Report. Regional potential analysis biomass as energy feedstock in regional economic cycles in region Havelland-Flaeming

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1 Report Regional potential analysis biomass as energy feedstock in regional economic cycles in region Havelland-Flaeming in context of CENTRAL Europe-Project RUBIRES Rural Biological Resources in Regions Submitted by: Dr. Philipp Grundmann 1, Dr. Hilde Klauss 1 Prof. Dr. Hans-Peter Piorr 2, Dipl. Geoökol. Sybille Brozio 2, Dipl.-Ing. (FH) Mirella Zeidler 2 1 Leibniz Institute for Agricultural Engineering Potsdam-Bornim Max-Eyth-Allee 100, D Potsdam, Germany 2 University of Applied Sciences Eberswalde, Faculty Land use and nature protection Friedrich-Ebert-Str. 28, D Eberswalde, Germany Contractor: Regionale Planungsgemeinschaft Havelland-Flaeming his operation is implemented through the CENTRAL EUROPE Programme co-financed by the ERDF T Project partners:

2 Table of Contents 1 Assignment of task and procedure Assignment of tasks Procedure of Processing Region Havelland-Flaeming Geographical and climate conditions Agriculture in the region Havelland-Flaeming Current Situation of bioenergy production Bioenergy potential analysis Method of potential calculation Reference scenario Site potential Economic potential Bioenergy product lines and input material Spatial data Administration units Land use Precipitation Soil fertility rate Statistical data Bioenergy modelling Reference: Bioenergy potential based on agricultural statistics Reference scenario: biogas Reference scenario: bio fuels Site potential based on geodata and biomass yield model Site scenario: biogas Site scenario: biodiesel Site scenario: bioethanol Modelling transformation scenarios The resource use model SunReg Limitations of quantitative modelling and the SunReg model Transformation scenarios... 38

3 5.2.1 Scenarios with increasing mineral oil prices Scenarios increasing product prices Scenarios expansion of bioenergy production Scenario simulation results Evaluation of the transformation scenarios simulation results Calculation of cumulated energy demand and emissions of greenhouse gases Evaluation of results Potential for scenario expansion of bioenergy production Data transfer from economic model to biomass yield model Evaluation of crop rotations Bioenergy potential of bioenergy crop rotation scenarios Summary and concluding remarks Literature and data sources Literature Data sources of statistics and geo data

4 Figures: Figure 1: Climate diagram with precipitation (blue) and temperature (red) per month at weather station Potsdam (WIKIPEDIA 2008) Figure 2: Nature parts of federal state Brandenburg Figure 3: Structure of agricultural farms 2007 (Statistische Ämter des Bundes und der Länder, 2009) Figure 4: Livestock farming (cattle and pigs) in the counties Figure 5: Agricultural land use: conventional and organic farming system in 2008 (INVEKOS 2009) Figure 6: Bioenergy sites in the investigation area Figure 7: Biomass yield model (July 2007) Figure 8: Calculation and aggregation procedure of available bioenergy material Figure 9: The average ferments of 438 biogas plants in Germany 438 (top agrar 2007) Figure 10: Figure 11: Figure 12: Figure 13: Administration levels in Brandenburg and investigation area Havelland- Flaeming INVEKOS: Arable land and grassland differenced by farming system in region Havelland-Flaeming Average and minimum of the annual precipitation in the investigation area Mean Ackerzahl per municipality (GEMDAT 1995) in the region Havelland- Flaeming Figure 14: Acreages of dominant crops in region Havelland-Flaeming Figure 15: Figure 16: Figure 17: Figure 18: Figure 19: Figure 20: Figure 21: Biomass yield of the year 2007 in 100 % dry matter of dominant crops in the region Havelland-Flaeming Average yield in 1991 to 2007 of dominant crops in the region Havelland- Flaeming Reference scenario: amount of potential biogas plants in the region Havelland-Flaeming Site scenario biogas based on manure and silages with balanced humus carbon (0 kg Humus-C ha -1 ) and precipitation scenarios normal and dryness Site scenario - biodiesel: yield level of winter rape in region Havelland- Flaeming (precipitation scenario normal ) Share of arable land by crops in the basic scenario and the transformation scenarios: Area of cultivation areas of dominant crops Share of energy crops cultivation according to their uses for bioethanol, biodiesel or biogas as percentage of the total available arable land Figure 22: Area dedicated to energy crops cultivation (in ha) Figure 23: Gross bioenergy production potential (in ha) Figure 24: Figure 25: Cumulative energy requirement (CER) in agricultural production by uses of agricultural products in the Havelland-Flaeming region (in GJ) Greenhouse gas emissions from agricultural production by uses of agricultural products in the Havelland-Fläming region (in kg CO 2 -equivalent) Figure 26: Modelling course of crop rotations per municipality

5 Figure 27: Figure 28: Figure 29: Crop rotations in site scenario (bym) and economic scenarios Bioenergy (BE) + 50% / + 100% / + 200% : Percentage of cultivation areas of dominant crops Evaluation of the sustainability of crop rotations in bioenergy scenarios Bioenergy + 50% / + 100% / + 200% : Percentage of municipalities with amount of several evaluations higher than Evaluation of bioenergy crop rotations in municipalities of the bioenergy scenario + 200%: amount of evaluation values higher than Figure 30: Livestock farming (cattle and pigs) in the counties 2007 and Figure 31: Figure 32: Biogas potentials of scenario Bioenergy +200% and potential of alternative biogas input ferments Biogas potentials (biomass yield model / Bioenergy +200% / Bioenergy +200% plus) in comparison to existing and planned biogas sites Figure 33: Energy potential [GJ] of bioethanol, biodiesel and biogas in all scenarios

6 Tables Table 1: Agricultural biogas sites and their capacities... 9 Table 2: Calculation of available amount of grain and rapes seed from national statistics Table 3: Dry matter, cereal straw index (KTBL 2005, Piorr 2007) and starch content and ethanol calculation (Senn 2005) for bioenergy crops Table 4: Percentage of kind of livestock farming systems with production of liquid manure (Dämmgen 2007) Table 5: Comprehensive geodata sets in Brandenburg Table 6: Reference scenario: number of potential biogas facilities in region Havelland-Flaeming Table 7: Reference scenario: biodiesel potential in region Havelland-Flaeming Table 8: Reference scenario: bioethanol potential based on grain and cereal straw (without rye) in region Havelland-Flaeming Table 9: Site scenario: Biogas potential in region Havelland-Flaeming with humus balance = 0 kg Humus-C ha Table 10: Site scenario: biodiesel potential in region Havelland-Flaeming Table 11: Site scenario: bioethanol potential based on grain and cereal straw (without rye) in region Havelland-Flaeming Table 12: Bioenergy potential: Site scenarios of municipalities with balanced humus carbon and precipitation scenarios normal and dryness Table 13: Basic structure of the resource use model Table 14: Parameter values of the scenarios increasing mineral oil prices Table 15: Parameter values of the scenarios increasing product prices Table 16: Parameter values in the scenarios expansion of bioenergy production Table 17: Cultivation breaks of several crops (Müller 1986) Table 18: Output of economic modelling: cultivation acreages of crops in 4 municipalities: bioenergy scenario +50% Table 19: Calculation of acreages in percentage and parts of seventh in 4 municipalities: bioenergy scenario +50% Table 20: Evaluation values of crop rotation: effect of previous crop to yield of crop Table 21: Theoretical crop rotation and evaluation in 4 municipalities: bioenergy scenario +50% Table 22: Bio fuel potentials of various scenarios Table 23: Biogas potentials of various scenarios Table 24: Biogas potentials of economic scenario Bioenergy +200% in counties of the investigation area Table 25: Municipalities with highest biogas potentials in counties Table 26: Biogas potentials and biogas sites in counties of investigation Table 27: Energy potential [GJ] of bioethanol, biodiesel and biogas in all scenarios

7 1 Assignment of task and procedure Against a background of rising energy demand, the finite reserves of fossil raw materials and increasing environmental problems, an increased interest in expanding the use of energy crops is recorded. Alongside a significant reduction in CO 2 emissions, a reduction of dependence on fossil fuels and an increase of the added value in rural areas are aspired. The market for renewable resources and renewable energy has developed in recent years to an important industry in Germany. It is already in evidence that in the future the increased expansion of biomass-based energy industry will be connected with considerable changes of land use systems and regional flows of biomass. This development confronts individual agricultural and forestry holdings as well as regional economic stakeholders with a series of existential decisions. In this regard a substantial problem results from the fact that economic and ecological effects of the production and energy recovery of biomass are not yet sufficiently evaluated. Thus the estimation of the consequences of an expansion of biomass use for energy production represents an indispensable precondition. Further it shows that the lack of instruments for the evaluation of the consequences of biomass-based development projects is displaying a difficulty during decision making. During past decision-making for the expansion of energy crop use often competing or complementary requirements to the production of renewable resources were not sufficiently considered. An outstanding example for this is the one-sided aid for the energy crop cultivation for the production of energy without due consideration of public interest for available resources (i.e. arable land area) for the production of food, feed and / or materials. These studies consistently work with valuations of potential and expansion strategies for bio-energy use (so-called normative scenarios). The competition with nature and landscape protection for arable land area is partly considered. A possible competition to food production so far only has been discussed rudimentary. 1.1 Assignment of tasks The main task in the project is to determine the existing biomass potentials in the region Havelland- Flaeming (counties Havelland, Potsdam-Mittelmark, Teltow-Flaeming, City Brandenburg an der Havel, and Potsdam) in Brandenburg on basis of available current data and sorted by main groups of substances from agriculture and forestry. This includes a presentation of their current spatial distribution on a scale of 1: or higher (ArcView). A derivate task within the project is to determine the energy demand and emissions of green house gases connected with the provision of these biomass potentials. The calculated biomass potentials should be examined regarding their usability (e.g. biogas, biodiesel, ethanol, gasification and other energy recovery (solid fuels)). Operational conditions, quality and quantity variability, pre-and post-treatment processes (e.g. drying, packaging), logistic parameters and cost structures should be used as evaluation criteria. Furthermore it should be examined, whether climate changes, as forecasted by regional models, will have effect on the cultivation of particular crops. 2

