Soil Carbon Storage and Greenhouse Gas Abatement
in Commercial Cropping Systems
Contents
Key findings ............................................................................................................................................. 1
Soil Organic Carbon (SOC) levels on-farm .......................................................................................................... 1
Abating greenhouse gas emissions from soils.................................................................................................... 2
Background ............................................................................................................................................. 3
The Project ......................................................................................................................................................... 3
Participants ........................................................................................................................................................ 4
Methods .................................................................................................................................................. 8
Soil sampling design ........................................................................................................................................... 8
Laboratory analysis .......................................................................................................................................... 11
Soil sample preparation ................................................................................................................ 11
Soil measurements........................................................................................................................ 11
Abating greenhouse gas emissions from soils.................................................................................................. 13
Configuration of the APSIM model ............................................................................................... 14
Model parameterisation, validation, and operation .................................................................... 16
The scenarios ................................................................................................................................ 17
Calculation of global warming potential ....................................................................................... 18
Results & Discussion ............................................................................................................................. 19
Soil Morphology ............................................................................................................................................... 19
Soil type and texture ........................................................................................................................................ 19
Initial soil carbon content (SOC) ....................................................................................................................... 24
Spatial variability in initial SOC ......................................................................................................................... 26
Relationship between soil texture and SOC ..................................................................................................... 28
Change in carbon stock (SCS) over time ........................................................................................................... 29
Soil phosphatase activity .................................................................................................................................. 34
Greenhouse gas abatement ............................................................................................................................. 39
Parameterisation and validation of the APSIM model to field sites ............................................. 39
Scenarios with potential to mitigate greenhouse gas emissions.................................................. 39
Greenhouse gas abatement conclusions ......................................................................................................... 43
Conclusions and implications for the future ......................................................................................... 43
Next Steps ............................................................................................................................................. 44
Outreach ............................................................................................................................................... 45
References ............................................................................................................................................ 46
Appendix 1. Example Profile Morphological Report ............................................................................. 52
Appendix 2: Kambodia greenhouse gas abatement simulations ......................................................... 59
Model parameterisation .................................................................................................................................. 59
Soils ............................................................................................................................................... 59
Climate .......................................................................................................................................... 59
Management practices ................................................................................................................. 59
Biophysical modelling....................................................................................................................................... 61
Parameterisation of APSIM to the Across Rail paddock at Kambodia .......................................... 61
Modelling of the scenarios............................................................................................................ 64
Parameterisation of APSIM to the New paddock at Kambodia .................................................... 68
Modelling of the scenarios............................................................................................................ 70
Appendix 3: Livingston greenhouse gas abatement simulations ......................................................... 74
Model parameterisation .................................................................................................................................. 74
Soils ............................................................................................................................................... 74
Climate .......................................................................................................................................... 74
Management practices ................................................................................................................. 74
Biophysical modelling....................................................................................................................................... 76
Parameterisation of APSIM to the Airport paddock at Livingston................................................ 76
Modelling of the scenarios............................................................................................................ 78
Parameterisation of APSIM to the JKL No Till paddock at Livingston ........................................... 82
Modelling of the scenarios............................................................................................................ 84
Appendix 4: Lachlan Downs greenhouse gas abatement simulations ................................................. 88
Model parameterisation .................................................................................................................................. 88
Soils ............................................................................................................................................... 88
Climate .......................................................................................................................................... 88
Management practices ................................................................................................................. 88
Biophysical modelling....................................................................................................................................... 89
Parameterisation of APSIM to the Wheat paddock at Lachlan Downs ........................................ 89
Modelling of the scenarios............................................................................................................ 92
Appendix 5: Kilnyana greenhouse gas abatement simulations ............................................................ 96
Model parameterisation .................................................................................................................................. 96
Soils ............................................................................................................................................... 96
Climate .......................................................................................................................................... 96
Management practices ................................................................................................................. 96
Biophysical modelling....................................................................................................................................... 98
Parameterisation of APSIM to the Boatrock paddock at Kilnyana ............................................... 98
Modelling of the scenarios.......................................................................................................... 101
Parameterisation of APSIM to the Middleplain paddock at Kilnyana ........................................ 106
Modelling of the scenarios.......................................................................................................... 109
Appendix 6: Merrilong greenhouse gas abatement simulations........................................................ 113
Model parameterisation ................................................................................................................................ 113
Soils ............................................................................................................................................. 113
Climate ........................................................................................................................................ 113
Management practices ............................................................................................................... 113
Biophysical modelling..................................................................................................................................... 115
Parameterisation of APSIM to the Dimby1 paddock at Merrilong ............................................. 115
Modelling of the scenarios.......................................................................................................... 118
Parameterisation of APSIM to the Dimby5 paddock at Merrilong ............................................. 122
Modelling of the scenarios.......................................................................................................... 124
Parameterisation of APSIM to the Willows paddock at Merrilong............................................. 128
Modelling of the scenarios.......................................................................................................... 131
Appendix 7: Warili greenhouse gas abatement simulations .............................................................. 135
Model parameterisation ................................................................................................................................ 135
Soils ............................................................................................................................................. 135
Climate ........................................................................................................................................ 135
Management practices ............................................................................................................... 135
Biophysical modelling..................................................................................................................................... 137
Parameterisation of APSIM to the Buttenshaw paddock at Warili ............................................ 137
Modelling of the scenarios.......................................................................................................... 139
Parameterisation of APSIM to the Cattleyard paddock at Warili ............................................... 143
Modelling of the scenarios.......................................................................................................... 145
Appendix 8: Wilgo greenhouse gas abatement simulations .............................................................. 150
Model parameterisation ................................................................................................................................ 150
Soils ............................................................................................................................................. 150
Climate ........................................................................................................................................ 150
Management practices ............................................................................................................... 150
Biophysical modelling..................................................................................................................................... 152
Parameterisation of APSIM to the Blackstump paddock at Wilgo ............................................. 152
Modelling of the scenarios.......................................................................................................... 155
Parameterisation of APSIM to the Clearview paddock at Wilgo ................................................ 159
Modelling of the scenarios.......................................................................................................... 161
Appendix 9: Eurie Euire greenhouse gas abatement simulations ...................................................... 165
Model parameterisation ................................................................................................................................ 165
Soils ............................................................................................................................................. 165
Climate ........................................................................................................................................ 165
Management practices ............................................................................................................... 165
Biophysical modelling..................................................................................................................................... 166
Parameterisation of APSIM to the P4 paddock at Eurie Eurie .................................................... 166
Modelling of the scenarios.......................................................................................................... 169
Soil Carbon Storage and Greenhouse Gas Abatement in Commercial
Cropping Systems: Science to Best Agronomic Practice
Action on the Ground Project 2012 – 2015
Brett Whelan1, Kanika Singh1, Peter Thorburn2, Jeda Palmer2, Elizabeth Meier2, David Eyre3.
1
Precision Agriculture Laboratory, Faculty of Agriculture and Environment, University of Sydney
CSIRO Agriculture, Queensland Biosciences Precinct, St Lucia, Brisbane, QLD
3
Research, Development & Innovation Division, NSW Farmers
2
Key findings
Soil Organic Carbon (SOC) levels on-farm
x Total soil organic carbon (SOC) levels (0-30cm) were found to be significantly higher in
pasture/native areas compared to cropped areas.
x The farms with higher SOC levels also generally exhibit the largest variability in SOC which may
have cost implications for sampling design and intensity in any long-term direct
monitoring/auditing of SOC content. Increased costs may impact on the propensity for farmers to
participate in market-based carbon farming/trading schemes.
x While there is some evidence that fields under no-till (NT) management may have slightly higher
SOC levels than conventionally cultivated (CT) fields, the differences are not statistically
significant.
x Across the farms in the study, large spatial variability of soil texture was found within different
areas of the same farms
x SOC levels appear to be significantly higher in Silty Clay soil compared to Clay and Sandy Clay soil
across the study farms.
x Where crop yield maps were available, significant within-field relationships between yield and
SOC levels, soil texture and total gammaradiometric count were identified.
x Therefore, areas with lower than average SOC and high silt/clay fractions may be areas with the
greatest potential to increase SOC in the future. Management options within cropping or pasture
phases that increase organic matter entering the soil in these areas may be the most effective
way to increase overall field/farm SOC levels. One way of achieving this goal in a cropping
situation would be to ensure that seasonal crop yield/biomass production potential is achieved
on these areas.
x Over the two year sampling period, SOC levels in the majority of fields (49%) did not significantly
change, 17% rose significantly and 34% decreased significantly. The decreases in SOC can be
partly attributed to a below average total rainfall for the period contributing to reduced
vegetative input in some areas.
x Soil enzyme activity, reflecting biological activity in the soil, was higher in no-till systems (NT) as
compared to conventionally tilled systems (CT).
1
x Soil enzyme activity significantly dropped between 2013 and 2015 where lack of rainfall restricted
cropping operations for one season. Soil enzyme activity remained unchanged in areas where
local rainfall for the period remained close to long-term averages.
x The baseline data has provided information to the participating farmers that often wasn’t as
expected, but quantification of SOC levels has enabled clearer decision making regarding future
management change trials.
Abating greenhouse gas emissions from soils
x Of the wide range of management practices assessed using production modelling, SOC was
estimated to increase compared with baseline practices when there was an increase in organic
matter inputs (e.g. summer legume crops, winter legume pasture crops, or manure additions).
x Generally, nitrous oxide emissions were estimated to increase compared to baseline emissions
under the management practices in which soil carbon increased.
x In many cases, the simulated greenhouse gas abatement provided by the sequestered carbon
was greater than the rise in greenhouse effect predicted from the corresponding increase in
nitrous oxide emissions, and therefore led to net abatement.
x Management practices that include a summer crop or a pasture phase in the rotation have the
capacity to provide greenhouse gas abatement for many grain farming sites in New South Wales.
Exceptions include irrigated sites with high fertiliser use. Management strategies that can provide
abatement at irrigated sites include reductions to nitrogen fertiliser.
x Other management practices (such as additions of manure, or changes to nitrogen fertiliser
application rate) have the capacity to provide abatement at selected sites and therefore may
warrant site-specific consideration.
2
Background
The Project
NSW Farmers, The University of Sydney, CSIRO and 10 NSW farm enterprises collaborated on a three
year Department of Agriculture Food and Fisheries (DAFF) funded project aimed at exploring the
potential impact of management changes on the storage of carbon in the soil (SOC) and the
emissions of greenhouse gases from commercial farming systems. Improving knowledge of the baseline situation and then starting down the path of testing potential changes that are commercially
and agronomically appropriate for local production systems was undertaken.
The impetus is multifaceted. It draws on the societal discussion around the importance of SOC and
nitrogen (N) in the climate and food security debates, improving the resource use efficiency of
agricultural production and improving the quality, productivity and profitability of farming systems.
These issues have a wide-ranging global impact, but it is expected that the practical importance for
managing SOC and greenhouse gas emissions in NSW will be highly dependent on changes in
local/regional environmental variables such as climate, soil type and farming operation as
highlighted by the work of the NSW DPI (Chan, 2008; Chan et. al, 2009).
Understanding the management of SOC and greenhouse gas emissions at the farm or field scale, and
the success of any market-based storage/emission reduction schemes, will also be dependent on
establishing baseline and change-over-time estimates within an acceptable degree of accuracy
(Walcott et al., 2009). Methodologies for achieving these goals continue to be discussed. For SOC, a
number of sampling strategies for direct measurement have been recently proposed by the NSW DPI
(Murphy et al., 2013) and the CSIRO (Chappell et al., 2013). These protocols aim to identify sample
site locations that will best estimate the average SOC within the area of interest by stratifying the
area to encompass the expected variation in SOC using design-based or model-based systems
respectively. These two methodologies acknowledge the impact that actual variation in SOC and
sample number will have on the costs of direct measurement.
This project aimed to contribute to improving the understanding of the practical challenges and
costs of establishing, implementing, monitoring and verifying SOC and greenhouse gas emissions for
such market-based storage and abatement schemes. In particular, the project gathered data that
will help inform industry discussions around the stratification methods, and associated costs, for the
soil sampling required to provide valid SOC baseline and SOC change-over-time data. The
3
participatory aspect of the project also explored the appetite of farmers for the work and
investment involved in participating in market-based carbon farming/trading schemes.
Participants
Ten cropping/mixed farming enterprises located across five Biogeoregions of NSW collaborated in
the project (Figures 1 and 2). The measurement of SOC was performed in accordance with the
National SC Research Program (SCaRP) field and laboratory techniques which require the calculation
of total soil organic carbon (SOC). The greenhouse gas budget was estimated using the emission
factor approach described in the Australian National Greenhouse Accounts: National Inventory
Report 2011.
Initial on-farm planning meetings were held in March 2013 to formulate the desired farm-driven
research goals for each farm and select appropriate field-scale trial sites (Figure 2). At least ten
samples are required by the SCaRP protocol to produce a good estimation of the SOC at each trial
site, which meant that three trial sites per farm were possible within the project budget.
The majority of farmers were interested in exploring the impact of both current and trial farming
practices on SOC and greenhouse gas emissions, so a native/pasture site was included on most of
the properties. This enables a comparison with relatively ‘natural’ SOC and greenhouse gas emission
levels for both current and future practices (Table 1). The general practices that farmers chose to be
included in the trials are depicted in Figure 3.
Figure 1: 10 farm site locations included in the project, Kambodia (Moree), Livingston (Moree), Eurie Eurie
(Walgett), The Plantation (Bundella), Merrilong (Yarraman), Warili (Forbes), Lachlan Downs (Rankin Springs),
Merribee (Binya), Kilnyana (Mulwala) and Wilgo (Mulwala).
4
Figure 2: Photographs taken during initial meetings and soil surveys
5
Table1: Participant field summaries.
Farm ( Location)
Merribee
(Binya)
Paddock and intended plan
1. Pump: Fourth year rice 2014/15.
2. Merribee: Wheat 2014 (harvest Dec 14)
3. Wet area: Second year rice 2014/15
Two types of stubble burning is undertaken on this farm:
x
x
Full burn- when going from rice into rice
Flag leaf burn- when going from rice into cereal crop
Crop rotation with rice and impact on SOC. Cropping versus native comparison.
Warili
(Forbes)
1. Old cattle yard: Perennial pasture moved to sorghum 2013/14. Double cropped
Canola 2014
2. Buttenshaws: Continuous cropping for 6 years with wheat and canola rotation
3. Scrubby lane: Native area for a baseline soil carbon comparison will remain
uncultivated.
Canola accidently sprayed out in 2013, changed to summer sorghum crop 2013/2014.
Impact of pasture phase versus continuous cropping. Cropping versus native
comparison.
Kilnyana
(Mulwala)
Lachlan Downs
Organic farmers
(Rankin Springs)
1. Boatrock west (red soil): east side was treated with TM21 a couple of years ago
whereas west was untreated for crop yield comparison. 2014 the whole Boatrock
area was under uniform treatment.
2. Middleplain area: Middleplain has various N treatments in 6 strip trials for
2013/4.
3. Prarie: Native area for comparison of soil carbon content with Middleplain.
4. Native red: Native area for comparison of soil carbon content with Boatrock west
area.
1. Cell area: undergoes high intensity (large numbers of animals for 1 day) rotational
cell grazing; it contains 16 cells in a block design with differential crops sown into
native pastures. All cells have been treated with ‘Nutri-Life Platform’ at the rate of
50kg/ha. No crop sown 2014 due to drought.
2. Uncelled wheat area: Pasture cropping and 2 week rotational grazing is followed
in this area. Within this area the farmer moves cattle 80 m every day. Field
treated with ‘Nutri-Life Platform’ at the rate of 50kg/ha. No crop sown 2014 due
to drought.
3. Native section: A section has been fenced off where the stock has not been for
the past 5 yrs.
Comparison of cell treatments. Comparison of pasture cropping versus native.
Drought 2014.
Eurie Eurie
(Walgett)
1. College green area: This area was moved over from grazing (never cropped) to
cropping in 2012. Not cropped 2013 and 2014 due to drought.
2. Bullock area: pasture area (never cropped).
3. P4 area: cropping since 2004. Originally wheat on wheat production. Now wheat
and chickpea rotation. Not cropped 2013 and 2014 due to drought.
Crop rotation impact on SOC. Cropping versus native comparison.
Merrilong
(Yarraman)
1. Dimby 1: Irrigated rotational cropping: wheat-sorghum-sorghum-wheat-cottonwheat.
2. Dimby 5: Irrigated rotational cropping: Sorghum-corn-sorghum-wheat-sorghumwheat.
3. Willows: no-till for over 20 yrs.
Crop rotation comparison and no-till versus history of tillage comparison.
6
The Plantation
(Bundella)
1. Brigalow area: New country. Sorghum 2013/14.
2. Pasture area: Grazing area that has never been cultivated.
3. Gurley area: Old area- it contains contour banks and stubble retention for cover.
It has been cultivated since the 1940’s.
No-till versus history of tillage comparison. Cropping versus native comparison.
Kambodia
(Moree)
1. New area: This country has been cropped for the past 12-15 years. It has been
undergoing stubble retention and no till.
2. Across rail: this section of new country undergoes the same cropping pattern.
3. Old area: this country has been cropped for 65 years. Wheat 2014 harvested Nov
1.0 t/ha.
4. Pasture area: Bench mark for soil carbon in native country for comparison with
new/old country.
In 2014:Gypsum treatment and control was applied in ‘Old’. Wheat stubble
bailing/removal trial in ‘Across Rail’.
Livingston
(Moree)
1. Airport site: this site dry land conventionally cultivated cropping country. Not
sown 2014 due to drought.
2. No till site: this country is dryland with 25 years of no-till cropping. Not sown
2014 due to drought.
3. Pasture: Benchmark for soil carbon in native country
No-till versus history of tillage comparison. Cropping versus native comparison.
Wilgo
(Mulwala)
1. Blackstump area: Canola in 2014. Variable-rate from aircart in 2015.
2. Two Tanks area: Canola in 2014 Variable-rate from aircart in 2015.
3. Clearview Front area: Faba beans with a nil treatment applied in 2013. Wheat in
2014 and monitoring impact of Faba beans vs no Faba beans.
PATHWAYS TO INCREASED SOIL CARBON CONTENT
Improved crop productivity
x
x
x
x
No-till farming
Nitrogen fixing plants
Perennial crops and crop
rotation
Precision agriculture
Soil amelioration
x
x
Organic
amendments
Physical &
Chemical
amendments
Figure 3: Summary of pathways for increasing SOC by different management practices to be explored.
7
Methods
Soil sampling design
A targeted sampling scheme was devised for each trial site using broad-scale soil gammaradiometric
data, landscape terrain information and where available, historic soil or crop yield maps. The
sampling schemes were designed to help ensure the full extent of SOC variability at a site was
sampled for the baseline study and subsequent monitoring for changes in SOC at the end of the
project (Figure 4). An example of the data used in the sampling design process for a field is shown in
Figure 5 and the resulting sampling scheme is shown in Figure 6.
Soil sample sites were discussed with farmers (Figure 7) and were extracted to a depth of 30cm
using a hydraulic coring rig mounted on an ATV fitted with high accuracy GPS navigation capabilities
(Figure 8). This depth range is in line with requirements for Australia to report any changes in SOC
levels under the Kyoto Protocol. A single 0-90cm core was taken for morphological description and
the measurement of profile soil properties. The profile properties were also used to parameterise
the APSIM software used in greenhouse gas predictions at each site.
Data layers (as available)
Crop yield, soil ECa, elevation, gamma radiometric total
count (TC) and predicted 0 -30cm soil carbon content.
k-means clustering conducted on the layers
to form soil sampling strata
Random sampling within strata
Cropping fields: A minimum of 10 samples (0-30cm) were taken from each field. Depending on
treatments, a maximum of five of the sampling points were randomly chosen for duplicate
sampling.
Pasture areas: A minimum of 10 samples (0-30cm) were taken from each designated pasture
area. Given the generally smaller size of the pasture areas, on most farms the sampling comprised
5 sets of duplicate samples.
Soil profiles: A single core (0-90cm) was taken for morphological description from each cropping
field.
Figure 4: Summary of sampling design process.
8
(b)
(a)
Total count
High : 74.6347
Elevation (m)
High : 463.004
Low : 70.1068
Low : 417.288
Ü
(c)
(d)
Wheat yield (t/ha)_Brigalow
High : 5.99914
Low : 2.39054
Sorghum yield (t/ha)_Brigalow
High : 7.39224
Low : 1.45308
Figure 5: The data available for designing the sampling scheme in Brigalow paddock. Elevation (a), gamma
radiometric total count (b) wheat yield (c), and sorghum yield (d).
Figure 6: 10 sampling classes with random sampling site locations for Brigalow paddock.
9
(a)
(b)
Figure 7: (a) David Brownhill ‘Merrilong’ discusses soil sampling design with Brett Whelan (University of
Sydney); (b) Controlled traffic, no-till farming on ‘The Plantation’ (Bundella).
Figure 8: DGPS-guided soil sampling rig.
10
Laboratory analysis
Soil sample preparation
Full description of soil profiles was conducted at the Precision Agriculture Laboratory, University of
Sydney. Each soil sample was air-dried at 400C and weighed. Soil samples were gently broken down
by hand, and coarse root and leaf material was removed with the help of tweezers with precaution
to avoid soil loss. The soil sample was ground and sieved to less than 2-mm, ground soil was weighed
again and any unground material >2mm was weighed as gravel.
Soil measurements
Soil profile morphology
Properties measured from each soil profile core (0-90cm) included discernible soil layers and their
thickness, colour, texture and the accumulation of compounds such as carbonates and organic
matter (see Figure 9). The soil morphological properties were described in accordance with the
Australian Soil and Land Survey Field Handbook.
Soil chemical properties
Soil from the profiles at each site were anyalysed using XRay Fluorescence to provide farmers with
more detailed information on the representative soil profiles (see Figure 10).
Soil Organic Carbon (SOC )
A 60g subsample was taken from each homogenised soil sample, and any noticeable fine root/plant
material and charcoal was removed. The subsample was ground to less than .53 mm using an
automatic porcelain grinder. Any carbonate was chemically removed by excess 5% H2SO3 solution on
a hot plate in a fume cabinet. The residual soil was washed with de-ionised water 3 times and
measured for carbon by Leco C analyses and results reported as % SOC on an oven-dry (1050C) basis.
SOC stock (SCS)
Soil carbon stocks are calculated on a constant depth basis as the soil in this project has been
sampled at a regional scale which encompasses different soil types and management/landuse
contributing to large variability in soil bulk densities. Therefore, SOC stocks at 0-30 cm depth
increment for each soil sample were calculated using the following formula:
= × × × 11
Eq. 1
Where, is the soil carbon stock at 0-30 cm depth in Mg C/ha, is the SOC % in (mg C/100 g
soil), is the depth in cm, is the bulk density (Mg/m3), and is the gravel corrected soil
mass (g >2 mm/g soil).
Soil phosphatase activity (APA)
The measurement of APA was used as an estimate of biological activity in the soil. APA was assayed
using the method of Tabatabai and Bremmer (1969). This includes calorimetric estimation of pnitrophenol released by incubating 1 g of soil at 370C for 1 hour with 0.25 ml toluene, 4 ml universal
buffer set at pH 6.5, and 1 ml of p-nitrophenyl phosphatase solution in an Erlenmeyer flask. After
incubation 1 ml of 0.5M calcium chloride and 4 ml of 0.5M sodium hydroxide was added to the
Erlenmeyer flask and swirled to stop the reaction. The soil suspension was then filtered through
Whatman No. 42, and transferred to a transparent cuvette and the absorbance of the yellow colour
was measured with a calorimeter at 400 nm.
A control was prepared for each sample whereby the process described for assay of phosphatase
activity was followed but the addition of 1ml p-nitrophenyl phosphatase solution was made after
addition of 1 ml of 0.5M calcium chloride and 4 ml of 0.5M sodium hydroxide to halt any reaction.
This enabled the absorbance of the materials without any reaction to be measured. The final
absorbance reading for the sample was obtained by subtracting the absorbance measured for the
control from the absorbance measured for the reacted samples.
