Whole farm systems analysis of
greenhouse gas emission abatement
strategies for dairy farms.
UT12945
Final report to Dairy Australia on the
investigation and analysis into greenhouse gas
abatement strategies, modelling and decision tools for
the Australian dairy industry.
Prepared by:
Karen Christie, Dr Richard Rawnsley, and Dr Danny Donaghy
(Tasmanian Institute of Agricultural Research,
University of Tasmania)
August 2008
Contents
Executive summary.......................................................................................... 1
Review of proposed project outputs .............................................................. 3
1. Project objectives ........................................................................................ 4
2. Project activities .......................................................................................... 7
2.1. Collate and synthesise existing information relevant to greenhouse gas
abatement strategies for dairy farming systems............................................................ 7
2.2. Identify key abatement strategies for differing dairy farm systems ...................... 13
2.3. Quantifying the pre-farm embedded greenhouse gas emissions......................... 17
2.4. Modelling analysis to quantify the greenhouse gas emissions of differing
dairy farm systems ..................................................................................................... 20
2.5. Selection of key abatement strategies for each differing farming systems .......... 35
2.6 Modelling analysis of each abatement strategy ................................................... 39
2.7 Costs, benefits and synergies of adopting abatement strategies in a whole
farm systems context.................................................................................................. 44
2.8 Evaluation of tools and processes to monitor greenhouse gas emissions ........... 47
2.9 Development of a spreadsheet model that enables a whole of system
comparison between indicative systems and changes in imports and
management practices. .............................................................................................. 50
3. Way forward for the Australian dairy industry ........................................ 53
3.1. Educate and promote best management practices ............................................. 53
3.2. On-going research requirements ........................................................................ 55
4. Conclusion ................................................................................................. 57
5. References ................................................................................................. 58
6. Appendices ................................................................................................ 62
ii
List of tables
Table 1. Annual non-carbon dioxide greenhouse gas emissions for the dairy,
beef, sheep and pigs and poultry industries .................................................................. 12
Table 2. Embedded greenhouse gas emissions from key farm inputs. .......................... 18
Table 3. Description of the low supplementary feeding farm system. ............................ 23
Table 4. Description of the high supplementary feeding 1 farm system. ........................ 25
Table 5. Description of the high supplementary feeding 2 farm system. ........................ 27
Table 6. Description of the total mixed ration 1 farm system. ......................................... 29
Table 7. Description of the total mixed ration 2 farm system. ......................................... 31
Table 8. A comparison of the total farm and intensity of greenhouse gas emission
and the percentage of emissions from each source for the five baseline farming
systems. ........................................................................................................................ 33
Table 9. A comparison of the results of the baseline farm system and the three
most effective abatement strategies in reducing greenhouse gas emissions per
tonne milksolids, in terms of total farm and the carbon pollution reduction scheme
liability. .......................................................................................................................... 40
Table 10. Ease of implementation and/or relevance of each abatement strategy
for a low supplementary feeding system, a high supplementary feeding system
and a total mixed ration feeding system. ....................................................................... 45
Table 11. A comparison of the capacities of five greenhouse gas calculator models ..... 48
iii
List of figures
Figure 1. Australian total and percentage of total greenhouse gas emissions from
various industry sectors. ................................................................................................ 10
Figure 2. Australian total greenhouse gas emissions across the various agricultural
sectors. ......................................................................................................................... 11
Figure 3. Estimates of potential reduction in enteric methane and nitrous oxide
with the adoption of abatement strategies . ................................................................... 15
Figure 4. The intensity of greenhouse gas emissions from the low supplementary
feeding farm system . .................................................................................................... 24
Figure 5. The intensity of greenhouse gas emissions from the high supplementary
feeding 1 farm system. .................................................................................................. 26
Figure 6. The intensity of greenhouse gas emissions from the high supplementary
feeding 2 farm system . ................................................................................................. 28
Figure 7. The intensity of greenhouse gas emissions from the total mixed ration 1
farm system................................................................................................................... 30
Figure 8. The intensity of greenhouse gas emissions from the total mixed ration 2
farm system................................................................................................................... 32
Figure 9. Total farm greenhouse gas emissions for each of the five farming
systems. ........................................................................................................................ 33
Figure 10. Total on-farm methane and nitrous oxide emissions for the baseline
farm system and three selected abatement strategies .................................................. 42
Figure 11. A copy of the results page from the Dairy Greenhouse gas Abatement
Strategies calculator. ..................................................................................................... 52
iv
List of common abbreviations
CH4- methane
CO2- carbon dioxide
CO2-e- carbon dioxide equivalents
CP- crude protein
CPRS- carbon pollution reduction scheme
DGAS- Dairy Greenhouse gas Abatement Strategies calculator
DMD- dry matter digestibility
DM- dry matter
DMI- dry matter intake
GHG- greenhouse gas
ha- hectare
HSF- high supplementary feeding
kg- kilogram
kWh- kilowatt hour
L- litre
LSF- low supplementary feeding
ME- metabolisable energy
MJ- megajoule
MS- milksolids
N- nitrogen
N2O- nitrous oxide
t- tonne
TMR- total mixed ration
v
Acknowledgements
The assistance from the following people and organisations has been gratefully
received:

Department of Agriculture, Fisheries and Forestry for project funding

Dairy Australia for project funding

Dr Richard Eckard and Cathy Phelps (project steering committee)

Colin and Erina Thompson (TMR dairy farmers), Brian Crockart (CRC
Agrisolutions), Jess Coad (TIAR), Dr Cameron Gourley (Victorian DPI), Will
English (Victorian DPI) and Neil Lane (Interlact) for farm data

Dr Chris Grainger (Victorian DPI), Dr Deli Chen and Dr Helen Suter (University of
Melbourne) for abatement strategy information

Tim Grant (Life Cycle Strategies) for farm input life cycle assessments

Dr Bill Slattery and Rob Waterworth (Australian Department of Climate Change)
for NCAT model information

Robert Kildare for DGAS model development
vi
Executive summary
The Australian dairy industry is a diverse and complex industry, in terms of location,
climatic variability and management systems. While there is diversity within the industry,
farms can be categorised into one of three farming systems. This report has examined
the greenhouse gas emissions (GHG) of these three farming systems found throughout
the industry, namely a low supplementary feeding system (LSF), a high supplementary
feeding system (HSF) and a total mixed ration feeding system (TMR).
Greenhouse gas emissions for these three farming systems were determined in terms of
total farm GHG emissions, as the sum of the pre-farm embedded emissions associated
with farm inputs and the on-farm carbon dioxide, methane and nitrous oxide emissions.
To compare between farming systems, the total farm GHG emissions were divided by
farm milk production and reported as tonnes of GHG emissions per tonne of milksolids (t
CO2-e/t MS). Greenhouse gas emissions were 12.2 and 12.8 t CO2-e/t MS for the two
modelled TMR systems, were 14.0 and 15.3 t CO2-e/t MS for the two modelled HSF
farm systems, and was 17.5 t CO2-e/t MS for the modelled LSF farm system.
Farm GHG emissions were also calculated in terms of emissions directly associated with
the farm (i.e. only on-farm methane and nitrous oxide emissions). Under an emission
trading scheme, farmers could potentially become liable for any on-farm source of
emissions so a second series of figures were calculated and termed CPRS liability.
Greenhouse gas emissions under a CPRS liability ranged from 9.5 and 9.8 t CO2-e/t MS
for the TMR farm systems, were 10.6 and 11.3 t CO2-e/t MS for the HSF farm systems
and was 14.0 t CO2-e/t MS for the LSF farm system.
These figures highlighted that as farm intensity increases (greater per cow milk
production, greater feed intakes etc), the intensity of GHG emissions (t CO2-e/t MS)
decreases. This concept contradicts the edict that farm profitability for the Australian
dairy industry has traditionally been associated with predominantly grass based diets
with low to medium grain inputs. Detailed farm economic analysis is required to highlight
which farm system will result in reducing farm GHG emissions without jeopardising milk
production and farm business profitability.
Previous research has highlighted that adopting abatement strategies could reduce GHG
emissions on dairy farms by 20-30%. However, a review of the impacts and outcomes
of different abatement strategies, at a whole dairy system level, has not been undertaken
previously. This report has explored the impact of abatement strategies on the whole
1
farm system. We have modelled the effect of adopting either single strategies or an allinclusive abatement strategy, in reducing GHG emissions.
Each strategy can be
classified into one of three categories- namely herd, feed and soil.
For each of the three farming systems, the greatest reduction in GHG emissions/t MS
occurred with the all-inclusive abatement strategy of increased herd weight, feed intakes
and milk production while feeding additives to reduce emissions and applying fertilisers
coated with a nitrification inhibitor. This strategy reduced GHG emissions by up to a
maximum of 22% for the LSF farm system, with lower reductions for the other two farm
systems. When assessing a potential CPRS liability emission, the reductions achievable
were up to a maximum of 25.5%. While there were several strategies available to farms
operating as either LSF or HSF farm systems, there was very little scope for TMR farm
systems to reduce their GHG emissions.
When viewing this report, it must be remembered that this is a desktop analysis of GHG
emissions. We have highlighted some of the issues with adopting some strategies. For
example increasing milk production per cow to very high levels could possibly result in
deceasing reproductive performance, resulting in the need to raise more replacement
stock therefore increasing farm GHG emissions. In addition, we have not taken into
consideration the full implications of adopting any strategies in terms of farm economics,
changes to labour inputs and skill base and/or changes to farm infrastructure, due to
difficulty in quantifying these changes in a simulated study. Many of the abatement
strategies discussed are also time dependant. It would take many years of selective
breeding within the farm system to increase feed conversion efficiency, herd weight
and/or milk production to reduce the intensity of GHG emissions.
The current models available to audit farm GHG emissions are based on inventory type
algorithms. None are dynamic mechanistic models that calculate the impact of a feeding
abatement strategy on animal performance and GHG emissions.
They are also
dependant on Australian emission factors based on limited field experimental data. For
example, nitrous oxide emissions losses from irrigated pastures are based on flood
irrigation results from inland Victoria. What losses could we expect to have under other
forms of irrigation, under different soil types and/or formulations of N fertiliser? Better
understandings of regional specific emission factors are critical if individual farms are
made accountable for their GHG emissions. To facilitate this, further research needs to
continue to build upon our current knowledge of minimising GHG emissions, in terms of
reducing GHG emissions from the dairy herd and from nitrogen fertiliser management.
2
Review of proposed project outputs
1. Report quantifying the whole of system implications, including ‘opportunity costs’
and ‘benefits’ of adopting key GHG abatement strategies across a range of dairy
farming systems.
This outcome has been achieved. The following report details the GHG
emissions associated with differing dairy farming systems and reviews potential
GHG abatement strategies for each system.
2. Discussion paper evaluating tools and processes to monitor GHG emissions at a
farming systems level.
This outcome has been achieved. A discussion paper detailing the approaches
used to assess GHG emissions from dairy farm systems and an evaluation of
available GHG tools is given in Appendix 1. The discussion paper is essentially a
summarised version of this final report.
3. Develop a spreadsheet model that farmers and industry can use to quantify
changes in GHG emissions at a farming systems level in response to changes in
imports and management practices.
This outcome has been exceeded. The project has developed a spread sheet
model which contains 13 “worksheets” and four “userforms” as its core and is
viewed using Visual Basic for Applications. A CD copy of the model is provided
and a user manual of the model is given in Appendix 2. The models allows for the
quantification of GHG emission from differing dairy farm systems which include
emission sources from imports (pre-farm) and on farm sources (carbon dioxide,
methane and nitrous oxide). The model allows for comparison between systems
where differing abatement strategies can be assessed. The model has been
designed with the flexibility to allow future comparisons to be explored as they
become available.
3
1. Project objectives
Previous research undertaken in Australia had highlighted numerous strategies which
could result in reducing GHG emissions in the order of 20 to 30% from dairy farms.
However, the assessment of the impact of adopting abatement strategies has generally
not taken into consideration the whole of farm systems implications or the potential GHG
emissions associated with key farm inputs such as fertilisers, grain and other feed
sources. The current project aimed to quantify the potential reduction in GHG emissions
for selected abatement strategies from a farming systems viewpoint and consisted of
four major objectives:

Quantify the greenhouse gas (GHG) emissions (including embedded
emissions in key farm inputs) from three typical dairy farming systems:

A system based predominantly on pasture, with low levels of
supplementation (15% of total diet supplementary feeding),

A system based on high levels of supplementary feed (40-50% of the total
diet from supplementary feeding),

A total mixed ration system (zero grazing with all feed supplied in an
enclosed area);

Quantify the impacts on those systems (GHG, costs, benefits and synergies)
of a range of GHG abatement strategies in a whole farm systems context;

Identify likely methods of validating GHG abatement and associated costs;

Evaluate the usefulness of science based modelling, including existing tools
such as OVERSEER and WFSAT (Whole Farm Systems Analysis Tools), to
estimate changes in GHG emissions resulting from changes in management
practices, and to provide a possible monitoring and reporting strategy.
Greenhouse gas emissions have been reported in two ways. The first method was total
farm GHG emissions (t CO2-e/farm), as the sum of four sources: pre-farm embedded
GHG emissions from imported products, on-farm carbon dioxide, on-farm methane and
on-farm nitrous oxide. In addition, a GHG emission intensity figure was calculated by
dividing total farm GHG emission by tonnes of milksolids produced (t CO2-e/t MS).
The second method involved assessing the GHG emissions that potentially could be a
direct liability under an emissions trading scheme. The Australian Federal Government
is in the process of introducing an emissions trading scheme titled the Carbon Pollution
4
Reduction Scheme (CPRS). This CPRS will come into effect in 2010, with agriculture
potentially liable for their on-farm methane and nitrous oxide emissions from 2015
onwards. Similarly to the total farm GHG emissions, two figures have been reportedCPRS farm GHG emissions (total methane and nitrous oxide; t CO2-e) and CPRS
emissions intensity (t CO2-e/t MS).
A series of project activities have been undertaken to meet the four key project
objectives and are described in details in each of the following sections:
Objective 1- Quantify the greenhouse gas (GHG) emissions (including embedded
emissions in key inputs) from three typical dairy farming systems
The following activities were undertaken to meet this objective.

A collation and synthesise of existing information relevant to GHG abatement
strategies for dairy farm systems,

A review of the potential abatement strategies for differing dairy farm
systems,

A quantification of the embedded GHG emissions in dairy farm imports,

An assessment of the GHG emissions from differing dairy farming systems.
Objective 2- Quantify the impacts differing dairy systems for a range of GHG abatement
strategies in a whole farm systems context
The following activities were undertaken to meet this objective:

Selection of the key abatement strategies for each of the differing farming
system,

A whole farm system modelling analysis for each abatement strategy for each
farming system.
Objective 3- Identify likely methods of validating GHG abatement and associated costs
The following activity was undertaken to meet this objective:

A review of the potential opportunity costs, benefits and synergies of differing
GHG abatement strategies at a farming systems level.
5
Objective 4- Evaluate the usefulness of science based modelling, including existing tools
such as OVERSEER and WFSAT (Whole Farm Systems and Analysis Tools), to
estimate changes in GHG emissions resulting from changes in management practices,
and to provide a possible monitoring and reporting strategy
The following activities were undertaken to meet this objective:

An evaluation of tools and process to monitor greenhouse gas emissions,

Development of a spreadsheet model that enables a whole of system
comparison between indicative systems and changes in imports and
management practices.
6
2. Project activities
2.1. Collate and synthesise existing information relevant to greenhouse
gas abatement strategies for dairy farming systems
Sources of greenhouse gases
It is widely accepted that since the beginning of the 20th century, human activity has
resulted in global warming due to the increase in greenhouse gas (GHG) emissions into
the atmosphere (Dalal et al. 2003b). The three major gases that are widely accepted as
contributing to global warming are carbon dioxide (CO2), methane (CH4) and nitrous
oxide (N2O). Molecule for molecule, carbon dioxide is a weak gas in terms of its global
warming potential. Compared to carbon dioxide, on a 100-year timescale, the global
warming potential of methane and nitrous oxide are 21 and 310 times greater,
respectively (IPCC 2001). Multiplying the GHG by its global warming potential converts
all three gases to a carbon dioxide equivalent (CO2-e) to allow for easier comparisons
between gas sources.
A global annual carbon dioxide emission rate of approximately 23.9 gigatonnes has been
estimated by the Carbon Dioxide Information and Analysis Centre (Marland et al. 2005).
Methane emissions amounted to ~ 7.6 gigatonnes of CO2-e/annum while nitrous oxide
emissions have been estimated to be within the range of 3.1 to 5.4 gigatonnes of CO2e/annum (Marland et al. 2005).
Carbon dioxide
Carbon dioxide is a heavy gas and is approximately 380 parts per million (ppm) of the
atmosphere. Carbon dioxide emissions have risen by approximately 100 ppm since
1750 (starting date considered to be practically uninfluenced by human activity such as
increasing specialised agriculture, land clearing and the combustion of fossil fuels;
Blasing and Smith (2006)).
Carbon dioxide is released into our atmosphere when carbon-based fossil fuels such as
oil, natural gas and coal are burned.
Increased global burning of fossil fuels has
contributed to an increasing amount of carbon dioxide in the atmosphere. Carbon
dioxide is also produced by all animals, plants, fungi and micro-organisms during
respiration and organic decomposition.
Carbon dioxide is used by plants during
7
photosynthesis to make sugars which can either be used for plant growth or be
consumed in the process of plant respiration.
While electricity usage does not result in carbon dioxide emission at a farm level, there is
an embedded carbon dioxide emission associated with its production. There is also an
emission associated with the production and consumption of fuels such as diesel. When
analysing the dairy whole farm system, the emission from the production and
consumption of energy have been grouped together as CO2- energy.
While these
emissions have been accounted for at a whole farm system analysis, they are unlikely to
be accounted for at a farm systems level in any future CPRS.
Methane
Methane is a colourless, odourless gas with a wide distribution in nature. It is the
principal component of natural gas and is derived from the anaerobic breakdown of
organic matter. A recent figure of the abundance of methane in the Earth’s atmosphere
from the northern hemisphere was 1847 parts per billion (ppb) with a southern
hemisphere figure of 1730 ppb. These figures are 2.5 times greater than pre-1750
figures (Blasing and Smith 2006).
Methane is an important greenhouse gas with a global warming potential of 21 times
greater than that of carbon dioxide over a 100 year period (UNFCCC 1995). However,
the global warming potential of methane, over a 20 year time period is markedly greater
at up to 72, due to methane having an estimated half life between 7 and 10 years (IPCC
2007). There are six different sources of atmospheric methane; wetlands, fossil fuels,
landfills, ruminant animals, rice paddies and biomass combustion.
In ruminant animals, methane originates from the anaerobic microbial fermentation
process in the gastrointestinal tract. As feed is ingested by ruminants, the proteins,
starch and plant cell-wall polymers of the feed is hydrolysed into amino acids and simple
sugars by the bacteria, protozoa and fungi that reside in the rumen.
Primary and
secondary digestive micro-organisms further ferment the amino acids and sugars into
volatile fatty acids, hydrogen, carbon dioxide and other end products.
Methanogen
micro-organisms then reduce the carbon dioxide to methane so as to prevent the
accumulation of hydrogen in the rumen (McAllister et al. 1996).
8
The emission of methane from livestock represents a direct loss of energy to the
ruminant, as between 4 and 10% of all ingested energy is excreted as methane
(Johnson et al. 1997). This represents a significant loss of energy from the production
system that could be redirected to produce more milk (Eckard 2006). A small amount of
methane is also emitted with the excretion and decomposition of manure.
Enteric methane emissions from dairy farm systems can be reduced by improving the
digestibility of the diet, reducing the number of unproductive animals and/or modifying
the rumen outputs by feeding additives such as fats, condensed tannins and ionophores.
Nitrous oxide
Nitrous oxide, commonly known as laughing gas, is a colourless non-flammable gas that
is released naturally from a wide range of biological sources in soils and the oceans. It is
a by-product of the aerobic nitrification process when ammonium is oxidised into nitrate.
Nitrous oxide can also be a result of the anaerobic denitrification process when nitrate is
microbially converted into either di-nitrogen or nitrous oxide. This process is maximised
in warm, anaerobic soil conditions when there is large amounts of nitrate and available
carbon present (de Klein and Eckard 2008).
The atmospheric concentration of nitrous oxide has increased from 270 ppb pre-1750 to
a current value of ~319 ppb and continues to increase (Blasing and Smith 2006). Nitrous
oxide concentrations are increasing due to land-use changes, burning of vegetation,
industrial emissions and fertiliser usage.
Agriculture is the main source of human-
induced nitrous oxide emissions. Nitrous oxide is lost from agricultural soils as a result of
cultivation, legumes, nitrogen fertilisers and animal excreta (Eckard 2006).
Nitrous oxide emissions from a dairy farm system can be reduced by balancing the
animals’ diet to minimise excess nitrogen in their urine and dung. Emissions can also be
reduced by minimising anaerobic soil conditions, reducing soil compaction and/or
improving fertiliser management practices through the use of current best management
practices and in using various products such as nitrification inhibitors.
9
Australia’s greenhouse gas emissions
Australia’s net GHG emissions across all sectors totalled 576 million tonnes of carbon
dioxide equivalents (Mt CO2 –e) in 2006. The stationary energy sector contributed 287.4
Mt CO2 –e or approximately half of these emissions, with agriculture the second largest
contributor, with 90.1 Mt CO2 -e or approximately 15.6% of the nation’s GHG emissions
(Australian Department of Climate Change 2008a; Figure 1).
350
300
50%
Mt CO2-e
250
200
150
16%
100
14%
7%
50
6%
5%
Fugitive
Emissions
Industrial
Processes
3%
0
Stationary
Energy
Agriculture
Transport
Land Use,
Land Use
Change and
Forestry
Waste
Figure 1. Australian total and percentage of total greenhouse gas emissions from various
industry sectors for 2006 (Australian Department of Climate Change 2008a).
Australia committed itself to playing a major role in addressing its contribution to the
global issue of climate change by ratifying the Kyoto Protocol in late 2007.
This
ratification has committed Australia to limiting the growth of its GHG emissions to 108%
of its 1990 baseline by 2012. While the major focus in reducing GHG emissions has
been placed on the stationary energy, industrial and transport sectors, the agricultural
sector will also be required to implement their own series of measures to tackle the issue
of GHG emissions and climate change.
Australia’s agriculture greenhouse gas emissions
Within agriculture, there are six broad sources of GHG emissions.
Based on 2006
figures, enteric fermentation was the most significant at 59.3 Mt CO2 –e or ~66% of the
total agricultural emissions. Agricultural soils contributed 15.2 Mt CO2 –e, followed by
the prescribed burning of savannas at 11.5 Mt CO2 –e, manure management at 3.6 Mt
CO2 –e, with the field burning of agricultural residues and rice cultivation both totalling
10
0.3 Mt CO2 –e (Figure 2). Agriculture was the dominant source of methane and nitrous
oxide emissions, accounting for 59% and 84% of the nations’ total methane and nitrous
oxide gas emissions, respectively (Australian Department of Climate Change 2008a).
70
65.7%
60
Mt CO2-e
50
40
30
16.9%
20
12.7%
10
4.0%
0.3%
0.3%
Rice cultivation
Burning crop
residuals
0
Enteric
fermentation
Agricultural soils
Burning
savannahs
Manure
management
Figure 2. Australian total greenhouse gas emissions across the various agricultural
sectors for 2006 (Australian Department of Climate Change 2008a).
Australia’s livestock greenhouse gas emissions
Within the agricultural sector, livestock was the biggest contributor of emissions at 62.8
Mt which was 69.7% of agricultural emissions or approximately 11% of the nations’ total
emissions (Australian Department of Climate Change 2008a). The production of
methane from livestock enteric fermentation was the biggest source of GHG emissions
for Australian agriculture. Table 1 shows the GHG emission rates from methane and
nitrous oxide for the dairy, beef, sheep, pigs and poultry industry in 2006 (Australian
Department of Climate Change 2008a).
11
Table 1. Annual non-carbon dioxide greenhouse gas emissions, expressed as kilotonnes
of carbon dioxide equivalents, for the dairy, beef, sheep and pigs and poultry industries in
2006 (Australian Department of Climate Change 2008a).
Methane from enteric
Dairy
Beef
Sheep
Pigs &
cattle
Cattle
6,802
38,653
15,570
81
524
1,129
4
1,889
656
2,036
1,390
neg
83
351
neg
266
Poultry
fermentation (kt CO2-e)
Methane from manure
management (kt CO2-e)
Nitrous oxide from animal excretion
directly onto pastures/rangelands
(kt CO2-e)
Nitrous oxide from animal waste
stored and applied to soils
(kt CO2-e)
The extensive agricultural industries of beef and sheep farming are major contributors to
the nation’s emissions, primarily due to the significantly larger number of animals within
each industry (2005 figures of ~ 24 and 100 million beef cattle and sheep, respectively,
compared to 3.2 million dairy cattle (inc. 2 million milking cows); ABARE 2007a, b & c).
Implementing abatement strategies to reduce their contribution to GHG emissions could
result in substantial reductions in GHG emissions. However, the very nature of these
industries could add a degree of difficulty in implementing abatement strategies.
Beef and sheep farms are generally reliant on low to medium digestibility pasture to
supply the majority of the herd or flocks diet, with supplementary feeding generally
restricted to times of drought and when finishing off stock for slaughter. Contact with the
herd or flock can be highly varied, from daily to weekly contact on smaller farms, through
to annual contact on some of the large (million plus hectares) northern Australian cattle
properties.
The reduction in stock contact, together with low to medium pasture
digestibility and extensive grazing constrain the opportunities to reduce GHG emissions
for the Australian beef and sheep industries.
12
Dairy farming, on the other hand, involves daily contact with the milking herd, and
coupled with medium to high quality pastures, allows for a variety of abatement
strategies to be implemented. Therefore, there is a more practical pathway available to
dairy farmers to implement abatement strategies on farm to reduce GHG emissions.
Australia’s dairy industry greenhouse gas emissions
Methane from enteric fermentation was the biggest source of GHG for the dairy industry,
with an estimated 6,802 kt CO2-e emissions in 2006 (Table 1). The second biggest
source was nitrous oxide from urine and faeces deposition while stock grazed pastures,
at 656 kt CO2-e. Approximately 500 kt CO2-e of this was from urine and the balance
from faeces. Methane produced from animal manures was the third biggest at 524 kt
CO2-e, with 79% of this from manures deposited into anaerobic ponds. There was also
a small amount of nitrous oxide emitted from the application of manures onto soils at ~
83 kt CO2-e. No figures were presented in terms of nitrous oxide emissions from the
application of nitrogenous fertilisers onto dairy pastures. Assuming that all synthetic
fertilisers applied to irrigated and dryland pastures could be attributed to the dairy
industry (RJ Eckard pers comm.), it could be assumed that the dairy industry contributed
an additional 797 kt CO2-e (Australian Department of Climate Change 2008a).
Therefore, in 2006, the Australian dairy industry contributed approximately 2% or 8,870
kt CO2-e towards the nations’ GHG emissions.
2.2. Identify key abatement strategies for differing dairy farm systems
There have been many reviews of potential abatement strategies to reducing non-carbon
dioxide emissions from agricultural and/or livestock practices (Dalal et al. 2003a; O’Hara
et al. 2003; Tamminga et al. 2007; Beauchemin et al. 2008; de Klein and Eckard 2008).
A review of key abatement strategies that are both currently available and relatively
easily adoptable by farmers was undertaken in milestone 3 (refer to Appendix 3 for
details).
The impacts of abatement strategies from these research review papers have been
reported in several ways. Methane emissions has been reported as a total (e.g. 135 g
CH4/cow.day), in terms of a percentage of gross energy intake (e.g. 5.5% GEI) or in
terms of the amount of emissions per kg of dry matter intake (e.g. 20g CH 4/ kg DMI).
Nitrous oxides emissions have been reported as a total (eg. 22.3 Mt CO2-e/year), as
13
nitrous oxide emissions per unit area (eg. 11 kg N2O-N/ha) or as a fractional loss per unit
application (eg 1.5% N2O-N/kg N applied).
A summary of the potential reduction in GHG emissions from the adoption of abatement
strategies can be seen in Figure 3. However, it must be noted that the adoption of one
or more of these strategies may result in a lower reduction than reported here,
depending on which of the three broad farming systems the strategy is applied to. For
example, a TMR diet is generally formulated by animal nutritionalists and is balanced in
terms of supplying the correct amounts of energy, protein and fibre concentration.
Therefore there is very little scope in improving the quality of the TMR diet. Total mixed
rations also frequently contain sources of fats such as whole cottonseed and as such,
there is also less scope to increase the fat content of the diet to assist in reducing
methane production. Therefore, while feeding fats and oils could potentially reduce
methane production by 10-25%, for a TMR farm, this abatement strategy is either
unlikely to be adopted or if adopted, would result in a significantly lower GHG emission
reduction than reported.
14
GHG emissions
Enteric Methane
Nitrous oxide
Herd based strategies
Herd based strategies
10-20% potential
10-50% potential reduction in
urinary nitrogen
Extended lactations
Reduced herd size
Higher feed conversion efficiency
Extended longevity in
the herd
Condensed tannins
Nitrification inhibitors in urine
Higher feed conversion efficiency
Balance crude protein in the diet
Feed based strategies
Soil based strategies
10-20% potential
10-20% potential
Feeding fats & oils
Nitrification inhibitors
oioioioils
Feeding condensed tannins
Stand-off pads during winter
Improved drainage
Feeding ionophores
Improved irrigation management
Maximise diet digestibility
Fertiliser managementrate/ timing/ formulation
Figure 3. Estimates of potential reduction in enteric methane and nitrous oxide with the
adoption of abatement strategies (Adapted from pers. comm. Eckard & Grainger 2007,
refer to Appendix 3 for more details).
We have not focussed on abatement strategies that are currently unavailable for farmers
to adopt. For example, while CSIRO have been developing and testing various vaccines
to reduce methane production, these vaccines have not been commercially released and
therefore are not currently available for use. In a press release Dr Rob Kelly predicted
“we expect that the commercial vaccine will be able to reduce methane emissions by
about 20%” (ScienceDaily, 11th June, 2001). Once this vaccine is available, we will be
able to incorporate it as an abatement strategy into the new Dairy Greenhouse gas
Abatement Strategies (DGAS) calculator.
15
In undertaking this project, we have reviewed three strategies which are not currently
available but have the potential to significantly reduce GHG emissions. Researchers
only began to genome sequence the methanogen micro-organisms responsible for
methane production in the 1990’s. According to Attwood and McSweeney (2008), there
are several methanogenesis pathways present in the rumen. Research is required to
develop a better understanding of the complex process in which methanogens produce
methane, especially under the grazing conditions prevalent in the Australian dairy
industry (Jarvis et al. 2000).
This will hopefully lead to future options for methane
mitigation. Examples of such mitigation are the enhancement of rumen micro-organisms
that combine carbon dioxide and hydrogen to form acetate instead of methane or to
promote organisms that divert excess hydrogen away from methane production into
propionate.
Researchers in the USA and other countries are developing new lucerne (Medicago
sativa) and other forage cultivars that contain adequate levels of condensed tannins to
assist in improving protein absorption (Grabber et al. 2002). Recent research on the
benefits of feeding condensed tannins to dairy cows in Victoria, administered as an
extract of the Black Wattle tree (Acacia mearnsii) in a water-diluted drench twice daily,
found that methane emissions were reduced by 10 to 22% on a DMI basis (Grainger et
al. 2008). However, this method of administering a source of condensed tannin would
be very tedious and time consuming in the context of a commercially operated dairy
farm.
Breeding tannins back into forages would have several additional benefits beyond being
a delivery mechanism for the tannin. Lucerne is an accepted source of forage, unlike
other condensed tannin-rich forages such as birdsfoot trefoil (Lotus corniculatus) and
sulla (Hedysarum coronarium). Therefore farmers would be more supportive of growing
and feeding lucerne to their herd, compared to these other species. Lucerne is a legume
forage that synthesises its own N fertiliser to maintain growth, therefore reducing any
manufacturing and fertiliser-related nitrous oxide emissions. Lucerne will generally
supply adequate energy and protein requirements for milk production and it is grown
widely throughout dairying regions of Australia.
16
It has long been recognised that there is variation in methane production both within and
between animals (Blaxter and Clapperton 1965). Animals with greater feed conversion
efficiency will result in reducing methane production per kg DMI, as research by Hegarty
et al. (2007) demonstrated that residual feed intake could partly explain the variation in
high and low methane producing beef cattle. We believe that research into identifying
and isolating the sequence of DNA responsible for feed conversion efficiency could
provide a significant strategy in reducing methane emissions. In the same way that
farmers currently select semen from bulls that will lead to improvements in milk quality,
temperament or bone structure, they may in the future be able to also select semen from
a line of bulls that’s offspring will have greater feed conversion efficiency and reduced
enteric methane emissions per unit of intake.
Further research into establishing a greater understanding of the rumen microflora, in
breeding forages than could assist in supplying condensed tannins directly to the animal
rather than as a feed additive and/or breeding animals that exhibit greater feed
conversion efficiency, therefore requiring less forage to produce the same level of milk
production, will all result in significant improvements in reducing on-farm GHG
emissions. However, the implementations of the outcomes from these areas of research
are still many years away.
2.3. Quantifying the pre-farm embedded greenhouse gas emissions
The GHG emissions generated with the production of farm inputs such as fertilisers,
feeds and pasture chemicals was calculated by Tim Grant from Life Cycle Strategies Pty
Ltd, using Simapro software. An emission factor (EF; kg CO2-e/kg product) for a range
of inputs can be seen in Table 2. To calculate the GHG emissions generated with the
production of each farm input product, the amount of product was multiplied by the EF to
convert into t CO2-e. For example, grain has an EF of 0.302 so for every tonne of grain
that is imported onto farm; there is an associated pre-farm embedded GHG emission of
0.302 t CO2-e. We grouped similar farm inputs together to calculate a pre-farm GHG
emission for grain, other feed sources (hay, silage, grass seed etc), fertilisers and
herbicides. Once pre-farm embedded GHG emissions were calculated, they were added
to on-farm GHG emissions to determine the total farm GHG emissions. Details of the life
cycle assessment of each of the embedded calculations are given in Appendix 4.
17
Table 2. Embedded greenhouse gas emissions from key farm inputs.
Farm input (kg product)
Wheat
Total
CO2
CH4
N2O
Sequestration
Other
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
0.302
0.190
0.007
0.104
0.000
1.09E-06
Source of data
Australian inventory data based on average wheat crop
1.3 t/ha yield
Lupins
0.204
0.107
-0.001
0.098
0.000
1.67E-06
Model from typical data in
NSW cropping
Grass silage1
0.25
0.075
0.000
0.146
0.000
2.22E-05
Ecoinvent data
Grass hay1
0.25
0.091
0.000
0.132
0.000
4.69E-05
Ecoinvent data
Maize silage
0.371
0.142
0.004
0.225
0.000
2.99E-06
From maize study in Griffith
Urea
0.891
0.839
0.050
0.002
0.000
1.99E-05
Adapted from ecoinvent data
Ammonium sulphate, as
2.534
2.522
0.000
0.010
0.000
2.05E-03
Adapted from ecoinvent data
2.602
2.573
0.001
0.023
0.003
1.17E-03
Adapted from ecoinvent data
Triple superphosphate
0.834
0.816
0.015
0.003
0.000
1.03E-04
Adapted from ecoinvent data
Diammonium
2.737
2.722
0.000
0.013
0.000
1.187E-3
Adapted from ecoinvent data
1.592
1.580
0.001
0.008
0.000
5.64E-4
Adapted from ecoinvent data
N
Single superphosphate,
as P2O5
phosphate, as N
Monoammonium
phosphate, as P2O5
1 Calculated
figure based on European farming conditions, so total GHG emissions were adjusted to reflect Australian conditions
18
Table 2 cont. Embedded GHG emissions from key farm inputs.
Farm input (kg product)
Total
CO2
CH4
N2O
Sequestration
Other
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
(kg CO2-e)
Potassium chloride
0.131
0.128
0.002
0.001
0.000
1.91E-05
Adapted from ecoinvent data
Limestone
0.019
0.019
0.000
0.000
0.000
1.80E-05
Australian inventory data
Pesticide unspecified
7.017
6.956
0.001
0.058
0.000
2.57E-03
Adapted from ecoinvent data
Canola meal
0.308
0.256
0.006
0.045
0.000
5.82E-06
Based on data from study
Source of data
done for AGO and Caltex
Soybean meal
0.485
0.373
0.009
0.102
0.000
4.25E-05
Based on data from study
done for AGO and Caltex
Palm kernel oil
2.723
0.615
0.000
0.502
1.605
1.97E-04
Ecoinvent data
Palm kernel meal
0.186
0.042
0.000
0.034
0.109
1.34E-05
Ecoinvent data
Maize seed
1.923
1.000
0.000
0.922
0.000
3.95E-04
Ecoinvent data
Grass seed
3.367
1.093
0.000
2.272
0.000
4.69E-04
Ecoinvent data
Clover seed
0.291
0.108
0.004
0.179
0.000
1.83E-06
From maize study in Griffith
Glyphosate (41.5%)
8.945
8.708
0.210
0.027
0.000
1.37E-04
Adapted from ecoinvent data
MCPA
4.000
3.968
0.001
0.030
0.000
9.39E-04
Ecoinvent data
Diuron
6.574
6.518
0.002
0.052
0.000
2.09E-03
Ecoinvent data
Diesel
0.719
0.671
0.046
0.002
0.000
1.38E-05
Australian inventory data
19
2.4. Modelling analysis to quantify the greenhouse gas emissions of
differing dairy farm systems
Methodology to determine on-farm greenhouse gas emissions
To model the on-farm GHG emissions, we used the Dairy Greenhouse Framework
calculator (referred to as the GHG calculator) which was developed by Dr Richard
Eckard, Dr Roger Hegarty and Mr Geoff Thomas (Dairy Australia funded UM10778
project). The GHG calculator was developed using Intergovernmental Panel on Climate
Change (IPCC) and Australian-specific algorithms and emission factors to determine
carbon dioxide, methane and nitrous oxide emissions that conform to international
guidelines developed by the United Nations Framework Convention on Climate Change.
Methane emissions from enteric fermentation is calculated based on Australian
methodologies, as the IPCC approach uses a fixed methane conversion rate for each
livestock category. This IPCC method was deemed unsuitable for the Australian dairy
industry due to the industries vast differences in terms of location and types of feeds
used. Methane production from manure management uses a combination of IPCC and
Australian-specific methodologies, as research measuring methane production from
dairy cattle manure under field conditions found that the IPCC conversion factor of 1.5%
was too high for Australian conditions, resulting in a reduction of the factor to 1.0%.
Nitrous oxide emissions from manure management is based on algorithms developed in
Australia from the Australian Standing Committee on Agriculture (1990) and by Freer et
al. (1997), rather than applying IPCC default values. The methodology for determining
GHG emissions from N fertiliser is based on Australian-specific emission factors was
reduced to 0.3 and 0.4% for crops and pastures, respectively, as the IPCC default factor
of 1.25% across all classes of crops and pastures was shown to be too high for
Australian conditions.
Since the development of the GHG calculator, based on the 2003 national methodology
inventory report, there have been two significant changes. Communications with Dr
Mick Meyer from CSIRO has confirmed that nitrous oxide loss from soil disturbance has
been removed from the inventory. At the same time a nitrous oxide emission from
indirect sources has been added.
The indirect sources are from the ammonia
20
volatilisation and the leaching/runoff of N fertilisers and animal waste. Modelling in this
report reflects these inventory changes.
Modelling the three dairy farming systems
The first objective of this project was to quantify the GHG emissions from three typical
dairy farming systems. These were defined as a low supplementary feeding system
(LSF) where approximately 10-15% of the total diet to the milking herd was from
concentrates/grain, with the balance (85-90%) from pasture. The second system was
defined as a high supplementary feeding system (HSF) where approximately 50-60% of
the herd’s feed intake was derived from pastures with the balance (40-50%) of the diet
derived from grain and other supplementary feeds. The third system was defined as a
total mixed ration feeding system (TMR) where the milking herd was maintained in an
enclosed area year round and their diet was either a cut and carry system from home
grown forages or sourced from off-farm feeds.
The process adopted to model the LSF and HSF farm systems was to model a farm
system where annual milk production totalled 2 million litres or 150 t MS and was located
in Victoria. For the LSF farm system, this was achieved by milking 445 cows, with each
cow producing 4,500L/lactation and the diet consisting of 89% pasture and 11% grain.
From here on this farm system is referred to as LSF.
For the HSF farm system, the milk production target was achieved by one of two
methods:

A pasture and grain system where 310 cows produced 6,500L/lactation and
the diet consisted of 56% pasture and 44% grain. From here on this farm
system is referred to as HSF 1;

A pasture, grain and maize silage system where 333 cows produced
6,000L/lactation and the diet consisted of 56% pasture, 26% grain and 18%
maize silage from an off-farm source. From here on this farm system is
referred to as HSF 2.
21
Two TMR dairy systems were modelled based on farm data from commercially operated
TMR dairies both located in New South Wales. The two systems were:

A traditional TMR farm, with cows housed in a large freestall system with the
diet consisting of 42% forage, conserved both on and off farm, 30% grain and
28% by-products (waste from Manildra Mills, cottonseed meal, molasses and
whole cottonseed). This farm produced 3.8 million litres or 285 t MS per
annum. From here on this farm system is referred to as TMR 1;

A farming system which has converted from a traditional pasture grazing
system to a cropping system due to reduced water allocations, with the diet
consisting of 43% on-farm conserved forage, 36% grain and 21% ‘waste’
products (canola meal, soybean meal, dried distillers grain and cottonseed
meal). This farm produced 6.4 million litres or 487 t MS per annum. From
here on this farm system is referred to as TMR 2.
To allow easier comparison of the total farm GHG emissions between the farming
systems, the two TMR systems were scaled down to replicate farms that produced 150t
MS/annum (the same as the LSF and HSF systems), by dividing their baseline total farm
GHG emissions by their total MS production and then multiplying by 150. Only these
scaled downs figures are shown in this report.
In addition, the TMR 1 farm agist their replacement stock off farm, thus reducing their onfarm GHG emissions. Reintroducing the replacement stock on farm allowed for all farm
systems to be comparative in terms of herd structure.
It is generally accepted that daily feed intake can be determined from bodyweight. Daily
DMI is generally 3.0 to 3.5% of a cows bodyweight so for every 100kg of body weight, a
cow can consume 3 to 3.5 kg DM/day. For the LSF baseline system we assumed that
the cows were consuming 3% of their bodyweight or 15kg DM/day, increasing to 16.5 kg
DM/day when the weight was increased to 550kg. This resulted in a feed conversion
efficiency of 1L milk/kg DMI, which according to Buckley et al. (2007), is on the lower
scale of the feed conversion efficiency scale.
For the HSF baseline system, we
assumed that the cows could consume 3.25% of their bodyweight or 18 kg DM/day,
increasing to 19.5 kg DM/day when the weight was increased to 600kg. We assumed
22
that for the HSF 1 system, every kg DMI resulted in 1.2L milk to achieve 6,500L from 5.4
t DMI, while the lower quality diet of the HSF 2 farm system resulted in 1.1L milk from
every kg of feed to achieve 6,000L from 5.4 t DMI.
Low supplementary feeding system
The LSF farm system consists of 445 milkers, each producing 4,500L/cow.lacation from
a predominantly pasture based diet (Table 3).
Table 3. Description of the low supplementary feeding farm system.
Description
Baseline
Cow numbers
445 milkers
Cow weight
500 kg
Milk production
4,500L/cow or 15.0 L/day
Heifer replacement rate
20% or 90 heifers/year
Farm size- milking area
300 ha at 1.5 cows/ha with 20% irrigated
Farm size- heifer area
120 ha at 1.5 heifers/ha
Total farm size
420 ha
Annual diet for milkers 1
4.0 t pasture & 0.5 t grain
Diet of 4.5 t DM, 71.1% DMD,
ME 10.4 MJ/kg DM & CP 19.1%
Diet for heifers
70% DMD, ME 10.2 MJ/kg DM & CP 16%
Total N fertiliser applied 2
72.0 t
Grain imported
223 t DM
Diesel
15,000 L
Electricity 3
120,000 kWh
1
see Appendix 5 for diet quality calculation; DMD-dry matter digestibility, ME- metabolisable
energy, MJ-megajoules & CP-crude protein
2 N fertiliser based on 150kg N/ha.annum for dryland pastures, 300 kg N/ha.annum for irrigated
pastures
3 Electricity based on dairy consuming 0.67 kWh/cow for 365 days and irrigators consuming 8
kWh/ha.day for 150 days on a 6 day rotation
Total farm GHG emissions for the LSF system was 2,623 t CO2-e/farm or 17.5 t CO2-e/t
MS, with enteric methane production equating to 9.3 t CO2-e/t MS or ~ 53% of the total
farm emissions. As a percentage of the total farm GHG emissions, pre-farm, on-farm
23
carbon dioxide, on-farm methane and on-farm nitrous oxide emissions were 11.4, 8.3,
54.5 and 25.7%, respectively (Figure 4).
Assessment of the on-farm liability GHG
emissions showed that CPRS farm GHG emissions was 2,105 t CO2-e/farm or 14.0 t
CO2-e/t MS, with enteric methane ~ 67% of the on- farm CPRS emissions.
CPRS liability
Total farm
Tree plantings
N2O - Indirect
N2O - Dung & urine
N2O - N Fertiliser
N2O - Effluent
CH4 - Effluent ponds
CH4 - Enteric
CO2 -Energy
Other feed sources
Grain
Herbicide
Fertiliser
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
GHG emissions (t CO2-e/t MS)
Figure 4. The intensity of greenhouse gas emissions from the low supplementary feeding
farm system calculated as pre-farm (■), on-farm carbon dioxide (■), on-farm methane (■)
and on-farm nitrous oxide emissions (■), total farm (■; t CO2-e/t MS) and that liable under a
carbon pollution reduction scheme (■; t CO2-e/t MS). The percentage of total farm
emissions from each source shown as the enclosed pie chart.
High supplementary feeding system
For the HSF system we have modelled two contrasting farm systems. The first was a
high grain based diet, where the cows received ~ 56% of their diet from pasture and the
balance of their diet from grain (HSF 1). The second system replicated many of the
aspects of the first system with the major difference being that this diet consisted of ~
56% of the diet from pasture, 26% from grain and 18% from maize silage sourced offfarm (HSF 2). Feeding silage was estimated to increase the diesel usage on farm from
15,000L/annum to 20,000 L/annum.
The HSF1 farming system consists of 310 milkers, with each cow producing
6,500L/cow.lactation from a diet that was 56% pasture and 44% grain (Table 4).
24
Table 4. Description of the high supplementary feeding 1 farm system.
Description
Baseline
Cow numbers
310 milkers
Cow weight
550 kg
Milk production
6,500L/cow or 21.7 L/day
Heifer replacement rate
25% or 78 heifers/year
Farm size- milking area
125 ha at 2.5 cows/ha with 40% irrigated
Farm size- heifer area
100 ha at 1.5 heifers/ha
Total farm size
225 ha
Annual diet for milkers 1
3.0 t pasture & 2.4 t grain
Diet of 5.4 t DM, 74.4% DMD,
ME 10.9 MJ/kg DM & CP 16.4%
Diet for replacement stock
70% DMD, ME 10.2 MJ/kg DM & CP 16%
Total N fertiliser applied 2
41.3 t
Grain imported
745 t DM
Diesel
15,000 L
Electricity
3
85,000 kWh
1
see Appendix 5 for diet quality calculation
N fertiliser based on 150kg N/ha.annum for dryland pastures, 300 kg N/ha.annum for irrigated
pastures;
3 Electricity based on dairy consuming 0.67 kWh/cow for 365 days and irrigators consuming 8
kWh/ha.day for 150 days on a 6 day rotation
2
Total farm GHG emissions for the HSF 1 system was 2,118 t CO2-e/farm or 14.0 t CO2e/t MS, with enteric methane production equating to 7.5 t CO2-e/t MS or ~ 54% of the
total farm emissions. As a percentage of the total farm GHG emissions, pre-farm, onfarm carbon dioxide, on-farm methane and on-farm nitrous oxide emissions were 16.7,
7.9, 54.9 and 20.5%, respectively (Figure 5). Assessment of the on-farm liability GHG
emissions showed that CPRS farm GHG emissions was 1,596 t CO2-e/farm or 10.6 t
CO2-e/t MS, with enteric methane ~ 71% of the on- farm CPRS emissions.
25
CPRS liability
Total farm
Tree plantings
N2O - Indirect
N2O - Dung & urine
N2O - N Fertiliser
N2O - Effluent ponds
CH4 - Effluent ponds
CH4 - Enteric
CO2 -Energy
Other feed sources
Grain
Herbicide
Fertiliser
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
GHG emissions (t CO 2-e/t MS)
Figure 5. The intensity of greenhouse gas emissions from the high supplementary feeding
1 farm system calculated as pre-farm (■), on-farm carbon dioxide (■), on-farm methane (■)
and on-farm nitrous oxide emissions (■), total farm (■; t CO2-e/t MS) and that liable under a
carbon pollution reduction scheme (■; t CO2-e/t MS). The percentage of total farm
emissions from each source shown as the enclosed pie chart.
The HSF 2 farm system consisted of 333 milkers, with each cow producing
6,000L/cow.lactation from a diet that was ~ 56% pasture, 26% grain and 18% maize
silage. As the overall diet quality was lower for this diet compared to the HSF 1 diet, this
equated to a reduction in milk production from 6,500 to 6,000L/cow.lactation.
To
maintain a total farm milk production of 2 million litres, we increased herd size to 333
milkers (Table 5).
26
Table 5. Description of the high supplementary feeding 2 farm system.
Description
Baseline
Cow numbers
333 milkers
Cow weight
550 kg
Milk production
6,000L/cow or 20.0 L/day
Heifer replacement rate
25% or 83 heifers/year
Farm size- milking area
125 ha at 2.67 cows/ha with 40% irrigated
Farm size- heifer area
100 ha at 1.67 heifers/ha
Total farm size
225 ha
Annual diet for milkers 1
3.0 t pasture, 1.4 t grain and 1.0 t maize silage
Diet of 5.4 t DM, 71.7% DMD,
ME 10.5 MJ/kg DM & CP 16.8%
Diet for replacement stock
70% DMD, ME 10.2 MJ/kg DM & CP 16%
Total N fertiliser applied 2
41.3 t
Grain and silage imported
466 & 365 t DM
Diesel
20,000 L
Electricity
3
91,000 kWh
1 see
Appendix 5 for diet quality calculation
N fertiliser based on 150kg N/ha.annum for dryland pastures, 300 kg N/ha.annum for irrigated
pastures;
3 Electricity based on dairy consuming 0.67 kWh/cow for 365 days and irrigators consuming 8
kWh/ha.day for 150 days on a 6 day rotation
2
Total farm GHG emissions for the HSF 2 farm system was 2,290 t CO2-e/farm or 15.3 t
CO2-e/t MS, with enteric methane production equating to 8.0 t CO2-e/t MS or ~ 53% of
the total farm emissions. As a percentage of the total farm GHG emissions, pre-farm,
on-farm carbon dioxide, on-farm methane and on-farm nitrous oxide emissions were
17.7, 8.5, 53.8 and 20.0%, respectively (Figure 6). Assessment of the on-farm liability
GHG emissions showed that CPRS farm GHG emissions was 1,690 t CO2-e/farm or
11.3 t CO2-e/t MS, with enteric methane ~ 71% of the on- farm CPRS emissions.
27
CPRS liability
Total farm
Tree plantings
N2O - Indirect
N2O - Dung & urine
N2O - N Fertiliser
N2O - Effluent ponds
CH4 - Effluent ponds
CH4 - Enteric
CO2 -Energy
Other feed sources
Grain
Herbicide
Fertiliser
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
GHG emissions (t CO2-e/t MS)
Figure 6. The intensity of greenhouse gas emissions from the high supplementary feeding
2 farm system calculated as pre-farm (■), on-farm carbon dioxide (■), on-farm methane (■)
and on-farm nitrous oxide emissions (■), total farm (■; t CO2-e/t MS) and that liable under a
carbon pollution reduction scheme (■; t CO2-e/t MS). The percentage of total farm
emissions from each source shown as the enclosed pie chart.
Total mixed ration feeding system
While the number of dairy farms in Australia that would be classed as a TMR farming
system is small, there is a very strong likelihood that as irrigation water becomes
increasingly more difficult to acquire and forage production becomes increasingly more
expensive due to increasing fertiliser and other costs, farmers will try to achieve as much
milk production as possible out of their forages and purchased feeds. While the dairy
industry will never progress down the path of fully housed free-stall stanchion style farms
found in North America and Europe, we will start to see more farms consolidate their
herd into a small area around the dairy facilities and cut and carry most and/or all of the
on-farm grown forages to the milking herd.
This is evident from the two commercial farms that we have modelled in this report. The
first farm (TMR 1) was established when the dairy business relocated from the south
coast of NSW to the central west of NSW, with funds from the Dairy Structural
Adjustment Scheme deregulation payments. They constructed an enclosed free-stall
feeding system to feed their milking herd year round, with access to loafing paddocks
when not feeding or milking.
28
The TMR 1 farm systems consisted of year round milking of 300 cows, with ~ 50 dry
cows maintained on-farm while the replacement stock were agisted off-farm from
weaning age to just prior to calving. This farm produced the bulk of their forage on a
second property 3 km from the home farm. However, the biggest proportion of the
milking herds’ diet was supplied from off-farm sources. To assist in comparing farm
systems, we re-introduced the replacement stock on-farm. Farm details are shown in
Table 6.
Table 6. Description of the total mixed ration 1 farm system.
Description
Baseline
Cow numbers- milkers
300 milkers
Cow weight
700 kg
Milk production
11,500L/cow or 38.3L/day
Dry cows
50
Heifer replacement rate
33% or 110-120 heifers/year
Farm size- pasture/cropping area
100 ha at 3.0 cows/ha with 50% irrigated
Farm size- dry cow area
50 ha
Total farm size 1
220 ha
Annual diet for milkers 2
2.85 t DM ryegrass & sorghum silage,
2.25 t DM grain, 2.1 t DM by-products &
0.3 t DM cereal hay
Diet of 7.5 t DM, 72.0% DMD,
ME 10.5 MJ/kg DM & CP 16.0%
Diet for dries
65% DMD, ME 9.4 MJ/kg DM & CP 14%
Diet for replacement stock
70% DMD, ME 10.2 MJ/kg DM & CP 16%
Total N fertiliser applied
52 t
Grain, silage and hay imported
800, 300 and 200 t DM
Diesel
24,500 L
Electricity
225,000 kWh
1 Farm
consists of an additional 70ha consisting of non-farming activities
consist of 0.63 t DM cottonseed meal, 0.63 t DM mill waste from the Manildra Flour
Mills, 0.45 t DM whole cottonseed and 0.4 t DM molasses; see Appendix 5 for diet quality
calculation
2 By-products
29
To allow comparison between the farming systems, we scaled down the total farm GHG
results to replicate a farm producing 150t MS by dividing the total farm GHG emissions
by 285 (t MS achieved) and then multiplying by 150. Total farm GHG emissions for the
TMR1 system was greater than any previously modelled system at 3,658 t CO2-e/farm.
However, when converted to GHG emissions from a 150t production level, this was
reduced to 1,925 t CO2-e.
Greenhouse gas emissions /t MS for this farm was 12.8 t CO2-e/t MS, with enteric
methane production equating to 5.7 t CO2-e/t MS or ~ 45% of the total farm emissions.
As a percentage of the total farm GHG emissions, pre-farm, on-farm carbon dioxide, onfarm methane and on-farm nitrous oxide emissions were 17.1, 9.2, 47.9 and 25.7%,
respectively (Figure 7). Assessment of the on-farm liability GHG emissions showed that
CPRS farm GHG emissions was 1,418 t CO2-e/farm or 9.5 t CO2-e/t MS, with enteric
methane ~ 61% of the on- farm CPRS emissions.
CPRS liability
Total farm
Tree plantings
N2O - Indirect
N2O - Dung & urine
N2O - N Fertiliser
N2O - Effluent
CH4 - Effluent ponds
CH4 - Enteric
CO2 -Energy
Other feed sources
Grain
Herbicide
Fertiliser
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
GHG emissions (t CO2-e/t MS)
Figure 7. The intensity of greenhouse gas emissions from the total mixed ration 1 farm
system calculated as pre-farm (■), on-farm carbon dioxide (■), on-farm methane (■) and onfarm nitrous oxide emissions (■), total farm (■; t CO2-e/t MS) and that liable under a carbon
pollution reduction scheme (■; t CO2-e/t MS). The percentage of total farm emissions from
each source shown as the enclosed pie chart.
The TMR 2 farm system is located in the NSW Riverina region. They traditionally grew
pastures and forages, on 350ha of flood irrigation, to feed their herd. However, due to
drought conditions and significant reductions in water allocation, they have modified their
30
farm from a traditionally grazed pasture system to a cut and carry system, where
pastures have been replaced with an irrigated maize crop on a proportion of the farm,
with the balance of the farm sown down to either a grazing wheat variety or a legume
based forage (lucerne/vetch/clover) that was conserved as silage and hay and the whole
diet is delivered to the herd as a TMR diet. Unlike the TMR 1 farm system, this farm
maintains all their replacement stock on farm. Farm details are shown in Table 7.
Table 7. Description of the total mixed ration 2 farm system.
Description
Baseline
Cow numbers
680 milkers
Cow weight
550 kg
Milk production
8,600L/cow or 28.7 L/day
Dry cows
65
Heifer replacement rate
350 heifers aged 0-1 yr (47%) and
230 heifers aged 1-2 yr (30%)
Farm size- home farm cropping
390 ha
area/ dries/ heifers 1
Farm size- runoff cropping area
250 ha
Total farm size
675 ha1
Annual diet for milkers 2
2.4 t DM grain, 1.4 t DM by-products, 1.3 t
DM maize silage, 0.9 t DM cereal hay, 0.5 t
DM lucerne hay & 0.2 t DM cereal hay;
Diet of 6.7 t DM, 72.9% DMD,
ME 10.7 MJ/kg DM & CP 17%2
Diet for dries & replacement herd
65% DMD, ME 9.4 MJ/kg DM & CP 16%
Total N fertiliser applied
32.5 t
Grain imported
2,280 t DM
Diesel
50,000 L
Electricity
210,000 kWh
1 Farm
consists of ~ 35ha consisting of non-farming area
By-products consist of 0.7 t DM of canola meal/soybean meal and/or lupins and 0.7 t DM of
cottonseed meal; see Appendix 5 for diet quality calculation
2
Total farm GHG emissions for the TMR 2 system was the greatest of any of the farm
systems modelled in this report at 5,959 t CO2-e/farm, as a direct result of being the
31
largest milking herd and producing the greatest total milk production. When total farm
GHG emissions were scaled down to total farm GHG emissions from a 150t production
level, this was reduced to 1,835 t CO2-e. Greenhouse gas emissions /t MS was 12.2 t
CO2-e/t MS, with enteric methane production equating to 6.2 t CO2-e/t MS or ~ 51% of
the total farm emissions. As a percentage of the total farm GHG emissions, pre-farm,
on-farm carbon dioxide, on-farm methane and on-farm nitrous oxide emissions were
13.4, 6.3, 54.3 and 26.0%, respectively (Figure 8).
Assessment of the on-farm liability
GHG emissions showed that CPRS farm GHG emissions was 1,474 t CO2-e/farm or 9.8
t CO2-e/t MS, with enteric methane ~ 63% of the on-farm CPRS emissions.
CPRS liability
Total farm
Tree plantings
N2O - Indirect
N2O - Dung & urine
N2O - N Fertiliser
N2O - Effluent
CH4 - Effluent ponds
CH4 - Enteric
CO2 -Energy
Other feed sources
Grain
Herbicide
Fertiliser
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
GHG emissions (t CO2-e/t MS)
Figure 8. The intensity of greenhouse gas emissions from the total mixed ration 2 farm
system calculated as pre-farm (■), on-farm carbon dioxide (■), on-farm methane (■) and onfarm nitrous oxide emissions (■), total farm (■; t CO2-e/t MS) and that liable under a carbon
pollution reduction scheme (■; t CO2-e/t MS). The percentage of total farm emissions from
each source shown as the enclosed pie chart.
Comparison of the differing dairy farming systems
The total farm GHG emissions varied from 1,836 t CO2-e for the TMR 2 system to 2,623
t CO2-e for the LSF system (Figure 9). As each system produced ~ 150t MS, there was
a corresponding variation in GHG emissions/t MS from 12.2 to 17.5 t CO2-e/t MS for the
TMR 2 and LSF systems, respectively (Table 8). While there was variation between the
differing farm systems, analysis of the GHG emissions from the dairy systems indicated
that:

on-farm methane emissions were 48 to 55% of total emissions;

on-farm nitrous oxide emissions were 20 to 26% of total emissions;
32

pre-farm embedded GHG emissions were 11 to 18% of total emissions;

on-farm carbon dioxide emissions were 6 to 9% of total emissions.
Total farm GHG emissions (t CO2-e)
3000
2500
2000
1500
1000
500
0
LSF
On-farm methane
HSF 1
HSF 2
On-farm nitrous oxide
TMR 1
Pre-farm embedded
TMR 2
On-farm carbon dioxide
Figure 9. Total farm greenhouse gas emissions and the source of greenhouse gas
emissions, expressed as tonne of carbon dioxide equivalents, when achieving 150 tonnes
milksolids per annum for each of the five farming systems under review.
Table 8. A comparison of the total farm and intensity of greenhouse gas emission and the
percentage of emissions from each source, for the five baseline farming systems.
LSF
HSF 1
HSF 2
TMR 1
TMR 2
Total farm GHG emissions (t CO2-e)
2,623
2,118
2,290
1,925
1,836
Annual milk production (t MS)
150.2
151.1
149.9
150.0
150.0
GHG emissions/t MS (t CO2-e/t MS)
17.5
14.0
15.3
12.8
12.2
Pre farm GHG emission %
11.8
17.1
18.2
17.5
13.9
On-farm carbon dioxide emission %
8.0
7.5
8.0
8.8
5.8
On-farm methane emission %
54.5
54.9
53.8
47.9
54.3
On-farm nitrous oxide emission %
25.7
20.5
20.0
25.7
26.0
CPRS farm GHG emissions1 (t CO2-e)
2,105
1,596
1,690
1,418
1,474
CPRS emissions/t MS1 (t CO2-e/t MS)
14.0
10.6
11.3
9.5
9.8
1Only
On-farm methane and nitrous oxide emissions are considered.
33
The LSF system emitted between 15 to 23% and 36 to 43% more total farm GHG
compared to the HSF and TMR farm systems, respectively. This was a result of the LSF
having the greatest comparative herd size.
While the pre-farm GHG emission was
comparative to all other systems, the greater herd size resulted in greater electricity
consumption, increasing the on-farm carbon dioxide emissions.
The higher stock
numbers and lower digestibility of the diet resulted in greater methane emissions.
Nitrous oxide emissions were also elevated due to the greater herd size and the
application of significantly more N fertiliser compared to the other systems.
Overall, total farm GHG emissions and the GHG emission from all sources was greater
for the HSF 2 farm system compared to the HSF 1 farm system, as a result of replacing
some grain with maize silage for the HSF 2 system. Pre-farm GHG emissions was
greater, primarily due to the higher emission factor for maize silage compared to grain
(each tonne of maize silage produces 0.371 t CO2-e compared to 0.302 t CO2-e for
grain) and because we assumed a 10% ‘wastage’ value of using maize silage (i.e. for
every tonne of grain not brought on, 1.1 t of silage was brought on). On-farm carbon
dioxide emission increased due to the greater herd size and due to the extra diesel
required to feed out silage to the herd. Diet quality for the HSF 2 system was also lower
due to the maize silage, increasing this farms’ methane and nitrous oxide emissions.
While total GHG emission from each source was greater for the HSF 2 system, in terms
of the percentage of the total from each source, pre-farm and on-farm carbon dioxide
emissions were greater for the HSF 2 system, while the percentage of on-farm methane
and on-farm nitrous oxide emissions were slightly lower for the HSF 2 farm system
compared to the HSF 1 farm system.
Overall, total farm GHG emissions and the GHG emission from all sources, other than
methane, was greater for the TMR 1 farm system compared to the TMR 2 farm system.
Pre-farm embedded emissions were higher due to greater reliance on off-farm feed
sources and fertiliser inputs. The higher fertiliser inputs also contributed to increased onfarm nitrous oxide emissions.
On-farm carbon dioxide emission was substantially
greater as irrigation took place all year, compared to the TMR 2 system which only
irrigated forages over summer. In addition, the TMR 1 farm milked their herd three times
a day, rather than the typical twice a day, therefore consuming more electricity.
However, on-farm methane emission was lower for the TMR 1 farm system as a result of
34
a higher quality diet for that system compared to the TMR 2 system.
While total
emission from each source, other than on-farm methane, was greater for the TMR 1
farm system, in terms of the percentage of the total for each source, pre-farm and onfarm carbon dioxide were greater for the TMR 1 farm system while on-farm methane and
on-farm nitrous oxide emissions were slightly lower than the TMR 2 farm system.
2.5. Selection of key abatement strategies for each differing farming
systems
The second objective was to quantify the impact of abatement strategies on the three
farming systems.
Not every abatement strategy was relevant to each of the three
farming systems. Some abatement strategies have a time dependency. For example,
feeding fats and oils as an abatement strategy will only be of maximum benefit during
the summer and autumn months (especially under rain-fed conditions) when pasture
digestibility and energy supply is generally lower than in winter and spring. Lower diet
digestibility will increase methane production while lower energy content of the diet will
also reduce milk production.
Abatement strategies for the low supplementary feeding system
For the LSF farm system, we modelled 8 abatement strategies. These were:
1. Increasing per cow milk production by 10% to 4,950L, but reducing the size of the
herd to 404 milkers to maintain farm milk production at ~ 2 million litres or 150t
MS/annum (assumed to be from greater feed conversion efficiency (1.1L/kg
DMI), not from altering feed management);
2. Increase milk production by increasing cow weight and feed intake- increase milk
production to 5,000L by increasing cow weight to 550kg and feed intake to 16.5
kg DMI (feed source proportions remained the same so diet quality remained the
same, feed intake to be 3% bodyweight and a feed conversion efficiency of 1L/kg
DMI);
3. Increase milk production by increasing grain intakes- increase milk production to
5,500 L by increasing grain to 1.0t DM/cow.lactation (assumed no pasture
substitution effect therefore changing the overall diet quality; assumed each kg of
grain would supply the necessary energy to produce an extra 2 litres of milk);
4. Feeding fats- reducing enteric methane production by 20% for the milking herd
for 6 months of the year (summer and autumn; diet quality assumed to not alter
35
from the baseline quality as the increased energy from the fats over summer and
autumn would offset the lower pasture quality and therefore milk production
would remain the same as the baseline system);
5. Feeding condensed tannin year round to the milking herd- reduce enteric
methane production by 10%, increase dung N output by 50% and reduce urine N
output by 33%;
6. Using an N-inhibitor year round- reduce the N fertiliser emission factor from 0.4 to
0.25%;
7. All-inclusive (404 cows)- producing 5,500L from 550kg cows from 4.4 and 1.0 t
DM/cow.lactation of pasture and grain, respectively, while feeding fats for 6
months, feeding a source of condensed tannin for 12 months and using an Ninhibitor year round;
8. All-inclusive (445 cows)- same as previous all-inclusive strategy except maintain
herd size at 445 milkers.
Abatement strategies high supplementary feeding system
For the HSF 1 farm system, there were fewer abatement strategies that could be
implemented compared to the LSF farm system. The baseline system already fed 2.4t
grain/lactation or 8kg DM/day. Grain intakes were at their upper limit in terms of adverse
health risks associated with high grain feeding (e.g. acidosis or lameness). Also the
increasing cost of purchasing grain was taken into consideration so we did not increase
grain feeding to any of the strategies.
Where feed intakes were increased due to
increased liveweight, the extra feed intake was from pasture only.
For the HSF 1 farm system, we modelled 7 abatement strategies. These were:1. Increasing per cow milk production by 10% to 7,150L, but reducing the size of the
herd to 280 milkers to maintain farm milk production at ~ 2 million litres or 150t
MS/annum (assumed to be from greater feed conversion (1.3 L/kg DMI), not from
altering feed management);
2. Increase milk production by increasing cow liveweight and feed intake- increase
milk production to 7,000L by increasing cow liveweight to 600kg and feed intake
to 19.5 kg DMI (assumed feed intake to be 3.25% bodyweight and a feed
conversion efficiency of 1.2 L/kg DMI);
36
3. Feeding fats- reducing enteric methane production by 20% for the milking herd
for 6 months of the year (summer and autumn);
4. Feeding condensed tannin year round to the milking herd- reduce enteric
methane production by 10%, increase dung N output by 50% and reduce urine N
output by 33%;
5. Using an N-inhibitor year round- reduce the N fertiliser emission factor from 0.4 to
0.25%;
6. All- inclusive (280 cows)- producing 7,150L from 600kg cows from 19.5 kg
DMI/day while feeding fats for 6 months of the year, feeding a condensed tannin
for 12 months and using an N-inhibitor year round; and
7. All-inclusive (310 cows)- same as previous all- inclusive abatement strategy
except maintain herd size at 310 milkers.
For the HSF 2 farm system, we modelled 8 abatement strategies. These were:1. Increasing per cow milk production by 10% to 6,600L, but reducing the size of the
herd to 303 milkers to maintain farm milk production at ~ 2 million litres or 150t
MS/annum (assumed to be from greater feed conversion efficiency (1.2L/kg
DMI), not from altering feed management);
2. Increase milk production by increasing cow weight and feed intake- increase milk
production to 7,000L by increasing cow weight to 600kg and feed intake to 19.5
kg DMI (assumed feed source proportions remained the same so diet quality
remained the same, feed intake to be 3.25% bodyweight and a feed conversion
efficiency of 1.2 L/kg DMI);
3. Increasing milk production by increasing grain feeding- increasing milk production
to 7,000L by increasing grain feeding to 1.9t DM/cow.lactation (assumed no
pasture substitution so increasing overall diet quality; assumed each kg of grain
would supply the necessary energy to produce an extra 2 litres of milk);
4. Feeding fats- reducing enteric methane production by 20% for the milking herd
for 6 months of the year (summer and autumn);
5. Feeding condensed tannin year round to the milking herd- reduce enteric
methane production by 10%, increase dung N output by 50% and reduce urine N
output by 33%;
6. Using an N-inhibitor year round- reduce the N fertiliser emission factor from 0.4 to
0.25%;
37
7. All- inclusive (303 cows)- producing 7,000L from 600kg cows from 19.5 kg
DMI/day while feeding fats for 6 months of the year, feeding a condensed tannin
for 12 months and using an N-inhibitor year round; and
8. All-inclusive (333 cows)- same as previous all-inclusive abatement strategy
except maintain herd size at 333 milkers.
Abatement strategies total mixed ration system
For the TMR1 farm, there was little scope to adopt abatement strategies. The milking
herd already produces 11,500L/cow.lactation from very large framed Holstein-Friesian
cows. There was little scope to increase milk production, herd weight or feed intakes.
Grain and by-products feed totalled 7.5 and 7.0 kg DM/cow.day, respectively, so there
was little scope to increase these feed sources in the diet. Similarly, there was no scope
to increasing the fat content of the diet to assist in reducing methane production.
For the TMR 1 farm system, we modelled 4 abatement strategies. These were:1. Agisting the replacement stock off-farm (to reflect current management
practices);
2. Feeding a condensed tannin year round to the milking herd- reduce enteric
methane production by 10%, increase dung N output by 50% and reduce urine N
output by 33%;
3. Using an N-inhibitor year round- reduce the N fertiliser emission factor from 0.4 to
0.25%;
4. Feeding condensed tannin year round and using an N-inhibitor year round.
For the TMR 2 farm system, we modelled 7 abatement strategies. These were:1. Agisting the replacement stock off-farm (to compare with the TMR 1 system)
2. Increasing per cow milk production by 10% to 9,500L, but reducing the size of the
herd to 615 to maintain farm milk production at ~ 6.4 million litres or 487t
MS/annum (assumed to be from greater feed conversion efficiency (1.4L/kg
DMI), not from altering feed management);
3. Increase milk production by increasing cow weight and feed intake- increase milk
production to 9,500L by increasing cow weight to 600kg and grain intake by 2 kg
DM/day (total intake to 24.3 kg DM/day, with a feed conversion efficiency of
1.3L/kg DMI);
38
4. Feeding condensed tannin year round to the milking herd- reduce enteric
methane production by 10%, increase dung N output by 50% and reduce urine N
output by 33%;
5. Using an N-inhibitor year round- reduce the N fertiliser emission factor from 0.4 to
0.25%;
6. All- inclusive (615 milking herd)- producing 9,500L from 600kg cows from 24.3 kg
DMI/day while feeding a source of condensed tannin year round and using an Ninhibitor year round; and
7. All-inclusive (680 milking herd)- same as previous all-inclusive abatement
strategy except maintain herd size at 680 milkers.
2.6 Modelling analysis of each abatement strategy
The results of the baseline farm system and the three most effective abatement
strategies in reducing GHG emissions/t MS for each system can be seen in Table 9. For
the LSF and HSF systems, the most effective abatement strategy was adopting the allinclusive abatement strategy of increased cow weight, intakes and milk production while
feeding fats and condensed tannins and using an N-inhibitor. While the all inclusive
abatement strategy with a reduction in herd size resulted in the second greatest
reduction, it was more relevant to show the results of adopting individual abatement
strategies. For this reason, the 2nd and 3rd abatement strategies and results are from
individual abatement strategies. The result of each individual abatement strategy for
each of the five farming systems is given in Appendix 6.
As there was little scope to reduce GHG emissions/t MS for the two TMR systems, we
have shown the effect of agisting the replacement stock off farm (assuming that there
would be no GHG accountability cost associated with this management practice) as the
abatement strategy that could produce the greatest reduction in GHG emissions/t MS.
The 2nd and 3rd abatement strategies are either individual strategies or in the case of the
TMR 2 farm system, the all-inclusive abatement strategy.
39
Table 9. A comparison of the results of the baseline farm system and the three most effective abatement strategies in reducing
greenhouse gas emissions per tonne milksolids, in terms of total farm and the carbon pollution reduction scheme liability.
Total farm
Farm
system
LSF




HSF 1




HSF 2
Annual
MS (t)
Strategy




Baseline
AS 1- All inclusive 1
AS 2- Increase grain & milk
AS 3- Increase milk production &
reduce herd size

Baseline
AS 1- All inclusive 2
AS 2- Increase milk production &
reduce herd size
AS 3- Feeding condensed tannin

Baseline
AS 1- All inclusive 1
AS 2- Increase weight, intakes &
milk production
AS 3- Increase grain & milk
production
t CO2-e/
farm
150.2
183.6
183.6
150.0



151.1
166.2
150.2


t CO2-e/
t MS
2,623
2,503
2,765
2,434



2,118
1,960
1,923
151.1



149.9
174.8
174.8

174.8





CPRS liabilities
%
reduced
17.5
13.6
15.1
16.2



14.0
11.8
12.8
2,003



2,290
2,194
2,425

2,450





t CO2-e/
farm
n/a
22.0
13.8
7.1



n/a
15.9
8.7
13.3



15.3
12.6
13.9

14.0





t CO2-e/
t MS
2,105
1,917
2,180
1,936



1,596
1,438
1,431
5.4



n/a
17.8
9.2

8.3





%
reduced
14.0
10.4
11.9
12.9



10.6
8.7
9.5

n/a
18.1
9.8
1,481

9.8

7.2


11.3
8.8
10.4


1,690
1,534
1,801

n/a
22.2
8.6

1,789

10.3

9.2















n/a
25.5
15.3
7.9
1
Maintain milking herd size while increasing liveweight, milk production and all feed type intakes, feed a fat source for 6 months, a condensed
tannin source for 12 months and use an N-inhibitor year round
2 Maintain milking herd size while increasing liveweight, milk production and pasture intakes (no increase in grain), feed a fat source for 6 months,
a condensed tannin source for 12 months and use an N-inhibitor year round
40
Table 9 cont. A comparison of the results of the baseline farm system and the three most effective abatement strategies in reducing
greenhouse gas emissions per tonne milksolids.
Total farm
Farm
system
TMR 1
Annual
MS (t)
Strategy
t CO2-e/
farm
t CO2-e/
t MS
CPRS liabilities
%
reduced
t CO2-e/
farm
t CO2-e/
t MS
%
reduced

Baseline

150.0

1,925

12.8

n/a

1,418

9.5

n/a

AS 1- Agist heifers off-farm

150.0

1,654

11.0

14.1

1,212

8.1

14.5

AS 2- Feeding condensed tannin

150.0

1,851

12.3

3.9

1,343

9.0

5.3
& N-inhibitor
TMR 2

AS 3- Feeding condensed tannin

150.0

1,859

12.4

3.4

1,352

9.0

4.7

Baseline

150.0

1,835

12.2

n/a

1,474

9.8

n/a

AS 1- Agist heifers off-farm

150.0

1,569

10.5

14.5

1,233

8.2

16.3

AS 2- Increase milk production &

150.0

1,671

11.1

9.0

1,332

8.9

9.6

150.0

1,739

11.6

5.3

1,369

9.1

7.1
reduce herd size

AS 3- All inclusive 3
3 Maintain
milking herd size while increasing liveweight, milk production and grain intakes, feed a condensed tannin source for 12 months and use
an N-inhibitor year round
41
While various abatement strategies in isolation have been shown to reduce GHG
emissions by up to 40% from that particular source (e.g. use of N-inhibitors in reducing
nitrous oxide emissions from fertilisers), our modelling has shown that this level of
reduction can not be achieved in terms of a whole farm systems context. Abatement
strategies reduced GHG emission/t MS by up to a maximum of 22% for the LSF system,
by 16 to 18% for the HSF system and by 3 to 9% for the TMR system, compared to their
3000
20
2500
16
2000
12
1500
8
1000
4
500
0
GHG emissions (t CO 2 -e/t MS)
GHG emissions (t CO 2 -e)
corresponding baseline farming systems (Figure 10).
0
B
AS AS AS
1
2
3
LSF
B
AS AS AS
1
2
3
HSF 1
B
AS AS AS
1
2
3
B
HSF 2
AS AS AS
1
2
3
TMR 1
B
AS AS AS
1
2
3
TMR 2
Figure 10. Total on-farm methane and nitrous oxide emissions (t CO2-e/farm; ▌), total onfarm carbon dioxide and pre-farm embedded emissions (t CO2-e/farm; ▌), emissions under
a carbon pollution reduction scheme per tonne milksolids (t CO 2-e/t MS; ♦) and total farm
greenhouse gas emissions per tonne milksolids (t CO 2-e/t MS; ♦) for the baseline farm
system (B) and three selected abatement strategies (AS).
For all farm systems, abatement strategy 1 resulted in reducing total farm GHG
emissions below that achieved for the baseline farm system. This resulted in GHG
emissions/t MS being reduced by 14 to 22%, compared to their corresponding baseline
farm system. For the LSF and the two HSF farm systems, this was due to combining all
potential abatement strategies together to reduce total farm GHG emission while
increasing milk production. For the two TMR farm systems, a significant reduction in
total farm GHG emissions was achieved with the removal of the replacement stock. This
reduced total farm GHG emissions and, as milk production remained the same, GHG
emissions/t MS was also significantly reduced.
42
Adopting the 2nd most effective abatement strategy reduced GHG emissions/t MS by 4 to
14% compared to their corresponding baseline farm systems. For the HSF 1 and the
two TMR farm systems, total farm GHG emissions were also reduced. However, for the
LSF and the HSF 2 farm system, total farm GHG emissions were greater under this 2 nd
most effective strategy than under the baseline farm system.
In the LSF farm system, where the highlighted strategy was feeding more grain which
resulted in more milk production, total farm GHG emission was 5.4% greater for the
strategy system compared to the baseline system.
Grain inputs were increased by
100%, increasing pre-farm GHG emissions by 22%.
The extra feed intake also
increased methane and nitrous oxide emissions by 3%. However, the 22% increase in
milk production achieved with this strategy diluted the extra total farm GHG emission
such that GHG emissions/t MS was 13.8% lower for the strategy compared to the
baseline LSF system.
In the HSF 2 farm system, where the highlighted strategy was increasing herd weight to
increase daily feed intakes and therefore greater milk production, total farm GHG
emission was 5.9% greater for the strategy system compared to the baseline system.
The greater feed intakes was achieved by increasing intakes from pasture, grain and
maize silage, therefore increasing pre-farm GHG emissions by 5.9%. The extra feed
intake also increased methane and nitrous oxide emissions by 6.5% each. However, the
17% increase in milk production achieved with this strategy diluted the extra total farm
GHG emission such that GHG emissions/t MS was 9.2% lower for the strategy
compared to the baseline HSF 2 system.
Adopting the 3rd most effective abatement strategy resulted in reducing GHG emissions/t
MS by 3.4 to 7.5% compared to the corresponding baseline farm system for all five farm
systems. In all instances, milk production remained similar between the baseline and
the abatement strategy. For the LSF and HSF 2 farm systems, the reduction in total
farm GHG emissions was achieved by increasing milk production per cow and reducing
herd size, therefore reducing methane and nitrous oxide emissions associated with
animal numbers. For the HSF 1 and TMR 1 farm systems, the reduction in total farm
emissions was achieved by feeding a source of condensed tannin. For the TMR 2 farm
43
system, the reduction in total farm GHG emissions was achieved by adopting an allinclusive strategy.
We have highlighted the strategy of agisting replacement stock off-farm as the strategy
that would result in the greatest reduction in GHG emissions/t MS for both TMR farm
systems. In reality, this strategy would result in the greatest reduction in emissions for
all farming systems. However, not all farms can agist their replacement stock off-farm.
If stock were agisted off-farm, they are still emitting GHG, so does the cost of this
emission lay with the farm that owns the stock or the farm that is raising the stock?
2.7 Costs, benefits and synergies of adopting abatement strategies in a
whole farm systems context
Assessing the potential reduction in GHG emissions only partly quantifies the whole of
farm implications of implementing abatement strategies. Before adopting an abatement
strategy, an assessment of the ease of implementation and relevance to the farming
system needs to be undertaken. For example, increasing milk production per cow for a
TMR farm could be detrimental to the reproductive performance of the herd, if milk
production is already at an upper limit as was shown in the TMR 1 farm. Increasing milk
production beyond this level would most likely result in extending the anoestrus phase of
the reproductive cycle, thus resulting in cows taking longer to be successfully re-bred,
increasingly the potential number of days that they could be unproductive and could also
require more replacement stock to be maintained, further increasing the farms GHG
emissions.
We have endeavoured to rank the ease and/or relevance to adopting the different
abatement strategies that we have been examined for the three broad farming systems
(Table 10). It must be noted that this ranking is subjective and that individual farm
systems, management structure, skill level of farm workers, farm infrastructure and
economics also need to be considered. An abatement strategy that could be easily
adopted on one TMR system may not be so easily adopted on another TMR system.
For example, with the two real farm TMR systems modelled earlier, the TMR 1 farm
already had very large framed animals at 700kg compared to 550kg for the TMR 2 farm,
so there was very little scope to increasing herd weight to increase feed intake and milk
production for the TMR 1 farm.
We have also included some of the abatement
44
strategies given in Figure 3 which were not individually modelled for each of the farming
systems, especially the soil abatement strategies. These are difficult to model with the
calculators available to us (other than with DairyMod) and generally follow the concept
that using the best management practices that are currently advocated, will be the best
adoption strategy in reducing nitrous oxide emissions.
Table 10. Ease of implementation and/or relevance of each abatement strategy for a low
supplementary feeding system, a high supplementary feeding system and a total mixed
ration feeding system.
Herd

Strategy
LSF
HSF
TMR
Increasing per cow milk production
xxx
xx
x
xxx
xx
x
to reduce herd size

Increasing herd weight and milk
production

Reducing herd replacement rate
xx
xx
x

Extending lactation lengths
x
xx
xxx

Increased feed conversion
x
xx
xxx
efficiency
Feed

Increased grain feeding
xxx
x - xx
x

Feeding fats and oils
xxx
xx
x

Feeding monensin
x
xx
xxx

Feeding condensed tannins
xx
xx
xx

Maintaining the crude protein
x
xx
xx
content of the diet within the optimal
range
Soil

Nitrification inhibitor
xxx
xxx
xxx

Paddock management (drainage,
xxx
xx
x
irrigation, minimise waterlogging)

Winter stand-off areas
xx
xx
x

N fertiliser rate and timing
xxx
xx
xx
X, XX and XXX= low, medium and high degree of relevance or potential to be an effective
abatement strategy
Overall, we have shown that adopting one or a combination of abatement strategies will
result in reducing total farm GHG emissions. However, some abatement strategies do
45
have a time factor to consider. Increasing herd weight and therefore milk production is
an ongoing process. It would take years of selective breeding to increase a herds’
average weight by an amount that would provide for significant increases in feed intakes.
Increasing feed intakes then highlights other issues.
Does the farm have surplus
pasture most years to meet this increased demand for forage or will the additional feed
intake need to be achieved through off-farm sources, thus increasing pre-farm
embedded GHG emissions?
One abatement strategy that was not modelled for the five farming systems is extended
lactations. Traditionally, farmers would calve down their herd in late winter and/or early
spring, milk their herd for 270-300 days, before drying them off over late autumn and
winter to allow the cows to recover before calving next season. To facilitate this milking
pattern, most milk processors that predominantly manufacture long-life products, such as
butter, cheese and milk powders, would also cease processing during this dry period.
While this pattern will remain for most farming systems, there could be the potential to
continue milking herds beyond 300 day lactations, especially for high milk production
systems such as TMR farms that supply milk to processors year round.
Extending
lactations beyond 10 months, for year round calving herds, reduces the number of
animals dry at any particular time of the year. For example to milk 300 cows on 10month lactations year round, you would need to calve 30 cows each month. If however,
you milked 300 cows on 15-month lactations year round, you would only need to calve
20 cows each month. This would reduce the herd size from 360 to 340 milkers and
potentially reduce your replacement herd size, thus reducing pre-farm and on-farm GHG
emissions.
Reducing herd replacement rates could also assist in reducing total farm GHG emissions
as less replacement stock need to be raised each year. However, this could reduce the
ability of farmers to cull stock based on factors such as lower milk production and/or an
inability to reach a 270 to 300 day lactation length. These two aspects would most likely
increase GHG intensity figures, due to a reduction in annual milk production.
46
2.8 Evaluation of tools and processes to monitor greenhouse gas
emissions
Currently available greenhouse gas models
There is an array of tools that have or are currently being developed to assist agricultural
industries in accounting for GHG emissions. A national carbon accounting toolbox
(NCAT) model is available, with ongoing model developments, to account for GHG
emissions across a range of agricultural industries (e.g. forestry, cropping). However, the
current version of the NCAT model is not suitable to account for GHG emissions from
dairy farms system due to the omission of enteric methane estimates from livestock.
Greenhouse gas accounting tools relevant to the dairy industry include OVERSEER,
DairyMod and the Dairy Greenhouse Framework calculator (herein named the GHG
calculator).
One major issue with all these models is that they are not dynamic
mechanistic models, but inventory type models, based on equations and algorithms
where ‘one factor fits all situations’ is generally accepted. For example, in OVERSEER it
is assumed that each kilogram of dry matter intake of good quality pasture would result
in an emission of 26.5 g of methane (Wheeler et al. 2008).
A review of the capacity of each of the above mentioned models in addition to the newly
developed DGAS calculator can be seen in Table 11. While OVERSEER appears to the
most capable model in determining GHG emissions, two aspects made this model less
capable for Australian dairying conditions. Firstly, the model was developed for New
Zealand conditions, so contains algorithms and emission factors more relevant for their
dairying conditions. Secondly, there is little access to the mechanics of the model to
alter the algorithms and factors as required, to reflect Australian conditions. It is for
these reasons that we developed the new DGAS calculator, based on the GHG
calculator.
47
Table 11. A comparison of the capacities of five greenhouse gas calculator models.
GHG
Methane
Nitrous oxide
Carbon dioxide
Source of GHG emission
GHG
OVERSEER
Enteric fermentation (grazed pasture)
x
x
Enteric fermentation (conserved feed)
x
x
Enteric fermentation (concentrate feed)
x
x
x
x
x
calculator
NCAT
DGAS
DairyMod
calculator
Enteric fermentation (total diet)
x
Effluent stored
x
x
x
Effluent spread
x
x
x
Fertiliser application
x
x
x
x
Direct dung and urine deposits
x
x
x
x
Effluent stored
x
x
x
Effluent spread
x
x
x
Fuel and electricity
x
x
x
Lime breakdown
x
Carbon
Stored in trees
x
Other farm
Herd structure
x
aspects
Soil type and irrigation influence
x
x
x
Climate
x
x
x
Daily
Yearly
Daily
Time step
Yearly
x
x
x
x
Seasonal
X= ability of the model to determine each source of GHG emissions
48
Issues with currently available greenhouse gas models
Kebreab et al. (2006) reviewed models that predicted methane emissions from enteric
fermentation in North American dairy cattle. They stated that “Tier 1 linear inventory
type models (as is used in the GHG and DGAS calculators) are adequate for general
national inventory of methane emissions. However, when considering mitigation options
in relation to dietary manipulation, mechanistic models are better formulated to
determine the impact of mitigation strategies on methane emissions, both in the context
of the rumen and of the whole animal”. Therefore while the most appropriate tools
available to Australian dairy farmers are inventory type models, there could be some
degree of error in the estimation of GHG emissions when selecting feed abatement
strategies.
Using inventory type models that segregate farm systems based on state borders can
also introduce issues in terms of accuracy of results.
For example, calculating the
amount of nitrous oxide lost with leaching and runoff for dryland pastures assumes a
emission fraction of 0.991 in Tasmania, while in Queensland, the fraction is 0.128 (i.e.
for every kilogram of N fertiliser, potentially up to 0.991 kg is available to be leached in
Tasmania compared to 0.128 kg in Queensland).
These fractions do not take into
consideration rainfall events, soil type or farm management practices.
Using a ‘one factor fits all situations’ does not recognise that farmers who are already
managing their fertiliser inputs using current best management practices, such as
applying a maximum of 40-50 kg N/ha per application, not applying when soils are
waterlogged or when a significant rainfall event is expected in the immediate future, will
have already reduced their nitrous oxide emissions.
An inventory-type model is
therefore often limited in its ability to determine the impact of varying management
practices, not only between farms but also within a farm over seasons or years.
If the dairy industry wants to accurately model individual farm GHG emissions and the
effect of adopting abatement strategies, there will be discrepancies between the real
farm outputs and the modelling outputs. If, however, an understanding that the outcome
from the models are accurate for the outcome that they are designed for, to show
variation between farming systems, management practices and adopting abatement
strategies, then current models will suffice.
49
2.9 Development of a spreadsheet model that enables a whole of system
comparison between indicative systems and changes in imports and
management practices.
We have developed the new DGAS calculator based on the previously developed GHG
calculator as it contains all of the relevant Australian emission factors and is easily
altered as required. The major advancements and advantages of the DGAS calculator
over the other tools are:

