5: Unadjusted mean data 62 Appendix 6: Land Management Survey Form 67 Appendix 7: Farmer Fact Sheet 69 3
EXECUTIVE SUMMARY
The aims of this project were;
1. To determine the levels of soil organic carbon and nitrogen in different soil types on
agricultural land used for both pasture and cropping in Tasmania.
2. To determine the effect of agricultural land management practices and environmental
influences on both soil organic carbon and nitrogen in different soil orders in
Tasmania.
3. To contribute data about soil organic carbon in Tasmania to the national SCaRP
project in order to calibrate a more economical and efficient method of measuring soil
organic carbon using mid infrared (MIR) spectroscopy.
4. To measure soil bulk density to allow calculations of carbon stocks on a mass per
hectare basis.
5. To physically fractionate the soil carbon and examine difference these fractions and
how it changes over time.
There were two aspects to meeting these aims – 1) establishing a baseline in soil carbon,
total nitrogen and soil bulk density in key soil orders and major land use types of Tasmania
for SCaRP and 2) expanding on an existing temporal study of soil carbon and carbon size
fractions in one soil order (Ferrosols).
The Tasmanian component of the SCaRP investigated organic carbon, total nitrogen and
bulk density levels in four key soil orders: dark cracking clay soils (Vertosol), reddish iron
oxide rich soils (Ferrosol), structured uniform to gradational textured soils (Dermosol) and
strong texture contrast soils (Chromosol/Sodosol/Kurosol). For each soil order the samples
have been further split into two land uses, “Cropping” and “Pasture”. For each of these land
uses, land management data such as tillage, fertiliser application, crop type, periods of
fallow etc., were collected from farmers to determine impacts on soil carbon and nitrogen
levels. Environmental data such as rainfall total and timing, temperature, altitude and aspect
were included in the monitoring and analysis.
The temporal component of the work involved further sampling and analysis of a 25-year
long term study on the soil order “Ferrosol” in northern Tasmania initiated by Sparrow et al.
(1999) which was also sampled in 2005 and 2010. The purpose of this study was to
determine both the change in Total Organic Carbon (TOC) levels in pasture and cropping
sites with time and to examine how two carbon fractions (> and < 50 µM) were affected by
land use.
The results indicate that rainfall, soil order and land use were all strong explanatory variables
for differences in TOC, soil carbon “Stock”, total nitrogen (TN) and bulk density (BD) in
Tasmania. Cropping sites had 29 - 36% less carbon in surface soils than pasture sites as
well as high bulk densities. The difference between cropping and pasture was most
pronounced in the top 0.1 m. The clay rich soils, Ferrosols and Vertosols, contained the
greatest carbon Stocks.
For all soil orders there is a significant difference in TOC between land uses at depths of 0 –
0.1 m and 0.1 – 0.2 m at P <0.001. For carbon Stocks there is a significant difference at 0 –
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0.1 m depth to P<0.001, but no significance at depths below this. The highest carbon Stocks
to 0.3 m depth occurs in Ferrosols at 150 Mg ha-1 under “Pasture” and 125 Mg ha-1 under
“Cropping”, while the lowest are under Texture Contrast (Kurosols/Sodosols/Chromosols)
soils at 65 Mg ha-1 and 58 Mg ha-1 respectively.
The highest TOC values also occurred in Ferrosols at 0 – 0.1 m depth, at 73 mg g-1 under
“Pasture” and 47 mg g-1 under “Cropping”, with the lowest at the same depth under Texture
Contrast soils with 33 mg g-1 and 23 mg g-1 respectively.
Measuring Carbon as a Stock in Mg ha-1 can mask the true carbon story as land use affects
soil bulk density. The carbon Stocks as measured in the 0 – 0.3 m depth can be significantly
influenced by compaction causing increased bulk density of the soil. Also any simple or
quick field assessment of soil carbon will be hampered by the need to take adequate bulk
density measurements needed to calculate stocks.
Land management effects on soil carbon were minor when compared to rainfall, soil order
and land use. The land management variables that had the most effect on carbon were the
number of years cropped, and the number of years of conventional tillage.
The 13-year temporal study of Ferrosol carbon showed that:
1. Total organic carbon (TOC) in surface horizons decreased with increasing years of
prior cultivation, i.e., cropping intensity.
2. Total organic carbon levels did not decrease significantly between 1997 and 2010,
suggesting that after many years of agricultural management equilibrium in carbon
levels has been reached.
3. Sites which had been predominantly used for pasture had higher organic carbon
levels than cropped sites.
4. Soil carbon associated with two soil particle size fractions (> and < 50 M) were
uniformly affected by land use.
There are limited options for farmers to sequester soil carbon in productive agricultural land.
Of the factors that most influence soil carbon only the land use options and type and
regularity of cultivation selected by the primary producer. The other dominating factors such
as rainfall and soil order are exogenic parameters beyond the control of most farmers.
Management practices can however have an impact. Increasing pasture leys, strategic
irrigation and minimum till in cropping management may increase SOC or minimise further
loss.
This project was designed to look at soil carbon and total nitrogen levels in particular soil
orders that have been influenced by a variety of management criteria over the last ten years.
It was not a measure of the soil carbon stocks at the paddock level or for the most part
changes in carbon levels over time.
Further work is required to quantify the effects of management practices on soil carbon
balances, and to identify the potential for sequestration or further carbon loss in particular
soils. The possibility of soil carbon plateau at either the degradation or sequestration ends of
the spectrum need to be identified before any carbon based compensation is considered.
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BACKGROUND
The world’s soils have been both a source and a sink for atmospheric carbon since
terrestrial life on earth began. Agriculture has driven the balance of this dynamic toward soil
being a net source of atmospheric carbon. The challenge we face today is to reverse this
trend in the face of a growing population, forecast to grow to over 9 billion by 2050 that will
make increasing demands on agricultural production. Most agricultural soils have lost 30%
to 75% of their antecedent soil organic carbon (SOC) pool or 30 to 40 t C ha-1 (Lal et al.
2007). This equates to about 30% of the post industrial revolution emissions of CO2. It has
been estimated that by 2050 world demand for cereals alone will need to increase by 50%
(Lal 2010). This will put additional pressure on the Soil Organic Carbon (SOC) pools of
agricultural soils requiring better soil management strategies to retain soil carbon. It is
imperative that agricultural management practices are identified that either minimise or
reverse this trend.
One of the challenges to adjusting land management is the need for accurate measurement
and monitoring of levels in SOC under agricultural production. In order to achieve this a
number of technical obstacles need to be overcome. Firstly the existing SOC levels of a
particular soil need to be assessed, and the potential of these soils to increase SOC. The
difficulty in doing this arises from the high cost of SOC analysis using existing techniques in
landscapes where SOC levels and soil type may vary across relatively small spatial
increments. In addition to these analytical issues a sampling protocol that is representative
of a particular spatial unit (paddock, farm and region) needs to be implemented.
The type of land use has a major impact on how SOC levels change (Guo and Gifford 2002).
Traditional cultivation methods cause a decline in SOC levels from the virgin or pastoral
state. High input pastoral uses lead to soils with relatively high SOC levels (Cotching 2012).
Even within these land use groups SOC levels fluctuate across climatic gradients of mean
annual temperature and rainfall. For better carbon farming it is important to know the range
of possible SOC levels that might be reasonably expected on each soil type under a
particular land use and management practice.
Soil Organic Matter (SOM) comprises a large range of carbon compounds mixed with
mineral particles. Most soil carbon originates from plant debris that is progressively broken
down by a range of organisms and incorporated into the mineral soil. Thus soil organic
carbon is in a constant state of flux. High levels of inert charcoal can result in a soil with high
TOC but with low levels of the functioning carbon needed for good soil health. Decaying
organic matter is initially incorporated into the soil forming transient and then more persistent
carbon pools (Baldock and Skjemstad 1999; Christensen 2001; Skjemstad et al. 2004).
Protection of SOM against decomposition occurs in two main ways; either as part of the soil
mineral matrix or by biochemical recalcitrance (Christensen 2001). Mineral protection occurs
by soil aggregates physically protecting the SOM or it may be adsorbed on the reactive
surfaces of mineral particles (Kaiser et al. 2002; Schulten and Leinweber 2000) with both
processes inhibiting microbial access to the organic substrate (Lützow et al. 2006).
Carbon exists in soils in a variety of forms and a range of classification systems exist. In
some soils there is a mineral carbon component present as calcium and/or magnesium
carbonate which is removed by acid leaching prior to determination of the total organic pool
of carbon in soils (TOC). Charcoal (char-C) is the carbon which has accumulated due to fires
6
on and in the soil and this exists as a predominately inert pool. In Australia size separation
has been used to divide the organic pool in to two parts. The first is defined as Particulate
Organic Carbon (POC) that is considered to represent a more transient and active pool of
decomposing coarser (>50 µm) organic material. The finer fraction (<50 µm) is considered to
be a more stable carbon pool and is called the humate fraction (HUM). It is postulated to be
more protected from oxidation either by chemical structure or the soil matrix of silt and clay
sized particles and aggregates (Hassink et al. 1997).
Active SOC simulation models such as the RothC model predict changes that would be
associated with environmental and management criteria. They rely on the concept of TOC
pools decomposing at various rates. In the case of the RothC model the pools are
represented by resistant plant material RPM, inert organic matter IOM and humate pools.
These pools are conceptual and cannot be physically or chemically measured. Skjemstad
and Janik (1996) proposed a measurable system whereby the soil was physically
fractionated at 53 µm giving a POC (=RPM) fraction >53 µm and a HUM and charcoal C
(=IOM) fraction<53 µm. Each of these fractions could be individually measured. The large
number of samples collected for SCaRP, and analysed empirically, will assist in such future
modelling.
It has long been established that rainfall and temperature can have an effect on SOC levels.
The basic trend being that warm-moist soils have less TOC than cool-dry or cold-wet soils
(Potter et al. 2007). A lack of adequate soil moisture results in the reduction of the biomass
required to sequester carbon into the soil. There is evidence to suggest that the timing of
rainfall can also effect SOC levels (Aanderud et al. 2010).
Temperature has been found to be a major contributing factor to SOC levels (Potter et al.
2007). Low temperatures can result in the accumulation of undecomposed organic matter on
the surface. Potter (2007) also found that SOC increased at cooler temperatures but as
temperatures increased under the same tillage practice more crop residue returns were
required to maintain SOC levels. Zimmermann et al.(2012) found that both SOC levels in
mineral soil and the thickness of the O horizon increased with altitude. How global warming
will affect soil carbon is another matter of research interest.
Carbon sequestration is dependent on biomass accumulation that results originally from
photosynthesising plant material through to the diverse ecology of microbial and biochemical
decomposition. It has generally been accepted that C sequestration is also dependent on N
inputs. Nitrogen application ought to promote humus formation by reducing the C:N ratio of
carbonaceous crop residues. The lack of available N can reduce the amount of biomass
produced and reduce the amount of crop waste and roots converted to humus (Lal 2001).
However a number of long term studies have shown N application to have no effect on SOC
accumulation (Halvorson et al. 2002; López-Bellido et al. 2010). Both these studies
demonstrated that tillage and crop rotation had far more significant impacts. This was further
backed up by Khan (2007) who demonstrated that NPK applications on identical soil, crop,
climate and tillage had no impact on SOC after 50 years.
A great deal of information has been generated over the years relating management
practices with soil conditions. Much less has been achieved relating management practice
with the various carbon pools. Agricultural land management practices have a major impact
on the levels of soil carbon. Data about current soil carbon levels in different agricultural
7
lands is needed to help better understand how farming practices affect SOC levels. Previous
studies have shown that variations in tillage for example can have significant effects on TOC
levels (Ussiri and Lal 2009). Implementation of no till or controlled traffic, stubble retention,
grass leys etc. may all make a contribution to slowing down or reversing the loss of SOC to
the atmosphere.
SOC can also be restored in soils by the adoption of management systems that add
biomass to the soil, cause minimal soil disturbance, conserve soil and water, improve soil
structure, enhance microbial activity and species diversity and strengthen mechanisms of
nutrient cycling (Batjes 2004). Such practices include conservation tillage, decrease in
fallow periods, use of cover crops, change from monoculture to crop rotation systems and
increasing primary production by means of irrigation, fertiliser, manure (Jarecki and Lal
2003) and the application of lime (Batjes 2004).
Tasmania soil carbon balances
The present project forms one part of a national study examining the balances in soil carbon
levels in a range of soils each with a range of different land uses. Tasmania as a relatively
small island has a number of advantages when looking at SOC levels. While mean annual
temperatures are relatively uniform across agricultural districts, rainfall, lithology and
landforms types vary widely. All twelve Australian soil orders are found in Tasmania.
The Tasmanian SCaRP project was designed to answer or expand our understanding of the
following challenges and questions.
1) How to establish a robust sampling protocol that would be representative of a
particular soil type under a given land use?
2) How to take a current snapshot of the SOC pool as it exists in specific soil orders
under particular land uses and management practises?
3) Determine how current management practices effect SOC levels.
4) Determine if MIR analyses and partial least squares regressions (MIR PLS) of the
derived data act as a reliable and economical technique for measuring SOC levels.
5) Determine the efficacy of MIR to measure the carbon levels of the various SOC
fractions.
6) Determine the relevance of parameters such as clay content and C:N ratios on SOC
levels.
7) Determine how has soil carbon changed in Ferrosols first sampled in 1997?
The Tasmanian project has focussed on determining the differences in TOC levels in soil
orders as classified under the Australian Soil Classification (Isbell 1996). The project
recognised that different soil types have the potential to store and protect varying levels of
SOC (Cotching 2012; Tan et al. 2004; Verheijen et al. 2005). Sample sites were selected to
represent four key agricultural soil types: dark, reactive, cracking clay soils (Vertosols),
reddish brown, iron oxide rich, clayey soils (Ferrosols), strong texture contrast soils
(Chromosols/Sodosols/Kurosols), and other, structured soils (Dermosols).
For each soil order identified the sites were further selected across two key land use types
defined as dominantly “Cropping” and dominantly “Pasture”. In order to ensure a robust
sample set at least 25 sites for each soil type land use combination were selected. Wherever
8
possible sites were pre-selected using published soil and topographic maps and then
verified in the field. This reduced the possibility of biasing the sampling by relying on
engaged or proactive farmer groups for site selection.