8 The results of the potential analysis are to be varied using a set of scenarios. Different scenarios shall show whether and in which magnitude the energy potential changes depending on price trends for agricultural and forestry products. The primary question regards the quantities of available sustainable energy feedstock under favourable as well as unfavourable conditions. The calculated potentials should be compared to the demand of biomass (of defined energy conversion plants) in the region in order to develop models of material flows for the region. Energy expenditure as well as emissions of greenhouse gases and other environmental relevant gases connected with the material flow are to be determined. 1.2 Procedure of Processing The processing of the project is divided in four consecutive work packages: 1) determination of the biomass potential, 2) analysis of the biomass potential, 3) simulation and assessment of transformation scenarios, and 4) adjustment to trends of demand and regional material flows. In this chapter the approach of project processing is described. The status of work packages 1) and 2) is presented in Chapters 2 to 4 of this interim report. The description of the contents of the packages 3) and 4) provide an outlook on the analyses during the second part of the project. In Chapter 5 completed preliminary work used for the analysis of transformation scenarios is described. Work Package 1: Determination of biomass potentials Land use, particularly for energetic use of agriculturally produced biomass, is substantially affected by the geographic and climatic conditions. In addition, information about the structure of agriculture in Brandenburg and the current situation of biogas generation is important. For this reason an important basis for further analysis is the characterization of the region at the beginning of the project. The use of agricultural products is subject to an increasing competition between food and/or feed industry and the production of bioenergy. Based on geo data and by means of a biomass yield model (developed by university of applied science Eberswalde) regional biomass potentials were calculated and evaluated. They are a precondition for the estimation of infrastructure requirements and the evaluation of the sustainability of patterns of utilisation. For modelling with geographic information systems (GIS) geo data are needed with the highest possible spatial resolution and detail. The first step of the determination of the agricultural biomass potential is the estimation of the theoretical biomass potential. This includes a. Identification of the yield level of the regional biomass and biomass production b. Annual yield potential on dry mass yield c. Annual yield potential of bio-energy input materials Work package 2: Analysis of biomass potentials The estimates of available bioenergy potentials from agriculture, based on the results of the biomass yield model of the university of applied science Eberswalde, were calculated as follows: 3

9 Under consideration of regional needs for food and feed, the available quantities of biomass were determined and converted into specific bioenergy yields. Moreover, modelling of the humus balance as a parameter of sustainable agricultural production was included. a. Modelling of availability of biomass for bioenergy production: need of food and feed are determined and subtracted from the biomass potentials, distinguished by region. b. biogas: potentially installable power and biogas quantities on the basis of defined bioenergy fruits in consideration of competitions to food and feed industry (including humus balance). c. biodiesel: potentially usable winter rape quantities in consideration of competitions to food industry and spatial differentiated biodiesel potentials related to the available winter rape. d. bioethanol from grain or beet: potentially usable quantities of grain or beets in consideration of competitions to food and feed industry and spatially differentiated bioethanol potentials related to the available harvested quantities. e. scenarios of biomass availability. f. determination of potentials in normal year/dry year with a balanced humus balance. g. bio-energy estimation based on regional adapted energy crop rotation in cooperation with Leibniz Institute for Agricultural Engineering Potsdam-Bornim (ATB) in second half-year Work Package 3: Simulation and evaluation of transformation scenarios: Agriculture is characterized by a large dependence on external basic conditions, particularly the demand for agricultural products and subsidies of agriculture by the European Union. These exogenous factors are currently characterized by a change which decisively affects the characteristics of land use. The development and political orientation of the framework conditions for the provision of bioenergy is of crucial importance for the medium-and long-term perspective of biomass production on agricultural area. The production of bioenergy in agriculture will develop only if it not only is an ecological option for the economy, but also is an economic alternative to competitive land use forms and finds acceptance in the socioeconomic context. In this work package on the basis of economic, legal and administrative framework data, transformation scenarios are derived, that should be simulated. For these scenarios parameters are defined, which characterize the model and are considered crucial parameters in the simulations. Farm type models and material flow models, which were developed at the ATB (Klauss et al. 2009, Grundmann et al. 2008), will be used in the project. By scenario technique various regional development possibilities are simulated that potentially affect biomass production and the biomass energy sector. On the one hand the point is to create regional scenarios for biomass production and on the other hand to assess the resulting land use changes and their impacts. 4

10 Work package 4: adaptation on trend of demand and regional material flow models. The implementation of the existing biomass potential is attended by planning and coordination of current and future needs on the actual availability of biomass. Prerequisites are knowledge of the demand and supply structures and the development of demand and supply routes. On this basis the allocation of available resources to the utilisation facilities should take place according to relevant criteria like feasibility, profitability, supply guarantee, etc. In this work package the results of the potential analysis are contrasted with existing and future demand for biomass, which can be expected from selected plants for energy generation and utilization in the region. A limited selection of relevant facilities is planned in the second half of 2009, in consultation with Regional Planning Authority Havelland-Flaeming. Basis for this will be information about demand of biomass for facilities in the region which is collected by Regional Planning Authority Havelland-Flaeming. In a further step sources of biomass are adapted to biomass sinks, as well as a representation of the material flows in the region. Based on the material flows energy expenditure and environmental emissions of climate gases for the supply of biomass are determined. For this determination key data, which are defined for respective scenarios in WP 1, are transferred into the model. Simulation calculations based on this will subsequently be evaluated. 5

11 2 Region Havelland-Flaeming The kind of land use and the production of biomass in agriculture are influenced by geographical and climate conditions. Furthermore the structure of agriculture, the intensity of cultivation and the current situation of bioenergy production in Federal state Brandenburg is interesting for a study of bioenergy potentials in the region Havelland-Flaeming. This region covers the counties Havelland, Potsdam- Mittelmark and Teltow-Flaeming with cities Potsdam and Brandenburg. This region is situated in the western part of federal state Brandenburg. 2.1 Geographical and climate conditions The investigation area has maritime climate, the Eastern part of the federal state Brandenburg increasingly continental climate. The mean monthly temperature is -1 C in January and 18 C in July. The annual precipitation varies between 500 und 700 mm. According to statistics in every third or fourth year climate in early summer is extremely dry, as for instance in On dominant sandy soils with low water holding capacity this drought is leading to yield decreases mainly of spring and catch crops. The federal state Brandenburg is situated in lowlands with low relief energy. The grasslands are located in valleys, arable land on ground moraine with till and loam. The Flaeming is a highland in the south of the investigation area. Further to the north are the Mittelbrandenburgische Platten und Niederungen. The Luchland and Elbtalniederung are situated in the Northern and western parts of the region. Figure 1: Climate diagram with precipitation (blue) and temperature (red) per month at weather station Potsdam (WIKIPEDIA 2008). 6

12 Figure 2: Nature parts of federal state Brandenburg. 2.2 Agriculture in the region Havelland-Flaeming Following the political changes in 1989 changes in agricultural structure and intensity took place like employment reduction, privatisation and the partly liquidation of big farms. Currently the investigation area is cultivated by nearly farms; 28 % of these cultivate an area of more than 100 ha and cover 92 % of the agricultural land area of the region. The mean farm size of 200 ha is much higher than the European average of 35 ha. 92,3% Figure 3: Structure of agricultural farms 2007 (Statistische Ämter des Bundes und der Länder, 2009). < = > 100 ha According to the statistic of 2007 in the region Havelland-Flaeming 125,456 cattle and 195,121 pigs were counted (Figure 4). 7

13 More than the half of pigs is animal husbandry in the county Teltow-Flaeming. The number of dairy cows was reduced from 50,000 in 1996 to 35,000 in The number of suckler cows was increasing with lower trends in recent years since 2001 the livestock is balanced with 20,000 cows cattle pig Havelland Potsdam-Mittelmark Teltow-Fläming Figure 4: Livestock farming (cattle and pigs, number of animals) in the counties The investigation area is covered with an agricultural area of 290,752 hectare (= 93 % of the whole area). Of this area 216,548 ha are arable land and 71,896 ha are used as grassland. organic Havelland conventional Potsdam-Mittelmark Teltow-Fläming hectare Figure 5: Agricultural land use: conventional and organic farming system in 2008 (INVEKOS 2009). More than 21,700 ha are cultivated in organic farming system ( ha arable land) corresponding to 7.5 % of agricultural land use. The percentage of organic farming in the investigation area is lower than 8

14 the average of federal state Brandenburg with 10.1 %, but it has increased since 2003, when it occupied only 5.2 % of the agricultural area. 2.3 Current Situation of bioenergy production The number of bioenergy and biogas plants increased since the introduction of the Renewable Energy Sources Act. In 2008 there are 24 biogas plants with differing capacities from 80 kw el to 1.2 MW el (in the average = 500 kw el ) (MLUV, 2008). In total these sites have an electrical capacity of 12.7 MW el from agricultural biomass. Additionally one biogas plant is based on waste in county Potsdam- Mittelmark. Nine projected plants with a capacity of MW el will be build in the next years; one of them is an innovative biogas plant in Rathenow which will supply biogas to the gas distribution system equivalent to annually 44 million kwh, starting in the year Table 1: Agricultural biogas sites and their capacities Counties kw electrical capacity projected other biogas sites Status 2008 in planning Sum Brandenburg an der Havel Havelland 3,315 1,563 4,878 1 Potsdam-Mittelmark 3,828 6,701 10,529 Teltow-Flaeming 5, ,970 1 Region 12,783 9,219 22,002 2 More and more biogas plants are projected with combined heat and power generation or feed-in of biogas into the gas distribution system for higher efficiency of the biogas use. In the region no bio-fuel plants are realized. In neighbourhood counties and federal states following plants allow for marketing potential for farmers: 9

15 Figure 6: Bioenergy sites in the investigation area Biodiesel: northern county Ostprignitz-Ruppin: Biodiesel Kyritz GmbH (annual capacity = 35,000 t biodiesel), county Oberhavel in Oranienburg (annual capacity = 10,000 t biodiesel), county Prignitz: Biodiesel Wittenberge GmbH (annual capacity = 90,000 t biodiesel), EOP Biodiesel AG Falkenhagen (annual capacity = 30,000 t biodiesel) federal state Sachsen-Anhalt: Lutherstadt Wittenberg, Piesteritz, Magdeburg Bioethanol: Sachsen-Anhalt: Lutherstadt Wittenberg with an annual capacity of 7,500 t bioethanol In the ongoing discussion of the last years the construction of a bioethanol plant in county Havelland in Premnitz with an annual capacity of 150,000 t bioethanol was debated. 10

16 3 Bioenergy potential analysis For bioenergy potentials of the product lines of biogas, biodiesel and bioethanol following scenarios are presented: Reference potential: Calculation of bioenergy potentials based on agricultural statistics Site potential: Calculation of bioenergy potentials based on geodata and biomass yield model (bym) Economic potential: Calculation of bioenergy potentials based on geodata and biomass yield model (bym) with economical adaption of crop rotations based on: balanced humus carbon = 0 kg Humus-C/ha o o average of annual precipitation ( mean ) and minimum of annual precipitation ( dryness ) 3.1 Method of potential calculation Reference scenario Based on agricultural statistics bioenergy potentials will be predicted. However the lowest spatial scale is the county level, because of the agricultural statistics. The amount of yields and development of yields are analyzed and converted to bioenergy contents. Other than the technical biomass potentials calculated using biomass yield model and geodata sets the reference potentials give an overview of the status quo. Data of the year 2007 are the basis of the calculation of the reference potential. The results of other bioenergy scenarios are to be compared to the reference potential Site potential Different to the reference potential the site potential is based on site specific conditions. The spatial scale depends on the availability of a comprehensive geodata set. The biomass yield model was developed on the UAS Eberswalde. It calculates the annual theoretical biomass potential in regard to regional and site specific crop rotations. An overview of the structure is given in figure 7. The main input geodata are climate and soil data. The model contains crop specific yield functions and crop rotation algorithms differenced by cultivation intensity of conventional farming or organic farming system. The crop rotations are adapted to regional specific cultivation systems and differentiated by soil conditions. Sustainability aspects and cross compliance rules are included in the biomass yield model. 11

