Ministry of Food, Agriculture and Fisheries
Danish Institute of Agricultural Sciences
Internal Report
J.E. Olesen, A. Weiske, W.A.H. Asman, M.R. Weisbjerg, J. Djurhuus & K. Schelde
FarmGHG
A model for estimating greenhouse gas emissions from livestock
No. 202• June 2004
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Printing: www.digisource.dk Internal Report no. 202 • June 2004
FarmGHG
A model for estimating greenhouse gas emissions from livestock farms
J.E. Olesen, A. Weiske, W.A.H. Asman, M.R. Weisbjerg, J. Djurhuus & K. Schelde Department of Agroecology
Box 50
DK-8830 Tjele
1
FarmGHG
A model for estimating greenhouse gas emissions from livestock farms
Documentation
J.E. Olesen, A. Weiske, W.A. Asman, M.R. Weisbjerg, J. Djurhuus &
K. Schelde
March 2004
Danish Institute of Agricultural Sciences
2
3 Preface
FarmGHG is a model for estimating greenhouse gas emissions from a whole-farm, including emissions from imported goods to the farm. The model has been programmed in Delphi using a compartmentalised approach.
The model was developed in the Midair project funded under contract EVK-CT2-2000-00096 of the EU 5th Framework programme.
Research Centre Foulum March 2004
Jørgen E. Olesen
4
5 Contents
Preface... 3
Contents... 5
1. Introduction ... 7
2. Model overview... 9
2.1 System ... 9
2.2 Extern ... 9
2.3 Farm ... 9
2.4 Crop rotation ... 10
2.5 Housing ... 10
2.6 Fields and crops... 10
2.7 Animals ... 11
2.8 Feed storage... 11
2.9 Manure storage... 11
2.10 Ammonia volatilisation... 12
2.11 Methane emissions ... 12
2.12 Nitrous oxide emissions ... 12
3. Prechain emissions ... 15
3.1 Energy sources ... 15
3.2 Fertilisers... 15
3.3 Pesticides... 16
3.4 Imported feeds and seeds ... 16
3.5 Water ... 18
3.6 Animal production... 18
3.7 Housing ... 18
3.7 Field operations ... 19
4. Farm system ... 21
5. Livestock ... 23
5.1 Feed composition and energy values ... 23
5.2 C and N flow in the animal herd ... 23
5.3 Feeding ... 25
5.4 Replacement of cows and young stock ... 25
5.5 Methane emissions ... 27
6. Housing ... 29
6.1 Ammonia volatilisation ... 30
6.2 Methane emissions ... 30
6.2.1 Slurry based systems ... 30
6.2.2 Deep litter systems ... 31
6.3 Nitrous oxide emissions ... 31
7. Manure store and manure treatment... 33
7.1 Ammonia volatilisation ... 33
7.2 Methane emissions ... 34
7.2.1 IPCC methodology... 34
7.2.2 Default methodology... 35
7.3 Nitrous oxide emissions ... 36
7.3.1 IPCC methodology... 36
7.3.2 Default methodology... 37
7.4 Anaerobic digestion... 37
6
8. Feed store ... 39
9. Fields and crops... 41
9.1 Crops and crop rotations ... 41
9.2 Fertiliser and manure application... 42
9.3 Nitrogen fixation ... 44
9.3.1 IPCC methodology... 44
9.3.2 Default methodology... 45
9.4 Crop residues... 46
9.4.1 IPCC methodology... 46
9.4.2 Default methodology... 46
9.5 Nitrate leaching ... 47
9.6 Ammonia volatilisation ... 47
9.6.1 IPCC methodology... 47
9.6.2 Default methodology... 47
9.7 Methane emissions ... 48
9.8 Nitrous oxide emissions ... 49
10. References ... 51
7 1. Introduction
A simple flow-based simulation model was adapted for calculating nitrogen and carbon budgets for the dairy farming systems. The model system is designed with the aim of allowing a quantification of all direct and indirect gaseous emissions from dairy farms, so that the model can be used for assessment of mitigation measures and strategies. This requires modelling the flow of all products through the internal chains on the farm and also allowing for import and exports from the farm. The model thus allows assessments of emission from both the production unit and all prechains. The model draws on experience from both nutrient balance models (Olesen and Vester, 1995), from life cycle assessments (LCA) (Halberg et al., 1999) and reported model studies from dairy farms (Brown et al., 2001; Phetteplace et al., 2001). The model includes both matter balances of C and N, and allows calculation of environmental effect balances for greenhouse gas emissions (CO2, CH4 and N2O) and eutrophication (NO3 and NH3).
The model includes not only the farm gate budget components (input/output), but also the internal flows in the system (Watson and Atkinson, 1999). These internal flows are represented as flows between compartments in the system (Figure 1.1). Internal flows are important for the estimation of the overall effectiveness of different measures in reducing ammonia volatilisation (Hutchings et al., 1996). The energy use is calculated for each
compartment and is converted to import and to emissions of CO2 and other greenhouse gases.
The energy costs are calculated using generally accepted rates for direct and indirect energy use per input unit for field operations and per input or animal unit for livestock management (Refsgaard et al., 1998).
The imports of C and N in manure, fertiliser, bedding, feed, seed and irrigation are given from the description of model farms, but are distributed between the model compartments in the simulations. Milk production and herd size are also given from the definition of the model farms. This will imply a certain export of C and N in milk and meat. The farms are assumed to be best managed and not to export manure. The net crop production that is not used for feed is exported.
Figure 1.1. Flows of carbon (C) and nitrogen (N) in and out of the total model farm system and between compartments within the system represented in the FarmGHG model.
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9 2. Model overview
The model is divided in compartments, which can be aggregated within other compartments.
Each compartment handles imports, exports and its own operations. This leads to emissions of greenhouse gases (CO2, N2O and CH4), which is aggregated for each compartment.
The following types of compartments are distinguished:
• System, which has the overall control of all compartment
• Extern, which is everything external to the farm, including prechain emissions
• Farm, which is an aggregation of other types of compartments.
• Crop rotation, which is an aggregation of all fields on the farm. The crop rotation also defines the sequence of crops, including permanent grasslands.
• Housing, which contains the animals and the related management.
• Field, which defines a specific crop in one year.
• Animals
• Feed storage
• Manure storage
2.1 System
The model is run at monthly time steps to account for annual variation in operations and in flows of feed and manure. In order to properly account for the flows of manure, daily time steps are used for the animal, house and manure storage compartments.
Each import type is assigned a unique product name and a unit. However, products can also be internal to the model, e.g. crop produce. All model compartments can import any type of product. The imports also contain information on energy use and other greenhouse gas emissions (CH4 and N2O) for their manufacture.