The Australian Soil Classification System (ASC) is a hierarchical national system designed
to suit Australian conditions (Isbell 1996). It does not necessarily differentiate a soils capacity
to store or protect carbon; although it recognises carbon content in one soil order
(Organosols) and is used to characterise certain classes of horizon e.g., humus and peaty
topsoils. It has been suggested that the physical properties of soil such as clay content and
level of aggregation are better indicators of soil carbon storage potential (Saidy et al. 2012).
For this reason field texture was assessed on all samples taken including an estimate of clay
content. This clay content estimate will be analysed to determine any correlation to SOC.
Dermosols which have developed across different soil parent materials (SPM) were used to
look at the impact of lithology and likely soil mineralogy on SOC within this single soil order.
Dermosol are defined as soils with a structured B2 horizon and lacking strong texture
contrast between A and B horizons (Isbell 1996). In Tasmania it is possible to sample
Dermosol formed from igneous parent materials (both Tertiary basalt and Jurassic dolerite),
Tertiary sediments (mostly clays) and those derived from Quaternary alluvium (mostly
clayey). MIR data sets calibrated to these soil parent materials may provide more accurate
predictions of TOC and its fractions although delays with this work will mean the detailed
relationships will need to be reported elsewhere.
Land use diversity in Tasmania
The Tasmanian component of the project tried to incorporate the main agricultural
management practises on the most common soil types used for agricultural production.
Cropping is undertaken on much smaller land areas than the more typical broad acre grain,
cotton and vegetable operations found on mainland Australia. Land use and management is
characterised by a greater variety of crops and more diversity in crop rotations. There is also
a mixture of irrigated and dry land operations. Recent, current and projected irrigation
developments and a trend of low wool prices have seen increases in the areas of land
opened up to both irrigated and arable uses. The cooler mean annual soil temperatures and
generally higher mean annual rainfalls in Tasmania result in higher TOC levels within each
soil order (Cotching 2012).
All farmers were surveyed for records of the previous ten years of land use and
management and data are presented below (Figure 1). Based on the surveyed data a
cropping intensity value was applied to each site to examine the intensity of the cropping,
this is a figure ranging from 0 (continuously pasture) to 1 (continuously cropped) for the
period that we were able to obtain management data for. This value is referred to as the
‘Crop Ratio’.
The SCaRP project has also generated data on total nitrogen (TN) levels and its impact of
SOC levels. This was aided by the fact the University of Tasmania (UTAS) oxidative
combustion elemental analyser (EA) allows for the simultaneous analysis of TN and TOC
and hence C:N ratios can be calculated. In addition the land use histories give a record of
fertiliser application for the last ten years. These data sets allow an analysis of the impact of
total and applied nitrogen under varying soil types and land management systems on SOC.
9
Frequency of land management, including both Cropping and Pasture managements
3.2% 2.7%
6.1%
Perennial Pasture
Crop Cereal
8.2%
35.2%
Mixed Pasture
Annual Pasture
8.9%
Crop Veg Other
Crop Root Veg
Fallow
9.8%
Crop Perennial
26.0%
Figure 1
Land use types, based on farmers ten year records, for the Tasmanian SCaRP
project.
Mid-Infrared (MIR) data and associated partial least square regression estimates
The traditional analytical methods used to quantify and fractionate soils are time consuming
and expensive. One of the aims of this project was to generate empirical data to facilitate the
development of a more efficient and economical methods of measuring soil organic carbon
using a dispersive Mid-Infrared (MIR) spectrometer. The MIR traces were analysed with
partial least squares regression techniques against measured data to provide predictions.
Diffuse reflectance spectroscopy provides an opportunity to monitor soil properties at a level
of intensity that would be economically prohibitive using conventional methods of soil
analysis. Dispersive MIR spectroscopy is rapid, inexpensive and non-destructive.
Furthermore, a single spectrum allows for simultaneous characterisation and estimation of a
diverse range of soil properties such as pH, CEC, silt, clay, exchangeable calcium (Ca)
potassium (K) and aluminium (Al ) (Viscarra Rossel et al. 2006). This in addition to estimates
of the carbon fractions; TOC, POC, HUM, carbonates and char-C (Janik et al. 2007).
The predictive accuracy of dispersive MIR spectra is highly dependent on the quality and
applicability of the calibration data sets used. For this reason it is the intent of this project to
generate regional calibration sets that will enhance the accuracy of the MIR spectra. The full
extent of this analysis will be reported elsewhere by Baldock et al (Report 2 - this issue).
The SCaRP project has established an extensive MIR calibration set based on empirical
measurements of thousands of different soils. These calibrations can later be refined to
specific regions or soil orders to improve the predictability of soil properties such as TOC,
the POM and HUM fractions, carbonates and charcoal.
10
METHODOLOGY
Sample site selection
A total of 291 sites were used for sampling for the Tasmanian component of SCaRP. These
sites were selected across the key agricultural regions of Tasmania (Figure 2).
Figure 2
Location of sampled sites in Tasmania by soil order and land use.
Potential sample sites were derived by a desktop study of topographic, geological and soil
maps, rather than using existing farmers groups or TIA networks in an attempt to give a
more representative cross section of land use and management across the agricultural
regions of Tasmania. Where possible the existing Department of Primary Industries, Parks,
Water and Environment (DPIPWE) ‘Soil Condition Evaluation And Monitoring’ (SCEAM)
sites were included as SCaRP sites. The purpose of which was to embed SCaRP data into
the ongoing temporal SCEAM project. This resulted in a total of 61 complementary sites with
an additional four sites being nearby to SCEAM sites which were not available for sampling
11
due to crop rotation, inappropriate land use history or because the sites were too steep and
rocky. A total of 21 sites sampled for the SCaRP project were at locations that had
previously been used by Sparrow et al. (1999) for an investigation of soil carbon in Ferrosols
soils over time.
Although we endeavoured to capture a representative of the four targeted soil orders across
agricultural areas of Tasmania, some limitations on sampling dictated where sampling was
undertaken. Sample sites were selected at generally more flat, uniform and less rocky areas,
given that steep rocky slopes are difficult to access and sample. Flatter more uniform sites,
are often of lower rock content, and this should be taken into account if analysing corrections
for rocks and gravels. The regional sample site labelling code can be found in Appendix 4.
Sampling procedure
The national SCaRP project has chosen to sample soils cores to 0.3 m on a 25 × 25 m grid
basis at each soil type by land use combination. At least 25 soil order by land use
combinations were required to provide sufficient representative samples for statistical
analysis. By sampling from a relatively small area within each paddock potential errors due
to sampling across soil types was minimised. However the carbon levels across the entire
paddock selected remains unknown; although it might be estimated if the areas of each soil
type are know. As SCaRP was not set up to generate baseline carbon contents on paddocks
or farms, the issue of representativeness of the sampling site to the paddock was not as
important as the “within soil order” information. It only mattered that the sampling site was a
random representation of the management by soil type combination under investigation
(SCaRP methods). Baseline assessments of carbon contents at the paddock and farm level
provide problems that need to be overcome in the future. It may however suffice to know
whether SOC pools are trending up or down for that sampled part of the paddock.
Temporal Ferrosol study
The study re-sampled and also re-analysed 25 sites on Ferrosols which were previously
sampled as part of a long-term study in northern Tasmania. These sites were initially
sampled in 1997 and then again in both 2005 and 2010 (Sparrow et al. 1999; Sparrow et al.
2011). The initial study looked at four land uses described as “intermittent” and “continuous”
cropping and “high input” and “low input” pasture. The importance of further sampling these
sites is to have some indication of temporal changes. A similar study of continuously
cropped Ferrosols in subtropical Queensland found that declining SOC levels eventually
plateau out at a level that reduced the productivity of these soils (Bell et al. 1995).
In addition to TOC levels the archived 1997 soils and the 2010 samples were fractionated
into two size fractions <50µm (HUM) and >50µm (POM). The purpose of which was to look
at the change over time of the apparently transient POM and recalcitrant HUM pools. This
was possible because the 1997 soil samples had been archived and were made available
for this project by Drs Sparrow and Cotching (TIA-UTAS).
The original 1997 and 2005 samples were analysed for TOC using the Walkley-Black
method. For the sake of continuity the 2010 samples were also analysed with this method.
The SCaRP project however used the oxidative combustion method. As a result both the
1997 and 2010 samples (in storage) were also re-analysed using oxidative combustion.
Although restricted to Ferrosols this allows for the comparison of the different SOC analytical
methods.
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Sampling methods - temporal Ferrosol study
This research forms a part of a long term study, in which the SOC content of Tasmanian Red
Ferrosols was measured to determine the extent of management related change. Composite
sampling was conducted at two depths (0 – 0.15 m and 0.15 – 0.30 m) over a total of 25
sites in Northern Tasmania, in 1997, 2005 and 2010. However unfortunately the 2005
samples were not archived.
The 25 sites, for which we retained archived samples collected in 1997, were re-sampled in
2010. In this study individual sites were allocated to groups based on the dominant
agricultural treatment received between 1997 and 2010. The two main land use categories
were ‘continuously cropped’ and ‘predominantly pasture’. Sub-samples of composite cores
were taken at 0 – 0.15 m and 0.15 – 0.30 m using a 100 mm Jarret auger at 20 points within
a grid. The soil was then processed as per standard SCaRP methodology. Bulk density was
taken at three separate sites within the grid using 60 mm length x 60 mm diameter rings.
Oxidative combustion was used for the determination of TOC and TN using a Perkin Elmer
CHN-S 2400 analyser. Walkley-Black was used for TOC determination on the original
samples and for confirmation on the 2010 samples. Fractionation of these samples was
performed prior to the finalisation of the SCaRP procedure. Several steps were involved in
the process of dividing each of the 100 samples into the two size fractions of POC (>50 µm)
and HUM (<50 µm). Each sample was ‘disaggregated’ by adding 20 g of soil and 90 ml of 5
g/L sodium hexametaphosphate solution to a 250 ml plastic container. The container was
then placed on its side and shaken horizontally at 180 – 200 rpm for 12 – 16 hours by use of
a Gio Gyrotory ® Shaker. Fractionation at 50 µm was carried out as per the SCaRP protocol.
The fractions were then dried at 40oC, reground and analysed for TOC using oxidative
combustion.
SCaRP sampling protocol
The details of the sampling methodology are outlined in the SCaRP methods section
(Sanderman, chapter 1 - this volume). The Tasmanian project differed slightly in that two
different sample acquisition techniques were employed. On the soils with compact clayey
subsoils, such as Vertosols and Texture Contrast soils, a truck mounted pneumatically
assisted hydraulic push tube system was predominantly employed, whereas on the better
structured more friable soils such as Ferrosols and Dermosols a 100 mm Jarret hand auger
was predominantly used. In the case of the push tubes the extracted core was checked for
compaction and the individual depth cores sliced to length using a modified mortice block.
Samples acquired using the Jarret auger were taken at each of the depth intervals, mixed,
quartered and sub-sampled into a composite sample bag.
Laboratory methods
With the exception of MIR and NMR the laboratories at UTAS performed all the processing,
fractionation, analysis and freeze drying. This was undertaken in accordance with the
standard SCaRP analytical protocols. TOC and TN analysis was performed using a Perkin
Elmer CHN-S 2400 analyser and a Thermo Finnigan EA 1112 Series Flash Elemental
Analyser.
Air-dried, sieved and homogenised soil samples were later subjected to texture testing.
Approximately 50 g of dried soil was wetted to field capacity, moulded into a ribbon and the
field texture assessed for clay content (McDonald et al. 1990). Estimated clay contents were
recorded increments of five percent. Higher clay soils >55% were attributed the arbitrary
13
value of 75%. It should be noted that 0.1 m interval homogenised field textures may not be
reflective of natural horizon field textures.
Statistical methods
The nature of any possible spatial variation was evaluated by the use of variograms and the
distribution of residuals by the use of quantile-quantile plots, all within PROC MIXED, SAS
version 9.2. The alternative spatial covariance structures examined were no spatial
correlation or the spherical, exponential, Gaussian, linear, log-linear, power, and Mateern
structures. Of these the exponential proved most successful. Further examination by the use
of the Akaike Information Criterion (AIC) indicated that an isotropic version was preferred to
an anisotropic version of the exponential structure. The residuals plots indicated that the
TOC, carbon stock, bulk density, TN, and C:N results at each depth required a log
transformation to obtain Gaussian residuals distributions. This was done prior to model
development.
Model development proceeded in two steps. First, explanatory models for the data were
developed for each sampling depth separately using PROC GLMSELECT, SAS version 9.2,
to conduct stepwise selection to obtain models with the minimum corrected AIC. All models
examined involving the main effects and also interactions of the categorical factors; soil
order, land use, and aspect, with the continuous variables for April – October and November
– March over both the previous five years and over the previous 30 years for rainfall,
temperature and vapour pressure deficit, plus the continuous variables of elevation, slope,
topographic wetness index, focal median of slope (300 m), focal range of elevation, plan
curvature and profile curvature, and also the soil management data. The goodness-of-fit
statistics, r2 and root mean square deviation (RMSD) were generated for the models. The r2
provides a measured of the degree of fit. The RMSD quantifies the degree of fit by indicating
the variation of the model from the observed data, in the same units as the observed logtransformed data. In the second stage a mixed modelling approach was used to allow for
spatial variation and which used the Satterthwaite degrees of freedom correction. The fitted
models were used to construct adjusted outcome variables by subtracting away the
continuous effect of 30-year mean annual rainfall. Mixed models were fitted to the adjusted
outcomes and least square means calculated. Where appropriate, this was followed by
multiple comparison adjustments using Tukey's method at the 95% confidence level to
compare the means by soil order, land use and depth effects. The dependent variables TOC,
Carbon stock, TN, BD, and C:N were analysed at each depth separately and for all land
uses together with all the explanatory variables. Ratios of the means of the adjusted
outcomes were plotted relative to the pasture land use. The grid sampling methodology used
in this research means that type 1 errors may be increased and so only effects that were
significant at P < 0.01 were accepted in the initial models. Tables of explanatory models are
found in Appendix 2.
Examination and presentation of unadjusted data in the graphs provide error bars set as +/one standard error from means. Average is a synonym for the arithmetic mean.