The absorbance was converted to concentration of p-nitrophenol produced using a calibration
equation built from measuring the absorbance of solutions containing 0, 10, 20, 30, 40 and 50 μg of
p-nitrophenol. Where the colour intensities for the filtrate was more than that of 50 μg of pnitrophenol, the filtrate diluted with water to bring the absorbance within calibration range.
Chemical reaction:
+ !"!"#$#% %&'(%
) (,-) + (Phosphate)
= Disodium salt of p-Nitrophenyl phosphate (phosphate monoester)
R-OH = p-nitrophenol (alcohol) - yellow colour intensity reflects the amount of phosphatase
Statistical Assessment of soil data
Summary statistics (mean, standard deviation and CV) was used to describe the measured soil
properties for site, farm and regional scale data. To determine the spatial dependence, spherical
variogram models were fitted to the observed SOC (0-30 cm). These variograms can be used to
determine the expected scale of spatial variability for SOC at the farm scale. There were insufficient
data to fit independent variograms for each paddock.
12
Figure 9: Soil morphology and texture analysis at the Precision Agriculture Laboratory.
Figure 10: XRay Fluorescence (XRF) analysis for soil elemental properties at the Precision Agriculture
Laboratory.
Abating greenhouse gas emissions from soils
A range of management practices, many which were beyond those that the collaborating farmers
had already put in place, were assessed for their potential contribution to greenhouse gas
abatement.
Carbon sequestration and nitrous oxide emissions were considered because sequestering carbon in
soils is not the only path to abating greenhouse gas emissions from soils. Nitrous oxide, a potent
greenhouse gas with approximately 300 times the global warming potential of carbon dioxide, is
13
emitted from soils. Nitrous oxide emissions are driven by soil carbon, soil nitrate, water and
temperature levels. The interaction between soil carbon and nitrous oxide makes it important to
consider both carbon sequestration and nitrous oxide emissions when assessing the net greenhouse
gas abatement potential of different management practices.
Sufficient data were available from eight of the participating farms for this part of the study. These
farms represent a wide variety of climates, soils, crops and practices in the NSW Australian grains
industry (Table 2). Field histories gathered for the participating farms for the period 2008 to 2014
(described in Appendices 2-9) and soil sampling data and operational information for 2013 and 2015
formed the basis for an analysis of the greenhouse gas abatement potential of different
management practices. The analysis was undertaken using the Agricultural Production Systems
sIMulator (APSIM), a comprehensive farming systems model with a proven capacity to predict
carbon sequestration and nitrous oxide emissions as well as crop growth and management
(Holzworth et al., 2014). The APSIM approach is as follows:
Configure and test APSIM to give realistic predictions of yields and other relevant variables for
possible historical (termed ‘baseline’) cropping systems on the farms.
Develop scenarios describing potential changes to cropping systems that farmers could adopt to
reduce greenhouse gas emissions from soils.
Predict the change in greenhouse gas emissions (soil organic carbon sequestration, nitrous oxide
emissions reduction) for scenarios relative to the baseline systems using APSIM simulations.
Configuration of the APSIM model
The APSIM model
APSIM (Holzworth et al., 2014) v 7.5 was parameterised with the climate, soil/crop properties, and
management practices used at each of the eight farms. APSIM was configured with modules for soil
carbon and nitrogen (APSIM-SoilN; Probert et al., 1998; Thorburn et al., 2010), soil water dynamics
(APSIM-SoilWat; Probert et al., 1998), soil temperature (APSIM-SoilTemp2, following Campbell,
1985), residue (APSIM-SurfaceOM; Probert et al., 1998; Thorburn et al., 2001), and crop growth
(Holzworth et al., 2014).
14
Table 2: Location, rainfall, soils, crops for the case study farm sites.
Case study
farm
Paddock
Local region
Location
Mean
rainfall
(mm/yr)
Dominant
soils
Winter
1
Crops
Eurie Eurie
P4
Walgett
-29.947069,
479
Grey
vertosol
Wt, Cp,
148.275267
Kambodia
Across Rail
Moree Plains
-29.391452,
150.0277
618
Grey
vertosol
Wt
Kambodia
New
Moree Plains
-29.400282,
618
Grey
vertosol
Wt, Cp,
Cn,
593
Black
vertosol
By,Cp,
Wt
593
Black
vertosol
Cp, Wt
451
Red
chromosol
Wt
150.028065
Livingston
Airport
Moree Plains
-29.499608,
149.848437
Summer
1
crops
Ct
Livingston
JKL No Till
Moree Plains
-29.510928,
Lachlan
Downs
Wheat
Carrathool
-33.625808,
Kilnyana
Boatrock
Berrigan
-35.789855,
145.964108
445
Red
ferrosol
Wt, Ot,
Cn, By
Kilnyana
Middleplain
Berrigan
-35.803714,
145.913471
445
Grey
vertosol
Wt, Cn,
By
Warili
Buttenshaw
Forbes
-33.435097,
147.47795
443
Brown
sodosol
Wt, Cn,
Sg
Warili
Cattleyard
Forbes
-33.430749,
443
Brown
sodosol
Wt, Ln,
Cn
Sg
149.848437
146.011537
147.47795
Sg
Merrilong
Dimby1
Liverpool
Plains
-31.492999,
150.277746
603
Black
vertosol
Wt
Sg
Merrilong
Dimby5
Liverpool
Plains
-31.50953,
150.291233
603
Brown
vertosol
Wt
Sg, Mz
Merrilong
Willows
Liverpool
Plains
-31.52875,
603
Brown
vertosol
Wt
Sg, Sn
Berrigan
-35.84075,
502
Grey
vertosol
Wt, Cn
502
Grey
vertosol
Wt, Fb
Wilgo
Blackstump
150.19875
145.908607
Wilgo
Clearview
Berrigan
-35.862487,
145.92186
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower.
APSIM soil modules
APSIM-SoilN and APSIM-SoilWat were parameterised with data collected from each field,
supplemented by data representative of local soils that had been previously sampled for other
projects and were available in the APSoil database (http://www.apsim.info/Products/APSoil.aspx).
The data taken from APSoil included the soil water characteristics, crop lower limits, bulk density,
15
and soil water conductivity for each soil layer, as well as runoff curve numbers and first and second
stage evaporation coefficients. Actual SOC measured in the soil profile to 1 metre at each field was
used to parameterise soil organic carbon content in the APSIM model. Measured soil pH values were
also used to parameterise the APSIM soil pH. Initial soil mineral nitrogen was notionally set at 10.5
kg/ha in the soil profile for nitrate-N and 0.36 kg/ha for ammonium-N. Initial soil water was set to 15
% of maximum available soil water.
APSIM crop modules
Simulated cropping rotations for the parameterisation and long-term parameterisation simulations
were based on actual crop rotations for the eight farms from the last two to seven years. For three
sites, the most recent cropping rotation grown on the farm was not considered to be a likely longterm rotation. Thus for the scenarios a region-appropriate general crop rotation was simulated for
the Across Rail and New paddocks at the Kambodia farm and for the Blackstump paddock at the
Wilgo farm. Crops were modelled using default parameters for varieties grown at the case study
farms or commonly used local varieties. Agronomic details of crop management including plant
density, fertiliser type, rates and timing, sowing depth and sowing window, were based on actual
management practices at the farms or general agronomic practices for the region.
Climate
Climate data were obtained from the SILO data base (Jeffrey et al. 2001) for weather stations close
to the farms. Each 100 year period was composed of a recent 50 year period of climate data that
was repeated twice. The 50 year periods commenced either in 1964 or 1965. For example, the 19142013 climate file consisted of the 1964-2013 climate record repeated for the 1914-1963 period.
Model parameterisation, validation, and operation
Parameterisation simulations
For the parameterisation simulations, a the crop rotation practice recorded for the last two to seven
years at the 15 fields from the eight farms was simulated. If the first crop rotation was a winter crop,
simulations were started on the 1st of November in the prior year prior in order to provide a run-in
prior to crop planting. If the first crop was a summer crop, the simulations were started on the first
day of the year of the first crop. Two simulations were modelled. One simulation used general
management rules (e.g. average fertiliser application rate for a specific crop), while the other used
16
specific management rules that occurred at the site (e.g. the actual fertiliser amount applied to a
specific crop in a given year).
Validation of the model performance was undertaken through comparison of simulated yield from
both the general and specific management simulations to: (1) the measured yield at each farm, and
(2) the average yields from 1992 -2010 for the statistical local area (SLA; roughly equivalent to local
shires or district councils; Australian Bureau of Statistics, 2010). Measured grain yields were
compared to simulated grain yield weights at 13 percent moisture.
Long-term parameterisation simulations
The general management scenarios were then simulated for approximately 50 years (dependent on
crop rotation) to assess long-term crop, water, and carbon dynamics. Cumulative water inputs from
rainfall and irrigation was compared to total water losses via evapotranspiration, runoff and deep
drainage. Soil organic carbon was assessed for its long-term stability.
The scenarios
Practices that sequester carbon and mitigate greenhouse gas emissions have been well documented
(e.g. Sanderman et al., 2010; Snyder et al., 2009; Johnson et al., 2007). For the farms in this study,
practices with the potential to provide greenhouse gas abatement include:
x
Management of the nitrogen in fertilisers and residues (to mitigate nitrous oxide emissions),
and
x
Increasing organic matter inputs by increasing crop size/duration or by adding organic
matter to sequester soil carbon (to reduce atmospheric concentrations of carbon dioxide).
Accordingly, scenarios were developed (Table 3) to be applied at all sites to demonstrate the effect
of alternative practices on greenhouse gas emissions. Recent practices on the farms included stubble
retention, which is represented by the No Burn scenario. This scenario reflects the general
management practices at the sites (e.g. fertiliser rates, sowing and harvesting dates) and is deemed
to be the ‘baseline’ scenario, against which all alternative scenarios are compared. Some alternative
scenarios were developed that burn stubble (Burn, Burn+N, Burn-N) as this can reduce nitrous oxide
emissions. Stubble is retained in all other scenarios as this can enhance carbon sequestration. The
nitrogen fertiliser rate was varied to 75 and 125% from the No Burn scenario in combination with
stubble retention or burning, in the Burn+N, Burn-N, No Burn+N, No Burn-N scenarios. Inputs of
organic matter (in addition to stubble retention) were applied in the Manure, Summer Crop, and
Pasture scenarios. In the Summer Crop and Combination scenarios, a sacrificial summer cowpea crop
17
was sown in place of bare summer fallows (WANTFA, 2015). In the Pasture and Combination
scenarios, field peas were sown for one winter fallow per crop rotation in rotations with one or
more winter fallows. At some case study sites, the cropping rotation did not include a winter fallow.
Thus for those sites, the Pasture and Combination scenarios could not be simulated.
Table 3: The No Burn scenario which represents typical management for the sites and alternative scenarios
modelled for the case study farms.
No.
1
2
3
4
5
6
7
8
9
10
Name
Burn
No Burn
Burn+N
Burn-N
No Burn+N
No Burn-N
Manure
Summer Crop
Pasture
Combination
Description
Stubble burnt, bare summer fallow
Stubble retained, bare summer fallow
Stubble burnt, 125% of baseline nitrogen fertiliser rate
Stubble burnt, 75% of baseline nitrogen fertiliser rate
Stubble retained, 125% of baseline nitrogen fertiliser rate
Stubble retained, 75% of baseline nitrogen fertiliser rate
-1
Stubble retained, 5 t ha manure applied every 5 years
Stubble retained, summer cowpea crop in place of fallow
Stubble retained, field pea pasture in place of one winter fallow
Stubble retained + cowpea summer crop + field pea pasture
All scenarios were simulated for a 100 year period. Simulations were started at the beginning of a
year. Each simulation was repeated using two different start years (1914 or 1915) in order to capture
seasonal variation in rainfall and temperature that affect carbon sequestration, nitrous oxide
emissions, and yields.
Scenarios were assessed for carbon sequestration, nitrous oxide emissions, and net global warming
potential (described in the section: Calculation of global warming potential) for each site at 25 and
100 years into the simulation period. These time periods were selected as they are the carbon
storage permanence periods outlined in the Emissions Reduction Fund guidelines (Department of
the Environment 2014). Annual carbon sequestration, nitrous oxide emissions, and global warming
potential are provided for each site in Appendices 2-9.
Calculation of global warming potential
Global warming potential quantifies the contribution of different greenhouse gases emitted from a
natural or anthropogenic practice to global warming. The greenhouse gases carbon dioxide and
nitrous oxide are the main gases of interest in this study, but have different global warming
potential. Calculating global warming potential permits the combined effect of greenhouse gases to
be compared by converting them to a common unit, carbon dioxide equivalent (CO2e; IPCC, 2013).
The factors used to convert each greenhouse to its global warming potential depend on the
18
cumulative radiative forcing of the gas and the time period over which the climate effects are of
concern. Global warming potentials were therefore calculated as follows using IPCC (2013) factors:
CO2e from sequestered carbon =
(%/0# (# 123
41
(%/0# (# 1
CO2e from nitrous oxide-N emissions =
Eq. 2
(%/0# (# 63 2
4298
(%/0# (# 6
Eq. 3
Net global warming potential = CO2e from sequestered carbon + CO2e from nitrous oxide-N
emissions
Eq. 4
Results & Discussion
Soil Morphology
Most common soil profiles found in Australia generally contain A, B and C horizons. The A horizon
(topsoil) can be further sub-ordered into A1 or A2 based on different colour and organic matter
content. The main horizon of a soil profile is usually the B horizon (subsoil) which often consists of
higher concentrations of clay, iron, and aluminum and is often a different colour compared to the
horizon above or below. The B horizon can be sub-ordered into B1, B2 or B3 horizons based on
colour and soil development. The C horizon usually comprises partially weathered parent material.
The soil morphology and important attributes of each horizon for the trial sites are shown in Table 4.
Soil type and texture
Figure 11 classifies the soil texture at each trial site on each farm by plotting the average clay, silt
and sand content (0-30cm) on an Australian texture triangle. Given the geographical spread of the
participating farms, it was not unexpected that the soil texture/type was quite variable between
farms, but the sampling confirmed that large spatial variability of soil texture can be found within
different areas of the same farm.
Variation in soil type/texture may directly influence the yield potential of a site by contributing to
the variation in nutrient storage and availability, moisture holding and transport capacity and soil
stability (Mott, 1988). In general an increase in clay content increases these attributes at a site and
would be expected to lead to an increase in SOC levels and an ability to cycle more nitrogen
compounds.
19
Table 4: Soil type, horizon descriptions and depths, SOC and other chemical properties in the soil profile for
each cropping paddock. N = nitrogen, K = Potassium, Ca= Calcium, Mn = Manganese, Fe= Iron, Zn = Zinc.
Farm
Paddock
(Location)
(Soil type)
Merribee
(Binya)
Horizon
(cm)
SOC
%
CaCl
Pump
A11
1.74
5.4
(Grey
Chromosol)
(0-5)
0.88
A12
pH
N%
C/N
ratio
Clay
%
K
Ca
Mn
Fe
Zn
ppm
ppm
ppm
ppm
ppm
0.17
10.43
43.2
9390
1207
354 22387
56.8
6.6
0.06
13.83
65.4
11384
3033
573 33457
68.2
0.78
5.8
0.04
21.44
61.9
11468
11332
427 32763
67.8
1.73
5
0.16
11.11
43.3
963
730
283 26708
59.5
1.97
6.5
0.12
17.21
43
9377
945
448 27553
60.1
0.50
6.2
0.06
8.75
63
10558
2597
408 35993
68.1
0.82
5.8
0.04
21.43
59.9
10330
4283
441 31781
63.6
3.45
6
0.36
9.73
49.9
10741
1661
406 25425
68.3
1.20
5.2
0.12
9.87
59.8
10536
2090
492 25369
62.2
0.53
5.7
0.06
8.46
66.2
11821
2142
487 32618
70.4
0.46
6.6
0.04
12.19
62.8
12719
5036
540 34103
75.9
1.18
6.4
0.09
13.43
39.5
5917
5429
1040 53044
88
0.95
6.5
0.05
18.07
65.81
5575
8115
1031 48924
76.2
0.92
6.9
0.05
20.46
73.3
9653
9214
750 42818
80.9
1.22
6.8
0.07
16.52
46.1
7505
9955
969 53904
86.5
1.33
6.6
0.05
27.35
43.2
6408
11531
972 49617
74.8
1.19
7.3
0.06
19.28
68.6
7284
13864
998 49526
77.2
(5-38)
B2
(38-70)
Merribee
(Brown
Chromosol)
A1
(0-17)
A2
(17-50)
B21
(50-70)
B22
(70-90)
Wet
A11
(Brown
Dermosol)
(0-5)
A12
(5-23)
B1
(23-52)
B2
(52-70)
The
Plantation
(Bundella)
Brigalow
A1
(Black
Vertosol)
(0-10)
B21
(10-47)
B22
(47-80)
Gurley
(Brown
Vertosol)
A1
(0-12)
B21
(12-85)
B22
(85-90)
Merrilong
Dimby 1
A1 (0-5)
1.45
6.4
0.13
11.52
40
10655
10008
1017 80886
108
(Yarraman)
(Black
Vertosol)
B21
1.07
6.2
0.09
11.96
59.4
8634
11048
1248 78954
98
1.07
6.2
0.09
12.37
72.3
8091
10259
1073 73019
87
1.16
7.7
0.04
28.27
73.2
7496
18966
1066 75730
86
(5-20)
B22
(20-70)
B3
(70-90)
20
Dimby 5
A11
(Brown
Vertosol)
(0-5)
A12
1.11
6.8
0.09
12.42
39.9
7367
16272
1147 76894
97
1.18
6.5
0.07
16.95
53.2
65.74
21353
1116 73096
89
1.11
6.2
0.06
19
62.8
73.85
24683
1105 74317
85
1.12
7.3
0.05
24.81
66.6
7487
22862
1122 72393
91
1.05
5.8
0.08
12.59
43.3
7994
5560
1194 84594
145
1
6.5
0.07
14.91
43
7925
6091
1394 77480
89
1.27
6.2
0.07
17.43
63.1
12873
1443 75070
85
1.23
7.1
0.06
21.68
59.9
6874
76243
1145 64615
82
0.98
5.4
0.05
17.77
50
1447
2146
895 22298
49.6
0.84
6.6
0.04
22.49
56.6
955
2456
580 18862
45.7
0.62
6.6
0.04
14.61
56.6
1568
2256
532 21049
45.9
0.62
6.9
0.03
18.89
65.9
1007
3395
645 23784
52.8
1.43
6.3
0.12
12.02
32.2
11932
2568
499 24924
60.2
0.88
6.5
0.08
11.17
49.8
11378
1620
472
3379
66.8
0.70
6.9
0.04
16.32
49.6
11310
5437
551 32807
69.7
0.58
7
0.03
17.05
52.5
8200
9231
501 31790
75.6
1.14
6.5
0.11
10.36
33.3
13378
2075
722 28207
65.7
1.19
6.2
0.08
14.44
49.8
12638
2019
662 26015
66.3
0.65
6.9
0.05
12
56.3
12307
1911
585 35489
69.7
0.42
7.2
0.03
12.83
49.9
11090
2462
665 32244
62.8
1.17
6.5
0.10
11.82
69.2
16738
1315
299 37361
81.6
0.52
6.8
0.07
7.55
72.8
18259
7508
456 39650
88.6
0.50
6.8
0.05
10.86
75.6
17225
1278
305 36940
78.8
(5-20)
B21
(20-75)
B22
(75-90)
Willows
A11
(Brown
Vertosol)
(0-5)
A12
(5-48)
B21
7915
(48-73)
B22
(73-90)
Livingston
(Moree)
Airport
(Black
Vertosol)
A1
(0-18)
AB
(18-80)
No-till
(Black
Vertosol)
A1
(0-15)
AB
(15-90)
Warili
(Forbes)
Old cattle
yard
A11
(Red Sodosol)
A12
(0-5)
(5-46)
B21
(46-80)
B22
(80-90)
Buttenshaws
A11
(Red Sodosol)
(0-5)
A12
(5-20)
B21
(20-63)
B22
(63-90)
Wilgo
(Mulwala)
Blackstump
(Grey
Vertosol)
A1
(0-10)
B21
(10-55)
B22
(55-75)
21
B3
0.79
7.3
0.04
22.52
73.0
18373
7466
388 35543
85.8
1.49
7.2
0.07
52.21
56.6
174497
2527
582 34916
79.1
0.50
6.6
0.04
8.60
69.3
20412
32380
286 34253
85.7
0.28
6.8
0.03
7.48
66.6
12176
1880
252 17304
56.7
1.30
6.8
0.09
14.20
43.3
12933
1984
286 19568
57.3
0.43
6.5
0.05
8.60
63.3
15686
1307
276 29908
74
0.30
6.2
0.03
8.68
63.3
13795
2422
397 26771
71.5
0.32
7.6
0.03
10.57
76.1
15959
8387
3384 27242
75
1.80
5.7
0.04
45
16.6
8200
871
404 16654
41.8
0.93
7.6
0.06
14.77
49.9
9458
27384
441 25671
53.8
0.68
7.7
0.01
19.09
49.8
9632
1522
315 31444
62.4
0.81
5.9
0.09
8.81
33.9
12819
NA
412 26300
60.4
0.51
7.2
0.07
7.64
65.8
18116
4695
406 39636
82
0.48
7.6
0.06
8.37
66.4
17700
2885
445 35338
81.4
0.96
6.2
0.07
14.26
56.3
12323
9350
561 33843
77.7
0.88
6.5
0.05
17.81
59.4
11666
24990
740 33171
78.7
0.88
6.8
0.04
23.47
53.2
11565
9101
610 33136
76
A1 (0-8)
0.79
6.9
0.07
11.21
59.9
12611
5806
734 40641
83.8
B21
0.53
7.2
0.04
12.33
62.5
12262
7145
728 38469
82
0.50
6.3
0.03
14.52
66.4
12073
9463
789 38444
85.4
0.60
7.3
0.03
20
49.2
1143
1521
521 22439
30.21
0.65
6.9
0.03
21.67
59.9
1045
5420
753 22572
19.53
0.73
6.3
0.05
14.6
46.6
1003
1973
671 20384
46.7
(75-90)
Two Tanks
A1
(Grey
Vertosol)
(0-26)
B21
Clearview
Front
(26-72)
(Grey
Vertosol)
(72-90)
B22
A1
(0-10)
B21
Boatrock
(10-56)
(Red
Ferrosol)
B22
(56-80)
B3
(80-90)
A1
(0-20)
Kilnyana
(Mulwala)
B1
Middleplain
(Grey
Vertosol)
(20-64)
B21
(64-90)
A1
(0-12)
B21
College
Green
(Brown
Vertosol)
(12-66)
B22
(66-90)
A1
(0-15)
Eurie Eurie
(Walgett)
B21
P4
(Brown
Vertosol)
New
(Grey
Vertosol)
(14-66)
B22
(66-90)
(8-70)
B22
(70-90)
A1
(0-10)
Kambodia
(Moree)
AB
Old
(Grey
Vertosol)
22
(10-90)
A1
(0-12)
B21
Cell
(Red
Chromosol)
0.62
7.2
0.04
15.5
53.1
9995
1500
829 15943
41.7
0.82
6.9
0.02
41
59.5
8645
2013
602 23910
51
0.82
6.3
0.08
10.31
43.2
10323
272
542 35478
66.3
0.63
6.5
0.07
9
43
11028
428
461 39482
67.8
0.89
6.74
0.07
12.71
63.1
11409
11988
448 36710
67.8
0.43
7.9
0.04
10.75
59.9
12550
40125
508 36962
68.1
A1 (0-5)
0.81
5.8
0.07
11.57
43.3
10265
1578
776 31638
60.8
B21
0.51
5.6
0.06
8.5
43
10283
887
550 35993
59.5
0.33
6.44
0.04
8.25
63.1
9591
1343
426 35354
59.3
0.55
7.64
0.03
18.33
59.9
9843
7019
390 32304
63
(12-52)
B22
(52-82)
A1
(0-5)
Lachlan
Downs
Wheat
(Rankin
Springs)
(Red
Chromosol)
B21
(5-40)
B22
(40-66)
B3
(66-99)
(5-20)
B22
(20-68)
B3
(68-90)
For example, on ‘Kilnyana’ there were two distinct soil texture classifications catalogued as Sandy
Clay and Clay, which had average clay contents of 35% and 77% respectively. Such vast differences in
physical properties would be expected to require different soil management approaches and
influence the potential for carbon storage and nitrogen emission within-farm.