Each compartment is assigned a number of operations, which are performed at specified time intervals. All model compartments can use any operation. Each operation has a number of characteristics, including energy use, type of energy (electricity or diesel) and any required additional inputs (e.g. fertiliser or pesticides).
2.2 Extern
The extern compartment handles the emissions associated with import of energy, fertiliser, pesticides, feedstuffs etc. A simple emission factor approach is used, where the emission of CO2, CH4, N2O, NH3 and NO3 with each type of import is accounted for. In addition the extern compartment accumulates exports of crop and livestock production from the farm.
2.3 Farm
The farm is a simple aggregation of all other compartments. However, the farm must also ensure that these compartments are properly linked, i.e. so that the outputs of compartments are directed to inputs of other compartments, if needed. The total product surplus is exported as crop or livestock products. In addition losses of N in ammonia and nitrate leaching is calculated. This is converted to N2O emissions.
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2.4 Crop rotation
The crop rotation is a simple ordered sequence of individual crops, and the emissions from the crop rotation are simple aggregations of these emissions. The crop rotation is defined in the model farms.
2.5 Housing
The housing is primarily necessary to distinguish different manure management strategies.
The housing requirements are distinguished between different animal categories, and a housing system is therefore linked with an animal category to provide inputs on manure outputs. The IPCC methodology does not consider emissions from animal houses per se, but links the emissions of methane and nitrous oxide to the manure management and storage. This model also allows the emissions of CH4, NH3 and N2O to be calculated from the housing system using alternative approaches depending e.g. on emission factors for each manure handling type, and depending on temperature in the house. The housing therefore needs to be specified in terms of insulation and type of manure handling (manure type and storage time in the house).
The housing will deliver manure to the manure storages, and the housing also defines the amount of straw and bedding material that is added to the manure.
2.6 Fields and crops
The individual crop is characterised by the crop type, the management (fertilisation, pesticides etc) and the use (grazing, silage, grains etc.). The fields take manure from the manure storages and deliver feed to the feed storages. Data on management, yields and the use of the crops are taken from the model farms.
The following sources of N2O emissions are distinguished from the fields:
• Application of animal manure. IPCC uses an emission coefficient of 1.25%. A higher emission factor may be used, e.g. 2.5% of applied N.
• Application of mineral nitrogen fertiliser. IPCC uses an emission coefficient of 1.25%. A lower emission factor may be used, e.g. 0.8% of applied N.
• Nitrogen excreted from grazing animals. IPCC uses an emission coefficient of 2%. It may be relevant to distinguish emission from urine and faeces.
• Emissions from biological nitrogen fixation. IPCC uses an emission coefficient of 1.25%.
It may be relevant to ignore this component.
• Emissions from crop residues. IPCC uses an emission coefficient of 1.25% and fixed values for amount of crop residues.
• Indirect emissions from nitrogen lost by leaching and runoff. IPCC uses an emission coefficient of 1.25%. IPCC also assumes that 30% of applied N is lost by leaching.
Alternative methods of estimating leaching are incorporated.
• Indirect emissions from nitrogen lost by ammonia volatilisation. IPCC uses an emission coefficient of 1%, and assumes that 10% of N in synthetic fertiliser and 20% of N in nitrogen excreted by livestock is volatilised. This model assumes specific volatilisation factors for each fertiliser and manure type.
11 2.7 Animals
Animals are separated in two categories, cows and heifers. Bull calves are sold from the farm and thus not considered in the model. The animals have a given production level and an associated feed plan, which is defined by the model farm.
The methane emissions can be calculated using the IPCC tier 1 and tier 2 methods. In tier 1 an emission factor is defined per animal, whereas for tier 2 the emissions are calculated from net energy intake. This net energy intake is calculated from the feed plan. In addition the
procedure for estimating methane emissions proposed by Kirchgessner et al. (1995).
The nitrogen excretion in urine and faeces (nitrogen and volatile solids) is calculated from the feed plan and the production level based on the estimates of Poulsen et al. (2001).
2.8 Feed storage
The feed storage stores feed for later consumption, typically during winter. Different storages may be considered, e.g. cereal grains, silage and root crops. The IPCC methodology does not consider emissions from these sources. In particular storage of silage may lead to emissions of ammonia and N2O, in particular for low silage quality. There is, however, very little
information available on this.
2.9 Manure storage
The manure is stored as solid or liquid manure. The liquid manure may be stored in closed tanks (e.g. the urine from a separate systems) or in open tanks (e.g. slurry) with or without a cover of straw or other materials.
The emissions of both methane and nitrous oxide from solid manure stores strongly depend on the temperature and flow in the manure heap (Hellmann et al., 1997; Sommer, 2000).
During composting there may be a methane emissions, whereas nitrous oxide emissions primarily occur at lower temperatures in the heap. Temperature has been found to strongly affect methane from slurry storages, but the level varies considerably between stores.
Slurry stores are anaerobic, and the methane emission depends on the amount of volatile solids in the manure and on the temperature (Khan et al., 1997; Sommer et al., 2000). N2O can be produced in porous surface covers such as natural surface crusts, straw or leca pebbles of slurry stores, while no N2O was emitted from uncovered slurry (Sommer et al., 2000). The emission was significantly related to the water balance, i.e., the difference between
evaporation and rain, during dry periods; during wet periods no N2O was emitted.
The IPCC method for estimating methane emission from manure is based on amount of volatile solids excreted and the climate region, which is the same for all model farms in Midair. However, the model for solid manure may be improved by taking account of the bulk density of the slurry (i.e. composting of the manure). The methane emissions from the slurry can be improved through accounting for the actual temperatures and for any surface layer that may oxidise methane.
12
The IPCC methodology calculates N2O emissions from manure on the basis of total N content and climate region only. Possibly, estimates of N2O emissions from slurry stores could be improved by considering surface area, ammonium content and water balance as input variables. Emissions from solid manure heaps should consider surface area and the potential for composting, as reflected in bulk density and moisture content.
2.10 Ammonia volatilisation
The CORINAIR simple method (CORINAIR, 2002) only gives one emission factor for each of the following subcategories: Emission from housing, storage, application and during grazing. There is no discrimination between slurry and farmyard manure. Moreover, it is assumed that no measures are taken to reduce the emission. This cannot really be used within MIDAIR as the emission e.g. depends on the excretion rate, type of manure, fraction of the time cattle is in the housing, storage method (incl. measures to reduced the emission) and application methods (incl. measures to reduce the emission).
The CORINAIR detailed method is only a framework within which countries have to report the emissions. For some countries some information is provided on published emission factors. It should be noted that a difference in published detailed emission factors of a factor of 2-3 between different countries is quite common.