Inclusion of the updated methodology to determine indirect nitrous oxide
emissions from leaching/runoff and volatilisation;

Calculation of the GHG emissions per tonnes of milksolids for the whole farm
system and the liability under a carbon pollution reduction scheme;

Ability to determine the GHG emissions associated with importing products
such as grain, fertiliser and some farm chemicals;

Ability to model a baseline farm system and then select one or a combination
of abatement strategies to compared outcomes;

Ability to define and calculate the seasonal diet quality (digestibility and crude
protein %) for the milking herd;

Ability to define and calculate the annual diet quality (digestibility and crude
protein %) for all other stock classes;

Inclusion of a dry cow category for farms that milk year round and therefore
have a proportion of the herd continually dry (not relevant for seasonal or split
calving herds);

Predictive feed intakes required to achieve either the current or the projected
milk production;

Ability to select one of four manure management systems, depending on the
pathway that the herds’ effluent is managed;

Farm abatement strategy page allowing the user to alter aspects relevant to
the farm such as planting trees, altering the N fertiliser inputs, the effect of
increasing the amount of grain imported onto farm etc;

Herd abatement strategy page allowing the user to alter aspects relevant to
the herd such as diet quality, increasing herd size and milk production and/or
in the adoption of one or several of the three currently available additional
abatement strategies (feeding fats and oils, feeding a condensed tannin and
using an N-inhibitor);
50

Results page graphing each source of GHG emissions/t MS for the baseline
and abatement farm system. The results page contains the total farm GHG
emissions and a breakdown into the four major sources of GHG emissions
(as text and as a pie chart; Figure 11). The results can be printed and the
calculator can be saved in Microsoft Excel for further analysis;

The inclusion of a user defined ad-hoc strategy whereby a percentage
reduction in emissions from the seven sources of GHG emissions (carbon
dioxide, the two methane and the four nitrous oxide sources) can be entered.
This can be used to provide an indication of the level of reduction in
emissions required to meet a farm GHG emission target.

Ability to add new abatement strategies and technologies (e.g. vaccines) as
they become available for the dairy industry to implement.
The DGAS calculator is still based on an inventory type model, where either international
(IPCC) or Australian specific algorithms are used to estimated GHG emissions. There is
still a degree of uncertainty in the actual estimates of GHG emissions from such
calculations (as explained in section 2.8).
While there are a number of best management practices that can be adopted to reduce
emissions from dairying it must be noted that the modelling activities undertaken for this
report have ignored a number of other aspects of farming that need to also be included
in any decision to adopt abatement strategies. These include:

What are the financial implications of improving diet quality, and therefore
reducing methane production, by feeding higher levels of grain?

Does adopting an abatement strategy require new farm infrastructure, such
as a feed pad area and/or a feed-out wagon?

Is there a change to the labour inputs and/or skills requirements to adopt
certain abatement strategies?
All these aspects of farm management need to be considered, not just the implications
of adopting a strategy to reduce GHG emissions.
51
Figure 11. A copy of the results page from the Dairy Greenhouse gas Abatement
Strategies calculator.
52
3. Way forward for the Australian dairy industry
The Australian Federal Government funded Green Paper has already highlighted the
issues of adopting an emissions trading scheme for the agriculture industry. Emissions
are highly variable in response to management practices and climatic conditions.
Nitrous oxide emissions vary geographically and over time, according to rainfall patterns,
soil types, fertiliser application rates and formulation, and farm management practices.
Methane emissions vary according to diet quality, from the highly digestible temperate
pastures of southern Australia to the relatively lower digestible tropical pastures and
crops of northern Australia. These characteristics pose challenges for their inclusion in a
national carbon pollution reduction scheme.
One of the major issues facing the dairy industry will be how are farms’ GHG emissions
reported, whether on a total emission basis or an emission/unit of product basis.
Comparing farm systems based on GHG emissions/t MS allows for an easy comparison
between dairy farm systems. However, there is a risk of assuming that each tonne of
milksolids has resulted in emitting a certain level of GHG emissions, irrespective of farm
management practices, farm location and/or soil type. Two farms that produced the
same level of milk production could result in vastly different GHG emissions.
For
example, taking a dairy farm system from Victoria and relocating it in Queensland (i.e. all
things being equal other than location), resulted in a reduction in GHG emissions/t MS of
over 5%.
Therefore assuming a single factor to convert a per cow or a per milk
production unit to a known GHG emission value can not accurately determine a farms’
GHG emissions.
3.1. Educate and promote best management practices
Adopting current best management practices will always work towards reducing GHG
emissions.
To adopt these practices, farmers need to be better informed of to the
source of GHG emissions and ways that they can assist in reducing emissions. Some
examples of ways that farmers can reduce their farms’ emissions include balancing diets
to maximise digestibility while maintaining the level of crude protein in the diet to within
the optimal range, better managing their fertiliser practices to minimise nitrous oxide
losses and monitoring their irrigation scheduling to reduce excessive irrigating to save on
electricity usage and reduce nitrous oxide emissions.
53
There is already a strong focus on fertiliser best management practices through such
programs as DairySAT and FertCare.
These programs facilitate farmers in
understanding and adopting best management practices that assist in reducing their
farms’ nitrous oxide emissions.
Developing a communications program designed to
highlight the implications of diet quality on methane emissions, will facilitate farmers in
understanding and adopting management practices that will assist in capping and/or
reducing their farms’ methane emissions. A focus on reducing enteric methane losses is
viewed as a priority for the dairy industry as across all farming systems, as enteric
methane is the major source of total farm GHG emissions.
To assist farmers in working towards reducing their farms’ GHG emissions, milk
processing companies and co-operatives could play a major role. Processors want to be
able to trade their products domestically and internationally based on a ‘clean green’
image. As consumers become increasingly aware of the GHG emissions generated with
the production of fresh produce, there could become increasing pressure on milk
processors to validate the GHG emissions associated with their products.
Milk processors are in one of the best positions to assist and educate farmers in the
nutritional requirements of the milk herd.
The use of by-products such as whole
cottonseed, canola meal and/or brewers grain will play an important role in reducing total
farm GHG emissions as these products contain no additional embedded emission
beyond their primary purpose of cotton for clothing, oil for cooking and barley for beer
brewing.
Milk companies could also adopt a GHG emissions target range that needs to be met by
farms to supply milk to their factory. This target range would be linked to a pricing
schedule, where milk supplied with lower GHG emissions/kg milksolids could receive a
price bonus while milk above a target range could receive a price discount.
If a pathway of utilising milk processors to implement a pricing schedule based on GHG
emissions does become operative, care will need to be taken in setting optimal GHG
emission ranges. Emission ranges will need to be set according to the milk factory
location, not a ‘one rule fits all suppliers’.
A single milk processor such as Dairy
Farmers, that accesses milk throughout the country (from the northern tropics of the
54
Atherton Tablelands, through to the temperate regions of New South Wales, Victoria and
South Australia), should expect their suppliers to emit varying levels of GHG emissions
based on the variation in climatic conditions, feeding regimes and soil types found
throughout these regions.
How we audit farms to determine their GHG emissions will need to be considered. The
cost of auditing individual farms, based on the calculators available to us at present,
would be very costly and time-consuming. Methane emissions, and to a lesser extent,
nitrous oxide emissions are very dependant on diet quality. Most farmers would not
have accurate estimates of their diet quality throughout the seasons. If, however, we
assume that we can simplify emissions to a small range (i.e. 12-14 t CO2-e/t MS), and
simply multiply farm production by this factor, then although this simple methodology
would significantly reduce auditing costs, it also removes any feedback to farmers in
regards to how their individual management practices are affecting their farms’ GHG
emissions.
Therefore a balance between accuracy of GHG emissions and efficiency of time and
cost needs to be identified so that farmers are receiving an accurate indication of their
farms’ GHG emissions and any implications this may pose to their price scheduling.
3.2. On-going research requirements
What is clear from the findings of the current modelling of dairy farm systems is that
strategies to significantly reduce GHG emissions need to focus on reducing methane
emissions. Farmers will continue to identify practices for improving the diet quality to
their herd, but this may only result in small reductions in methane emissions.
Researchers need to continue to find ways to reduce the production of methane by
methanogens in the rumen, without jeopardising milk production. Continuation of the
research of developing vaccines that manipulate the rumen microflora to assist in
reducing methane emissions, better understanding and isolating the genetic markers for
stock that naturally have a greater feed conversion efficiency and re-breeding
condensed tannins into forages such as lucerne and sorghums will all assist in reducing
GHG emissions. However, these are all long term strategies which can not be adapted
immediately on farm but need to be implemented over time.
55
There are number of abatement strategies for reducing nitrous oxide that are currently
available which could be potentially adapted on farm immediately. Research has shown
that the use of nitrification inhibitors can reduce nitrous oxide emissions from fertiliser by
up to 60%. However, the effectiveness of N inhibitors is climate and location specific.
As the dairy industry has a vast variation in production systems and soil conditions, there
is a need to better understand the efficacy of this technology under varying soil, location
and climatic conditions.
The Australian dairy industry has traditionally focused its feedbase systems research
efforts on the production and consumption of home grown forage. One of the major
international competitive advantages of the Australian dairy industry is its ability to
produce milk at a low cost due to the production and consumption of high amounts of
home grown forage. However, our modelling has indicated that such systems have a
higher intensity of GHG emissions than TMR type systems currently used in the US and
Europe. If farmers are going to adopt strategies such as a greater reliance on off-farm
grain and forages, they need to be aware of the financial and management implications
of this, above and beyond the GHG emission implications. It is suggested that future
developments of the DGAS calculator could incorporate some economic analysis of
adopting the various abatement strategies.
In addition, an analysis of the how the
profitability of current dairy farming systems will be modified by the costs incurred in a
carbon pollution reduction scheme is of high importance to the industry.
56
4. Conclusion
This report has highlighted several abatement strategies that could be adopted on farm
to reduce GHG emission intensity. However, not all abatement strategies will result in
lowering total farm GHG emissions. If farmers are directly ‘charged’ according to their
total farm GHG emissions, this could result in disadvantaging the industry by deterring
farmers from expanding their businesses. A whole farm analysis of the implications of
adopting strategies, not just the GHG implications, but also aspects such as the financial
and farm infrastructure implications, will be the next crucial step.
Education will hold the key to better inform farmers of ways that they can reduce their
farms’ GHG emissions. Research needs to be maintained and continued into the future
in areas where maximum results can be achieved, especially in better understanding
and isolating the methanogens that produce methane in the rumen. Research also
needs to continue to better formulate emission factors that are more location specific.
Having national or state-based emission factors does not account for differences in farm
management practices, nor does it accurately quantify individual farm GHG emissions.
Individual accounting of GHG emissions from dairy farms into a national accountability
scheme by the adoption of a ‘one rule fits all’ scenario (such as a carbon tax per animal)
will undoubtedly penalise high producing farms that have very little scope to reduce their
current on-farm GHG emissions, while reducing the incentive to improve farm
productivity for those farms that do.
The link between business profitability and environmental stewardship is not a new
concept to the Australian dairy industry. The interrelationship between climate change,
global warming and GHG emissions is potentially one of the largest issues to face the
dairy industry. There are a number of GHG abatement strategies currently available to
the dairy industry and also a number of strategies that should be available in the near
future. Many of the abatement strategies discussed, if adopted, could potentially result
in both economic and environmental improvements. However, there is a need to further
develop our modelling capacity to analyse the interactions between farm profitability and
farm GHG abatement strategies. Armed with this, the industry will be better informed so
that they can view the implications of a carbon pollution reduction scheme as a positive
step forward for the industry, the nation and the world.
57
5. References
Attwood, G., and McSweeney, C. (2008). Methanogen genomics to discover targets for
methane mitigation technologies and options for alternative H2 utilisation in the rumen.
Australian Journal of Experimental Agriculture 48, 28-37.
Australian Bureau of Agricultural and Resources Economics (2007a). Australian Beef
Financial Performance and Production to 2006-07. Australian Beef 07.2 Report.
Australian Bureau of Agricultural and Resources Economics (2007b). Benefits of
Adjustment in Australia’s Sheep Industry. Australia Lamb 07.1 Report.
Australian Bureau of Agricultural and Resources Economics (2007c). Financial
Performance of Farms to 2006-07. Australian Dairy 07.2 Report.
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61
6. Appendices
Appendix 1. Discussion paper
Appendix 2. User manual for the DGAS calculator
Appendix 3. Literature review from the Milestone 3 report on greenhouse gas
abatement strategies
Appendix 4. Details of the life cycle assessment of each of the embedded
calculations
Appendix 5. Overall diet quality calculation for each farming system
Appendix 6. Greenhouse gas emissions for individual abatement strategies
across each of the dairy farming systems
62
Appendix 1. Discussion paper
Whole farm systems analysis of
greenhouse gas emission abatement
strategies for dairy farms.
Discussion paper on the outcomes of the investigation
and analysis into greenhouse gas abatement strategies,
modelling and decision tools for the
Australian dairy industry.
Prepared by:
Karen Christie, Dr Richard Rawnsley and Dr Danny Donaghy
(Tasmanian Institute of Agricultural Research,
University of Tasmania)
August 2008
Contents page
1. Summary ....................................................................................................... 1
2. Background .................................................................................................. 2
Methane ......................................................................................................... 3
Nitrous oxide .................................................................................................. 3
Carbon dioxide............................................................................................... 4
3. Greenhouse gas emissions on dairy farm systems .................................. 5
Defining the baseline farming systems .......................................................... 5
Greenhouse gas emissions for baseline farming systems ............................. 7
Abatement strategies for dairy farm systems ............................................... 10
Impact of adopting abatement strategies ..................................................... 10
4. Way forward for the Australian dairy industry ........................................ 14
Educate and promote best management practices ...................................... 14
Development of a greenhouse gas emission accountability and
abatement tool ............................................................................................. 16
Modelling implications .................................................................................. 18
On-going research requirements ................................................................. 19
5. References .................................................................................................. 21
1. Summary
The following paper examines abatement strategies that could potentially be adopted on
dairy farms to reduce greenhouse gas (GHG) emission intensity (tonnes of carbon
dioxide equivalents per tonne of milksolids; t CO2-e/t MS). Climate change, global
warming and greenhouse gas emissions are potentially some of the largest issues to
face the dairy industry. There are a number of GHG abatement strategies currently
available to the dairy industry and also a number of strategies that should be available in
the near future.
Education will hold the key to better inform farmers of ways that they can reduce their
farm GHG emissions. Research needs to be maintained and continued into the future in
areas where maximum results can be achieved, especially in better understanding and
isolating the methanogens that produce methane in the rumen. Research also needs to
continue to better formulate emission factors that are more location specific. Having
national or state-based emission factors does not account for differences in farm
management practices, nor does it accurately quantify individual farm GHG emissions.
Individual accounting of GHG emissions from dairy farms into a national accountability
scheme by the adoption of a ‘one rule fits all’ scenario (such as a carbon tax per animal)
will undoubtedly penalise high producing farms that have very little scope to reduce their
current on-farm GHG emissions, while reducing the incentive to improve farm
productivity for those farms that do.
Abatement strategies aimed at reducing the intensity of GHG emissions for dairy farm
systems will not necessarily result in lowering total farm GHG emissions. If farmers are
directly ‘charged’ according to their total farm GHG emissions, this could result in
disadvantaging the industry by deterring farmers from expanding their businesses. In
addition, a whole farm analysis of the implications of adopting strategies, not just the
GHG implications, but also aspects such as the financial and farm infrastructure
implications are critical to the analysis.
The link between business profitability and environmental stewardship is not a new
concept to the Australian dairy industry. Many of the abatement strategies discussed, if
adopted, could potentially result in both economic and environmental improvements;
however, there is a need to further develop our modelling capacity to analyse the
interactions between farm profitability and farm GHG abatement strategies. Armed with
this, the industry will be better informed so that they can view the implications of a
carbon pollution reduction scheme as a positive step forward for the industry, the nation
and the world.
1
2. Background
Climate change is a feature of the 21st century. Increases in atmospheric concentration
of greenhouse gases (GHG) has been attributed to changes in global climatic conditions.
The three major gases that are widely accepted as contributing to global warming are
carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Molecule for molecule,
carbon dioxide is a weak GHG in terms of its global warming potential. Compared to
carbon dioxide, on a 100-year timescale, the global warming potential of methane and
nitrous oxide are 21 and 310 times greater, respectively (IPCC 2001). Multiplying the
GHG by its global warming potential converts all three gases to a carbon dioxide
equivalent (CO2-e) to allow for easier comparisons between gas sources.
Australia’s net GHG emissions across all sectors totalled 576 million tonnes of CO 2-e in
the year 2006 (Department of Climate Change 2008). Australia has committed itself to
playing a major role in addressing its contribution to the global issue of climate change
by ratifying the Kyoto Protocol in late 2007. This ratification has committed Australia to
limiting the growth of its GHG emissions to 108% of its 1990 baseline by 2012. While
the major focus in reducing GHG emissions has been placed on the stationary energy,
industrial and transport sectors, the agricultural sector will also be required to implement
their own series of measures to tackle the issue of GHG emissions and climate change.
Based on 2006 figures, agriculture was responsible for 16% of Australia's total GHG
emissions and was estimated to cause 84% of nitrous oxide emissions and 59% of
methane emissions (Department of Climate Change 2008).
Australian dairy farms
contributed approximately 2% (8,870 kt CO2-e) of Australia’s GHG emissions, with the
major factors being methane lost during rumen digestion (enteric) and nitrous oxide lost
from nitrogen fertilisers, animal excreta and soils.
2
Methane
Methane is a by-product of feed digestion in the rumen in a process known as enteric
fermentation.
Micro-organisms called methanogens found in the rumen of ruminant
livestock (cattle, sheep and goats) convert excess hydrogen and carbon dioxide into
methane, which is then excreted predominantly via the mouth, while smaller proportions
are either absorbed into the bloodstream and released through the lungs or passed
through the animal via the intestinal tract.
A small proportion of methane is also
produced with the excretion of manure of dairy cattle.
The emission of methane from livestock represents a direct loss of energy to the
ruminant, as between 4-10% of all ingested energy is excreted as methane (Johnson et
al. 1997). Therefore any process which can reduce the level of methane production in
ruminants should provide more energy to increase production of milk, meat or wool. A
small amount of methane from livestock is also emitted with the decomposition of
manure.
Methane emissions from dairy farm systems can be reduced by improving the
digestibility of the diet, reducing the number of unproductive animals and/or modifying
the rumen outputs by feed additives such as fats and condensed tannins.
Nitrous oxide
Nitrous oxide is released naturally from soils via one of two separate processes, known
as nitrification and denitrification. Anywhere between 20 and 80% of nitrogen applied to
soils (either as fertiliser or animal waste) escapes into the atmosphere without being
utilised by plant growth and production (Peoples et al. 2004), due to variations in
environmental and management conditions. This represents a direct loss of nitrogen
from the farming system, which has environmental and economical consequences.
Nitrous oxide emissions can be reduced by balancing the animal’s diet to minimise
excess nitrogen in their urine and dung. Emissions can also be reduced by minimising
anaerobic soil conditions, reducing soil compaction and/or improving fertiliser
management practices through the use of nitrification inhibitors.
3
Carbon dioxide
Carbon dioxide is released into the atmosphere when carbon-based fossil fuels are
burned to create electricity and by the production and consumption of fuels for farm
vehicles. While electricity usage does not result in carbon dioxide emission at a farm
level, there is an embedded carbon dioxide emission associated with its production.
There is also an emission associated with the production and consumption of fuel such
as diesel. When analysing the whole farm system, these three sources of emissions
from the production and consumption of energy are grouped together as CO 2- energy.
While these emissions have been accounted for at a whole farm system analysis, they
are not likely to be accounted for at a farm systems level in any future carbon pollution
reduction scheme.
4
3. Greenhouse gas emissions on dairy farm systems
Previous research undertaken in Australia had highlighted numerous strategies which
could result in reducing GHG emissions in the order of 20 to 30% from dairy farms.
However, the assessment of the impact of adopting these abatement strategies has
generally not taken into consideration implications across the whole of farm system, or
the potential GHG emissions associated with key farm inputs such as fertilisers, grain
and other feed sources.
This reports details the following:

A quantification of the greenhouse gas emissions (including embedded
emissions from farm inputs such as fertiliser and grain) from three typical
dairy farming systems:

A system based predominantly on pasture, with low levels of
supplementary feed (10-15% of the total diet from supplements)

A system based on high levels of supplementary feed (40-50% of the total
diet from supplements)

A total mixed ration system (zero grazing with all feed supplied in an
enclosed area);

An assessment of the impact of adopting a range of abatement strategies for
these three systems in a whole farm system context;

A discussion of the adoption of GHG abatement implications for the
Australian dairy industry;

An evaluation of currently available modelling tools to estimate whole farm
system GHG emissions, including the development of a new dairy GHG
abatement calculator.
Defining the baseline farming systems
The process adopted to model the low and high supplementary feeding systems was a
farm system where annual milk production totalled 2 million litres or 150 tonne milksolids
(t MS). For the low supplementary feeding system, this was achieved by milking 445
cows, with each cow producing 4,500L/lactation and the diet consisting of 89% pasture
and 11% grain. From here on this farm system is referred to as LSF.
5
For the high supplementary feeding system, this was achieved by one of two methods:

A pasture/grain system where 310 cows produced 6,500L/lactation with the
diet consisting of 56% pasture and 44% grain.
From here on this farm
system is referred to as HSF 1;

A
pasture/grain/maize
silage
system
where
333
cows
produced
6,000L/lactation with the diet consisting of 56% pasture, 26% grain and 18%
maize silage from an off-farm source. From here on this farm system is
referred to as HSF 2.
Two total mixed ration (TMR) dairy systems were modelled based on farm data from
commercially operated TMR dairies. The two systems were:

A traditional TMR farm, with cows housed in a large freestall system with the
diet consisting of 42% forage, conserved both on and off farm, 30% grain and
28% by-products (waste from Manildra Mills, cottonseed meal, molasses and
whole cottonseed). This farm produced 3.8 million litres of 285 t MS per
annum. From here on this farm system is referred to as TMR 1;

A farming system which has converted from a traditional pasture grazing
system to a cropping system due to reduced water allocations, with the diet
consisting of 43% forage conserved on farm, 36% grain and 21% by-products
(canola meal, soybean meal, dried distillers grain and cottonseed meal). This
farm produced 6.4 million litres or 487 t MS per annum. From here on this
farm system is referred to as TMR 2.
To allow easier comparison of the total farm GHG emissions between the farming
systems, the two TMR systems were scaled down to replicate farms that produced 150t
MS/annum (the same as the LSF and HSF systems), by dividing their baseline total farm
GHG emissions by their total MS production and then multiplying by 150. In addition,
the TMR 1 farm agist their replacement stock off farm, thus reducing their GHG
emissions. Reintroducing the replacement stock on farm allowed for all farm systems to
be comparative in terms of herd structure.
6
Greenhouse gas emissions have been reported in two ways. The first was total farm
GHG emissions, as the sum of four sources:

Pre-farm embedded GHG emissions from imported products;

On-farm carbon dioxide;

On-farm methane;

On-farm nitrous oxide.
In addition, a GHG emission intensity figure was calculated by dividing total farm GHG
emissions by 150 t MS.
The second method involved assessing the GHG emissions that would be a direct
liability under an emissions trading scheme. The Australian Federal Government is in
the process of introducing an emissions trading scheme named the Carbon Pollution
Reduction Scheme (CPRS). This CPRS will come into effect in 2010, with agriculture
potentially liable for their on-farm methane and on-farm nitrous oxide emissions from
2015 onwards.
Similarly to the total farm GHG emissions, two figures have been
reported- total farm GHG emissions and GHG emissions intensity. These two figures
have been reported as CPRS farm GHG emissions (total methane and nitrous oxide; t
CO2-e) and CPRS emissions/t MS (t CO2-e/t MS).
Greenhouse gas emissions for baseline farming systems
The total farm GHG emissions varied from 1,836 t CO2-e for the TMR 2 system to 2,623
t CO2-e for the LSF system (Figure 1). As each system produced 150t MS, there was a
corresponding variation in GHG emissions/t MS from 12.3 to 17.5 t CO2-e/t MS for the
TMR2 and LSF systems, respectively (Table 1). While there was variation between the
differing farm systems, analysis of the GHG emissions from the dairy systems indicated
that:

on-farm methane emissions were 48 to 55% of total emissions;

on-farm nitrous oxide emissions were 20 to 26% of total emissions;

pre-farm embedded GHG emissions were 11 to 18% of total emissions;

on-farm carbon dioxide emissions were 6 to 9% of total emissions.
The CPRS farm GHG emissions varied from 1,418 t CO2-e for the TMR 1 system to
2,105 t CO2-e for the LSF system. This equated to a 20 to 26% reduction on total farm
7
GHG emissions. The CPRS emissions/t MS varied from 9.5 to 14.0 t CO 2-e/t MS. Onfarm methane emissions were 65 to 73% of the CPRS farm GHG emissions.
Table 1. Pre-farm, on-farm carbon dioxide, on-farm methane and on-farm nitrous oxide
greenhouse gas emissions, total farm and carbon pollution reduction scheme farm
greenhouse gas emissions, and greenhouse gas emissions and carbon pollution
reduction scheme emissions per tonne milksolids to achieve 150 tonne milksolids per
annum.
LSF
HSF 1
HSF 2
TMR 1
TMR 2
Pre-farm (t CO2-e)
309
363
417
338
256
On-farm carbon dioxide (t CO2-e)
209
159
182
170
106
1,431
1,162
1,232
923
996
674
434
458
495
477
Total farm GHG emissions (t CO2-e)
2,623
2,118
2,290
1,925
1,836
GHG emissions/t MS (t CO2-e/t MS)
17.5
14.0
15.3
12.8
12.2
CPRS farm GHG emissions (t CO2-e)
2,105
1,596
1,690
1,418
1,474
CPRS emissions/t MS (t CO2-e/t MS
14.0
10.6
11.3
9.5
9.8
On-farm methane (t CO2-e)
On-farm nitrous oxide (t CO2-e)
Total farm GHG emissions (t CO2-e)
3000
2500
2000
1500
1000
500
0
LSF
On-farm methane
HSF 1
HSF 2
On-farm nitrous oxide
TMR 1
Pre-farm embedded
TMR 2
On-farm carbon dioxide
Figure 1. Total farm greenhouse gas emissions and the source of greenhouse gas
emissions, expressed as tonne of carbon dioxide equivalents, when achieving 150 tonnes
milksolids per annum for each of the five farming systems under review. Note- under the
carbon pollution reduction scheme, only the on-farm methane and on-farm nitrous oxide
emissions would be a direct liability to farmers.
8
The LSF system emitted between 15 to 23% and 36 to 43% more total farm GHG
compared to the HSF and TMR farm systems, respectively. This was a result of the LSF
system having the greatest comparative herd size. While the pre-farm GHG emission
was comparative to all other systems, the greater herd size resulted in greater electricity
consumption, increasing the on-farm carbon dioxide emissions.
The higher stock
numbers and lower digestibility of the diet resulted in greater methane emissions.
Nitrous oxide emissions were also elevated due to the greater herd size and the
application of significantly more N fertiliser compared to the other systems.
The difference in total farm GHG emissions between the two HSF systems was a result
of replacing some grain with maize silage for the HSF 2 system. All sources of GHG
emissions were greater for the HSF 2 system compared to the HSF 1 system. Pre-farm
GHG emissions was greater, primarily due to the higher emission factor for maize silage
compared to grain (each tonne of maize silage produces 0.371 t CO2-e compared to
0.302 t CO2-e for grain) and because an assumption was made that a 10% ‘wastage’
value of using maize silage was required to replace the grain (i.e. every one tonne of
grain was replaced with 1.1 tonnes of silage).
On-farm carbon dioxide emissions
increased due to extra diesel required to feed out silage to the herd. Diet quality for the
HSF 2 system was also lower due to the maize silage, increasing the methane and
nitrous oxide emissions.
Overall, there was a greater total farm GHG emission for the TMR 1 farm system
compared to the TMR 2 farm system, as a result of greater pre-farm embedded
emissions, on-farm carbon dioxide emissions and nitrous oxide emissions. Pre-farm
embedded emissions were higher due to greater reliance on off-farm feed sources and
fertiliser inputs. The higher fertiliser inputs also contributed to increased on-farm nitrous
oxide emissions.
On-farm carbon dioxide emission was substantially greater as
irrigation took place all year, compared to the TMR 2 system which only irrigated forages
over summer. In addition, the TMR 1 farm milked their herd three times a day, rather
than the typical twice a day, therefore consuming more electricity. However, the on-farm
methane emission was greater for the TMR 2 system as a result of a lower quality diet
compared to the TMR 1 system.
9
Abatement strategies for dairy farm systems
Research undertaken in Australia and New Zealand has identified an array of potential
abatement strategies for dairy farm systems. These are broadly categorised as:

Herd abatement strategies- including reducing the size of the herd, reducing
the replacement rate so fewer heifers require raising, agisting replacement
stock off-farm, increasing milk production per cow, increasing live weight to
increase feed intake and improved feed conversion efficiency resulting in
greater milk production per kg of feed intake;

Feed abatement strategies- including increasing the quality of the diet by
either feeding higher quality feed sources such as grain or increasing the
digestibility of the current feed source through improved management
practices. Strategies also include reducing the amount of enteric methane
produced per kg of feed intake through the use of feed additives including
fats and oils, condensed tannins and ionophores;

Soil abatement strategies- including using a nitrification inhibitor (N-inhibitor)
to reduce nitrous oxide emissions and improved irrigation scheduling to
reduce anaerobic soil conditions which cause increased nitrous oxide
emissions.
Following a review of the potential abatement strategies, the most appropriate strategies
for each system were investigated (refer to Final Report for further details).
Impact of adopting abatement strategies
This paper details the two strategies that resulted in the greatest reduction in GHG
emissions/t MS, compared to the baseline farm system, for all five farm systems (Table
2; refer to Final Report for further details).
10
Table 2. A description of the two selected strategies that resulted in the greatest reduction
in greenhouse gas emissions per tonne of milksolids for each farming system.
System
Abatement strategy (AS)

AS1 - All inclusive; increasing per cow milk production by increasing
cow weight and daily feed intake; use of feed additives; fats and
LSF
condensed tannins; all nitrogen fertiliser applied with a N-inhibitor.