However, there were also a number farms where the sand content of the soil remained relatively
constant at the trial sites but the differences in texture were driven by less dramatic changes in the
relative amounts of silt and clay. For example, the three trial sites at ‘Wilgo’ had average sand
contents of 22%, 22% and 24% and silt contents that ranged from 7% to 13%. These changes might
generally be considered to have less potential impact on the potential for carbon storage and
nitrogen emission within-farm.
23
Clay (%)
Sand (%)
Figure 11: Plot of average soil texture for the soil samples in each trial area on each farm. Texture
component symbols are: Cl = clay, Si = silt, Sa = sand, Lo = loam.
Initial soil carbon content (SOC)
Table 5 reports the average SOC for each site as measured in February/March 2013, each sie
management category and each whole farm. Figure 12 displays the whole-farm data and shows that
the farms in the northern biogeoregions have in general registered the highest overall SOC. The
notable exceptions to this generalisation being ‘Merribee’ at Binya, ‘Kambodia’ (Moree) and ‘Eurie
Eurie’ (Walgett).
The farms with higher SOC are also generally the most variable (Table 5, Figure 12), which may have
implications for sampling design and intensity in any long-term monitoring/auditing of SOC content
(0-30cm). Greater variability is likely to require more direct sampling or more sophisticated
stratification/modelling of SOC in an area using prior information to obtain accurate baseline and
change-over-time estimates of SOC, which as pointed out by Singh et al. (2012) may increase the
cost of estimating SOC at the field scale.
24
Table 5: The results of baseline sampling in 2013 for SOC (30 cm) at each trial site. Annotation with the same
letter within each farm means values are not significantly different.
Farm
(Location)
Paddock (management
category)
SOC
(%)
Merribee
Pump (cropping)
1.37a
(Binya)
Merribee (cropping)
1.31a
Wet (cropping)
1.34a
Native
1.79b
The
Plantation
(Bundella)
Brigalow (NT cropping)
1.33a
Gurley (long term cropping)
Merrilong
(Yarraman)
Livingston
(Moree)
Warili
(Forbes)
Average SOC (%)
cropping = blue,
native/pasture =
yellow
Whole-farm
average SOC
(%) and CV
(%)
1.34
1.57
1.79
(16.4)
1.25a
1.29
1.50
Pasture
1.70b
1.7
(13.7)
Dimby 1 (Irrigated cropping)
1.09a
1.12
Dimby 5 (Irrigated cropping)
1.16a
1.33
Willows (NT cropping)
1.10a
(27.3)
Native
1.53b
Airport (long-term cropping)
1.02a
No till
1.06a
1.04
1.25
Pasture
1.46b
1.46
(14.7)
Buttenshaws (Irrigated
cropping)
1.09a
1.09
Old cattle yard
1.32b
1.27
(10.8)
1.09
1.09
Summary
Native SOC is
significantly
higher than
cropping.
Pasture SOC is
significantly
higher than
cropping.
Native SOC is
significantly
higher than
cropping.
1.53
1.18
Pasture SOC is
significantly
higher than
cropping.
Perrenial pasture
has higher SOC.
(perennial pasture)
Scrubby lane (native)
1.22b
Wilgo
Blackstump (cropping)
1.01b
(Mulwala)
Two Tanks (cropping)
1.10ab
Kilnyana
(Mulwala)
Eurie Eurie
(Walgett)
Kambodia
(Moree)
Lachlan
Downs
Middleplain has
significantly
lower SOC.
Clearview Front (cropping)
1.19a
Boatrock west (cropping)
1.03a
Middleplain (cropping)
0.81b
0.96
0.97
Native red (native)
1.09a
0.98
(12.3)
Prairie (perennial pasture)
0.87a
College green (new cropping)
0.96a
P4 (longer-term cropping)
0.91a
0.94
0.96
Bullock (native pasture)
0.97a
0.97
(12.8)
New (cropping)
0.70a
Across rail (No-till cropping)
0.66a
Old (long-term cropping)
0.73a
0.70
0.86
Pasture
1.01b
1.01
(19.7)
Cell (cropping)
0.83a
Wheat (cropping)
(Rankin Springs) Native
25
(17.5)
Clearview has
signicantly higher
SOC than
Blackstump.
0.76b
0.80
0.78
0.76b
0.76
(12.2)
No significant
difference in SOC
between cropped
and pasture.
Pasture SOC is
significantly
higher than
cropping.
Cell treated area
has higher SOC
Figure 12: Box plots of the average SOC (solid black line) and variability in SOC (size of each box) for the 10
farms in top 30 cm for samples taken in 2013.
Spatial variability in initial SOC
Table 6 describes the model parameters for the semivariograms fitted to the baseline SOC data on
each farm. The semivariograms are shown in Figure 13. These data describe the spatial dependence
of SOC content across the farms. The sill values (C0+C1; the height of the semivariogram) depict the
overall variation to be found. The nugget values (C0; the intercept of the semivariogram with the Y
axis) represent small distance variation. The structure (C1) represents the amount of variation that
can be spatially modelled and the range value is the distance at which the maximum variability (sill)
is reached. In effect it is the distance to which samples of SOC can be related.
The data confirms that the total variation in SOC changes between farms and that the spatial
relationships between SOC samples changes across farms. This information will impact on the design
of universal SOC sampling schemes, with the average semivariogram (Figure 13) potentially useful in
determining future sampling spacing.
These baseline results on the levels and variability of SOC across the farms suggest that:
x
in general, cropping activities on these farms may have reduced/limited the SOC content relative
to the native/pasture areas. Native and pasture areas are most likely to contribute to higher and
more stable levels of SOC due to reduced soil disturbance and sustained vegetative cover. This
general result is in line with previous studies across south-east NSW (e.g. Chan, 2008; Chan et al.
2011). Previous work (Toole, 2009) has also suggested that in lower rainfall areas where the SOC
is expected to be lower, the reduction in SOC due to the introduction of cropping may be
relatively smaller than higher rainfall areas. The results here from Walgett and Rankin Springs
appear to confirm this pattern;
26
x
while there is some evidence that fields under no-till management have a slightly higher SOC
level when compared to fields with a longer cultivation history, there is no statistically significant
difference in SOC on these farms between conventionally cropped and no-till areas. It is
generally believed that no-till management would lead to higher crop production and greater
quantities of biomass and root exudates entering the carbon cycle (Chan et al, 2009). One
possible hypothesis for the results here is that there is an increase in carbon entering the system
from no-till management, but the influx of carbon, in conjunction with the improvements in soil
structure and moisture regimes provides a priming effect that increases microbial activity and
raises the mineralisation of SOC. While they didn’t find a statistically significant result, the work
of Cowie et al. (2013) provides evidence that increased microbial activity appears to follow on
from increased organic inputs;
x
the farms with higher SOC also generally display the most variability across the sampled fields.
Where variability is high, options for audit sampling and practices to improve SOC content may
need to be considered in more detail than more uniform areas;
x
where crop yield data was available, there were relationships between yield and measured soil
properties. In some cases a significant positive relationship between SOC and crop yield was
observed which could suggest that an increase in crop production was contributing to greater
SOC. However a positive relationship between crop yield and SOC was not consistent throughout
the farms, which may be a function of the relatively small number of farms combined with
different management practices and considerable soil type variability.
Table 6: Farm-scale SOC semivariogram parameters for all the farms.
Farm
C0
C1
C0+C1
A1
(nugget)
(structure)
(sill)
(range)
Nugget Ratio
(%)
Model
Merribee
0.028
0.020
0.048
131.8
58
Spherical
The Plantation
0.0005
0.116
0.1165
388.7
0
Spherical
Merrilong
0.071
0.014
0.085
103.2
84
Spherical
Livingston
0.015
0.019
0.034
907.1
44
Spherical
Warili
0
0.037
0.037
564.4
0
Spherical
Wilgo
0.011
0.027
0.038
215.1
29
Spherical
Kilnyana
0.01
0.002
0.012
238.4
83
Spherical
Eurie Eurie
0.005
0.011
0.016
831.0
31
Spherical
Kambodia
0.014
0.018
0.032
455.7
44
Spherical
Lachlan Down
0
0.006
0.006
451.0
0
Spherical
Average
0.01
0.03
0.05
428.6
37
Spherical
27
Figure 13: Variogram for SOC in top 30 cm for samples taken in 2013 for individual farms, model used=
Spherical/no of pairs
Relationship between soil texture and SOC
Using the individual sample site data, SOC and soil texture were then compared to explore the
influence of soil texture on SOC. Table 7 shows that SOC was significantly higher in Silty Clay soil
across these farms. Soil dominated by coarse soil particles (sand fractions) are generally known to
hold less organic matter than soil with greater silt and clay content (finer soil fractions), so it is
expected that as the quantity of fine soil fractions increases, so does the potential to store SOC.
Heywood and Turpin (2013), among others, have confirmed that increasing soil clay content within a
Biogeoregion is often associated with increased SOC. The results presented here suggest that a
relatively moderate increase in silt content in soil with a high percentage of fine soil fractions may be
related to a significant increase in ability to store SOC.
Table 7: Relationship between soil texture and SOC across all farms.
Soil texture
Silty Clay
28
Cropping and Pasture SOC (%)
a
1.34
Cropping SOC (%)
1.51
b
1.11
b
1.03
1.22
b
1.01
b
0.79
Clay
1.02
Sandy Clay
0.95
Pasture SOC (%)
a
a
a
a
Change in soil carbon (SOC) over time
Detailed summary statistics for the SOC and SCS for 2013 and 2015 are provided in Tables 8 and 9
respectively. Overall the soils this project had average SOC (%) and SCS (Mg/ha) in 2013 and 2015 of
1.09%, 42.55 Mg/ha, and 1.01% and 39.17 Mg/ha respectively in the 0-30 cm layer. This data
documents a small average decrease over the two year period. This data documents a small average
decrease over the two year period.
However as can be seen from Table 10, both increases and decreases in SOC and SCS were observed
across the individual field sites. To assess the significance of any change in SOC between 2013 and
2015, the standard error from the 2013 measurements were used to calculate the 95% confidence
interval for the mean SOC in each individual field and for the mean of all fields.
The average SOC and SCS measured between 2013 and 2015 for all the fields combined are not
significantly different. Table 11 documents whether the changes in SOC within the individual fields
were significantly different between 2013 and 2015. SOC levels in the majority of fields (49%) did not
significantly change, 17% rose significantly and 34% decreased significantly.
The decreases in SOC can be partly attributed to a below average total rainfall for the period
contributing to reduced vegetative input in some areas (especially ‘Livingston’ and ‘Eurie Eurie’). The
five fields where SOC significantly increased were either on the Liverpool Plains where rainfall was
generally close to average or on fields which had relatively low levels of SOC in 2013.‘
To explore the impact of reduced rainfall on changes in SOC, the change in SOC (Delta SOC calculated as SOC in 2015 minus SOC in 2013) was compared to the two year total rainfall deficit
(TRD) from the long-term average (Equation 5)
TRD = (Long term average annual rainfall X 2) – (rainfall 2013 + rainfall 2014)
Eq. 5
The data is shown in Table 12. A linear relationship between Delta SOC and the TRD was observed
across the farms (Figure 14) with a regression model providing a fit of R2 = 0.40. Previous studies
(e.g. Toole, 2009) have found lower SOC in lower rainfall areas and the significant decrease in SOC %
at ‘Walgett’ and ‘Livingston’ appears to confirm this pattern.
29
Table 8: Soil carbon levels (SOC and SCS) in 2013 (0-30 cm).
Soil organic carbon (SOC )
Farm
Paddock
(Location)
Mean
Farm
SOC (%) s.d. s.e. CV (%) Mean CV (%)
Soil carbon stock (SCS)
Mean
SCS
Farm
s.d. s.e. CV (%) Mean CV (%)
(Mg/ha)
Merribee
Pump
1.37 0.12 0.03 8.89
16.40
(Binya)
Merribee
1.31 0.22 0.06 16.73
47.10 8.62 2.23 18.20
Wet
1.34 0.13 0.03 9.36
49.96 5.37 1.39 10.75
Native
1.79 0.55 0.24 30.63
64.80 20.35 9.10 31.40
The Plantation Gurley
1.25 0.12 0.03 9.67
(Bundella)
Brigalow
1.33 0.23 0.06 17.48
51.04 9.51 2.45 18.63
Native
1.70 0.57 0.18 33.53
62.59 21.17 6.70 33.83
Merrilong
Dimby 1
1.09 0.28 0.07 25.63
(Yarraman)
Dimby 5
1.16 0.29 0.07 24.93
43.43 10.71 2.76 24.66
Willows
1.10 0.24 0.06 21.57
41.22 8.60 2.22 20.86
Native
1.53 0.57 0.25 37.22
53.16 20.45 9.15 38.48
Livingston
Airport site
1.02 0.15 0.04 14.92
(Moree)
No till site
1.06 0.16 0.04 15.26
Native
1.46 0.18 0.06 12.41
20.23
27.34
14.20
48.91 5.70 1.47 11.66
48.96 5.29 1.37 10.80
40.59 9.20 2.38 22.67
45.34 7.25 1.87 15.99
26.67
14.96
59.99 7.37 2.32 12.28
Old cattle yard 1.32 0.16 0.04 12.00
(Forbes)
Buttenshaws
1.09 0.14 0.04 13.10
39.44 7.31 1.89 18.53
Native
1.22 0.09 0.03 7.34
48.98 6.63 2.09 13.54
Wilgo
Blackstump
1.01 0.20 0.05 19.37
(Mulwala)
Two tanks
1.10 0.17 0.04 15.22
40.26 5.75 1.48 14.29
Clearview
1.19 0.22 0.06 18.89
44.29 8.23 2.12 18.58
Kilnyana
Boatrock
1.03 0.17 0.06 16.74
(Mulwala)
TM21
1.05 0.12 0.05 11.79
44.73 5.77 2.18 12.89
Middleplain
0.81 0.11 0.03 13.66
30.12 5.12 1.21 17.01
Prarie
0.87 0.08 0.03 9.54
33.99 2.82 0.89 8.29
Native
1.09 0.10 0.03 9.04
42.52 4.59 1.45 10.79
Eurie Eurie
College green
0.96 0.16 0.04 17.43
(Walgett)
P4
0.91 0.12 0.03 12.86
34.29 5.94 1.53 17.32
Bullock
0.97 0.08 0.02 8.10
36.73 3.41 1.08 9.27
Kambodia
Across rail
0.66 0.11 0.03 16.39
(Moree)
New
0.70 0.21 0.06 30.17
30.57 8.71 2.51 28.48
Old
0.73 0.12 0.03 15.94
31.05 4.80 1.39 15.47
Native
1.01 0.16 0.05 16.23
43.64 6.40 2.02 14.65
Lachlan Down Celled
0.83 0.08 0.01 9.28
(Rankin Springs) Uncelled
0.76 0.10 0.03 13.68
32.64 5.22 1.65 16.00
Lock
0.70 0.12 0.05 16.84
30.81 4.64 2.07 15.05
Unlock
0.81 0.05 0.02 6.06
35.10 3.09 1.38 8.80
All farms
1.09 0.190.06 16.33
42.55 7.545 2.33 17.23
30
21.09
45.92 7.62 1.96 16.60
Warili
Total
18.00
10.80
17.83
12.24
12.80
19.68
11.46
49.53 6.38 1.65 12.89
36.14 7.25 1.87 20.07
43.12 6.93 2.45 16.08
35.47 6.38 1.65 17.99
28.07 4.26 1.23 15.20
37.23 4.18 0.73 11.21
15.42
17.65
13.02
14.86
18.45
12.77
Table 9: Soil carbon levels (SOC and SCS) in 2015 (0-30 cm).
Soil organic carbon (SOC )
Farm
Paddock
(Location)
Mean
Farm
Soil carbon stock (SCS)
Mean
Farm
SOC (%) s.d. s.e. CV (%) Mean CV SCS s.d. s.e. CV (%) Mean CV
(%) (Mg/ha)
(%)
Merribee
Pump
1.16
0.09 0.02 7.81
(Binya)
Merribee
1.29
0.11 0.03 8.57
45.24 4.35 1.12 9.61
Wet
1.24
0.19 0.05 15.23
47.52 7.99 2.06 16.82
Native
1.34
0.19 0.08 14.06
50.22 6.45 2.88 12.84
The Plantation Gurley
NA
NA
(Bundella)
Brigalow
1.57
0.81 0.21 51.77
56.43 28.43 7.34 50.38
Native
2.11
0.96 0.43 45.59
71.89 31.85 14.25 44.31
Merrilong
Dimby 1
1.20
0.53 0.14 43.89
(Yarraman)
Dimby 5
1.33
0.59 0.15 44.36
43.57 22.62 5.84 51.91
Willows
1.31
0.49 0.13 37.65
48.34 18.15 4.69 37.53
Native
1.55
0.59 0.27 38.38
54.89 20.16 9.02 36.74
Livingston
Airport site
0.60
0.05 0.01 8.98
(Moree)
No till site
0.60
0.05 0.01 9.16
Native
NA
NA
11.42
48.68
41.07
16.57
42.41 3.06 0.79 7.22
NA
NA
NA
NA
42.77 19.54 5.05 45.68
26.90 2.59 0.67 9.61
0.93
0.29 0.13 31.58
Old cattle yard 1.25
0.21 0.05 16.78
(Forbes)
Buttenshaws
1.18
0.14 0.04 11.69
42.67 5.64 1.46 13.26
Native
1.13
0.17 0.07 14.68
46.33 9.21 4.12 19.89
Wilgo
Blackstump
0.72
0.17 0.04 23.33
(Mulwala)
Two tanks
0.67
0.12 0.03 18.54
29.05 5.69 1.47 19.60
Clearview
0.76
0.17 0.05 22.87
31.07 7.21 1.86 23.22
Kilnyana
Boatrock
0.96
0.09 0.03 9.73
(Mulwala)
TM21
1.01
0.11 0.04 11.22
41.81 5.99 2.26 14.33
Middleplain
0.91
0.11 0.03 12.24
34.81 4.14 0.98 11.90
Prarie
0.84
0.14 0.06 16.62
33.48 5.20 2.32 15.53
Native
1.09
0.02 0.01 2.15
42.90 1.98 0.88 4.61
Eurie Eurie
College green
0.69
0.09 0.02 13.00
(Walgett)
P4
0.78
0.15 0.04 19.20
28.99 6.06 1.56 20.89
Bullock
0.63
0.06 0.03 9.65
25.64 2.48 1.11 9.66
Kambodia
Across rail
0.61
0.09 0.03 14.29
(Moree)
New
0.82
0.12 0.04 14.85
35.15 5.27 1.52 15.00
Old
0.87
0.09 0.03 10.41
36.59 4.78 1.38 13.07
Native
0.84
0.04 0.02 4.81
34.16 0.45 0.20 1.33
Lachlan Down Celled
0.85
0.09 0.02 10.29
(Rankin Springs) Uncelled
0.80
0.12 0.04 15.60
35.28 5.77 1.83 16.36
Lock
0.77
0.21 0.12 27.39
33.22 9.27 5.35 27.90
Unlock
0.80
0.15 0.09 18.91
33.45 5.76 3.33 17.23
All farms
1.01
0.22 0.07 19.29
39.16 8.39 2.83 19.70
31
47.35
42.97
16.76
27.27 2.86 0.74 10.49
Warili
Total
11.63
40.19 12.13 5.43 30.19
14.39
21.58
10.39
13.95
11.09
18.07
46.73 7.70 1.99 16.47
28.15 6.83 1.76 24.26
40.67 4.95 1.75 12.16
26.11 3.55 0.92 13.58
26.41 3.79 1.09 14.33
36.74 4.27 0.76 11.63
16.54
22.36
11.71
14.71
10.93
18.28
Table 10: Soil Carbon change over two years (0-30 cm).
Soil organic carbon (SOC )
Farm
Paddock
(Location)
2013
Soil carbon stock (SCS)
2015
Difference 2013
2015
(%)
SOC (%) s.e. SOC s.e.
SCS
SCS s.e.
(Mg/ha)
(%)
(Mg/ha)
s.e.
Difference
(%)
Merribee
Pump
1.37
0.03 1.16 0.02
-0.21
48.91 1.47
42.41
0.79
-6.5
(Binya)
Merribee
1.31
0.06 1.29 0.03
-0.02
47.10 2.23
45.24
1.12
-1.86
Wet
1.34
0.03 1.24 0.05
-0.1
49.96 1.39
47.52
2.06
-2.44
-0.45
64.80 9.10
50.22
2.88
-14.58
NA
NA
NA
Native
1.79
0.24 1.34 0.08
The Plantation Gurley
NA
NA
NA
NA
(Bundella)
Brigalow
1.33
0.06 1.57 0.21
0.32
51.04 2.45
56.43
7.34
7.47
Native
1.70
0.18 2.11 0.43
0.41
62.59 6.70
71.89
14.25
9.3
Merrilong
Dimby 1
1.09
0.07 1.20 0.14
0.11
40.59 2.38
42.77
5.05
2.18
(Yarraman)
Dimby 5
1.16
0.07 1.33 0.15
0.17
43.43 2.76
47.45
5.26
4.02
Willows
1.10
0.06 1.31 0.13
0.21
41.22 2.22
48.34
4.69
7.12
Native
1.53
0.25 1.55 0.27
0.02
53.16 9.15
54.89
9.02
1.73
Livingston
Airport site
1.02
0.04 0.60 0.01
-0.42
45.34 1.87
26.90
0.67
-18.44
(Moree)
No till site
1.06
0.04 0.60 0.01
-0.46
45.92 1.96
27.27
0.74
-18.65
Native
1.46
0.06 0.93 0.13
-0.53
59.99 2.32
40.19
5.43
-19.8
Old
cattleyard
1.32
0.04 1.25 0.05
-0.07
49.53 1.65
46.73
1.99
-2.8
Buttenshaws
1.09
0.04 1.18 0.04
0.09
39.44 1.89
42.67
1.36
3.23
Native
1.22
0.03 1.13 0.07
48.98 2.09
46.33
4.12
Warili
(Forbes)
NA
NA
NA
Wilgo
Blackstump
1.01
0.05 0.72 0.04
-0.09
-0.29
36.14 1.87
28.15
1.76
-2.65
-7.99
(Mulwala)
Two tanks
1.10
0.04 0.67 0.03
-0.43
40.26 1.48
29.05
1.47
-11.21
Clearview
1.19
0.06 0.76 0.05
-0.43
44.29 2.12
31.07
1.86
-13.22
Kilnyana
Boatrock
1.03
0.06 0.96 0.03
-0.07
43.12 2.45
40.67
1.75
-2.45
(Mulwala)
TM21
1.05
0.05 1.01 0.04
-0.04
44.73 2.18
41.81
2.26
-2.92
Middleplain
0.81
0.03 0.91 0.03
0.1
30.12 1.21
34.81
0.98
4.69
Prarie
0.87
0.03 0.84 0.06
-0.03
33.99 0.89
33.48
2.32
-0.51
Native
1.09
0.03 1.09 0.01
0
42.52 1.45
42.90
0.88
0.38
Eurie Eurie
College green
0.96
0.04 0.69 0.02
-0.27
35.47 1.65
26.11
0.92
-9.36
(Walgett)
P4
0.91
0.03 0.78 0.04
-0.13
34.29 1.53
28.99
1.56
-5.3
Bullock
0.97
0.02 0.63 0.03
-0.34
36.73 1.08
25.64
1.11
-11.09
Kambodia
Across rail
0.66
0.03 0.61 0.03
-0.05
28.07 1.23
26.41
1.09
-1.66
(Moree)
New
0.70
0.06 0.82 0.04
0.12
30.57 2.51
35.15
1.52
4.58
Old
0.73
0.03 0.87 0.03
0.14
31.05 1.39
36.59
1.38
5.54
Native
1.01
0.05 0.84 0.02
-0.17
43.64 2.02
34.16
0.20
-9.48
Lachlan Down Celled
0.83
0.01 0.85 0.02
0.02
37.23 0.73
36.74
0.76
-0.49
(Rankin
Springs)
Uncelled
0.76
0.03 0.80 0.04
0.04
32.64 1.65
35.28
1.83
2.64
Lock
0.70
0.05 0.77 0.12
0.07
30.81 2.07
33.22
5.35
2.41
Unlock
0.81
0.02 0.80 0.09
-0.01
35.10 1.38
33.45
3.33
-1.65
32
Total
All farms
1.09
0.06 1.01 0.07
-0.08
42.55 2.33
39.17
2.83
-3.38
Table 11: Comparison of mean 2015 SOC% to mean 2013 SOC% in 0-30cm using 2013 confidence interval.
