1. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 1 GHG balance from biofuel crop cultivation in Europe – simulations with DNDC-EUROPE – Adrian Leip, Lorenzo Orlandini European Commission – Joint Research Centre Institute for Environment and Sustainability Climate Change Unit

2. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 2 Outline → Introduction → The CAPRI – DNDC-EUROPE framework → Set-up of bio-crop simulations → Results → Conclusions

3. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 3 Introduction

4. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 4 Benefit of biofuels? → Biofuels: is the cure worse than the disease? (OECD, 2007) → N 2 O negates global warming reduction (Crutzen et al., 2007) → Support for energy crops is unlikely to be justified. It is more important to direct support to second generation crops (Executive Summary MEACAP project, 2007)

5. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 5 Global Biofuels production Million litres Brazil16,500 United States16,230 China2,000 European Union950 India300 1000 tonnes Germany2,662 France743 Italy447 United Kingdom192 Austria123 Worldwatch Institute, 2006 Fuel Ethanol Producers in 2005 EU biodiesel Producers in 2006 European Biodiesel Board, 2007

6. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 6 European legislation Biofuel directive 2003 → Target for min. proportion for biofuels: 5.75% (energy content) of all petrol and diesel for transport purposes by dec. 2010 Commission Communication: Limiting Global Climate Change to 2ºC (2007) and EU Spring Summit 2007 → Binding targets for the overall share of renewable energy (20%) and for the share of biofuels in petrol and diesel (10%) in 2020 Public consultation "Biofuel issues in the new legislation on the promotion of renewable energy“ → Sustainability criteria Achieving a minimum level of GHG savings Avoiding major reduction in carbon stocks through land use change Avoiding major biodiversity loss from land use change

7. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 7 CAPRI - DNDC-EUROPE framework

8. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 8 CAPRI-MODEL A “multi-purpose” modeling system for EU’s agriculture, allows to analyze → Market policies (administrative prices/tariffs/preferential agreements) → Premium systems/quotas/set-aside at regional level → Environmental policies (standards/market solutions) → Changes in exogenous drivers (population/inflation/exchange rates/consumption behavior/technical progress)

9. Li et al., 1992, 2000,2003

10. Socio-economic database DNDC European national and international statistics GIS environmental database CAPRI Regional statistics National market/trade Regional agricultural system + economic and environmental indicators Policy framework Global trade framework Production level and farm input estimation at spatial calculation units Agricultural land use map Definition of environmental scenario Aggregation to modeling spatial units Climate data and N deposition Soil information CAPRI - DNDC-EUROPE Farm Management Simulation at modeling spatial unit Environmental indicators -N2O, N2 - NOx - CH4 - NH3 - Nitrate leaching - Carbon Stocks - … Environmental indicators -N2O, N2 - NOx - CH4 - NH3 - Nitrate leaching - Carbon Stocks - … Geographic data Developed in CAPRI-DynaSpat Leip et al, 2007, Biogeosciences Discussions For each Spatial Unit: -Crop area and livestock density -Mineral fertilizer and manure application rate by crop and polygon -Potential yield

11. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 11 CAPRI-DynaSpat Agricultural Land Use Maps

12. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 12 Model setup

13. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 13 Set-up of DNDC Climate → MARS 50 km2, daily (1982-2006) → Deposition: EMEP Soil: → ESB 1 km raster data (Hiederer & Jones: SOC, base saturation, clay content, packing density) → 65 year spin-up run Land Use: → CAPRI-DynaSpat Land Use Map Farm Management → N application: IFA/FAO + CAPRI → Potential yield: MARS + CAPRI → Sowing dates: Bouraoui & Aloe. → Irrigation: FAO → Other farm data: Li et al. GIS Database HSMU-layer DNDC- EUROPE * * modified DNDC V.89 to accommodate simulations of HSMU for in- and output handling

14. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 14 Introduction of crop rotations → Rape seed is characterized by a deep rooting systems which favors nutrient availability for the succession crop (reduced tillage possible) → Rape and sugar beets with a rotation share of more than 25% reduced productivity → It is possible to grow rape and sugar beet in one rotation of ≥ 5 years Source: farmers pages, industry and government recommendations, web-articles (Germany, France, Italy)

15. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 15 Estimation of crop rotation - approach → Key crops: Rape and Sugar beet → Cereals in rotation: wheat (soft, durum), barley, rye → Procedure (according to farm practice recommendations) Select ‘polygons’ in land use map with rape or sugar beet cultivation (minimum agricultural area in ‘polygon’) Identify cereals in these ‘polygons’ for rotations –If only one cereal is grown, repeat it (3-years rotation) –If no cereal is grown in HSMU, add most frequent cereal in region –Max. 4 cereals for 5-years rotation Rape and sugar beet are grown in a single rotation only if 3 cereals cultivated

16. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 16 Crop rotations – results Examples → Barley – Rape – Barley → Wheat – Rape – Wheat → Barley – Sugar beet – Wheat → Wheat – Rape – Wheat – Barley – Sugar beet → Rye – Rape – Wheat – Barley Result: Rotations are generated consistent with national statistics, environmental conditions, and farm practice recommendations

17. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 17 Results

18. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 18 RESULTS

19. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 19 N2O fluxes by crop

20. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 20 Carbon stock changes by crop cerealsrapesugarbeet

21. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 21 Calculating CO2-savings Adapted from Crutzen et al., 2007 Savings of CO 2 emissions kg CO 2-eq Savings of CO 2 emissions kg CO 2-eq Harvest kg C Harvest kg C Conversion efficiency kg C in biofuel/kg C in dry biomass Conversion efficiency kg C in biofuel/kg C in dry biomass Savings efficiency kg C in fossil fuel/kg C in biofuel Savings efficiency kg C in fossil fuel/kg C in biofuel Molecular ratio kg CO 2 /kg C Molecular ratio kg CO 2 /kg C

22. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 22 Yield 1300kg C Conversion efficiency 0.58 kg C in biofuel/kg C in feedstock biocrop (after Crutzen et al., 2007) Avoided CO2 from oil ca. 3000kg CO2 N2O emissions ca. 1500kg CO2-equivalent - direct (simulated)1.70kg N2O-N527kg CO2-equivalent - indirect (estimated)3.2kg N2O-N992kg CO2-equivalent (N leaching)100kg N (N volatilization)70kg N GHG balance biodiesel from rape Saving: ca. 50% r c = 0.44 g C / g dry biomass f energy = 1.07 All values expressed per hectare and year

23. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 23 Yield 9500kg C Conversion efficiency 0.37 kg C in biofuel/kg C in feedstock biocrop (after Crutzen et al., 2007 for corn) Avoided CO2 from oil ca. 13000kg CO2 N2O emissions ca. 1100kg CO2-equivalent - direct (simulated)1.10kg N2O-N527kg CO2-equivalent - indirect (estimated)2.5kg N2O-N992kg CO2-equivalent (N leaching)80kg N (N volatilization)50kg N GHG balance bioethanol from sugarbeet Saving: ca. 90% r c = 0.44 g C / g dry biomass f energy = 1.07 All values expressed per hectare and year

24. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 24 Conclusions → The current (preliminary) simulation results suggest significant GHG savings (CO2 savings vs. N2O emissions) Uncertainty remains high → Crop specific differences (farm management!) and regional can be decisive for the climate impact of biofuels Current simulation results suggest a higher greenhouse gas reduction for ethanol from sugar beet compared to biodiesel from rape seed

25. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 25 → The methodology allows a detailed analysis of N2O emissions from biofuel production Direct N2O fluxes are simulated, indirect fluxes estimated from simulated volatilization and leaching rates Consistency with cultivation pattern and farming practices → Next to N2O fluxes also the effect on soil carbon stocks must be considered Both positive and negative effects on the GHG balance are possible Considering also energy input and C stock changes, GHG savings might become insignificant

26. JRC-AL – 4 th International Nitrogen Conference – Costa do Sauipe – 01.10.2007 26 Acknowledgements → Changsheng Li, University of New Hampshire → Giulio Marchi, Joint Research Centre → Markus Kempen, Universität Bonn → Renate Köble, Joint Research Centre (now IER Stuttgart) → Wolfgang Britz, Universität Bonn (now Joint Research Centre) CAPRI