As far as possible more detailed information is used as well as a parameterization that is a function of important factors if this information was available. This means that for some emission activities (e.g. for slurry after application) more detailed information is used than for others (e.g. for application of farmyard manure).
2.11 Methane emissions
Methane (CH4) is assumed to have global warming potential that is 21 times higher than for CO2. In literature, some of the results are presented as g CH4 whereas others are presented as g CH4-C. The conversion is 1 g CH4 = 12/14 g CH4-C.
The emission from stored slurry is expressed in g CH4 per g VS/day. As the excretion also can be expressed in g VS/day this will not give any problems to calculate the emission.
Information on the storage period is of course needed.
When making farmyard manure, straw is added to the manure and an often a substantial part of the CH4 emission comes from the straw. In the parameterization of the emission from stored farmyard manure here (composting and non-composting) it is assumed that the possible differences VS content of the manure due to feeding practise are negligible compared to the sum of the minimum VS content originating from the manure and the VS content originating from the straw. This means that it is assumed that there is no relation between the VS content of the manure and the CH4 emission of the combination manure + straw.
2.12 Nitrous oxide emissions
Nitrous oxide (N2O) is assumed to have global warming potential that is 310 times higher than for CO2. In literature, some of the results are presented as g N2O whereas others are presented as g N2O-N. The conversion is 1 g N2O = 14/22 g N2O-N.
13 Some emissions are defined per LU (livestock units = an animal of 500 kg). Normally this is equivalent to about 127 kg total N (Holsten cow) and 105 kg total N (Jersey cow).
14
15 3. Prechain emissions
The prechain emissions are the emissions associated with imports of production goods to the farm. In the model this includes consumption of energy, fertilisers, pesticides and feedstuffs.
However, energy included in buildings and farm machinery are not considered by the model.
3.1 Energy sources
Two types of energy sources are used by the model farms; diesel and electricity. The emissions associated with the production, transport and use of these two energy sources are shown in Table 3.1. These data were taken as values being representative for Central Europe.
Table 3.1 Greenhouse gas emissions associated with production, transport and use of diesel and electricity.
Energy source Emissions (mg MJ-1 electricity) (g kg-1 diesel)
CO2 CH4 N2O NH3
Diesel 3668 4.565 0.346 0.0204
Electricity 200600 400 7.5 0.042
3.2 Fertilisers
The fertiliser production, especially the production of nitrogen, represents in the conventional production (with often more than 50% of the fossil energy input) the essential share of the whole energy balance. Nitrogen can be applied in different fertiliser forms. However in the model only the pre-chain emissions associated with an average N-fertiliser is considered. Also the model does not consider the energy used for providing potassium and phosphate fertilisers as the use of these fertilisers are assumed to be minimal on the dairy farms.
The N2 fixation of modern N-fertilisers via the synthesis of ammonia (NH3) from hydrogen (H2) and atmospheric nitrogen (N2) by the Haber-Bosch ammonia synthesis is the dominating production process in Europe. There is a tendency for decreasing energy comsumption per kg of nitrogen fertiliser in recent years (Patyk and Reinhardt, 1997). This can be explained with an increasing efficiency of older production systems and the implementation of new
manufacturing processes, by which e.g. the net energy use could be lowered for the basic material for N fertilizer, NH3, within the last 30 years. Likewise, in the 1970s the energy consumption for the production of N fertilizers was 47 MJ end energy kg-1 NH3-N for the best production technology compared to 34.5 MJ end energy kg-1 NH3-N in modern fertiliser plants. On European average, however, the developments result in a consumption of 39 MJ end energy kg-1 NH3-N (Kongshaug, 1998).
The average greenhouse gas emissions associated with the supply of nitrogen fertiliser representative for the conditions in Germany (transportation distances, energy mix etc.) are used in the model (Patyk and Reinhardt, 1997) (Table 3.2).
Table 3.2 Greenhouse gas emissions (g kg-1) associated with production and transport of average nitrogen fertilisers (Patyk and Reinhardt, 1997).
Fertiliser type CO2 CH4 N2O NH3
Average N 2829 7.45 15.1 6.69
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3.3 Pesticides
The pesticides used in agriculture primarily include herbicides, fungicides and insecticides (95%). The growth regulators are added to the herbicides. The active substance in pesticides is a consequence of more or less complicated reaction steps in the production. Particularly the synthesis of new active substances, which are applied in lower concentrations than older pesticides, is connected with substantial energy expenditure.
Taking the different need of herbicides, fungicides and insecticides of the various crops into account, a differentiation of the energy and emission balance according to these groups would be meaningful. However, because of the major gaps of knowledge with respect to energy and emission factors such a differentiation is not possible. The available energy data are relatively old and the data separation is not clear and does not refer to representative active substances (Biskupek et al., 1997). Besides, information of the market share of active substances and countries of origin are often incomplete. Unfortunately, the chemical industry is still not willing to publish the required information (Olsson, 2000).
In this study the expenditure of energy was estimated according to the production of a high number of active substances. As values of the pure active substance production the
unweighted average of all examined materials were adopted according to Green (1987). The values of the formulation and packaging were also taken from Green (1987). The primary energy use and the associated GHG emissions of the average pesticide production according to Kaltschmitt and Reinhardt (1997) are shown in Table 3.3.
Table 3.3 Greenhouse gas emissions (g kg-1) associated with production and transport of an average pesticide.
Pesticide type CO2 CH4 N2O NH3
Average pesticide 4921 0.18 1.50 0.16
3.4 Imported feeds and seeds
Using the energetic assessment of the supply of seeds and seedlings it can be distinguished between products, which are morphologically identical with the harvested product, as for example grain, potatoes, peas and beans, and products where the seeds show other
morphologic qualities. This is applicable to e.g. sugar beets or grass seeds. For the first group derivatives from standardised production processes can be calculated. For the second group it is necessary to define specific production procedures (Kaltschmitt and Reinhardt, 1997;
Ratke, 1999).
Kaltschmitt and Reinhardt (1997) have calculated the energy amounts necessary for the supply of seeds as well as the associated GHG emissions for the most important field products. The calculated values refer to the seed production in the conventional agriculture.
Because of the directives for the organic agricultural production the applied seeds and seedlings must be produced in the organic farming system. The treatment of the seeds with chemical-synthetic plant protection agents (disinfectants) is forbidden. Besides, it has to be considered that the energy and emission values of the seeds and seedlings applied in the organic agricultural system do not correspond with those of the conventional system.
17 Because, however, no corresponding information is available for the organic cultivation, the same values have to be applied for both production systems.