AS 2 - Increasing per cow milk production by increasing the level of
grain feeding.

AS1 - All inclusive; increasing per cow milk production by increasing
cow weight and daily feed intake; use of feed additives; fats and
HSF 1
condensed tannins; all nitrogen fertiliser applied with a N-inhibitor.

AS2 - Increasing per cow milk production by 10%, while reducing
herd size to maintain total farm milk production.

AS1 - All inclusive; increasing per cow milk production by increasing
cow weight and daily feed intake; use of feed additives; fats and
HSF 2
condensed tannins; all nitrogen fertiliser applied with a N-inhibitor.

AS2 - Increasing per cow milk production by increasing cow weight
and the level of feed intake (pasture, grain and silage).

TMR 1

AS1 - Replacement stock agisted off-farm1
AS2 - Milking herd fed a source of condensed tannins year round and
an N-inhibitor fertiliser applied year round
TMR 2

AS1 - Replacement stock agisted off-farm.

AS2 - Increasing per cow milk production by 10%, while reducing
herd size to maintain total farm milk production.
1
Replacement stock remain on-farm to maintain similar herd structure between all systems
Figure 2 shows the total farm GH emissions and GHG emissions/t MS for the baseline
farm system and the two abatement strategies with the greatest reduction compared to
the baseline farm system, for each of the five farming systems.
11
20
GHG emissions (t CO2-e)
2500
16
2000
12
1500
8
1000
4
500
0
GHG emissions (t CO2-e/t MS)
3000
0
B
AS 1 AS 2
LSF
B
AS 1 AS 2
HSF 1
B
AS 1 AS 2
HSF 2
B
AS 1 AS 2
B
TMR 1
AS 1 AS 2
TMR 2
Figure 2. Total on-farm methane and nitrous oxide emissions (t CO 2-e/farm; ▌), total onfarm carbon dioxide and pre-farm embedded emissions (t CO2-e/farm; ▌), carbon pollution
reduction scheme emissions per tonne milksolids (t CO 2-e/t MS; ♦) and total farm
greenhouse gas emissions per tonne milksolids (t CO 2-e/t MS; ♦) for the baseline farm
system (B) and two selected abatement strategies (AS).
While various abatement strategies in isolation have been shown to reduce GHG
emissions by up to 40% from that particular source (e.g. use of N-inhibitors in reducing
nitrous oxide emissions from fertilisers), our modelling has shown that this level of
reduction can't be achieved in terms of a whole farm systems context.
For all farm systems, abatement strategy 1 resulted in reducing total farm GHG
emissions below that achieved for the baseline farm system. This resulted in reducing
GHG emission/t MS by 14 to 22% compared to the corresponding baseline farm system.
For the LSF and the two HSF farm systems, this was due to combining all potential
abatement strategies together to reduce total farm GHG emissions while increasing milk
production. For the two TMR farm systems, this was due to a significant reduction in
total farm GHG emissions with the removal of the replacement stock. As milk production
remained the same, GHG emissions/t MS was significantly reduced.
Adopting the 2nd most effective abatement strategy (refer to AS 2 in Table 2 above)
reduced GHG emissions/t MS by 4 to 14% compared to their corresponding baseline
farm systems. For the HSF 1 and the two TMR farm systems, total farm GHG emissions
12
were also reduced. However, for the LSF and the HSF 2 farm system, total farm GHG
emissions were greater under this strategy than under the baseline farm system.
In the LSF farm system, where the highlighted strategy was feeding more grain which
resulted in more milk production, total farm GHG emissions was 5.4% greater for the
strategy system compared to the baseline system. Grain inputs were increased by
100%, increasing pre-farm GHG emissions by 22%.
The extra feed intake also
increased methane and nitrous oxide emissions by 3%. However, the 22% increase in
milk production achieved with this strategy diluted the extra total farm GHG emission
such that GHG emissions/t MS was 13.8% lower for the strategy compared to the
baseline LSF system.
In the HSF 2 farm system, where the highlighted strategy was increasing herd weight to
increase daily feed intakes and therefore result in greater milk production, total farm
GHG emissions was 5.9% greater for the strategy system compared to the baseline
system. The greater feed intake was achieved by increasing intakes from pasture, grain
and maize silage, therefore increasing pre-farm GHG emissions by 5.9%. The extra
feed intake also increased methane and nitrous oxide emissions by 6.5%. However, the
17% increase in milk production achieved with this strategy diluted the extra total farm
GHG emissions such that GHG emissions/t MS was 9.2% lower for the strategy
compared to the baseline HSF 2 system.
Agisting replacement stock off-farm resulted in the greatest reduction in GHG
emissions/t MS for the two TMR farm systems. In reality, this strategy would result in
the greatest reduction in emissions for all farming systems. However, not all farms can
agist their replacement stock off-farm.
If stock were agisted off-farm, they are still
emitting GHG, so does the cost of this emission lay with the farm that owns the stock or
the farm that is raising the stock?
13
4. Way forward for the Australian dairy industry
The Australian Federal Government funded Green Paper has already highlighted the
issues of adopting an emissions trading scheme for the agricultural industry. Emissions
are highly variable in response to management practices and climatic conditions.
Nitrous oxide emissions vary geographically and over time, according to rainfall patterns,
soil types, fertiliser application rates and formulation, and farm management practices.
Methane emissions vary according to diet quality, from the highly digestible temperate
pastures of southern Australia to the relatively lower digestible tropical pastures and
crops of northern Australia. These characteristics pose challenges for their inclusion in
the national emissions trading scheme.
One of the major issues facing the dairy industry will be how farms’ GHG emissions are
reported, whether on a total emission basis or an emission/unit of product basis.
Comparing farm systems based on GHG emissions/t MS allows for an easy comparison
between dairy farm systems. However, there is a risk of assuming that each tonne of
milksolids has resulted in emitting a certain level of GHG emissions, irrespective of farm
management practices, farm location and/or soil type. Two farms that produced the
same level of milk production could result in vastly different GHG emissions.
For
example, taking a farm system from Victoria and placing it in Queensland (i.e. all things
being equal other than location), resulted in a reduction in GHG emissions/t MS of over
5%. Therefore assuming a single factor to convert a per cow or a per milk production
unit to a known GHG emission value can not accurately determine a farms’ GHG
emission.
Educate and promote best management practices
Adopting current best management practices will always work towards reducing GHG
emissions and to adopt these practices, farmers need to be better informed of the
source of GHG emissions and ways that they can assist in reducing emissions. Some
examples of ways that farmers can reduce their farms’ emissions include balancing diets
to maximise digestibility while maintaining the level of crude protein in the diet to within
the optimal range, better managing their fertiliser practices to minimise nitrous oxide
14
losses, and monitoring their irrigation scheduling to reduce excessive irrigating to save
on electricity usage and reduce nitrous oxide emissions.
There is already a strong focus on fertiliser best management practices through such
programs as DairySAT and FertCare.
These programs facilitate farmers in
understanding and adopting best management practices that assist in reducing their
farms’ nitrous oxide emissions.
Developing a communications program designed to
highlight the implications of diet quality on methane emissions, will facilitate farmers in
understanding and adopting management practices that will assist in capping and/or
reducing their farms’ methane emissions. A focus on reducing enteric methane losses is
viewed as a priority for the dairy industry as across all farming systems, enteric methane
is the major source of total farm GHG emissions.
To assist farmers in working towards reducing their farms’ GHG emissions, milk
processing companies and co-operatives could play a major role. Processors want to be
able to trade their products domestically and internationally based on a ‘clean green’
image. As consumers become increasingly aware of the GHG emissions generated with
the production of fresh produce, there could become increasing pressure on milk
processors to validate the GHG emissions associated with their products.
Milk processors are in one of the best positions to assist and educate farmers in the
nutritional requirements of their herd.
The use of ‘by-products’ such as whole
cottonseed, canola meal and/or brewers grain will play an important role in reducing total
farm GHG emissions as these products contain no additional embedded emission
beyond their primary purpose of cotton for clothing, oil for cooking and barley for beer
brewing.
Milk companies could also adopt a GHG emissions target range that needs to be met by
farms to supply milk to their factory. This target range would be linked to a pricing
schedule, where milk supplied with lower GHG emissions/kg milksolids could receive a
price bonus while milk above a target range could receive a price discount. Adopting
this practice of price scheduling according to GHG emissions does carry with it some
issues.
For example, seasonal variation in pasture quality over summer, especially
under non-irrigated conditions, would be likely to result in lowering pasture digestibility,
15
resulting in an increase in methane production. What degree of flexibility would a price
schedule need to have to be able to accurately reflect the effect of varying farm
management conditions on GHG emissions?
If a pathway of utilising milk processors to implement a pricing schedule based on GHG
emissions does become operative, care will need to be taken in setting optimal GHG
emission ranges. Emission ranges will need to be set according to the milk factory
location, not a ‘one rule fits all suppliers’.
A single milk processor such as Dairy
Farmers, that accesses milk throughout the country (from the northern tropics of the
Atherton Tablelands, through to the temperate regions of New South Wales, Victoria and
South Australia), should expect their suppliers to emit varying levels of GHG emissions
based on the variation in climatic conditions, feeding regimes and soil types found
throughout these regions.
How we audit farms to determine their GHG emissions will need to be considered. The
cost of auditing individual farms, based on the calculators available to us at present,
would be very costly and time-consuming. Methane emissions, and to a lesser extent,
nitrous oxide emissions are very dependant on diet quality. Most farmers would not
have accurate estimates of their diet quality throughout the seasons. If, however, we
assume that we can simplify emissions to a small range (i.e. 12-14 t CO2-e/t MS), and
simply multiply farm production by this factor, then although this simple auditing
methodology would significantly reduce auditing costs, it also removes any feedback to
farmers in regards to how their individual management practices are affecting their
farms’ GHG emissions.
Therefore a balance between accuracy of GHG emissions and efficiency of time and
cost needs to be identified so that farmers are receiving an accurate indication of their
farms’ GHG emissions and any implications this may pose to their price scheduling.
Development of a greenhouse gas emission accountability and abatement tool
There is an array of tools that have or are currently being developed to assist agricultural
industries in accounting for GHG emissions.
A national carbon accounting system
model is available, with ongoing model developments, to account for GHG emissions
16
across a range of agricultural industries (e.g. forestry, cropping). However, the current
version of this model is not suitable for to account for GHG emissions from dairy farms
system due to the omission of enteric methane estimates from livestock.
Greenhouse gas accounting tools relevant to the dairy industry include OVERSEER,
DairyMod and the Dairy Greenhouse Framework calculator. One major issue with all of
these models is that they are not dynamic mechanistic models, but inventory type
models, based on equations and algorithms where ‘one factor fits all situations’ is
generally accepted. For example, in OVERSEER it is assumed that each kilogram of
dry matter intake of good quality pasture would result in an emission of 26.5 g of
methane (Wheeler et al. 2008).
While each of the named models exhibited some
advantages over the others, the most appropriate model to estimate GHG emissions
was the Dairy Greenhouse Framework calculator, developed by Dr R Eckard, R Hegarty
and G Thomas. The Dairy Greenhouse Framework calculator uses Intergovernmental
Panel on Climate Change (IPCC) equations with emission factors specific to Australian
conditions. It is from the Dairy Greenhouse Framework calculator model that a new
calculator, the Dairy Greenhouse gas Abatement strategies (DGAS) calculator has been
developed:
The major advancements and advantages of the DGAS calculator over the above tools
are:

Ability to determine the GHG emissions associated with importing products
such as grain and fertiliser;

Ability to model a baseline farm system and then select one or a combination
of abatement strategies to compare outcomes;

Ability to define and calculate seasonal variation in diet quality for the milking
herd;

Inclusion of the updated methodology to determine indirect nitrous oxide
emissions from leaching/runoff and volatilisation;

Ability to add new abatement strategies and technologies (e.g. vaccine) as
they become available for the dairy industry to implement.
17
A copy of the results page of the DGAS calculator can be seen in Figure 3, showing a
reduction of 11.4 % in total farm GHG emissions/t MS by adopting an abatement
strategy.
Greenhouse Gas Abatement Strategy Comparison
CPRS liability
Total farm
Tree plantings
N2O - Indirect
N2O - Dung, Urine & Spread
N2O - N Fertiliser
N2O - Effluent ponds
CH4 - Effluent ponds
CH4 - Enteric
CO2 -Energy
Other feed sources
Grain
Herbicide
Fertiliser
Baseline
Strategy
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
tonnes C02 eqiv. / tonne Milksolids
Fertiliser
Herbicide
Grain
Other feed
sources
CO2 Energy
CH4 Enteric
CH4 Effluent
ponds
N2O Effluent
ponds
N2O - N
Fertiliser
N2O Dung, Urine
& Spread
N2O Indirect
Tree
plantings
Total farm
CPRS
liability
Baseline
1.5
0.0
0.4
0.0
1.5
9.3
0.2
0.0
0.9
1.2
2.3
0.0
17.5
14.0
Strategy
1.3
0.0
0.7
0.0
1.2
8.3
0.2
0.0
0.8
1.1
1.9
0.0
15.5
12.3
18.2%
18.2%
-63.2%
0.0%
18.2%
10.9%
11.0%
13.3%
18.2%
13.2%
15.5%
0.0%
11.4%
12.3%
% change
Figure 3. Results graph from the Dairy Greenhouse gas Abatement Strategy calculator
showing individual sources of greenhouse gas emissions per tonne of milksolids for a
baseline system and an abatement strategy system.
Modelling implications
The DGAS calculator is still based on an inventory-type model, where either international
or Australian-specific algorithms are used to estimate GHG emissions.
There is a
degree of uncertainty in the actual estimates of GHG emissions from such calculations.
For example, calculating the amount of nitrous oxide lost with leaching and runoff
assumes that the fraction of fertiliser available for leaching and runoff can be categorised
along state borders.
For dryland pastures, there is an assumption of an emission
fraction of 0.991 in Tasmania, while in Queensland, the fraction is 0.128 (i.e. for every
kilogram of N fertiliser, potentially up to 0.991 kg is available to be leached in Tasmania
compared to 0.128 kg in Queensland). These fractions do not take into consideration
rainfall events, soil type or management practices.
18
Using a ‘one factor fits all situations’ does not recognise that farmers who are already
managing their fertiliser inputs using current best management practices, such as
applying a maximum of 40-50 kg N/ha per application, not applying when soils are
waterlogged or when a significant rainfall event is expected in the immediate future, will
have already reduced their nitrous oxide emissions.
An inventory-type calculator is
therefore often limited in its ability to determine the impact of varying management
practices, not only between farms but also within a farm over seasons or years.
This paper has highlighted a number of best management practices that could be
adopted to reduce emissions from dairying.
However, it must be noted that the
modelling activities undertaken for this paper have ignored a number of other aspects of
farming that need to also be included in any decision to adopt abatement strategies.
These include:

What are the financial implications of improving diet quality, and therefore
reducing methane production, by feeding higher levels of grain?

Does adopting an abatement strategy require new farm infrastructure, such
as a feed pad area and/or a feed-out wagon?

Is there a change to the labour inputs and/or skills requirements to adopt
certain abatement strategies?
On-going research requirements
What is clear from the findings of the current modelling of dairy farm systems is that
strategies to significantly reduce GHG emissions need to focus on reducing methane
emissions. Farmers will continue to identify practices for improving the diet quality to
their herd, but this amy only result in small reductions in methane emissions.
Researchers need to continue to find ways to reduce the production of methane by
methanogens in the rumen, without jeopardising milk production. Continuation of the
research into developing vaccines that manipulate the rumen microflora to assist in
reducing methane emissions, better understanding and isolating the genetic markers for
stock that naturally have a greater feed conversion efficiency and re-breeding
condensed tannins into forages such as lucerne and sorghums will all assist in reducing
GHG emissions. However, these are all long term strategies which can not be adapted
immediately on farm but need to be implemented over time.
19
There are number of abatement strategies for reducing nitrous oxide that are currently
available which could be potentially adapted on farm immediately. Research has shown
that the use of nitrification inhibitors can reduce nitrous oxide emissions from fertiliser by
up to 60%. However, the effectiveness of N-inhibitors are climate and location specific.
As the dairy industry has a vast variation in production systems and soil conditions, there
is a need to better understand the efficacy of this technology under varying soil, location
and climatic conditions.
Condensed tannins have been shown to reduce methane and nitrous oxide emissions.
Research in Australia and overseas has explored the benefits of feeding a source of
condensed tannin, using a range of pasture species (birdsfoot trefoil, chicory, sulla and
lotus; refer to Final Report Appendix 3 for more detail). However, the herbage quality of
these species may be lower compared to the more traditional pasture species. This,
along with greater difficulty in management, may provide a barrier to farmers integrating
these species into their pastures.
Plant breeders in the USA are using genomic
techniques to improve the condensed tannin concentrations in species which
traditionally used to contain tannins, but have over time reduced these to minimal
concentrations (e.g. lucerne and sorghum). Once these condensed tannin rich species
are available to farmers, this will provide an abatement option that may be readily
adopted by farmers.
The Australian dairy industry has traditionally focused its feedbase systems research
efforts on the production and consumption of home grown forage. One of the major
international competitive advantages of the Australian dairy industry is its ability to
produce milk at a low cost due to the production and consumption of high amounts of
home grown forage. However, our modelling has indicated that such systems have a
higher intensity of GHG emissions than systems currently used in the US and Europe. If
farmers are going to adopt strategies such as a greater reliance on off-farm grain and
forages, they need to be aware of the financial and management implications of this,
above and beyond the GHG emission implications.
It is suggested that future
developments of the DGAS calculator could incorporate some economic analysis of
adopting the various abatement strategies.
In addition, an analysis of how the
profitability of current dairy farming systems will be modified by the costs incurred in a
carbon pollution reduction scheme is of high importance to the industry.
20
5. References
Australian Department of Climate Change (2008). Australian Greenhouse Emissions
Information System (sourced 10/7/2008;
http://www.ageis.greenhouse.gov.au/GGIDMUserFunc/QueryModel/Ext_QueryModelRe
sults.asp#resultStartMarker).
Intergovernmental Panel on Climate Change (IPCC; 2001). The Scientific Basis
Technical Summary (Cambridge University Press, UK).
Johnson, DE., Ward, GM., and Bernal, G. (1997). Biotechnology mitigating the
environmental effects of dairying; greenhouse gas emissions. In ‘Milk composition,
production and biotechnology’, pp 497-511. (Ed RAS Welch; CAB International, USA).
Peoples, MB., Boyer, EW., Goulding, KWT., Heffer, P., Ochwoh, VA., Vanlauwe, B.,
Wood, S., Yagi, K., and van Cleemput, O. (2004). Pathways of Nitrogen Loss and Their
Impacts on Human Health and the Environment. In ‘Agriculture and the Nitrogen Cycle’),
pp 53-70. (Eds. AR Mosier, JK Syers, and JR Freney; Island Press, USA.
Wheeler, DM., Ledgard, SF., and de Klien, CAM. (2008). Using the OVERSEER nutrient
budget model to estimate on-farm greenhouse gas emissions. Australian Journal of
Experimental Agriculture 48, 99-103.
21
Appendix 2. User manual for the DGAS calculator
Dairy Greenhouse gas Abatement
Strategy calculator
(DGAS)
User Manual
Version DGASv1.0
CONTENTS PAGE
Purpose of Software ........................................................................................................... 1
System Requirements ........................................................................................................ 1
Structure of Software .......................................................................................................... 2
Navigation......................................................................................................................... 2
Form Inputs and Controls.................................................................................................... 5
Farm Inputs ............................................................................................................................. 6
Herd Inputs ............................................................................................................................. 9
Additional Abatement Strategies .......................................................................................... 15
Results .................................................................................................................................. 16
Workbook View ............................................................................................................... 20
WorkSheets .......................................................................................................................... 22
Acknowledgements and Licensing ..................................................................................... 22
The Dairy Greenhouse gas Abatement System (DGAS) has been developed by the
Tasmanian Institute of Agricultural Research (TIAR) to address the greenhouse gas
emissions concerns of dairy farmers. It draws upon more general calculators and
incorporates the most recent scientific knowledge in its modelling. This manual refers to
DGAS version 1.0, August 2008. A full discussion of the modelling is beyond the scope
of this Manual and can be found in the documentation for the DGAS project. The model
is constructed as a Microsoft Excel Workbook and incorporates MSForms for ease of
use.
Purpose of Software
DGAS software is intended to give the dairy farmer an understanding of the greenhouse
gases emitted from their enterprise, both in absolute terms and relative to milksolids
produced. The gases, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are
expressed in tonnes of CO2 equivalent. These are then totalled and compared to the
tonnes of milksolids produced by the enterprise.
The software is specifically engineered to enable the farmer to vary the key inputs to the
enterprise and compare the effects of changes on emission levels. Both baseline and a
strategy data are processed and the outputs graphed for easy comparison. A report of
inputs and results can be printed out or saved as an MSExcel spreadsheet.
Other users are expected to include researchers and consultants.
System Requirements
The system is constructed using the Microsoft Office environment and has been tested
using Excel 2000 and Excel 2003. The libraries used in the programming of the interface
forms are compatible with Office 97 and 2007. All development has been done on Intelbased machines running Microsoft Windows XP. The software has not been tested on
other operating systems or hardware, but should run on systems that support Microsoft
Office 97 or later.
The security settings of Excel (Office2000: Tools/Macro/Security… or Office2003:
Tools/Options/Security…) should be set to Medium. When you open the DGAS Excel
workbook you will be asked whether or not to enable macros. You should do so (“Enable
1
Macros”), for the interface forms to run. After a brief pause, the first input form will
appear and the spreadsheet will be minimised.
To exit the system, the Excel workbook must be closed. At this point any changes you
have made can be saved. If you do not wish to overwrite previous data and still wish to
save current data, then use “SaveAs” and save as a different DGAS Excel workbook.
Structure of Software
The software has, at its core, 13 “worksheets” in an MSExcel “workbook” and five
“userforms”. The 13 “worksheets” sheets are the functional components of the software
(Figure 1.). Four userforms are devoted to accepting the Baseline and Strategy inputs
and one is used to access the results.
Inputs
Calculator
Outpu
Report
Strat
Baselin
ts
e
egy
Farm
Results
Herd
Figure 1. The functional operation of DGAS. Baseline and strategy inputs are passed to
the calculator and to the results and report pages. The calculator applies transformations
before passing that data to the results page. Results are included in the report.
Navigation
The software is designed to take the user through a specific path of data entry via 5
userforms. The user is asked to enter details of the current farm practices first. These
are divided into Farm Inputs (the first form to open when the user chooses “Enable
Macros”) and Herd Information. The current farming practice data is referred to as the
“Baseline” scenario and is associated with green navigation buttons (see Figure 2.).
2
Figure 2. The first data input form – enter the current practices for the farm here. To enter
the current herd information, click the green button.
Having entered the herd information, it is possible to go back and edit the farm inputs or
navigate to the Results form, associated with red navigation buttons. You can view the
Results and the Herd Information at the same time and edit the herd information, or
navigate back to Farm Inputs from there. The “Back” button simply closes Results
(Figure 3.).
The user is then able to enter a speculative scenario referred to as a “Strategy” or
“Abatement Strategy” for both the farm inputs and herd information. These are
associated with pink navigation buttons. The “Farm Strategy” button closes Results and
takes the user to the Farm Input Abatement Strategy form. From there it is possible to
move to the Herd Abatement Strategy form and then back to the Results form where
output from both scenarios can be compared and records obtained.
3
Soil based strategies
BASELINE
FARM
Soil based strategies
HERD
FARM
HERDpotential
10-20%
RESUL
RESUL
TS
TS
Figure 3. Navigation sequence of the userforms. Baseline information is entered before the
user can enter an abatement strategy. Results are viewed after each scenario. The results
form permits navigation between scenarios.
The User is able to navigate from the forms to the Excel workbook by clicking the
“EXCEL” navigation button found on the Farm Input, Farm Inputs Abatement Strategy
and Results forms.
The User is also able to navigate from the Excel workbook to the baseline Farm Input
form by either selecting the RESTART button on the “Backdrop” spreadsheet or by
launching the Auto_Open macro. From the “Tools” menu select “Macro../Macros”. The
Auto_Open macro should be highlighted. If not, select it and click the “Run” button
(Figure 4.).
4
Figure 4. Start the userforms from the Excel workbook by going to “Tools/Macro/Macros”
Choose “Auto_Open” then click “Run”.
Form Inputs and Controls
Both the baseline and the strategy Farm Input forms have identical data entry fields
(Figure 5.), with only a small difference in the two Herd Input forms. The baseline Herd
Information form allows the user to copy baseline data to the strategy forms as a
convenience. The Herd Information Abatement Strategy form allows the addition of fixed
abatement strategies as well as altering herd and diet information.
5
Farm Inputs
Figure 5. The Farm Input Strategy form. All text input is selected from dropdown lists. The
only data entered using the keyboard is numeric. Data is logically grouped and can be
navigated using both tab and enter keys.
Both the Farm Input and Farm Input Abatement Strategy userforms are similar in layout.
The “pre-farm” emissions such as those from electricity generation, fertiliser and
chemical manufacturing, feed inputs and diesel usage are calculated from the inputs
entered on this form.
Validation
A series of dropdown lists allow the user to select options that require text. All other data
requires the user to enter numbers. Text characters cannot be entered (Figure 6.).
6
Figure 6. Fields requiring numbers will not accept text characters such as the lowercase
“L” shown. When the user clicks OK on the error message, the field is reset to zero.
If the user moves to a new form without adding all necessary data, they will be allowed
to move, but will be shown a list of the missing data (Figure 7.). This warning is only
generated if the user has filled in at least one of the essential fields. Otherwise the user
is assumed to have reset the form.
Figure 7. Leaving the farm input baseline and strategy forms will raise a warning if
essential data is omitted.
Tips and help
Where appropriate, tips for data entry (such as unit conversions and expansion of
abbreviations) will appear if the mouse hovers over an input field or label. More
extensive assistance is also provided where complex decisions are required and can be
obtained by clicking on the blue “Help” beside complex fields (Figure 8.).
7
Figure 8. Help for selecting the Manure Management System from the dropdown list.
Reset button
If you wish to clear data from the entire form, click the “RESET” button. The user will be
warned and able to cancel the action before data is deleted (Figure 9.).
Figure 9. The user has an opportunity to reconsider when clicking the reset button.
Excel button
The “EXCEL” button allows the user to close the form interface and open the MSExcel
workbook. All the usual menu items, formulas, format options, etc are available to the
user. The sheets may be “protected” but can be unprotected via the Tools/Protection/
menu options.
WARNING: Changes to the workbook may destroy the calculator!
8
The calculator is highly dependent upon the values in specific cell locations. Even
inserting or deleting a row is likely to cause damage that will be difficult to repair. It is
best to experiment with a copy of the file first before making any long term alterations.
The userforms have similar dependencies since data must be recorded in specific
locations.
Navigation to other forms
The baseline farm input form has a green button allowing the user to navigate to the
second input page where herd information must be entered. A similar arrangement
exists for the abatement strategy input form, with the navigation button being pink and
leading to the Herd Information Abatement Strategy form (Figure 10.).
Herd Inputs
Figure 10. The Herd Abatement Strategy page allows for the inclusion of three fixed
greenhouse gas abatement strategies: feeding fats and/or tannins and using a Nitrification
inhibitor. These strategies can be used together by checking the appropriate boxes.
9
Milk production details are needed to determine the level of energy from the diet that is
available for methane production. Herd details are segregated into 6 major classes. If
your farming system is a seasonal or split calving system, enter total number of milking
cows in the stock class milking section. If however, you milk year round with cows
calving each month, enter the average number of dry cows in the dry cow stock class.
For the Heifers and Bulls less than one year old (< 1 y.o.) and greater than one year old
(> 1 y.o.), enter their average weight and weight gain at the half way point of each
category. For example, what is the average weight of the heifers at 6 months of age,
enter this in the <1 y.o. section. Generally heifers at 6 months of age will weigh
approximately 150 kg while at 18 moths they will weight approximately 400-425 kg.
Average daily weight gain is generally 0.7-1.0 kg/day.
Five specific feed types are considered and a sixth category can be used to incorporate
“Other” feed types. If you are feeding two types of silage but no hay, the second silage
figures can be entered in the hay section to determine the diets’ digestibility and crude
protein %.
All feed data needs to be entered on a dry matter basis, by converting all wet weights to
dry weights. For example, grain is generally 90% dry and 10% moisture. If you feed 5
kg of grain (as fed), multiply this by 0.9 to get 4.5 kg DM. Likewise, if you feed 3 kg of
silage (as fed) that is 33% dry, then by multiplying 3 kg by 0.33, you are feeding 1 kg DM
of silage. Average dry matter % for an array of feed sources can be seen in Table 1 at
the end of this manual.
Entering the daily intake of each feed source with their corresponding digestibility and
crude protein % is essential to determine seasonal diet quality figures for the milking
herd. These are the basis for determining methane and nitrous oxide emissions.
Average digestibility and crude protein % figures for an array of feed sources can be
seen in Table 1 at the end of this manual.
10
Calculation of feed intake
Labels below the herd details data block (Figure 10.) automatically estimate the optimal
quantity of daily feed intake required for each class of animal to achieve their daily milk
production and/or liveweight gain entered on the form. This is intended as a convenience
for the user, so that the quantities of feed they enter in the feed data block are realistic.
It also assists if one abatement strategy is increasing milk production. The user is able
to ascertain the level of feed intake required to achieve this increase in milk production
and therefore increase feed intakes accordingly.
The feed intake calculation assumes an average feed digestibility of 75% and will not be
shown until all three figures (livestock numbers, weight and daily weight gain) are
entered. For the milkers, the average daily milk production figure also needs to be
entered before the intake calculation will be displayed.
Navigation of Nutrition Details
The milkers are treated differently to the other stock in terms of nutritional data. As
variation in diet quality has a significant effect on methane production, and milkers are
the largest stock class in terms of numbers, we have segregated the milkers diet into the
4 seasons. For all other classes, an average diet quality for an annual basis is required
(Figure 11.).
Figure 11. Herd Data entry for nutrition of animals associated with dairying only. Users
enter data for six feed types, on a seasonal basis for milkers and annually for the other 5
stock classes.
11
The Cattle Type field and the Feed Period field can be seen on the left of Figure 10.
These fields are fixed lists. Below these fields are navigation buttons that allow the user
to loop through the lists in either direction. The fields can only be changed via these
navigation buttons. The possible combinations are:









Stock Class Milkers / Spring
Stock Class Milkers / Summer
Stock Class Milkers / Autumn
Stock Class Milkers / Winter
Dry Cows / Annual
Heifers <1 y.o. / Annual
Heifers >1 y.o. / Annual
Bulls < 1 y.o. / Annual
Bulls > 1 y.o. / Annual
The user should enter feed data where appropriate. Repeat data for different seasons if
this matches the farm practice for milkers. Omit data if not applicable (if there are no
young bulls on the farm, for example).
Display of dietary summary
Summary figures of the actual feed for the current cattle Type/Time Period are displayed
at the bottom of the Nutrition Details entry fields (Figure 12.). This is a convenience for
the user, so that actual levels can be compared with recommended levels calculated in
the herd details section at the top of the form.
12
Figure 12. Nutrition summary details are displayed for each Cattle Type / Feed period.
Copy baseline data
Once farm and herd data is entered on their corresponding forms, all entered data can
be transferred to their corresponding abatement forms. This is achieved by clicking on
the “Copy Data to Strategy” button, found only on the Herd Information (baseline) form
(Figure 13). This reduces the re-entry of the same data needed on the abatement
strategy pages. When the user navigates to the strategy forms, they need only edit the
particular inputs they wish to change.
13
Figure 13. “Copy Data to Strategy” Button. The Herd Information form has a button for
simplifying the process of entering data to create a speculative abatement scenario.
Navigation to other forms
Once the Herd information has been entered, the software sequence permits navigation
to a results form which displays the emissions that result from either the baseline
scenario or the baseline and strategy scenarios. A red “Results” or “Strategy Results”
button can be found at the bottom of the Herd input form, depending on which of the
herd forms the user is working.
A second button is also available to enable the user to move back to either the Farm
Inputs form (the green “Farm Inputs” button) or Farm Inputs Abatement Strategy form
(the pink “Farm Inputs Strategy” button). Figure 10 shows the latter arrangement.
Navigation section and Figure 3 explain the underlying flow of the software in more
detail.
Reset button
Both herd forms have a reset button to allow the user to clear data from the entire form.
In the case of the Herd Information Abatement Strategy form, this will also clear the
section for “Additional Abatement Strategies”.
14
Additional Abatement Strategies
Three additional abatement strategies have been included on the Herd Information
Abatement Strategy form. The user can select to feed Fats and Oils, Tannins and/or use
a Nitrification Inhibitor to reduce emissions. The three approaches can be used
concurrently (Figure 14.).
Figure 14. Additional strategies can be selected for reducing Greenhouse gas emissions.
Help is available to assist with each of these.
Fats and oils supplementation
Feeding fats and oil can reduce methane emissions. Only select this option if you know
the fat content of the diet. Cows can be fed fats to a maximum of 6-7% of their diet. All
feeds contain fats so by calculating the fat content of the diet, you can determine if this
abatement strategy is suitable for your farming system.
For each 1% additional fat in the diet, there is a 5.6% reduction in methane. Therefore if
your herd’s current summer diet contains 3% fat, you could safely feed another 3%, with
a 16.8% reduction in methane. Type 16.8 into the summer box and add the quantity and
quality data of the fat into the summer nutritional details.
Generally there is some scope to feed fats and oils over summer and autumn periods,
especially under dryland conditions.
Seek nutritional advice if you wish to adopt this abatement strategy, as feeding fats
above 6-7% will decrease feed intakes and milk production.
15
Tannins supplementation
Tannins bind proteins so they are better digested. This assists in reducing methane and
alters the nitrogen content of dung and urine. Selecting tannins will reduce methane by
10%, reduce urine N output by 33% and increase dung N output by 50%.
Nitrification Inhibitor
Nitrification Inhibitors can reduce the emission of nitrous oxide from the application of
nitrogen fertilisers. Selecting the default strategy calculates a 40% reduction in nitrous
oxide emissions. The User may select the default 40% reduction figure or can define
their own reduction potential figure.
Results
The graphs shown on the Results form are images copied from the ResultsSheet in the
workbook (Figure 15.). Each time you make a change and return to the Results form this
image is updated. A refresh button is provided so that you can re-assure yourself that
you are looking at the most up-to-date results from the calculator. If the results are not
what you expect, you can return to the baseline Farm Inputs form by selecting the green
box or to the Herd Abatement Strategy form by selecting the back button, and re-check
your data entry.
16
Figure 15. The results form shows the comparison of baseline and strategy greenhouse
gas emissions. A summary of these results together with the two input scenarios can be
printed or saved as a separate file from this form.
Bar graph
The bar graph shows the contributing factors and total emissions for both the baseline
farm system and for an abatement strategy system. The units of measurement are
tonnes of carbon dioxide (CO2) equivalent per tonne of milksolids produced by the farm.
The contribution of tree plantings in sequestering CO2 is subtracted from the total
emissions from the farm, but is shown on the graph as being a positive value. This is
17
only for formatting reasons and has been identified on the axis label as “Tree Plantings
(-ve)”.
The top two bars represent total farm emissions and emissions under an emissions
trading scheme. Total farm emission is the sum of pre-farm embedded emissions, onfarm carbon dioxide emissions, on-farm methane emissions and on-farm nitrous oxide
emissions minus any tree planting sequestration.
At the time of developing this model, a Carbon Pollution Reduction Scheme (CPRS) was
in the process of being introduced and a decision on the inclusion of agricultural in a
CPRS will not be made until 2013. The CPRS liability used in this calculator is the sum
of on-farm methane and on-farm nitrous oxide emissions minus any tree planting
sequestration. Which emissions will be accounted for under a CPRS for dairy farms is
currently unknown. We have used the above calculation as a guide only.
The data table below the bar graph indicates the actual values of each contributor and
the percentage change in emissions achieved adopting an abatement strategy for each
contributor.
Pie chart
The Pie charts (one for the baseline and one for the strategy) show the proportional
contributions of the pre-farm embedded emissions and the three on-farm gases
associated with the enterprise.
Absolute values list
The absolute tonnage of greenhouse gases for the farm (as CO2 equivalents) are listed
in two tables between the pie charts.
Navigation to other forms
The Results form will allow the user to navigate between scenarios, with the colour and
caption of the navigation button dependent upon the form from which the user has come.
18
If the user has come from the Herd Information form (baseline section) then the button
will be pink and take the user to the beginning of the strategy section (“Strategy Inputs”).
If the user has come from the Herd Information Abatement Strategy form, then the
button will be green and entitled “Baseline Inputs” (Figure 15.).
When the user navigates to the Results form, the previous form is not closed, for
convenience, although it is hidden behind the Results form. The user can work back and
forth between these two windows, or use the “Back” button to close the Results form,
and then use the Results button to return.
The “EXCEL” button closes all forms and maximizes the DGAS Workbook. This is the
recommended way to exit the forms as it makes all worksheets in the workbook visible.
Print results
The “PRINT” button will compile a report on the “PrintSheet” worksheet and send a print
instruction to the default printer. It uses the print setup and print configurations of the
Excel program running the DGAS file, and thus of the computer running Excel. The
report is set out to fit into 5 or 6 landscape A4 pages. The user is able to reconfigure all
of this by altering the layout of the “PrintSheet” worksheet.
WARNING: It is suggested that the user only alter the formatting if they absolutely must
and that they do so only on a copy of the file. The report draws on all the input values
as well as a copy of the actual bar graph and pie chart. In particular, the nutrition details
for the herd are copied to specific blocks of cells at the end of the sheet. Empty data is
omitted.
Save results
The “SAVE RESULTS” button allows the user to save the report worksheet “PrintSheet”
to a separate workbook. It does not save the whole DGAS workbook. The default
filename of this workbook is “Results.xls” but can be changed in the usual manner
19
through the “SaveAs” dialogue box that is launched when you click the button (Figure
16.).
As the PrintSheet contains Excel charts without their data tables, the user will be asked
if they wish to update the charts. Typically the user would choose “No” – especially if the
original DGAS file has been moved.
Figure 16. The “SAVE RESULTS” button will allow a report to be saved as a separate Excel
workbook.
NOTE: The only way to save changes made to the DGAS Workbook as a result of using
the forms is to close the forms, then save from Excel. When trying to close the workbook
itself, Excel will prompt you, asking if you wish to save the changes. Choose “Yes” if you
want to keep the state of the forms for later use.
Ad-hoc strategy calculator
The user is able to enter values for a single strategy that is not included in the calculator
(Figure 17.). The ad-hoc strategy allows for the user to define a percentage reduction in
emission from the seven sources of GHG’s. This can be used to provide an indication of
the level of reduction in emissions required to meet a farm emission target. The user
should enter a percentage for each type of emission affected by the strategy. Help is
provided to assist with use of the ad-hoc strategy calculator. The strategy series on the
graphs and absolute value lists change to reflect the new strategy, when the user moves
20
to a new field or uses the tab key. Ad-hoc scenarios are only retained for the session.
They are removed when the workbook is next opened.
Figure 17. The Ad-hoc Scenario Calculator allows the user to alter DGAS’s strategy output.
Workbook View
The DGAS MSExcel file is editable, to enable further refinement of the model. There are
three functional categories of worksheets in the workbook. In order to protect the
workbook from inadvertent damage, all sheets except the “BackDrop” sheet are hidden
by the macros driving the forms (Figure 18.). If the user closes the forms without
deliberately navigating via “EXCEL” buttons, the other worksheets will remain hidden.
These buttons are found on the Farm Inputs form (the form that automatically opens
when the DGAS file is opened) and the Results form.
Figure 18. The BackDrop worksheet is the only sheet to be visible unless form-based
navigation is used. The restart button will launch the userforms.
21
Once the worksheets are shown, it is possible to swap freely between the forms and the
worksheets via the restart button on the “BackDrop” sheet or via the Auto_Open macro
(see Navigation section).
The user must deliberately choose to open the worksheets with a view to editing. Such a
step is not to be taken lightly, as mentioned earlier, and should be done using a copy of
the DGAS file.
WorkSheets
Input Sheets
Two input worksheets are used to store the descriptions of the baseline scenario
(“FarmSheet” and “HerdSheet”) and two mirror sheets are used to store the descriptions
of the abatement strategy scenario (“Farm_A_Sheet” and “Herd_A_Sheet”). Pre-farm
emissions are calculated on the farm input sheets.
Calculator Sheets
The models used for calculating the emissions resulting from the enterprise of dairying
are divided into “Enteric Methane”, “Fecal Methane”, “Nitrous Oxide-Fecal”, “Nitrous
Oxide – Soils & Ferts”, “trees” and “Electricity & Diesel”.
Output Sheets
The last sheet in the workbook is the “ResultsSheet” where the output for the two
scenarios is separated and graphed. The penultimate worksheet, “PrintSheet”, is a
summary of both the inputs and the results. It can be saved separately and printed –
both from worksheet view and, more easily from the Results userform.
Acknowledgements and Licensing
DGAS is funded by The Department of Agriculture, Fisheries and Forestry (DAFF), Dairy
Australia (DA) and the University of Tasmania; compiled by staff of the Tasmanian
Institute of Agricultural Research (Robert Kildare, Karen Christie and Dr Richard
Rawnsley) with the assistance of Dr Richard Eckard, University of Melbourne.
22
This calculator is a further development of the Dairy Greenhouse Framework calculator,
compiled by Richard Eckard, Roger Hegarty and Geoff Thomas.
The University of Tasmania and its employees do not guarantee that the tool or
information contained therein is without flaw of any kind and therefore disclaims all
liability for any error, loss or other consequence which may arise from reliance on any
information contained herein.
Note:
a) The calculator is subject to development at all times,
b) The methods are continually changing so we take no responsibility for the currency of
the tool, and
c) Professional advice should be sought on the interpretation of the results.
23
Table 1. Average dry matter %, dry matter digestibility and crude protein % figures for various feed sources (data sourced
from the Diet Check program, referencing feed quality data from FEEDTEST, DPI Victoria)
Dry matter %
Average
Dry matter digestibility %
Range
Average
Range
Crude protein %
Average
Range
Barley grain
Barley hay
Barley silage
Barley straw
Brewer's grain
Canola meal
Carrot pulp
Citrus pulp
Cottonseed meal
Clover hay generic
Clover silage generic
Grape marc
Grass hay
Grass silage
Legume/grass silage (grass dom.)
Legume/grass silage (legume dom.)
Lucerne hay
Lucerne silage
Lucerne straw
Lupin seed
Maize grain
Maize silage
Oats
88.7
87.0
39.0
89.3
28.2
90.5
10.0
14.3
89.8
86.6
41.9
55.1
86.3
43.2
86.4
42.1
87.8
49.5
86.1
91.6
84.2
30.9
91.1
81.2
66.1
20.9
73.4
13.9
87.4
8.0
10.6
87.5
61.3
20.9
19.6
51.9
17.1
45.2
13.7
36.0
15.8
68.2
86.1
60.3
9.2
80.0
97.0
93.7
64.3
93.6
60.6
93.5
15.5
17.3
95.3
93.2
79.5
93.9
94.0
89.3
95.9
68.3
96.1
87.7
93.4
95.5
96.4
84.5
93.3
79.5
56.9
58.8
42.0
69.8
78.2
82.1
83.4
71.8
57.5
62.1
40.7
51.7
60.1
56.9
60.8
60.1
60.8
36.9
81.5
89.2
68.5
66.6
55.6
27.2
35.6
14.2
53.7
63.4
56.9
62.1
62.1
40.1
52.4
14.9
31.7
31.0
33.6
38.1
34.3
31.0
27.8
72.4
79.5
32.3
38.1
87.3
72.4
74.3
55.0
90.5
102.1
91.8
93.7
82.1
72.4
68.5
78.2
67.9
77.6
73.7
73.7
73.1
70.5
44.0
96.3
96.3
84.0
91.8
10.8
8.2
10.7
2.8
21.6
37.5
9.8
8.6
43.5
17.6
19.3
12.1
8.0
13.2
14.5
16.0
18.9
20.0
8.9
32.0
10.0
7.7
9.0
6.3
1.2
5.5
0.2
9.8
27.4
6.5
6.0
39.5
6.3
12.4
5.4
0.7
5.1
4.1
7.3
5.7
5.3
5.9
21.3
7.3
3.4
4.0
19.0
14.6
22.9
28.8
28.8
42.1
15.3
11.9
48.0
26.1
27.2
17.2
17.7
26.6
25.4
28.6
29.7
32.1
14.1
43.2
21.9
17.1
15.4
Oaten hay
88.9
40.2
96.4
54.3
29.1
73.1
6.9
1.1
16.3
24
Dry matter %
Average
Oaten silage
Oaten straw
Pasture hay
Pasture silage
Persian clover hay
Persian clover silage
Rice bran
Rice straw
Sorghum grain
Soyabean meal
Subclover hay
Subclover silage
Sunflower meal
Tomato pulp
Triticale grain
Triticale hay
Triticale silage
Triticale straw
Turnip tops*
Turnip bulbs*
Wheat bran
Wheat grain
Wheat hay
Wheat silage
40.9
89.4
86.2
43.1
85.6
42.9
90.4
85.2
89.6
85.4
86.8
37.1
90.8
27.3
89.4
86.6
42.9
89.8
29.1
23.7
34.0
89.4
87.9
44.9
Dry matter digestibility %
Range
18.1
80.2
48.6
10.9
67.8
23.7
89.9
52.2
86.2
11.9
71.7
20.6
86.4
16.6
80.3
54.3
20.1
62.7
8.5
4.7
15.1
80.2
46.8
27.5
Average
82.2
93.8
95.5
87.6
93.5
81.9
90.8
93.5
94.4
93.7
93.9
59.9
92.0
30.2
96.9
93.9
71.0
95.7
87.7
87.4
89.6
92.9
95.1
69.1
56.2
40.1
54.3
60.8
62.1
64.0
89.9
43.3
86.0
96.3
56.9
61.4
64.0
49.8
84.0
55.6
58.8
40.1
86.6
89.9
77.6
84.7
56.2
56.9
Range
38.1
27.8
34.3
14.2
45.3
53.0
60.1
34.3
80.8
86.0
42.0
33.6
54.3
26.5
75.0
31.0
45.9
26.5
69.2
75.6
70.5
67.9
31.7
29.7
72.4
64.7
72.4
76.3
75.6
72.4
97.6
57.5
93.1
104.7
68.5
67.9
90.5
60.1
87.3
69.2
72.4
58.2
93.7
95.7
85.3
91.2
71.1
69.2
Crude protein %
Average
9.8
2.8
10.8
14.1
16.2
17.6
15.5
4.0
10.6
43.5
17.2
18.8
34.1
19.4
11.4
7.3
10.8
2.8
15.9
14.8
17.9
12.9
8.2
10.0
Range
3.8
0.1
1.7
3.2
5.3
8.0
12.9
1.9
9.6
29.3
7.7
12.6
20.4
5.0
6.6
1.3
4.0
0.7
7.2
4.6
8.4
7.4
0.1
6.5
19.4
11.9
30.0
27.3
23.3
23.4
19.6
5.0
13.2
53.7
25.7
26.9
39.1
22.6
18.8
16.2
24.0
6.7
29.6
26.7
29.8
22.7
17.4
16.0
25
Appendix 3. Literature review from the Milestone 3 report on greenhouse gas
abatement strategies
Methane
A number of potential CH4 abatement strategies from dairy production systems have
been identified, with most of these options currently being addressed by researchers in
Australia and New Zealand (Figure 1). While some of these options are currently
possible and achievable (e.g. reducing animal numbers or increasing forage quality),
other options are still in the research phase (e.g. changing the rumen microbial
population through vaccinations) or there is uncertainty about the long term efficiency of
abatement strategies aimed at reducing enteric CH4 emissions (e.g. monensin). Figure
3 shows a summary of the potential reduction in CH4 from various abatement strategies.
Enteric methane
Animal-based
Management
Dietary
strategies
strategies
strategies
Breeding for higher
Animal numbers
Concentrates
feed conversion
efficiency
Animal size and
Fats & Oils
production
supplementation
Rumen microbial
Ionophore
population changes
Diet quality
Finishing off
stock earlier
supplementation
Tannin
supplementation
Figure 1. Potential options for enteric methane abatement from ruminants (Adapted from
RJ Eckard pers. comm. 2007).
113
Animal-based strategies
Breeding for higher feed conversion efficiency
Variation in CH4 production both within and between animals has long been recognised
(Blaxter and Clapperton 1965). Waghorn et al. (2006) found that more efficient animals
could emit 10-20% less CH4 than animals with a lower feed conversion efficiency.
Grainger et al. (2007) found a coefficient of variation (CV) of 4.3% within animals and
17.8% between animals when using open-circuit calorimetry. Lassey et al. (1997), Boadi
and Wittenberg (2002) and McNaughton et al. (2005) obtained between animal CV’s of
11.5, 15.5 and 25%, respectively. O’Hara et al. (2003) found that approximately 10% of
all animals were always high emitters and 10% were always low emitters, irrespective of
their diet. Therefore, if researchers can make advances in tracking the genes
associated with higher CH4 production, there will be scope to select genetic lines that will
result in reducing CH4 emissions.
Rumen microbial population changes
There is ongoing research into formulating a vaccination that will reduce methane
emissions by changing the rumen microbial population (CSIRO 2000). However, the
vaccine has not been commercially released and so will not be considered in this report
as a strategy.
Management strategies- stock
Animal numbers
Decreasing stock numbers will have an immediate effect of the amount of CH4 emitted
per farm. However, reducing stock numbers alone is not seen as an acceptable
strategy, as emissions per kg MS may remain the same under this strategy. However, if
farmers are able to feed their herd a higher digestibility diet and increased milk
production, this could result in lower emissions per kg MS and therefore be a suitable
strategy.
Maintaining the longevity of the herd should result in less replacement stock needing to
be raised. O’Mara (2004) showed that for a 100 cow herd, annual farm emissions could
be reduced from 15.8 to 13.8 t CH4/yr by increasing the average number of lactations
from 2.5 to 5 years (based on each cow emitting 118 kg CH4/yr and each heifer emitting
100 kg CH4 up to entering the milking herd).
114
Animal size and production
Kirchgessner et al. (1995) compared the total CH4 and CH4/kg milk emissions for cows
weighing 500, 600 or 700 kg and producing either 4000, 5000 or 6000 kg milk/yr. For
each weight category, increasing milking production resulted in more total CH4
emissions. However, when cows produced more milk, the CH4 emissions/kg milk
decreased due to the dilution effect. For example a 600kg cow producing 4000 and 6000
kg milk emitted 103 kg and 113 CH4/yr, respectively. Their emissions /kg milk reduced
from 25.8 g to 18.3 g CH4/kg milk due to the increased milk. The lowest emission/kg
milk was achieved with a 500kg cow producing 6000 kg/yr at 17.5 g CH4/kg milk.
The best strategy for reducing per cow emissions is to increase milk yields to dilute the
amount GEI over more milk. The best strategy to reduce the farms emissions is to
produce the same amount of milk from fewer animals and/or to reduce the number of
unproductive surplus stock, such as carrying empty cows through to the next breeding
season or raising extra heifers. Therefore adopting these strategies could potentially
reduce emissions by 10-20%.
Diet quality and availability
Methane emissions can be influenced by diet quality and availability. Ominski and
Wittenberg (2006) found that emissions were greatest when feed quality and availability
were low (11% GEI went into CH4 production). When feed quality was high but
availability was low, approximately 6.9% GEI went into CH4 production, while 7.1 to 9.4%
GEI went into CH4 production when quality was low coupled with higher availability. No
figures were presented when both quality and availability were high. However, the
authors speculated that the percentage of GEI into CH4 production would be lowest
under these conditions.
The influences of seasonal pasture quality on enteric CH4 emissions were clearly shown
by Auldist et al. (2006). Total CH4 emissions were the lowest in early spring at 344 g
CH4/cow.day, increased to 487 g CH4/cow.day in late spring, peaked in late summer at
623 g CH4/cow.day before decreasing to 521 g CH4/cow.day in mid autumn. Emissions
per litre of milk followed a similar seasonal trend as total emissions until autumn, when
emissions peaked at 36.8 g CH4/litre compared to 35.4 g CH4/litre milk in summer. The
115
reason for the greater emissions in autumn was due to lower milk production compared
to summer, coupled with lower feed quality.
Diet quality can also have an effect on CH4 production from manures. Hegarty (2001)
stated that to minimise CH4 production from manures, we need to ‘ensure that the
energy requirements of the animals are met from the highest digestibility feed available,
fed only at levels required for the desired animal performance’.
It is clear that maintaining high levels of pasture quality will reduce enteric CH4
emissions; however, the potential to control this is heavily influenced by the farm system.
It is therefore difficult to quantify the potential annual reduction in emissions due to
improvements in pasture quality. If differing farm systems can achieve some level of
improvement in this area then enteric CH4 emissions per animal will certainly decrease.
Dietary strategies
Feeding concentrates
Feeding a higher proportion of the diet as concentrate has been shown to reduce the
enteric methane emission per unit of product. In study by Johnson et al. (2002b), feeding
a diet containing 57, 39 and 4% DMI of concentrates resulted in emissions of 1.26, 1.38
and 1.62 CO2-e/L milk. While feeding grain will result in lower emissions, it must be
assessed in terms of a life cycle assessment. Will the reduction in emissions offset the
increases in emissions associated with the growing and transporting of the concentrate
to the farm gate? Total on- and off-farm emissions associated with feeding differing level
of concentrates in the diet was 1.15, 1.10 and 1.04 kg CO2-e/L milk for the low, medium
and high concentrate levels, respectively (Lovett et al. 2006).
Fats and oils supplementation
There is an increasing body of literature indicating that the supplementation of diets with
fats and oils that are not protected from ruminal digestion will reduce enteric CH4
fermentation. In a summary of results for 33 varying treatments, there was a 5.6%
reduction in CH4 for every 1% increase in DMI. However, there was considerable
variation both within and between fat sources (Beauchemin et al. 2008). When feeding
fats and oils, it must be remembered that there is an upper limit of 6-7% DMI before
116
reduction in intakes will occur due to the digestibility of the total diet (Grainger et al.
2008b).
In a study conducted in the USA, dairy cows were fed a 2.3, 4.0 and 5.6% fat DMI diet of
