Mitigation of GHG emissions from crop production - governing factors and assessment

Transcription

1 GHG Emissions in Renewable Feedstock Production Mitigation of GHG emissions from crop production - governing factors and assessment Henning Kage Ingo Pahlmann, Astrid Knieß, Thomas Räbiger, Dorothee Neukam Institut für Pflanzenbau und Pflanzenzüchtung CAU Kiel M. Andres, R. Fuß, H. Hegewald, K. Kesenheimer, S. Koepke, T. Suarez, H. Flessa, M. Rohwer, S. Fiedler, G. Heintze, M. Pohl, J. Augustin, K. Dittert, U. Hagemann, G. Jurasinski, K.H. Mühling

2 GHG Emissions in Renewable Feedstock Production Arable crops as renewable feedstock in Germany 2.4 Mio ha energy crops ( 20% of arable land) 1.2 Mio ha silage maize for biogas 0.6 Mio ha oilseed rape (OSR) for biofuel 0.24 Mio ha industry crops (starch & oil) 2

3 GHG Emissions in Renewable Feedstock Production Outline Model based analysis of GHG emissions Maize & Oilseed rape (OSR) Indirect (Leaching) & direct N 2 O-emissions Short remarks on Crop rotation effects Preceding crop effects of OSR on GHG emissions in wheat production Assessment Specific emission vs. GHG balance as indicator Mitigation options 3

4 GHG Emissions in Renewable Feedstock Production Two coordinated projects GHG Emissions Maize Oilseed rape 4

5 GHG Emissions in Renewable Feedstock Production Calculation rules for biofuel GHG emissions in Europe follow(ed) IPCC Tier 1 Direct N 2 O emission: 1% of N input (fertilizer + crop residues) Indirect N 2 O emisson: Ammonia volatilization Mineral: 10% of total fertilizer-n Organic: 20% of total fertilizer-n 1% of ammonia volatilization as indirect N 2 O N-leaching: 30% of N input (fertilizer + crop residues) 0.75% of N leached as indirect N 2 O 5

6 Field Study Oilseed Rape % of OSR-cultivated area in Germany Hohenschulen Merbitz Ihinger Hof Berge Dedelow 5 sites Established in 2012 Randomized block design 4 replicates Treatments: N treatments Mineral: 0, 120, 180, 240 kg total N/ha (CAN) 180 kg Ammonia N as Org. N Digestates, digestates + Nitr. Inhib. Weekly measurements of N 2 O fluxes (static chamber method) NH 3 emissions after fertilisation 3 years continuously measured 6

7 Field Study Oilseed Rape Hohenschulen Dedelow Map of annual rainfall rates 3 sites with low rainfall! Berge Merbitz Ihinger Hof 7

8 Field study Silage/Biogas Maize Hohenschulen Guelzow Dedelow Dornburg Ascha 5 sites Established in 2011 Randomized block design 4 replicates Treatments: N rates, N form Focus on digestates 50, 75, 100, 125, 200% of opt. supply 100% N supply + nitrif. Inhib. 100% CAN (160 kg N/ha) as control Measurements Weekly measurements of N 2 O fluxes (static chamber method) Ammonia emissions after N application 3 years continuously measured 8

9 Model based analysis of GHG emissions Dynamic cropping system model Domestic model of our group Modular, component based Crop growth modules Winter oil seed rape (WOSR) Maize Wheat (as succeeding crop) Soil water and nitrogen transport model C/N based mineralisation model (4 Pools) Denitrification model (Del Grosso, APSIM, Daisy) Developed and calibrated on independent data 9

10 Simulated nitrogen dynamics OSR crop system Depth, SMN [kg N/ha] [mm/d] Site: Hohenschulen 100% CAN Oilseed rape Fallow Wheat precipitation 770 mm cum N leaching cum mineralisation cum N uptake Rooting depth [cm] SMN [kg N/10cm] SMN 0-30cm measured Jul 2013 Oct 2013 Jan 2014 Apr 2014 Jul 2014 Oct 2014 Jan 2015 SMN 0-30cm [kg N/ha] SMN 0-60cm [kg N/ha] SMN 0-90cm [kg N/ha]