Soil organic carbon (SOC )
Farm
Paddock
2013 (%) s.e.
C.I.
(Location)
33
2015 Significant change
(%)
Merribee
Pump
1.37
0.03 0.06 1.16
down
(Binya)
Merribee
1.31
0.06 0.12 1.29
none
Wet
1.34
0.03 0.06 1.24
down
Native
1.79
0.24 0.47 1.34
none
The Plantation Gurley
NA
NA
NA
NA
(Bundella)
Brigalow
1.33
0.06 0.12 1.57
up
Native
1.70
0.18 0.35 2.11
up
Merrilong
Dimby 1
1.09
0.07 0.14 1.20
none
(Yarraman)
Dimby 5
1.16
0.07 0.14 1.33
up
Willows
1.10
0.06 0.12 1.31
up
Native
1.53
0.25 0.49 1.55
none
Livingston
Airport site
1.02
0.04 0.08 0.60
down
(Moree)
No till site
1.06
0.04 0.08 0.60
down
Native
1.46
0.06 0.12 0.93
down
Warili
Old cattle yard 1.32
0.04 0.08 1.25
none
(Forbes)
Buttenshaws
1.09
0.04 0.08 1.18
none
Native
1.22
0.03 0.06 1.13
none
Wilgo
Blackstump
1.01
0.05 0.10 0.72
down
(Mulwala)
Two tanks
1.10
0.04 0.08 0.67
down
Clearview
1.19
0.06 0.12 0.76
down
Kilnyana
Boatrock
1.03
0.06 0.12 0.96
none
(Mulwala)
TM21
1.05
0.05 0.10 1.01
none
Middleplain
0.81
0.03 0.06 0.91
up
Prarie
0.87
0.03 0.06 0.84
none
Native
1.09
0.03 0.06 1.09
none
Eurie Eurie
College green
0.96
0.04 0.08 0.69
down
(Walgett)
P4
0.91
0.03 0.06 0.78
down
Bullock
0.97
0.02 0.04 0.63
down
Kambodia
Across rail
0.66
0.03 0.06 0.61
none
(Moree)
New
0.70
0.06 0.12 0.82
none
Old
0.73
0.03 0.06 0.87
up
Native
1.01
0.05 0.10 0.84
down
Lachlan Down Celled
0.83
0.01 0.02 0.85
none
(Rankin Springs) Uncelled
0.76
0.03 0.06 0.80
none
Lock
0.70
0.05 0.10 0.77
none
Unlock
0.81
0.02 0.04 0.80
none
NA
Table 12: Delta SOC % value for each farm between 2013 and 2015 with recorded rainfall in 2013 and 2015,
mean total rainfall for 2014 and 2015 and calculated rainfall deficit (TRD).
North to South
Farm
Rainfall data
Delta
SOC (%)
Rainfall 2013
Rainfall 2014
Mean
(mm)
(mm)
for all years (mm)
Total Rainfall
Deficit
(mm)
Kambodia
0.01
565.8
536.8
618.3
-134
Livingston
-0.47
499.4
354.8
592.9
-331.6
Walgett
-0.25
248.4
270.2
437.5
-356.4
The Plantation
0.33
513.4
453.2
631.7
-296.8
Warili
-0.02
388.2
462.3
442.6
-34.7
Merrilong
0.13
680
612
679
-66
Lachlan downs
0.03
221
381
412.6
-223.2
Merribee
-0.195
381.4
391.8
419.3
-65.4
Kilnyana
-0.008
338
384.4
445
-167.6
Wilgo
-0.38
347.4
446
502.3
-211.2
Figure 14: Plot of soil carbon (SOC %) change between 2013 and 2015 against the Total Rainfall Deficit (mm).
Soil acid phosphatase activity
No significant difference in SOC was observed between No-till (NT) and conventionally tilled areas
(CT) on the farms studied. Research has noted that SOC sequestration is highly dependable on
ancillary variables, such as site-specific environmental conditions, crop type and management, and
the natural storage capacity of a given soil (Kravchenko, Robertson et al. 2006; Wong, Murphy et al.
2008). With respect to SOC management, there have been several studies that have shown higher
34
SOC levels in perennial pasture, pasture-cropping, and rotational irrigated cropping (Luo, Wang et al.
2010). However, elevation of SOC via No till (NT) practices is still a conundrum.
Scientific literature provides evidence that Australian agricultural soils have the potential to hold
SOC through the introduction of conservation practices (Heenan, Chan et al. 2004), however, there
hasn’t been a detectable benefit recorded for Vertosols (McLeod, Schwenke et al. 2013). This mixed
response to NT management is a challenge for understanding the SOC sequestration potential. It is
quite possible that NT management may improve the overall ‘condition’ of the soil, creating higher
microbial activity, nutrient cycling, and thereby assisting SOC cycling.
Estimating microbial activity can be achieved using indicators that measure soil enzyme activity,
because the enzymes are known to play an important role in the biochemical functions of the soil
and decomposition of soil organic matter. Soil enzymes are formed by plants and microorganisms.
Measurement of the activities of soil enzymes such as phosphatase, glycosidase and lipases, is
known to show a quick response to any change in biophysical properties of soil (Mathew, Feng et al.
2012). For this project the soil acid phosphatase activity (APA) was compared for soil under NT and
CT management regimes on four farms of NSW (Table 13). This enzyme is sensitive to soil tillage and
is a good indicator of soil quality (Ekenler and Tabatabai 2003; Gil-Sotres, Trasar-Cepeda et al. 2005).
Table 13: Farms and paddocks studied for APA comparison.
Farm
Management information
Code
‘Livingston’,
Moree
‘Airport’ is an area with a recent history of soil cultivation.
CT
‘No-Till’ has been under no-till management for 25 years.
NT
‘Kambodia’,
Moree
‘New’ is an area cropped for the past 12-15 years under stubble retention and
no-till management
NT
‘Old’ has been under cropping for 65 years
CT
‘Dimby 1’ and is irrigated rotational cropping
CT
‘Dimby 5’ and is irrigated rotational cropping
CT
‘Willow’ has been under dryland, no-till management for 25 years.
NT
‘The Plantation’ is now under no-till management but ‘Gurley’ is an area with a
previous history of soil cultivation since the 1940’s.
CT
‘Brigalow’ is a newer cropping paddock with predominant history of no-till
management.
NT
‘Merrilong’,
Yarraman
‘The
Plantation’,
Bundella
In Figure 15 the APA activity measured from the whole soil profiles is plotted with soil profile depth
on each farm paddock. The colours are sequential with paler colours representing lower content and
35
darker colours representing higher content respectively. There are stark differences between the
measured phosphatase activities over the different depth horizons. The top soil horizon (~0-10 cm)
has the strongest response to measured APA and the activity then declines with depth in all soil
profiles. In Table 14, the APA data for the topsoil (0-30cm) samples measured in 2013 is displayed.
The two farms in the Moree area (Livingston and Kambodia) show significantly greater APA activity
in the NT sites as compared to the CT sites. The two farms on the Liverpool Plains (Merrilong and
The Plantation) show no significant differences.
It has been known for some time now that the top layers of a soil profile have high microbial activity
compared to the subsoil (Trumbore, Davidson et al. 1995). This may be a consequence of more root
growth and development in the top layers of a soil profile. The topsoil is expected to hold more of
the labile forms of carbon, as opposed to the subsoil that is known to hold more stable and
recalcitrant forms (Fontaine, Barot et al. 2007). Top soil carbon is usually added via different
microbial and plant activities and exists in the form of chemical complexes such as sugars, peptides,
amino acids, plant enzymes and organic acids (Rovira 1969).
Under NT management systems, generally more organic matter is maintained at the surface which
should encourage microbial activity (Angers, Bissonnette et al. 1993; Mathew, Feng et al. 2012).
Residue retention in NT system consists of layers of litter at the soil surface, which over years go
through several levels of decomposition processes (Bosatta and Agren 1985). Catabolism is the
process where soil microbes chemically breakdown this surface litter for energy and is controlled by
factors such as the quality of litter, types of flora and fauna, resident soil enzymes, soil pH, soil C/N
ratio, environmental factors, crop rotations, and soil moisture and structure (Heal, Anderson et al.
1997; Baldock and Skjemstad 2000; Krull, Baldock et al. 2003). Critically, the higher the quality of the
surface litter, the higher the microbial activity below ground. It is also possible that rhizosphere
priming may be occurring in NT systems, whereby increases in the turnover rate of soil organic
matter are induced by the increased addition of carbon and/or nutrients However, the actual impact
on SOC turnover still remains under question.
36
Livingston
Kambodia
Merrilong
Plantation
Figure 15: Soil APA within the profiles of each paddock.
Table 14: APA in 0 - 30 cm soil samples across trial sites in 2013.
Mean
Farm
Site ID
SOC % s.e.m
APA
s.e.m
BD
pH
EC
Clay
-3
(g cm ) (H20) (μS/cm) (%)
‘Livingston’,
Airport(CT)
1.02±0.2a 0.04 114.71±15.97a 4.12 1.48 8.23 133.4 59.52
Moree
No till (NT)
1.06±0.2a 0.04 166.03±44.12b11.39 1.44 8.04 193.6 65.59
‘Kambodia’,
New (NT)
0.68±0.2a 0.03 128.36±26.71a 5.45 1.43 7.91
83.3 61.31
Moree
Old (CT)
0.73±0.1 a 0.03 103.70±21.59b 6.23 1.42 6.98
79.4 53.51
‘Merrilong’,
Dimby 1 (CT) 1.09±0.3a 0.07 312.30±65.21a16.84 1.26 7.77
146 50.75
Yarraman
Dimby 5 (CT) 1.16±0.3a 0.07 335.90±49.46a12.77 1.23 8.03 153.1 44.26
Willows(NT) 1.10±0.2a 0.06 335.67±68.42a17.67 1.25 7.91 127.6 50.12
‘The Plantation’,Brigalow (NT) 1.33±0.2a 0.06 283.62±63.72a16.45 1.28 8.02 206.3 52.16
Bundella
Gurley(CT)
1.25±0.1a 0.03 259.25±28.34a 7.32 1.30 7.83 260.5 61.43
Mean, ± standard deviation not connected by same letter are significantly different. Student t-test was used to
SOC %, soil organic carbon (%); s.e.m, standard
error mean; APA, Acid phosphatase activity (μg of p-nitrophenol released/g/hr); BD, Bulk density; EC,
Electrical conductivity; management code is given in parentheses as CT, tilled, and NT, no-till.
37
The APA data for the top soil (0-30cm) samples from 2015 was used to calculate the change in APA
() from 2013 (Table 15). The results show that on ‘Livingston’ both SOC and APA have
decreased significantly as compared to ‘Kambodia’ and ‘Merrilong’. The changes in APA were also
compared to the calculated Total Rainfall Deficit (TRD) in Table 15 where a strong correlation is
observed between the decline in APA and rainfall deficit. On ‘Livingston’ there is considerable
decline in APA in No-till as compared to the airport site. Similarly Huang W et al (2011) found that
APA follows a seasonal trend with an increase during wet seasons. No-till and Airport were left
uncultivated in 2014 due to drought, the consequences of no crop and drought is evident in the
results.
Table 15: Delta SOC and APA change over two years with rainfall deficit.
Farm
Site ID
!%
Rainfall deficit (mm)
‘Livingston’,
Airport(CT)
No till (NT)
-0.42
-0.46
-17.52
-50.12
-331.6
New (NT)
Old (CT)
0.04
0.14
12.26
7.63
-134
Dimby 1 (CT)
Dimby 1 (CT)
Willows (NT)
0.11
0.17
-10.61
-11.64
-11.63
Moree
‘Kambodia’,
Moree
‘Merrilong’,
Yarraman
38
0.21
-66
Greenhouse gas abatement
Parameterisation and validation of the APSIM model to field sites
Realistic yields, water balances, and soil carbon dynamics were simulated for all fifteen sites
(farm/paddock combinations; Appendices 2 to 9).
Scenarios with potential to mitigate greenhouse gas emissions
Differences in the simulated values obtained between alternative scenarios and the No Burn
scenario (‘baseline’ practice) are presented for all fifteen sites for carbon sequestered (0-0.3 m),
nitrous oxide emissions (0-1.0 m), and global warming potential at 25 years (Figure 16) and 100
years into the simulation period (Figure 17). While the effect of the management practices imposed
by the scenarios varies across sites (because sites have different soils, climate, and management
practices) and over time, general trends are apparent.
Change in soil carbon by scenarios
The simulated change in soil carbon stored in the top 0.3 m of the soil was between -0.2 % and 0.3 %
relative to the No Burn (‘baseline’) scenario at 25 years into the simulation period (Figure 16). At all
sites, soil carbon increased relative to the No Burn (‘baseline’) scenario in response to increased
inputs of organic matter. The increase was greatest in the Summer Crop and Combination scenarios,
but soil carbon also increased when a pasture was grown (Pasture) or manure was applied (Manure).
At most sites, the addition of increased nitrogen fertiliser with the retention of stubble (No Burn+N)
also increased soil carbon, but only by a small amount. Soil carbon decreased at all sites in scenarios
where stubble was burnt and/or nitrogen fertiliser was reduced (Burn, Burn+N, Burn-N, and No BurnN). At 100 years into the simulation period, the scenarios had very similar effects on soil carbon
storage as at 25 years, but soil carbon storage changed to between -0.5 % and 0.5 % relative to the
No Burn (‘baseline’) scenario.
39
40
Figure 16: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m)
and net global warming potential (GWP) for 15 case study sites. Data points displayed represent values at 25 years into the simulation period. The simulated values
represent the average obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios are described in Table 3.
Figure 17: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for net global
warming potential (GWP) for 15 case study sites. Data points displayed represent values at 100 years into
the simulation period. The simulated values represent the average obtained from simulations with two
starting points over 100 years (1914-2013 and 1915-2014). Scenarios are described in Table 3.
Change in nitrous oxide emissions by scenarios
The change in simulated nitrous oxide emissions in the top 1.0 m of the soil was between -50 kg
N2O-N/ha and 100 kg N2O-N/ha relative to the No Burn (‘baseline’) scenario at 25 years into the
simulation period (Figure 16). Simulated nitrous oxide emissions increased compared to the No Burn
(‘baseline’) scenario in the No Burn+N scenario at all sites. Simulated nitrous oxide emissions also
increased compared to the No Burn (‘baseline’) scenario at most sites in the Manure, Summer Crop,
Pasture, and Combination scenarios. Simulated soil organic carbon increased in these scenarios. The
increase in nitrous oxide in scenarios where soil organic carbon increased is not surprising, as soil
carbon is a substrate for microbes that facilitate nitrous oxide production. Simulated nitrous oxide
emissions at all sites were less than or similar to emissions in the No Burn (‘baseline’) scenario in the
Burn, Burn-N and No Burn-N scenarios. Nitrous oxide emissions were also less than or similar to
emissions in the No Burn (‘baseline’) scenario in the Burn+N scenarios at all sites, with the exception
of the Merrilong Willows site (where relatively high amounts of nitrogen fertiliser were used). At 100
years into the simulation period, the scenarios had very similar effects on nitrous oxide emissions as
at 25 years, but the quantity of simulated nitrous oxide emissions changed to between -300 kg N2ON/ha and 500 kg N2O-N/ha relative to the No Burn (‘baseline’) scenario.
41
Net global warming potential by scenarios
The net amount of greenhouse gas abatement resulting from the effects of the different farming
practices represented in the scenarios was determined by different factors at different sites.
However, in general, carbon storage was the main driver of the change in net global warming
potential obtained for dryland sites, while nitrous oxide emissions were also important drivers for
change in net global warming potential at irrigated sites.
Accordingly, the large increase in soil carbon stocks obtained under the Summer Crop and
Combination scenarios resulted in a net reduction in global warming potential (i.e. provided
greenhouse gas abatement) compared with the No Burn (‘baseline’) scenario (Figure 16) at most
sites at 25 years into the simulation period. By comparison, relatively high nitrous oxide emissions
from the Summer Crop and Combination scenarios at the Dimby1 and Dimby5 irrigated sites at the
Merrilong farm resulted in a net increase in global warming potential, despite large increases in soil
carbon. However, at the two other irrigated sites investigated (Warili farm), the change in soil
carbon storage from these practices was sufficient to offset the relatively high nitrous oxide
emissions and resulted in a net decrease in global warming potential.
Management practices in the Pasture scenario sequestered sufficient carbon to off-set the nitrous
oxide emissions, resulting in a net decrease in global warming potential after 25 years for many sites.
For scenarios where stubble was burnt, the reduction in nitrous oxide emissions was not sufficient to
offset the reduction of soil carbon storage, resulting in a net increase in global warming potential for
most sites. In the No Burn+N, No Burn-N, and Manure scenarios the change in net global warming
potential was variable and the effects were site-specific.
After 100 years into the simulation period, the amount of abatement provided by some of the
scenarios had changed. This occurred because soils that are low in carbon are able to sequester large
amounts of carbon initially, but this diminishes over time. By comparison, nitrous oxide emissions
are produced at a relatively regular rate. Thus, for the Summer Crop and Combination scenarios, the
change in carbon storage was sufficient to offset the nitrous oxide emissions at a fewer number of
sites than at 25 years (Figure 17). In the Burn, Burn-N, No Burn-N, Manure, and Pasture scenarios,
the change in net global warming potential varied depending on the site and was higher than, similar
to, or lower than the No Burn (‘baseline’) scenario. In scenarios where management practices
increased nitrogen fertiliser compared with the No Burn (‘baseline’) scenario, net global warming
potential was higher than or similar to that in the No Burn (‘baseline’) scenario.
42
Greenhouse gas abatement conclusions
Soil carbon was predicted to increase in response to alternative management practices that increase
organic matter inputs (e.g. summer legume crops, winter legume pasture instead of winter fallow,
and manure additions), and retain crop stubble in combination with increasing nitrogen fertiliser
application rate.
Generally, nitrous oxide emissions were predicted to increase compared to baseline emissions in
response to alternative management practices in the same scenarios where soil carbon increased.
Net global warming potential from combined sequestered carbon and nitrous oxide emissions was
predicted to decrease (i.e. provide greenhouse gas abatement) compared with the baseline for most
of the case study sites under management practices that include a summer crop or have a pasture
phase in the rotation, particularly in the initial simulation period (25 years), when relatively high
quantities of carbon can be sequestered. The exceptions to this were irrigated sites that used high
levels of nitrogen fertiliser. Management strategies that could provide abatement at such sites
include reductions to nitrogen fertiliser. Other management practices (such as additions of manure,
or changes to nitrogen fertiliser application rate) have the capacity to provide abatement at selected
sites.
Conclusions and implications for the future
At the state level it is farm location (or associated agro-climatic factors), rainfall, soil texture and
land-use that are major drivers of variability in SOC across the farms in this project. The findings that
pasture or native areas have higher SOC levels than cropping areas implies that there is ‘room’ to
improve SOC levels across cropping fields. The inclusion of a pasture phase in the rotation would be
expected to help increase the SOC levels in cropping fields as suggested by Chan et al. (2010). The
impacts on crop yield, farm operations and enterprise economics would need to be considered.
The results here also tend to confirm that areas with lower SOC and high silt/clay fractions may be
areas with the greatest potential to increase SOC in the future. Management options within
cropping or pasture phases that increase organic matter entering the soil in these areas may be the
most effective way to increase overall field/farm SOC levels. Given the evidence here of a positive
relationship between crop yield and SOC, one way of achieving this goal in a cropping situation
would be to ensure that seasonal crop yield/biomass production potential is achieved on these
areas.
43
It is important to note that these results are based on a short term sampling interval, but the
preliminary findings here are important for researchers and policy makers considering the
development of managerial/agronomic methods for increasing the storage of carbon in soil. They
also have implications for carbon accounting systems based on physical soil sampling given that the
variability in SOC across fields was generally highest on the farms with highest SOC levels. It implies
that the number of samples required and/or the location of the samples across the landscape would
need to be targeted to ensure a robust estimate of the average SOC was obtained.
Modelling of the grain cropping systems found there is potential for alternative farming
management practices to abate greenhouse gas emissions across a range of sites through practices
that increase organic matter inputs (e.g. summer legume crops, winter legume pasture, or manure
additions). Exceptions to this include irrigated sites that use high levels of nitrogen fertiliser; here
management strategies that reduce nitrogen fertiliser application rate would be most likely to abate
greenhouse gas emissions. For management strategies that provide abatement through
sequestering carbon, abatement potential is higher in the first 25 years when relatively high
quantities of carbon can be sequestered, compared with 100 years into the simulation period.
Next Steps
While some alternative farming management practices have the capacity to abate greenhouse gas
emissions, this may not be achieved because these practices may either reduce farm profitability or
fit poorly within the overall practicalities of farm management. Further research could take into
account the trade-offs and/or interactions associated with farming practicalities and economics.
44
Outreach
Media coverage during a soil survey at Moree (Kambodia) with farmer Oscar Pearce
Demonstrating soil sample extraction and description to the Faculty of Agriculture and Environment
students during 2015 sampling.
45
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50
51
Appendix 1. Example Profile Morphological Report
Example of a profile morphological report provided to farmers - The Plantation
Soil profile description
Figure 1 visually displays the horizon depths and colours observed in each profile.
Brigalow
The soil type in ‘Brigalow’ is classified as a Black Vertosol, a black cracking clay soil with a strong
increase in texture with depth, and a notably brownish black, clayey B2 horizon. These soils have
self-mulching properties and often referred to as Black Earths.
Gurley
The soil type in ‘Gurley’ is classified as a ‘Brown Vertosol’, a cracking clay soil with a gradual change
in soil texture down the profile.
Figure 1: Soil horizon depth classification and colour.
Soil profile clay and carbon content
In Figures 2 and 3 the clay (%) and TC (%) content distribution is plotted with depth. The colours are
sequential with paler colours representing lower content and darker colours representing higher
content respectively.
52
Brigalow
A significant increase in clay content from A1 to B2 horizon in ‘Brigalow’ may impose restrictions to
crop root growth in this horizon and may be a factor in the reduced TC content.
Gurley
The generally lighter textured A1 and B21 horizons in ‘Gurley’ should provide greater rooting depth
for crop production. The impact of this is that the TC content remains high to a greater depth in this
profile relative to ‘Brigalow’. The longer history of cultivation may also be a factor.
Figure 2: Soil clay content (%) distribution with depth.
Figure 3: Soil TC (%) distribution with depth.
53
A possible explanation as to why the SOC content in the profile is lower under no-till
Soil carbon can be divided between the active and passive pools (Trumbore 2000) . The active pool
contains fresh forms of carbon such as microbes, animal and root exudates in the form of sugars,
peptides, amino acids, plant enzymes, nutrients and organic acids (Rovira 1969). The passive pool is
a more stable form of carbon and includes lignin, resins, and charcoal. The top layer of the soil
profile is known to comprise mostly active pool carbon and the subsoil is known to have more
passive pool carbon.
In a no-till system on Vertosol soil, the vertical redistribution of soil carbon can occur via natural
processes such as bioturbation and movement down deep cracks. On the other hand, soil tillage can
promote top soil carbon redistribution and potential storage in the passive pool in the subsoil
(Benham, Vanguelova et al. 2012).