17 Figure 7: Biomass yield model (July 2007). 12

18 3.1.3 Economic potential By analysing the changes of land use patterns, crop rotations systems can be identified, that are well adapted to economic objectives and regional requirements. These crop rotation systems are integrated in the biomass yield model. The results of the biomass yield model are used to calculate an economic potential analysis, which bases on an optimisation model that simulates land use as a result of decision making at farm and regional level. The methodological procedure includes the simulation of future resources by means of multi-criteria optimization applying linear programming models for regionalized farms. Each regionalized farm represents a set of extensively homogenous farms of the region. The model includes all regionalized farms of a region. The model regards the scarce resources of arable land, water, humus carbon, workforce, and means of production. By integration of these resources the modelling accounts for the competition for cropland and other crucial restrictions. Moreover technologic and ecological basic conditions as well as legal requirements are integrated, that limit the use of the resources. Prime objective is the economic optimisation of the options of usage in regard of the profit contribution of cultivation methods and of whole farms. On basis of the regional and farm operation characteristics and resources endowment the model simulates the optimal allocation of the available resources. An extensive description and discussion of the farm type model as well as the methods and restrictions used therein can be found in Grundmann and Kimmich (2008) Bioenergy product lines and input material The demand for feed and fodder is analyzed to model the available amount of bioenergy material in the region, especially rape seed, maize, grain straw, silages etc. This accounts for the competition of bioenergy production with conventional marketing of agricultural products for cropland. Only the surplus is the input material for bioenergy production. The calculated demand for food and fodder was based on agricultural national statistics for Germany 2007 (Eurostat 2009). By comparison between these consumptions and the harvested amounts a percentage of used material is calculated the rest is available for bioenergy production. The demand is a non spatial percentage index for the whole investigation area. Table 2: Calculation of available amount of grain and rapes seed from national statistics Germany amount in tons wheat rye barley triticale rape seed Yield amount demand food demand fodder demand sum rest amount percentage to yield amount [%] 18,9 24,1 19,7 9,8 81,1 First, the spatial differentiation of food demand was tested using the demand-per-head. However the results showed, that the demand of rye grain per head with 10 kg per person is to low for summarizing the demand in this area (demand for Berlin and Brandenburg = t). A yield of 688,541 tons in the 13

19 year 2007 in Brandenburg means that 91 % of the yield amount would be available for bioenergy. However, this result doesn t account for the fact, that Brandenburg is the main rye production area of Germany. For this reason national statistics were used as basis for the calculation of food consumption. For calculation of livestock supply, i.e. demand for bedding material and fodder, spatial regional data are analysed, differentiated by kind of livestock. The demand of maize silages for feeding purposes is based on a calculation model of UAS Eberswalde in co-operation with Dr. Neuberth (LVLF Brandenburg) HUMUS CARBON BALANCE The biomass yield model includes a module of humus carbon balances. The calculation of the humus carbon balance in the region integrates the crop specific humus carbon loss and the yield amounts as a result of the biomass yield model. On the other hand the humus reproduction by grain straw and organic fertilizer is calculated based on livestock farming statistics by methods of VDLUFA (2004). The aim is a balanced humus carbon proportion of 0 kg Humus-C ha -1. The first part of humus reproduction is modelled according to the application of organic fertilizers (liquid manure and dung). The other part of humus reproduction origins from by-products and harvesting residuals as beet leafs and rape straw. 100 % of these by-products are left on the fields. Other byproducts (e.g. cereal straw) may be used for bedding (calculated based on livestock amount) and to balance the humus reproduction. Only the surplus of cereal straw is available for bioenergy production BIOENERGY CONVERSION RATES In this model the amount of biomass is the input material for bioenergy production, which is not required for food, fodder, carbon balances or bedding material. Using the conversion rates and the crop specific bioenergy contents the potentials of the product lines biogas, biodiesel or bioethanol are calculated. To eliminate the competitions between bioenergy product lines the bioethanol potentials include only grain of wheat, barley and triticale not the grain of rye. Rye whole-plant silage and maize whole-plant silage are the main co-ferments in biogas plants in this region. 14

20 Figure 8: Calculation and aggregation procedure of available bioenergy material. Bioethanol Bioethanol is produced by fermentation of starch or sugar in crops. The input materials of bioethanol production may be grain, maize, sugar beet or potatoes. In Europe main input materials for bioethanol production are sugar beets or grain often of wheat, triticale or rye. Table 3: Dry matter, cereal straw index (KTBL 2005, Piorr 2007) and starch content and ethanol calculation (Senn 2005) for bioenergy crops Crop Parameter for biomass and cereal straw calculation Dry matter content in % Relation of straw yield to grain yield (conventional farming system) (KTBL 2005, Piorr 2007) Parameter for ethanol calculation Starch content for 100 % dry matter Litre ethanol per ton fresh matter grain Wheat % Barley % Triticale % t starch = 64 litre ethanol 15

21 In this study the quantities of grain of wheat, barley and triticale are the basis for the bioethanol potentials with their crop specific bioethanol conversion rates. The ethanol output depends on the starch content in the grain (Table 3). The available grain yield quantities are converted in starch quantities and into bioethanol quantities according to the following relation: 0.1 tons of starch are 64 litre ethanol (Senn 2005). Bioethanol quantities produced from available cereal straw are calculated according to IOGEN (2007) as follows: 250 litre ethanol may be produced from 1 ton of straw. If the whole cereal plant is used for bioethanol production the yield of one ton of fresh mass is 600 to 700 litre ethanol. Biodiesel Biodiesel is a bio fuel which may substitute fossil fuels. It is produced generally from rape seeds and sunflower seeds. In the investigation region mainly rape is cultivated; rape seeds have an oil content of %. By extrusion of the seeds in oil mills plant oil and as a by-product coarse colza meal for livestock fodder are produced: Plant oil is converted to biodiesel by transesterification. The oil content in this study relate to information of a regional biodiesel plant in Northern Brandenburg: one ton rape seed (fresh matter) will produce 0.35 tons of plant oil and 358 litre biodiesel. Biogas The potential of biogas is calculated based on silages of rye and maize (whole plants) and liquid manure. The calculation of liquid manure is based on the kind of livestock farming (bedding or no bedding) and the amount of livestock, spatially differentiated. Table 4: Percentage of kind of livestock farming systems with production of liquid manure (Dämmgen 2007) cattle % pigs % dairy cows 58.2 sows 1.1 heifers 40.0 piglets 84.6 fattening bull 56.7 fattening pigs 84.6 suckler cows 0 boars 1.9 breeding bull 57.1 pigs 71.8 other 29.0 The amount of liquid manure is calculated from livestock farming according to KTBL (2007): 1 animal unit = 1 cattle over 2 years old or 1.43 cattle between 1-2 years = 29 t per year of dairy cow manure (liquid manure with 8 % dry matter content); 23 t liquid manure for calf und other cattle 1 animal unit = 9.07 pigs; 3.3 sows; 7.7 fattening pigs = 21 t liquid manure per year with 6 % dry matter content To include the loss of dry matter of biogas manure the dry matter content of liquid manure is adjusted to the low value of 4 % in the humus balance calculation. That means that the manure is used at first as 16

22 biogas ferment and after that the biogas manure is covering the field and is used for humus reproduction in the model. The biogas yield of livestock manure is the same in all scenarios. Whole plants of maize and rye are the main co-ferments used in biogas plants in Brandenburg: % of ferments are silages of whole maize plants, 2-20 % silages of whole rye plants. Manure, mainly of cattle, is used as ferment in % of the biogas plants. The main ferments according to national statistics are are maize silage (40 %) and manure ( %) (top agrar 2007). maize silage cattle manure pig manure 14% pig manure grassland silage whole-plant silage cattle manure 31% cattle dung rye (grain) wheat (grain) maize silage 40% chicken dung corn green rye other ferments sudan grass Figure 9: The average ferments of 438 biogas plants in Germany (top agrar 2007). In processing of silages for storage the input material loses 10 % of its dry mass this mass loss is accounted for. Based on the regional availability of biomass of silages and manure the potential electrical power and the potential number of biogas facilities are calculated. The power and methane yield are ferment specific. The energy efficiency of the conversion of methane to electricity was assumed to be 35 % (KTBL 2007): 311 kwh el or 171 m³ biogas or 89 m³ methane per ton fresh matter rye whole-plant silage 315 kwh el or 173 m³ biogas or 90 m³ methane per ton fresh matter maize silage 45 kwh el or 24 m³ biogas or 13 m³ methane per ton fresh matter liquid manure of cattle 40 kwh el or 19 m³ biogas or 11 m³ methane per ton fresh matter liquid manure of pigs 17

23 The potential number of biogas facilities is calculated on an average of electrical power of 500 kw el. At present most biogas sites are constructed with this capacity. An utilisation duration of hours per year is assumed. 3.2 Spatial data At the outset of this study the availability of spatial and statistical data was investigated. Especially statistical data regard the agricultural sector. Spatial data also contain ecologic information. Table 5: Comprehensive geodata sets in Brandenburg. data sets Scale Format area model input digital administration units land use 1: shape Brandenburg administration area and regions ATKIS Basis-DLM 1: shape Brandenburg INVEKOS 1: shape Brandenburg precipitation and relief DWD: annual precipitation Soil 1 km² raster Deutschland arable land / grassland minimum average GEMDAT 1: shape Brandenburg mean Ackerzahl per municipality DIBOS: Folie 42 1: shape not available digital soil fertility rate with Ackerzahl Administration units Agricultural statistics are available in different scales for spatial administration units. This scales are national Units = NUTS in level 0 to 3. NUTS level 0 is the border of the nation, level 1 are federal states (= Brandenburg), NUTS level 2 are districts (northern and southern Brandenburg), NUTS level 3 are the counties. On level 3 most of the statistics are available. Furthermore the counties are subdivided into municipalities, departments and independent towns. These levels are visualized in Figure

