Henning Kage, Antje Herrmann, Klaus Dittert, Andreas Pacholski, Babette Wienforth. Competence Center Biomass Utilisation
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1 Versuch Hohenschulen Multi-Criteria Assessment and Optimization of Cropping Systems: What do we have and what do we need Experiences from a Case Study for Bioenergy Cropping Systems Henning Kage, Antje Herrmann, Klaus Dittert, Andreas Pacholski, Babette Wienforth Sponsored by EU Regionalprogramm 2000
2 Outline Evaluation of Cropping Systems Bioenergy Case study 4 Levels of Evaluation Experimental Results Energy Balance/GHG Calculations Combined Data/Modelling Approach System Model Conclusions
3 Problems in Assessment and Optimization of Cropping Systems Multiple Criteria Strong Interaction with local Soil and Weather Conditions Rapid Changes in Boundary Conditions Social Economical Technical Environmental (Climate Change) Tradition and Experience not longer sufficient Optimum Cropping system: Running Target!
4 Case Study: Evaluation of a Bioenergy Cropping System Bioenergy Main Driver for Change in Cropping Systems Effects on GHG reduction? Environmental Ressources & Services?
5
6 Open Questions Engergy efficiency Energy balances GHG mitigation effects Land Use Efficiency/Productivity Optimal Crop Rotation (Alternatives to maize mono-culture) Water Use Efficiency Ground water recharge Nitrogen Use Efficiency N-use efficiency N-leaching/Groundwater Quality Ammonia- und N 2 O losses Soil fertility Carbon balances (GHG effects)
7 Open Questions Engergy efficiency Energy balances GHG mitigation effects Land Use Efficiency/Productivity Optimal Crop Rotation (Alternatives to maize mono-culture) Water Use Efficiency Ground water recharge Nitrogen Use Efficiency N-use efficiency N-leaching/Groundwater Quality Ammonia- und N 2 O losses Soil fertility Carbon balances (GHG effects)
8 Approach of Biogas-Expert 2-Year Field Experiments on two Sites (Sandy Soil, Loamy Sand Soil) Experimental Evaluation of: Biomass Production Methane yield per Hectar N-uptake, N-utilisation efficiency, N-losses Finished 2-Year Field Trial on Marsh site Running Development of dynamic Models Dry matter production N-uptake N-losses (NH3, N2O, Leaching) Running
9 Hohenschulen (HS) Karkendamm (KD) Hohenschulen (HS) mm - Ca. 8.5 C - Sandy loam Karkendamm (KD) mm - Ca. 8.8 C -Sand Climatic water balance Average annual values
10 Experimental sites Hohenschulen (Sandy Loam Soil) Karkendamm (Sandy soil) Cropping Sequences Evaluated Maize - Silage Wheat - Grass (intercrop) Permanent Grassland (4 cuts y -1 ) Maize Maize Maize Maize (Mono crop) Maize - Grain Wheat - Catch Crop (Mustard) 10
11 N-treatments Fertilizer type Mineral-N (CAN) Pig slurry Cattle Slurry Biogas slurry (Co-Ferment) Biogas slurry (Energy Crops) N-Amounts 0 kg N ha -1 (N1) 120 kg N ha -1 (N2) 240 kg N ha -1 (N3) 360 kg N ha -1 (N4)
12 Hohenschulen (Sandy Loam Site) Christian-Albrechts-University 240 plots Kiel Karkendamm (Sandy Site) Competence 96 plots Center Field Experiments Fall 2006 Spring Types of Fertilizer / Manure, 3 N Levels + Control 12