It must be remembered that the results shown here are based on one soil profile analysed for soil
carbon from ‘Gurley’ and ‘Brigalow’, but the topsoil results provided in the last report also found no
significant difference in topsoil organic carbon between ‘Gurley’ and ‘Brigalow’. Several studies have
found similar results: no difference in soil carbon levels between till/no-till systems or higher soil
carbon levels in a tilled system at, and below, the plough depth (Rasse, Mulder et al. 2006; Gal, Vyn
et al. 2007; Angers and Eriksen-Hamel 2008).
One possibility is that the active pool carbon in the soil under no-till is larger than in the tilled
system, which would promote greater microbial activity and therefore breakdown of SOM at a faster
rate. Greater amounts of OM may be going into the soil, but being cycled more quickly and therefore
not moving into the passive carbon pool. The benefits of this should be seen in improved soil
structure, water holding capacity and crop production.
Currently we are running soil microbial activity assessments, but it would be good to get access to
any spatial yield data from these two fields to match with the TC data.
C/N ratio and decomposition of soil organic matter
There are several important factors responsible for soil organic matter (SOM) decomposition by
microbes, such as oxygen supply, water, soil pH, temperature, nitrogen and the C/N ratio. Soil TN
content represents the total amount of nitrogen present in soil, usually locked up in the organic
matter. A decline in TN levels down a profile contributes to a lowering of SOM decomposition rate.
Linking the TC to the TN is the C/N ratio to which the activity of SOM decomposing microbes is
sensitive. Higher C/N ratios are known to slow decomposition of organic matter and vice versa
54
(Figure 4). The C/N ratio in the surface layers for ‘Brigalow’ is lower as compared to ’Gurley’ which
could suggest a rapid breakdown of organic matter and mineralization in the surface layers of
‘Brigalow’. The assumed active soil carbon in a no-till system could be promoting this mineralization.
Figure 4: A schematic diagram to illustrate what occurs when the C/N ratio is high or low
Other elements
Tables 1 and 2 contain the values for all soil properties within the individual soil horizons in each soil
profile.
Titanium content is high in both the paddocks, and tends to be decreasing with depth in ‘Brigalow’,
and a reverse trend is seen in ‘Gurley’. These levels could be related to catalytic functions in nitrogen
fixation by symbiotic microbes. Iron content is very high in both the paddocks and tends to decrease
with depth, iron is required in metabolism of bacteria, formation of chlorophyll, and contributes to
soil colour. High zirconium is usually found in nodules of roots, and promotes microbial activity.
55
Table 1: Soil property results for profile horizons in ‘Brigalow’
Element
Soil properties in Brigalow with soil horizon (red= high and blue= lower
than mean)
A1
B21
B22
Comments
TN
0.09
Medium
0.05
Low
0.05
Low
Nitrogen tends to decrease with depth.
TC
1.18
0.95
0.92
TC tends to decrease with depth.
C/N Ratio
13.43
18.07
20.46
Increasing with depth.
pH (CaCl2)
6.4
6.5
6.9
Electrical conduct. (μS/cm)
284
267
271
Clay %
39.54
65.81
73.33
Chlorine
NA
NA
NA
Potassium
5917
5575
9653
Low tends to increase with depth.
Calcium
5429
8115
9214
Low tends to increase with depth.
8445
6547
High
Titanium
Tends to increase with depth,
Vanadium
106
105
93
High
Chromium
133
99
102
High
Manganese
1040
1031
750
High
Iron
53044
48924
42818
High
Nickel
74
62
79
High
Copper
46
43
29
High
Zinc
88
76.2
80.9
High
Arsenic
8.8
5.5
7.1
Selenium
14.9
14.4
15.2
Rubidium
44.3
42.3
76
Strontium
189
220
211
Yttrium
32.7
30.3
35.1
Zirconium
399
437
361
High
Niobium
48.2
47.1
39.3
High
Rhodium
NA
NA
NA
Tin
NA
NA
9
Barium
314
296
400
Lanthanum
NA
70
NA
Cerium
NA
NA
87
Tantalum
NA
19
28
Platinum 20-75
NA
NA
NA
Mercury
NA
NA
6.7
Lead
NA
5.7
11.1
Thorium
16.3
16.5
17.1
High, increasing with depth
Uranium
14.3
13.2
13.8
High, increasing with depth
56
High, increasing with depth
High
Table 2: Soil property results for profile horizons in ‘Gurley’
Element
Soil properties in Gurley with soil horizon (red= high and blue= lower
than mean)
A1
B21
B22
Comments
TN
0.07
0.05
0.06
Nitrogen decreasing down the profile.
TC
1.22
1.33
1.19
Higher TC in lower depths
C/N Ratio
16.52
27.35
19.28
High C/N ratio in B21 horizon
pH (CaCl2)
6.8
6.6
7.3
Electrical conduct.(μS/cm)
308
250
262
Clay %
46.14
43.25
68.61
Chlorine
NA
NA
NA
Potassium
7505
6408
7284
Calcium
9955
11531
13864
Titanium
8381
7820
7649
High
Vanadium
101
95
97
High
Chromium
102
154
108
High
Manganese
969
972
998
High
Iron
53904 49617
49526
High
Nickel
102
86
88
High
Copper
42
36
34
High
Zinc
86.5
74.8
77.2
High
Arsenic
5.6
3.6
4.3
low
Selenium
17
16.2
14.7
High, increasing with depth
Rubidium
46.2
41.3
43.2
Strontium
257
298
261
Yttrium
29.3
30.3
27.8
Zirconium
394
377
363
High
Niobium
53.6
51.8
49.1
High
Rhodium
1
NA
NA
Tin
NA
NA
NA
Barium
287
267
254
Lanthanum
NA
NA
NA
Cerium
NA
NA
95
Tantalum
23
24
27
Platinum 20-75
NA
NA
NA
Mercury
NA
NA
6.5
Lead
6
6.9
7.2
Thorium
19
16.9
18.3
High, increasing with depth
Uranium
14.2
13.2
14.3
High, increasing with depth
57
Fine textured as compared to Brigalow.
High, increasing with depth
low
High
References
Angers, D. A. and N. S. Eriksen-Hamel (2008). Full-inversion tillage and organic carbon
distribution in soil profiles: A meta-analysis. Soil Science Society of America Journal
72: 1370-1374.
Benham, S. E., E. I. Vanguelova, et al. (2012). Short and long term changes in carbon,
nitrogen and acidity in the forest soils under oak at the Alice Holt Environmental
Change Network site. Science of the Total Environment 421: 82-93.
Gal, A., T. J. Vyn, et al. (2007). Soil carbon and nitrogen accumulation with long-term no-till
versus moldboard plowing overestimated with tilled-zone sampling depths. Soil &
Tillage Research 96(1-2): 42-51.
Rasse, D. P., J. Mulder, et al. (2006). Carbon turnover kinetics with depth in a french loamy
soil. Soil Science Society of America Journal 70(6): 2097-2105.
Rovira, A. D. (1969). Plant root exudates. Botanical Review 35(1): 35-37.
Trumbore, S. (2000). Age of soil organic matter and soil respiration: Radiocarbon constraints
on belowground C dynamics. Ecological Applications 10(2): 399-411.
58
Appendix 2: Kambodia greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Across Rail and
New paddocks.
Table 1: Soil properties used to simulate the Kambodia farm soils at the Across Rail and New paddocks.
Parameter
Across Rail
New
APSoil number
APSoil name
Soil type
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted – crop dependant (m)
Plant available water capacity – crop dependant
(mm)
Crop rotation for the parameterisation simulations1
Crop rotation for the simulations simulations1
78
Terry Hie Hie
Grey vertosol
0.66
12
7.3
84
Moderate (0.3)
1.5 -1.8
195-207
78
Terry Hie Hie
Grey vertosol
0.70
12
7.3
84
Moderate (0.3)
1.2-1.8
168-221
WtxWtxxCtxxWtxWt WtxCpxWtxCnxWtxWt
WtxCpxWtxxSg
WtxCpxWtxxSg
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Pallamallawa Post Office meteorological station (station number 53033).
Average rainfall for the area is 618 mm/yr.
Management practices
The crop rotation for the parameterisation simulations and the long-term parameterisation
simulations represented the actual crop rotation grown most recently at the site (Table 1). However,
this crop rotation was not considered sensible for a long-term rotation for the scenarios. Thus for
the scenarios a region-appropriate typical crop rotation was simulated (Table 1).
59
Crop variety, sowing windows, fertiliser rates and application dates, and tillage management
practices for (1) the general management practices simulated for the parameterisation simulations
and the long-term parameterisation simulations, (2) the specific management for the
parameterisation simulations, and (3) the general management for the scenarios for the Kambodia
case study farm are described in Tables 2 and 3.
Table 2: General management practices for the parameterisation simulations and the long-term
parameterisation simulations, the specific management used to represent on-site management for the
parameterisation simulations, and the general management used for the scenarios for the Across Rail
paddock for the Kambodia.
Practice
General management for
parameterisation
simulations and long-term
parameterisation
simulations
Specific management for
parameterisation
simulations
General management for
scenarios
Variety
S71BR (cotton); spitfire
(wheat)
S71BR (cotton); spitfire or
sunvale (wheat)
Sowing
date
15-Oct to 15-Nov (cotton);
14-May to 7-Jul (wheat)
N
fertiliser
at
sowing
Apply: 50 kg N/ha (cotton);
40 kg N/ha (wheat): N is
applied as urea at 50 mm
depth
Apply: 30 kg N/ha (cotton):
N is applied as urea at 50
mm depth
15-Oct to 15-Nov (cotton); 1Jun to 28-Jun (wheatsunvale); 12-Jun (wheatspitfire 2013); 29-May
(wheat-spitfire 2014)
Apply: 50 kg N/ha (cotton);
60 kg N/ha (wheat-sunvale);
12 kg N/ha (wheat-spitfire
2013) ; 11 kg N/ha (wheatspitfire 2014): N is applied as
urea at 50 mm depth
Apply: 64 kg N/ha (wheatspitfire 2013) on 22-may2013; 67 kg N/ha (wheatspitfire 2014) on 28-apr2014: N is applied as urea at
50 mm depth
Apply: 55 kg N/ha (cotton): N
is applied as urea at 50 mm
depth
spitfire (wheat); amethyst
(chickpea); medium
(sorghum)
14-May to 7-Jul (wheat); 7May to 28-Jun (chickpea); 7Sep to 7-Nov (sorghum)
Nil – plant into standing
stubble
Zero till; plant stubble left
on paddock after harvest
Nil – plant into standing
stubble
Zero till; plant stubble left on
paddock after harvest
N
fertiliser
on a
fixed
date
N
fertiliser
40 days
after
sowing
Tillage
Residues
60
Apply: 40 kg N/ha (wheat); 3
kg N/ha (chickpea); 80 kg
N/ha (sorghum): N is applied
as urea at 50 mm depth
Nil
Scenario dependent
Table 3: General management practices for the parameterisation simulations and the long-term
parameterisation simulations, the specific management used to represent on-site management for the
parameterisation simulations, and the general management used for the scenarios for the New paddock for
the Kambodia.
Practice
General management for
parameterisation simulations
and long-term
parameterisation simulations
Specific management for
parameterisation
simulations
General management for
scenarios
Variety
sunvale (wheat); amethyst
(chickpea); hyola42 (canola)
bellaroi, spitfire or sunvale
(wheat); amethyst
(chickpea); hyola42 (canola)
Sowing
date
1-May to 21-Jun (wheat); 7May to 28-Jun (chickpea); 21Apr to 14-Jun (canola);
N
fertiliser
at
sowing
Apply: 40 kg N/ha (wheat); 3 kg
N/ha (chickpea); 40 kg N/ha
(canola): N is applied as urea at
50 mm depth
1-Jun to 21-Jun (wheatsunvale); 1-Jul to 14-Aug
(wheat-bellaroi); 12-Jun
(wheat-spitfire 2013); 3-Jun
(wheat-spitfire 2014)
47 kg N/ha (wheat-sunvale);
66 kg N/ha (wheat-bellaroi);
12 kg N/ha (wheat-spitfire
2013); 3 kg N/ha (chickpea);
70 kg N/ha (canola): N is
applied as urea at 50 mm
depth
Apply: 64 kg N/ha (wheatspitfire 2013) on 22-may2013; 67 kg N/ha (wheatspitfire 2014) on 1-may2014: N is applied as urea at
50 mm depth
Nil – plant into standing
stubble
Zero till; plant stubble left on
paddock after harvest
sunvale (wheat); amethyst
(chickpea); medium
(sorghum)
1-May to 21-Jun (wheat); 7May to 28-Jun (chickpea); 7Sep to 7-Nov (sorghum)
N
fertiliser
on a
fixed
date
Tillage
Nil – plant into standing stubble
Residues
Zero till; plant stubble left on
paddock after harvest
Apply: 40 kg N/ha (wheat); 3
kg N/ha (chickpea); 80 kg
N/ha (sorghum): N is applied
as urea at 50 mm depth
Nil
Scenario dependent
Biophysical modelling
Parameterisation of APSIM to the Across Rail paddock at Kambodia
Parameterisations simulations
The crop rotation (Table 1) practiced at the Across Rail paddock at the Kambodia farm was simulated
with representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop
yields were obtained for the cotton and wheat crops. Average simulated wheat yield in the general
61
and specific management practices was estimated to be within 0.16 t/ha of the average yield
measured at the farm (Figure 1). Average simulated cotton yield in the general and specific
management practices was estimated to be within 0.22 bales/ha of the average yield measured at
the farm. Average simulated crop yields were simulated to be above the regional average crop
yields.
Figure 1: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatwheat-cotton-wheat-wheat crop rotation for the Across Rail paddock at the Kambodia farm. The simulation
was run from 2008 to 2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 2). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was predicted to be
very slowly declining (Figure 3). The general management practices included bare fallow periods
combined with variable within-crop rainfall. Accordingly, there were low and discontinuous inputs of
carbon throughout the simulation which led to an overall net mineralisation of carbon from this soil.
62
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat-cotton-wheat-wheat crop rotation for the Across Rail paddock at the Kambodia farm. The
simulation was run from 1961 to 2014.
63
Figure 3: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat-cotton-wheat-wheat crop rotation for the Across
Rail paddock at the Kambodia farm. The simulation was run from 1961 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Across Rail paddock at the Kambodia farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
64
Figure 4: Crop yield simulated in response to 10 scenarios for a wheat-chickpea-wheat-sorghum crop
rotation for the Across Rail paddock at the Kambodia farm. Values displayed represent the crop yield from
simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios are described in
Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a wheatchickpea-wheat-sorghum crop rotation for the Across Rail paddock at the Kambodia farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
65
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheat-chickpeawheat-sorghum crop rotation for the Across Rail paddock at the Kambodia farm. Values displayed represent
the overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased in scenarios
relative to the No Burn (‘baseline’) scenario when residues were not burnt and increased nitrogen
fertiliser was applied, a summer crop or pasture was grown, or manure was applied. These scenarios
all involved increased inputs of organic matter. Soil carbon decreased in scenarios where stubble
was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased and were reduced in all scenarios where soil organic carbon
decreased. The increase in nitrous oxide in scenarios where soil organic carbon increased is not
surprising, as soil carbon is a substrate for microbes that facilitate nitrous oxide production.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In scenarios where management practices considerably increased soil
carbon stocks compared with the No Burn (‘baseline’) scenario (Summer Crop, Pasture and
Combination), the greenhouse gas abatement provided by the sequestered carbon was greater than
66
the global warming impact of the nitrous oxide emissions, resulting in a reduction in net global
warming potential. Global warming potential in the No Burn+N and No Burn-N scenarios was similar
to the No Burn (‘baseline’) scenario. In scenarios where management practices reduced soil carbon
stocks compared with the No Burn (‘baseline’) scenario (Burn, Burn+N, and Burn-N), net global
warming potential increased, irrespective of any reduction of nitrous oxide emissions. When soil
carbon stabilised in these scenarios, the net global warming potential began to decrease (although it
was still positive by the end of the simulation period).
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-chickpea-wheat-sorghum crop rotation for the Across Rail paddock at the Kambodia farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
67
Parameterisation of APSIM to the New paddock at Kambodia
Parameterisation simulations
The crop rotation (Table 1) practiced at the New paddock at the Kambodia farm was set up with
representative crop management and soil outlined in Tables 1 and 3. Realistic simulated crop yields
were obtained for the wheat, canola, and chickpea crops. Average simulated wheat and canola yield
in the general and specific management practices was estimated to be within 0.60 t/ha of the
average yield measured at the farm (Figure 8). Average simulated chickpea yield in the general and
specific management was estimated to be within 1.15 t/ha of the average chickpea yield measured
at the farm. Average simulated crop yields were simulated to be above the regional average crop
yields.
Figure 8: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatchickpea-wheat-canola-wheat-wheat crop rotation for the New paddock at the Kambodia farm. The
simulation was run from 2008 to 2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
68
(Figure 9). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was predicted to be
relatively stable over time (Figure 10).
Figure 9: Water balance for the long-term parameterisation simulation using general management for a
wheat-chickpea-wheat-canola-wheat-wheat crop rotation for the New paddock at the Kambodia farm. The
simulation was run from 1961 to 2014.
69
Figure 10: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-chickpea-wheat-canola-wheat-wheat crop rotation for
the New paddock at the Kambodia farm. The simulation was run from 1961 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the New paddock at the Kambodia farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 11, 12 and 13.
70
Figure 11: Crop yield simulated in response to 10 scenarios for a wheat-chickpea-wheat-sorghum crop
rotation for the New paddock at the Kambodia farm. Values displayed represent the overall average
obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios
are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 12: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a wheatchickpea-wheat-sorghum crop rotation for the New paddock at the Kambodia farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
71
Figure 13: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheatchickpea-wheat-sorghum crop rotation for the New paddock at the Kambodia farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and No Burn scenario (considered
to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous oxide
emissions (0-1.0 m) and global warming potential (Figure 14). Soil carbon increased in scenarios
relative to No Burn (‘baseline’) scenario when residues were not burnt and increased nitrogen
fertiliser was applied, a summer crop or pasture was grown, or manure was applied. These scenarios
all involved increased inputs of organic matter. Soil carbon decreased in scenarios where stubble
was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased and were reduced in all scenarios where soil organic carbon
decreased. The increase in nitrous oxide in scenarios where soil organic carbon increased is not
surprising, as soil carbon is a substrate for microbes that facilitate nitrous oxide production.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In scenarios where management practices considerably increased soil
carbon stocks compared with the No Burn (‘baseline’) scenario (Summer Crop, Pasture and
Combination), the greenhouse gas abatement provided by the sequestered carbon was greater than
72
the global warming impact caused by the simulation of the nitrous oxide emissions resulting in a
reduction in global warming potential compared with the No Burn (‘baseline’) scenario. Global
warming potential in the No Burn+N and No Burn-N scenarios was similar to the No Burn (‘baseline’)
scenario. In scenarios where management practices reduced soil carbon stocks compared with the
No Burn (‘baseline’) scenario (Burn, Burn+N, and Burn-N), net global warming potential increased,
irrespective of any reduction of nitrous oxide emissions. When soil carbon stabilised in these
scenarios, global warming potential began to decrease (although it was still positive by the end of
the simulation period).
Figure 14: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-chickpea-wheat-sorghum crop rotation for the New paddock at the Kambodia farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
73
Appendix 3: Livingston greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Airport and JKL
No Till paddocks.
Table 1: Soil properties used to simulate the Livingston farm soils for the Airport and JKL No Till paddocks.
Parameter
Airport
JKL No Till
APSoil number
APSoil name
Soil type
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted – crop dependant (m)
Plant available water capacity – crop
dependant (mm)
Crop rotation1
235
Black Vertosol Moree
Black vertosol
1.02
12
5.4
84
Moderate (0.3)
1.2
171-175
235
Black Vertosol Moree
Black vertosol
1.06
12
6.6
84
Moderate (0.3)
1.2-1.8
171-208
ByxByxByxCpxWtxCpxx CpxWtxWtxxSgCpxWtxx
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Moree Aero meteorological station (station number 053115). Average
rainfall for the area is 593 mm/yr.
Management practices
The crop rotation for the parameterisation simulations, the long-term parameterisation simulations,
and the scenarios represented the actual crop rotation grown most recently at the site. Crop variety,
sowing windows, fertiliser rates and application dates, and tillage management practices for the
Livingston farm are described in Tables 2 and 3.
74
Table 2: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Airport paddock for the Livingston farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
sunbri (wheat); amethyst (chickpea);
commander (barley)
7-Apr to 7-Jun (wheat); 7-May to 28-Jun
(chickpea); 1-May to 21-Jun (barley)
sunbri (wheat); amethyst (chickpea);
commander (barley)
4-May to 7-Jun (wheat); 20-Jun to 28-Jun
(chickpea 2011); 20-Jun (chickpea 2013);
1-May to 21-Jun (barley)
Apply: 50.5 kg N/ha (wheat); 4.5 kg N/ha
(chickpea 2011); 5 kg N/ha (chickpea
2013); 0 kg N/ha (barley): N is applied as
urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
Apply: 40 kg N/ha (wheat); 5 kg N/ha
(chickpea); 20 kg N/ha (barley): N is applied
as urea at 50 mm depth
Tillage
Residues
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
Table 3: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the JKL No Till paddock for the Livingston farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
janz (wheat); amethyst (chickpea); buster
(sorghum)
1-May to 21-Jun (wheat); 7-May to 28-Jun
(chickpea); 7-Oct to 7-Nov (sorghum)
bellaroi, wylie, or janz (wheat); amethyst
(chickpea); buster (sorghum)
14-May to 7-Jul (wheat-bellaroi); 1-Jul to
21-Jul (wheat-wylie); 27-May (wheatjanz); 7-May to 28-Jun (chickpea); 7-Oct
to 7-Nov (sorghum)
Apply: 50.5 kg N/ha (wheat-bellaroi); 50.5
kg N/ha (wheat-wylie); 55 kg N/ha
(wheat-janz); 4.5 kg N/ha (chickpea); 0 kg
N/ha (sorghum): N is applied as urea at
50 mm depth
Apply: 46 kg N/ha (wheat-janz) on 25-jun2013: N is applied as urea at 50 mm
depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
N fertiliser
on a fixed
date
Tillage
Residues
75
Apply: 55 kg N/ha (wheat); 4.5 kg N/ha
(chickpea); 40 kg N/ha (sorghum): N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
Biophysical modelling
Parameterisation of APSIM to the Airport paddock at Livingston
Parameterisation simulations
The crop rotation (Table 1) practiced at the Airport paddock at the Livingston farm was set up with
representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop yields
were obtained for the barley, chickpea, and wheat crops. Average simulated chickpea and wheat
yield in the general and specific management practices was estimated to be within 0.73 t/ha of the
average yield measured at the farm (barley yield was not measured; Figure 1). Average simulated
crop yields were simulated to be above the regional average crop yields, with the exception of barley
yield in the specific management, which was simulated to be slightly below the regional average.
Figure 1: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single barleybarley-barley-chickpea-wheat-chickpea-fallow crop rotation for the Airport paddock at the Livingston farm.
The simulation was run from 2007 to 2014.
76
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 2). Soil carbon both in the total soil profile and in the top 0.3 m was predicted to be slowly
declining under the general management practices scenario (Figure 3). The general management
practices included bare fallow periods combined with variable within-crop rainfall. Accordingly,
there were low and discontinuous inputs of carbon throughout the simulation which led to an
overall net mineralisation of carbon from this soil.
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
barley-barley-barley-chickpea-wheat-chickpea-fallow crop rotation for the Airport paddock at the Livingston
farm. The simulation was run from 1959 to 2014.
77
Figure 3: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a barley-barley-barley-chickpea-wheat-chickpea-fallow crop
rotation for the Airport paddock at the Livingston farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Airport paddock at the Livingston farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
78
Figure 4: Crop yield simulated in response to 10 scenarios for a barley-barley-barley-chickpea-wheatchickpea-fallow crop rotation for the Airport paddock at the Livingston farm. Values displayed represent the
overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a barleybarley-barley-chickpea-wheat-chickpea-fallow crop rotation for the Airport paddock at the Livingston farm.