24 Figure 10: Administration levels in Brandenburg and investigation area Havelland-Flaeming Land use For regional datasets of land use INVEKOS Feldblockkataster of 2008 are available (Figure 11). The spatial distribution is of the scale of 1:5,000. On basis of this digital dataset arable land and grassland are identified. These data are used as model input parameters to model the agricultural bioenergy potentials. The dataset also contains information about the farming system, i.e. organic or conventional farming. The model distinguishes between these two farming intensities. 19

25 Figure 11: INVEKOS: Arable land and grassland differenced by farming system in region Havelland-Flaeming Precipitation The spatial datasets of climate parameters are given as raster datasets with a spatial distribution of 1 km² of one raster cell. Of these climate parameters the annual sum of precipitation is used as model input parameter for the biomass yield model. The data are provided by the German weather institute (DWD). We used the annual data from 1991 to 2005 and calculated the minimum and the average of this period per raster cell. On the average of annual precipitation is modelled the scenario normal, on the minimum dataset the scenario dryness. 20

26 Figure 12: Average and minimum of the annual precipitation in the investigation area. On average the amount of precipitation is 561 mm ( mm) per year. The scenario dryness is based on an extreme year with a precipitation amount of only 395 mm ( mm) per year. The south-western part of the investigation area generally has higher amounts of precipitation, i.e. 600 mm precipitation in an average year, 500 mm precipitation in a dry year Soil fertility rate Another crucial input parameter to the biomass yield model is the information of soil fertility. The German system of soil fertility evaluation expresses soil fertility in a scale of up to 100 points of Ackerzahl. Non fertility soils have only 10 to 25 points Ackerzahl. The soil with the highest potential fertility like tschernozem has 100 points Ackerzahl. In federal state Brandenburg a digital dataset of GEMDAT 1995 contains a database of municipalities of 1995 with information of soil fertility. Per municipality the average of soil fertility rate Ackerzahl is given. The Ackerzahl in municipalities of the investigation area (shown in Figure 13) ranges from 13 to 52 points. The average is 29 points with suitable soil for cultivation of rye and potato. Primarily in Nuthe- Urstromtal and Baruth/Mark in county Teltow-Flaeming soils with low fertility rates and averages of Ackerzahl below 20 points are located. The detailed dataset of Ackerzahl based on DIBOS (digital soil data on field scale) has not been made available for this application. The spatial resolution of the potential analyses is a municipality scaled data set. 21

27 Figure 13: Mean Ackerzahl per municipality (GEMDAT 1995) in the region Havelland-Flaeming. 3.3 Statistical data The statistical data are used to model a reference-scenario, the humus balances and for the calculation of food, fodder and bedding demands. The agricultural statistics contain data of livestock farming, acreages, yields and yield amounts of crops. The spatial resolution is given in national units level 2 or 3, depending on counties and federal state. In the investigation area the statistical data are available on county-level (NUTS level 3) from the LDS (Landesbetrieb für Datenverarbeitung und IT- Serviceaufgaben). 22

28 4 Bioenergy modelling 4.1 Reference: Bioenergy potential based on agricultural statistics The reference scenario is based on agricultural statistics and describes the current situation of agriculture in the investigation area. Acreages, dominant crops, yield amounts and yield developments are calculated. Figure 14: Acreages of dominant crops in region Havelland-Flaeming Figure 15: Biomass yield of the year 2007 in 100 % dry matter of dominant crops in the region Havelland-Flaeming In region Havelland-Flaeming rye is the dominant crop, which was cultivated on more than 27 % of the arable land in 2007; rape and maize each covered 11 % of the acreages in On 9% and 6 % of 23

29 the arable land wheat and barley were cultivated. Since 1991 the cultivation of winter rape and triticale was increasing. In 2007 also the acreage of corn was increasing: while in former years only 2,000 ha corn was cultivated, 2007 corn covered 5,800 ha. Mainly the acreages of spring grain (wheat and barley) were decreasing. The cultivation of ley has doubled from 20,000 ha (2004) to more than 42,000 ha in The yield amount of agricultural products in statistics is given in crop specific fresh matter. For better comparison the results in this scenario are calculated into 100 % dry matter. The dry matter content of maize silage is 30 %, the dry matter content of grain is 86 %. As expected, maize silage produces the highest yield of dry mass due to the high biomass yield per hectare. Rye provides the highest percentage of grain yield with 21 %. Yields vary each year depending on the climate conditions. [dt/ha] winter wheat 10 spring wheat [dt/ha] winter barley 10 spring barley [dt/ha] rye [dt/ha] triticale [dt/ha] [dt/ha] winter rape maize Figure 16: Average yield in 1991 to 2007 of dominant crops in the region Havelland-Flaeming Most of the crops respond to low precipitation in early summer by yield decreases. So in 2003 yields of grain were very low because of an extreme dryness in spring and early summer. On the other side in 2007 the precipitation in summer was high and the yield level of maize was optimized, while the yields 24

30 of grains of this year were at average level. Significant yield increases of any crop in the last 15 years were not found. Based on the statistics of yield amounts and livestock statistics bioenergy potentials corresponding to the reference year 2007 were calculated including humus balance modelling and calculations of food, fodder and bedding demand of livestock. The balance of 0 kg Humus-C ha -1 on scale of county was reached on an availability of 15 % cereal straw Reference scenario: biogas The input materials to calculate the biogas potential of the biogas scenario are available amounts of maize, rye (whole-plant) and liquid manure. This sums up to a potential total electrical capacity of 22,675 kw el in the region Havelland--Flaeming, based on 8,000 working hours per year and an electrical efficiency of 35 %. Figure 17: Reference scenario: amount of potential biogas plants in the region Havelland-Flaeming 25

31 In comparison to biogas status quo and projected biogas facilities in the region (Figure 6) the potential of new biogas sites in the reference scenario is low. The biogas production may increase only in the county Teltow-Flaeming without impact on the agricultural structure. Table 6: Reference scenario: number of potential biogas facilities in region Havelland-Flaeming number of potential biogas facilities in reference scenario (statistic 2007) (8,000 working hours per year / standard 500 kw el) based on ferments: cattle manure pig manure maize rye sum Havelland Potsdam-Mittelmark Teltow-Flaeming investigation area Reference scenario: bio fuels The input material to calculate the biodiesel potential of the reference scenario are available amounts of winter rape seeds. Rapeseed oil is converted to biodiesel by transesterification in a biodiesel plant. Table 7: Reference scenario: biodiesel potential in region Havelland-Flaeming Yield amount winter rape Reference: amount in tons per year (statistic 2007) Availability = 81 % Biodiesel Diesel equivalent Havelland 23,107 18,716 5,896 4,999 Potsdam-Mittelmark 14,486 11,733 3,696 3,134 Teltow-Flaeming 22,215 17,994 5,668 4,806 investigation area 59,807 48,444 15,260 12,940 The input material to calculate the bioethanol potential of the reference scenario are available amounts of grain of wheat, barley and triticale. Table 8: Reference scenario: bioethanol potential based on grain and cereal straw (without rye) in region Havelland-Flaeming Reference scenario without rye available amount Grain amount in tons per year Ethanol gasoline equivalent available amount Cereal straw amount in tons per year Ethanol gasoline equivalent Havelland 10,903 3,592 2,217 13,727 2,332 1,439 Potsdam-Mittelmark 7,813 2,574 1,588 11,181 1,899 1,172 Teltow-Flaeming 12,798 4,217 2,602 15,622 2,653 1,637 investigation area 31,514 10,383 6,407 40,530 6,884 4,248 26

32 4.2 Site potential based on geodata and biomass yield model The area of arable land is the basis of the potential analysis with geodata and the biomass yield model. 100 % of the arable land is used in the potential analysis. However the area data of various datasets differ strongly. Statistical data of 2007 [MLUV 2008] counted an acreage of 216,548 ha in the counties of the investigation area. The digital data of INVEKOS (2007) cover an area of 231,136 ha. This is the input area used in the model. The average of annual precipitation in the investigation area is 561 mm. The scenario dryness is based on a dry year of 395 mm precipitation. The mean Ackerzahl of the region is 29 points. The geodata (chapter 3.2) are the model input of the biomass yield model. The modelling varies two farming systems (conventional and organic) and is optimized for good practice farming management. The whole annual biomass potential is modelled (theoretical potential). The useful potential (=technical) is minimized by humus reproduction rate of grain straw, the food, fodder and bedding demand. The site scenario contains a balanced humus carbon rate and annual precipitation scenarios normal and dryness Site scenario: biogas The assessment of the biogas potential contains the modelling of a humus carbon balance of 0 kg Humus-C ha -1. With disregard of the competition to the bio fuel production and taking the energy crops winter rye and maize (both whole-plant silages) as well as the manure of cattle and pigs into account, the biogas potential in the investigation area amounts to 173,424 MWh electrical power. This equals to 43 standardised biogas facilities, which have a mean capacity of 500 kw el. The biogas potential of counties and municipalities differs, because of site specific yields of silages and regional demand of livestock farming. The potential of silages in normal years summarized to a potential of 86.7 million m³ biogas methane. The yield decrease of maize and rye in extreme dry years cuts the number of sufficiently supplied standardized biogas facilities to 21 and the amount of methane produced with silages to 42.2 million m³. Table 9: Site scenario: Biogas potential in region Havelland-Flaeming with humus balance = 0 kg Humus-C ha -1 number of 500 kw el biogas plants (with manure) precipitation scenario normal dryness Havelland Potsdam-Mittelmark Teltow-Flaeming investigation area The regional planning of biogas sites has to consider this fact, as the security of supply in dry years may be compromised. The yield of maize (whole plant silage) in the dryness scenario is reduced to half of the precipitation scenario normal from 76,301 kwh el to 39,768 kwh el. The availability of winter rye (whole-plant silage) 27

33 in a normal precipitation scenario is 62,007 kwh el. In the dryness scenario it decreases to 9,549 kwh el, i.e. 15 % of the availability in the normal precipitation scenario. The biogas potentials of silages and manure in the municipalities of the investigation area are visualized in Figure 18. Light colours represent a potential for a biogas facility with small electrical capacity in the municipality, darker colours a potential for a biogas facility with high capacity; the gray colour stands for a very low potential in the municipality. Figure 18: Site scenario biogas based on manure and silages with balanced humus carbon (0 kg Humus- C ha -1 ) and precipitation scenarios normal and dryness Site scenario: biodiesel The biomass yield model is designed for a sustainable regional biomass production. One of the parameters is a low intensity of acreages of winter rape. On arable sites with dry soil conditions and low fertility rates the yield level of winter rape is low and the cultivation of rape is not recommended. In crop rotations the percentage of rape production should not exceed a value of 14 %. The mean potential is 11,751 tons biodiesel. In scenario dryness the winter rape yield decreases and equals a potential of only 6,946 tons per year. In organic farming system rape is cultivated as a crop for better soil fertility and as a spring crop. This potential was not considered in this potential analysis. 28

