Modelling the Marginal Abatement Cost of Reducing Nitrogen Pollution in Agriculture

2. Modelling the Marginal Abatement Cost of Reducing Nitrogen Pollution in Agriculture Aksana Chyzheuskaya, Dr. Cathal O’Donoghue, Dr. Stephen Green, Mark Gibson and Dr. Stan Lalor Teagasc Rural Economy and Development Programme (REDP) IMA Conference Ashtown, Teagasc May 18th, 2012

3. Component of the chlorophyll molecule • Component of amino-acids (essential for protein synthesis) • Essential for carbohydrates utilization • Component of enzymes • Stimulative of root development and activity • Supportive to uptake of other nutrients [W. Ritter] Motivation: Environmental and Economic

4. Theory: How to Mitigate N pollution • Restrict Excessive Inputs • Soil Testing • Higher Performing Cattle breeds • Inorganic / Organic Fertiliser Reduction • Livestock Numbers Reduction • Change of Feed Mix • Calibration of Spreading Equipment/ Injection vs overland spreading • Restrict N Losses • Livestock exclusion (fencing off streams) • Wetland Development/ Restoration • Riparian Buffer Zones/ Filter Strips • Cover crops/ minimising periods when the soil is left bare • Timing of Fertiliser Application • Restricting of old grassland ploughing

5. Motivation: Environmental and Economic • Where the resources are limited and a number of option exists the policy decisions should be made on the basis of economic efficiency, or cost-effectiveness. • A pollution abatement measure is cost-effective, if it attains a target at a minimum cost. • MAC represents the cost of different mitigation measures to reduce pollution from a number of sources.

6. MAC as a tool for Policy Decision-Making • Two types of MACC: • 1. Bottom-up engineering based • Involves modeling individual technologies and measures and their abatement potential – • a static “snap shot” – reduction of emissions and average costs associated it them. • 2. Top-down macroeconomic general equilibrium models. (Moran et. al. 2009 ) • Top-down models take emission reduction as exogenous and • costs are calculated on the economy –wide level.

7. Step 1 – Data Analysis • NFS 2008: collected annually since 1972 • Dairy + Dairy Other Farms: defined on the basis of the prevalent enterprise on the farm GIS data How we did it. • Scenarios: • Baseline • Fert. reduction by 10% • Fert. reduction by 20% • LU reduction to achieve 170kgN/ha • LU reduction by 20% • Use new feed mix • Fencing off streams • Increase breeding index • Slurry Efficiency • Step 2 – Estimation of Production and Cost functions • Estimate Production and Cost Functions for each enterprise using Log – Polynomial Regression, • dependent log variables are GO, DC per LU • explanatory variables related enterprise variables • Y = B0+B1X1+....BnXn + e • Save betas and error terms for microsimulation. • Step 3 – Microsimulation • Change independent variables according to scenarios • Estimate new dependent variables using changed Xs and betas & error terms from previous estimation Step 4 -Calculate Nitrogen production MAC

8. How we did it: MAC • MAC: Δprofit/ΔN • Farm Profit: = Incomei – Variable Costi – Fixed Costi • Output: Incomeij = βjXij + ɛyij • Cost: Variable Costij= γjXij + ɛcij • Where X is ... • Δ Profit = Profitsimulated - Profitoriginal • Nitrogen: N = F(animal numbers, fertiliser) • ΔN = Nsimulated - Norginal

9. Results

10. If the ranking was the same for all farms, the curves would be parallel. But they cross, meaning no measure is strictly dominant for all farms Results: Cumulative MAC for individual farms Farms Ranked on the basis of average country most cost-effective measure – Increasing the breeding Index

11. Policy Implications • Any environmental policy aiming at Nitrogen reduction should be flexible. Rigid policies in command- control manner will not yield efficient results. • Our model aims at assisting farmers in cost assessment of the possible reduction measures.

12. Thank you!!!