Values displayed represent the overall average obtained from simulations with two starting points over 100
years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions
from soils: Table 3.
79
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a barley-barleybarley-chickpea-wheat-chickpea-fallow crop rotation for the Airport paddock at the Livingston farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased in scenarios
relative to the No Burn (‘baseline’) scenario when residues were not burnt and increased nitrogen
fertiliser was applied, a summer crop or pasture was grown, or manure was applied. These scenarios
all involved increased inputs of organic matter. Soil carbon decreased in scenarios where stubble
was burnt and/or nitrogen fertiliser was reduced.
For the first 20 years of the simulation period, nitrous oxide emissions were similar to the No Burn
(‘baseline’) scenario in all scenarios. After this period, nitrous oxide emissions increased in all
scenarios where soil organic carbon increased and decreased in all scenarios where soil organic
carbon decreased. The increase in nitrous oxide in scenarios where soil organic carbon increased is
not surprising, as soil carbon is a substrate for microbes that facilitate nitrous oxide production.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In scenarios where management practices considerably increased soil
80
carbon stocks compared with the No Burn (‘baseline’) scenario, the greenhouse gas abatement
provided by the sequestered carbon was greater than the global warming impact of the nitrous
oxide emissions, resulting in a reduction in net global warming potential. This occurred in the
Summer Crop, Pasture and Combination scenarios. The Manure scenario was also able to provide a
small reduction in net global warming potential for the majority of the simulation time frame. The
net global warming potential in the No Burn+N and No Burn+N scenarios was similar to the No Burn
(‘baseline’) scenario. In scenarios where management practices reduced soil carbon stocks
compared with the No Burn (‘baseline’) scenario, net global warming potential increased irrespective
of any reduction in nitrous oxide emissions, until the time in which soil organic carbon became
relatively stable. At this point, global warming potential in these scenarios began to decrease and by
the end of the simulation period, it was similar to the No Burn (‘baseline’) scenario.
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
barley-barley-barley-chickpea-wheat-chickpea-fallow crop rotation for the Airport paddock at the Livingston
farm. Values displayed represent the overall average obtained from simulations with two starting points
over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas
emissions from soils: Table 3.
81
Parameterisation of APSIM to the JKL No Till paddock at Livingston
Parameterisation simulations
The crop rotation (Table 1) practiced at the JKL No Till paddock at the Livingston farm was set up
with representative crop management and soil outlined in Tables 1 and 3. Realistic simulated crop
yields were obtained for the wheat, sorghum, and chickpea crops (Figure 8). Average simulated
chickpea yield for the general and specific management practices was estimated to be within 0.08
t/ha of the average yield measured at the farm. Average simulated sorghum yield for the specific
management practices was over-predicted (within 1.6 t/ha) of the average yield measured at the
farm. Average simulated wheat yield in the general and specific management was within 0.46 t/ha
of the average yield measured at the farm. The low average simulated wheat yield may be explained
in part by the variety of durum wheat grown at the site. The bellaroi variety of durum was grown at
the farm in 2009 and yielded 3.8 t/ha. APSIMs capacity to simulate durum wheat is still under
development and the estimated the yield for this crop was substantially lower than the measured
yield. Average simulated crop yields were simulated to be above the regional average crop yields.
Figure 8: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single
chickpea-wheat-wheat-fallow-sorghum-chickpea-wheat-fallow crop rotation for the JKL No Till paddock at
the Livingston farm. The simulation was run from 2007 to 2014.
82
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 9). Soil carbon both in the total soil profile and in the top 0.3 m was predicted to be slowly
declining under the general management practices scenario (Figure 10). The general management
practices included bare fallow periods combined with variable within-crop rainfall. Accordingly,
there were low and discontinuous inputs of carbon throughout the simulation which led to an
overall net mineralisation of carbon from this soil.
Figure 9: Water balance for the long-term parameterisation simulation using general management for a
chickpea-wheat-wheat-fallow-sorghum-chickpea-wheat-fallow crop rotation for the JKL No Till paddock at
the Livingston farm. The simulation was run from 1959 to 2014.
83
Figure 10: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a chickpea-wheat-wheat-fallow-sorghum-chickpea-wheat-fallow
crop rotation for the JKL No Till paddock at the Livingston farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the JKL No Till paddock at the Livingston case study farm. The No Burn scenario was developed
from the parameterisation simulation with the general management practices. It represents the
common practice at the site and was considered to be the ‘baseline’ scenario against which all other
scenarios were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3
m of the soil for all scenarios are presented in Figures 11, 12 and 13.
84
Figure 11: Crop yield simulated in response to 10 scenarios for a chickpea-wheat-wheat-fallow-sorghumchickpea-wheat-fallow crop rotation for the JKL No Till paddock at the Livingston farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 12: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a chickpeawheat-wheat-fallow-sorghum-chickpea-wheat-fallow crop rotation for the JKL No Till paddock at the
Livingston farm. Values displayed represent the overall average obtained from simulations with two starting
points over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse
gas emissions from soils: Table 3.
85
Figure 13: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a chickpeawheat-wheat-fallow-sorghum-chickpea-wheat-fallow crop rotation for the JKL No Till paddock at the
Livingston farm. Values displayed represent the overall average obtained from simulations with two starting
points over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse
gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 14). Soil carbon increased in
scenarios relative to the No Burn (‘baseline’) scenario when a summer crop or pasture was grown, or
manure was applied. These scenarios all involved increased inputs of organic matter. Soil carbon was
similar to that in the No Burn (‘baseline’) scenario when the residues were not burnt and increased
nitrogen fertiliser was applied. Soil carbon decreased relative to the No Burn (‘baseline’) scenario
where stubble was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased and were reduced or similar to the No Burn (‘baseline’) scenario
in all scenarios where soil organic carbon decreased. The increase in nitrous oxide in scenarios
where soil organic carbon increased is not surprising, as soil carbon is a substrate for microbes that
facilitate nitrous oxide production.
86
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by both soil carbon and nitrous oxide emissions. While management practices increased soil carbon
stocks compared with the No Burn (‘baseline’) scenario in a number of scenarios, this was sufficient
to reduce the net global warming potential for the majority of the simulation time frame only in the
Combination scenario. The Manure, Pasture and Summer Crop scenarios were only able to provide
intermittent reductions in global warming potential for the first 30 years of the simulation time
frame, after which they caused an increase in global warming potential. All other simulations either
resulted in a net global warming potential that was similar to or greater than the No Burn (‘baseline’)
scenario.
Figure 14: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
chickpea-wheat-wheat-fallow-sorghum-chickpea-wheat-fallow crop rotation for the JKL No Till paddock at
the Livingston farm. Values displayed represent the overall average obtained from simulations with two
starting points over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating
greenhouse gas emissions from soils: Table 3.
87
Appendix 4: Lachlan Downs greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Wheat paddock.
Table 1: Soil properties used to simulate the Lachlan Downs farm soils for the Wheat paddock.
Parameter
Wheat
APSoil number
APSoil name
Soil type
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted (m)
Plant available water capacity – crop dependant
(mm)
Crop rotation1
689
West Wyalong
Sand loam over a sandy clay
0.76
12
5.8
68
well drained (0.5)
1.0
124
WtxWtx
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Naradhan (Uralba) meteorological station (station number 075050).
Average rainfall for the area is 451 mm/yr.
Management practices
The crop rotation for the parameterisation simulations, the long-term parameterisation simulations,
and the scenarios represented the actual crop rotation grown most recently at the site. Crop variety,
sowing windows, fertiliser rates and application dates, and tillage management practices for the
Lachlan Downs farm are described in Table 2.
88
Table 2: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations Wheat paddock for the Lachlan Downs farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
livingston (wheat)
Sowing date
21-Apr to 14-Jun (wheat)
N fertiliser
at sowing
Apply: 40 kg N/ha (wheat): N is applied as
urea at 50 mm depth
Tillage
Residues
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
livingston (wheat 2012); livingston (wheat
2013)
28-May (wheat 2012); 21-May (wheat
2013)
Apply: 0 kg N/ha (wheat 2012); 0 kg N/ha
(wheat 2013): N is applied as urea at 50
mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Biophysical modelling
Parameterisation of APSIM to the Wheat paddock at Lachlan Downs
Parameterisation simulations
The crop rotation (Table 1) practiced at the Wheat paddock at the Lachlan Downs farm was set up
with representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop
yields were obtained for the wheat crops. Average simulated wheat yield in the specific
management practices was estimated to be above (1.46 t/ha) the average yield measured at the
farm (Figure 1). The average measured yield at the farm was very low (average ~0.3 t/ha) due to
heavy thistle infestation. Furthermore, no fertiliser was recorded to have been applied to the crops,
which also may have contributed to low yields. Average simulated wheat yield in the general
management simulations was within 0.44 t/ha of the regional average yield.
89
Figure 1: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatwheat crop rotation for the Wheat paddock at the Lachlan Downs farm. The simulation was run from 2011
to 2013.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 2). Soil carbon both in the total soil profile and in the top 0.3 m was predicted to be slowly
declining under the general management practices scenario (Figure 3). This scenario included bare
fallow periods combined with variable within-crop rainfall. Accordingly, there were low and
discontinuous inputs of carbon throughout the simulation which led to mineralisation of carbon
from this soil.
90
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat crop rotation for the Wheat paddock at the Lachlan Downs farm. The simulation was run from
1964 to 2013.
91
Figure 3: Soil carbon 0-1.2 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat crop rotation for the Wheat paddock at the
Lachlan Downs farm. The simulation was run from 1964 to 2013.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Wheat paddock at the Lachlan Downs case study farm. The No Burn scenario was developed
from the parameterisation simulation with the general management practices. It represents the
common practice at the site and was considered to be the ‘baseline’ scenario against which all other
scenarios were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3
m of the soil for all scenarios are presented in Figures 4, 5 and 6.
92
Figure 4: Crop yield simulated in response to eight scenarios for a wheat-wheat crop rotation for the Wheat
paddock at the Lachlan Downs farm. Values displayed represent the overall average obtained from
simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios are described in
Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to eight scenarios for a wheatwheat crop rotation for the Wheat paddock at the Lachlan Downs farm. Values displayed represent the
overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
93
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to eight scenarios for a wheat-wheat
crop rotation for the Wheat paddock at the Lachlan Downs farm. Values displayed represent the overall
average obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014).
Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). The Pasture and Combination
scenarios were not simulated at this site as there was no winter fallow in the crop rotation. Soil
carbon increased in scenarios relative to the No Burn (‘baseline’) scenario when residues were not
burnt and increased nitrogen fertiliser was applied, a summer crop was grown, or manure was
applied. These scenarios all involved increased inputs of organic matter. Soil carbon decreased in
scenarios where stubble was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased in the Manure and No Burn+N scenarios compared to the No Burn
(‘baseline’) scenario but reduced in all other scenarios (for the majority of the simulation time
period). An increase in nitrous oxide in scenarios where soil organic carbon increased is not
surprising, as availability of soil carbon is one of the precursors for nitrous oxide production.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In the Summer Crop scenario, management practices considerably
94
increased soil carbon stocks and reduced nitrous oxide emissions compared with the No Burn
(‘baseline’) scenario, thus providing a substantial reduction in global warming potential. In the
Manure scenario, the greenhouse gas abatement provided by the small amount of sequestered
carbon was greater than the global warming impact caused by the simulation of the nitrous oxide
emissions resulting in a small reduction in global warming potential compared with the No Burn
(‘baseline’) scenario. In scenarios where management practices had reduced or similar soil carbon
stocks compared with the No Burn (‘baseline’) scenario, net global warming potential increased or
was similar irrespective of any reduction in nitrous oxide emissions.
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-wheat crop rotation for the Wheat paddock at the Lachlan Downs farm. Values displayed represent
the overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
95
Appendix 5: Kilnyana greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Boatrock and
Middleplain paddocks.
Table 1: Soil properties used to simulate the Kilnyana farm soils for the Boatrock and Middleplain paddocks.
Parameter
Boatrock
Middleplain
APSoil number
APSoil name
Soil type
184
Tocumwal
Sandy Clay Loam over
Light Clay
1.03
12
5.7
73
Well drained (0.5)
1.8
168
213
Rand
Clay Loam over Clay
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted – crop dependant (m)
Plant available water capacity – crop
dependant (mm)
Crop rotation1
0.81
12
5.9
73
Moderate (0.4)
0.9-1.8
118-172
WtxWtxOtxCnxWtxByxCn WtxWtxWtxWtxxxCnxBy
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Berrigan Post Office meteorological station (station number 74009).
Average rainfall for the area is 445 mm/yr.
Management practices
The crop rotation for the parameterisation simulations, the long-term parameterisation simulations,
and the scenarios represented the actual crop rotation grown most recently at the site. Crop variety,
sowing windows, fertiliser rates and application dates, and tillage management practices for the
Kilnyana farm are described in Tables 2 and 3.
96
Table 2: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Boatrock paddock for the Kilnyana farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
sunvale (wheat); hindmarsh (barley);
hyola42 (canola); coolabah (oats)
1-May to 21-Jun (wheat); 7-May to 21-Jul
(barley); 14-Apr to 7-Jun (canola); 21-Mar to
21-May (oats)
sunvale (wheat); hindmarsh (barley);
hyola42 (canola); coolabah (oats)
1-May to 21-Jun (wheat); 26-May
(barley); 14-Apr to 7-Jun (canola 2011);
14-Apr to 7-Jun (canola 2014); 21-Mar to
21-May (oats)
Apply: 7 kg N/ha (wheat); 9 kg N/ha
(barley); 7 kg N/ha (canola 2011); 24 kg
N/ha (canola 2014); 7 kg N/ha (oats): N is
applied as urea at 50 mm depth
Apply: 73.6 kg N/ha (canola 2014): N is
applied as urea at 50 mm depth
Sowing date
N fertiliser
at sowing
Apply: 7 kg N/ha (wheat); 9 kg N/ha (barley);
10 kg N/ha (canola); 30 kg N/ha (oats): N is
applied as urea at 50 mm depth
N fertiliser
40 days
after sowing
N fertiliser
on a fixed
date
Tillage
Apply: 40 kg N/ha (wheat); 45 kg N/ha
(barley); 15 kg N/ha (canola): N is applied as
urea at 50 mm depth
Nil – plant into standing stubble
Apply: 45 kg N/ha (barley) on 1-aug-2013;
64.4 kg N/ha (wheat) on 14-jul-2012: N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Table 3: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Middleplain paddock for the Kilnyana farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
sunvale (wheat); hindmarsh (barley);
hyola42 (canola)
1-May to 21-Jun (wheat); 7-May to 21-Jul
(barley); 14-Apr to 7-Jun (canola)
Apply: 40 kg N/ha (wheat); 3 kg N/ha
(barley); 21 kg N/ha (canola): N is applied as
urea at 50 mm depth
Apply: 40 kg N/ha (barley); 59 kg N/ha
(canola): N is applied as urea at 50 mm
depth
sunvale (wheat); hindmarsh (barley);
hyola42 (canola)
1-May to 21-Jun (wheat); 1-Jun to 21-Jul
(barley); 21-May to 7-Jun (canola)
Apply: 7 kg N/ha (wheat); 3 kg N/ha
(barley); 21 kg N/ha (canola): N is applied
as urea at 50 mm depth
Apply: 74 kg N/ha (barley): N is applied as
urea at 50 mm depth
Sowing date
N fertiliser
at sowing
N fertiliser
40 days
after sowing
N fertiliser
on a fixed
date
97
Apply: 2.1 kg N/ha (canola) on 15-jul2013; 46 kg N/ha (canola) on 11-aug2013; 10.5 kg N/ha (canola) on 26-aug2013 : N is applied as urea at 50 mm
depth
Tillage
Residues
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Biophysical modelling
Parameterisation of APSIM to the Boatrock paddock at Kilnyana
Parameterisation simulations
The crop rotation (Table 1) practiced at the Boatrock paddock at the Kilnyana farm was set up with
representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop yields
were obtained for the barley, canola, oats and wheat crops. Average simulated canola yield in the
general and specific management practices was estimated to be within 0.19 t/ha of the average yield
measured at the farm (Figure 1). Wheat yield was well simulated in the specific management
practices for the 2012 crop (3.14 t/ha; data not shown), which was the only year wheat yield was
recorded on the farm (3 t/ha; data not shown). Simulated wheat yield was low in the other two
years wheat crops were simulated (the yield not recorded on these years at the farm) and this
reduced the average yield to the values (2.03 or 2.34 t/ha) displayed on the graph. Average barley
yield was under-predicted in both general and specific management, but was estimated to be within
0.37 t/ha of the regional average. The oat crop was grown for hay and thus the yield was not
recorded at the farm. In addition, a very small amount of fertiliser was recorded to have been
applied to the oat crop.
98
Figure 1: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatwheat-oats-canola-wheat-barley-canola crop rotation for the Boatrock paddock at the Kilnyana farm. The
simulation was run from 2007 to 2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 2). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was predicted to be
very slowly declining (Figure 3). The general management practices included bare fallow periods
combined with variable within-crop rainfall. Accordingly, there were low and discontinuous inputs of
carbon throughout the simulation which led to an overall net mineralisation of carbon from this soil.
99
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat-oats-canola-wheat-barley-canola crop rotation for the Boatrock paddock at the Kilnyana farm.
The simulation was run from 1959 to 2014.
100
Figure 3: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat-oats-canola-wheat-barley-canola crop rotation for
the Boatrock paddock at the Kilnyana farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Boatrock paddock at the Kilnyana farm. The No Burn scenario was developed from the
parameterisation simulation using the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
101
Figure 4: Crop yield simulated in response to eight scenarios for a wheat-wheat-oats-canola-wheat-barleycanola crop rotation for the Boatrock paddock at the Kilnyana farm. Values displayed represent the overall
average obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014).
Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
102
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to eight scenarios for a wheatwheat-oats-canola-wheat-barley-canola crop rotation for the Boatrock paddock at the Kilnyana farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to eight scenarios for a wheat-wheatoats-canola-wheat-barley-canola crop rotation for the Boatrock paddock at the Kilnyana farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
103
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). The Pasture and Combination
scenarios were not simulated at this site as there was no winter fallow in the crop rotation. Soil
carbon increased in scenarios relative to the No Burn (‘baseline’) scenario when residues were not
burnt and increased nitrogen fertiliser was applied, a summer crop was grown, or manure was
applied. These scenarios all involved increased inputs of organic matter. Soil carbon decreased in
scenarios where stubble was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased in the No Burn+N scenario compared to the No Burn (‘baseline’)
scenario, were similar for the Manure scenario, but reduced in all other scenarios (for the majority
of the simulation time period).
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In the No Burn+N scenario, where management practices increased soil
carbon stocks compared with the No Burn (‘baseline’) scenario, the greenhouse gas abatement
provided by the sequestered carbon was greater than the global warming impact caused by
increased nitrous oxide emissions, resulting in a reduction in global warming potential. Increased soil
carbon also drove the reduction in global warming potential in the Summer Crop and Manure
scenarios, however, in the Summer Crop scenario reductions to nitrous oxide emissions compared
with the No Burn (‘baseline’) scenario also contributed. Scenarios were soil organic carbon
decreased compared with the No Burn (‘baseline’) scenario led to a net increase in global warming
potential irrespective of reductions in nitrous oxide emissions.
104
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-wheat-oats-canola-wheat-barley-canola crop rotation for the Boatrock paddock at the Kilnyana farm.
Values displayed represent the overall average obtained from simulations with two starting points over 100
years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions
from soils: Table 3.
105
Parameterisation of APSIM to the Middleplain paddock at Kilnyana
Parameterisation simulations
The crop rotation (Table 1) practiced at the Middleplain paddock at the Kilnyana farm was set up
with representative crop management and soil outlined in Tables 1 and 3. Realistic simulated crop
yields were obtained for the wheat, canola, and barley crops. Average simulated canola yield in the
general and specific management practices was estimated to be within 0.90 t/ha of the average yield
measured at the farm and within 0.50 t/ha of the regional average yield (Figure 8). Average
simulated wheat yield in the general and specific management was estimated to be within 0.49 t/ha
of the regional average wheat yield (no measurements of wheat yield were recorded at the farm).
Average simulated barley yield in the specific management practices was estimated to be within
0.48 t/ha of the average yield measured at the farm.
Figure 8: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatwheat-wheat-wheat-fallow-canola-barley crop rotation for the Middleplain paddock at the Kilnyana farm.
The simulation was run from 2007 to 2014.
106
A single measurement of ammonium in the top 0.6 m of the soil was well simulated, but the amount
of nitrate and total nitrogen was under estimated by approximately 50 kg N/ha (Figure 9).
Figure 9: Measured (point) and predicted (line) nitrate, ammonium, total nitrogen (TotalN) in the top 0.6 m
of the soil for the specific management simulation for a single wheat-wheat-wheat-wheat-fallow-canolabarley crop rotation for the Middleplain paddock at the Kilnyana farm. The simulation was run from 2007 to
2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 10). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was predicted to
be very slowly declining (Figure 11). The general management practices included bare fallow periods
combined with variable within-crop rainfall. Accordingly, there were low and discontinuous inputs of
carbon throughout the simulation which led to an overall net mineralisation of carbon from this soil.
107
Figure 10: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat-wheat-wheat-fallow-canola-barley crop rotation for the Middleplain paddock at the Kilnyana
farm. The simulation was run from 1959 to 2014.
108
Figure 11: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat-wheat-wheat-fallow-canola-barley crop rotation
for the Middleplain paddock at the Kilnyana farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Middleplain paddock at the Kilnyana farm. The No Burn scenario was developed from the
parameterisation simulation using the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 12, 13 and 14.
109
Figure 12: Crop yield simulated in response to 10 scenarios for a wheat-wheat-wheat-wheat-fallow-canolabarley crop rotation for the Middleplain paddock at the Kilnyana farm. Values displayed represent the
overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 13: Average Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a
wheat-wheat-wheat-wheat-fallow-canola-barley crop rotation for the Middleplain paddock at the Kilnyana
farm. Values displayed represent the overall average obtained from simulations with two starting points
over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas
emissions from soils: Table 3.
110
Figure 14: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheat-wheatwheat-wheat-fallow-canola-barley crop rotation for the Middleplain paddock at the Kilnyana farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased in scenarios
relative to the No Burn (‘baseline’) scenario when residues were not burnt and increased nitrogen
fertiliser was applied, a summer crop or pasture was grown, or manure was applied. These scenarios
all involved increased inputs of organic matter. Soil carbon decreased in scenarios where stubble
was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in some scenarios
(Manure, Pasture, and No Burn+N) where soil organic carbon increased. Conversely, while soil
carbon increased in the Summer Crop and Combination scenarios, variable nitrous oxide emissions
were simulated. In scenarios were soil carbon decreased relative to No Burn (‘baseline’) scenario,
nitrous oxide emissions were reduced.
111
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In scenarios where management practices considerably increased soil
carbon stocks compared with the No Burn (‘baseline’) scenario (Summer Crop, Pasture, and
Combination) the greenhouse gas abatement provided by the sequestered carbon was greater than
any negative global warming impact caused by nitrous oxide emissions, resulting in a reduction in
global warming potential. In scenarios where management practices reduced soil carbon stocks
compared with the No Burn (‘baseline’) scenario, net global warming potential was either similar to
the No Burn (‘baseline’) scenario or increased, irrespective of any reduction of nitrous oxide
emissions.
Figure 15: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-wheat-wheat-wheat-fallow-canola-barley crop rotation for the Middleplain paddock at the Kilnyana
farm. Values displayed represent the overall average obtained from simulations with two starting points
over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas
emissions from soils: Table 3.
112
Appendix 6: Merrilong greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Dimby1 and
Dimby5 paddocks.
Table 1: Soil properties used to simulate the Merrilong farm soils for the Dimby1, Dimby5, and Willows
paddocks.