whole cottonseed (WCS) and canola oilseed. Results showed that the addition of fats
into the diet did not directly affect CH4 emissions (388, 396 and 456 g CH4/day).
However, there was an increase of approximately 6kg of milk for the two higher fat
content diets, resulting in 11.4, 10.0 and 11.5g CH4/litre milk, respectively, indicating that
the 4% diet resulted in the lowest emissions per litre (Johnson et al. 2002a).
In a study undertaken by Grainger et al. (2008b), dairy cows were fed a diet of lucerne
hay, pasture silage and cracked grain (13.8 kg DM/cow.day). Half of the animals were
fed an additional 2.7 kg /cow. day of WCS. Total CH4 emission were lower for the cows
fed WCS compared to the control herd (453 and 399 g CH4/cow.day, respectively), milk
production was greater for the cows fed WCS and therefore emissions per kg milk solids
was lower for the cows on the WCS diet (351 and 279 g CH4/kg MS, respectively).
While there are a varying array of feed sources high in fats and oils, there are some
practical, financial and environmental considerations. Feed sources high in mediumchian fatty acids (e.g. coconut oil, high-laurate canola oil and pure myristic) are effective
in reducing emissions. However, they are unlikely to be cost effective (Beauchemin et
al. 2008). Long-chain fatty acid oilseeds (e.g. sunflower seeds) may need to be
mechanically broken down to release the oil content of the seed to aid rumen digestibility
(Beauchemin et al. 2008). While pure oil sources may be more effective in lowering
emissions, feeding oilseeds is seen as a preferred option due to less adverse sideeffects on intakes and fibre digestibility. During the recent drought in Australia, some
farmers have used Palm kernel meal to supply fibre to their herds’ diet. It is important
that any meal sourced has been certified to ensure no native forest areas are destroyed
in the production of the meal (C Phelps, pers. comm. 2008).
The inclusion of a fat or oil source, to an upper dietary limit of 6-7% can be useful in
reducing CH4 emissions, while potentially providing extra energy to the diet to increase
milk production. However, there can be some negative side-effects in reducing feed
intake, reducing milk fat percentage and changing the fatty acid profile of the milk so
117
care must be shown when feeding a fat source to dairy cattle (Beauchemin et al. 2008).
By increasing the fat content of the diet, there is a potential to reducing emissions by 1025%.
Monensin supplementation
Monensin has typically been used in commercial beef and dairy cattle production to
modulate intake, control bloat and improve efficiency of meat and milk production,
especially in feedlot situations (McGuffrey et al. 2001). Monensin can increase the
acetate to propionate ratio of the volatile fatty acids in the rumen and can reduce the
number of protozoa present in the rumen. These are two important mechanisms that
contribute to the reduction in methanogenic microbes in the rumen producing CH4
(Beauchemin et al. 2008).
The effect of monensin on lowering CH4 production may be dose-dependant. Feeding <
15mg/kg DMI resulted in no lowering of CH4 production in terms of total or yield /kg DMI
(Grainger et al. unpublished data in Beauchemin et al. (2008)). van Vugt et al. (2005)
found that feeding < 20 mg//kg DMI reduced total CH4 production but did not reduce
CH4/kg DMI. Several researchers have identified that the optimal dosage is in the 24-35
mg/kg DMI range as total production was reduced by 4-10% g/day and by 3-8% g/kg
DMI (Sauer et al. 1998; McGinn et al. 2004; van Vugt et al. 2005; Odongo et al. 2007).
There would also appear to be a time factor involved in the efficiency of monensin.
Guan et al. (2006) found that after 2 months, the monensin was no longer effective in
reducing CH4 production, due to the protozoa population adapting to the monensin over
time. However, Odongo et al. (2007) found that monensin was still effective in lowering
CH4 production in dairy cows over a 6 month period.
While the effectiveness of monensin in reducing CH4 production has been shown, the
effectiveness over time and the potential public pressure associated with using an
antimicrobial chemical in animal production may result in monensin having a limited role
in reducing CH4 emissions. The potential reduction in emissions is also low at 10%,
further reducing the suitability of monensin as a single abatement strategy.
118
Condensed tannin supplementation
The potential of plant secondary constituents in reducing enteric CH4 production has
only been recently recognised (Beauchemin et al. 2008). While there is a large selection
of potential plants that could provide a source of condensed tannins (CT), they are
predominantly tropical shrub legumes. These tropical shrub legumes generally lack the
agronomic rigor of traditional temperate pasture species and high concentrations of CT
can impede forage digestibility and therefore reduce animal productivity (Beauchemin et
al. 2008).
Woodward et al. (2004) found that dairy cows fed a diet consisting of 2.6% DMI Lotus
corniculatus (Birdsfoot trefoil) had an increase in DMI, a 4% reduction in total CH4
production and a 32% reduction in CH4/kg MS compared to cows on a perennial
ryegrass diet. Methane emissions on sheep fed either perennial ryegrass or Lotus
pedunuculatus (lotus) were 25.7 and 11.5 g CH4/kg DMI, respectively (Waghorn et al.
2002). In another sheep study, Carulla et al. (2005) found a ~ 12% reduction in
emissions when sheep were fed a diet containing 2.5% DMI of Acacia mearnsii extract
(Black wattle). Similar reductions in emissions have also been observed in dairy cattle,
with a 14 and 29% reduction in CH4 emission for cattle fed either a 0.9 or 1.5% CT diet
(Grainger et al. 2008a). The results of these studies indicate that the feeding of CT has
the potential of reducing CH4 emissions by up to 12%. They are also a useful abatement
strategy in reducing N2O emissions through the process of protecting the protein within
the diet from degradation in the rumen (de Klein and Eckard, 2008).
In summary, there are several abatement strategies than can reduce CH4 emissions.
Some of these methods will have a permanence aspect to their effect on emissions (e.g.
reducing stock numbers), while others will have an additive effect on emissions (e.g.
feeding fats and oils). There may also be a time-dependant factor to consider with a
greater potential to reducing CH4 emissions during the summer months, by feeding feed
sources higher in their fat content or by feeding greater concentrates, when pasture
digestibility can be lower, especially under dryland conditions, and supplementary feed is
required to maintain milk production and feed intake.
119
Nitrous oxide
A number of abatement strategies have been identified as potential sources of reducing
N2O. Some of these strategies would be best classified as best farm management
practices such as minimising soil compaction from pugging over wet winter months.
Other strategies require changes to farm practices, such as feeding condensed tannins.
Several strategies do not have a direct reduction potential as they may be hard to define
(e.g. using a stand off pad over winter) while many of the strategies we have identified
are inter-related. For example, reducing the N concentration of the herds’ diet by not
over fertilising pastures with higher than required concentrations of N fertiliser, especially
during spring time.
On intensively managed and grazed pastures there is a cycling of N via urine and dung
from the grazed animals. Most of the N excreted is in an organic form and has to be
mineralised before it can be taken up by grass (Koops et al. 1997). The main N
component in urine is urea which is rapidly hydrolysed in the soil to NH4+ and
subsequently nitrified into NO3-, with N2O a by-product of this process. In contrast, the
main N component in dung is in the organic form and as such, it is generally slower in
converting into NH4+, NO3- and subsequently N2O (de Klein and Eckard 2008). The daily
excretion of N in urine and dung is variable and dependant on many factors, with typical
ranges of 100-350 g and 100-150g N from urine and faeces, respectively (Whitehead
1995). Therefore, urine patches are the dominant source of N2O emissions from animal
excrement.
Potential N2O abatement strategies that could be adapted to dairy farm systems and are
considered in the current study are given in Figure 2.
120
Nitrous oxide
Animal-based
Soil strategies
strategies
strategies
Waterlogging
Dietary
N inhibitors
& drainage
aspectsfeeding tannins
Management
Stand off
Irrigation
pads
Dietary
Fertiliser
aspects-
aspects
reducing N
content of diet
Effluent
management
Figure 2. Potential options for methane abatement from ruminants (Adapted from RJ
Eckard pers. comm. 2007).
Animal based strategies
Feeding condensed tannins
Condensed tannins interact with proteins in the rumen to protect them from microbial
digestion, resulting in either more efficient digestion of the proteins in the lower intestine
or the tannin-protein complex being excreted in the dung (de Klein and Eckard, 2008).
Misselbrook et al. (2005) found cows fed a diet containing 3.5% DMI of CT, excreted
24% less urinary N with an increase of total N excreted by 8% compared to cows on a
1% DMI CT diet. The increase in CT in the diet reduced the urine to dung ratio from
60:40 to 40:60. In a more recent study, Grainger et al. (2008a) fed dairy cows either a 0,
0.9 or 1.5% DMI CT diet. The low and high CT supplementation resulted in a decrease
of 38 and 57% less urinary N than the control treatment. There was a corresponding
increase in the percentage of total N appearing as faeces N, from 29.3% for the control
group, up to 41.5 and 49.6% for the low and high CT diets.
121
The inclusion of condensed tannins into the diet of dairy cows could potentially reduce
urinary N emissions by up to 59%, with a corresponding increase in dung N. This,
coupled with the reduction in CH4 production as described above, is an emission
strategy that can have a two-fold effect on lowering a dairy farms total GHG emission.
Reducing N content of the diet
Animals on a maintenance-only diet require about 7% of their DMI as crude protein (CP;
CP equal to N * 6.25), non-lactating pregnant animals require a 10-12% CP diet while
lactating animals require 15-20%. Of the dietary N consumed by ruminant animals, less
than 30% is actually utilised for the production of milk, wool or live weight gain
(Thompson and Poppi 1990). The excretion of N in dung is fairly constant per unit of DM
consumed, so any changes in dietary N intakes are reflected in the amount of N
excreted in the urine (Whitehead 1995).
Misselbrook et al. (2005) found that dairy cows fed on a 14% CP diet excreted 29% less
total N than cows on a 19% CP diet. The amount of N excreted in urine was reduced by
45%, with a slight increase of 4% of dung N. Supplementing a higher N ryegrass only
diet with a lower protein/ high sugar supplementary feed such as maize silage has been
shown to reduce the total N and urinary N excretion by 6-9% and 10-20%, respectively
(van Vuuren et al. 1993).
Another area of research resulting in lower urinary N excretion is with the substitution of
highly degradable proteins (e.g. soyabean meal) with a much lower digestible protein
source such as sopralin (formaldehyde treated soyabean meal). Kebreab et al. (2001)
found that while cows fed on the lower digestible protein meal excreted a total of 314 g
N/day compared to 338 g N/day for the higher degradable protein, there was a 24%
reduction in urinary N associated with the lower digestible protein source. However,
there is a possibility of increasing CH4 emissions due to a lower digestible forage being
fed; this requires further research.
There is also a potential of reducing urinary N excretion concentration with feeding
grasses that are higher in water soluble carbohydrates (WSC). Clark et al. (2001) found
that dairy cows fed a diet containing a higher WSC ryegrass cultivar excreted ~ 24% less
122
urinary N compared to cows fed a standard ryegrass diet. The higher WSC ryegrass
grass tended to contain less protein and thus N.
Miller et al. (2001) found that cows fed a ryegrass pasture diet containing a high WCS
concentrate diet (165 g/kg DMI compared to the standard ryegrass diet of 126 g/kg DMI),
emitted significantly less N to urine compared to the cows on the lower WCS diet (71.3
and 100 g N/day, respectively).
Monitoring and altering the CP% of the dairy cows diet so that there is minimal excess N
in the diet is both a cost-effective and GHG emission reduction effective strategy, with a
potential of reducing urinary N by 20-45%.
Soil strategies
Avoidance of waterlogging conditions and improved drainage
Soils that are waterlogged will denitrify and thus produce N2O more readily than well
drained soils, with field and laboratory studies suggesting a 2 to 5 times greater N2O
emission from the waterlogged soils compared to free draining soils (de Klein and
Eckard, 2008).
Whitehead (1995) suggests that water-filled pore spaces (WFPS) is an accurate method
of expressing the soil moisture content and is closely related to denitrification, as it is
percentage of the total pore space and comparable between soil texture classifications.
Aulakh et al. (1992) found that there was a rapid increase in denitrification when finemedium and course textures soils reached a WFPS of 70 and 80%, respectively. While
farmers are unable to make major changes to their soil texture, by improving soil
drainage there could be a reduction in denitrification by improving the aeration of the soil.
However, there is then most likely going to be an increase in soil nitrate leaching
(Whitehead 1995). It is difficult to directly calculate a potential reduction in N2O
emissions by either reducing the length of time that soils are waterlogged or by
improving soil drainage, although reducing the length of time that paddocks are
waterlogged by improving drainage, will result in lowering N2O emissions.
123
Irrigation management
Grazed irrigated pasture systems in south-eastern Australia have high N inputs either
from dung and urine or from N fertilisers and therefore are likely to generate significant
N2O emissions (Galbally et al. 2005). Phillips et al. (2007) found that on a recently
fertilised established perennial ryegrass/white clover/paspalum pasture, N2O emissions
remained relatively low immediately following flood irrigation when the WFPS was
>~95%, rapidly increasing to 55-150 g N2O-N/ha.day for 1 to 2 days, before a gradual
decrease to pre-irrigation levels of 2-4g N2O-N/ha.day once the soil dried out to ~65%
WFPS. The pattern of emission increases with irrigation and/or significant rainfall were
similar, although the rate of emissions varied between years and each irrigation/rainfall
event. The annual N2O emissions from this study were 2.9 to 3.6 kg N2O-N/ha.year and
were 57-59% lower than the 1996 IPCC default emission factor of 1.25%. Based on the
finding of this research, a new Australian factor of 0.4% was established for irrigated
pastures.
The potential to reduce N2O emissions from irrigation is inter-related to other strategies
such as fertiliser management (i.e. only applying N fertiliser during pasture growth
phases and not above the recommended 40-50 kg N/ha), soil texture and drainage.
Therefore it is difficult to determine a potential reduction the emissions from optimal
irrigation practices.
Management strategies
Nitrogen inhibitors
Nitrogen inhibitors are chemical compounds that can delay the formation of NO3- from
NH4+ in soils and thus reduce N2O emissions from urea and NH4+-based fertilisers or
from urine patches (Di and Cameron 2002). Of the commercially developed nitrification
inhibitors, the most widely used are nitrapyrin (N-serve®), 3, 4-dimethylpyrazole
phosphate (DMPP®) and dicyandiamide (DCD®) (de Klein and Eckard 2008; Suter and
Chen 2008). Recent research undertaken in New Zealand and Australia has shown a
reduction in emissions in the range of 61-91% (Di and Cameron 2002, 2003, 2006; Di et
al. 2007). However, in the review by de Klein and Eckard (2008), the authors suggested
care in extrapolating these potential reduction results as these studies were undertaken
when conditions were optimal for maximum N2O emissions.
124
Kelly et al. (2008) researched the effect of using DCD on urine applications to soils and
found that urine (at a rate of 1000kg N/ha) treated with DCD emitted 3.0 and 3.2 kg N2ON/ha in spring and summer, respectively, compared to 5.6 and 4.7 N2O-N/ha from urine
alone.
In a study assessing the effectiveness of applying DCD to soil cores that had been
fertilised with urea (100kg N/ha), there was a 75% reduction in N2O emissions from 0.43
to 0.11 N2O-N/ha for urea and urea + DCD, respectively (Singh et al. 2004). Application
of 100 kg N/ha is double the suggested best management practice for N fertilising
(Mundy 1999). Therefore, if lower rates had been applied, it could be speculated that
the reduction in emissions would have been significantly lower than the reported 75%
achieved. In another study by Singh et al. (2004), they examined the effectiveness of
using DCD and a urease inhibitor (Agrotain®) together in reducing emissions. Soil cores
with DCD and Agrotain emitted 0.19 kg N2O-N/ha compared to 0.56 kg N2O-N/ha for
cores fertilised with Agrotain alone.
It is concluded from reviewing the outcomes of a number of studies, that nitrification
inhibitors could potentially reduce emissions of N2O by up to 40-50% during times of the
year when emissions are high.
Using wintering stand-off pads
Animals grazing paddocks during wet winter conditions when soils are waterlogged can
cause substantial compaction and pugging damage. Recent research has indicated that
restricted grazing during this period can reduce N2O emissions by up to 10% (de Klein et
al. 2006; Luo et al. 2007). Therefore this could be a strategy worth considering for farms
that have an extended wet winter period when soils are most susceptible to being
damaged.
Best management practice to nitrogen fertilisers
The rate, source and timing of N fertiliser applications are important management factors
affecting the efficiency of pasture growth responses and the magnitude of N loss (de
Klein and Eckard, 2008). Galbally et al. (2005) reported a baseline N2O emission of 1.01.2 kg N2O-N/ha.year from irrigated pastures, increasing to 2.4 kg N2O-N/ha.year with
the application of urea at 50 kg N/ha for 6 times over a two year period. In a recent N2O
125
abatement strategy review by de Klein and Eckard (2008), they suggest that apart from
reducing the total amount of N fertiliser used and tightening up on the timing of
applications in relation to soil moisture conditions, further fertiliser abatement strategies
may be limited for pasture-based grazing systems.
Effluent management
As most Australian dairy farms are predominantly grazing systems as compared to
northern hemisphere dairy farms, there is significantly less effluent needing to be stored
and deposited onto pastures at a later stage. While there are an increasing number of
larger farms converting to a free stall feedlot system that requires various technologies to
manage their effluent, in this report we have not focussed on these farming systems.
We have only focussed on how farmers can use the effluent collected from the dairy as a
source of water and nutrients to apply to pastures. In a review by Saggar et al. (2004), it
was found that N2O emissions from effluent were greater when applied to wet soils
compared to drier soils, with a peak flux of N2O occurring within 24 hours of application
and taking 1-2 weeks before emissions were back to background levels. Therefore, if
farms are able to store effluent during periods of soil saturation, rather than needing to
apply effluent every day, this could be a source of potential reduction in emissions.
Farmers also need to view their effluent as a source of valuable nutrients that could also
reduce their reliance on synthetic fertilisers.
In summary, there are several on-farm strategies that dairy farmers can immediately
adopt to reduce their CH4 and N2O emissions. The addition of condensed tannins to a
herd diet could result in a win-win scenario by reducing CH4 and N2O emissions. Most
abatement strategies reported here do fall under the banner of ‘best management
practices’. Improving the quality of the herd diet with an increased reliance on grain
feeding or by being aware of the effect of digestibility on CH4 emissions when conserving
surplus feed, by monitoring soil moisture content to assess when stock grazing
paddocks could cause compaction or by matching the N requirements of pasture growth
to fertiliser applications can all play their role in reducing emissions on farm.
126
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partial replacement or ryegrass by low protein feeds on rumen fermentation and nitrogen
loss by dairy cows. Journal of Dairy Science 76, 2982-2993.
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forages fed to sheep. Proceedings of the New Zealand Grassland Association 64, 167171.
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132
Appendix 4 – Details of the life cycle assessment of each of the embedded calculations
Appendix 4a. Embedded GHG emissions produced with the production of one kilogram of wheat and lupins
1 kg
Wheat/AU U
0.30194 kg CO2 e
1 kg
Lupins/AU U
0.20431 kg CO2 e
0.026994 kg
Diammonium
phosphate, at
regional store/AU U
0.039419 kg CO2 e
0.026994 kg
Diammonium
phosphate/AU U
0.0378 kg CO2 e
0.001227 kg
Active pesticide/AU
U
0.013891 kg CO2 e
0.50589 MJ
Energy, from
natural gas/AU U
0.0297 kg CO2 e
1.6104 MJ
Tractor, low
population area, per
MJ fuel input/AU U
0.13711 kg CO2 e
0.039196 kg
Diesel, at
consumer/AU U
0.026886 kg CO2 e
1.6104 MJ
Tractor tailpipe, low
population area per
MJ fuel input/AU U
0.112 kg CO2 e
0.020551 kg
Lime, Calcined/AU
U
0.0013699 kg
Active pesticide/AU
U
6.8493 m2
Emission from land
use/AU U
0.023251 kg CO2 e
0.015509 kg CO2 e
0.097671 kg CO2 e
0.015287 kg CO2 e
0.043934 MJ
Australian average
electricity mix, high
voltage/AU U
4.6394E-5 m3
Diesel,
automotive/AU U
0.011962 kg CO2 e
0.026868 kg CO2 e
-0.061058 kg CO2 e
0.1238 kg CO2 e
0.033086 kg
Diesel, at
consumer/AU U
0.022694 kg CO2 e
0.024524 kg
Phosphoric acid
70%/AU U
-0.068493 kg
Urea, at regional
store/AU U
1.4541 MJ
Tractor, low
population area,
per MJ fuel
1.4541 MJ
Tractor tailpipe,
low population
area per MJ fuel
0.10113 kg CO2 e
-0.068493 kg
Urea
(granulated)/AU U
-0.056951 kg CO2 e
3.9538E-5 m3
Diesel,
automotive/AU U
-0.068493 kg
Urea
compounds/AU U
0.022897 kg CO2 e
-0.054622 kg CO2 e
-0.038823 kg
Ammonia/AU U
0.043934 MJ
Electricity, high
voltage, Australian
average/AU U
0.011962 kg CO2 e
4.5578E-5 m3
Crude oil,
imported/GLO U
-0.029112 kg CO2 e
0.010716 kg CO2 e
-0.23626 MJ
Energy, from
natural gas/AU U
-0.01387 kg CO2 e
6.6634E-5 m3
Crude oil
exploration and
extraction/AU U
0.013023 kg CO2 e
133
Appendix 4b. Embedded GHG emissions produced with the production of one kilogram of grass silage and grass hay
1 kg
hay intensive IP, at
farm/kg/CH
0.22241 kg CO2 e
1 kg
grass silage IP, at
farm/kg/CH
0.002522 kg
ammonium nitrate,
as N, at regional
storehouse/kg/RER
0.22058 kg CO2 e
0.02208 kg CO2 e
0.0068725 m3
slurry spreading,
by vacuum
tanker/m3/CH
0.0079508 kg CO2 e
4.1152 m2
mowing, by rotary
mower/ha/CH
0.0025101 kg
ammonium nitrate,
as N, at regional
storehouse/kg/RE
4.1152 m2
swath, by rotary
windrower/ha/CH
0.0057143 p
baling/p/CH
0.0092192 kg CO2 e
0.021976 kg CO2 e
0.0063794 kg CO2 e
0.032827 kg CO2 e
0.0056536 kg
nitric acid, 50% in
H2O, at
plant/kg/RER
0.0011984 kg
tractor,
production/kg/CH/
I
0.018477 kg CO2 e
0.0065961 kg CO2 e
0.0056834 kg
nitric acid, 50% in
H2O, at
plant/kg/RER
0.018575 kg CO2 e
0.0017219 kg
agricultural
machinery,
general,
0.01088 kg
diesel, at regional
storage/kg/CH
0.0058344 kg
polyethylene,
HDPE, granulate,
at plant/kg/RER
0.0062218 kg CO2 e
0.0055088 kg CO2 e
0.0091307 kg CO2 e
0.0038776 kg
steel, converter,
unalloyed, at
plant/kg/RER
10.773 m2
haying, by rotary
tedder/ha/CH
0.021815 m3
fodder loading, by
self-loading
trailer/m3/CH
0.011136 kg CO2 e
0.013059 kg CO2 e
1 kg
dried roughage
store, cold-air dried,
conventional,
0.033586 kg CO2 e
0.47355 MJ
electricity, low
voltage, at
grid/kWh/CH
0.016574 kg CO2 e
0.000253 m3
dried roughage
store, cold-air dried,
conventional/m3/CH
0.017078 kg CO2 e
0.5601 MJ
electricity, medium
voltage, at
grid/kWh/CH
0.017214 kg CO2 e
0.57341 MJ
electricity, high
voltage, at
grid/kWh/CH
0.017133 kg CO2 e
0.0057512 kg CO2 e
0.57909 MJ
electricity
mix/kWh/CH
0.016822 kg CO2 e
0.066965 MJ
electricity,
production mix
DE/kWh/DE
0.011676 kg CO2 e
134
Appendix 4c. Embedded GHG emissions produced with the production of one kilogram of maize silage and urea
1 kg
Maize, off
specification,
used for silage
0.37118 kg CO2 e
1 kg
Urea, at regional
store/AU U
0.89145 kg CO2 e
0.66667 m2
Maize harvested,
average
1 kg
Urea
(granulated)/AU U
0.35528 kg CO2 e
0.6 m3
Average water
supply to maize,
griffith