13 Dry Matter Yield 18.5 t 17.3 t 17.1 t 16.7 t 10.9 t Sandy Loam Sandy Soil Dry Matter Yield [t ha -1 a -1 ] Average N3, Average of Fertilizer Type
14 Outline Evaluation of Cropping Systems Bioenergy Case study Experimental Results Energy Balance/GHG Calculation Combined Data/Modelling Approach Simulation Modelling Conclusions
15 Field Energy Input Average of N-Types
16 Total Energy Balance (Including Estimates for Conversion) Numbers in Chart gives Difference between Output and Input Sandy Loam Site Sandy Soil Site
17 Energy Balance Largely positive (up to 70 GJ/ha) Output/Input Ratios From 4.2 to 2.7 [GJ/GJ] Fertilizer & Diesel Major Inputs Frequent Harvest Operations reduces Efficiency
18 Calculation of CO 2 -Savings CO bal 2 (Y L) * CVE * Refem + Heatbonus + Slurrybonus Inp Emf NO em CH 25 4em NH em ΔSoilC 3.6 i = i Y = DM-Yield L = Losses (6%) CVE = Conversion efficiency biogas plant (1283 kwh/t substrate DM) Refem = Reference Emission (0.627 kg CO 2 äqu./kwh) Inp i = Inputs for Production (Fertilizer, Diesel, ind. Inputs) Emfi i = CO2-Emission per Unit of Input (NPK, Diesel, )
19 N 2 O-N [kg N ha -1 ] Wheat Maize March 2007-March 2008 Cumulative N 2 O- Emissions 2007 and 2008 Sandy Loam (Hohenschulen) N 2 O-N [kg N ha -1 ] Wheat Maize March 2008-March Cont. Min- N N1 C.- Slurry Min- N C.- Slurry N3 Mono Biogas R N1 N kg N ha -1
20 N 2 O-N [kg N ha -1 ] Grass Maize March 2007-March 2008 Cumulative N 2 O- Emissions 2007 und 2008 Sandy Soil Site (Karkendamm) 0 6 March 2008-March 2009 N 2 O-N [kg N ha -1 ] Cont. Min- N N1 C.- Slurry Min- N C.- Slurr y N3 Mono Biogas R. N1 N3 Maize kg N ha -1 Grass kg N ha -1
21 N 2 O-Emission Measurements Emissions Large Site Effects Sandy Loam Site Hohenschulen > Sandy Soil Site Karkendamm Large Year Effects 2007 > 2008 (Wet Summer 2007) Large Crop Effects Maize>Wheat Maize>Grass Small Fertilizer Effects Mineral N same as slurry Not all treatments could be measured: Final Assessment needs Modelling!
22 CO 2 -Saving per ha Numbers in Chart gives Difference between Output and Input Sandy Loam Site Sandy Soil Site
23 Comparison of CO 2 -Savings per ha for different Bio-Energy Chains 20 Heat Electricity & Heat Fuel Chains t CO 2äq /ha t 10 t 5 2 to 3 t 0 Wood- Chips- Heating Cereal- Grain- Heating Biogas (Electr. Biogas (Electr. & Heat.) Biogas Wood- (In- Chips- Feeding) Power- Plant Straw Co- Combustion Wood- Chips Co- Combustion Biodiesel Ethanol (Wheat Biogas (Fuel) stof) Source: Isermeyer et al. (2008).
24 CO 2 -Balance Considerable CO 2 -Savings possible Biogas-Chain (Electricity/Heating) better than Bio-Fuel Chains Own values slighty higher than Reference study Relative Importance for GHG-Balance: Yield>Energy Input Land use > N 2 O-Emissions> Energy Input Biogas Plant, CH4 Emissions Biogas Plant >> NH 3 -Emissions
25 Experimental Analysis/GHG Calculation Interesting Results, Comprehensive Data Set But: 2 Sites x 2 Year may not be representative for Relevant Population of Environments (Sites, Climate, ) Many Measurements give not goal parameters Simulation Modelling?
26 Comprehensive System Model Desirable, but Validated Crop and other Sub-Models not available Difficult to paramterize Lack of Accuracy?