Parameter
Dimby1
Dimby5
Willows
APSoil number
APSoil name
Soil type
Organic carbon (Total %,
0.00-0.15 m)
123
Breeza
Grey-Black Vertosol
1.09
94
Spring Ridge
Black Vertosol
1.16
94
Spring Ridge
Black Vertosol
1.10
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted –
crop dependant (m)
Plant available water
capacity – crop dependant
(mm)
Crop rotation1
12
6.4
84
Moderate (0.3)
1.8
12
6.8
84
Moderate (0.3)
1.8
12
5.8
Moderate (0.3)
1.2-1.8
264-273
254-302
254-266
WtxxSgxSgWtxxCtWt
SgxMzxSgWtxxSgWt
WtxxSgxSnWtxxSg
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Pine Ridge meteorological station (station number 055037). Average
rainfall for the area is 603 mm/yr.
Management practices
Crop variety, sowing windows, fertiliser rates and application dates, and tillage management
practices for (1) the general management practices simulated for the parameterisation simulations,
long-term parameterisation simulations, and scenarios, and (2) the specific management for the
parameterisation simulations for the Merrilong case study farm are described in Tables 2, 3 and 4.
113
The Dimby1 and Dimby5 paddocks are both irrigated cropping systems while the Willows paddock is
a dryland cropping system.
Table 2: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Dimby1 paddock for the Merrilong farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
livingston (wheat); buster (sorghum); S71BR
(cotton)
14-May to 7-Jul (wheat); 1-Oct to 21-Nov
(sorghum); 15-Oct to 15-Nov (cotton)
Apply: 120 kg N/ha (wheat); 160 kg N/ha
(sorghum); 140 kg N/ha (cotton): N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
livingston (wheat); buster (sorghum); ;
S71BR (cotton)
14-Jul to 28-Jul (wheat); 1-Nov to 21-Nov
(sorghum); 15-Oct to 15-Nov (cotton)
Apply: 115 kg N/ha (wheat); 138 kg N/ha
(sorghum); 138 kg N/ha (cotton): N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
Tillage
Residues
Table 3: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Dimby5 paddock for the Merrilong farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
livingston (wheat); buster (sorghum);
hycorn_424 (maize)
14-May to 7-Jul (wheat); 1-Oct to 21-Nov
(sorghum); 1-Sep to 14-Nov (maize)
Apply: 140 kg N/ha (wheat); 150 kg N/ha
(sorghum); 200 kg N/ha (maize): N is applied
as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
livingston (wheat); buster (sorghum);
hycorn_424 (maize)
21-Jul to 25-Jul (wheat); 7-Nov to 21-Nov
(sorghum); 1-Nov to 14-Nov (maize)
Apply: 138 kg N/ha (wheat); 138 kg N/ha
(sorghum); 138 kg N/ha (maize): N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
Tillage
Residues
114
Table 4: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Willows paddock for the Merrilong farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
livingston (wheat); buster (sorghum);
hyoleic31 (sunflower)
14-May to 7-Jul (wheat); 1-Oct to 21-Nov
(sorghum); 1-Sep to 7-Nov (sunflower)
Apply: 115 kg N/ha (wheat); 115 kg N/ha
(sorghum); 115 kg N/ha (sunflower): N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
livingston (wheat); buster (sorghum);
hyoleic31 (sunflower)
14-May to 7-Jul (wheat); 1-Oct to 21-Nov
(sorghum); 1-Sep to 7-Nov (sunflower)
Apply: 115 kg N/ha (wheat); 115 kg N/ha
(sorghum); 115 kg N/ha (sunflower): N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
Tillage
Residues
Biophysical modelling
Parameterisation of APSIM to the Dimby1 paddock at Merrilong
Parameterisation simulations
The crop rotation (Table 1) practiced at the Dimby1 paddock at the Merrilong farm was set up with
representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop yields
were obtained for the sorghum, wheat, and cotton crops. Average simulated wheat yield in the
general management practice was estimated to be within 0.66 t/ha of the average yield measured at
the farm (Figure 1). Average simulated sorghum yield in the general and specific management
practices was estimated to be within 0.67 t/ha of the average yield measured at the farm. Average
simulated cotton yield in the general and specific management practices was estimated to be within
1.23 bales/ha of the average yield measured at the farm. Regional averages were excluded from the
comparison as they were not available for irrigated crops.
115
Figure 1: Average measured crop yield versus average predicted crop yield for the parameterisation
simulations using general and specific management for a single wheat-sorghum-sorghum-wheat-cottonwheat crop rotation for the Dimby1 paddock at the Merrilong farm. The simulation was run from 2008 to
2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
and irrigation were simulated to be in balance with the losses from evapotranspiration, runoff, and
deep drainage (Figure 2). Soil carbon in the total soil profile (0-1.8 m) was estimated to be relatively
stable while soil carbon in the top 0.3 m was predicted to be very slowly increasing (Figure 3). This
increase of soil carbon in the top 0.3 m could be attributed to inputs of organic matter from high
yielding irrigated crops.
116
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-sorghum-sorghum-wheat-cotton-wheat crop rotation for the Dimby1 paddock at the Merrilong farm.
The simulation was run from 1964 to 2014.
117
Figure 3: Total Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-sorghum-sorghum-wheat-cotton-wheat crop rotation for
the Dimby1 paddock at the Merrilong farm. The simulation was run from 1964 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Dimby1 paddock at the Merrilong farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
118
Figure 4: Crop yield simulated in response to 10 scenarios for a wheat-sorghum-sorghum-wheat-cottonwheat crop rotation for the Dimby1 paddock at the Merrilong farm. Values displayed represent the overall
average obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014).
Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a wheatsorghum-sorghum-wheat-cotton-wheat crop rotation for the Dimby1 paddock at the Merrilong farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
119
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheat-sorghumsorghum-wheat-cotton-wheat crop rotation for the Dimby1 paddock at the Merrilong farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased
considerably compared with the No Burn (‘baseline’) scenario, in scenarios where a summer crop
was grown (Summer Crop and Combination). These scenarios both involved relatively large inputs of
organic matter in the form of the cowpea residue. Soil carbon was similar to that in the No Burn
(‘baseline’) scenario in scenarios where there were smaller inputs of organic matter (Manure,
Pasture, and No Burn+N scenarios). Soil carbon decreased in scenarios where stubble was burnt
and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased. The increase was very small in the Pasture and Manure
scenarios. The increase in nitrous oxide in scenarios where soil organic carbon increased is not
surprising, as soil carbon is a substrate for microbes that facilitate nitrous oxide production. Nitrous
oxide emissions were reduced in all scenarios where soil organic carbon decreased.
120
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in nitrous oxide emissions. In the Summer Crop, Combination and No Burn+N scenarios
where management practices considerably increased nitrous oxide emissions compared with the No
Burn (‘baseline’) scenario, global warming potential increased, irrespective of any increased carbon
sequestration. In the Burn, Burn-N and No Burn-N scenarios where management practices reduced
nitrous oxide emissions compared with the No Burn (‘baseline’) scenario, net global warming
potential reduced compared with the No Burn (‘baseline’) scenario, irrespective of reduced carbon
sequestration.
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-sorghum-sorghum-wheat-cotton-wheat crop rotation for the Dimby1 paddock at the Merrilong farm.
Values displayed represent the overall average obtained from simulations with two starting points over 100
years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions
from soils: Table 3.
121
Parameterisation of APSIM to the Dimby5 paddock at Merrilong
Parameterisation simulations
The crop rotation (Table 1) practiced at the Dimby5 paddock at the Merrilong farm was set up with
representative crop management and soil outlined in Tables 1 and 3. Realistic simulated crop yields
were obtained for the sorghum, maize, and wheat crops. Average simulated wheat yield in the
general and specific management practices was estimated to be within 0.12 t/ha of the average yield
measured at the farm (Figure 8). Average simulated sorghum yield in the general and specific
management practices was estimated to be within 0.93 t/ha of the average yield measured at the
farm. Maize yields were under predicted in both general and specific management. Regional
averages were excluded from the comparison as they were not available for irrigated crops.
Figure 8: Average measured crop yield versus average predicted crop yield for the general and specific
management simulations for a single sorghum-maize-sorghum-wheat-sorghum-wheat crop rotation for the
Dimby5 paddock at the Merrilong farm. The simulation was run from 2009 to 2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
and irrigation were simulated to be in balance with the losses from evapotranspiration, runoff, and
122
deep drainage (Figure 9). Soil carbon in the total soil profile (0-1.8 m) was estimated to be relatively
stable while soil carbon in the top 0.3 m was predicted to be very slowly increasing (Figure 10). This
increase of soil carbon in the top 0.3 m could be attributed to inputs of organic matter from high
yielding irrigated crops.
Figure 9: Water balance for the long-term parameterisation simulation using general management for a
sorghum-maize-sorghum-wheat-sorghum-wheat crop rotation for the Dimby5 paddock at the Merrilong
farm. The simulation was run from 1961 to 2014.
123
Figure 10: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a sorghum-maize-sorghum-wheat-sorghum-wheat crop rotation
for the Dimby5 paddock at the Merrilong farm. The simulation was run from 1961 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Dimby5 paddock at the Merrilong farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 11, 12, and 13.
124
Figure 11: Crop yield simulated in response to 10 scenarios for a sorghum-maize-sorghum-wheat-sorghumwheat crop rotation for the Dimby5 paddock at the Merrilong farm. Values displayed represent the overall
average obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014).
Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 12: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a sorghummaize-sorghum-wheat-sorghum-wheat crop rotation for the Dimby5 paddock at the Merrilong farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
125
Figure 13: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a sorghum-maizesorghum-wheat-sorghum-wheat crop rotation for the Dimby5 paddock at the Merrilong farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 14). Soil carbon increased
considerably compared with the No Burn (‘baseline’) scenario, in scenarios where a summer crop
was grown (Summer Crop and Combination). These scenarios both involved relatively large inputs of
organic matter in the form of the cowpea residue. Soil carbon was similar to that in the No Burn
(‘baseline’) scenario in scenarios where there were smaller inputs of organic matter (Manure,
Pasture, and No Burn+N scenarios). Soil carbon decreased in scenarios where stubble was burnt
and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased. The increase was very small in the Pasture and Manure
scenarios. The increase in nitrous oxide in scenarios where soil organic carbon increased is not
126
surprising, as soil carbon is a substrate for microbes that facilitate nitrous oxide production. Nitrous
oxide emissions were reduced in all scenarios where soil organic carbon decreased.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by a combination of changes in nitrous oxide emissions and soil carbon storage. In the Summer Crop,
Combination and No Burn+N scenarios, where management practices considerably increased nitrous
oxide emissions compared with the No Burn (‘baseline’) scenario, global warming potential
increased irrespective of any increase in carbon sequestration. In the Burn-N scenario, a reduction in
nitrous oxide emissions resulted in a decrease in global warming potential relative to the No Burn
(‘baseline’) scenario. (This scenario had a small effect on soil carbon storage.) In the Burn and Burn-N
scenarios, net global warming potential was positive initially but decreased over time.
127
Figure 14: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
sorghum-maize-sorghum-wheat-sorghum-wheat crop rotation for the Dimby5 paddock at the Merrilong
farm. Values displayed represent the overall average obtained from simulations with two starting points
over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas
emissions from soils: Table 3.
Parameterisation of APSIM to the Willows paddock at Merrilong
Parameterisation simulations
The crop rotation (Table 1) practiced at the Willows paddock at the Merrilong farm was set up with
representative crop management and soil outlined in Tables 1 and 4. Realistic simulated crop yields
were obtained for the sorghum, sunflower, and wheat crops. Average simulated wheat yield in the
general and specific management practices were estimated to be within 0.16 t/ha of the average
yield measured at the farm (Figure 15). Average simulated sorghum and sunflower yield in the
128
general and specific management practices were estimated to be within 0.46 t/ha of the average
yield measured at the farm. General and specific management produced very similar yields for all
crops. Crop yields were simulated to be above the average regional yields.
Figure 15: Average measured crop yield and regional average (1992-2010) crop yield versus average
predicted crop yield for the parameterisation simulations using general and specific management for a
single wheat-sorghum-sorghum-sunflower-wheat-sorghum crop rotation for the Willows paddock at the
Merrilong farm. The simulation was run from 2007 to 2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 16). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was predicted to
be very slowly declining (Figure 17). The general management practices included bare fallow periods
combined with variable within-crop rainfall. Accordingly, there were low and discontinuous inputs of
carbon throughout the simulation which led to an overall net mineralisation of carbon from this soil.
129
Figure 16: Water balance for the long-term parameterisation simulation using general management for a
wheat-sorghum-sorghum-sunflower-wheat-sorghum crop rotation for the Willows paddock at the Merrilong
farm. The simulation was run from 1959 to 2014.
130
Figure 17: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-sorghum-sorghum-sunflower-wheat-sorghum crop
rotation for the Willows paddock at the Merrilong farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Dimby5 paddock at the Merrilong farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 18, 19, and 20. Simulated nitrous oxide emissions were
higher at the Willows paddock than at other dryland paddocks in this study as relatively high
amounts of fertiliser were applied to the Willows paddock.
131
Figure 18: Crop yield simulated in response to 10 scenarios for a wheat-sorghum-sorghum-sunflower-wheatsorghum crop rotation for the Willows paddock at the Merrilong farm. Values displayed represent the
overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 19: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a wheatsorghum-sorghum-sunflower-wheat-sorghum crop rotation for the Willows paddock at the Merrilong farm.
Values displayed represent the overall average obtained from simulations with two starting points over 100
years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions
from soils: Table 3.
132
Figure 20: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheatsorghum-sorghum-sunflower-wheat-sorghum crop rotation for the Willows paddock at the Merrilong farm.
Values displayed represent the overall average obtained from simulations with two starting points over 100
years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions
from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 21). Soil carbon increased
considerably compared with the No Burn (‘baseline’) scenario in scenarios where a summer crop was
grown (Summer Crop and Combination scenarios). These scenarios both involved relatively large
inputs of organic matter in the form of the cowpea residue. In scenarios with small inputs of organic
matter (Manure and Pasture scenarios) soil carbon was only slightly higher than that in the No Burn
(‘baseline’) scenario. Soil carbon decreased or was similar to that in the No Burn (‘baseline’) scenario
in scenarios where stubble was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased or additional nitrogen fertiliser was used. The increase in nitrous
oxide in scenarios where soil organic carbon increased is not surprising, as soil carbon is a substrate
for microbes that facilitate nitrous oxide production. Scenarios with additional nitrogen fertiliser had
the highest nitrous oxide emissions. This was driven by the relatively high amounts of nitrogen
fertiliser used at this paddock. Nitrous oxide emissions were reduced in all scenarios where nitrogen
fertiliser was reduced as well as in the Burn scenario.
133
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in nitrous oxide emissions. Net global warming potential decreased for the duration of
the simulation time frame in scenarios where nitrous oxide emissions were reduced (No Burn-N,
Burn-N, and Burn). In the Summer Crop and Combination scenarios where management practices
considerably increased soil carbon stocks compared with the No Burn (‘baseline’) scenario, the
greenhouse gas abatement provided by the sequestered carbon was greater than the global
warming impact of the nitrous oxide emissions for the first 75 years of the simulation period
resulting in a small reduction in global warming potential for that period. In scenarios where
nitrogen fertiliser was increased or small additions of organic matter were added (Burn+N, No
Burn+N, Manure, and Pasture scenarios), there was an increase in net global warming potential.
Figure 21: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-sorghum-sorghum-sunflower-wheat-sorghum crop rotation for the Willows paddock at the Merrilong
farm. Values displayed represent the overall average obtained from simulations with two starting points
over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas
emissions from soils: Table 3.
134
Appendix 7: Warili greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Buttenshaw and
Cattleyard paddocks.
Table 1: Soil properties used to simulate the Warili farm soils for the Buttenshaw and Cattleyard paddocks.
Parameter
Buttenshaw
Cattleyard
APSoil number
APSoil name
Soil type
190
Parkes
Sandy Clay Loam over
Heavy Clay
1.09
12
6.5
68
Well drained (0.5)
1.2-1.8
176-225
190
Parkes
Sandy Clay Loam over
Heavy Clay
1.32
12
6.3
68
Well drained (0.5)
1.2-1.8
176-225
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted – crop dependant (m)
Plant available water capacity – crop
dependant (mm)
Crop rotation1
WtxWtxCnxWtxxSgCn WtxWtxLnLnxSgCn
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Warroo meteorological station (station number 050020). Average
rainfall for the area is 443 mm/yr.
Management practices
Crop variety, sowing windows, fertiliser rates and application dates, and tillage management
practices for (1) the general management practices simulated for the parameterisation simulations,
long-term parameterisation simulations, and scenarios, and (2) the specific management for the
parameterisation simulations for the Warili farm are described in Tables 2 and 3. The Buttenshaw
and Cattleyard paddocks at the Warili farm are both irrigated cropping systems.
135
Table 2: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Buttenshaw paddock for the Warili farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
livingston (wheat); buster (sorghum);
hyola42 (canola)
14-May to 28-Jun (wheat); 14-Oct to 7-Dec
(sorghum); 7-Apr to 21-May (canola)
livingston (wheat); buster (sorghum);
hyola42 (canola)
14-May to 28-Jun (wheat); 7-Dec to 18Dec (sorghum); 7-Apr to 21-May (canola
2010); 7-Apr to 21-May (canola 2014)
Apply: 61 kg N/ha (wheat); 10 kg N/ha
(sorghum); 61 kg N/ha (canola 2010); 54
kg N/ha (canola 2014): N is applied as
urea at 50 mm depth
Apply: 58 kg N/ha (wheat); 46 kg N/ha
(sorghum); 46 kg N/ha (canola 2010); 106
kg N/ha (canola 2014): N is applied as
urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
Apply: 65 kg N/ha (wheat); 10 kg N/ha
(sorghum); 65 kg N/ha (canola): N is applied
as urea at 50 mm depth
N fertiliser
40 days
after sowing
Apply: 60 kg N/ha (wheat); 50 kg N/ha
(sorghum); 50 kg N/ha (canola): N is applied
as urea at 50 mm depth
Tillage
Residues
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
Table 3: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations Cattleyard paddock for the Warili farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
livingston (wheat); buster (sorghum);
hyola42 (canola); trifecta (lucerne)
14-May to 28-Jun (wheat); 14-Oct to 7-Dec
(sorghum); 7-Apr to 21-May (canola); 1-Sep
to 15-Oct (lucerne)
Apply: 65 kg N/ha (wheat); 10 kg N/ha
(sorghum); 55 kg N/ha (canola): N is applied
as urea at 50 mm depth
Apply: 60 kg N/ha (wheat); 50 kg N/ha
(canola): N is applied as urea at 50 mm
depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
livingston (wheat); buster (sorghum);
hyola42 (canola); trifecta (lucerne)
14-May to 28-Jun (wheat); 7-Dec to 18Dec (sorghum); 7-Apr to 21-May (canola);
1-Sep to 15-Oct (lucerne)
Apply: 61 kg N/ha (wheat); 10 kg N/ha
(sorghum); 54 kg N/ha (canola): N is
applied as urea at 50 mm depth
Apply: 58 kg N/ha (wheat); 69 kg N/ha
(canola): N is applied as urea at 50 mm
depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Sowing date
N fertiliser
at sowing
N fertiliser
40 days
after sowing
Tillage
Residues
136
Biophysical modelling
Parameterisation of APSIM to the Buttenshaw paddock at Warili
Parameterisation simulations
The crop rotation (Table 1) practiced at the Buttenshaw paddock at the Warili farm was set up with
representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop yields
were obtained for the canola, sorghum, and wheat crops. Average simulated yield in the general and
specific management practices was estimated to be within 1.08 t/ha of the average yield measured
at the farm (Figure 1). Regional averages were excluded from the comparison as they were not
available for irrigated crops.
Figure 1: Average measured crop yield versus average predicted crop yield for the general and specific
management simulations for a wheat-wheat-canola-wheat-wheat-fallow-sorghum-canola crop rotation for
the Buttenshaw paddock at the Warili farm. The simulation was run from 2007 to 2014.
137
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
and irrigation were simulated to be in balance with the losses from evapotranspiration, runoff, and
deep drainage (Figure 2). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was
predicted to be very slowly declining (Figure 3). The general management practices included bare
fallow periods combined with variable within-crop rainfall. Accordingly, there were low and
discontinuous inputs of carbon throughout the simulation which led to an overall net mineralisation
of carbon from this soil.
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat-canola-wheat-wheat-fallow-sorghum-canola crop rotation for the Buttenshaw paddock at the
Warili farm. The simulation was run from 1959 to 2014.
138
Figure 3: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat-canola-wheat-wheat-fallow-sorghum-canola crop
rotation for the Buttenshaw paddock at the Warili farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Buttenshaw paddock at the Warili farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
139
Figure 4: Crop yield simulated in response to 10 scenarios for a wheat-wheat-canola-wheat-wheat-fallowsorghum-canola crop rotation for the Buttenshaw paddock at the Warili farm. Values displayed represent
the overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a wheatwheat-canola-wheat-wheat-fallow-sorghum-canola crop rotation for the Buttenshaw paddock at the Warili
farm. Values displayed represent the overall average obtained from simulations with two starting points
over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas
emissions from soils: Table 3.
140
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheat-wheatcanola-wheat-wheat-fallow-sorghum-canola crop rotation for the Buttenshaw paddock at the Warili farm.
Values displayed represent the overall average obtained from simulations with two starting points over 100
years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions
from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased
considerably compared with the No Burn (‘baseline’) scenario in scenarios where a summer crop was
grown (Summer Crop and Combination scenarios). These scenarios both involved relatively large
inputs of organic matter in the form of the cowpea residue. In scenarios with small inputs of organic
matter (Manure, Pasture, and No Burn+N scenarios) soil carbon was only slightly higher than that in
the No Burn (‘baseline’) scenario. Soil carbon decreased or was similar to that in the No Burn
(‘baseline’) scenario where stubble was burnt and/or nitrogen fertiliser was reduced (Burn, Burn+N,
Burn-N, and No Burn-N).
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario where soil organic
carbon increased. The exception to this was the Pasture scenario, where nitrous oxide emissions
were similar to the No Burn (‘baseline’) scenario. The increase in nitrous oxide emissions in scenarios
where soil organic carbon increased is not surprising, as availability of soil carbon is one of the
precursors for nitrous oxide production. Nitrous oxide emissions were reduced in all scenarios where
stubble was burnt and/or nitrogen fertiliser was reduced.
141
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by a combination of changes in soil carbon storage and nitrous oxide emissions. There was limited
potential for consistent abatement for this paddock. In scenarios where management practices
considerably increased soil carbon stocks compared with the No Burn (‘baseline’) scenario (Summer
Crop and Combination), the greenhouse gas abatement provided by the sequestered carbon was
greater than the global warming impact caused by the stimulation of the nitrous oxide emissions for
the first 60 years of the simulation period, resulting in a reduction in global warming potential for
this time period. When the rate of carbon sequestration slowed (after 60 years), the nitrous oxide
emissions produced by this scenario led to a net positive global warming potential. In scenarios were
nitrogen fertiliser was reduced, global warming potential decreased, but only after 50 - 75 years.
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-wheat-canola-wheat-wheat-fallow-sorghum-canola crop rotation for the Buttenshaw paddock at the
Warili farm. Values displayed represent the overall average obtained from simulations with two starting
points over 100 years (1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse
gas emissions from soils: Table 3.
142
Parameterisation of APSIM to the Cattleyard paddock at Warili
Parameterisation simulations
The crop rotation (Table 1) practiced at the Cattleyard paddock at the Warili farm was set up with
representative crop management and soil outlined in Tables 1 and 3. Realistic simulated crop yields
were obtained for the wheat, sorghum, and canola crops. Average simulated wheat and sorghum
yields in the general and specific management practices was estimated to be within 1.11 t/ha of the
average yields measured at the farm (Figure 8). Average simulated canola yield was over-simulated
in the general and specific management practices (within 1.87 t/ha of the average yields measured
at the farm). Note that lucerne yield is not presented as the crop was ‘grazed’. Regional averages
were excluded from the comparison as they were not available for irrigated crops.