In addition to the greenhouse gas emissions associated with the energy use, it is also
necessary to consider the N2O and NH3 emissions associated with use of fertilisers. Standard yields and recommended N fertiliser rates for the crops were taken from Danish Plant
Directorate (1997). The N2O emissions were estimated using an emission factor of 1.25% for fertiliser and N fixation and 2.5 for N lost by nitrate leaching, which was assumed to be 30%
of applied N in fertiliser and N fixation. Ammonia emission was estimated as 4% of applied fertiliser N. The sum of the emissions from energy consumption (Kaltschmitt and Reinhart, 1997; FAL, 2000) and from the fertiliser use are shown in Table 3.4.
Table 3.4 Greenhouse gas emissions (g kg-1) associated with production and transport of seeds.
Crop CO2 CH4 N2O NH3
Spring barley 151 0.00 1.73 1.13
Winter rye 154 0.00 1.44 1.20
Winter wheat 130 0.00 1.67 1.08
Spring wheat 130 0.00 1.67 1.08
Triticale 150 0.00 1.64 1.31
Spring oat 145 0.00 1.52 0.96
Pea 188 0.10 2.44 0.30
Field beans 118 0.10 2.72 0.30
Maize 151 0.00 1.56 1.00
Lucerne 194 0.10 2.60 0.20
Grass-clover 900 0.00 14.50 2.80
Red clover 900 0.00 24.00 0.70
Potato 39 0.00 0.20 0.15
Phacelia1 316 0.00 4.42 3.04
Mustard1 316 0.00 4.42 3.04
1 taken as the emission values for winter rape.
The feedstuffs that are imported to the farms are used solely for concentrates. The organic model farms are assumed to be self sufficient with feed and all the imported feedstuffs are therefore assumed to be produced in conventional production systems.
The data on energy use for domestically produced feeds were taken from FAL (2000). The additional emissions of N2O and NH3 from application of fertilisers and from N-fixation were calculated as described above for seeds. Data on emissions from production of soybeans were provided by R. Dalgaard (pers. comm.). The resulting greenhouse gas emissions are shown in Table 3.5.
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Table 3.5 Greenhouse gas emissions (g kg-1) associated with production, processing and transport of feedstuffs.
Crop CO2 CH4 N2O NH3
Spring barley 196 0.24 1.80 1.19
Wheat 184 0.25 1.46 1.15
Oats 195 0.24 1.49 1.02
Rye 222 0.24 1.80 0.96
Maize 200 0.24 1.53 1.06
Pea 200 0.10 2.05 0.00
Field beans 203 0.10 2.63 0.00
Rapeseed cake1 453 0.69 4.48 3.11
Soybean 300 0.10 2.71 0.00
Soybean meal 350 0.10 2.71 0.00
1 taken as the emission values for winter rape.
3.5 Water
Water is used both in the house and in the field for irrigation. The energy required for providing the water depends on the source of the water. Here it is assumed that groundwater is used and the energy required was set to 5 MJ m-3 in electricity (Dalgaard et al., 2002).
3.6 Animal production
HEA (1996) indicates an average of the overall electricity consumption of about 400 kWh cow-1 yr−1 of which 2% account for indoor manure transports, 3% for lighting, 35% for feeding and 60% for milk extraction and cooling. Generally, the electric energy consumption does not differ between organic and conventional production systems.
3.7 Housing
In houses with mechanical ventilation systems there is an additional energy requirement for the ventilation. This requirement has been estimated to 121 kWh cow-1 yr-1 (Dalgaard et al., 2000).
Use of scrapers for cleaning the floor has an additional energy requirement, which has been estimated at 40 kWh cow-1 yr-1.
Straw is used for bedding especially in deep litter systems. Some farms do not produce sufficient straw and therefore needs to import straw. Cederberg (1998) presented data for the energy (diesel) input for the gathering, baling and transportation of straw in the organic and conventional production. The yield data used by Cederberg (1998) have here been adjusted to the average yield level used in the organic farms in the current study. No additional N2O emissions from the production associated with use of fertilisers were assumed, and the resulting greenhouse gas emissions are shown in Table 3.6.
19 Table 3.6 Greenhouse gas emissions (g kg-1) associated with production and transport of straw for bedding.
Production type CO2 CH4 N2O NH3
Conventional 408 0.58 0.010 0.002
Organic 643 0.91 0.016 0.003
3.7 Field operations
Only few literature data are available on the diesel consumption of specific activities in agriculture. Therefore, Biskupek et al. (1997) have varied the diesel consumption per ha and engines capacity (big engines and big fields have a low diesel consumption per ha). For this study the data of FAL (2000) for small fields (5 ha) were used. The machine type and diesel use of different treatments are summarized in Table 3.6.
Table 3.6. Diesel use depending on machine type according to KTBL (1996), Biskupek et al.
(1997), FAL (2000) and Moerschner (2000).
Machine type (kW) Diesel use
Ploughing 90 20.5 l ha-1
Harrowing 56 7.6 l ha-1
Manure application 90 0.4 l t-1
Sowing (grain / rape) 56 5.5 l ha-1
Sowing (maize) 56 1.8 l ha-1
Plant of potatoes 56 3.5 l ha-1
Chemical application 56 1.3 l ha-1
Fertilizer application 56 1.3 l ha-1
Limestone application 90 6.7 l ha-1
Weed harrowing 56 3.2 l ha-1
Chaff cutter (silage) - 0.5 l ha-1
(Swath) Cutting 45 3.0 l ha-1
Turn-over, windrow etc. 56 2.3 l ha-1
Harvesting > 4 t ha-1 90 2.4 l t-1
Harvesting < 4 t ha-1 90 3.8 l t-1
Harvesting potatoes 56 1.0 l ha-1
Harvest transport 56 0.2 l t-1
Silage baling/storing 56 0.5 l t-1
Straw baling 90 1.1 l t-1
Straw transport 90 3.9 l ha-1
Grain drying (per percentage point) - 1.22 l t-1
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21 4. Farm system
The farm handles all flows between the compartments on the farm. The model applies a basic time step of one month. However, in order to properly reflect the flows and emissions, daily time steps are used to represent the animals, house and manure storage compartments. The model is initialised by clearing the contents of all storages on the farm. However, this gives an unrealistic representation of the manure storages, and the model is therefore run for an initial year in order to equalise compartments before doing the actual simulations.
The following steps are included for each monthly update of the model:
1. The demand of the crops for nitrogen from manure is estimated and satisfied if manure is available from the manure storages, i.e. manure is moved from the manure storages to the respective fields. Mineral nitrogen is added on conventional farms to achieve the required N demand in the specific month.
2. The animal compartments are updated and milk and meat is transferred to the extern compartment. Emissions of methane from the animals are estimated.