34 Figure 19: Site scenario - biodiesel: yield level of winter rape in region Havelland-Flaeming (precipitation scenario normal ) Table 10: Site scenario: biodiesel potential in region Havelland-Flaeming Site scenario Amount in tons per year Normal amount winter rape availability = 81 % biodiesel diesel equivalent Havelland 19,138 15,521 4,889 4,146 Potsdam-Mittelmark 11,150 9,043 2,849 2,415 Teltow-Flaeming 15,710 12,740 4,013 3,403 investigation area 45,998 37,304 11,751 9,964 dryness Amount in tons per year Havelland 12,709 9,116 2,872 2,435 Potsdam-Mittelmark 8,067 5,786 1,823 1,545 Teltow-Flaeming 9,969 7,150 2,252 1,910 investigation area 30,745 22,052 6,946 5,890 29

35 4.2.3 Site scenario: bioethanol The bioethanol potentials of counties are listed in Table 11. They are classified in the potential of grain and the potential of cereal straw. The use of grain is possible because of the bioethanol facilities in the neighbourhood regions. Using of cereal straw for bio fuel production is a future scenario, as there is no projected site in the investigation area. In the dryness scenario the grain bioethanol potential and the straw potential decrease considerably. Table 11: Site scenario: bioethanol potential based on grain and cereal straw (without rye) in region Havelland-Flaeming Site scenario (without rye) Normal available amount Grain amount in tons per year ethanol gasoline equivalent available amount Cereal straw amount in tons per year ethanol gasoline equivalent Havelland 15,031 5,012 3,093 18,430 3,130 1,932 Potsdam- Mittelmark Teltow- Flaeming investigation area dryness 13,074 4,345 2,681 21,725 3,690 2,277 14,789 4,925 3,039 18,386 3,123 1,927 42,895 14,282 8,813 58,541 9,943 6,136 Havelland 9,990 3,330 2,055 1, Potsdam- Mittelmark Teltow- Flaeming investigation area 9,550 3,171 1,957 2, ,534 3,175 1,959 2, ,075 9,676 5,971 6,973 1,

36 Table 12: Bioenergy potential: Site scenarios of municipalities with balanced humus carbon and precipitation scenarios normal and dryness County Municipality Bioethanol based on grain Bioethanol based on cereal straw in tons per year Biodiesel Biogas (plants with 8,000 working hours per year) ID Name normal dryness normal dryness normal dryness normal dryness Havelland Brieselang Dallgow-Döberitz Falkensee Friesack Gollenberg Großderschau Havelaue Wiesenaue Ketzin Kleßen-Görne Kotzen Märkisch Luch Milower Land Mühlenberge Nauen Nennhausen Paulinenaue Pessin Premnitz Rathenow Retzow Rhinow Schönwalde-Glien Seeblick Stechow-Ferchesar kw el

37 Table 12: Bioenergy potential: Site scenarios of municipalities with balanced humus carbon and precipitation scenarios normal and dryness County Municipality Bioethanol based on grain Potsdam- Mittelmark Bioethanol based on cereal straw in tons per year Biodiesel Biogas (plants with 8,000 working hours per year) ID Name normal dryness normal dryness normal dryness normal dryness Wustermark Beelitz Beetzsee Beetzseeheide Belzig Bensdorf Borkheide Brück Buckautal Golzow Görzke Gräben Groß Kreutz Havelsee Kloster Lehnin Linthe Michendorf Mühlenfließ Niemegk Nuthetal Päwesin Planebruch Planetal Rabenstein/Flaeming kw el 32

38 Table 12: Bioenergy potential: Site scenarios of municipalities with balanced humus carbon and precipitation scenarios normal and dryness County Municipality Bioethanol based on grain Teltow- Flaeming Bioethanol based on cereal straw in tons per year Biodiesel Biogas (plants with 8,000 working hours per year) ID Name normal dryness normal dryness normal dryness normal dryness Rosenau Roskow Schwielowsee Seddiner See Stahnsdorf Teltow Treuenbrietzen Wenzlow Werder (Havel) Wiesenburg/Mark Wollin Wusterwitz Ziesar Am Mellensee Baruth/Mark Blankenfelde- Mahlow kw el Dahme/Mark Dahmetal Großbeeren Ihlow Jüterbog Luckenwalde Ludwigsfelde Niedergörsdorf

39 Table 12: Bioenergy potential: Site scenarios of municipalities with balanced humus carbon and precipitation scenarios normal and dryness County Municipality Bioethanol based on grain Bioethanol based on cereal straw in tons per year Biodiesel Biogas (plants with 8,000 working hours per year) ID Name normal dryness normal dryness normal dryness normal dryness Niederer Flaeming Nuthe-Urstromtal Rangsdorf Trebbin Zossen kw el 34

40 5 Modelling transformation scenarios The farm type model SunReg is used within this project to assess the land use for energy crop cultivation in representative regions of Germany (Klauss et al. 2009, Volkswagen AG 2009, Murach 2009). The SunReg model considers the availability of arable land as well as other limiting resources like water, soil humus carbon, manpower, etc. By integration of additional resources further crucial parameters are integrated in the model to simulate the competitions between food crops and dedicated energy crops. The goal or objective of the model application is the optimisation of the resource use by maximization of the total farm profit. In addition to this goal further requirements and restrictions oriented towards nature conservation and environmental sustainability are incorporated in the model (Grundmann and Kimmich 2008). Therefore, the model primarily provides as results of a scenario simulation the distribution of land use and energy crop production by pathways of conversion for a farm or a region, as well as the profit contribution and the marginal return of different value chains. Based on these results the model delivers as secondary output the humus carbon balance at farm and regional level, as well as the emissions of greenhouse-gases and the energy supply and demand. 5.1 The resource use model SunReg The schematic diagram of the resource use model in table 13 shows the economic, technological, ecological and environmental interrelations and restrictions. The model is arranged in two dimensions the resources are listed vertically, the economic activities and methods are listed horizontally. The points of intersection of the activities with the resources provide the logical interconnection. The objective is the economic optimisation of the activities, which is provided by the target function Max!. The result of the simulation is generated using the simplex linear programming algorithm, integrated in the solver of Frontline Systems Inc. Land use for crop cultivation, energy crop cultivation and set-aside represent central activities, which are listed first. The detailed subdivision of areas according to soil fertility was merged in one category in this scheme. The relation with the endowment of the region with arable land, grassland and set-aside land is provided by the logical interconnection 1. The parameter a ij also represents the connection of an activity with possible attainable yields on the different cultivation sites, with land subsidy and bonuses for renewable resources cultivation, with the operating time of the machinery for ploughing, sowing, plant protection and harvesting, with the demand of seeds, fertilizers and biocides and with irrigation requirements. The ecological restriction of land use is implemented by restrictions of crop rotation, the humus carbon loss limitation and the water availability. The model also integrates the methods of green manuring and the processing of agricultural products. Washing, conditioning, transport, drying and storage were merged in the scheme. Further activities, e.g. purchasing production means and selling agricultural products, are an interface to the market. Methods and value added of bioenergy production in conversion plants also are part of the model. Animal husbandry is represented by the activities of dairy cattle, livestock farming of cattle and pigs and selling of their products. Technological and procedural processes with their demand of time, production means and manpower connect the equipment with appropriate activities. The economic perspective is completed by the available liquidity and the required raising of capital.

41 Table 13: Basic structure of the resource use model. activities crop cultivation for food, feed, materials energy crop cultivation grassland set-aside land incorporating into the soil washing, conditioning, loading, drying, storage selling dairy cattle breeding pig breeding chicken breeding selling milk selling meat conversion selling bio energy selling renewable resources products buying seeds selling/buying fertilizers buying biocides tillage machines machines of fertilizing machines if seeding machines of plant protection machines of irrigation machines of harvesting, loading, washing, transport, drying, livestock breeding, conversion buying/selling feeding stuff electricity, diesel, lubricant repair and maintenance work for cultivation, conversion work for livestock breeding renting arable land, buying /selling land raising capital payment of subsidies, bonuses target function = Max! / / /+ - + resources area of arable land ha area of grassland ha 1 area of set-aside land ha 1 yield of main product A- quality dt -aij -aij -aij +/ Yield of main product B- quality dt -aij -aij -aij -aij +/ /+1 - /+1 - /+1 yield coproduce dt -aij -aij -aij land subsidy /ha renewable resources bonus /ha 1 demand of machine usage h aij aij aij aij -1 demand of seeds kg aij aij aij aij -1 demand of fertilizers kg aij aij aij aij -aij -aij -aij 1 - /+1 demand of biocides aij aij aij aij -1

42 demand of irrigation l a ij a ij a ij demand of storage capacity dt demand of electricity, diesel and lubricants kw a ij a ij a ij a ij a ij a ij -a ij demand of machine maintenance a ij a ij a ij a ij a ij a ij -a ij demand of manpower Akh a ij a ij a ij a ij a ij a ij -a ij fodder dt a ij a ij a ij milk l -a ij a ij - /+1 meat kg -a ij -a ij -a ij a ij machines livestock breeding h a ij a ij a ij manpower livestock breeding Akh a ij a ij a ij -a ij conversion facilities kw / dt a ij restriction conversion capacity dt 1 bio energy kw -a ij 1 renewable resources products dt -a ij 1 restrictions crop rotation ha restrictions marketing dt 1 restriction humus carbon loss kg aij aij a ij -a ij restriction water availability l a ij a ij a ij greenhouse gas emissions/ summer smog/ eutrophication /acidification Unit -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij -a ij restrictions stable capacity Unit a ij a ij a ij liquidity -a ij -a ij -a ij -a ij -a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij a ij -a ij -a ij 37

