Presentation by Hazelle Tomlin, GRA, at the CLIFF-GRADS workshop on 6-7 October 2019 in Bali.
The two-day workshop was organized by the CCAFS Low Emissions Development Flagship and the Global Research Alliance on Agricultural Greenhouse Gases (GRA). Read more: https://ccafs.cgiar.org/cliff-grads-workshop
2. Agriculture and Energy are the largest contributors to New Zealand’s
gross emissions, at 48.1 per cent and 40.7 per cent respectively
NZ Emissions Profile
3. Institutional Arrangements
• NZ Ministry for the Environment (MfE) is the centralized agency
that compiles the national inventory submission (NIS) and
submit to the UNFCCC
• MPI compiles the agriculture sector inventory, chapter 5 of the
NIS
• Country specific (CS) emission factors (EF’s) are necessary for
NZ because IPCC defaults do not accommodate NZ’s
agricultural circumstances
5. • Livestock:
• Major - Dairy cattle, Beef cattle, Sheep, Deer
• Minor– Pigs, goats, horses, Alpaca and Llama, Mules and Asses, Poultry
• Crops:
• Barley, wheat, oats, potatoes, maize seed, other seed crops, onions, squash,
and sweetcorn.
• N-fixing – peas, lentils, forage legume seed, grass/clover pasture, lucerne.
• Fertilisers:
• N containing synthetic fertilisers, dolomite and lime
• Mitigation practices:
• Nitrification and Urease inhibitors
NZ Agriculture Inventory - Source categories
6. 2017 Agricultural Emissions Profile by Livestock Industry
• In 2017, agriculture emissions
were 38.9 Mt CO2-e.
7. Contributors to the change in agricultural emissions
between 1990 and 2017
Categories:
• Dairy
• Beef
• Sheep
• Other
NB Emissions expressed in kilotonnes of carbon dioxide equivalent (kt CO2-e)
8. • Energy requirements model
estimates feed intake and emissions
for dairy cattle, sheep and deer
• Production data and physiological
characteristics used to estimate
metabolisable energy (ME)
requirements
• This is combined with pasture quality
data to estimate intake (DM) per
animal
• There is a linear relationship
between intake and emissions
NZ Ag Inventory - Livestock Model
9. • The APS is the most important data source for the
inventory
• Smaller data sets are sourced from other organisations
NZ Ag Inventory - Data sources
10. • 92% of NZ’s agricultural emissions are calculated using a
‘tier 2’ method
• Dairy and beef cattle, sheep and deer
• Cropping emissions
• The remaining proportion are calculated using tier 1
methods, but still use country-specific emission factors
where available
NZ’s Ag Inventory Methodology Tiers
11. Tier 1
Alpaca (D)
Goats (CS)
Horses (D)
Mules and asses (D)
Swine (CS, D)
Poultry (CS, D)
Direct N2O* (CS,D)
Indirect N2O* (CS,D)
Tier Increase % Change in Estimated Emissions
Tier 2 / 3 (CS)
Dairy cattle
Beef cattle
Sheep
Deer
Crops
Where CS = Country Specific and D = Default Examples of country specific values
14. • Continual improvement of inventory
estimates is required to account for new
farming techniques and mitigation
technologies
• Comprehensive process for incorporating
new science and technology into the
inventory methodology and emission
estimates
• Any significant inclusion requires peer-
reviewed published research and data
Improving the agriculture inventory
15. • Gaps in our inventory data; some years of missing data (extrapolate and
uncertainty). Other AD gaps limit tier 2.
• Data sharing issues; cross agency, data privacy regulations
• MfE as centralised inventory compiler: subject to their deadlines despite
ag. complexity
• Agriculture Inventory Model (AIM) Reprogram
• Providing information at the right level of complexity to inform policy is
also important, i.e. not too detailed or too aggregate.
MRV Challenges faced in New Zealand’s Agricuture
Inventory
The GRA secretariat is currently hosted by NZ’s MPI
The other half of my role with MPI is agricultural emission reporting in the national agriculture inventory team
This includes contributing to preparation of our annual national inventory report (NIR) (actual quantification of emissions and writing: https://www.mfe.govt.nz/sites/default/files/media/Climate%20Change/nz-greenhouse-gas-inventory-2019.pdf)
Contributing to preparation of the biennial report which tracks trends, progress toward targets and projects emissions into the future (http://unfccc.int/files/national_reports/biennial_reports_and_iar/submitted_biennial_reports/application/pdf/148395_new_zealand-br3-1-new_zealand_third_biennial_report.pdf)
Contributing to our national communication which provides summary of NIR and advice for CC policy and tracks progress on trends (https://www.mfe.govt.nz/sites/default/files/media/Climate%20Change/21-12-17%20Web%20FINAL%20-%20Seventh%20National%20Communication%202017.pdf)
Responding to information requests, both internal at MPI in the domestic and wider CC teams and external from wider NZ public and industry
I’m going to give an overview of NZ’s emission profile
some trends we see in agricultural emissions
the structure of our MRV and institutional arrangements
And some key challenges in developing and implementing MRV systems we face in NZ
Students now know, following Ngoni presentation:
Purpose of inventory is to track progress towards emission reduction targets
Provides basis for New Zealand’s policies for emissions reductions
NZ small country with some 85% of food production going to the international market
Therefore significant dependence on ag. sector
Almost half (48.1%) of New Zealand’s gross emissions were from the agriculture sector (more than half if include energy used for heat processing for milk products)
Energy makes up another significant portion at over 40%
LULUCF offsets net emissions in NZ by 30 %: calculated by subtracting the 24 Mt CO2-e of net removals from LULUCF
NZ’s target under the Paris agreement is to reduce emissions by 30% on 2005 baseline by 2030
New Zealand’s country-specific methods were developed under a comprehensive and ongoing research programme, including modelling to calculate emissions from cattle, sheep, and deer.