27 Outline Evaluation of Cropping Systems Bioenergy Case study Experimental Results Energy Balance/GHG Calculation Combined Data/Modelling Approach System Model Conclusions
28 Combined Data/Modelling Approach Regularly Non-Destructive Measurement of Leaf Area Index Model Modules used for Interpolation and Calculation of Additional Data (Crop Height, fractional Radiation Interception, Root Growth) Coupling with Modules for Calculation of Evapoptranspiration Soil Water Balance Computation of Ressource Use Efficiency for Radiation Water
29 Semi-Descriptive Model Contructed using HUME-Modelling Environment
30 Measured and Interpolated Leaf Area Index for the Cropping Systems on the Sandy Loam Soil Site Maize Maize Silage-Wheat Grass Maize Grain-Wheat Catch-Crop Maize
31 Measured and Interpolated Leaf Area Index for the Cropping Systems on the Sandy Soil Site Maize Maize Grass 1st 2nd 3rd 4th cut 1st 2nd 3rd 4th cut
32 Maize Maize Silage-Wheat Grass Maize Grain-Wheat Catch-Crop Maize
33 Maize Maize Grass 1st 2nd 3rd 4th cut 1st 2nd 3rd 4th cut
34 Simulated Water Contents for different Cropping Systems Sandy Loam Site Christian-Albrechts-University Kiel Maize Maize Silage-Wheat Grass Maize Grain-Wheat Catch-Crop Maize
35 Simulated Water contents for different Cropping Systems Sandy Soil Site Maize Maize Grass
36 Radiation Use and Radiation Use Efficiency of Cropping Systems for Biogas Substrate Production Site/ Soil Type Sandy Loam Sandy Loam Sandy Loam Sandy Soil Sandy Soil Rotation Maize Mono Silage Wheat - Grass Maize W.-Wheat- CC-Maize Mais Mono Grass Cum. Int. PAR [MJ/m 2 ] (% of Avail.) 748 (42%) 1000 (55%) 940 (52%) 763 (43%) 1455 (79%)
37 Radiation Use and Radiation Use Efficiency of Cropping Systems for Biogas Substrate Production Site/ Soil Type Rotation Cum. Int. PAR [MJ/m 2 ] (% of Avail.) DM Yield [g/m 2 ] LUE [g/parint] Sandy Loam Maize Mono 748 (42%) Sandy Loam Silage Wheat - Grass Maize 1000 (55%) Sandy Loam W.-Wheat- CC-Maize 940 (52%) Sandy Soil Mais Mono 763 (43%) Sandy Soil Grass 1455 (79%)
38 Radiation Use and Radiation Use Efficiency of Cropping Systems for Biogas Substrate Production Site/ Soil Type Rotation Cum. Int. PAR [MJ/m 2 ] (% of Avail.) DM Yield [g/m 2 ] LUE [g/parint] ft Tact/Tpot Sandy Loam Maize Mono 748 (42%) Sandy Loam Silage Wheat - Grass Maize 1000 (55%) Sandy Loam W.-Wheat- CC-Maize 940 (52%) Sandy Soil Mais Mono 763 (43%) Sandy Soil Grass 1455 (79%)
39 Water Use and Water Use Efficiency of Cropping Systems for Biogas Substrate Production Site/ Soil Type Rotation DM Yield [g/m2] CumTI act [mm/a] TUE [g/l] Sandy Loam Maize Mono Sandy Loam Sandy Loam Silage Wheat - Grass Maize W.-Wheat- CC-Maize Sandy Soil Mais Mono Sandy Soil Grass
40 Water Use and Water Use Efficiency of Cropping Systems for Biogas Substrate Production Site/ Soil Type Rotation DM Yield [g/m2] CumTI act [mm/a] TUE [g/l] Cum. E act [mm/a] WUE [g/l] Sandy Loam Maize Mono Sandy Loam Sandy Loam Silage Wheat - Grass Maize W.-Wheat- CC-Maize Sandy Soil Mais Mono Sandy Soil Grass
41 Water Use and Water Use Efficiency of Cropping Systems for Biogas Substrate Production Site/ Soil Type Rotation DM Yield [g/m2] CumTI act [mm/a] TUE [g/l] Cum. E act [mm/a] WUE [g/l] Cum. Drainage [mm/a] Sandy Loam Maize Mono Sandy Loam Sandy Loam Silage Wheat - Grass Maize W.-Wheat- CC-Maize Sandy Soil Mais Mono Sandy Soil Grass