43 5.1.1 Limitations of quantitative modelling and the SunReg model Quantitative modelling has limitations, that have to be kept in mind when applying this modelling tool. The tool presents a strongly simplified image of an object of investigation. When using linear programming it has to be considered, that erratic changes of the solutions can not be eliminated. The model calculations can highlight changes, which in practice only appear to a smaller degree or after a longer period of time, as the adaption rate of the land users differs. Therefore quantitative modelling can represent real competitions only conditionally and not accurately. The results are based on several assumptions (Carpenter, Pingali et al. 2005). Two basic assumptions of the model used are: the region acts/reacts rationally as business unit and optimises according to economic criteria the region passively accepts market prices and does not affect external factors. The model is connected to the trend of demand through economic basic data, but it does only contain reactions of the supply side. The adaption of the supply doesn t affect the market. Therefore possible developments and deductions for recommendations, that emerge from interactions of supply and demand, cannot be detected. Further, ecologic feedbacks which have effect e.g. in a closed systemdynamic model, are not integrated. The model only considers agricultural resource uses. Landscape and resource uses, which emerge e.g. by soil sealing in areas of settlement, are only indirectly integrated into the model through limitation of arable land and grassland. As the agricultural land use is the main focus of the model, its ecological consequences as emissions of greenhouse gases are exactly recorded, yet consequences of changes of landscape use as soil sealing on emissions cannot be accounted for. Also it is accounted for an analysis of possible competitions with land uses as e.g. through settlements and soil sealing only by restrictions on land use in the model. 5.2 Transformation scenarios For simulating transformation scenarios basic parameters of the model (e.g. prices of products and production means) are adapted to current trends or possible future developments. The model s simulation estimates changing production structures, which result from changes of key factors, that are politically influenced or market driven. Three scenarios were analyzed: 1) increasing mineral oil prices, 2) increasing product prices, and 3) expansion of bioenergy production. The assumptions made respectively in the scenarios are described in the following sections. A detailed derivation of the assumptions can be found in the final report of SunReg I (Schindler 2008) Scenarios with increasing mineral oil prices The parameter values of the transformation scenarios with increasing mineral oil prices are listed in table 14. The scenario low increase of mineral oil prices considers an increase of the mineral oil price of 25 % in relation to the basic scenario, which assumes a mineral oil price of 60 US-$, equal to The scenario moderate increase of mineral oil prices is based on an increase of 50 %, the scenario high increase of mineral oil prices on an increase of 100 %, the scenario extreme increase

44 of mineral oil prices on an increase of 200 % of mineral oil prices. It s assumed that increases of the mineral oil price cause accordant increases of the prices of production means and products Scenarios increasing product prices The parameter values of the transformation scenarios regarding increasing product prices are listed in table 15. The price level of the reference wheat is increased by 15 % in the scenario moderate increase of product prices, by 33 % in the scenario high increase of product prices, and by 50 % in the scenario extreme increase of product prices. Moreover it is assumed that increases in the price level of wheat result in different increases in the price levels of other agricultural products Scenarios expansion of bioenergy production The parameter values of the transformation scenarios regarding an expansion of the bioenergy production capacities are listed in table 16. The reference bioenergy production capacity is the sum of the capacities of the operational biogas facilities described in chapter The scenario moderate expansion of bioenergy production considers an increase in capacity of 50 %, the scenario high expansion of bioenergy production an increase in capacity of 100 %, the scenario extreme expansion of bioenergy production an increase in production capacity of 200 %. The corresponding changes of the prices of production factors were assumed as described in table

45 Table 14: Parameter values of the scenarios increasing mineral oil prices Scenarios base scenario low increase of mineral oil prices moderate increase of mineral oil prices high increase of mineral oil prices mineral oil price US-$/Barrel % +50% +100% diesel price /l 1.097, +14% +28% +55% benzine price /l % +25% +50% lubricant oil price /l % +13% +25% labour costs /h % +8% +15% fertilizer N /kg % +18% +35% fertilizer P 2 O 5 /kg % +10% +20% fertilizer K 2 O + MgO /kg % +8% +15% fertilizer CaO /kg % +3% +5% plant protection % 100% +7% +13% +26% variable machine costs % 100% +6% +12% +23% fixed machine costs % 100% +6% +12% +23% product seeds product seeds product seeds product seeds prices /dt /kg % % % % % % wheat (food) % +23% +45% wheat (fodder quality) % +6% +23% +13% +47% wheat (ethanol) % +25% +49% rye (food) % +23% +47% +25% rye (fodder quality) % +4% +25% +8% +50% +15% rye (ethanol) % +25% +49% rye silage % +6% % +13% % +25% barley (fodder quality) % +6% +24% +13% +48% +25% oats % +6% +24% +13% +47% +25% Triticale % +6% +24% +13% +47% +25% corn /unit +11% +4% +21% +8% +42% +15% maize silage (fodder) % +18% +35% 75 /unit +4% +8% +15% maize silage (Biogas) % % % rape seeds (food) % +20% +40% % +8% rape seeds (energy) % +20% +40% +15% Sunflower seeds /unit +10% +3% +20% +5% +40% +10% table potatoes % +6% +6% +13% +13% +25% starch potatoes % +6% +15% +13% +30% +25% sugar beet (sugar) % +23% +47% 150 /unit +4% +8% sugar beet (ethanol) % +30% +60% +15% Sudan grass % +4% % +8% % +15% Hybrid Sorghum % +4% % +8% % +15% ley grass % +4% % +8% % +15% cereal straw % +23% +45% 40

46 Table 15: Parameter values of the scenarios increasing product prices scenarios base scenario moderate increase of product prices high increase of product prices extreme increase of product prices wheat price /t % 33% 50% diesel price /l % +0% +0% benzine price /l % +0% +0% lubricant oil price /l % +0% +0% costs of labour /h % +5% +10% fertilizer N /kg % +10% +20% fertilizer P 2 O 5 /kg % +8% +15% fert. K 2 O + MgO /kg % +10% +20% fertilizer CaO /kg % +3% +5% plant protection % 100% +6% +13% +25% Var. machine costs % 100% +4% +8% +15% fixed machine costs% 100% +4% +8% +15% product seeds product seeds product seeds product seeds prices /dt /kg % % % % % % wheat (food) % +33% +50% wheat (fodder quality) % +8% +34% +21% +52% wheat (ethanol) % +36% +55% rye (food) % +34% +52% rye (fodder quality) % +3% +37% +8% +56% rye (ethanol) % +36% +55% +39% +14% rye silage % +8% % +22% % +40% barley (fodder quality) % +7% +35% +21% +54% +38% oats % +8% +35% +23% +53% +42% Triticale % +8% +35% +21% +53% +39% corn /unit +14% +0% +31% +1% +47% +3% maize silage (fodder) % +27% +40% +0% +1% maize silage (Biogas) 4.09 /unit % % % rape seeds (food) % +34% +52% % +1% rape seeds (energy) % +34% +52% Sunflower seeds % +2% 95 /unit +16% +3% +40% +5% +52% +10% table potatoes % +3% +11% +7% +17% +14% starch potatoes % +3% +16% +6% +23% +11% sugar beet (sugar) % +29% +44% +4% +8% sugar beet (ethanol) 2.70 /unit +14% +31% +47% +15% Sudan grass % +4% % +8% % +15% Hybrid Sorghum % +4% % +8% % +15% ley grass % +4% % +8% % +15% cereal straw % - +8% - +15% - 41

47 Table 16: Parameter values in the scenarios expansion of bioenergy production scenarios biogas production cap. MWh el /year base scenario moderate expansion of bioenergy production high expansion of bioenergy production extreme expansion of bioenergy production 113, % + 100% + 200% diesel price /l % +0% +0% benzine price /l % +0% +0% lubricant oil price /l % +0% +0% cost of labour /h % +0% +0% fertilizer N /kg % +3% +5% fertilizer P 2 O 5 /kg % +0% +0% fertilizer K 2 O + MgO /kg % +3% +5% fertilizer CaO /kg % +0% +0% costs of plant protection% 100% +2% +4% +8% variable machine costs % 100% +1% +3% +5% fixed machine costs % 100% +1% +3% +5% product seeds product seeds product seeds product seeds prices /dt /kg % % % % % % wheat (food) wheat (fodder quality) % +2% +8% +3% +16% +4% +8% +15% wheat (ethanol) % +8% +16% rye (food) rye (fodder quality) % +1% +8% +1% +17% +4% +8% +16% rye (ethanol) % +8% +16% rye silage % +2% +6.81% +3% % +8% barley (fodder quality) % +2% +8% +3% +16% +7% oats % +2% +8% +4% +16% +8% triticale % +2% +8% +3% +16% +8% corn /unit +4% +0% +7% +0% +14% +0% maize silage (fodder) % +6% +12% 75 /unit +0% +0% maize silage (Biogas) % +6.81% % rape seeds (food) % +8% +16% % +0% rape seeds (energy) % +8% +16% Sunflower seeds /unit +4% +3% +8% +5% +16% +10% table potatoes % +1% +3% +1% +5% +2% starch potatoes % +0% +4% +1% +7% +2% sugar beet (sugar) % +7% +13% 150 /unit +1% +3% sugar beet (ethanol) % +7% +14% Sudan grass % +3% +6.81% +5% % +10% Hybrid Sorghum % +3% +6.81% +5% % +10% ley grass % +1% +6.81% +3% % +5% cereal straw % +4% +8% +8% +3% +0% +0% +5% 42

48 5.2.4 Scenario simulation results The following diagrams show the simulation results for the previously described scenarios. Figure 20 depicts the share of arable land by crops in the basic scenario and the transformation scenarios. Figure 20: Share of arable land by crops in the basic scenario and the transformation scenarios: Area of cultivation of dominant crops. The columns for the scenarios with an expansion of bioenergy production show that the cultivation of rye is reduced, and to a lesser extent also the cultivation of wheat, as a consequence of an increasing production of bioenergy. In other words, the growing cultivation of maize and other energy crops take place at the expense of a decreasing production of wheat and rye. The scenarios with increasing oil prices prove to be unfavourable to the cultivation of crops with a high demand for energy (i.e. fuel) along its production process, especially when these do not have a high profit value. Crops belonging to this category include corn maize and potatoes. In the scenarios with increasing prices for agricultural commodities corn maize production increases significantly, since a high profit margin is achieved at constant energy costs. The production of wheat and rye is reduced instead. 43

49 Looking at the allocation of the land according to the cultivated crops and their dedication either for bioethanol, biodiesel or biogas, figure 21 indicates that the cultivation of energy crops occupies around 14.3 % of the arable land. This share increases in the scenarios with an expansion of bioenergy production to 23.2 %. The main cause for this change of land use is the expansion of biogas crops, that occupy altogether 12.1 % of the arable land to feed a total biogas plant power of 42,516 kw el. Figure 21: Share of energy crops cultivation according to their uses for bioethanol, biodiesel or biogas as percentage of the total available arable land. In the scenarios with oil price increases and agricultural commodity prices increases the share of land dedicated to the production of energy crops decreases to 12.8 % (scenarios with oil price increases) and 12.2 % (scenarios with agricultural commodity prices increases), respectively. This is mainly because of the falling cultivation of rapeseed. The share of bioethanol remains nearly constant in all scenarios. This is the consequence of the model assumption, that prices for ethanol remain stable. 44