0.062299 kg CO2 e
0.39 m3
Water supply
from bore (50m)
0.062299 kg CO2 e
0.312 m3
Water - from 50m
bore (diesel)
0.046439 kg CO2 e
0.5434 MJ
Diesel pump (MJ
fuel input)
0.83149 kg CO2 e
0.66667 m2
Maize growing,
average
1 kg
Urea
compounds/AU U
0.35473 kg CO2 e
0.79748 kg CO2 e
0.66876 m2
Maize sowing,
irrigated,
average
0.083402 kg CO2 e
0.66876 m2
Field preparation
for maize
0.029586 kg CO2 e
0.010005 kg
N application
emission stubble
incorp. kg N
0.077975 kg CO2 e
0.010005 kg
N application
emission stubble
burn kg N applied
0.13647 kg CO2 e
0.56682 kg
Ammonia/AU U
0.77579 MJ
Australian average
electricity mix,
high voltage/AU U
0.21122 kg CO2 e
0.42504 kg CO2 e
0.43873 kg
Natural gas, high
pressure/AU U
8.9212 MJ
Energy, from
natural gas/AU U
0.77579 MJ
Electricity, high
voltage, Australian
0.18676 kg CO2 e
0.52374 kg CO2 e
0.21122 kg CO2 e
0.56993 m3
Natural gas, high
pressure /AU U
0.18681 kg CO2 e
0.56993 m3
Natural gas,
processed/AU U
0.16428 kg CO2 e
0.047106 kg CO2 e
0.70738 MJ
Agricultural
machinery, heavy
1.1378 MJ
Oil & gas
production
0.055018 kg
Venting - gas
processing plant
0.08395 kg CO2 e
0.081592 kg CO2 e
0.061321 kg CO2 e
135
Appendix 4d. Embedded GHG emissions produced with the production of one kilogram of ammonium sulphate and triple
superphosphate
1 kg
Triple
superphosphate at
regional store/AU U
0.83436 kg CO2 e
1.0001 kg
Ammonium
sulphate, as N, at
regional
1 kg
Triple
superphosphate/AU
U
2.5343 kg CO2 e
0.77439 kg CO2 e
1.9041E-9 p
Chemical plant,
organics/RER/I U
0.20044 kg CO2 e
1.4892 MJ
Electricity, medium
voltage,
production UCTE,
0.1915 kg CO2 e
1.4424 MJ
Heat, at hard coal
industrial furnace
1-10MW/RER U
0.17529 kg CO2 e
22.312 MJ
Heat, natural gas,
at industrial furnace
>100kW/RER U
1.5013 kg CO2 e
4.4027 MJ
Heat, heavy fuel
oil, at industrial
furnace 1MW/RER
0.46442 kg
Phosphoric acid
70%/AU U
0.41052 kg CO2 e
1.8602 MJ
Australian average
electricity mix, high
voltage/AU U
0.28951 kg CO2 e
2.022 MJ
Electricity, high
voltage,
production UCTE,
1.9229 MJ
Hard coal, burned
in industrial furnace
1-10MW/RER U
23.632 MJ
Natural gas, burned
in industrial furnace
>100kW/RER U
4.6662 MJ
Heavy fuel oil,
burned in industrial
furnace 1MW,
0.25549 kg CO2 e
0.18694 kg CO2 e
1.5112 kg CO2 e
0.41286 kg CO2 e
2.0436 MJ
Electricity,
production mix
UCTE/UCTE U
23.806 MJ
Natural gas, high
pressure, at
consumer/RER U
0.2546 kg CO2 e
0.17706 kg CO2 e
0.71284 kg
Sulphuric acid/AU U
0.38951 kg
Phosphate rock
beneficiated/GLO U
0.059659 kg CO2 e
0.11422 kg CO2 e
0.23524 kg
Sulphur/AU U
0.059659 kg CO2 e
0.50647 kg CO2 e
2.2925 MJ
Energy, from
natural gas/AU U
0.13459 kg CO2 e
0.58884 MJ
Electrictiy black coal
NSW, sent out/AU U
0.16007 kg CO2 e
1.8602 MJ
Electricity, high
voltage, Australian
average/AU U
0.50647 kg CO2 e
0.46392 MJ
Electrictiy brown
coal Victoria, sent
out/AU U
0.17017 kg CO2 e
0.42966 MJ
Electrictiy black coal
QLD, sent out/AU U
0.11427 kg CO2 e
0.8979 MJ
Energy, from
diesel/AU U
0.075904 kg CO2 e
136
Appendix 4e. Embedded GHG emissions produced with the production of one kilogram of diammonium phosphate and
monoammonium phosphate
1 kg
diammonium
phosphate, as N, at
regional
1 kg
monoammonium
phosphate, as
P2O5, at regional
2.7366 kg CO2 e
1.5921 kg CO2 e
1.8244E-9 p
chemical plant,
organics/p/RER/I
1.3769 kg
phosphoric acid,
fertiliser grade,
70% in H2O, at
1.2227 kg
ammonia, steam
reforming, liquid, at
plant/kg/RER
0.30495 kg CO2 e
1.3033 kg CO2 e
2.2329 kg CO2 e
0.00029943 m3
building,
multi-storey/m3/RE
R/I
0.022987 kg
facilities, chemical
production/kg/RER/I
0.14651 kg CO2 e
0.15608 kg CO2 e
0.088568 kg
reinforcing steel, at
plant/kg/RER
1.751 MJ
electricity, medium
voltage, production
UCTE, at
0.11848 kg CO2 e
0.24525 kg CO2 e
2.0283 MJ
electricity, high
voltage, production
UCTE, at
0.27852 kg CO2 e
30.213 MJ
natural gas, high
pressure, at
consumer/MJ/RER
0.22664 kg CO2 e
0.82829 m3
natural gas, at
long-distance
pipeline/m3/RER
0.20751 kg CO2 e
0.28788 m3
natural gas,
production RU, at
long-distance
0.13118 kg CO2 e
2.4929 kg
sulphuric acid,
liquid, at
plant/kg/RER
2.2869 MJ
electricity,
medium voltage,
production UCTE,
5.0938 MJ
heat, natural gas,
at industrial
furnace
1.0182 kg
phosphate rock,
as P2O5,
beneficiated, dry,
0.33069 kg CO2 e
0.32032 kg CO2 e
0.3434 kg CO2 e
0.22254 kg CO2 e
0.53348 kg
secondary
sulphur, at
refinery/kg/RER
1.0822E-9 p
chemical plant,
organics/p/RER/I
2.6901 MJ
electricity, high
voltage,
production UCTE,
5.5522 MJ
natural gas,
burned in
industrial furnace
0.16486 kg CO2 e
0.1809 kg CO2 e
0.3694 kg CO2 e
0.35574 kg CO2 e
1.8336 MJ
refinery gas,
burned in
furnace/MJ/RER
2.7179 MJ
electricity,
production mix
UCTE/kWh/UCTE
0.11499 kg CO2 e
0.36798 kg CO2 e
2.0492 MJ
electricity,
production mix
UCTE/kWh/UCTE
0.27745 kg CO2 e
137
Appendix 4f. Embedded GHG emissions produced with the production of one kilogram of potassium chloride and limestone
1 kg
Limestone
(calcilte)/AU U
1 kg
Potasium chloride,
AU, at regional
store/AU U
0.13092 kg CO2 e
1 kg
Potasium
chloride/AU U
0.006 kg
Kraftliner-Brown,
mass allocation of
wood products/AU
0.0048443 kg CO2 e
0.070956 kg CO2 e
0.1837 MJ
Australian average
electricity mix,
high voltage/AU U
0.050014 kg CO2 e
0.37056 MJ
Energy, from
diesel/AU U
0.031326 kg CO2 e
0.1837 MJ
Electricity, high
voltage, Australian
average/AU U
0.050014 kg CO2 e
0.062739 MJ
Electrictiy black
coal NSW, sent
out/AU U
0.017055 kg CO2 e
0.046476 MJ
Electrictiy brown
coal Victoria, sent
out/AU U
0.017048 kg CO2 e
0.019184 kg CO2 e
0.14039 MJ
Energy, from
natural gas/AU U
0.0082423 kg CO2 e
0.2 tkm
Rigid truck
transport, freight
task/AU U
0.049603 kg CO2 e
0.42803 MJ
Rigid truck
operation,
diesel/AU U
0.049607 kg CO2 e
0.085738 MJ
Transport infrast.
pub sect/AU U
0.035628 MJ
Energy, from
diesel/AU U
0.0030118 kg CO2 e
0.018316 MJ
Australian average
electricity mix,
high voltage/AU U
0.11001 tkm
Articulated Truck,
28 tonne load on
30 tonne truck,
0.0049866 kg CO2 e
0.010591 kg CO2 e
0.018316 MJ
Electricity, high
voltage, Australian
average/AU U
0.0015624 kg CO2 e
0.0015773 kg CO2 e
0.097908 MJ
Articulated truck
operation/AU U
0.0049866 kg CO2 e
0.0057475 MJ
Electrictiy black
coal NSW, sent
out/AU U
0.060001 tkm
Shipping,
domestic
freight/AU U
0.010591 kg CO2 e
0.0045604 MJ
Electrictiy brown
coal Victoria, sent
out/AU U
0.0016728 kg CO2 e
0.0022769 kg
Diesel, ultralow
sulphur, at
consumer/AU U
0.0015619 kg CO2 e
0.01792 MJ
Transport infrast.
priv. sect/AU U
0.0011866 kg CO2 e
0.0072481 kg CO2 e
0.043044 MJ
Electrictiy black
coal QLD, sent
out/AU U
0.011448 kg CO2 e
3.8349E-6 m3
Diesel,
automotive/AU U
0.0022208 kg CO2 e
138
Appendix 4g. Embedded GHG emissions produced with the production of one kilogram of pesticide and canola meal
1 kg
Pesticide
unspecified, at
regional
7.017 kg CO2 e
18.459 MJ
Electricity, low
voltage, production
UCTE, at grid/UCTE
2.6454 kg CO2 e
1.0816 kg
Naphtha, at
refinery/RER U
0.38072 kg CO2 e
34.706 MJ
Heat, heavy fuel
oil, at industrial
furnace 1MW/RER
3.2361 kg CO2 e
0.20028 kg
Disposal, hazardous
waste, 25% water,
to hazardous waste
0.36721 kg CO2 e
1 kg
Canola meal
22.147 MJ
Electricity, medium
voltage, production
UCTE, at grid/UCTE
2.8479 kg CO2 e
36.783 MJ
Heavy fuel oil,
burned in industrial
furnace 1MW,
3.2545 kg CO2 e
22.784 MJ
Electricity, high
voltage, production
UCTE, at grid/UCTE
2.8788 kg CO2 e
1.0445 kg
Heavy fuel oil, at
regional
storage/RER U
0.40826 kg CO2 e
23.013 MJ
Electricity,
production mix
UCTE/UCTE U
2.867 kg CO2 e
6.2604 MJ
Electricity,
production mix
DE/DE U
1.1296 kg CO2 e
1.5746 MJ
Electricity, hard
coal, at power
plant/DE U
0.42754 kg CO2 e
4.3739 MJ
Hard coal, burned
in power plant/DE
U
0.42754 kg CO2 e
1.0704 kg
Heavy fuel oil, at
refinery/RER U
0.308 kg CO2 e
0.619 kg
Canola seed, at
farm/AU U
0.193 kg
Steam, from
natural gas, in
kg/AU U
0.235 kg CO2 e
0.0546 kg
Fertiliser, NPKS 32
10, at regional
store/AU U
0.0328 kg
Lime, Calcined/AU
U
0.0619 kg CO2 e
0.0371 kg CO2 e
0.0419 kg CO2 e
0.955 MJ
Tractor, low
population area,
per MJ fuel
0.0821 kg CO2 e
9.1E-5 kg
Nitrogen
volatilisation from
fertiliser
0.0443 kg CO2 e
1.52 MJ
Energy, from
natural gas/AU U
0.0895 kg CO2 e
0.167 MJ
Electricity, high
voltage, Australian
average/AU U
0.0456 kg CO2 e
0.167 MJ
Electricity, high
voltage, Australian
average,
0.0456 kg CO2 e
0.38892 kg CO2 e
3.1073 MJ
Electricity,
production mix
IT/IT U
0.56657 kg CO2 e
0.0546 kg
Fertiliser, NPKS 32
10/AU U
0.0585 kg CO2 e
0.955 MJ
Tractor tailpipe,
low population
area per MJ fuel
0.0664 kg CO2 e
1.623 MJ
Electricity, lignite,
at power plant/DE
U
0.54495 kg CO2 e
4.914 MJ
Lignite, burned in
power plant/DE U
0.54495 kg CO2 e
139
Appendix 4h. Embedded GHG emissions produced with the production of one kilogram of soybean meal and palm kernel
oil
1 kg
Soybean meal,
economic
allocation/AU U
1 kg
Palm kernel oil, at oil
mill/MY U
0.485 kg CO2 e
2.72 kg CO2 e
0.87 kg
Meal processing/AU
U
0.87 kg
Oil extraction and
solvent recycling/AU
U
0.87 kg
Soybeans, crushed
and dehulled/AU U
0.0456 kg CO2 e
0.0969 kg CO2 e
0.342 kg CO2 e
0.27 kg
Steam, from natural
gas, in kg/AU U
0.357 MJ
Electricity, high
voltage, Australian
average/AU U
0.0586 kg CO2 e
0.0972 kg CO2 e
3.04 MJ
Energy, from
natural gas/AU U
0.179 kg CO2 e
0.357 MJ
Electricity, high
voltage, Australian
average,
0.0972 kg CO2 e
0.87 kg
Soybean/AU U
0.215 kg CO2 e
0.923 MJ
Tractor, low
population area,
per MJ fuel
0.0793 kg CO2 e
0.923 MJ
Tractor tailpipe, low
population area per
MJ fuel input/AU U
0.0642 kg CO2 e
6.49 kg
Palm fruit bunches,
at farm/MY U
0.649 tkm
Transport, lorry
3.5-16t, fleet
average/RER U
2.48 kg CO2 e
0.87 kg
Soybean Drying/AU
U
0.057 kg CO2 e
0.0409 kg
Ammonium
sulphate, as N, at
regional
4.55 m2
Irrigating/ha/CH U
0.108 kg CO2 e
0.124 kg CO2 e
0.974 MJ
Heat, natural gas,
at industrial furnace
>100kW/RER U
1.45 MJ
Electricity, low
voltage, at grid/CH
U
0.0657 kg CO2 e
0.0507 kg CO2 e
1.23 MJ
Natural gas, burned
in industrial furnace
>100kW/RER U
1.79 MJ
Electricity, medium
voltage, at grid/CH
U
0.0789 kg CO2 e
0.0551 kg CO2 e
0.208 kg CO2 e
7.04 kg
Wood chopping,
mobile chopper, in
forest/RER U
0.0951 kg CO2 e
0.104 m2
Provision, stubbed
land/MY U
1.03 kg CO2 e
286 m
Operation, lorry
3.5-16t, fleet
average/RER U
0.177 kg CO2 e
1.11 MJ
Diesel, burned in
building
machine/GLO U
0.0993 kg CO2 e
140
Appendix 4i. Embedded GHG emissions produced with the production of one kilogram of palm kernel meal and maize seed
1 kg
Maize for seed
0.291 kg CO2 e
1 kg
Palm kernel meal, at
oil mill/MY U
0.186 kg CO2 e
1.25 m2
Maize growing,
irrigated from
surface waters
0.29 kg CO2 e
0.443 kg
Palm fruit bunches,
at farm/MY U
0.0443 tkm
Transport, lorry
3.5-16t, fleet
average/RER U
0.169 kg CO2 e
0.0142 kg CO2 e
0.0335 kg
Urea delivered to
central NSW
0.0323 kg CO2 e
0.00279 kg
Ammonium
sulphate, as N, at
regional
0.31 m2
Irrigating/ha/CH U
0.00739 kg CO2 e
0.00848 kg CO2 e
0.0665 MJ
Heat, natural gas,
at industrial furnace
>100kW/RER U
0.0989 MJ
Electricity, low
voltage, at grid/CH
U
0.00448 kg CO2 e
0.00346 kg CO2 e
0.481 kg
Wood chopping,
mobile chopper, in
forest/RER U
0.00649 kg CO2 e
0.0758 MJ
Diesel, burned in
building
machine/GLO U
0.00709 m2
Provision, stubbed
land/MY U
0.0703 kg CO2 e
0.122 MJ
Electricity, medium
voltage, at grid/CH
U
0.00539 kg CO2 e
0.00376 kg CO2 e
0.0219 kg
N application
emission kg N
applied
0.135 kg CO2 e
0.16 kg CO2 e
0.0196 kg
NPKS 32 10
delivered to central
NSW AU
1.25 m2
Field preparation for
maize
0.0249 kg CO2 e
0.0558 kg CO2 e
19.5 m
Operation, lorry
3.5-16t, fleet
average/RER U
0.0121 kg CO2 e
0.0448 kg
Urea
(granulated)/AU U
0.0373 kg CO2 e
0.00678 kg CO2 e
0.0448 kg
Urea compounds/AU
U
0.0841 MJ
Natural gas, burned
in industrial furnace
>100kW/RER U
1.25 m2
Maize sowing,
irrigated, average
0.0358 kg CO2 e
0.468 MJ
Energy, from
natural gas/AU U
0.0275 kg CO2 e
0.0196 kg
Fertiliser, NPKS 32
10/AU U
0.021 kg CO2 e
1.25 m2
Tractor ploughing
maize field, average
0.0153 kg CO2 e
0.307 MJ
Agricultural
machinery, heavy
duty AU, per MJ fuel
0.0269 kg CO2 e
141
Appendix 4j. Embedded GHG emissions produced with the production of one kilogram of grass seed and clover seed
1.0163 kg
grass seed IP, at
farm/kg/C H
1.0191 kg
clover seed IP, at
farm/kg/CH
1.9227 kg C O 2 e
3.3664 kg CO2 e
10.163 m2
combine
harv esting/ha/C H
0.29036 kg
grain dry ing, low
temperature/kg/C
H
0.10174 kg
ammonium
nitrate, as N, at
regional
0.043191 kg
triple
superphosphate,
as P2O 5, at
0.15147 kg C O 2 e
0.21507 kg C O 2 e
0.8907 kg C O 2 e
0.086099 kg C O 2 e
2.0501 MJ
light fuel oil,
burned in industrial
furnace 1MW,
0.11938 kg
ammonia, steam
reforming, liquid,
at plant/kg/RER
0.22902 kg
nitric acid, 50% in
H2O , at
plant/kg/RER
0.61329 MJ
electricity ,
medium v oltage,
production U C TE,
0.17667 kg C O 2 e
0.21801 kg C O 2 e
0.74848 kg C O 2 e
0.085901 kg C O 2 e
0.067497 kg
ammonia, liquid,
at regional
storehouse/kg/RE
0.73206 MJ
electricity , high
v oltage,
production U C TE,
0.13621 kg C O 2 e
0.10053 kg C O 2 e
0.74 MJ
electricity ,
production mix
U C TE/kWh/U C TE
0.019108 kg
clover seed IP, at
regional
storehouse/kg/CH
25.478 m2
combine
harvesting/ha/CH
0.29117 kg
grain drying, low
temperature/kg/C
H
0.10191 kg
triple
superphosphate,
as P2O5, at
0.29301 kg
potassium chloride,
as K2O, at regional
storehouse/kg/RE
0.065479 kg CO2 e
0.37975 kg CO2 e
0.21568 kg CO2 e
0.20315 kg CO2 e
0.1426 kg CO2 e
0.016051 kg
harvester,
production/kg/CH/
I
2.0624 MJ
light fuel oil,
burned in industrial
furnace 1MW,
0.76554 MJ
electricity, medium
voltage,
production UCTE,
0.098513 kg
phosphoric acid,
fertiliser grade,
70% in H2O, at
0.067242 kg CO2 e
0.17773 kg CO2 e
0.10723 kg CO2 e
0.093242 kg CO2 e
0.90689 MJ
electricity, high
voltage,
production UCTE,
0.12453 kg CO2 e
0.91672 MJ
electricity,
production mix
UCTE/kWh/UCTE
0.12412 kg CO2 e
0.10019 kg C O 2 e
142
Appendix 4k. Embedded GHG emissions produced with the production of one kilogram of glyphosate and MCPA
1 kg
MCPA, at regional
storehouse/RER U
1 kg
G ly phosate
41.5% A U ,
production and
8.94 kg C O 2 e
4 kg CO2 e
1 kg
G ly phosate
41.5% in w ater,
A U , Deliv ered
8.63 kg C O 2 e
20.6 MJ
Heat, heavy fuel
oil, at industrial
furnace 1MW/RER
1.93 kg CO2 e
0.415 kg
G ly phosate, A U ,
from ecoinv ent
data
8.53 kg C O 2 e
21.8 M J
Electricity , high
v oltage,
A ustralian
5.93 kg C O 2 e
0.891 kg
N atural gas, high
pressure/A U U
0.379 kg C O 2 e
24.8 M J
Energy , from fuel
oil/A U U
2.15 kg C O 2 e
1.16 m3
N atural gas, high
pressure /A U U
0.38 kg C O 2 e
5.43 M J
Electrictiy brow n
coal V ictoria, sent
out/A U U
1.99 kg C O 2 e
5.03 M J
Electrictiy black
coal Q LD, sent
out/A U U
1.34 kg C O 2 e
0.2 kg
Disposal, hazardous
waste, 25% water,
to hazardous waste
1.12 kg CO2 e
0.372 kg CO2 e
9.38 MJ
Electricity, medium
voltage, production
UCTE, at grid/UCTE
1.94 kg CO2 e
1.31 kg CO2 e
0.593 kg
Heavy fuel oil, at
regional
storage/RER U
9.81 MJ
Electricity, high
voltage, production
UCTE, at grid/UCTE
1.35 kg CO2 e
0.678 kg
F uel oil, at
consumer/A U U
0.477 kg C O 2 e
0.611 kg
Heavy fuel oil, at
refinery/RER U
0.227 kg CO2 e
6.84 M J
Electrictiy black
coal N S W, sent
out/A U U
1.86 kg C O 2 e
0.335 kg CO2 e
7.08 MJ
Electricity, low
voltage, production
UCTE, at grid/UCTE
21.9 MJ
Heavy fuel oil,
burned in industrial
furnace 1MW,
0.236 kg CO2 e
21.8 M J
Electricity , high
v oltage,
A ustralian
5.93 kg C O 2 e
0.93 kg
Naphtha, at
refinery/RER U
9.91 MJ
Electricity,
production mix
UCTE/UCTE U
1.34 kg CO2 e
0.000835 m3
F uel oil/A U U
0.457 kg C O 2 e
2.27 MJ
Electricity,
production mix
DE/DE U
0.396 kg CO2 e
143
Appendix 4l. Embedded GHG emissions produced with the production of one kilogram of diuron and diesel
1 kg
Diesel, at
consumer/AU U
1 kg
Diuron, at regional
storehouse/RER U
6.57 kg C O 2 e
19.8 M J
Heat, heav y fuel
oil, at industrial
furnace 1M W/RER
1.85 kg C O 2 e
1.59 kg
N aphtha, at
refinery /RER U
0.574 kg C O 2 e
0.719 kg CO2 e
20.3 M J
Electricity , low
v oltage, production
U C TE, at
0.00118 m3
Diesel,
automotive/AU U
3.2 kg C O 2 e
0.696 kg CO2 e
21.2 M J
Heav y fuel oil,
burned in industrial
furnace 1M W,
24.6 M J
Electricity , medium
v oltage, production
U C TE, at
1.88 kg C O 2 e
3.44 kg C O 2 e
0.000545 m3
Crude oil,
domestic/AU U
0.107 kg CO2 e
25.3 M J
Electricity , high
v oltage, production
U C TE, at
0.00118 m3
Crude oil,
imported/GLO U
0.279 kg CO2 e
0.00173 m3
Crude oil
exploration and
extraction/AU U
3.48 kg C O 2 e
0.337 kg CO2 e
25.6 M J
Electricity ,
production mix
U C TE/U C TE U
0.95 MJ
Energy, from
natural gas, just
fuel, CO2,CH4, &
0.0566 kg CO2 e
14 tkm
Shipping, oil
transport/AU U
0.0698 kg CO2 e
3.47 MJ
Energy, from fuel
oil, just fuel,
CO2,CH4, &
0.277 kg CO2 e
0.0892 MJ
Electricity, high
voltage, Australian
average/AU U
0.0243 kg CO2 e
0.0892 MJ
Electricity, high
voltage, Australian
average,
0.0243 kg CO2 e
0.103 kg
Fuel oil, at
consumer/AU U
3.46 kg C O 2 e
0.0722 kg CO2 e
5.86 M J
Electricity ,
production mix
DE/DE U
1.02 kg C O 2 e
2.93 M J
Electricity ,
production mix
IT/IT U
0.495 kg C O 2 e
0.000126 m3
Fuel oil/AU U
1.5 M J
Electricity , lignite,
at pow er plant/DE
U
0.0692 kg CO2 e
0.504 kg C O 2 e
4.54 M J
Lignite, burned in
pow er plant/DE U
0.504 kg C O 2 e
144
Appendix 5. Overall diet quality calculation for the milking herd for each farming system
Farm
Feed source
Feed input
Digestibility
Crude
(kg DM/cow.day)
%
Protein %
Pasture
13.3
70.0
20.0
Grain
1.7
80.0
12.0
Daily average
15.0
71.1
19.1
Pasture
10.0
70.0
20.0
Grain
8.0
80.0
12.0
Daily average
18.0
74.4
16.4
Pasture
10.0
70.0
22.0
Grain
4.7
80.0
12.0
Maize silage
3.3
65.0
8.0
Daily average
18.0
71.7
16.8
Grain
7.5
80.0
12.0
Mill run
2.1
80.0
23.0
Cottonseed meal
2.1
85.0
45.0
Whole cottonseed
1.5
80.0
23.0
Molasses
1.3
65.0
4.0
Ryegrass silage
6.5
60.0
14.0
Sorghum silage
3.0
60.0
10.0
Cereal hay
1.0
55.0
8.0
Daily average
25.0
72.0
16.0
Wheat
8.0
85
12
Maize silage
4.3
65
8
Cereal silage
3.0
60
10
Canola meal
2.3
80
35
Whole cottonseed
2.3
70
44
Lucerne hay
1.7
60
18
Cereal hay
0.7
55
8
Daily average
22.3
72.9
17.0
system
LSF
HSF 1
HSF 2
TMR 1
TMR 2
145
Appendix 6 – Greenhouse gas emissions for individual abatement strategies across each
of the dairy farming system.
Appendix 6a. Effect of adopting abatement strategies for the low supplementary feeding farm
system on total farm and intensity of greenhouse gas emissions
Abatement strategy
Baseline farm system- 445
cows, weighing 500kg and
producing 4,500L
Increase milk to 4,950L
and decrease herd size to
404 milkers
Increase herd weight to
550kg and milk production
to 5,000L
Increase milk to 5,500L
and grain intake to 1.0t
DM/cow
Feeding fats to achieve a
20% reduction in CH4
emissions
Feeding condensed
tannins to alter enteric CH4
and urine/dung N2O
emissions
Applying a nitrification
inhibitor to alter soil N2O
emissions
All-inclusive abatement
strategy farm with reduced
herd numbers
All-inclusive abatement
strategy farm with
maintained herd numbers
Total farm
emission
( t CO2-e)
Milk
production
(t MS)
Emission
intensity
(t CO2-e/t MS)
Percentage
reduction1
CPRS2
liabilities
2,623
150.2
17.5
n/a
2,105 t CO2-e
14.0 t CO2/t MS
2,434
150.0
16.2
7.1
2,754
166.9
16.5
5.7
2,765
183.6
15.1
13.8
2,506
150.2
16.7
4.5
2,468
150.2
16.4
5.9
2,571
150.2
17.1
2.0
2,320
166.7
13.9
20.3
2,503
183.6
13.6
22.0
1,936 t CO2-e
12.9 t CO2/t MS
7.9%
2,226 t CO2-e
13.3 t CO2/t MS
5.0%
2,180 t CO2-e
11.9 t CO2/t MS
15.3%
1,988 t CO2-e
13.2 t CO2/t MS
5.6%
1,950 t CO2-e
13.0 t CO2/t MS
7.4%
2,052 t CO2-e
13.7 t CO2/t MS
2.5%
1,761 t CO2-e
10.4 t CO2/t MS
24.6%
1,917 t CO2-e
10.4 t CO2/t MS
25.5%
1
Percentage reduction in the intensity of GHG emissions (t CO2-e/t MS) from the baseline farm system.
2
Only on-farm methane and nitrous oxide emissions are considered.
146
Appendix 6b. Effect of adopting abatement strategies for the high supplementary feeding farm
system (HSF 1) on total farm and intensity of greenhouse gas emissions
Abatement strategy
Baseline farm system- 310
cows, weighing 550kg and
producing 6,500L
Increase milk to 7,150L
and decrease herd size to
280 milkers
Increase herd weight to
600kg and milk production
to 7,000L
Feeding fats to achieve a
20% reduction in CH4
emissions
Feeding condensed
tannins to alter enteric CH4
and urine/dung N2O
emissions
Applying a nitrification
inhibitor to alter soil N2O
emissions
All-inclusive abatement
strategy farm with reduced
herd numbers
All-inclusive abatement
strategy farm with
maintained herd numbers
Total farm
emission
( t CO2-e)
Milk
production
(t MS)
Emission
intensity
(t CO2-e/t MS)
Percentage
reduction1
CPRS2
liabilities
2,118
151.1
14.0
n/a
1,596 t CO2-e
10.6 t CO2/t MS
1,923
150.2
12.8
8.7
2,196
162.8
13.5
3.8
2,023
151.1
13.4
4.5
2,003
151.1
13.3
5.4
2,088
151.1
13.8
1.4
1,802
150.2
12.0
14.4
1,960
166.2
11.8
15.9
1,431 t CO2-e
9.5 t CO2/t MS
9.8%
1,674 t CO2-e
10.3 t CO2/t MS
2.7%
1,501 t CO2-e
9.9 t CO2/t MS
5.9%
1,481 t CO2-e
9.8 t CO2/t MS
7.2%
1,566 t CO2-e
10.4 t CO2/t MS
1.9%
1,311 t CO2-e
8.7 t CO2/t MS
17.4%
1,438 t CO2-e
8.7 t CO2/t MS
18.1%
1
Percentage reduction in the intensity of GHG emissions (t CO2-e/t MS) from the baseline farm system.
2
Only on-farm methane and nitrous oxide emissions are considered.
147
Appendix 6c. Effect of adopting abatement strategies for the high supplementary feeding farm
system (HSF 2) on total farm and intensity of greenhouse gas emissions
Abatement strategy
Baseline farm system- 333
cows, weighing 550kg
producing 6,000L
Increase milk to 6,600L
and decrease herd size to
303 milkers
Increase herd weight to
600kg and milk production
to 7,000L
Increasing grain feeding to
1.9t and milk production to
7,000L
Feeding fats to achieve a
20% reduction in CH4
emissions
Feeding condensed
tannins to alter enteric CH4
and urine/dung N2O
emissions
Applying a nitrification
inhibitor to alter soil N2O
emissions
All-inclusive abatement
strategy farm with reduced
herd numbers
All-inclusive abatement
strategy farm with
maintained herd numbers
Total farm
emission
( t CO2-e)
Milk
production
(t MS)
Emission
intensity
(t CO2-e/t MS)
Percentage
reduction1
CPRS2
liabilities
2,290
149.9
15.3
n/a
1,690 t CO2-e
11.3 t CO2/t MS
2,120
150.0
14.1
7.5
2,425
174.8
13.9
9.2
2,450
174.8
14.0
8.3
2,190
149.9
14.6
4.4
2,168
149.9
14.5
5.4
2,260
149.9
15.1
1.3
2,030
159.1
12.8
16.5
2,194
174.8
12.6
17.8
1,555 t CO2-e
10.4 t CO2/t MS
8.1%
1,801 t CO2-e
10.3 t CO2/t MS
8.6%
1,789 t CO2-e
10.2 t CO2/t MS
9.2%
1,591 t CO2-e
10.6 t CO2/t MS
5.9%
1,568 t CO2-e
10.5 t CO2/t MS
7.3%
1,661 t CO2-e
11.1 t CO2/t MS
1.8%
1,409 t CO2-e
8.9 t CO2/t MS
21.4%
1,534 t CO2-e
8.8 t CO2/t MS
22.2%
1
Percentage reduction in the intensity of GHG emissions (t CO2-e/t MS) from the baseline farm system.
2
Only on-farm methane and nitrous oxide emissions are considered.
148
Appendix 6d. Effect of adopting abatement strategies for the total mixed ration farm system (TMR
1) on total farm and intensity of greenhouse gas emissions
Abatement strategy
Baseline farm system- 300
cows, weighing 700kg and
producing 11,500L
Baseline farm system with
heifers agisted off-farm
Total farm
emission
( t CO2-e)
Milk
production
(t MS)
Emission
intensity
(t CO2-e/t MS)
Percentage
reduction1
CPRS2
liabilities
1,925
150.0
12.8
n/a
1,418 t CO2-e
9.5 t CO2/t MS
1,654
150.0
11.0
14.1
Feeding a condensed
tannin for 12 months
1,859
150.0
12.4
3.4
Using an N-inhibitor for 12
months
1,917
150.0
12.8
0.5
1,851
150.0
12.3
3.9
Feeding a condensed
tannin and using an Ninhibitor
1,212 t CO2-e
8.1 t CO2/t MS
14.5%
1,352 t CO2-e
9.0 t CO2/t MS
4.7%
1,409 t CO2-e
9.4 t CO2/t MS
0.6%
1,343 t CO2-e
9.0 t CO2/t MS
5.3%
1
Percentage reduction in the intensity of GHG emissions (t CO2-e/t MS) from the baseline farm system.
2
Only on-farm methane and nitrous oxide emissions are considered.
149
Appendix 6e. Effect of adopting abatement strategies for the total mixed ration farm system (TMR
2) on total farm and intensity of greenhouse gas emissions
Abatement strategy
Baseline farm system- 680
milkers, weighing 550kg
and producing 8,6000L
Baseline farm system with
the replacement stock
agisted off-farm
Increase milk to 9,500L
and decrease herd size to
615 milkers
Increasing herd weight to
600kg, increasing grain
feeding to 3.0 t and milk
production to 9,500L
Feeding condensed
tannins to alter enteric CH4
and urine/dung N2O
emissions
Applying a nitrification
inhibitor to alter soil N2O
emissions
All-inclusive abatement
strategy farm with reduced
herd numbers
All-inclusive abatement
strategy farm with
maintained herd numbers
Total farm
emission
( t CO2-e)
Milk
production
(t MS)
Emission
intensity
(t CO2-e/t MS)
Percentage
reduction1
CPRS2
liabilities
1,835
150.0
12.2
n/a
1,474 t CO2-e
9.8 t CO2/t MS
1,569
150.0
10.5
14.5
1,671
150.0
11.1
9.0
1,813
1,762
150.0
150.0
12.1
11.7
1,233 t CO2-e
8.2 t CO2/t MS
16.3%
1,332 t CO2-e
8.9 t CO2/t MS
9.6%
1.2
1,443 t CO2-e
9.6 t CO2/t MS
2.1%
4.0
1,440 t CO2-e
9.3 t CO2/t MS
5.0%
1,832
150.0
12.2
0.2
1,746
150.0
11.6
4.9
1,739
150.0
11.6
5.3
1,470 t CO2-e
9.8 t CO2/t MS
0.2%
1,367 t CO2-e
9.1 t CO2/t MS
7.3%
1,369 t CO2-e
9.1 t CO2/t MS
7.1%
1
Percentage reduction in the intensity of GHG emissions (t CO2-e/t MS) from the baseline farm system.
2
Only on-farm methane and nitrous oxide emissions are considered.
150