3. Feed is transferred from the feed storage to the animals to satisfy the need of the animals.
Fresh feed from grazing etc. are also virtually transferred to the animals via the feed storage.
4. The produced urine and faeces is allocated to grazed fields and to the house in proportion to the time spent in house or in the fields.
5. Bedding and water is added to the house according to rates needed for the specific house and animal numbers. The need for bedding and water is reduced in proportion to the time spent in the house.
6. The house compartment is updated and emissions of methane and nitrous oxide are estimated.
7. Manure is transferred from the house to the manure storages. Any additional organic waste (or crop produce) for biogas digestion is added. The methane production capacity of the organic matter is assigned (Table 4.1).
8. The manure storage is updated and emissions of methane and nitrous oxide are estimated.
If the manure storage includes a biogas digester then additional estimates of electricity and heat production are made.
9. The crop rotations are updated and emissions of methane and nitrous oxide are estimated.
Table 4.1. Methane producing capacities (Bo) of different organic materials.
Organic matter source Bo (m3 kg-1 VS)
Faeces from heifers 0.21
Faeces from cows 0.24
Grass-clover silage 0.34
Maize silage 0.53
Triticale whole crop silage 0.34
Potato 0.42
Cereal grain 0.43
Straw 0.28 Lucerne 0.34 Pea 0.47
Field beans 0.47
22
23 5. Livestock
The model herds are all based on Holstein Friesian (HF) cattle, with dairy cows and young stock, but where all male calves (bull calves) are sold as newborn.
5.1 Feed composition and energy values
Chemical compositions of feeds are taken from the Danish feed table (Møller et al., 2000).
Nitrogen free extract (NFE) and carbohydrate are calculated as:
NFE = 100 - [ash + crude fat + crude protein + crude fibre] (5.1) Carbohydrate = 100 - [ash + crude fat + crude protein] = crude fibre + NFE (5.2) where NFE, carbohydrate, ash, crude fat, crude protein and crude fibre are all in % in DM.
Gross energy in feeds is calculated from the chemical composition using the same energy factors as used for digestible nutrients in the Danish energy evaluation system (Weisbjerg and Hvelplund, 1993).
Gross energy = 24.237 crude protein + 34.116 crude fat + 17.300 carbohydrate (5.3) where gross energy is in MJ kg-1 DM, and crude protein, crude fat and carbohydrate all are in kg kg−1 DM.
5.2 C and N flow in the animal herd
The values for N and C are based on mole weight 12 for C and 14 for N. The concentration of C and N in different nutrients are shown in Table 5.1.
Table 5.1. Concentration of C and N in g per kg feed nutrients
Nutrient C (g kg-1) N (g kg-1)
Crude protein 520 160
Crude fat 760
Fibre carbohydrates 480
Starch 444
Sugar (disaccharide) 421
The milk composition is taken from Danish milk recording for HF cows in 2001-2002 (Lauritsen and Lind, 2002), where 1 kg milk contains 33.6 g protein and 40.9 g fat. The lactose content is 46.1 g kg-1 milk according to Sjaunja et al. (1991). However, the mean value for Danish HF cows is 47.3 g lactose kg-1 milk (Kjeldsen, personal communication), which is used in the calculations. The concentration of C and N in protein, fat and lactose is given in Table 5.2.
Table 5.2. Concentration of C and N in g kg-1 milk components.
Milk component C (g kg-1) N (g kg-1)
Protein 533.0 156.7
Fat 701.6 * Lactose 420.5
* 39.1% of fatty acids are de novo synthesised with mole weight 192 (11.4 mole C per mole fatty acid) and 60.9% are either from feed or from mobilisation with mole weight 276 (17.5 mole C per mole fatty acid).
24
The chemical composition of the animals is shown in Table 5.3. In the model herds animals leave the herd either as newborn (bull calves and dead calves), as heifers with a foetus or as cows (for slaughter or dead). Placenta etc. is assumed to be negligible.
Table 5.3. Body composition (kg) of herd animals Live
weight
Fat Protein Water Ash Total empty
body
Gastrointestinal content, milk and
urine4
Cows 600 1011 811 2751 221 471 129
Heifers (incl. foetus) 575 972 782 2642 212 460 115 Foetus (newborn) 40 2.65 7.43
1 Values for HF cows in lactation week 19, standardised to live weight 600 kg (Gibb et al., 1992)
2 Proportional to cows (575/600)
3 Protein from Børsting et al. (2001)
4 Gastro-intestinal content is omitted in calculations
5 Calculated from deposited energy in 40 kg foetus (AFRC, 1993), which with a 280 days pregnancy gives 282.5 MJ. Energy in protein = 7.5 kg × 24.1MJ kg-1 = 181.8 MJ. Energy in fat = 282.5-181.8 = 101.8. Weight of fat = 101.8/39.6 MJ/kg fat = 2.6 kg (energy factors for protein and fat from Møllgaard (1922)).
The concentration of C in animal protein and fat is given in Table 5.4. The glycogen content in the body is assumed to be negligible.
Table 5.4. Concentration of C and N in animal protein and fat
Component C (g kg-1)
Protein 520
Fat 758.11
1 Assuming fat to be tri-palmitin
The concentration of N in the total body is shown in Table 5.5.
Table 5.5. Concentration of N in animal total body (Børsting et al., 2001) Animal body N (g kg-1 total body)
Cow 25.6 Heifer 25.6 Foetus 29.6
N in faeces is calculated from the following equation assuming N excretion to be dependent on N concentration in the feed and the feed intake (Poulsen and Kristensen, 1998):
2
Faeces Intake
N =(1-0.96) N + (20 DM + 1.8 DM )/6.25 (5.4) where NFaeces is N excretion in faeces (g N day-1) and DM is dry matter feed intake (kg day-1) N in urine is calculated as the difference between N intake and N in products, gain, foetus and faeces:
urine feed milk gain foetus faeces
N = N - (N + N + N + N ) (5.5)
C excretion in faeces is assumed to be C in feed minus digested C, where C digestibility is assumed to be similar to organic matter (OM) digestibility. This is based on OM
25 digestibilities from the Danish feed table (Møller et al., 2000). This digestibility is based on sheep fed at maintenance level, and can therefore be regarded as a potential digestibility. For heifers the feeding level can be regarded as close to maintenance, therefore the values can be used directly. Digestibilities in cows fed on high production level will normally be
considerable lower. For the present model the following assumptions have been used for cows:
• OM digestibility is an estimate for C digestibility
• Digestibilities from the Danish Feed Table are used
• Reduction in digestibility with increasing feed level is calculated assuming digestibility follows feed efficiency, using the equation: FUused = -1.47 + (1.43 + 0.0161 (Milk – 7200)/1000) ⋅ FUintake – 0.0261 ⋅ (FUintake)2, where Milk is the milk yield level (kg ECM cow-1 year-1), and FU are feed units cow-1 day-1 (Kristensen and Aaes, 1989).