The unadjusted means data for TOC, Stock, BD, TN and C:N at all three depths are
summarised in tables in Appendix 5. These represent all eight land use by soil order
combinations and for all 14 sampling regions.
14
RESULTS OF THE PROJECT
Background to dataset
A total of 123 sites that were sampled were classified as ‘Pasture’. Of these Pasture sites,
104 had been permanently pasture (for the time period that management history was
provided for), and 19 sites had a cropping intensity value between 0.1 and <0.3 (Figure 3). A
total of 147 sites were classified as predominantly ‘Cropping’. Of these cropping sites 58 had
been continuously cropped (for the period that management history was provided for), and
89 sites had a mixed cropping history, with cropping intensity values ranging from 0.4 to 0.9
(Figure 3). Five sites did not fit into either criteria as they had cropping intensity values
between 0.3 and <0.4, these sites were labelled ‘Intermittent’ so that they would not be
considered when comparing Cropping with Pasture sites. For analysis of Cropping and
Pasture sites together, these data points were included, and are useful in making a linear
dataset of cropping intensity (Crop Ratio). The cropping intensity is illustrated below in
Figure 3, this shows the spread of land use of the sampled sites with a clusters of 0
(permanent pasture) and 1 (permanent cropping) and a fairly even spread of data points in
between. A total of 11 sites were sampled for which no land use management information
was provided.
Cropping Intensity
Crop ratio, 1 = site cropped 100% of the years
1
0.9
0.8
Cropping
0.7
0.6
0.5
0.4
0.3
Pasture
0.2
0.1
0
0
50
100
150
200
250
300
Cumulative number of sample sites
Figure 3
Cropping intensity of sites, measured as ‘Crop Ratio’ where 1 = continuous
cropping and 0 = continuous pasture in the last ten years.
It should be noted that to observe trends in the effect of the crop grown, our crop definitions
were condensed from 15 categories to four (see Appendix 1). These four categories were
15
selected by combining crops that were assumed to have similar effects on soil carbon, for
example ‘Crop Cereal’ contains Cereals, Poppies, Oilseed, Pasture Seed and Cereal Hay.
Whilst ‘Crop Root Vegetables’ contains both potatoes and bulb crops. A further breakdown
of land uses is visible in Appendix 1.
Table 1 below shows that cereal crops were dominant crop type, and perennial pasture was
the dominant pasture type. Rotational grazing is the dominant grazing management, 95% of
pasture sites had either rotational or set grazing, and only 5% mixed the two grazing
management practises.
Irrigation frequency is presented in Table 1 and shows irrigation to be common on cropping
sites and uncommon on pasture sites, however no data on irrigation volume or timing was
collected.
Table 1
Breakdown of land management practises for type of crop, type of pasture, type
of grazing management and irrigation frequency.
61%
Grazing
Management
Rotational
Grazing
Vegetables Other
19%
Set Stocking
Root Vegetables
14%
Crop Category
Percentage of cropping years
Cereal
Perennial
Percentage of total years
69%
31%
6%
Pasture Category
Percentage of pasture years
Perennial
68%
Mixed
21%
Annual
11%
Crop/Pasture/Fallow
Grazing
Management
Always
Rotational
Always Set
Mixed
Set/Rotational
Percentage of total years
Irrigation
Frequency
Percentage of sites
67%
28%
5%
Percentage of years irrigated
Cropping sites
Perennial Pasture
35%
Never
12%
Crop Cereal
26%
Intermittent
52%
Mixed Pasture
10%
Always
36%
Annual Pasture
9%
Crop Vegetables Other
8%
Percentage of years irrigated
Pasture sites
Crop Root Vegetables
6%
Never
75%
Fallow
3%
Intermittent
13%
Crop Perennial
3%
Always
12%
Table 2 shows the average of farmer recorded fertiliser applications and indicates cropping
sites are receiving 3 – 3.5 times the fertiliser inputs of the pasture sites. Approximately 20%
of fertiliser data was recorded as ‘unknown’.
Table 2
Mean Annual Fertiliser Applications.
Mean Annual Fertiliser Applications
16
N
P
K
Pasture
15.2
12.8
13.5
Cropping
44.5
41.5
39.6
Initial modelling showed stubble management to have no significant difference on soil
carbon, TN or bulk density, consequently it was dropped from further statistical modelling.
Rainfall data by soil order is illustrated in Table 3. Ferrosols have the highest average of
mean annual rainfalls while the Texture Contrast soils and Vertosols have the lowest
rainfalls.
Table 3
Rainfall data (mm) for the four soil orders analysed.
Rainfall (mm) by
Soil Order
Dermosol
Mean
Annual
637
Median
Annual
580
Standard
Deviation
179
Minimum
on Record
413
Maximum
on Record
1352
Ferrosol
902
892
165
495
1289
Texture Contrast
564
544
104
421
1027
Vertosol
562
540
107
413
921
Unadjusted means - carbon
To analyse the effect of land use on soil carbon, all soil orders were first combined together,
and later separated into the respective soil orders. The two land use groupings are Pasture
and Cropping as defined in the methods. The data presented in this section are unadjusted
means with +/- one standard error shown on all graphs. The log normal adjusted results will
be discussed later along with further analysis of statistical variables, affects and valid models.
Given that rainfall is a strongly correlated explanatory variable for soil carbon, a bias could
be expected between soil orders towards soil orders found in higher rainfall areas (Table 3).
Most of the data discussed in this section is summarised in Table 4.
The two different measures of carbon (TOC and Stocks) are shown for each depth in Figure
4. Mean TOC for all Tasmanian soils 0 – 0.1 m depth is 53 mg g-1 under pasture and 37 mg
g-1 under cropping, while Stock values were 48 Mg ha-1 and 37 Mg ha-1 respectively.
17
Carbon Results, all soil types combined TOC (mg g‐1) and Stock (Mg ha‐1)
50
40
30
20
TOC
Stock
TOC
0‐0.1m
Figure 4
Stock
0.1‐0.2m
TOC
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
0
Cropping
10
Pasture
mg g‐1 for TOC, t ha‐1 for Stock
60
Stock
0.2‐0.3m
TOC (mg g-1) and soil carbon Stocks (Mg ha-1) for each 0.1 m depth interval to
0.3 m.
Figure 4 shows a trend of a greater difference in both TOC and Stock in 0 – 0.1 m depth
between the two land use categories. There are differences in TOC at 0.1 – 0.2 m depth as
a result of land use, but not at 0.2 – 0.3 m depth. There are negligible differences in Stocks
below 0.1 m. The P- values for significance are calculated on data normalised for rainfall on
the log scale and indicate that for all soil types there is a significant difference in TOC
between land uses at depths of 0 – 0.1 m and 0.1 – 0.2 m at P <0.001, and at 0.2 – 0.3 m at
P <0.05. For Carbon Stocks there is a significant difference at 0 – 0.1 m depth to P<0.001,
but no significance for depths 0.1 – 0.2 m or 0.2 – 0.3 m.
It is clear that “Cropped” sites have less soil carbon than “Pasture” sites as TOC, especially
at 0 – 0.1 m depth but less so at 0.2 – 0.3 m depth. However when converted to stocks, the
difference between the two land uses is reduced. This is a result of the higher bulk density of
cropping sites reducing the differences. This suggests when measuring carbon stocks that
while cropping sites may have depleted carbon on a mass/mass basis, this loss is masked
by the fact that with higher bulk density more soil is being sampled for a given depth interval.
Figures 5 below show both the TOC and C Stocks in each soil order on the surface 0 – 0.1
m. TOC is reported as mg g-1 and Stock is reported as Mg ha-1 per unit area per 0.1 m depth
interval, both values are on the same Y-axis.
18
80
70
60
50
40
30
20
TOC
Stock
Dermosol
Figure 5
TOC
Stock
Ferrosol
TOC
Stock
Texture contrast
TOC
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
0
Cropping
10
Pasture
mg C/ g soil for TOC, Mg/ha per 0.1m depth for Stock
TOC and Stock means at 0 ‐ 0.1 m depth
90
Stock
Vertosol
Mean carbon content expressed as both TOC and Stock, for four soil types and
two land use categories, at 0 – 0.1 m sampling depth.
Figures 5 and 6 again show the difference between land uses is more pronounced in TOC
data than Stock data in each land use by soil order pairing. This is again a reflection of the
generally higher bulk density in Cropped sites compared with Pasture sites. Each soil order
behaves differently with respects to land use, with the differences between Cropping and
Pasture expressed as a percentage difference in Figure 6.
The highest TOC values at 0 – 0.1 m occurred in Ferrosols at 73 mg g-1 under Pasture and
47 mg g-1 under Cropping, with the lowest under Texture Contrast soils with 33 mg g-1 and
23 mg g-1 respectively.
19
40%
Carbon difference between Pasture and Cropping sites at 0 ‐ 0.1 m depth
35%
35%
35%
36%
33%
29%
27%
30%
25%
20%
20%
20%
15%
10%
5%
0%
TOC
Stock
Dermosol
Figure 6
TOC
Stock
Ferrosol
TOC
Stock
Texture contrast
TOC
Stock
Vertosol
Percentage change in both TOC and Stock between Cropping and Pasture sites
in 0 – 0.1 m sampling depth.
Figure 6 shows that Vertosols have the highest percentage change in TOC between land
use categories at 36%, this is very similar to Dermosols and Ferrosols at 35%. Texture
Contrast soils have the least difference in TOC between land use categories at 29%. The
lower percentage change in Texture Contrast soils could reflect lower impact tillage practises
and more ley phases within cropping rotations.
The carbon Stock figures are quite different to the TOC figures, which reflect the change of
bulk density between land uses being different from one soil order to the next. For Ferrosols
the change in Stock is not much lower than the change in TOC (33% compared with 35%).
This indicates that the Ferrosols soils do not increase in bulk density with cropping to the
same degree as Vertosols which have a greater difference between TOC and Stock
percentage changes with land use (36% compared with 20%).
20
TOC and Stock means at 0.1 ‐ 0.2 m depth
50
40
30
20
TOC
Stock
TOC
Dermosol
TOC
Stock
TOC
Texture contrast
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Stock
Ferrosol
Stock
Vertosol
TOC and Stock measures of carbon at 0.1 – 0.2 m depth for all land use and soil
order combinations.
TOC and Stocks at 0.2 ‐ 0.3 m depth
40
35
30
25
20
15
10
TOC
Stock
Dermosol
TOC
Stock
Ferrosol
TOC
Stock
Texture contrast
TOC
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Cropping
0
Pasture
5
Pasture
TOC as mg g‐1, Stock as Mg ha‐1
Figure 7
Figure 8
Pasture
Cropping
Pasture
Cropping
Cropping
0
Pasture
10
Pasture
TOC as mg g‐1, Stock as Mg ha‐1
60
Stock
Vertosol
TOC and Stock measures of carbon at 0.2 – 0.3 m depth for all land use and soil
order combinations.
Figures 5, 7 and 8 indicate that the difference between pasture and cropping is strongest in
the 0 – 0.1 m depth, and this difference is reduced with depth. The levels of significance for
are seen in the adjusted data which with a P<0.001 in the 0 – 0.1 m depth for Dermosol,
Ferrosols and Vertosols (for TOC), and Dermosol and Ferrosols (for Stock). However there
21
were no significant differences between land uses for either TOC or Stock at 0.1 – 0.2 m or
0.2 – 0.3 m depths using adjusted data.
The following two charts, Figures 9 and 10, show the percentage change between means of
TOC and Stock for the two land uses. These charts can be compared with Figure 6
illustrated the 0 – 0.1 m depth.
Carbon difference between Pasture and Cropping sites at 0.1 ‐ 0.2 m depth
40%
35%
30%
25%
20%
19%
15%
20%
18%
11%
11%
11%
10%
5%
0%
TOC
Stock
Dermosol
Figure 9
3%
0%
TOC
Stock
Ferrosol
TOC
Stock
Texture contrast
TOC
Stock
Vertosol
Percentage change between TOC and Stock means for Pasture and Cropping
land uses at 0.1 – 0.2 m depth.
Carbon difference between Pasture and Cropping sites at 0.2 ‐ 0.3 m depth
40%
35%
30%
25%
24%
20%
17%
15%
12%
9%
10%
4%
5%
‐4%
0%
TOC
‐5%
16%
Stock
Dermosol
TOC
Stock
Ferrosol
0%
TOC
Stock
Texture contrast
TOC
Stock
Vertosol
Figure 10 Percentage change between means for Pasture and Cropping land uses at 0.2 –
0.3 m depth.
22
Table 4
Surface soil properties (means) for Tasmanian soil orders under different land uses
Soil order
Land use
Dermosol
Ferrosol
Texture Contrast
Vertosol
No. of sites
Depth
(m)
Pasture
46
Cropping
47
Pasture
27
Cropping
54
Pasture
27
Cropping
29
Pasture
26
Cropping
26
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
TOC
-1
(mg g )
mean
std dev
49.7
18.1
31.1
14.3
21.0
11.2
32.3
13.2
25.2
10.9
16.0
6.3
72.2
21.0
52.2
14.6
34.7
11.2
47.0
12.5
42.7
10.2
33.2
9.5
32.5
12.7
16.1
7.1
10.3
4.9
23.0
6.2
14.4
6.2
8.6
3.8
58.9
19.8
36.1
11.7
25.6
9.2
37.9
13.1
28.8
12.0
21.5
10.1
C stock
-1
(Mg ha )
mean
std dev
48.1
13.1
34.6
12.5
24.0
9.6
35.9
12.0
31.1
11.6
21.3
7.5
69.6
14.6
52.3
12.9
35.7
9.6
46.8
10.9
46.2
9.3
36.9
9.5
34.4
11.0
20.4
7.8
13.8
5.7
27.5
7.8
20.0
8.4
12.6
5.7
50.4
11.7
35.4
8.9
26.3
7.2
39.8
11.8
34.1
12.9
26.3
11.3
1
C stock gc
-1
(Mg C ha )
mean
std dev
46.1
12.4
33.1
12.1
22.8
9.4
33.5
11.6
29.3
11.1
20.0
7.2
66.4
14.2
50.0
12.2
34.0
9.2
44.8
10.5
44.3
9.1
35.4
9.2
33.2
10.6
19.2
7.8
12.9
5.8
26.7
7.5
19.2
8.5
11.8
6.1
47.9
10.7
33.8
8.1
25.1
6.8
38.3
11.1
32.9
12.6
25.2
10.8
Total Nitrogen
-1
(mg g )
mean
std dev
4.02
1.47
2.48
1.18
1.65
0.89
2.67
0.95
2.06
0.72
1.33
0.45
5.89
1.77
3.99
1.11
2.50
0.89
3.50
0.93
3.16
0.82
2.40
0.79
2.66
0.81
1.28
0.45
0.86
0.25
1.95
0.54
1.23
0.56
0.77
0.31
4.99
1.79
2.99
1.02
2.02
0.65
3.14
1.13
2.36
1.00
1.68
0.70
Bulk density
-3
(Mg m )
mean
std dev
1.01
0.17
1.16
0.17
1.21
0.20
1.14
0.17
1.26
0.16
1.36
0.20
0.99
0.15
1.03
0.15
1.08
0.18
1.01
0.15
1.10
0.16
1.13
0.15
1.09
0.18
1.29
0.21
1.38
0.23
1.20
0.13
1.39
0.14
1.48
0.14
0.90
0.16
1.01
0.17
1.07
0.18
1.07
0.11
1.21
0.15
1.26
0.16
Carbon Stocks 0 – 0.3 m
The highest carbon Stocks to 0.3 m depth occurs in Ferrosols at 150 Mg ha-1 under Pasture
and 125 Mg ha-1 under Cropping, while the lowest are under Texture Contrast soils at 65 Mg
ha-1 and 58 Mg ha-1 respectively (table 5). All carbon stocks to 0.3 m depth for each soil
order and land use combination and percentage difference between land uses are shown in
Table 5. The effect of soil order on carbon stocks is evident as all four soil orders are quite
different from each other.