50 Figure 22 shows in a greater detail the importance of single crops as feedstock for the production of bioenergy. The production of ethanol is mainly based on rye, that is cultivated on 16,000 ha from a total of 213,180 ha arable land in the region. A minimal amount of wheat (900 ha) and no sugar beats are cultivated for the purpose of gaining ethanol. Figure 22: Area dedicated to energy crops cultivation (in ha). Rapeseed was considered as the only statistically significant feedstock for the production of biodiesel. The main feedstock for biogas production include maize, grain silages and Sudan grass. Since the cultivation of ley grass and Sorghum is comparatively costly, it does not play a major role in the regions cultivation patterns. The share of maize biogas (bioenergy) production reaches an extension of 15.0 % of the total arable land in the scenario expansion of energy production by 200 %. The total extension of maize cultivated in this scenario (i.e. including maize for food and bioenergy) is 21.6 % of the total arable area. The largest extension of maize being cultivated happens to be in the scenarios with increases of agriculture commodity prices by 50 %. In this case the crop maize accounts for 39.0 % of the total arable land. 5.3 Evaluation of the transformation scenarios simulation results Parameters of the global warming potential, the acidification potential, the eutrophication potential and of the photochemical active oxygen potential were integrated into the scalable database, the model is based on. This includes data of emissions that are generated by the application of production means 45

51 (fertilizer, biocides, agricultural machines, etc.). As part of the modelling the cumulated energy demand is calculated, i.e. the sum of all inputs of primary energy of all production processes of crop cultivation, including the energy demand of the fabrication of necessary production means. Moreover the greenhouse gas emissions originating at the production of energy crop biomass are calculated. The calculation methods are described in chapter Calculation of cumulated energy demand and emissions of greenhouse gases A simplified life cycle assessment is used to calculate cumulated energy demand and greenhouse gas emissions that result from energy crop biomass production. The life cycle assessment is simplified, as only greenhouse gases and cumulated energy demand are regarded. Data sources for the cultivation methods are Hanff et al. (2005) and KTBL (2006). The application of production means, that are relevant for greenhouse gas emissions or energy demands, was calculated according to the descriptions of the cultivation methods. This productions means are diesel oil, heating oil, electricity, seeds, biocides, fertilizers, high density polyethylene and the usage of agricultural machines. The application of each agricultural machine was put in relation to the average useful life of the machine and the emissions and energy demand of its fabrication as given in Scholz and Kaulfuß (1995) and Jolliet (1993). According to the factors of emissions and global warming potential of the production means (see Kaltschmidt and Reinhardt (1997), Green (1987), Scholz and Kaulfuß (1995), Jolliet (1993), Davis and Haglund (1999) and Patyk and Reinhardt (1997)) cumulated energy demand and greenhouse gas emissions are calculated for all production steps (tillage, seeding, fertilizing, plant protection, harvesting, transport, drying, compacting, lavation and storage) and summarized. The calculation of carbon dioxide equivalents complies with IPCC 100 (2007). Direct, soil-borne emissions of the greenhouse gas nitrous oxide from plant residuals and the application of fertilizers as well as indirect N 2 O emissions and nitrate leaching and volatilisation according to "IPCC Guidelines for National Greenhouse Gas Inventories Guidelines" (IPCC 2006) are calculated and added to the greenhouse gas emissions of the cultivation procedure. Regarding the supply of manure and dung for fertilizing it is assumed, that they are available in excess and part of livestock breeding processes. Therefore cumulated energy demand and gas emissions of manure and dung supply are not accounted for in regard of crop cultivation. The greenhouse gas emissions and the cumulated energy demand of crop cultivation methods were averaged according to the land use allocation of a region to calculate the average cumulated energy demand and greenhouse gas emission of crop cultivation of the region. As livestock breeding was not modified in the scenarios, their emissions and energy demand was not considered. 46

52 5.3.2 Evaluation of results The figure 23 indicates that the potential gross energy production in the scenarios with increasing oil prices and the scenarios with increasing agriculture commodity prices does not change substantially compared to the reference scenario, but in the scenario with increasing bioenergy production the increase of the potential gross energy production is prominent. The main increment in this scenario is due to the marked expansion of biogas production. Figure 23: Gross bioenergy production potential (in ha). The significance of straw pellets as potential source for bioenergy production in the region is also revealed in the figure 23. In amounts to 50 % or even more of the potential gross bioenergy production in all scenarios, even though this share decreases in the scenarios with increasing commodity prices and the scenarios with increasing bioenergy production. In contrast to that, the potential amount of bioenergy gained from straw pellets increases drastically in the scenarios with increasing oil prices. In the scenario Bioenergy +200 the production of biogas increases from 2,332,306 GJ to 3,498,459 GJ compared to 1,166,153 GJ in the reference scenario. However, the production of bioenergy from straw decreases by 466,753 GJ from 3,341,453 to 2,874,700 GJ. The amount of electricity produced from biogas in the reference scenario is 113,376,000 kwh. Assuming a workload of 8,000 hours per year this corresponds to an equivalent of kw el.. The amount of electricity produced remains stable at a similar in the scenarios with increasing oil prices and the scenarios with increasing agricultural commodity prices. In the scenarios with increasing 47

53 bioenergy production the electricity produced from biogas increases respectively to 170,064,000 kwh (or 21,258 kw el ) ( Energy scenario +50% ); 226,752,000 kwh (or 28,344 kwel) ( Energy scenario +100% ); and 340,128,000 kwh (or 42,516 kwel) ( Energy scenario +200% ). In the reference scenario 33.9 % of the gross energy production target from renewable for 2020 of 15,480,000 GJ for the region is fulfilled. In the scenarios increasing bioenergy production +200 the contribution of bioenergy to the target value is 45.8 %. This is higher then the contribution achieved in the scenario increasing oil price +100 with 38.3 % and the scenarios increasing commodity prices +50 with 23.6 % of the target value. Indeed, the production potential of bioenergy increases in the scenarios with increasing oil prices, not because of an increment of biodiesel production, but because of an augmented cultivation of rye and wheat, and hence a higher availability of straw as feedstock for bioenergy production. Another feature in the scenarios with increasing oil prices is, that the total cumulative energy consumption in agricultural production decreases. The higher prices for fossil fuels result in a higher comparative advantage of crops with a high ratio of profit to energy demand, such as wheat and rye. This two crops again deliver straw residues that may be used as feedstock for bioenergy production. Figure 24: Cumulative energy requirement (CER) in agricultural production by uses of agricultural products in the Havelland-Flaeming region (in GJ). In the scenarios with increasing agricultural commodity prices the production of bioenergy decreases, to some extent because of a decreasing production of rapeseed, but mainly because a strong decline of wheat and rye production in favour of maize (corn) cultivation, and hence a reduced availability of 48

54 straw residues as a feedstock for bioenergy production. The increasing cultivation of maize results in higher consumption of energy, i.e. a less favourable energy input-output ratio. Compared to other energy crops maize silage and sorghum have a relatively low energy demand per hectare. The crops are cultivated more extensively with respect to the amount of mineral fertilizer applied, and hence have a lower energy demand per hectare. Rapeseed and wheat do have a similarly high energy demand per hectare, while rye has a slightly lower energy demand per hectare compared to maize silage and sorghum. In the scenarios with increasing bioenergy production the expansion of energy crops for biogas is evident. Simultaneously the cultivation of rye and wheat decreases, that have a higher energy demand per hectare. Finally, the total energy demand in crop production remains stable, since the expansion of energy crops is compensated by the decrease of food production. In the scenarios with increasing oil prices no changes of the cumulative energy demand can be observed as a consequence of energy crop production. Nevertheless, the energy demand for food production decreases, since energy intensive crops like maize are cut down while relatively energy extensive crops like wheat increase in production. The opposite development of the cumulative energy demand (CER) takes place in the scenarios with increasing agricultural commodity prices. In this case, the cultivation of energy intensive maize is extended massively, while the total area cultivated with rye and wheat, two crops with a much lower energy demand per hectare, is cut down. This at the end leads to a strong increase of the total energy demand in crop production. In total, energy crops hold a relatively low share of greenhouse gas emissions from total agricultural production in the Havelland-Fläming region (figure 25). Rye, maize silage and sorghum are among the crops that cause less greenhouse gas emissions, while the cultivation of maize as food emits relatively more greenhouse gases on a hectare basis compared to rye and wheat. The total emissions of greenhouse gases is always in relation to the area under cultivation. In the reference scenario energy crops hold a share of 7.2 % of the total greenhouse gas emissions from crop production. This share increases to a maximum of 12.5 % in the scenario increasing bioenergy production The minimum share of 5.8 % of the total greenhouse gas emissions is reached in the scenario with increasing agriculture commodity prices +50 %. 49

55 Figure 25: Greenhouse gas emissions from agricultural production by uses of agricultural products in the Havelland-Fläming region (in kg CO 2 -equivalent) The emissions of greenhouse gases is higher in all the analysed scenarios compared with the reference scenario. The main reason for this is that in the reference year of the reference scenario the duty to set-aside was still existent. This duty is not anymore valid in the future scenarios. In turn, setaside areas do emit no or very little greenhouse gases. In the scenarios with increasing bioenergy production the totally emitted greenhouse gases from crop production remain at about the same level as in the reference scenario, even when bioenergy production is increased massively. In the scenarios with increasing oil prices it is possible to evidence the effect of the reduction of maize as food crop in favour of the expansion of wheat, that leads to a reduction of greenhouse gas emissions. On the other side, when maize as food crop is cultivated increasingly at the expense of wheat and rye, like it is the case in the scenarios with increasing agriculture commodity prices, greenhouse gas emission also increase significantly. 50

56 6 Potential for scenario expansion of bioenergy production The economic modelling (chapter 5) provided an economic optimization of land use in regard to an increased biogas production. These data were converted into crop rotations, which were the basis for the calculation of an economic bioenergy potential. As in the reference scenario (section 3.1.1), autarkic supply of food and fodder was included in the calculation. Three economic bioenergy scenarios were forecasted using the site specific modelling based on the biomass yield model: - Bioenergy +50%: based on energy +50% - Bioenergy + 100%: based on energy +100% - Bioenergy + 200%: based on energy +200%. 6.1 Data transfer from economic model to biomass yield model Part of the results of economic modelling (chapter 5) are predicted acreages of cultivated crops per municipalities in the investigation area. To meet the requirements of biomass yield model the acreages have to be converted into percentages of each crop for each municipality. These percentages will be translated into a crop rotation with seven crops: with parts (one, two or three parts) of seventh crops. Crop rotations distinguish a typical cultivation system with regional differentiated acreages of crops. The systematic and targeted sequence of crop cultivation in combination with the limitation of acreages of several crops (Table 17) reduces the danger of pests, weed infestation, soil erosion and soil exhaustion. The yield of crops may differ due to a previous crop up to 20 %. The crop rotation and the choice of previous crops have an effect on crop yield and crop health. Therefore, a balanced crop rotation is composed of winter and spring crops and the cultivation of catch crops preceding maize or after the cultivation of cereals. The percentage of cereals in crop rotation must not exceed 50 %. Table 17: Cultivation breaks of several crops (Müller 1986) crop break of X year(s) threat of crop yield Winter rye 1 fungi Winter wheat 2 fungi, nematode Winter barley 2 fungi Triticale 1 fungi Maize 3 fungi Winter rape 3-4 nematode, fungi To prepare and evaluate a new crop rotation for biomass yield modelling, the acreage of each crop is needed. Based on this data a module was developed and used for the selection of one specific crop rotation for each municipality. At first the statistics were evaluated and for each crop the percentage of cultivation area was calculated. Secondly, the crop rotations are ranged and assessed. (Figure 26) 51