C in urine is calculated according to values for lactating cows as a ratio of N excretion in urine (Hoffmann and Klein, 1980): g C i urine = 1.58 * (g N in urine).
5.3 Feeding
The feed is defined by a feed plan. There are separate feed plans for cows and heifers, and the feed plans can differ between the summer and the winter period. During the summer period cows and heifers will usually be grazing or given fresh feed.
The needs of the dairy cow vary during the lactation period (time since last calving) due to varying milk yield and faetal growth. Nevertheless, LR (1999) estimated an annual feed requirement of 6100 FU for a dairy cow weighing 600 kg and yielding 7800 kg ECM (energy corrected milk). This value has to be scaled to the actual milk yield for the cows in question.
At 100% feed efficiency, producing 2.5 kg ECM requires an energy intake of 1 FU (LR, 1999). At a feed efficiency of 83% common at Danish dairy farms (Poulsen et al., 2001), producing 2.075 kg ECM requires 1 FU. The equation used for downscaling is thus
down 0 0 down
FU = FU - (ECM - ECM )/2.075 (5.6)
where FUdown is the actual feed demand (FU), ECMdown is the actual annual milk yield (kg ECM), and the benchmark values FU0 and ECM0 are 6100 FU and 7800 kg ECM,
respectively.
In the farm scenario file, the standard feed demand (FU0) needs to be defined for both cows and heifers. For cows it is adjusted as described above. The standard value of 6100 FU applies to the basic replacement strategy, but needs to be adjusted for other replacement strategies as described in section 5.4.
5.4 Replacement of cows and young stock
In the model farms it has been pre-conditioned (for ease of calculations) that all bull calves are exported from the dairy model farm at a young age. Thus, only dairy cows and heifers are accounted for in the feed requirement calculations. In the basic settings an annual stock replacement with younger cows 40% is assumed. In such a stock, each ‘annual cow’ gives birth to 1.13 calves on average (LR, 1999). Assuming that 8% of the calves die at or soon after the calving (LR, 1999) leaves 1.05 living calves per annual cow. 50% of these are
assumed to be bulls and exported. It takes 24-26 months for a heifer to grow and later develop her first fetus so two generations of heifers co-exist with each annual cow. As a result, we
26
assume that there are 1.05 heifers per annual cow. This actually gives a surplus of heifers, and the surplus is sold as pregnant heifers.
In the farm scenario file the parameters for the cows include the following parameters related to replacement rate:
Replace Replacement rate, i.e. proportion of cows replaced by heifers per year (0.4) Calfexport Export of young calves in number of calves per cow per year. This is
calculated as the bull calves (1.13⋅0.5 = 0.565) plus the dead female calves (0.08⋅0.5 = 0.04) plus any exported female calves (0 in the standard case), which gives a total calf export rate of 0.605.
For the heifers the following parameters are related to the replacement rate:
Replace Replacement rate, i.e. proportion of heifers used to replace cows. This is calculated as Replace = ReplaceCow ⋅ Cows / Heifers, where Cows is the number of cows and Heifers is the number of heifers on the farm.
HeiferExport Export of heifers as pregnant heifers per heifer per year. The replacement rate can be calculated as Replace = (Calves – ReplaceCow) Cows / Heifers, where Calves is the number of live female calves produced per cow per year.
Replacement rate depends on many factors, where the most important are reproduction status, health and management in general in the herd. Therefore replacement rate is only partly a factor that can be changed with intent. However, in a herd with good reproduction status, health and management there will be some freedom to change the replacement rate. The lower the replacement rate, the higher is the surplus of female calves, which can either be exported as newborn or later, eventually as pregnant heifers before the first calving.
However, changes in replacement rate does not only affect the eventual surplus of female calves, but will also affect the composition of the cow herd. The number of cows in the different parities will change, and the ratio between lactation and dry period will also change.
This will affect milk yield, feed consumption and especially number of calves born on a herd basis.
The basic settings are assumed to result in a herd composed of 40% 1st lactation, 25% 2nd lactation and 35% later lactations (40:25:35). A reduced replacement rate of 30% is assumed to result in a herd composed of 30% 1st lactation, 25% 2nd lactation and 45% later lactations (30:25:45). A reduction in replacement rate from 40 to 30% will reduce the number of calves born from 1.13 to 1.08 per ‘cow year’ (LR, 2003).
Lactation yields (320 days lactation) will in 2nd lactation be 1.104 and in 3rd and later
lactations be 1,166 times the yield in first lactation, based on standard lactations for Holstein Friesian cows with a herd yearly milk yield of 7000 kg cow-1. This will result in an increase in yearly cow yield from 7000 to 7110 kg (factor 1.015) when the cow herd is changed from 40:25:35 to 30:25:45 (LR, 2003).
The increased yield will result in an increased feed requirement of 1/2.075=0.482 FU kg-1 milk, with a requirement of 1 FU per 2.5 kg milk and a feed efficiency of 83% as described earlier.
Decreased replacement rate will also change lactation stage of the herd and number of dry cows. However, in this context it is assumed that only the proportion of dry cows will change.
27 That reduced replacement rate will reduce proportion of cows in early lactation and increase proportion in late lactation is not taken into account, and therefore milk yield and feed consumption is not corrected due to that. Further it is assumed, that change from 40:25:35 to 30:25:45 will not affect the weight of the cows exported for slaughter or rendering. Number of dry cow days is calculated as 0 days for 1st lactation cows and 50 days for older cows, which result in an increase of 5 dry days per ‘cow year’ when changed from 40:25:35 to 30:25:45. Feed consumption is assumed to be 13 FU lower in the dry period compared with the mean consumption in the lactation period, which gives a reduced consumption of 65 FU per ’cow year’ when replacement rate is reduced from 40 to 30%.
5.5 Methane emissions
The methane emissions from enteric fermentation can be calculated in three different ways;
the IPCC tier 1, IPCC tier 2 or the default method, which uses the Kirchgessner equation.
With the IPCC tier 1 methodology a fixed emission rate is used by animal per year. The following values are used as standard values for cattle in Europe (IPCC, 1997); dairy cows:
100 kg CH4 head-1 yr-1, heifers: 48 kg CH4 head-1 yr-1.
