Modelling Agriculture Emissions: Methods & Applications

1. Modelling Agriculture Emissions:Methods & Applications Trevor Donnellan, Patrick Gillespie, Kevin Hanrahan Rural Economy Research Centre (RERC) Teagasc, Athenry, Co. Galway ESRI, Dublin, December 3rd, 2008

2. Overview • Background to work • National Objectives • Methodology • How it works • Relationship with rest of Model • Assumptions and uncertainties • Results

3. Background • Teagasc Sub-component is an element of the ISUS project • looking at agriculture • Mainly covering • emissions of GHGs and sequestration from forestry • Plan to also cover • herbicides, pesticides and fungicides • Using economic modelling to determine likely future size of sector • to derive emissions on that basis • Normally we begin with a Baseline view of the future • Based on policy being held constant into the future • This may not be the right starting point for our objective • More on this later

4. Agriculture & Emissions • Main issue is GHG emissions • Will focus on that today • Agriculture is a significant component (>25%) of Irish GHG Emission • Livestock based production is dominant in Ireland • Bovines are a major GHG source • Irish Agri sector is highly export focused • 90% of beef exported • 80% of dairy products exported • Need a global dimension to modelling framework • Prospects for agriculture are determined by factors external to Ireland

5. Irish Ag and the Outside World World Ag Model EU Ag Model Ireland Ag ISUS Module Hermes Model

6. How does the model fit with other models ? • Model uses exogenous data from ESRI projections • Consistent with ESRI in terms of macroeconomic assumptions • Model projects emissions from primary agriculture • Model does not capture downstream or upstream emissions, so does not include • Fertiliser manufacturing emissions • Food processing emissions • But these are in other sectors anyway for Kyoto purposes

7. Meeting National Objectives Agri Commodity Model used to produce: • Agricultural activity measures • e.g. no. of animals by type, fertiliser usage • GHG emissions projections • analogous to EPA’s own GHG projections • Simulations can be conducted under alternative assumptions relating to policy or other factors • For EPA, DAFF or Teagasc • New targets for Irish GHG emissions for 2020 • 10% reduction on 2005 (target about 17.1 Mt CO2 Eq.) • 20% reduction on 2005 (target about 15.3 Mt CO2 Eq.) • 30% reduction on 2005 (target about 13.3 Mt CO2 Eq.)

8. GHG Emissions • Commodity model provides • Livestock numbers, enterprise areas and input applications • Livestock emission factors provide • amount of methane produced by the animal on an annual basis • vary by animal type (dairy cow highest, horses, goats lowest) • Emissions factors for fertiliser & animal waste • Converted to Methane and Nitrous Oxide • GWP factors used to bring to common base of Carbon dioxide equivalents

9. Reference Scenario Process • GHG emissions modelling could start from a set of Baseline projections • Excludes policy developments not yet agreed • Designed to measure the impact of economic policy developments via scenario analyses (model shocks) • But Baseline projections may not be a best estimate of the future if we “know” that some policy change “is” going to happen • Projections are not Forecasts • We “know” that milk quotas will be abolished • Exact nature of their exit has only been agreed in last two weeks • For GHG purposes, Government effectively need our best forecast • That is including likely, but not yet agreed, policy changes

10. Reference Scenario Process • Means we need something slightly different from the Baseline projection • For GHG reporting purposes • For GHG purposes we’ll use the term “Reference Scenario” • So that projections incorporate future policy forecasts • For current Reference Scenario we assume that milk quota are abolished • we do not assume a WTO agreement

11. Reference Run Results2020 compared with 2005 • Total cattle population ↓ 11% • Suckler cow inventory ↓ 22% • Leads to decline in beef production of 9% • Cattle prices ↑14% • R3 Steer €297/100Kg • Output value ↑ 7% • Price increases offset impact of lower production • GHG of 10% reduction almost met

12. Results • Under the Reference Scenario GHG emissions from agriculture reduce by 8.5% • From 18.9 Mt CO2 Eq to 17.3 Mt CO2 Eq. • Decreases in suckler and ewe numbers and associated offspring • reduce GHG emissions • With no milk quota we see an increase in dairy cows • This offsets much of impact of reduced suckler cow and ewe numbers • Increase in milk yields per cow also contribute to increased emissions • Even though increase in emissions per cow is not proportionate to increase in milk yields per cow

13. Assumptions and uncertainties • Policy uncertainty is a major unknown • Agricultural policy is frequently changing • Global (WTO), EU (CAP Reform) and domestic (REPS) factors • Main driver in history • Agricultural policy • Main driver in the future • market based supply and demand factors • Macro outlook, exchange rates, population growth • Environmental policies – e.g. level of REPS participation • Macro factors such as €/US$ x rate • Impact on competitiveness, prices and production

14. Summary • Export orientation of Irish agriculture • Reason for high level of Ag GHG emissions • Agricultural GHG emissions are likely to fall over time • Probably by close to 10 percent • Any requirement for a reduction in agriculture GHG emissions in excess of 10% will have major implications • Policies continue to be important in determining future path of agricultural activity • Market conditions increasing in importance • Projected future level of emissions is likely to change

15. More information www.tnet.teagasc.ie/fapri