Agriculture’s potential and costs of reducing greenhouse gas emissions: how marginal abatement cost curve studies can help

Vera Eory

SRUC

Defra & Agricultural Economics Society one-day conference

17 January 2020, London

22Anna Pantelina/CERN

Avij/Wikipedia

Fortepan/Wikipedia

W. Röntgen/Wikipedia

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Not a new problem – still a problem

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The target

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UK greenhouse gas emissions

-100

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Mt

CO

2e

Agriculture Business Energy Supply Exports

Industrial Process Public Residential Transport

Waste Management Land Use ChangeNAEI, 2019

66

0%

20%

40%

60%

80%

100%

UK agricultural emissions

NAEI, 2019

77

Reducing food-related GHG emissions

On farm technologies

Land based C sinks

Consumption change

Reduced food waste

88

Mitigation scenarios (farm & land use)

Scenario (2050)

Assumptions (uptake of farm

practices, agrotech, agroforestry,afforestation, bioenergy, peatland

restoration, diet change)

Agricultural Land-based* Total

emissions (Mt CO2e)

2016 - 41.7 4.9 46.6

Business as usual - 45.7 12.4 65.7

Maximum food production

Medium: farm practices, agro-techLow: all others

45.3 12.4 65.3

Multifunctional land use

Medium: farm practices, agrotech,afforestation, peatland, bioenergy, diet changeHigh: agroforestry

35.5 -6.1 37.1

Technology push Low: agroforestryMedium: peatlandsHigh: all others

24.4 -13.9 18.1

High mitigation uptake

Medium: agrotech, diet change High: all others

32.9 -26.1 14.4

Thomson et al. 2018* Net difference due to mitigation measures

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On farm mitigation – how much?

• Farming for a Better Climate focus farms: 10%-11%

mitigation over a three year period (as captured by

AgRECalc©) (FFBC pers. comm.)

• Whole farm modelling exercises estimated 3-17% mitigation

in cattle farms (Beauchemin et al. 2011, Lengers et al. 2014, Adler et al. 2015)

• National level estimates: 10-20% cost-effective mitigation,

including carbon sequestration (Vermont & De Cara 2010)

UK 15%, Ireland 13%, France 33% (Schulte et al. 2012, Eory et al. 2015,

Pellerin et al. 2017)

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How?

Practical Acceptable

Cost-effectiveNo negative co-effects

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UK abatement, 2030 (central)

Eory et al. 2015

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A word of cautionAbate

ment

(kt

CO

2e/y

)

Eory et al. 2018

Uncertainty of the abatement potential of mitigation measures (Scotland)

1313© SRUC

… farmers’ willingness to change practices

• Fits into their farming objectives

• More benefits than disadvantages

• Low risk

• Information, advice, training, skills

• Financial aspects

… institutions to support the change

• Creating a strong incentive

• Robust accounting and monitoring methods

• Flexibility

• Trust

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What do farmers think about CC?

• US Midwest (Arbuckle et al. 2013)

8% mostly human causes

33% equally human and natural causes

25% mostly natural causes

31% uncertain if it occurs

3% doesn’t occur

• Australia (Kragt et al. 2017)

14% human cause

46% humans contribute

33% natural causes

• New Zealand (Niles & Mueller

2016)

12-21% not human induced

66-52% human induced

27-22% not occurring

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Climate change beliefs and attitudes to action

Arbuckle et al. 2013

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Current adoption in E&W

• 54% find it important to consider GHG emissions

• 48% agree that it would help profitability

• 58% take action to reduce emissions

50% waste recycling, 43% energy efficiency

40% nitrogen fertiliser accuracy

29% manure management

22% more clover, 16% more legumes in the rotation

16% nitrogen feed efficiency

Defra 2018

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Reasons

Defra 2018

0% 20% 40% 60% 80% 100%

Consider it good business practice

Concern for the environment

To improve profitability

Regulation

To meet market demands

Other motivation

Reasons to adopt

0% 20% 40% 60% 80% 100%

Not necessary (don't believe farm produces much GHG)

Lack of information

Unsure what to do - too many conflicting views

Lack of incentive

Don't believe farmers can do much

Too expensive

Already done all they can

Other reasons

Reasons not to adopt

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Current adoption (dairy farms)

Glenk et al. 2014

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Adoption intention (dairy farms)

Glenk et al. 2014

2020

Adopting C farming in Australia

• Drivers of / barriers to adopting

practices To improve soil quality and

productivity

Lack of information

Uncertainty on the practice’s effect

• Drivers of / barriers to joining the

scheme Knowing other C farmers

Policy and C price uncertainty

Kragt et al. 2017

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Voluntary policy schemes

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Effectiveness of a voluntary scheme

• Knowledge aquisition• Production of RE

• Stakeholder engagement

• Production of RE

• Implementation of nutrient management plan

• Soil testing

• Facilitation, small group work

• Egalitarian atmosphere

• Repeated meetings

• Open communication

• Multiple knowledge sources

Sociallearning

Practice adoption

Management skills

Resilience

Mixed: repetitive meetings but no

regular attendance; lack

of egalitarian atmosphere

Positive: practice adoption is higher

under PEP participants

Mixed: more renewable energy generation but not

attributable to participation

Neutral: recent participants show better knowledge,

but former participants don’t

Knook et al. submitted

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Climate Action Reserve (US)