Table 5
Carbon stocks to 0.3 m for each soil order and land use.
Land use
Soil order
Dermosol
Ferrosol
Texture
Contrast
Vertosol
Tonnes C/ha
Number of
% difference
mean
sites sampled
Pasture
102
46
Cropping
83
47
Pasture
150
27
Cropping
125
54
Pasture
65
27
Cropping
58
29
Pasture
107
26
Cropping
96
26
cropping/pasture
19%
17%
12%
10%
Carbon Stocks by Region
160
140
Carbon (t ha‐1)
120
147
135 131 130
124
105
93
100
80
91
87
84
81
79
77
63
60
40
20
0
Figure 11 Soil carbon Stocks 0 – 0.3 m by region.
Figure 11 shows the total carbon stocks to 0.3 m depth by region. Figure 12 shows a similar
trend with the carbon expressed as TOC, and includes all three depths.
Both Figures 11 and 12 illustrate higher carbon levels in the higher rainfall areas of;
Devonport, Burnie, North East and Deloraine, whilst the drier regions of Brighton, Derwent
Valley, Clarence have lower carbon values. Mean annual rainfall is plotted on Figure 12 to
illustrate this. The tables of explanatory models in Appendix 2 (Table A1) has rainfall as one
of the top two explanatory variables for TOC and C Stocks at all three depths and also for
the total stocks to 0.3 m.
TOC at three depth intervals by region with rainfall
0.0‐0.1m
7.0
0.1‐0.2m
6.0
0.2‐0.3m
Rainfall
5.0
TOC as %C
1000
800
600
4.0
3.0
400
2.0
200
1.0
Average annual mean rainfall of sites (mm)
1200
8.0
0
0.0
Figure 12 TOC by region illustrating all three sampling depths.
Results for adjusted data – land use affects
When the TOC values were adjusted to mean annual 30 year rainfall, it was found that land
use significantly (P < 0.0001) explained the variation in TOC when all soil orders and all
depths were analysed together. Rainfall adjusted TOC values at 0 - 0.1 m depth were
significantly (P < 0.01) greater under pasture than under cropping in Dermosols, Ferrosols
and Vertosols but not in Texture Contrast soils (Figure 13). There were no significant
difference between pasture and cropped sites on individual soil orders at 0.1 – 0.2 m or 0.2 –
0.3 m depths.
25
Figure 13 TOC values, data adjusted for rainfall on a log scale. Box plots with a dark
central line indicating the adjusted median, the central box indicates the middle
50% of the data, and the whiskers extend to 1.5 times the inter-quartile range.
Note 10 = 0 – 0.1 m, 20 = 0.1 – 0.2 m, 30 = 0.2 – 0.3 m.
26
Figure 14 Carbon Stocks at three depths adjusted for rainfall on a log scale. Box plots with
a dark central line indicating the adjusted median, the central box indicates the
middle 50% of the data, and the whiskers extend to 1.5 times the inter-quartile
range. Note 10 = 0 – 0.1 m, 20 = 0.1 – 0.2 m, 30 = 0.2 – 0.3 m.
Figures 13 and 14 highlight the difference between land use pairings for each soil order, are
more pronounced when measuring TOC rather than carbon Stocks.
27
Adjusted soil carbon – land management effects
Variables that explained variation in the data at lower levels of significance than soil order or
rainfall were identified by those variables that contributed to explanatory models with a level
of significance of P < 0.05 rather than P < 0.01, see table of explanatory variables in
Appendix 2 (Table A2). The two additional variables of ‘Crop Ratio’ and ‘Till 2’ were identified
as contributing to TOC and TN models indicating that the number of years cropped (crop
ratio) and the number of years of conventional tillage (Till 2) also contributed to determining
carbon content but to a lesser extent than either soil order or rainfall.
Further investigation of the influence of tillage intensity was undertaken by analysing data
from cropped sites after grouping according to whether a site had had five or more years of
no or minimum tillage as well as two or less years of conventional tillage in the past ten
years compared to sites that had had five or more years of conventional tillage in the past
ten years. There was a trend for cropped sites under conventional tillage to have lower TOC
concentrations than sites under no or minimum tillage but the differences were significant (P
< 0.05) only in Ferrosols at 0.2 – 0.3 m depth (Table 6). There was also a significant
difference in annual rainfall between these groups of sites that may have accounted for the
differences. C:N ratio was greater under conventional tillage than under no or minimum
tillage but the differences were significant only on Ferrosols. BD was significantly lower at all
depths sampled under no or minimum tillage than under conventional tillage on Dermosols
and Ferrosols but not on Texture Contrast soils or Vertosols. Degradation of Tasmanian
Texture Contrast soils has been associated with paddocks which had grown potatoes and
would have had deeper and more rigorous cultivation than for other crops, which adds
weight to the view that cropping rotation and associated soil management practices are
critical for sustainable management of these soils (Cotching et al. 2001). Vertosols are
thought to be resilient as they can redevelop good soil structure after only a few cycles of
wetting and drying (Wenke and Grant 1994). It would appear that the type of tillage can
influence TOC and C:N ratio but a more appropriately designed study is required to test this
hypothesis more fully.
The relevance of other variables (perennial crops, aspect, temperature, wetness index, and
slope) in explaining data variation, particularly at lower soil depths, is uncertain as each of
these variables appears only once in the series of models generated and so they are not
consistently explaining variations in the data across a number of soil properties. We are not
discounting the influence that these variables may have, but they would appear not to be
dominant in explaining variation in soil properties.
28
Table 6
Effect of tillage intensity on soil properties at different depths of cropped sites.
5 or more years no till or minimum tillage
n
Soil order
0-0.1
m
0.1-0.2
m
0.2-0.3
m
30 year annual
rainfall (mm)
41.8
45.4
24.7
36.5
28.7
43.7
13.3
29
18.8
42.3*
7.9
24.7
858
1024*
538
481*
1.0*
0.9*
1.3
1.1
1.2*
0.9*
1.5
1.1
1.2*
1.0*
1.5
1.2
5 or more years conventional tillage
N
0-0.1
m
0.1-0.2
m
0.2-0.3
m
30 year annual
rainfall (mm)
25
33
6
10
28.6
47.3
19
39.8
23.7
41.1
12
29.1
15.3
31.8
8.7
23
576
858
602
629
1.2
1.0
1.2
1.0
1.3
1.1
1.4
1.2
1.4
1.2
1.5
1.3
-1
Total organic carbon (mg g )
Dermosol
Ferrosol
Texture Contrast
Vertosol
7
4
5
5
-3
Bulk density (Mg m )
Dermosol
Ferrosol
Texture Contrast
Vertosol
C:N
Dermosol
Ferrosol
Texture Contrast
Vertosol
N fertiliser
applied (kg/ha/yr)
11.5
12.3*
12.1
11.5
11.7
12.6*
12.7
11.5
11.7
13.4*
12.5
12
41
nr
35
8
12.3
13.8
13.4
12.2
* Difference between tillage categories by T-test at P < 0.05
12.3
13.9
12.8
12.6
12.6
14.6
12.2
12.7
N fertiliser
applied (kg/ha/yr)
38
67
26
26
Soil bulk density
Figure 15 shows the bulk density (BD) of the four soil orders examined. The Texture
Contrast soils had the highest BD ranging from 1.09 to 1.48 with depth. The Ferrosols the
lowest ranging from 0.99 to 1.13 with depth. All soil types displayed an increase in BD with
depth, and a higher bulk density for Cropping sites than for Pasture sites. The difference
between BD between Cropping and Pasture land uses is given in Figure 16.
Bulk Density ‐ Dermosol
1.6
1.4
1.2
1.14
1.26
1.16
1.21
Bulk Density ‐ Ferrosol
1.36
1.6
1.4
1.2
1.01
0.8
0.6
0.4
0.4
0.2
0.2
0
0‐10
10‐20
Pasture Cropping Pasture Cropping Pasture Cropping
20‐30
0‐10
10‐20
Depth (cm) and land use
1.2
20‐30
Depth (cm) and land use
Bulk Density ‐ Texture Contrast
1.29 1.39 1.38
1.2
1.09
1.48
1
Bulk Density ‐ Vertosol
1.60
1.21
1.40
1.20
1.00
1.07 1.01
1.26
1.07
0.90
g cm‐3
g cm‐3
1.08 1.13
0
Pasture Cropping Pasture Cropping Pasture Cropping
1.4
1.1
0.8
0.6
1.6
1.03
1
g cm‐3
g cm‐3
1
0.99 1.01
0.8
0.80
0.6
0.60
0.4
0.40
0.2
0.20
0.00
0
Pasture Cropping Pasture Cropping Pasture Cropping
Pasture Cropping Pasture Cropping Pasture Cropping
0‐10
10‐20
0‐10
20‐30
10‐20
20‐30
Depth (cm) and land use
Depth (cm) and land use
Figure 15 Bulk Density for all three sample depths and for both land uses. The four charts
represent the four different soil orders. Error bars represent one Standard Error.
Percent Change in Bulk Density with Landuse and Depth
18%
16% 17%
16%
16%
15%
14%
12%
10%
11%
11%
9%
8%
8%
7%
6%
6%
4%
4%
2%
2%
0%
0‐10 10‐20 20‐30 0‐10 10‐20 20‐30 0‐10 10‐20 20‐30 0‐10 10‐20 20‐30
Dermosol
Figure 16
Ferrosol
Texture Contrast
Vertosol
Percentage increase in bulk density from Pasture to Cropping.
All cropping sites are consistently of higher BD than pasture sites. Ferrosols have the least
difference in bulk density between land uses, and Vertosols have the highest. This suggests
that Ferrosols, which typically have strong aggregation, are more resilient to compaction
when cultivated. Vertosols are the most susceptible to compaction and/or are able to build
structure under pasture. There seems to be no obvious trends with depth.
All values for the different soil orders are similar to those previously published for Sodosols
(Cotching et al. 2001), Dermosols (Cotching et al. 2002a), Ferrosols (Sparrow et al. 1999)
and Vertosols (Cotching et al. 2002b). Mean BD and TOC were significantly (P < 0.001)
negatively correlated (r2 = -0.69; P < 0.01 for all individual samples) as organic matter
generally lowers the mean density of soils. BD increased with increasing depth (Figure 15)
due to less organic matter and consolidation.
The differences in BD between pasture and cropped sites within particular soil orders after
data was adjusted for rainfall were significant only in Vertosols at 0 - 0.1 m depth (P =
0.0012 on log adjusted data) and at 0.2 - 0.3 m depth (P = 0.0032 on log adjusted data)
indicating that Vertosols are the most susceptible to compaction under cropping. Texture
Contrast soils have the greatest mean BD at each depth of the soil orders sampled. Figure
17 also illustrates a trend of increasing BD with the land use, and also of BD increasing with
depth.
BD exceeded 1.4 and 1.5 Mg m3 at 0 - 0.1 m and 0.1 - 0.2 m respectively in only a few
Dermosols and Texture Contrast soils under both pasture and cropping (data not shown).
These densities indicate that compaction has occurred at these sites and root growth is likely
to be inhibited. Soil compaction can occur under intensive stock grazing due to treading
damage (Houlbrooke et al. 2011) and under cropping (Cotching et al. 2002a) which has
implications for pasture production, soil hydrology and nutrient movement. BD in Ferrosols
and Vertosols rarely exceeded 1.2 and 1.3 Mg m-3 at 0 - 0.1 m and 0.1 - 0.2 m depths
respectively indicating that these soils are not severely compacted and root growth is not
likely to be inhibited.
31
Figure 17 Bulk density for each soil order by land use and depth adjusted for rainfall on a
log scale. Box plots with a dark central line indicating the adjusted median, the
central box indicates the middle 50% of the data, and the whiskers extend to 1.5
times the inter-quartile range. Note 10 = 0 – 0.1 m, 20 = 0.1 – 0.2 m, 30 = 0.2 –
0.3 m.
32
Figure 18 Ratio of means of bulk density between Cropping and Pasture land uses. The
higher the value above 1 the greater the relative bulk density of cropped soils.
Figure 18 shows the ratio of means for bulk density between cropping and pasture sites.
This illustrates that Vertosols have the largest difference between bulk density between the
two land uses, and Ferrosols the least. This indicates Vertosols are clearly the most
deleteriously affected by cropping. There seems to be no obvious trend with depth.
Vertosols are a swelling and reactive clay soil and due to their high plasticity, at lower water
contents, may be more prone to soil compaction. Also under pasture the additional root
mass and organic matter can contribute to better structure and consequently lower bulk
density.