With the IPCC tier 2 methodology methane production is a constant proportion (MCF) of gross energy intake. MCF is for developed countries taken to be 0.06 of the GE intake for both cows and heifers:
= / 55.65
Methane MCF GE (5.7)
where Methane is the methane production (kg CH4 animal-1 day-1), GE is gross energy intake (MJ animal-1 day-1) and 55.65 MJ kg-1 CH4 converts from energy to methane.
The default methodology is based on the empirical equation by Kirchgessner et al. (1995), which is based on the feed ration nutrient composition and feed intake:
= + 79 + 10 + 26 - 212
Methane a CF NFE CP Fat (5.8)
where a is the intercept (63 g CH4 day-1 for cows and 16 g CH4 day-1 for heifers), CF is intake of crude fibres (kg day-1), NFE is intake of nitrogen free extracts (kg day-1), CP is intake of crude protein (kg day-1), and Fat is intake of crude fat (kg day-1).
28
29 6. Housing
Three different housing types are considered:
• Tied stall. The animals are tied and fixed within separate stalls that serve as place for both resting and eating. The manure may be handled as either separate (solid and liquid) or slurry.
• Cubicles. This is a loose housing system, where the animals are allowed to move freely, and where the resting area is separated into separate cubicles. The walkways serve as traffic, manure and exercise areas. The manure may be handled as either separate (solid and liquid) or slurry.
• Deep litter. This is a loose housing system, where the animals are allowed to move freely.
The deep litter mat is also used for resting area. The entire floor area is assumed to be deep litter.
There are three types of manure handling systems allowed in the model:
• Separate. The urine and faeces is separated the house in a liquid and a solid fraction. It is assumed that all of the urine is transferred to the liquid fraction and the faeces and litter is transferred to the solid fraction. In practice some mixing of these fractions may occur.
• Slurry. The urine, faeces and any straw/litter is mixed and typically collected in a channel or a pit beneath slats.
• Deep litter. The floor is covered by a layer of straw/litter, and additional straw is added on the top every day. The urine and faeces in collected in the litter, and the deep litter mat builds up over time in the house.
There are a number of characteristics of the housing used by the model. The following main characteristics are defined in addition to the house type and the manure handling system:
• Animals. Number of cows in the house. This information is used for estimating energy requirement for ventilation and floor cleaning.
• Floor type. The floor type is determined partly by the house type. Possible floor types are solid, slatted, partly slatted and litter. This information is used for estimating ammonia volatilisation.
• Cleaning. Cleaning of the floor and other surfaces. Possibilities are none or scraper. This information is used for estimating ammonia volatilisation and energy requirement.
• Ventilation. The ventilation of the house can be either natural or forced. There is an energy requirement associated with forced (mechanical) ventilation, but no other effects.
• Temperature. The temperature of the house affects methane emissions from indoor slurry stores.
The need for water and bedding (straw) depends on the house type and manure handling system. The water demand is adjusted for the time that the animals spend in the house. The demand for water and bedding is shown in Table 6.1. The loss of dry matter in the manure in the house is shown in Table 6.2.
30
Table 6.1. Need for water and bedding for the manure management types considered in the model (Poulsen et al., 2001).
Manure Bedding (kg animal-1 day-1) Water (m3 animal-1 year-1)
management Cow Heifer Cow Heifer
Separate 1.2 0.9 0.1 0.25
Slurry 0.0 0.0 3.1 0.25
Deep litter 12.0 4.2 2.1 0.25
Table 6.2. Dry matter loss for manure stored in the house (Poulsen et al., 2001).
Manure DM loss (%)
Separate (solid) 0
Separate (liquid) 0
Slurry 10
Deep litter 28
6.1 Ammonia volatilisation
In the IPCC methodology there is no ammonia emission specifically from animal housing. All of the ammonia emission in the IPCC methodology is therefore attributed to the field
application.
The ammonia volatilisation in the default methodology is based on the standard values reported by Poulsen et al. (2001). The emission is estimated as a proportion of total-N excreted by the animals (Table 6.3).
Table 6.3. Ammonia emission from houses (% of excreted N) (Poulsen et al., 2001).
House Manure Ammonia emission (%)
Tied stall Separate 5
Tied stall Slurry 3
Loose (solid floor, no cleaning) Slurry 8
Loose (solid floor, scrapers) Slurry 4
Loose (slatted floor) Slurry 8
Loose (partly slatted floor) Slurry 6
Loose (litter floor) Deep litter 6
6.2 Methane emissions 6.2.1 Slurry based systems
The methane emissions from slurry-based systems are in the IPCC methodology calculated according to the following equation
4 0.67
CH MCF o
E =k VS B (6.1)
where ECH4 is the methane emission (kg CH4), VS is the amount of volatile solids or organic matter in input to the house (kg), Bo is the maximum methane producing capacity (m3 kg-1 VS), 0.65 converts from volume to kg of methane, and kMCF is the methane conversion factor.
The methane conversion factor depends on the climate region and on duration of storage in the house. All the model farms in the Midair study belong to the cool region, and only the cool region conversion factors are therefore shown in Table 6.4. There is also a difference between the values proposed in the original methodology (IPCC, 1997) and in the Good
31 Practice Guidelines (IPCC, 2000). The kMCF parameters is supplied to the program in the parameter file via the parameters Pit1 and Pit2.
Table 6.4. Methane conversion factors (kMCF) for slurry stored in a pit in the house in a cool region (IPCC, 1997, 2000).
Source Duration of storage kMCF
IPCC (1997) <= 30 days 0.05
> 30 days 0.10
IPCC (2000) <= 30 days 0.00
> 30 days 0.39
In the default methodology methane emissions are calculated according to the following equation:
4 0.67
CH m T o
E =k f VS B (6.2)
where VS is the amount of volatile solids or organic matter stored in the house (kg), km is a methane conversion factor at the reference temperature (15 °C) (proportion of methane producing capacity used per day), and fT is a function of temperature. The parameter km is defined in the parameter file via the Conversion parameter. The standard value used is km = 0.005.
The Arrhenius equation is used to describe the temperature dependence of the methane emission rate:
1 1
T exp
ref
f E
R T T
∆
= − (6.3)
where ∆E is the enthalpy of formation (J mol-1) taken to be an arbitrary value of -1.22×105 J mol-1, R is the gas constant (8.31 J mol-1 K-1), T is actual absolute temperature (K), and Tref is the reference temperature (15°C or 288.15 K).
6.2.2 Deep litter systems
There is no methane emission from deep litter systems in the IPCC (1997) methodology.
However, in the IPCC (2000) methodology the emissions are assumed to follow eqn (6.1).
The standard values of parameters are the same as shown in Table 6.4, but in parameter file they are referred to as Litter1 and Litter2. The emissions are calculated from the amount of volatile solids in the faeces only.