57 statistics of municipality crops acreages, percentages crops with percentage of 9 % of acreages preparation and evaluation of crop rotations selection of crop rotation with best evaluation value Figure 26: Modelling course of crop rotations per municipality Each crop rotation of the conventional cultivation system consists of 3 or 4 crops. In the biomass yield module crop rotations are defined with 7 crops. This provides the opportunity to build up the percentages of crops: crops with high percentage and dominant cultivation area occur several times in crop rotation. The results of the economic model are the acreages of crops per each municipality as in Table 18. Table 18: Output of economic modelling: cultivation acreages of crops in 4 example municipalities: bioenergy scenario +50% acreage in hectare of crops municipalities potato (food) rape (food) rape (energy) on fallow land maize (fodder) maize (biogas) Corn winter rye winter barley (fodder) spring barley (fodder) Oat grass on arable land total acreages The acreage of each crop in each municipality was summarized and calculated in percentage of arable land and parts of seventh (Table 19). 52

58 Table 19: Calculation of acreages in percentage and parts of seventh in 4 example municipalities: bioenergy scenario +50% acreage in percentages acreage in seventh part municipalities crop potato pot 0,0 15,0 0,0 0,0 1,1 rape ra 15,0 15,0 15,0 15,0 1,1 1,1 1,1 1,1 maize 3,0 11,7 18,3 14,0 0,2 0,8 1,3 1,0 corn 0,0 42,0 0,0 0,0 2,9 winter rye wr 0,0 0,0 28,6 54,1 2,0 3,8 winter barley wb 0,0 7,5 7,5 0,0 0,5 0,5 spring barley sb 18,4 0,0 0,0 7,5 1,3 0,5 oat 60,2 0,0 0,0 0,0 4,2 grass 3,3 8,8 30,6 9,4 0,2 0,6 2,1 0,7 As a whole the modelled crop rotations of the investigation area are dominated by cereals. In economic scenarios the acreages of triticale and barley are decreasing while acreages of wheat and maize are increasing. A new biogas crop gets more important: the cultivation area of sorghum increases in stronger bioenergy scenarios. The grass cultivation on arable land gets a higher percentage and replaces the cultivation of triticale (Figure 27). BE + 200% economic modelling BE + 100% BE + 50% bym regional crop rotation 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% winter wheat winter barley winter rye triticale maize winter rape sorghum grass sugar beet Figure 27: Crop rotations in site scenario (bym) and economic scenarios Bioenergy (BE) + 50% / + 100% / + 200% : Percentage of cultivation areas of dominant crops. 53

59 6.2 Evaluation of crop rotations Indicators were developed for the evaluation of crop rotations, using sustainability criteria and effects of preceding crops for the cultivation and yield of a current crop. The evaluation is a combination of the effect of the previous crop to the yield of the crop in organic and conventional farming systems (Table 20). First, the the effect of a organic farming system is assessed and is differentiated by the effect in a conventional farming system. Table 20: Evaluation values of crop rotation: effect of preceeding crop to yield of current crop Evaluation value organic farming conventional farming / * / m / * / m / - 3 (-) + 4 (-) * 5 (-) M 6 (-) / * / m / - ++ very favourable + favourable previous crop * favourable previous crop, but other crops will be better, m cultivation with restrictions (-) unfavourable previous crop - very unfavourable previous crop, impossible cultivation The evaluation system contains 7 values higher values account for an unfavourably effect of the previous crop to the main crop. In the cultivation system the value of an effect of a preceeding crop in crop rotation should not exceed 3. Each effect to the current crop by the previous crop is evaluated. Based on these evaluations several statistical parameters (sum, average and amounts of values higher than 2 or 3) of the whole crop rotation are derived. The evaluation values of the crop rotations in the module of biomass yield modelling (site scenario) achieve an average of 1.9. Not in a single municipality of the investigation area did values higher than 3 occur; one-third of municipalities contain only one value 3 in crop rotation. In the other two-third municipalities unfavourable evaluation occur once or even less. The economical crop rotations of the bioenergy scenarios calculated based on the acreages of crops per municipality are stepwise optimized by the minimization of the evaluation values in crop rotation (Table 21). 54

60 Table 21: Theoretical crop rotation and evaluation in 4 example municipalities: bioenergy scenario +50% municipalities Theoretical crop rotation Evaluation of each crop x number of evaluation value > 2 > 3 1 oat - oat - sb - ra - oat - oat - sb corn - grass - ra - wb - maize - pot - corn grass - maize - wr - grass - wb - ra - wr wr - sb - wr - maize - wr - grass - ra The average of the evaluation value of bioenergy optimized crop rotations in the investigation area is 2.5. In 38 of 80 municipalities, values higher than 3 occur 2 times in crop rotation. That means a crop rotation with some negative effects to crop yield or crop health. Evaluation of crop rotations: percantage of municvipalities. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Bioenergy +50% Bioenergy +100% Bioenergy +200% Amount of single evaluations with value > Figure 28: Evaluation of the sustainability of crop rotations in bioenergy scenarios Bioenergy + 50% / + 100% / + 200% : Percentage of municipalities with number of several evaluations higher than 3. With higher bioenergy scenario (+50% 200%) the number of high, unfavorable values and bad effects in crop rotations is declining because of the cultivation of new energy crops e.g. sorghum. In average the evaluation of crop rotations of the bioenergy scenarios resulted in higher values than the standard crop rotations in the biomass yield model. 55

61 Figure 29: Evaluation of bioenergy crop rotations in municipalities of the bioenergy scenario + 200%: number of evaluation values higher than Bioenergy potential of bioenergy crop rotation scenarios Since 2007 the livestock of the investigation area has been changed. In statistical data 2009 the number of pigs was decreasing in comparison to

62 amount in thousand County Teltow-Fläming Potsdam-Mittelmark Havelland cows pigs cows pigs Figure 30: Livestock farming (cattle and pigs) in the counties 2007 and Furthermore the biomass yield model was further adapted to the investigation area and the modelling of feeding amounts and kind of house / stable floor systems were included and specified. For that reason the biomass yield model was applied for the new livestock data and applications and new site scenarios are modelled. Furthermore the economic bioenergy (economic crop rotations) are also applied in the biomass yield model. The results of all scenarios are shown next. Table 22: Bio fuel potentials of various scenarios Bio fuels [t a -1 ] bioethanol without rye reference biomass yield model [bym] economic model + bym : mean precipitation 2007: dry 2009: mean precipitation bioenergy +50% bioenergy +100% bioenergy +200% 10,383 14,282 9,676 28,822 8,564 6,796 2,834 biodiesel 15,260 11,751 5,890 8,365 15,086 15,086 15,045 Potential of biofuels (Table 22) in the investigation area (in scenarios) is low and potentials will not increase significantly. The optimizations of economic scenarios are aimed to the cultivation of biogas crops (Table 23). 57

63 Table 23: Biogas potentials of various scenarios (based on 8000 working hours per year) Biogas potentials [kw el] reference biomass yield model [bym] economic model + bym : mean precipitation electricity based on manure + silages (rye, maize) 2007: dry 2009: mean precipitation bioenergy +50% bioenergy +100% bioenergy +200% 22,675 22,648 11,073 27,281 27,931 28,529 30,116 additional potentials based on (= Bioenergy +200% plus) sorghum 1,812 3,042 6,735 grain corn* 3,695 2,966 1,287 higher CH 4 yield (maize) total potential 34,004 35,197 39,061 * in competition to bioethanol The biogas potential consists on the biogas produced with manure and silage (rye, maize), on additional potentials of sorghum as a new cultivated biogas crop, on the biogas potential of grain corn (grain corn competes to the use of available corn for bioethanol production) and on predicted higher methane yields of maize. Based on all these biogas substrates the highest biogas potential is given out with rounded 78 biogas sites with 8,000 working hours per year and a standard electrical power of 500 kw el. The biogas potentials of the counties (Table 24) and municipalities (Figure 31) in the investigation area are different. Table 24: Biogas potentials of scenario Bioenergy +200% in counties of the investigation area County or City Bioenergy +200% (manure + silages) sorghum grain corn* higher CH 4 yield (maize) electrical power [kw el / 8,000 working hours per year] Bioenergy +200% plus number of sites Brandenburg (Hvl) Potsdam Havelland 7, , Potsdam-Mittelmark 8,856 3, , Teltow-Flaeming 13,799 2, , Investigation area 30,116 6,735 1, , * in competition to bioethanol 58

64 Figure 31: Biogas potentials of scenario Bioenergy +200% and potential of alternative substrates. The cultivation of bioenergy crops is specialized and concentrated to a few municipalities (Figure 31, Table 25). 59

65 Table 25: Municipalities with highest biogas potentials in counties County Municipality bioenergy +200% Electrical power [kw el] Teltow- Flaeming Potsdam- Mittelmark Niederer Flaeming Electrical power [kw el] bioenergy +200% plus number of biogas plants (500 kw el) 4,645 5, Dahme 2,697 2, Großberren 1,441 1, Nuthe-Urstromtal 215 1, Kloster Lehnin 175 1, Roskow 1,244 1, Havelland Wustermark 1,197 1, Nauen 1,170 1, High potentials of the municipalities Kloster Lehnin in county Potsdam-Mittelmark and Nuthe- Urstromtal in county Teltow-Flaeming may be the result of the cultivation of sorghum as a biogas crop. Following table and figure show the differences between the highest bioenergy potential (economic scenario Bioenergy +200% plus with additional potentials from sorghum, yield increases of biogas techniques and use of grain corn) and biogas sites (installed electrical power and planning sites 2009 / 2010). Table 26: Biogas potentials and biogas sites in counties of investigation County Bioenergy +200% plus [kw el] Biogas sites & plan 2010 [kw el] Difference [Potential Sites in kw el] Havelland 9,001 11, Potsdam-Mittelmark 13,173 12, Teltow-Flaeming 16,803 17, That means: The biogas potential in the investigation area is almost completely in use and the main substrates are modelled in this investigation. But few biogas plants shown by some regional data of biogas sites import substrates from other regions. Other biogas inputs like grassland may have additional potentials. But most of biogas site operators exclude these substrates because of the high heterogeneity of the biogas feedstock from grassland. The use of it will be only as an additional alternative substrate in small amounts. 60

66 Figure 32: Biogas potentials (biomass yield model / Bioenergy +200% / Bioenergy +200% plus) in comparison to existing and planned biogas sites

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