Total nitrogen
Total nitrogen (TN) levels showed similar trends to carbon, with respects to changes with
depth and between soil types. The TN exhibited a noticeable change between the two land
uses at the upper depth, tending to a negligible difference at depth. Figures 19 – 21 illustrate
TN for each depth.
33
Total Nitrogen
7.0
5.9
6.0
5.0
4.0
4.0
4.0
3.5
3.1
3.0
2.7
2.7
3.2
2.5
2.5 2.4
2.4
2.1
2.0
2.0
3.0
2.0
1.7
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
Pasture
Cropping
0.9 0.8
1.0
0.0
1.7
1.3
1.3 1.2
Pasture
Total nitrogn (mg g‐1)
5.0
Dermosol Ferrosol Texture Vertosol Dermosol Ferrosol Texture Vertosol Dermosol Ferrosol Texture Vertosol
Contrast
Contrast
Contrast
0‐10
10‐20
20‐30
Figure 19 Total nitrogen levels (unadjusted raw means) for each soil order, land use and
sampling depth.
TN was found to be strongly correlated with TOC (r2 = 0.952 to 0.998; P < 0.01) and the
influences of land use and depth are as described earlier for TOC (refer to Carbon Results).
TN concentrations are similar to previously published values in Tasmania except for
Dermosols which are lower in this study than those reported by Cotching et al. (2002a). This
difference may be due to the sites in this study being collected from greater area in
Tasmania than in the previous study. The SCaRP study covered greater numbers of lower
rainfall areas than Cotching et al. (2002a) especially for Dermosols, this is likely to have
contributed to lower TN in Dermosols. Soil order, land use and sampling depth were all
significant (P < 0.01) in explaining the variance in TN values, the rainfall adjusted data is
illustrated below in Figure 20.
34
Figure 20 Total nitrogen, adjusted for rainfall on a log scale. Box plots with a dark central
line indicating the adjusted median, the central box indicates the middle 50% of
the data, and the whiskers extend to 1.5 times the inter-quartile range. Note 10 =
0 – 0.1 m, 20 = 0.1 – 0.2 m, 30 = 0.2 – 0.3 m.
Carbon to nitrogen ratio
There were no significant differences in C:N ratio due to difference in land use or soil order
as there was a large amount of variability in the data (Figure 21). The only significant
differences in C:N were due to sampling depth under pasture in Ferrosols and Texture
Contrast soils which had significantly (P = 0.0011 and P = 0.0034 respectively) greater C:N
at 0.1 – 0.2 m than at 0.2 – 0.3 m depth. Ferrosols had the greatest increase in C:N on
cropped sites compared to pasture sites but this increase was not significant (P < 0.05).
35
Figure 21 Carbon to Nitrogen ratio, data adjusted for rainfall on a log scale. Box plots with
a dark central line indicating the adjusted median, the central box indicates the
middle 50% of the data, and the whiskers extend to 1.5 times the inter-quartile
range. Note 10 = 0 – 0.1 m, 20 = 0.1 – 0.2 m, 30 = 0.2 – 0.3 m.
MIR predictions
The results of the MIR predictions will be only briefly covered here, a comprehensive
analysis can be found in Baldock et al (Report 2 - this issue). Figure 22 shows MIR
predictions for TOC in all Tasmanian soils with an r2 of 0.852. This was based on a
calibration set of all Tasmanian soils rather than a soil order specific calibration. Of the soil
orders Vertosol was the most accurate with an r2 of 0.886 and Ferrosol the least with an r2of
0.599, refer to Table 7. The r2 values are calculated from a linear line of best fit set at Yintercept of zero.
36
MIR TOC plotted against measured TOC
120
y = 0.9433x
R² = 0.8522
TOC MIR Predicted
100
80
60
40
20
0
0
50
100
150
TOC Measured EA
Figure 22 MIR predicted TOC plotted against measured TOC. For all soil types and all
depths.
Table 7
r2 values calculated in Excel from a linear line of best fit set with Y intercept of
zero.
r2
0.82
0.60
Soil Order
Dermosol
Ferrosol
Texture
Contrast
Vertosol
0.73
0.89
Dermosols – A case study
Soils within the Dermosol order vary significantly throughout Tasmania. Dermosols are
formed on a variety of Soil Parent Materials (SPM) which have variable clay contents and
mineralogy. These variabilities within the soil order makes the Dermosol order a good case
study for soil carbon in Tasmania. Consequently Dermosols form the largest sample data set
(93 sample sites).
Effect of soil parent materials on Dermosols
Figure 23 summarises the carbon stocks for Dermosols formed on different SPM. These
include Tertiary basalt, Jurassic dolerite, Tertiary sediments and Quaternary alluvial deposits.
These figures are for both land uses combined. The chart on the left uses unadjusted data,
and illustrates an obvious difference between the two igneous SPM groups (basalt and
dolerite).
37
120
111
Carbon Stocks (t/ha)
98
89
74
60
40
20
0
Basalt (10) Dolerite (25) Tert + t/b
(23)
Rainfall Adjusted Stocks of Dermosols
10.4
10.1
10.0
100
80
12.0
C Stocks after rainfall adjustments
140
Mean Stocks of Dermosols on differing parent material
9.7
9.9
8.0
6.0
4.0
2.0
0.0
Alluvial (34)
Basalt (10) Dolerite (25) Tertiary (22) Alluvial (33)
Soil Parent Material
Soil Parent Material
Figure 23 Mean carbon stocks of Dermosols formed on different parent materials. Sample
size (n) is in brackets, Tert + t/b is an abbreviation for Tertiary clay deposits plus
soils mapped as formed on Tertiary clays but with obvious basalt influences.
After the carbon stocks have been adjusted for rainfall (chart on the right), the values are
now very similar across all SPM’s. When comparing the two charts in Figure 23 you will
notice that the error bars are reduced after adjusting for rainfall, this suggests that a lot of the
variation could be accounted for with rainfall. Also the highest value on the rainfall adjusted
chart (Alluvial) happens to be the SPM category with the highest ratio of pasture to cropping
sites (see Figure 24), this could account for the higher C stocks in that category.
Dermosols mapped as ‘Cressy soil series’ (Doyle 1993) were identified during sampling as
having three different SPM or substrate groups. These groups include; Tertiary sediments,
Tertiary sediments with basalt influence and Cressy Soil Series underlain by basalt bedrock.
When these three groups were analysed the mean Stocks at 0 – 0.3 m were 88, 89 and 91
Mg ha-1 respectively, and all groups were within one standard error of each other. The fact
that these soils were not significantly different illustrates that this narrow range of SPM or
substrate differences had negligible effect on carbon Stocks within the Cressy soil series.
This also justifies combining all Cressy series soils together in the one group – being Tertiary.
38
160
Carbon Stocks 0 ‐ 0.3 m (Mg ha‐1)
140
Carbon Stocks in Dermosol soils of different SPM and land use
121
107
120
107
96
100
85
75
72
75
80
60
40
20
0
pasture (6) cropping (4) pasture (14) cropping (11) pasture (4) cropping (19) pasture (25) cropping (9)
Basalt
Dolerite
Tert + T/B
Alluvial
Figure 24 Dermosols formed on various parent materials and illustrating land use categories.
Figure 24 shows the carbon Stocks to 0.3 m for Cropping and Pasture land uses for each
SPM class of Dermosol. There are differences are between each land use category with
Pasture always have greater stocks of carbon. The largest difference is stocks between
Pasture and Cropping land uses is found in the Alluvial category with a 30% difference while
the weakest is found in the Doleritic category with only a 4% difference.
Clay content impacts on Dermosols
Field textures undertaken on homogenised soil samples indicate that there is negligible
correlation between field textured clay content and TOC in Dermosols (data not presented).
Graphs of clay content plotted against TOC give r2 values are less than 0.01 at all depths,
indicating little or no relationship.
Clay content data generated by GRDC on 131 0.0 – 0.1 m depth samples also shows poor
correlations. Preliminary analysis undertaken through Excel generated X Y scatter charts
show the strongest relationship to be between percent silt plus percent clay within the
Dermosol order plotted against TOC, this yielded an r2 value of 0.26. R2 values for Ferrosols,
Dermosol and Ferrosol combined, and for rainfall adjusted data all yielded lower r2 values.
Temporal Ferrosol results – 1997 to 2010
At the depth of 0 – 0.15 m there were no significant changes in the mean percentage of TOC
between 1997 and 2010 in either the cropping or pasture land use categories (Figure 25).
TOC decreased in Cropped sites by 10%, while it increased by 1.5% in Pasture site over the
13 year period. There was a significant difference (P< 0.001) between Pasture and Cropping
land uses in TOC levels in 2010 but not 1997 (shown as C%, Figure 25). The mean TOC for
Cropped sites was 4.4 %, whereas the mean for Pasture sites was 6.6 % in 2010. TOC was
thus 33% higher in Pasture than in Cropped Ferrosols at the depth of 0 – 0.15 m in 2010,
and 25% higher in 1997. There were no significant differences in the mean values of TOC
between these categories in either 1997 and 2010 at the depth of 0.15 – 0.30 m. Therefore,
39
the type of management has had a larger influence on TOC in the 0 – 0.15 m depth than the
0.15 – 0.30 m depth.
Figure 25 Mean TOC difference between 1997 and 2010 for continuously cropped and
predominately pasture sites. Error bars = standard deviation of error.
40
Figure 26 Mean carbon (%) change in POC and HUM as proportion of the TOC between
1997 and 2010 for continuously cropped and predominately pasture sites, 0 –
0.15 mm depth.
There were no significant changes in the proportions of POC to HUM between 1997 and
2010, in either land use category (Figure 26).
1.0
0.9
R² = 0.3205
0.8
R² = 0.0012
Proportion of TOC (combustion)
0.7
HUM 0‐150 mm
0.6
HUM 150‐300
mm
POC 0‐150 mm
0.5
0.4
POC 150‐300 mm
0.3
0.2
R² = 0.3205
0.1
R² = 0.0012
0.0
0
10
20
30
Years cropped in past 38
40
Figure 27 Relationship between number of years cropped (out of 38) and proportion of
POC and HUM in TOC.
Figure 27 shows a weak trend in changing ratios of POC to HUM with years cropped at 0 –
0.15 m, and no trend at 0.15 – 0.3 m. The weak trends at 0 – 0.15 m suggests an increase
in the ratio of HUM at the expense of POC as cropping frequency increases. However this is
not significant (Figure 26) and has an r2of 0.32 (Figure 27).
Figure 28 shows a downward trend of % Carbon, measured by Walkley-Black method, in the
upper depth (0 – 0.15 m) with respect to years cropped. This relationship has a strong
r2value of 0.83. Whilst the lower horizon has a very poor relationship between years cropped
and % Carbon, with an r2 value of 0.23.
7
0‐150 mm
150‐300 mm
6
Carbon % (Wakley Black)
5
4
3
R² = 0.8276
2
R² = 0.234
1
0
0
5
10
15
20
25
30
35
40
Years cropped out of past 38
Figure 28 TOC (%) in 2010 at both depths, plotted against the number of years cropped
out of past 38.
42
DISCUSSION OF THE RESULTS OF THE PROJECT
Soil carbon (TOC and Stocks)
Total organic carbon (TOC in mg g-1) values were a more sensitive measure of soil carbon
changes than carbon Stocks (Mg C ha-1). This is because Cropping generally increases soil
bulk density (Mg m-3) while decreasing TOC levels thus masking soil carbon changes as
measured by Stocks.
Cropping sites had on average 35% less TOC than Pasture sites at 0 – 0.1 m depth on both
Dermosols and Ferrosols. This difference was 36% on Vertosols and 29% less on Texture
Contrast grouping of soils (Table 1). The corresponding difference at 0.1 – 0.2 m depth was
18 – 20% for all soil orders except the Texture Contrast group which was only 11%. This
change in TOC is similar to that reported in other Tasmanian studies of 30% on Ferrosols,
39% on Dermosol, 33% on Sodosols and 35% on Vertosols (Cotching et al. 2001; Cotching
et al. 2002a; Cotching et al. 2002c; Sparrow et al. 1999). These differences are less than the
59% reported by (Guo and Gifford 2002)and the 51% reported by (Luo et al. 2010).
The strongest influences on soil organic carbon concentration were soil order and mean
annual rainfall with land use also being a more dominant influence than soil management
variables including the number of crops grown and the type and amount of tillage practiced.
There was a trend for conventional tillage to be associated with lower TOC than with no or
minimum tillage but our results were not significant (P < 0.05). The influence of soil depth
decreased in a similar manner so that TOC was influenced at all depths in all soil orders by
mean annual rainfall but soil management had a greater influence closer to the soil surface.
We found no associations between TOC and other soil management variables including
grazing management, number of long fallows, type of crop grown, or rates and types of
fertilisers applied. We propose the following hierarchy of influence of variables on SOC:
Soil order > mean annual rainfall > land use > cropping frequency > tillage type
Adjusting carbon values for rainfall is a useful mechanism for normalising for climatic
variables to compare carbon amongst soil orders, depths and land uses.
Significantly greater soil bulk densities occurred on Cropped than on Pasture sites and
greater bulk density was also associated with conventional tillage on Dermosols and
Ferrosols rather than with no or minimum tillage. The results indicate that carbon changes
depending on whether the measured value is TOC (% Carbon) or Stocks (t ha-1). Quick
simple assessments of soil carbon will remain elusive if a series of bulk density
measurements are required for each paddock.
Our findings confirm the collective understanding about the variables that influence soil
organic carbon content (Ingram and Fernandes 2001). This being that potential SOC is
determined by inherent soil characteristics of clay content and clay type that are
encapsulated by the soil orders of the Australian Soil Classification, although we found no
relationship to clay content with a single soil order (Dermosols). Organic matter is adsorbed
onto clay surfaces, coated with clay particles or buried inside small pores or aggregates and
is physically protected from decomposition and so the amount of SOC stored in soil tends to
increase with increasing aggregation and clay content. The attainable level of SOC is
determined by the local climate – predominantly annual rainfall. Actual SOC levels are
determined by the type of land use and the soil management practices undertaken. Although
it is not possible to increase the attainable SOC of a given soil, management practices
determine whether or not the attainable storage of SOC in soil is achieved. Farmers can
influence soil organic carbon concentrations more by their choice of land use than their day
to day soil management but even though the influence of management is not as great as
other site inherent variables, farmers are still able to select practices that retain more soil
organic carbon than others. Farmers can retain more carbon by cropping less frequently and
by reducing the amount of tillage during soil preparation as long as these practices are
considered together with economic sustainability.