• Voluntary C market (78% market share) and California Cap and Trade

• Grasslands, forests, nitrogen management, rice cultivation, anaerobic

digester, soil C (coming)

• Not based on whole-farm C accounting; tools used e.g. DNDC (rice),

NMQuanTool (nitrogen)

• Non-agriculture as well (e.g. coal mines, waste)

• ~600 projects

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• Forest

Forest

• Grassland

• AD

Forest

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Emission Reduction Fund (Australia)

• Carbon credits issued

• Anaerobic digestion, beef efficiency, soil carbon,

fertiliser use efficiency (cotton only), nitrate feeding

for beef, fat feeding for dairy

• Specific tools for each option

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Other schemes

Arla

• Processor or retailer led initiatives

Market advantage, brand identity

Part of wider sustainability strategy

Self-set goals and a carbon calculator method

(independent verification?)

• Industry initiative

Across industry, voluntary goals

• Certification schemes

Suitable for smaller companies, products

Government/third sector led

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Farm GHG calculators

Sykes et al. 2017

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Bringing it all together

GHG mitigation

MACCs

Inventories

GHG calculators

Farmers’ intentions

Enabling technologies

Wider policy context

Supply chain

Policy instruments

Consumers, trade

Thank you!

Thank you for colleagues in SRUC and partner institutions over the past ten years and for the various funding bodies for supporting this research.

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References

• Adler, A. A. et al. (2015) Managing greenhouse gas emissions in two major dairy regions of New Zealand: A system-level evaluation. Agricultural Systems 135, 1-9.

• Arbuckle, J. G. et a. (2013). Climate change beliefs, concerns, and attitudes toward adaptation and mitigation among farmers in the Midwestern United States. Climatic Change, 117, 943-950.

• Barnes, A. P. et al. (2013). Heterogeneity in climate change risk perception amongst dairy farmers: A latent class clustering analysis. Applied Geography, 41,105-115.

• Beauchemin, K. A. et al. (2011) Mitigation of greenhouse gas emissions from beef production in western Canada - Evaluation using farm-based life cycle assessment. Animal Feed Science and Technology 166-167, 663-677.

• Committee on Climate Change (2019). Net Zero - The UK's contribution to stopping global warming. Committee on Climate Change.

• Eory, V. et al. (2015) Review and update of the UK agriculture MACC to assess the abatement potential for the 5th carbon budget period and to 2050, the Committee on Climate Change.

• Eory, V. et al. (2018). Addressing uncertainty in efficient mitigation of agricultural greenhouse gas emissions. Journal of Agricultural Economics, 69, 627-645.

• Kragt, M. E., et al. (2017). Motivations and barriers for Western Australian broad-acre farmers to adopt carbon farming. Environmental Science & Policy, 73, 115-123.

• Leinonen, I. et al. (2019). Comparative analysis of farm-based carbon audits ClimateXChange.

• Niles, M. T. & Mueller, N. D. (2016). Farmer perceptions of climate change: Associations with observed temperature and precipitation trends, irrigation, and climate beliefs. Global Environmental Change, 39, 133-142.

• Lengers, B. et al. (2014) What drives marginal abatement costs of greenhouse gases on dairy farms? A meta-modelling approach. Journal of Agricultural Economics 65, 579-599.

• Pellerin, S. et al. (2017) Identifying cost-competitive greenhouse gas mitigation potential of French agriculture. Environmental Science & Policy 77, 130-139.

• Schulte, R. P. et al. (ed) (2012) A marginal abatement cost curve for Irish agriculture, Teagasc.

• Sykes, A. J. et al. (2017). A comparison of farm-level greenhouse gas calculators in their application on beef production systems. Journal of Cleaner Production, 164, 398-409.

• Thomson, A. et al (2018) Quantifying the impact of future land use scenarios to 2050 and beyond - Final Report.

• Vermont, B. and De Cara, S. (2010) How costly is mitigation of non-CO2 greenhouse gas emissions from agriculture?: A meta-analysis. Ecological Economics 69, 1373-1386.

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Websites

• https://www.climateactionreserve.org/

• https://www.environment.gov.au/climate-change/government/emissions-reduction-fund/methods

• https://carbonfarmersofaustralia.com.au/

• http://idele.fr/no_cache/recherche/publication/idelesolr/recommends/france-carbon-agri-association.html

• http://idele.fr/reseaux-et-partenariats/ferme-laitiere-bas-carbone.html

• https://www.ecologique-solidaire.gouv.fr/label-bas-carbone

• https://www.woodlandcarboncode.org.uk/

• Resources from Carbon Farming Schemes in Europe (Roundtable, 2019-10-09): https://nx5846.your-

storageshare.de/s/tye6wTXwSe7fjMG, https://webcast.ec.europa.eu/carbon-farming-schemes-in-europe-roundtable#