11 Model based analysis of GHG emissions Nitrogen leaching 11

12 Indirect N2O emissions OSR system: N leaching Scenario analysis sim. N Leaching after oilseed rape (OSR) at 180 kg N/ha Calcium ammonium nitrate (CAN), 23 years weather data Moderate level Annual rain Sand/silt/clay /18/ /31/ /29/ /78/ (mm) 16/68/16 (%) 12

13 Indirect N2O emissions OSR system: N leaching Scenario analysis sim. N Leaching after oilseed rape (OSR) at 180 kg N/ha Calcium ammonium nitrate (CAN), 23 years weather data Experimental years quite representative! Annual rain Sand/silt/clay /18/ /31/ /29/ /78/ (mm) 16/68/16 (%) 13

14 Indirect N2O emissions OSR system: N leaching Scenario analysis sim. fractional N Leaching OSR at 180 kg N/ha Calcium ammonium nitrate (CAN), 23 years weather data Crop residues not considered as input! Lower than IPCC estimate, but large variation! Annual rain Sand/silt/clay /18/ /31/ /29/ /78/ (mm) 16/68/16 (%) 14

15 Indirect N2O emissions maize system: N leaching Scenario analysis N Leaching after Maize at 180 kg N/ha (CAN or digestates), 29 years Higher level than for OSR, large variation, experimental years representative Annual rain Texture 807 Loam sandy Loam clayey silt (mm) s. Loam s. Loam 15

16 Indirect N2O emissions maize system: N leaching Scenario analysis frac. N Leaching after Maize at 180 kg N/ha (CAN or digestates), 29 years Near IPCC estimate Annual rain Texture 807 Loam sandy Loam clayey silt (mm) s. Loam s. Loam 16

17 Indirect N2O emissions maize system: N leaching Sensitivity analysis Yearly leaching vs. N input 17

18 Indirect N2O emissions maize system: N leaching Sensitivity analysis Yearly leaching vs. N input Slightly higher emission for digestates (probably understimated) max. Median min. 18

19 Indirect N2O emissions maize system: N leaching Sensitivity analysis - Leaching vs. N input 19

20 Indirect N2O emissions maize system: N leaching Sensitivity analysis DM-Yield vs. N input 20

21 Indirect N2O emissions maize system: N leaching Sensitivity analysis - Leaching vs. N input N Leaching increases linearly at N rates > Nopt 21

22 Indirect N2O emissions maize system: N leaching Sensitivity analysis frac. Leaching vs. N input At Nopt: Leaching < 30% for all sites! 22

23 Direct N2O emissions Direct N2O emissions Weekly measurements (whole years) Linear interpolation 23

24 Direct N2O emissions OSR system N2O fluxes and SMN (0-30 cm) for OSR crops Spring: Fertilisation Autumn: Mineralisation 24

25 Direct N2O emissions OSR system Simulated N2O N loss (kg N/ha) Simulated vs. measured yearly N2O-N-emissions Moderate overestimation by the model! Measured N2O N loss (kg N/ha) 25

26 Direct N2O emissions OSR system Sensitivity analysis sim. frac N2O emissions at varying N rates Emission fraction at about 0.5 % 26

27 Direct N2O emissions maize system N2O fluxes and SMN for silage maize crops Spring: Fertilisation 27

28 Direct N2O emissions maize system Simulated vs. measured monthly N2O-emissions simulated (kg N2O-N/ha) measured 28

29 Direct N2O emissions maize system Fractional N2O-emissions Sensitivity analysis - 29 years local weather 29

30 GHG Emissions in Renewable Feedstock Production Model based analysis of GHG emissions Models simplify reality Validated models allow extrapolation and analysis GHG emissions in bioenergy crops maize and OSR N-Leaching Very variable (sites/years) OSR lower than maize Low values up to Nopt Direct N2O-emissions Lower than expected in OSR system Variable in maize systems with some sites higher than IPCC tier 1 Linear responce to N input because of high nitrification share? 30

31 GHG Emissions in Renewable Feedstock Production Outline Model based analysis of GHG emissions Maize & Oilseed rape (OSR) Indirect (Leaching) & direct N2O-emissions Crop rotation effects Preceding crop effects of OSR on GHG emissions in wheat production Assessment Specific emission vs. GHG balance as indicator Mitigation options Short summary 31