Figure 8: Average measured crop yield versus average predicted crop yield for the general and specific
management simulations for a single wheat-wheat-lucerne-fallow-sorghum-canola crop rotation for the
Cattleyard paddock at the Warili farm. The simulation was run from 2007 to 2014. Note that lucerne yield is
not presented as the crop was ‘grazed’.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
and irrigation were simulated to be in balance with the losses from evapotranspiration, runoff, and
143
deep drainage (Figure 9). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was
predicted to be very slowly declining (Figure 10). The general management practices included bare
fallow periods combined with variable within-crop rainfall. Accordingly, there were low and
discontinuous inputs of carbon throughout the simulation which led to an overall net mineralisation
of carbon from this soil.
Figure 9: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat-lucerne-fallow-sorghum-canola crop rotation for the Cattleyard paddock at the Warili farm.
The simulation was run from 1959 to 2014.
144
Figure 10: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat-lucerne-fallow-sorghum-canola crop rotation for
the Cattleyard paddock at the Warili farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Cattleyard paddock at the Warili farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 11, 12 and 13.
145
Figure 11: Crop yield simulated in response to 10 scenarios for a wheat-wheat-lucerne-fallow-sorghumcanola crop rotation for the Cattleyard paddock at the Warili farm. Values displayed represent the overall
average obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014).
Note that lucerne yield is not presented as the crop was ‘grazed’. Scenarios are described in Section Abating
greenhouse gas emissions from soils: Table 3.
Figure 12: Annual simulated emissions of nitrous oxide (N2O-N) in response to 10 scenarios for a wheatwheat-lucerne-fallow-sorghum-canola crop rotation for the Cattleyard paddock at the Warili farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
146
Figure 13: Soil carbon in the surface 0.3 m of soil simulated in response to 10 scenarios for a wheat-wheatlucerne-fallow-sorghum-canola crop rotation for the Cattleyard paddock at the Warili farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 14). Soil carbon increased
considerably compared with the No Burn (‘baseline’) scenario in scenarios where a summer crop was
grown (Summer Crop and Combination scenarios). These scenarios both involved relatively large
inputs of organic matter in the form of the cowpea residue. In scenarios with small inputs of organic
matter (Manure, Pasture, and No Burn+N scenarios) soil carbon was only slightly higher than that in
the No Burn (‘baseline’) scenario. Soil carbon decreased or was similar to that in the No Burn
(‘baseline’) scenario in scenarios where stubble was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in scenarios where
soil organic carbon increased. The exception to this was the Pasture scenario, where nitrous oxide
emissions were similar to the No Burn (‘baseline’) scenario. The increase in nitrous oxide in scenarios
where soil organic carbon increased is not surprising, as soil carbon is a substrate for microbes that
facilitate nitrous oxide production. The quantity of nitrous oxide emitted was relatively small for an
irrigated cropping system. Fertiliser inputs into this paddock were less than for the Buttenshaw
147
paddock. Lucerne grown during the cropping rotation fixed N, reducing the dependence on nitrogen
fertiliser. Nitrous oxide emissions were reduced in all scenarios where stubble was burnt and/or
nitrogen fertiliser was reduced.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by a combination of changes in soil carbon storage and nitrous oxide emissions. In scenarios where
management practices considerably increased soil carbon stocks compared with the No Burn
(‘baseline’) scenario (Summer Crop and Combination), the greenhouse gas abatement provided by
the sequestered carbon was greater than the global warming impact caused by the stimulation of
the nitrous oxide emissions resulting in a reduction in global warming potential compared with the
No Burn (‘baseline’) scenario. The Pasture, Manure and No Burn-N scenarios were also able to
provide a small reduction in global warming potential relative to the No Burn (‘baseline’) scenario.
Global warming potential was positive relative to the No Burn (‘baseline’) scenario for the majority
of the simulation period for scenarios were stubble was burnt and/or nitrogen fertiliser was
increased.
148
Figure 14: Difference between the baseline practice and scenarios for sequestered carbon (0-0.3 m), nitrous
oxide (N2O-N) emissions and net global warming potential (GWP) for a wheat-wheat-lucerne-fallowsorghum-canola crop rotation for the Cattleyard paddock at the Warili farm. Values displayed represent the
overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
149
Appendix 8: Wilgo greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the Blackstump and
Clearview paddocks.
Table 1: Soil properties used to simulate the Wilgo farm soils for the Blackstump and Clearview paddocks.
Parameter
Blackstump
Clearview
APSoil number
APSoil name
Soil type
541-YP
Urana
Grey Vertosol
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted – crop dependant (m)
Plant available water capacity – crop dependant
(mm)
Crop rotation for the parameterisation simulations1
Crop rotation for the scenarios1
1.01
12
6.5
84
Moderate (0.3)
0.9-1.2
164-251
213
Rand
Clay Loam over
Clay
1.19
12
6.8
73
Moderate (0.4)
0.9-1.8
106-172
WtxWtxWtxWtxxxCnxWt CnxWtxFbxWt
CnxWtxFbxWt
CnxWtxFbxWt
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation data were obtained from the SILO climate record
(Jeffrey et al., 2001) for the Mulwala Post Office meteorological station (station number 074081).
Average rainfall for the area is 502 mm/yr.
Management practices
The crop rotation for the parameterisation simulations and the long-term parameterisation
simulations represented the actual crop rotations grown most recently at the site (Table 1).
However, the crop rotation at the Blackstump paddock was not considered sensible for a long-term
rotation for the scenarios. Thus a region-appropriate typical crop rotation was simulated for the
Blackstump paddock scenarios (Table 1).
150
Crop variety, sowing windows, fertiliser rates and application dates, and tillage management
practices for (1) the general management practices simulated for the parameterisation simulations,
long-term parameterisation simulations, and scenarios, and (2) the specific management for the
parameterisation simulations for Wilgo farm are described in Tables 2 and 3.
Table 2: General management practices for the parameterisation simulations and the long-term
parameterisation simulations, the specific management used to represent on-site management for the
parameterisation simulations, and the general management used for the scenarios for the Blackstump
paddock for the Wilgo farm
Practice
General management for
parameterisation
simulations and long-term
parameterisation
simulations
Specific management for
parameterisation
simulations
General management for
scenarios
Variety
sunvale (wheat); hyola42
(canola)
21-Apr to 14-Jun (wheat);
14-Apr to 7-Jun (canola)
sunvale (wheat); hyola42
(canola)
14-Jun to 28-Jun (wheat); 14Apr to 7-Jun (canola)
N fertiliser
at sowing
Apply: 10 kg N/ha (wheat);
5 kg N/ha (canola): N is
applied as urea at 50 mm
depth
Apply: 6 kg N/ha (wheat); 5
kg N/ha (canola): N is
applied as urea at 50 mm
depth
N fertiliser
40 days
after sowing
Apply: 40 kg N/ha (wheat);
50 kg N/ha (canola): N is
applied as urea at 50 mm
depth
Apply: 46 kg N/ha (canola): N
is applied as urea at 50 mm
depth
sunvale (wheat); hyola42
(canola)
21-Apr to 14-Jun (wheat);
14-Apr to 7-Jun (canola);
14-Apr to 7-Jun
(fababean)
Apply: 10 kg N/ha
(wheat); 5 kg N/ha
(canola); 7 kg N/ha
(fababean): N is applied
as urea at 50 mm depth
Apply: 40 kg N/ha
(wheat); 50 kg N/ha
(canola): N is applied as
urea at 50 mm depth
Sowing date
N fertiliser
on a fixed
date
Tillage
Residues
151
Nil – plant into standing
stubble
Zero till; plant stubble left
on paddock after harvest
Apply: 37 kg N/ha (wheat) on
5-jul-2010; 60 kg N/ha
(wheat) on 3-jul-2011; 92 kg
N/ha (wheat) on 13-jul-2014:
N is applied as urea at 50
mm depth
Nil – plant into standing
stubble
Zero till; plant stubble left on
paddock after harvest
Nil – plant into standing
stubble
Zero till; plant stubble left
on paddock after harvest
Table 3: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, as well as the specific management practices used to represent on-site management in
the parameterisation simulations for the Clearview paddock for the Wilgo farm.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
sunvale (wheat); hyola42 (canola); fiord
(fababean)
21-Apr to 14-Jun (wheat); 14-Apr to 7-Jun
(canola); 14-Apr to 7-Jun (fababean)
Apply: 7 kg N/ha (wheat); 7 kg N/ha (canola)
7 kg N/ha (fababean): N is applied as urea at
50 mm depth
Apply: 30 kg N/ha (wheat); 50 kg N/ha
(canola): N is applied as urea at 50 mm
depth
sunvale (wheat); hyola42 (canola); fiord
(fababean)
21-Apr to 14-Jun (wheat); 14-Apr to 7-Jun
(canola); 14-Apr to 7-Jun (fababean)
Apply: 7 kg N/ha (wheat); 7 kg N/ha
(canola) 7 kg N/ha (fababean): N is
applied as urea at 50 mm depth
Apply: 46 kg N/ha (canola): N is applied as
urea at 50 mm depth
Sowing date
N fertiliser
at sowing
N fertiliser
40 days
after sowing
N fertiliser
on a fixed
date
Tillage
Residues
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
Apply: 92 kg N/ha (wheat) on 3-jun-2012;
46 kg N/ha (wheat) on 13-jun-2014: N is
applied as urea at 50 mm depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Biophysical modelling
Parameterisation of APSIM to the Blackstump paddock at Wilgo
Parameterisation simulations
The crop rotation (Table 1) practiced at the Blackstump paddock at the Wilgo farm was set up with
representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop yields
were obtained for the canola and wheat crops. Average simulated wheat yield in the general
management practices was estimated to be within 0.50 t/ha of the average yield measured at the
farm (Figure 1). Average simulated canola yield in the general management practices was estimated
to be within 0.95 t/ha of the average yield measured at the farm. Average simulated crop yields
were simulated to be above the regional average crop yields.
152
Figure 1: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatwheat-wheat-wheat-fallow-canola-wheat crop rotation for the Blackstump paddock at the Wilgo farm. The
simulation was run from 2007 to 2014.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 2). Soil carbon in both the total soil profile (0-1.5 m) and in the top 0.3 m was predicted to be
very slowly declining (Figure 3). The general management practices included bare fallow periods
combined with variable within-crop rainfall. Accordingly, there were low and discontinuous inputs of
carbon throughout the simulation which led to an overall net mineralisation of carbon from this soil.
153
Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-wheat-wheat-wheat-fallow-canola-wheat crop rotation for the Blackstump paddock at the Wilgo
farm. The simulation was run from 1959 to 2014.
154
Figure 3: Soil carbon 0-1.5 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-wheat-wheat-wheat-fallow-canola-wheat crop rotation
for the Blackstump paddock at the Wilgo farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Blackstump paddock at the Wilgo farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
155
Figure 4: Crop yield simulated in response to eight scenarios for a canola-wheat-fababean-wheat crop
rotation for the Blackstump paddock at the Wilgo farm. Values displayed represent the overall average
obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios
are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to eight scenarios for a canolawheat-fababean-wheat crop rotation for the Blackstump paddock at the Wilgo farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
156
Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to eight scenarios for a canola-wheatfababean-wheat crop rotation for the Blackstump paddock at the Wilgo farm. Values displayed represent
the overall average obtained from simulations with two starting points over 100 years (1914-2013 and 19152014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased in scenarios
relative to the No Burn (‘baseline’) scenario when residues were not burnt and increased nitrogen
fertiliser was applied, a summer crop was grown, or manure was applied. These scenarios all
involved increased inputs of organic matter. Soil carbon decreased in scenarios where stubble was
burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased in the No Burn+N scenario compared to the No Burn (‘baseline’)
scenario, were similar to the No Burn (‘baseline’) scenario in the Manure scenario, but reduced in all
other scenarios.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by a combination of changes in soil carbon and nitrous oxide emissions. In the Summer Crop
scenario, there was a substantial decrease in global warming potential as this scenario both
sequestered carbon and reduced nitrous oxide emissions. In the Manure scenario there was a small
157
decrease in global warming potential due to the small increase of carbon storage. For the first 50
years of the simulation, scenarios where management practices had reduced or similar soil carbon
stocks compared with the No Burn (‘baseline’) scenario, net global warming potential increased
slightly or was similar to the No Burn (‘baseline’) scenario irrespective of any reduction in nitrous
oxide emissions. However, in the last 50 years of the simulation, a very small amount of abatement
was provided by scenarios where stubble was burnt and/or nitrogen fertiliser was reduced.
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
canola-wheat-fababean-wheat crop rotation for the Blackstump paddock at the Wilgo farm. Values
displayed represent the overall average obtained from simulations with two starting points over 100 years
(1914-2013 and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils:
Table 3.
158
Parameterisation of APSIM to the Clearview paddock at Wilgo
Parameterisation simulations
The crop rotation (Table 1) practiced at the Clearview paddock at the Wilgo farm was set up with
representative crop management and soil outlined in Tables 1 and 3. Realistic simulated crop yields
were obtained for the wheat, canola and fababean crops. Average simulated wheat yield in the
specific management practices was estimated to be within 0.08 t/ha of the average yield measured
at the farm while for the general management practices simulated wheat yield was within 0.77 t/ha
(Figure 8). Average simulated canola and fababean yields in the specific management practices was
estimated to be within 0.52 t/ha of the average yields measured at the farm. Average simulated crop
yields were simulated to be above the regional average crop yields.
Figure 8: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single canolawheat-fababean-wheat crop rotation for the Clearview paddock at the Wilgo farm. The simulation was run
from 2010 to 2014.
159
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 9). Soil carbon in both the total soil profile (0-1.8 m) and in the top 0.3 m was predicted to be
very slowly declining (Figure 10). The general management practices included bare fallow periods
combined with variable within-crop rainfall. Accordingly, there were low and discontinuous inputs of
carbon throughout the simulation which led to an overall net mineralisation of carbon from this soil.
Figure 9: Water balance for the long-term parameterisation simulation using general management for a
canola-wheat-fababean-wheat crop rotation for the Clearview paddock at the Wilgo farm. The simulation
was run from 1959 to 2014.
160
Figure 10: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a canola-wheat-fababean-wheat crop rotation for the Clearview
paddock at the Wilgo farm. The simulation was run from 1959 to 2014.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the Clearview paddock at the Wilgo farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 11, 12 and 13.
161
Figure 11: Crop yield simulated in response to eight scenarios for a canola-wheat-fababean-wheat crop
rotation for the Clearview paddock at the Wilgo farm. Values displayed represent the overall average
obtained from simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios
are described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 12: Annual simulated emissions of nitrous oxide (N2O-N) in response to eight scenarios for a canolawheat-fababean-wheat crop rotation for the Clearview paddock at the Wilgo farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
162
Figure 13: Soil carbon in the surface 0.3 m of soil simulated in response to eight scenarios for a canolawheat-fababean-wheat crop rotation for the Clearview paddock at the Wilgo farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 14). Soil carbon increased in
scenarios relative to the No Burn (‘baseline’) scenario when residues were not burnt and increased
nitrogen fertiliser was applied, a summer crop was grown, or manure was applied. These scenarios
all involved increased inputs of organic matter. Soil carbon decreased in scenarios where stubble
was burnt and/or nitrogen fertiliser was reduced.
Nitrous oxide emissions increased in the No Burn+N and Manure scenarios compared with the No
Burn (‘baseline’) scenario but reduced in all other scenarios.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon and nitrous oxide emissions. In the Summer Crop scenario, there was a
substantial decrease in global warming potential as this scenario both sequestered carbon and
reduced nitrous oxide emissions. In the Manure scenario there was a small decrease in global
warming potential due to a small increase in carbon storage, irrespective of an increase in nitrous
163
oxide emissions. Global warming potential was similar to that in the No Burn (‘baseline’) scenario in
all other scenarios.
Figure 14: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
canola-wheat-fababean-wheat crop rotation for the Clearview paddock at the Wilgo farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
164
Appendix 9: Eurie Euire greenhouse gas abatement simulations
Model parameterisation
Soils
The simulated soils (Table 1) representing the farm soils were based on nearby APSOIL
parameterisations. Measured soil organic carbon and pH values were used for the P4 paddock.
Table 1: Soil properties used to simulate the Eurie Eurie farm soils for the P4 paddock.
Parameter
P4
APSoil number
APSoil name
Soil type
Organic carbon (Total %, 0.00-0.15 m)
Soil C:N
pH (CaCl2; 0.00-0.15 m)
Curve number
Drainage
Root depth restricted (m)
Plant available water capacity – crop dependant
(mm)
Crop rotation for parameterisation simulations1
Crop rotation for scenario simulations1
126
Merrywinebone
Grey vertosol
0.91
12
7.1
84
moderate (0.3)
180
206-263
WtxCpxWtxCpxWtx
WtxCpxWtxCpx
1
Wt, wheat; By, barley; Cp, chickpea; Cn, canola; Ot, oats; Ln, lucerne; Fb, faba bean; Sg, sorghum;
Ct, cotton; Mz, maize; Sn, sunflower; x, fallow.
Climate
Rainfall, evaporation, temperature and radiation climate data were obtained from the SILO climate
record (Jeffrey et al., 2001) for the Walgett Council meteorological station (station number 52026).
Average rainfall for the area is 479 mm/yr.
Management practices
The crop rotation for the parameterisation simulations, and the long-term parameterisation
simulations represented the actual crop rotation grown most recently at the site (Table 1). This crop
rotation was modified slightly to be appropriate as a long-term rotation for use in the scenarios
simulations (Table 1). Crop variety, sowing windows, fertiliser rates and application dates, and tillage
management practices for the Eurie Eurie case study farm are described in Table 2.
165
Table 2: General management practices used in the parameterisation, long-term parameterisation, and
scenario simulations, and the specific management practices used to represent on-site management in the
parameterisation simulations for the Eurie Eurie farm for the P4 paddock.
Practice
General management used in
parameterisation simulations, long-term
parameterisation simulations, and in
scenarios
Specific management used in
parameterisation simulations
Variety
Sowing date
janz (wheat); amethyst (chickpea)
1-May to 21-Jun (wheat); 7-May to 28-Jun
(chickpea)
N fertiliser
at sowing
Apply: 40 kg N/ha (wheat); 0 kg N/ha
(chickpea): N is applied as urea at 50 mm
depth
Nil – plant into standing stubble
Zero till; plant stubble left on paddock after
harvest
janz (wheat); amethyst (chickpea)
13-May (wheat2008); 12-Jun
(chickpea2009); 30-Apr (wheat2010); 11Jun (chickpea2011)
Apply: 0 kg N/ha (wheat); 0 kg N/ha
(chickpea)
Tillage
Residues
Nil – plant into standing stubble
Zero till; plant stubble left on paddock
after harvest
Biophysical modelling
Parameterisation of APSIM to the P4 paddock at Eurie Eurie
Parameterisation simulations
The crop rotation (Table 1) practiced at the P4 paddock at the Eurie Eurie farm was set up with
representative crop management and soil outlined in Tables 1 and 2. Realistic simulated crop yields
were obtained for the wheat and chickpea crops. Average simulated wheat yield in the general and
specific management practices was estimated to be within 0.53 t/ha of the average yield measured
at the farm (Figure 1). Average simulated chickpea yield in the general and specific management
practices was estimated to be within 0.34 t/ha of the average yield measured at the farm. Average
simulated crop yields were simulated to be above the regional average crop yields.
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Figure 1: Average measured crop yield and regional average (1992-2010) crop yield versus average predicted
crop yield for the parameterisation simulations using general and specific management for a single wheatchickpea-wheat-chickpea crop rotation for the P4 paddock at the Eurie Eurie farm. The simulation was run
from 2007 to 2012.
Long-term parameterisation simulations
Long-term parameterisation simulations used general management practises. Inputs from rainfall
were simulated to be in balance with the losses from evapotranspiration, runoff, and deep drainage
(Figure 2). Soil carbon both in the total soil profile and in the top 0.3 m was predicted to be slowly
declining under the general management practices scenario (Figure 3). The general management
practices included bare fallow periods combined with variable within-crop rainfall. Accordingly,
there were low and discontinuous inputs of carbon throughout the simulation which led to an
overall net mineralisation of carbon from this soil.
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Figure 2: Water balance for the long-term parameterisation simulation using general management for a
wheat-chickpea-wheat-chickpea crop rotation for the P4 paddock at the Eurie Eurie farm. The simulation
was run from 1962 to 2012.
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Figure 3: Soil carbon 0-1.8 m (top) and soil carbon 0-0.3 m (bottom) for the long-term parameterisation
simulation using general management for a wheat-chickpea-wheat-chickpea crop rotation for the P4
paddock at the Eurie Eurie farm. The simulation was run from 1962 to 2012.
Modelling of the scenarios
Scenarios (described in Section Abating greenhouse gas emissions from soils: Table 3) were applied
to the P4 paddock at the Eurie Eurie farm. The No Burn scenario was developed from the
parameterisation simulation with the general management practices. It represents the common
practice at the site and was considered to be the ‘baseline’ scenario against which all other scenarios
were compared. Crop yield, annual nitrous oxide emissions, and soil carbon in the top 0.3 m of the
soil for all scenarios are presented in Figures 4, 5 and 6.
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Figure 4: Crop yield simulated in response to eight scenarios for a wheat-chickpea-wheat-chickpea crop
rotation for the P4 paddock at the Eurie Eurie farm. Values displayed represent the overall average obtained
from simulations with two starting points over 100 years (1914-2013 and 1915-2014). Scenarios are
described in Section Abating greenhouse gas emissions from soils: Table 3.
Figure 5: Annual simulated emissions of nitrous oxide (N2O-N) in response to eight scenarios for a wheatchickpea-wheat-chickpea crop rotation for the P4 paddock at the Eurie Eurie farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
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Figure 6: Soil carbon in the surface 0.3 m of soil simulated in response to eight scenarios for a wheatchickpea-wheat-chickpea crop rotation for the P4 paddock at the Eurie Eurie farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
Differences in the values obtained between alternative scenarios and the No Burn scenario
(considered to be the ‘baseline’ practice) are presented for carbon sequestered (0-0.3 m), nitrous
oxide emissions (0-1.0 m) and global warming potential (Figure 7). Soil carbon increased in scenarios
relative to the No Burn (‘baseline’) scenario when residues were not burnt and increased nitrogen
fertiliser was applied, a summer crop was grown, or manure was applied. The increase was very
small for the Manure and No Burn+N fertiliser scenarios. These scenarios all involved increased
inputs of organic matter. Soil carbon decreased in scenarios where stubble was burnt and/or
nitrogen fertiliser was reduced.
Nitrous oxide emissions increased compared to the No Burn (‘baseline’) scenario in all scenarios
where soil organic carbon increased and were reduced in all scenarios where soil organic carbon
decreased. The increase in nitrous oxide in scenarios where soil organic carbon increased is not
surprising, as soil carbon is a substrate for microbes that facilitate nitrous oxide production.
The net amount of greenhouse gas abatement resulting from the different scenarios was dominated
by changes in soil carbon. In the Summer Crop scenario where management practices considerably
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increased soil carbon stocks compared with the No Burn (‘baseline’) scenario, the greenhouse gas
abatement provided by the sequestered carbon was greater than the global warming impact of the
nitrous oxide emissions, resulting in a reduction in net global warming potential. The Manure
scenario was also able to provide a small reduction in net global warming potential. In scenarios
where management practices considerably reduced soil carbon stocks compared with the No Burn
(‘baseline’) scenario (Burn, Burn+N, and Burn-N), net global warming potential increased irrespective
of reductions in nitrous oxide emissions.
Figure 7: Difference between the No Burn (‘baseline’) scenario and alternative scenarios for sequestered
carbon (0-0.3 m), nitrous oxide (N2O-N) emissions (0-1.0 m) and net global warming potential (GWP) for a
wheat-chickpea-wheat-chickpea crop rotation for the P4 paddock at the Eurie Eurie farm. Values displayed
represent the overall average obtained from simulations with two starting points over 100 years (1914-2013
and 1915-2014). Scenarios are described in Section Abating greenhouse gas emissions from soils: Table 3.
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