More frequent annual cropping is likely to result in decreased organic matter inputs as much
of the above ground crop biomass is invariably removed from the site and plants are not
growing all year round. We might have expected ‘Crop Ratio’ and ‘Till 2’ to be correlated but
this was not apparent as r2 = 0.002. This could suggest that frequently cropped sites are not
necessarily sites that are conventionally tilled. The association between the number of years
cropped and TOC on individual soil types was weaker than expected with r2 ≤ -0.10 at 0 –
0.1 m for cropped sites only and r2 < -0.35 for both cropped and pasture sites combined on
each soil order. This suggests that land use categories (Pasture/Cropping) gives a better
explanation of TOC than the linear cropping intensity measurement of ‘Crop Ratio’. These
results contrast with other Tasmanian studies where a significant negative correlation
between SOC and years cropped was reported with r2 = -0.822 for Ferrosols (Sparrow et al.
2011) and r2 = -0.633 for Dermosols (Cotching et al. 2002 b). The poorer correlation found in
this data set could be due to the shorter length of management history record of ten years
compared to the longer 25 year records in other studies, or the data set being dominated by
pasture sites with ten of the last ten years under perennial pasture. TOC takes many years
to adjust to different organic matter inputs (Baldock and Skjemstad 1999; Christensen and
Johnston 1997) and monitoring of TOC over a period of ten years or less appears unlikely to
demonstrate relationships to management history.
Preliminary investigations of the soil order Dermosol indicated neither soil parent material
nor field texture differences influenced TOC, however this requires further investigation. For
unadjusted data, higher TOC levels were observed in Dermosols formed on basalt than
those formed on dolerite. However after the TOC data was normalised for mean rainfall
there was no obvious difference across any SPM. The Dermosol family makes a good case
study to examine the effects of SPM and clay content, as they are an order with highly
variable SPM and clay contents. They are also the order that the most samples were collect
from, with 96 sites. Further work within the Dermosol soil order is ongoing, with Mark Downie
to present an oral poster at the joint ASSSI and NZSSS Soil Science Conference in
December.
Total nitrogen (TN)
Overall TN values closely mirrored TOC values. This is illustrated by fairly uniform C:N ratios
in the range of 10-15. The large difference in mean application rate of nitrogen fertiliser to
pasture and cropped sites (Table 2) might have been expected to result in differences in the
C:N ratio as greater availability of nitrogen in the soil can result in increased microbial
digestion of carbon rich organic material resulting in a reduction in the C:N ratio (Hoyle and
44
Murphy 2006). This effect was not noticed; in fact the data is suggesting the converse in
Ferrosols.
The Cropped Ferrosols had the highest C:N ratio in the surface 0 – 0.1 m yet they receive
the highest N fertiliser applications. Table 6 supports this and shows significantly higher C:N
ratios at all depths for sites with 5-years of conventional vs 5-years of no or minimum tillage.
This data appears converse to the relationship suggested by Hoyle and Murphy (2006). This
relationship may be specific to Ferrosols given they are very well structured and hence have
free draining characteristics. This could lead to short term stimulation of plant growth,
increasing soil carbon, followed by nitrate leaching, consequently reducing the TN and
resulting in a higher C:N ratio.
Nitrogen fertiliser applications were up to three times higher on cropped paddocks than
pasture paddocks. Yet both TN and TOC were markedly reduced in cropping paddocks.
Soil bulk density (BD)
Ferrosols appear more resilient to increases in bulk density (BD) under Cropping vs Pasture
while Vertosols had the greatest differences in BD across the two land uses. This suggests
Vertosols are more prone to compaction, as suggested by greater BD under Cropping, or
that Pasture may be increasing aggregation and hence porosity.
Temporal Ferrosol study – 1997 to 2010
There are three key conclusions that can be drawn from the 13-year temporal study of
Ferrosols. Firstly despite a declining trend TOC levels for Cropping sites and higher levels of
TOC in Pasture soils no significant change was measured in TOC levels between 1997 and
2010 in either land use class. This suggests that soil TOC levels are slow to change once
established in a given management system. Indeed Sparrow et al. (1999) posed the
question of whether TOC in cropped Red Ferrosols in Northern Tasmania had reached or
was approaching equilibrium levels.
Secondly there were no significant changes in the proportion of POC to HUM in either
management system over the thirteen years between measurements, they changed
uniformly. This appears counter to other studies and may be the result of the humate
fraction not being as well protected within the iron oxide-kaolin clay type structural system of
the Red Ferrosols.
Finally it was the upper sampling depth (0 – 0.15 m) which displayed the strongest
relationship between TOC and years cropped (r2 0.83). Whilst the lower depth had only a
very weak relationship (r2 0.23).
45
LIST OF FINDINGS OF THE PROJECT
1) Changes in TOC are generally only significant at 0 – 0.1 cm and so this depth offers
a potential simplification to further measurements or farm monitoring and sampling
for carbon accounting purposes.
2) Cropping compared to pastoral land uses lowers soil carbon by >30% in the top 0.1
m of most soil orders but changes are less significant and <20% lower below this
depth.
3) Changes in total nitrogen strongly mirrored changes in TOC.
4) Land use differences of Cropping vs Pasture appear as the key decision a farmer
can make to increase TOC in Tasmanian environments.
5) Soil order (ASC) has the biggest effect on soil carbon. Ranking impacts on soil
carbon is as follows:
ASC > Rainfall > Land Use > Cropping Frequency > Tillage Type.
6) TOC vs Stock approaches to carbon accounting assessment are important
considerations for government. It seems soil bulk density changes caused by soil
compaction may be masking soil carbon losses in many situations.
7) No significant change in TOC was measurable in established long term productive
Ferrosols despite a thirteen year assessment period (1997 – 2010).
8) A temporal study of Ferrosols indicates there is no change in the ratio of POC and
HUM fractions measured over 13 years (1997 – 2010).
9) Soil order (ASC) is the major variable controlling carbon levels in Tasmanian soils.
This is followed by mean annual rainfall and then land use. Other soil management
affects had the least measurable impact on soil carbon; but key ones indicated
include number of years cropped and the amount of conventional tillage vs minimum
tillage and no tillage, termed “conservation agriculture”.
10) Higher fertiliser nitrogen inputs in cropping soils do not contribute to increased C
stocks.
11) MIR as a method shows significant promise but will need further local calibration to
improve accuracy to detect the slow changes in TOC levels.
12) A temporal study of Ferrosols found at 0 – 0.15 m the number of years cropped (over
a 38 year period) was strongly correlated (r2 0.82) with a decline in TOC. However at
0.15 – 0 .30 m this was very weakly correlated (r2 0.23). This suggests land use
intensity maybe a long term explanatory for C loss in upper soil horizons.
13) Ferrosols are the most resilient, of the four soil orders assessed, to soil compaction
as measured by bulk density increases under cropping, while Vertosols appear most
susceptible.
14) Variations in soil parent material and field texture as assessed within the soil order
Dermosols appears to have little impact on carbon levels. This suggests soil
aggregation may be more significant than clay content per-se.
46
FUTURE RESEARCH NEEDS
The project has identified the following possible research gaps.







There is a need for more regional, appropriately designed, long-term and replicated
controlled trials if we are to understand the multiple variables which affect the
dynamics of soil carbon.
There is a need to establish a temporal study to determine how quickly “Cropped”
soils return to the higher TOC levels typically found under “Pasture”. This experiment
ought to be undertaken on each of the major soil orders.
Temporal studies require archival of collected samples. The value of such sample
archives was shown by the data set generated from Ferrosol samples collected in
1997.
Further work on the effects of soil mineralogy and clay content are needed to better
understand soil type impacts on carbon storage.
Although SPM is contributing little to carbon stocks in Dermosols in Tasmania, its
influence on the bulk density differences due to land use between the high clay
Ferrosol and Vertosol soils illustrates an effect that clay mineralogy has on soil
structure, and would suggest an indirect influence on Carbon stocks.
MIR calibrations based on specific soil orders or soil characteristics may be needed
to improve Carbon measurements.
In addition the 1997 samples could be re-analysed using the Walkley-Black method
to determine the extent of TOC degradation during storage.
47
PUBLICATIONS
Cotching, B. (2011) Soil Carbon Research in Tasmania TFGA Circular Head engaged
farmers 35 attendees Forum.
Oliver, G., Doyle, R., White, E. and Downie, M. (2011) An overview of the Tasmanian
component of the SCaRP project: Soil organic carbon balances in Tasmanian
Agricultural Systems. Academic and industry representatives 65 attendees
Parry-Jones, J. (2010) The effect of agricultural land use on the soil carbon fractions of Red
Ferrosols in North West Tasmania. Honours thesis. School of Agricultural
Science, University of Tasmania.
Parry-Jones, J., Oliver, G., White, E., Doyle, R., Cotching, B. and Sparrow, L. (2011) The
effect of agricultural land use on the soil carbon fractions of Red Ferrosols in
North West Tasmania Poster presented at the National Climate Change
Research Program for Primary Industries, Melbourne, 15-17 February 2011.
Scandrett, J., Oliver, G., Doyle, R. and White, E. (2010) Agricultural land use and soil carbon
in Tasmania. Poster presented at The 19th World Congress of Soil Science,
Brisbane 1-6 August 2010.
Scandrett, J. (2009) Soil carbon dynamics under different land uses in Tasmania. Honours
thesis. School of Agricultural Science, University of Tasmania.
Sparrow, L.A., Cotching, W.E., Parry-Jones, J., Oliver, G., White, E., Doyle, R. (2011)
Changes in carbon and soil fertility in agricultural soils in Tasmania, Australia.
Presented at the 12th International Symposium on Soil and Plant Analysis, June
2011, Crete.
48
PLAIN ENGLISH SUMMARY
Project Details
Project Title: Soil Organic Carbon Balances in Tasmanian Agricultural
GRDC Project Systems
Number:Primary CSA00019
Contact:Organisation: Richard Doyle
Phone: Tasmanian Institute of Agricultural Science (TIAR)
Fax: 03 62262621
Email: 03062262642
[email protected]
Objectives
1. To determine current levels of soil organic carbon in
different soil types on agricultural land used for pasture
and cropping.
2. To determine the effect of agricultural land management
practices and environmental influences on soil organic
carbon in different soil types.
3. To contribute data about soil organic carbon in Tasmania
to the national SCaRP project in order to calibrate a more
economical and efficient method of measuring soil organic
carbon using mid infrared (MIR) spectroscopy.
Background
Soils hold the potential for storing carbon and as such could help
to mitigate against the effects of climate change attributed to
carbon dioxide levels in the atmosphere. Agricultural land use and
management practices are thought to have an impact on soil
carbon levels. By acquiring data about current (2010-11) soil
carbon levels in a range of key soil types used for different
agriculture activities we can gain an understanding of how farming
practices, soil type and other environmental factors affect soil
organic carbon levels. Furthermore, the data will be used to
enhance calibration of a mid infrared system (MIR), which has the
potential to be a more efficient and economical method of
measuring soil carbon than the methods that are currently
available.
Research
There are two aspects to the Tasmanian component of the
SCaRP study.
The first aspect of the work involved investigating organic carbon
levels in a range of soil types throughout the state. The samples
came from four key soil groups: dark, cracking clay soils
(Vertosols), iron oxide rich soils (Ferrosols), strong texture
contrast soils (Chromosols/Sodosols/Kurosols) and other well
structured soils (Dermsols). For each soil order/group the samples
49
were further split into two land uses, Cropping and Pasture. For
each of these land uses, 10-year land management records such
as tillage, fertiliser application, crop type, periods of fallow etc
were collected to determine impacts on soil carbon levels.
Environmental data such as rainfall total and timing, temperature,
altitude and aspect were included in the monitoring.
Secondly component involved 25 long-term (13 years) field sites
on Red Ferrosols in northern Tasmania which were re-sampled.
This sampling contributed to a long term study initiated by TIAUTAS in 1997 and re-sampled in 2005 and 2010. The purpose of
this study was to determine not only the change in TOC levels in
pasture and cropping sites but also which carbon pools,either
Particulate Organic Carbon (POC) and/or Humus (HUM), are most
affected by land use.
Outcomes
Soil order, rainfall and land use were all strong explanatory
variables for differences in TOC, soil carbon stock, total nitrogen
(TN) and bulk density (BD) in Tasmania. Cropping sites had 29 36% less carbon in surface soils than pasture sites, they also had
2 – 16% greater bulk densities. The difference between cropping
and pasture was most pronounced in the top 0.1 m. Clay rich soils
(Ferrosols and Vertosols) contained the greatest carbon stocks.
Land management effects on soil carbon were minor when
compared to soil order, rainfall and land use. The number of years
cropped and the number of years of conventional tillage had the
most affect on soil carbon, i.e., both decreased soil carbon.
The long term field trial component of the project, conducted on
Ferrosols in the north of Tasmania, has been completed. This
aspect of the study showed that:
1. Total organic carbon decreased with increasing
years of cultivation. However, soil carbon levels did
not decrease between 1997 and 2010, suggesting
that after many years of agricultural management
equilibrium may have been reached.
2. Sites which had been predominantly used for
pasture had higher organic carbon levels than
cropped sites.
3. Carbon associated with two soil particle size
fractions (POC and HUM) which play different roles
in the soil, was uniformly affected by land use.
Implications
Farmers can influence TOC and carbon Stocks more by their
choice of land use than their day to day soil management but even
though the influence of management is not as great as other site
inherent variables, farmers are still able to select practices that
retain more soil organic carbon than others, i.e. minimum tillage
and increased ley phases.
50
The results of the long term field trial suggest that carbon
associated with the two soil fractions in Ferrosols is affected in a
similar way by land management. This conflicts with the findings of
previous studies which have generally shown that organic carbon
associated with the humic fraction is more resistant to depletion
than carbon associated with the particulate fraction. Red Ferrosols
may have different properties to other soil types in regard to soil
carbon storage.
Publications
Cotching, B. (2011) Soil Carbon Research in Tasmania TFGA
Circular Head engaged farmers 35 attendees Forum.
Oliver, G., Doyle, R., White, E. and Downie, M. (2011) An
overview of the Tasmanian component of the SCaRP project: Soil
organic carbon balances in Tasmanian Agricultural Systems.