32 Crop rotation effects Accounting of preceding crop effects? Szenario Reduction of WOSR cultivation for FAME production Increased wheat acreage for bio-ethanol with more 2nd wheats in crop rotation Approach Calculation of spec. emissions for bio-ethanol from 1st and 2nd wheat crops according to RED Difference emission as possible credit for OSR-FAME? 32 32

33 Crop rotation effects Wheat after different preceding crops 1st wheat 2nd wheat 3rd wheat Foto: Sieling

34 Crop rotation effects Wheat yield (t/ha) after different preceding crops as a function of N fertilisation Seed yield (t/ha) Yopt Nopt N-fertilisation (kg N/ha) , (Sieling et al. 2005) 1st wheat 2nd wheat

35 Accounting for crop rotation effects Optimum N level (Nopt), Yield at Nopt and spec. GHGemissions of Ethanol from 1st and second wheat Data: fiel trials Hohenschulen Rotation position Nopt [kg N/ha] Yopt [t/ha] GHGNopt [g CO2eq/MJ] 1st wheat nd wheat Difference Calculations following BioGrace 35

36 Accounting for crop rotation effects Preceding crop effects of OSR Higher specific GHG emissions for 2nd wheat crops Difference approx. 4-7 g CO2eq/MJ Crop rotation credits for OSR? Is it meaningful to calculate crop specific GHG-Balances? 36 36

37 GHG Emissions in Renewable Feedstock Production Outline Model based analysis of GHG emissions Maize & Oilseed rape (OSR) Indirect (Leaching) & direct N2O-emissions Crop rotation effects Preceding crop effects of OSR on GHG emissions in wheat production Assessment Specific emission vs. GHG balance as indicator Mitigation options Short summary 37

38 Assessment Renewable Feedstock GHG balance vs. spec. emission GHG balance Energy output*ref. em. Production emissions Spec. emission Energy output*ref.em. Production emissions Unit: g CO2-äqu. / ha Unit: g CO2-äqu. / MJ GHG-Reduction 38

39 GHG balance vs. spec. emission OSR- bzw. FAME-Yield OSR yield (t/ha) FAME yield (GJ/ha) as a function of N fertilisation N fertilisation (kg N/ha) Yield function: fiel trial Hohenschulen, (plot yields -10%, )

40 GHG balance vs. spec. emission GHG-Balance (per ha) and Efficiency (per MJ) GHG reduction by FAME (t CO2eq/ha) Spec. GHG reduction by FAME (t CO2eq/MJ) as a function of N fertilisation N fertilisation (kg N/ha) Max. CO2-balance 50% GHG/MJ

41 Mitigation options Options for GHG mitigation Yield level Yield is 3 to 10 times more important than emissions in GHG-balance (but not in spec. GHG reduction) Fertilisation More precise N rates (year, site, within field) Substitution of mineral N with organic N? (no production emissions, but less efficient, higher N2O-emissions) GHG- efficient mineral fertiliser Stabilised fertilisers? Crop rotation/cropping systems Taking advantage of crop rotation effects (Legumes, catch crops) Accounting benefits of renewable feed stock crops on following crops? Managing mineralisation/immobilisation dynamics (after harvest) 41 41

42 GHG Emissions in Renewable Feedstock Production Funding Thank you for your attention!

43 GHG Emissions in Renewable Feedstock Production GHG reduction assessment Based on specific emission (not on balance per area!) 43

44 GHG Emissions in Renewable Feedstock Production Specific GHG emission bioenergy (simplified) Field production emissions Inpi = inputs for production (fertilizer, diesel, ) Emfi = CO2-Emission per unit input Inp Emf direct N O i i N2O Field emissions (IPCC tier I) indirect N2Oem 296 Transport Re finery Allocation (Y L) * [E] * CVE 2 em Allocation Biofuel energy yield Total energy in yield / Energy of yield fraction for bioenergy Y L [E] CVE = DM-Yield = Losses (harvest+storage) = Energy concentration = Conversion efficiency 44