42 Abiotic Ressource Use Efficiency Maize Mono-Culture System is a Poor Collector but Efficient User of available Radiation Energy (Opposite true for Grass) High TUE of Maize is only partly leveled off by high Evaporation Losses leading to superior WUE Drainage Rate was much more dependent on Site than on Cropping System
43 Outline Evaluation of Cropping Systems Bioenergy Case study 4 Levels of Evaluation Experimental Results Energy Balance/GHG Calculations Combined Data/Modelling Approach System Model Conclusions
44 System Model First Step Maize Model with Soil Water Budget Modelling Approach Szenario Calculations
45 New (updated) Maize Modell Modules Crop Growth Dry matter production LUE approach Partitioning Allometric, Temp.-sums Leaf Area Expansion SLA (variable) aproach LUE = light use efficiency SLA = specific leaf area Development CERES-MAIS, adopted Soil Water Budget Potential Based Layer Model Evapotranspiration Penman-Monteith
46 Independent Validation of Silage Maize Model 3 Sites 3 Years 6 Cultivars n= 470
47 Szenario Calculation Magdeburg Warm + Continental Regensburg Warm + Moderate humid Hohenschulen Colder & Humid Approach Data 34 Years Weather Data ( ) 3 Sites with differin Soil and Climate Conditions Calculation of Average Yield Yield Potential Additioanl Water Requirement
48 Results Scenario Calculations Site Average sim. Yield [t/ha] cool + humid 18.3 warm + dry 19.6 warm + moderat humid 21.6
49 Results Scenario Calculations Site Average sim. Yield [t/ha] Yield Potential [t/ha] Temperature- und Radiation limited Without Drought Stress cool + humid warm + dry warm + moderat humid
50 Results Scenario Calculations Site Average sim. Yield [t/ha] Yield Potential [t/ha] Additional Irrigation Water mm Temperature- und Radiation limited Without Drought Stress cool + humid warm + dry warm + moderat humid
51 GHG-Balance Szenarios ??
52 Reduction Effects on Wheat Supply
53 Reduction Effects on Wheat Supply
54 Scenario Calculations Waterer Supply is limiting Yield Potential for Energy Maize on most Sites Average Yields > 22 t/ha achievable only with Irrigation Up to 30 t DM/ha achievable with high Irrigation Amounts CO 2 Reduction higher on warmer Sites in Southern Germany Effect on Wheat supply also smaller on Sites in Southern Germany
55 Summary on Bioenergy Study Maize Monoculture is sligtly superior in Energy Balance GHG Balance Water and Light use efficiency But: Crop Rotations Reduce Risk of Pests Increase Biodiversity May prevent excessive N-Leaching Losses
56 What we have learne so far Our Model approach seems useful Flexibel, modular Model integration feasible! Combination Field Experiment and Simulation Modelling sucessful Simulation models of the shelf may not exact enough System Modelling is the only way to generalise experimental Results
57 Assessment of Cropping Systems What we have Experimental Results from a (decreasing) number of Field Experiments Data from Farm Surveys Tools to Calculate Balances of different Indikators Increasingly Non-Destructive Sensor Data (Some) Models for Crop Growth and Soil Processes What we need More efficient ways to combine Experimental Data and Simulation Modelling More Models within Indicator Systems Modular Crop & Soil Models Modelling Standards
58 Thanks for your Attention!
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