Academic and industry representatives 65 attendees
Parry-Jones, J. (2010) The effect of agricultural land use on the
soil carbon fractions of Red Ferrosols in North West Tasmania.
Honours thesis. School of Agricultural Science, University of
Tasmania.
Parry-Jones, J., Oliver, G., White, E., Doyle, R., Cotching, B. and
Sparrow, L. (2011) The effect of agricultural land use on the soil
carbon fractions of Red Ferrosols in North West Tasmania Poster
presented at the National Climate Change Research Program for
Primary Industries, Melbourne, 15-17 February 2011.
Scandrett, J., Oliver, G., Doyle, R. and White, E. (2010)
Agricultural land use and soil carbon in Tasmania. Poster
presented at The 19th World Congress of Soil Science, Brisbane 16 August 2010.
Sparrow, L.A., Cotching, W.E., Parry-Jones, J., Oliver, G., White,
E., Doyle, R. (2011) Changes in carbon and soil fertility in
agricultural soils in Tasmania, Australia. Presented at the 12th
International Symposium on Soil and Plant Analysis, June 2011,
Crete.
51
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54
ACKNOWLEDGEMENTS
We wish to thank the following staff at TIA for their assistance in completing this work; Dr
Leigh Sparrow for assistance with the sampling of the temporal study and supply of the 1997
Ferrosol samples, Mr Suresh Panta for sample preparation and sample archiving work, Mr
Robert Brockman, Mrs Sally Jones, Ms Alicia Tracy and Ms Chantal Woodhams for their
work on financial management, travel arrangements and other administrative work
associated with the project.
Also at UTAS Drs Alistair Gracie (School of Agricultural Science-TIA) and Mark Hovenden
(School of Plant Science) were of great assistance in helping gain DAFF funding for this
project. Dr Hovenden also allowed us unfretted access to his carbon/nitrogen analyser and
sample mill.
We also wish to thank the staff at CSIRO for their helpful attitude to managing this project
and indeed the many other soil carbon projects around the nation. In particular Drs Jeff
Baldock and Elizabeth Schmidt have been extremely patient, prompt and helpful to the
Tasmanian soil carbon team.
55
APPENDICES
Appendix 1: Explanation of land management variables
The land manager (farmer) was consulted prior to sampling site selection to determine if
they were able to provide detailed 10-year land management information, sites were not
sampled if the farmer was not able to provide such information. The farmer was asked to
provide the information in the form of completing a survey form (Appendix 6), this data was
entered into a spreadsheet. We added four more land use categories to the eleven defined
by CSIRO namely; 12 – Pyrethrum, 13 – Grass Seed, 14 – Bulbs e.g., Tulips, 15 – Essential
Oils e.g., Peppermint/Fennel. Generally most landowners/managers did not measure dry
matter production of pastures, and almost half weren’t sure of the year of land clearance.
Once the land management data was collated several more calculations were performed to
generate quantifiable data to use for statistical analysis.
Cropping intensity was quantified with a ‘Crop Ratio’ number, calculated by dividing the
number of crops grown by the number of years of recorded data. The figure varied from 0.0
from a site that was pasture every year, to 1.0 for a site that was cropped every year.
Fertiliser values were calculated for N, P and K by dividing the total k ha-1 of respective
element added and divided by the number of years of fertiliser record. A standard deviation
of fertiliser application per year was also calculated for each respective element.
Irrigation Ratio was calculated by dividing years irrigated by the number of years of record.
This value ranged from 0.0 (no irrigation) to 1.0 (irrigated every year).
Set Stocking Ratio was calculated by dividing years set stocking grazing was used by the
number of years of Pasture. This value ranged from 0.0 (never set stocked) to 1.0 (set
stocked every year).
Rotational Grazing Ratio was calculated by dividing years that rotational grazing was used
by the number of years of Pasture. This value ranged from 0.0 (never rotationally grazed) to
1.0 (set stocked every year).
Hay Ratio was calculated by dividing years that hay was cut by the number of years of
Pasture. This value ranged from 0.0 (never cut for hay) to 1.0 (cut for hay every year).
Tillage Ratio was calculated by adding the tillage figure (0 = Zero Till), (1 = Minimal Till), (2 =
Conventional Till), and dividing this by the number of years that tillage data was provided.
This yields a figure ranging from 0 (always Zero Till) to 2 (always Conventional Till) for
cropping sites.
Till_1 (a measure of minimal tillage) figure was calculated by dividing the number of times
that a cropped site was cultivated with minimal till by the number of times cultivated with
either minimal and/or no tillage. Consequently this compares minimal tillage against no
tillage, and ignores conventional tillage.
Till_2 (a measure of conventional tillage) figure was calculated by dividing the number of
times that a cropped site was cultivated with conventional tillage i.e., two or more workings
divided by the total number of times that the site was cropped. Consequently this compares
conventional tillage to other, minimum, tillage practises.
56
Fallow Ratio was calculated by dividing years that the site was left in fallow for greater than
eight months by the number of years of fallow. This value ranged from 0.0 (never long fallow)
to 1.0 (long fallow every year).
We attributed quantitative figures to Pasture based on whether it was dominantly annual,
perennial or mixed. These values can be used when analysing sites categorised as
‘Pasture’.
1) Annual Pasture = number of years in annual pasture/total number of years in pasture.
To give a value from 0.0 (never) to 1.0 (always).
2) Perennial Pasture = number of years in annual pasture/total number of years in
pasture. To give a value from 0.0 (never) to 1.0 (always).
3) Mixed Pasture = number of years in annual pasture/total number of years in pasture.
To give a value from 0.0 (never) to 1.0 (always).
To gain quantitative figures for crop type, we condensed the fifteen categories down to four
broader categories. These four represented the impact that the crop was likely to have on
soil carbon. ‘Crop Cereal’ includes crops categorised as; Cereal, Oilseed, Poppies, Cereal
Hay, Grass Seed. ‘Crop Veg Other’ includes crops categorised as; Grain Legume or NonRoot Vegetable. ‘Crop Root Veg’ includes crops categorised as ‘Root Vegetables’ and
‘Bulbs’. ‘Crop Perennial’ includes crops categorised as ‘Pyrethrum’ and ‘Essential Oils’. The
figures can be used when analysing ‘Cropping Sites’. Each crop category was given a
quantitative figure as follows;
1) Crop Cereal = number of years in cereal crop categories/total number of years in any
crop. To give a value from 0.0 (never) to 1.0 (always)
2) Crop Veg Other = number of years in other vegetable crop categories/total number of
years in any crop. To give a value from 0.0 (never) to 1.0 (always)
3) Crop Root Veg = number of years in root vegetable crop categories/total number of
years in any crop. To give a value from 0.0 (never) to 1.0 (always)
4) Crop Perennial = number of years in perennial crop categories/total number of years
in any crop. To give a value from 0.0 (never) to 1.0 (always)
A quantitative ‘Crop Weighting’ value was generated by attributing a value to each of the
annual land use (crop or pasture) based on the likely impact that that land use would have
on the soil. The weighting was as follows; Pasture – 0, Perennial Crop – 1, Cereal Crop – 2,
Veg Other Crop – 3, Root Veg Crop -4. The values were totalled and divided by the number
of years that land use data (crop or pasture) was provided for. The resulting number for this
parameter varied from 0 (always pasture) to a maximum of 4 (always root vegetables).
Another Crop Weighting value was calculated to take into account long fallow. It was
calculated ‘Crop Weighting Plus Fallow’ is calculated the same was as ‘Crop Weighting’
(above) but it also had a weighting of 5 added to any year that had a long fallow of greater
than 8 months. The resulting number for this parameter varied from 0 (always pasture) to a
maximum of 9 (always root vegetables and long fallow).
57
Appendix 2: Tables outlining explanatory models
Table A1 Explanatory models (with minimum corrected Akaike Information Criterion –AICC) for soil properties under combined
pasture and cropping sites in Tasmania.
Soil property
Total organic carbon
(log)
Depth (m)
0-0.1
Model
Soil order + 30 year annual rainfall + Crop ratio
Carbon stock gc1 (log)
0.1-0.2
0.2-0.3
0-0.1
Soil order + 30 year annual rainfall + Elevation + Crop ratio
Soil order + 5 year annual rainfall
30 year annual rainfall + Crop ratio + Soil order
Carbon stock gc1 (log)
0.1-0.2
0.2-0.3
0-0.3
Soil order + 30 year annual rainfall + C:N
Soil order + 30 year annual rainfall + C:N + C:N x soil order
30 year annual rainfall + soil order + crop ratio + elevation + C:N + 30 year AprOct VPD4
Soil order + 30 year Nov-Mar rainfall + P fert applied
Soil order + crop ratio
Soil order + P fertiliser applied
Soil order + 30 year annual rainfall + crop ratio + C:N
Soil order + 30 year Apr-Oct rainfall + C:N + crop ratio + elevation
Soil order + 5 year annual rainfall + C:N
Bulk density (log)
Total nitrogen (log)
1
0-0.1
0.1-0.2
0.2-0.3
0-0.1
0.1-0.2
0.2-0.3
gravel corrected; 3 root mean square deviation, 4Vapour pressure deficit
r2
RMSD3
0.590
0.641
0.618
0.294
0.329
0.379
0.522
0.573
0.550
0.255
0.297
0.389
0.496
0.269
0.294
0.277
0.578
0.620
0.574
0.897
0.146
0.149
0.160
0.289
0.320
0.365
Table A2
Explanatory models for soil properties under cropping and pasture in Tasmania (with minimum corrected Akaike
information criterion)
Soil property
Depth
(m)
Cropping (P < 0.01)
r
Total organic carbon (log)
0-0.1
Soil order + 30 year Apr-Oct rainfall + elevation
0.1-0.2
Bulk density (log)
Total nitrogen (log)
C:N
2
1
RMSD
P<0.05
0.509
0.281
crop ratio + till 2 + C:N
5 year Apr-Oct rainfall + soil order
0.615
0.328
crop ratio + till 2
0.2-0.3
30 year April-Oct rainfall x soil order
0.673
0.357
0-0.1
Soil order 5 year Nov-Mar rainfall
0.270
0.129
till 2 x soil order
3
0.1-0.2
Soil order + till 2 + 30 year Nov-Mar VPD
0.409
0.122
5 year Nov-Mar VPD
0.2-0.3
Soil order + till 2
0.445
0.123
30 year Nov-Mar Temp
0-0.1
Soil order + 5 year Apr-Oct rainfall + elevation + till 2
0.471
0.276
crop ratio
0.1-0.2
Soil order + 5 year Apr-Oct rainfall + C:N
0.569
0.322
crop ratio
0.2-0.3
Soil order x 30 year annual rainfall + C:N
0.618
0.344
0-0.1
5 year Nov-Mar VPD
0.221
0.110
0.1-0.2
5 year Nov-Mar VPD
0.172
0.135
Perennial crops + aspect
0.2-0.3
5 year Nov-Mar VPD
0.174
0.172
soil order
0.600
0.284
Pasture
Total organic carbon (log)
Bulk density (log)
Total nitrogen (log)
C:N
1
0-0.1
Soil order + 30 year annual rainfall + crop ratio
0.1-0.2
Soil order + 30 year annual rainfall
0.638
0.337
0.2-0.3
Soil order + 5 year annual rainfall
0.619
0.366
0-0.1
Soil order + 5 year Nov-Mar rainfall
0.289
0.148
0.1-0.2
Soil order
0.264
0.153
5 year Nov-Mar rainfall
30 year annual VPD + wetness index
0.2-0.3
Soil order
0.341
0.156
0-0.1
Soil order + 30 year annual rainfall + crop ratio + C:N
0.587
0.280
0.1-0.2
Soil order + 30 year Apr-Oct rainfall + C:N
0.632
0.329
Focal median of slope within 300m
0.2-0.3
Soil order + 30 year annual rainfall + C:N
0.599
0.352
elevation
0-0.1
no effects
ns
ns
5 year annual Temp + 30 year Apr-Oct Temp
0.1-0.2
no effects
ns
ns
5 year Nov-Mar VPD
0.2-0.3
5 year Apr-Oct rainfall
0.188
0.204
N fert applied + 5 year Nov-MarVPD + elevation
3
root mean square deviation, Vapour pressure deficit, ns = not significant
59
Appendix 3: Significance of land use effects on carbon with adjusted P values
SIGNIFICANCE OF LAND USE CATEGORIES - TUKEY LOG CORRECTED
TOC
Significant Stock
Significant
L Depth
Soil Order
(m)
adjusted P P <0.01
adjusted P P <0.01
All soil
0.1
0.000000
Y
0.000000
Y
0.2
0.006552
Y
0.556775
N
0.3
0.041176
N
0.418173
N
Dermosol
0.1
0.000007
Y
0.001179
Y
0.2
0.904768
N
0.992667
N
0.3
0.709327
N
0.997644
N
Ferrosol
0.1
0.000793
Y
0.000731
Y
0.2
0.990094
N
0.999930
N
0.3
1.000000
N
1.000000
N
Texture
Contrast
0.1
0.146466
N
0.848489
N
0.2
1.000000
N
1.000000
N
0.3
0.988887
N
0.800315
N
Vertosol
0.1
0.003341
Y
0.683282
N
0.2
0.721043
N
1.000000
N
0.3
0.970693
N
1.000000
N
Appendix 4: Sample numbering key
The sites were identified with a TIA code as follows;
Three digit numbers beginning with 1, 2, 3, 4 or 5 indicate that it is a site that had been used
for Cotching and Sparrow in temporal Ferrosol studies.
Sites beginning with ‘E’ followed by two digits are sites identified by Dr Eve White in the
Cressy Region.
Sites beginning with S, N or C followed by two digits are sites that have been used by the
DPIPWE SCEAM project. The letter represents the three Tasmanian NRM regions of South
(S), North (N) and Cradle Coast (C).
Sites beginning with D were sites identified by Mr Mark Downie, and are further broken into
regions by the subsequent numbers. D1xx – Coal Valley and South East. D2xx – Derwent
Valley. D3xx Northern Midlands (Campbell Town and Cressy). D4xx – Southern Midlands
(Bothwell and Oatlands). D5xx - Deloraine and Devonport. D6xx - Scottsdale and East Coast.