Many of the IPCC's default values are based on Northern Hemisphere research
There is little to gain in terms of energy emission reductions as we already have high levels of renewable energy use, and agriculture production efficiencies have increased significantly since 1990
While ag. production has increased overall, absolute emissions per unit of sheepmeat, beef and dairy have decreased significantly
Result is that ag. emissions have remained relatively stable 2000kT since 2005
Trends:
Since 1990 emissions from agriculture have increased by 4.6 Mt CO2-e, or 13.5 per cent.
Agricultural emissions were at their highest point in 2005 and decreased significantly following a period of prolonged and severe drought resulting in significant livestock culling
They began increasing again in 2008 with more favourable growing conditions and demand for NZ animal ag. products.
Therefore, emissions in 2017 were 2.5 per cent lower than 2005 levels.
NZ uses enhanced LS classification with four major LS types: D, B, S, Deer
Deer is included in the ‘other’ category which includes all minor LS types which in NZ are goats, swine, poultry, llamas and alpacas, horses, mules and asses (and rabbits)
Included to show the four main contributors to agricultural emissions by livestock classification;
The graph clearly shows the gas breakdown per sector
Include example of equation here?
Means that we can document the improvements in emissions intensity that have occurred over the past few decades (around 1% per year since 1990)
Allows us to account for mitigation technologies that could be adopted on farms in the future
Internationally, the APS is exemplary of NZ government to farmer engagement
Likewise it is a good example of data collection for countries that do not yet have this process in place
Incorporating mitigation strategies into the inventory; currently only ag. inventory in the world (as far as I know) that incorporates mitigation
APS allows quantification of emission reductions from urease and nitrification inhibitors in our fertiliser estimate
There is a wider research programme funded by the Ministry for Primary Industries in addition to a specific fund, the Inventory Improvement Fund, that has an annual allowance for procurement of projects to inform shortfalls in the current inventory methodology and emission estimates
The Inventory Improvement Fund
Annual process includes a workshop for New Zealand’s technical agricultural greenhouse gas research community to collaborate on progress of the science since last year and establish priorities for the coming year of research to inform inventory improvements
The outcomes from the research community meeting are discussed with policy makers, as well as industry and sectoral representatives
Prioritisation of projects are also informed by questions and recommendations from the UNFCCC expert review team resulting from our inventory review (happening tomorrow in NZ)
Research is completed and provides recommendations on whether the methodology must be changed and how
Agriculture advisory panel
If methodological changes are recommended, and thus recalculations required, these must be approved by the Agriculture Inventory Panel AND MPI’s senior science advisor
Ag. advisory panel are an independent group of experts that meet annually, made up of N2O and CH4 emission experts, MfE and MPI representatives, NZAGRC representatives and Royal Society of NZ Experts
Research links:
https://www.mpi.govt.nz/news-and-resources/open-data-and-forecasting/greenhouse-gas-reporting/agricultural-greenhouse-gas-inventory-reports/
We are limited to a tier 2 inventory largely because of Activity Data limitations (which I believe most countries around the world face).
Further, as with everywhere we are constantly battling data sharing issues.
Another challenge of MRV is incorporating mitigation strategies, this is a hot topic in New Zealand at the moment; as far as I am aware there are not many inventories internationally that can quantify mitigation, the NZ inventory can quantify mitigation from urease inhibitors but we are hoping to incorporate more with time.
Although there really aren’t any other mitigation strategies that we are ready to quantify yet (vaccines and inhibitors are a long way off and changes to feed use are not scientifically robust enough yet for us to incorporate).
Agriculture Inventory Model (AIM) Reprogram
currently programmed in Excel: has data limitations where bugs can be carried through to different parts of the model
Need for separation of data and calculations / code
Need to ‘futureproof’ the model to more easily facilitate inventory changes
Providing information at right level of complexity
Better quantify regional emissions
Providing information at the right level of complexity to inform policy is also important, i.e. not too detailed or too aggregate.