D7xx – Burnie and Wynyard.
Sites with other codes are; A1 – Derwent Valley. UF1, UF2, UF3 – University Farm, Coal
Valley. CV8 = sites used in a study of Vertosol soils by Dr Bill Cotching in the Cressy region.
JS4 = sites used by Mt Joshua Scandrett for his honours project in the Southern Midlands
region.
If the sites and samples were deemed fit to be used they would also be given a unique site
SCaRP code ranging from TAS_0001 to TAS_0319. The samples were given a unique
SCaRP code ranging from tas000001 to tas001806.
61
Appendix 5: Unadjusted mean data
Unadjusted means for TOC, Stock, BD, TN and C:N, at all three depths are summarised in tables in Appendix 4,
for all 8 land use by soil order combinations, and for all 14 regions.
AVERAGE VALUES FOR DERMOSOL CROPPING
SITES
Depth (m)
0.0-0.1
3.23
Total Organic Carbon (%)
Carbon Stocks (t/ha)
33.5
0.27
Total Nitrogen (%)
Carbon to Nitrogen Ratio
12.1
1.14
Soil Bulk Density
Total Carbon Stocks to 0.3m (t/ha)
Number of Dermosol Cropping sites
Total Carbon Stocks to 0.3m (t/ha)
Number of Ferrosol Cropping Sites
0.2-0.3
2.10
22.8
0.17
12.7
1.21
0.1-0.2
4.27
44.3
0.32
13.5
1.10
0.2-0.3
3.32
35.4
0.24
13.8
1.13
0.1-0.2
5.22
50.0
0.40
13.1
1.03
0.2-0.3
3.47
34.0
0.25
13.8
1.08
124
54
AVERAGE VALUES FOR FERROSOL PASTURE
SITES
Depth (m)
0.0-0.1
Total Organic Carbon (%)
7.22
Carbon Stocks (t/ha)
66.4
Total Nitrogen (%)
0.59
Carbon to Nitrogen Ratio
12.3
Soil Bulk Density
0.99
Total Carbon Stocks to 0.3m (t/ha)
Number of Ferrosol Pasture Sites
150
27
0.1-0.2
3.11
33.1
0.25
12.6
1.16
102
46
AVERAGE VALUES FOR FERROSOL CROPPING
SITES
Depth (m)
0.0-0.1
Total Organic Carbon (%)
4.70
Carbon Stocks (t/ha)
44.8
Total Nitrogen (%)
0.35
Carbon to Nitrogen Ratio
13.4
Soil Bulk Density
1.01
0.2-0.3
1.60
20.0
0.13
12.1
1.36
83
47
AVERAGE VALUES FOR DERMOSOL PASTURE
SITES
Depth (m)
0.0-0.1
4.97
Total Organic Carbon (%)
Carbon Stocks (t/ha)
46.1
Total Nitrogen (%)
0.40
Carbon to Nitrogen Ratio
12.3
Soil Bulk Density
1.01
Total Carbon Stocks to 0.3m (t/ha)
Number of Dermosol Pasture sites
0.1-0.2
2.52
29.3
0.21
12.3
1.26
62
AVERAGE VALUES FOR TEXTURE CONTRAST CROPPING
SITES
Depth (m)
0.0-0.1
0.1-0.2
Total Organic Carbon (%)
2.30
1.44
Carbon Stocks (t/ha)
26.67
19.15
Total Nitrogen (%)
0.20
0.12
Carbon to Nitrogen Ratio
12.0
11.9
Soil Bulk Density
1.20
1.39
Total Carbon Stocks to 0.3m (t/ha)
Number of Texture Contrast Cropping
Sites
58
29
AVERAGE VALUES FOR TEXTURE CONTRAST PASTURE
SITES
Depth (m)
0.0-0.1
0.1-0.2
Total Organic Carbon (%)
3.25
1.61
33.2
19.2
Carbon Stocks (t/ha)
Total Nitrogen (%)
0.27
0.13
Carbon to Nitrogen Ratio
12.06
12.70
Soil Bulk Density
1.09
1.29
Total Carbon Stocks to 0.3m (t/ha)
Number of Texture Contrast Pasture
Sites
27
0.1-0.2
3.61
33.8
0.30
12.3
1.01
0.2-0.3
2.56
25.1
0.20
12.8
1.07
0.1-0.2
2.26
25.8
0.14
16.5
1.20
0.2-0.3
1.54
18.1
0.09
17.7
1.23
91
4
0.2-0.3
2.15
25.2
0.17
12.7
1.26
107
26
AVERAGE VALUES FOR SOUTHPORT REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
5.92
Carbon Stocks (t/ha)
46.8
Total Nitrogen (%)
0.41
Carbon to Nitrogen Ratio
14.3
Soil Bulk Density
0.83
Total Carbon Stocks to 0.3m (t/ha)
Number of Southport Region Sites
0.1-0.2
2.88
32.9
0.24
12.3
1.21
96
26
AVERAGE VALUES FOR VERTOSOL PASTURE
SITES
Depth (m)
0.0-0.1
Total Organic Carbon (%)
5.89
Carbon Stocks (t/ha)
47.9
Total Nitrogen (%)
0.50
Carbon to Nitrogen Ratio
12.0
Soil Bulk Density
0.90
Total Carbon Stocks to 0.3m (t/ha)
Number of Vertosol Pasture Sites
0.2-0.3
1.03
12.9
0.09
11.81
1.38
65
AVERAGE VAULES FOR VERTOSOL CROPPING
SITES
Depth (m)
0.0-0.1
Total Organic Carbon (%)
3.79
Carbon Stocks (t/ha)
38.3
Total Nitrogen (%)
0.31
Carbon to Nitrogen Ratio
12.2
Soil Bulk Density
1.07
Total Carbon Stocks to 0.3m (t/ha)
Number of Vertosol Cropping Sites
0.2-0.3
0.86
11.76
0.08
11.3
1.48
63
AVERAGE VALUES FOR SOUTHERN MIDLANDS REGION
Depth (m)
0.0-0.1
0.1-0.2
Total Organic Carbon (%)
3.94
2.74
Carbon Stocks (t/ha)
36.8
28.4
Total Nitrogen (%)
0.34
0.23
Carbon to Nitrogen Ratio
11.6
11.9
Soil Bulk Density
1.02
1.15
Total Carbon Stocks to 0.3m (t/ha)
Number of southern Midlands Region
Sites
87
41
AVERAGE VALUES FOR SORELL REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
5.12
Carbon Stocks (t/ha)
47.8
Total Nitrogen (%)
0.42
Carbon to Nitrogen Ratio
12.1
Soil Bulk Density
1.00
0.1-0.2
3.21
33.1
0.25
12.7
1.13
0.2-0.3
2.13
24.1
0.16
12.9
1.24
AVERAGE VALUES FOR NORTHERN MIDLANDS REGION
Depth (m)
0.0-0.1
0.1-0.2
Total Organic Carbon (%)
3.53
2.43
Carbon Stocks (t/ha)
35.3
27.1
Total Nitrogen (%)
0.29
0.20
Carbon to Nitrogen Ratio
12.0
12.0
Soil Bulk Density
1.11
1.26
0.2-0.3
1.61
18.7
0.13
12.0
1.36
Total Carbon Stocks to 0.3m (t/ha)
Number of Sorell Region Sites
Total Carbon Stocks to 0.3m (t/ha)
Number of Northern Midlands Region
Sites
105
9
81
65
AVERAGE VALUES FOR LAUNCESTON REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
5.16
Carbon Stocks (t/ha)
55.4
Total Nitrogen (%)
0.29
Carbon to Nitrogen Ratio
18.0
Soil Bulk Density
1.15
Total Carbon Stocks to 0.3m (t/ha)
Number of Launceston Region Sites
Total Carbon Stocks to 0.3m (t/ha)
Number of North East Region Sites
0.1-0.2
3.84
47.0
0.20
18.9
1.30
0.2-0.3
1.60
21.1
0.07
21.7
1.39
0.1-0.2
4.85
52.1
0.37
13.1
1.16
0.2-0.3
3.24
36.9
0.24
13.8
1.21
124
3
AVERAGE VALUES FOR NORTH EAST REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
5.95
Carbon Stocks (t/ha)
57.6
Total Nitrogen (%)
0.48
Carbon to Nitrogen Ratio
12.4
Soil Bulk Density
1.05
147
20
0.2-0.3
1.94
21.4
0.16
12.2
1.20
64
AVERAGE VALUES FOR EAST COAST REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
4.85
Carbon Stocks (t/ha)
42.0
Total Nitrogen (%)
0.40
Carbon to Nitrogen Ratio
12.2
Soil Bulk Density
0.96
Total Carbon Stocks to 0.3m (t/ha)
Number of East Coast Region Sites
Total Carbon Stocks to 0.3m (t/ha)
Number of Clarence Region Sites
0.2-0.3
1.18
15.6
0.10
11.6
1.39
0.1-0.2
4.89
46.4
0.37
13.2
1.00
0.2-0.3
3.01
28.6
0.23
13.4
1.02
0.1-0.2
2.32
28.8
0.18
12.8
1.31
0.2-0.3
1.70
21.7
0.14
12.6
1.39
84
20
0.1-0.2
1.82
24.5
0.16
11.5
1.39
130
11
AVERAGE VALUES FOR CLARENCE REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
3.04
Carbon Stocks (t/ha)
33.3
Total Nitrogen (%)
0.24
Carbon to Nitrogen Ratio
12.5
Soil Bulk Density
1.16
0.2-0.3
3.30
36.0
0.24
13.9
1.14
77
9
AVERAGE VALUES FOR DELORAINE REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
6.70
Carbon Stocks (t/ha)
55.2
Total Nitrogen (%)
0.52
Carbon to Nitrogen Ratio
13.0
Soil Bulk Density
0.90
Total Carbon Stocks to 0.3m (t/ha)
Number of Deloraine Region Sites
0.1-0.2
4.44
47.0
0.33
13.4
1.09
135
42
AVERAGE VALUES FOR DERWENT VALLEY
REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
2.99
Carbon Stocks (t/ha)
37.1
Total Nitrogen (%)
0.26
Carbon to Nitrogen Ratio
11.6
Soil Bulk Density
1.27
Total Carbon Stocks to 0.3m (t/ha)
Number of Derwent Valley Region Sites
0.2-0.3
1.97
21.1
0.16
12.5
1.23
93
18
AVERAGE VALUES FOR DEVONPORT REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
5.23
Carbon Stocks (t/ha)
52.0
Total Nitrogen (%)
0.40
Carbon to Nitrogen Ratio
13.2
Soil Bulk Density
1.02
Total Carbon Stocks to 0.3m (t/ha)
Number of Devonoport Region Sites
0.1-0.2
2.91
30.3
0.23
12.5
1.14
65
AVERAGE VALUES FOR CENTRAL HIGHLANDS REGION
Depth (m)
0.0-0.1
0.1-0.2
Total Organic Carbon (%)
3.64
2.14
Carbon Stocks (t/ha)
37.5
24.8
Total Nitrogen (%)
0.32
0.19
Carbon to Nitrogen Ratio
11.4
11.3
Soil Bulk Density
1.14
1.25
Total Carbon Stocks to 0.3m (t/ha)
Number of Central Highlands Region
Sites
79
24
AVERAGE VALUES FOR BURNIE REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
6.04
Carbon Stocks (t/ha)
51.1
Total Nitrogen (%)
0.47
Carbon to Nitrogen Ratio
12.9
Soil Bulk Density
0.91
Total Carbon Stocks to 0.3m (t/ha)
Number of Burnie Region Sites
0.2-0.3
4.05
35.7
0.29
13.9
0.95
0.1-0.2
1.64
19.3
0.14
11.7
1.26
0.2-0.3
1.17
15.2
0.10
11.5
1.38
63
6
0.1-0.2
5.08
44.6
0.38
13.3
0.94
131
13
AVERAGE VALUES FOR BRIGHTON REGION
Depth (m)
0.0-0.1
Total Organic Carbon (%)
2.64
Carbon Stocks (t/ha)
28.4
Total Nitrogen (%)
0.22
Carbon to Nitrogen Ratio
11.8
Soil Bulk Density
1.12
Total Carbon Stocks to 0.3m (t/ha)
Number of Brighton Region Sites
0.2-0.3
1.43
16.5
0.12
11.5
1.27
66
Appendix 6: Land Management Survey Form
Soil Carbon Research Program
Site Information Sheet
Farmer’s name & contact details:
Location: Road, paddock name:
Date:
GPS location (Easting/Northing):
Paddock size(ha):
Year of land clearance:
Sample collected by:
Land use history and management
Year since present
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
Cropping
Enter code
0 = no crop, 1=cereal, 2=oilseed, 3=grain legume, 4=sugarcane, 5=cotton, 6=non-root vegetables, 7=root
vegetables, 8=maize, 9=sorghum, 10=poppies, 11=cereal hay
Crop Yield
Tons/ha
Tillage
Enter code
0 = Zero till (no workings other than sowing), 1 = minimum till (1 working in addition to sowing), 2 = conventional (2
or more workings)
Stubble management
Enter code
0 = residue retained on surface, 1 = residue retained by worked in, 2 = residue grazed, 3 = residue baled and
removed, 4 = residue burnt
67
Pasture
Enter code
0 = no pasture, 1 = annual pasture - grass dominant (>75%), 2 = annual pasture - legumes dominant (>75%), 3 =
annual pasture - mixed grass/legume, 4 = perennial pasture - grass dominant (>75%), 5 = perennial pasture legume dominant (>75%), 6 = perennial pasture - mixed grass/legume, 7 = mixed annual/perennial – grass
dominant (>75%), 8 = mixed annual/perennial – legume dominant (>75%), 9 = mixed annual/perennial – missed
grass/legume
Pasture Yield
Tons dry matter/ha
Grazing management
Enter code
0 = no grazing, 1 = set stocking, 2 = rotational grazing
Cut for Hay (crop or pasture)
No=0, Yes=1
Long Fallow (>8 months)
No=0, Yes=1
Irrigation
No=0, Yes=1
N
Fertiliser
P
K
Total kg/ha
Soil conditioners
Enter code
0 = no soil conditioners added, 1 = agricultural lime, 2 = gypsum
NOTES
68
Appendix 7: Farmer Fact Sheet
69
70
71
72