There are no estimated methane emissions from deep litter in the default methodology.
6.3 Nitrous oxide emissions
There is no nitrous oxide emission from the house according to the IPCC (1997) methodology. However, in the IPCC (2000) methodology nitrous oxide emissions are estimated from both slurry and deep litter based systems as a proportion of excreted N. The emission factor depends on the duration of storage in house and is shown in Table 6.5.
32
Table 6.5. Nitrous oxide emission factors for slurry and deep litter systems in house (IPCC, 2000).
Manure system Duration of storage Parameter name EF (kg N2O-N kg-1 N)
Slurry <= 30 days Pit1 0.001
> 30 days Pit2 0.001
Deep litter <= 30 days Litter1 0.005
> 30 days Litter2 0.020
The default methodology includes nitrous oxide emissions from slurry, solid (farmyard) manure and deep litter systems. In all cases the parameterisation was taken from Amon (1998). For the N2O emission from slurry stored in the house the N2O emissions were estimated as:
( )
2 1.16 0.098 max 15,
EN O = − + T (6.4)
where EE2O is the nitrous oxide emission (g N2O day-1 LU-1), and T is the temperature (°C).
This is converted to an emission factor by assuming a N-excretion of 127 kg N LU-1. The N2O emission from solid manure and deep litter in the house is estimated as:
2 0.038 0.043
EN O = − + T (6.5)
33 7. Manure store and manure treatment
Four different types of manure storage types are considered:
• Slurry. A mixture of urine, faeces and other organic wastes is stored in a tank. The slurry may be treated in an anaerobic digester (digestion). The tank can be covered with a solid cover, straw or with a natural surface crust. Digested slurry will not form a natural surface crust.
• Liquid. The liquid manure is primarily the excreted urine plus some additional water. It is stored in a tank, which can be covered with a solid cover or straw. It will not form a natural surface crust.
• Solid. Solid manure is the faeces and added straw from a separate manure handling system.
It is stored in a heap. It may be composted, if the straw content is high enough, and if it is turned.
• Deep litter. Deep litter comes from housing system with deep litter and will have a high straw content, which will make it compost during storage in a heap.
For storage of solid manure and deep litter it is assumed that no seepage occurs. In practice this will occur, but the seepage will under good management practice be collected in the tank of liquid manure.
The storage of the manures will lead to loss of both dry matter and nitrogen. A major part of the nitrogen loss is associated with ammonia volatilisation as described in section 7.1. For the solid manure and deep litter there is assumed to be an additional loss through denitrification (Poulsen et al., 2001, Table 7.1). This denitrification loss is assumed to result in emission of N2 only. A small emission of N2O is also estimated as described in section 7.3. The estimated dry matter loss is also shown in Table 7.1.
Table 7.1. Loss of dry matter and N by denitrification during storage of different manure types (Petersen, 1996; Poulsen et al., 2001).
Manure type Treatment DM Loss (%) N denitrification loss (%)
Slurry None 5 0
Digestion 40 0
Liquid None 0 0
Solid None 10 10
Composting 45 5
Deep litter None 45 5
Composting 45 5
7.1 Ammonia volatilisation
In the IPCC methodology there is no ammonia emission specifically from manure stores. All of the ammonia emission in the IPCC methodology is attributed to the field application.
The ammonia volatilisation in the default methodology is based on emission factors related to total N in the manure (Table 7.2). Most of the information used for estimating the emission factors is based on studies from Germany and Denmark and no effect of climate conditions are included. The emission factors are not related to storage time.
34
Table 7.2. Ammonia emission factors for manure stores depending on manure type, treatment and storage cover (Hutchings et al., 2001; Poulsen et al., 2001; Sommer and Dahl, 1999;
Sommer, 2001; CORINAIR, 2002).
Manure type Treatment Cover Emission factor
Slurry None None 0.080
None Solid 0.008
None Straw 0.016
None Crust 0.024
Digestion, pre-store* None 0.040
Digestion, pre-store Solid 0.004
Digestion, pre-store Straw 0.008
Digestion, pre-store Crust 0.012
Digestion, post-store None 0.200
Digestion, post-store Solid 0.010
Digestion, post-store Straw 0.040
Digestion, post-store Crust 0.040
Liquid None None 0.160
None Solid 0.020
None Straw 0.040
None Crust 0.040
Solid None None 0.100
Composting None 0.200
Deep litter None None 0.200
Composting None 0.200
* For the digested treatments the emission factors for the pre-store and the final store are added.
7.2 Methane emissions 7.2.1 IPCC methodology
The IPCC methodology uses eqn (6.1) for the estimation. The methane emission depends on the volatile solids that are input to the manure store. The methane conversion factor depends on the climate region and on duration of storage in the house. All the model farms in the Midair study belong to the cool region, and only the cool region conversion factors are therefore shown in Table 7.3. There is also a difference between the values proposed in the original methodology (IPCC, 1997) and in the Good Practice Guidelines (IPCC, 2000).
35 Table 7.3. Methane conversion factors (kMCF) for stored manure in a cool region (IPCC, 1997, 2000). The parameter name used in the program parameter file is also shown.
Source Manure type Treatment Parameter name kMCF
IPCC (1997) Slurry None Slurry 0.100
Slurry Digestion Digested 0.100
Liquid None Liquid 0.100
Solid None Solid 0.010
Solid Composting Composted 0.010
Deep litter None Deep litter 0.010
IPCC (2000) Slurry None Slurry 0.390
Slurry Digestion Digested 0.100
Liquid None Liquid 0.390
Solid None Solid 0.015
Solid Composting Composted 0.005
Deep litter None Deep litter 0.005
7.2.2 Default methodology Slurry and liquid manure
For emissions from slurry tanks in the default methodology eqn (6.2) and (6.3) was used. This was based on studies of methane emission from open tanks with surface crust (Husted, 1994;
Sommer et al., 2000; Béline, 2003; Amon et al., 2003), which lead to the following conclusions:
• The CH4 emission rate increases with temperature. This was found by all authors, but the temperature dependence can be quite different. Husted (1994) reported a Q10 value of 3.4, which for the temperature range he investigated is about the same as a heat of formation (∆E) of –85 kJ mol-1. Sommer et al. (2000) found a value of –230 kJ mol-1 for summer measurements and -80 kJ mol-1 for winter measurements. Data from Amon (personal communication, 2003) show a value of –245 kJ mol-1 for summer measurements and -53 kJ mol-1 for winter measurements. The average over the summer and winter data of Amon was about –122 kJ mol-1. So the only conclusion one can draw is that the ∆E is likely to be lower at lower temperatures, which may reflect that different bacteria are active in
generating CH4 at different temperature ranges. Here a ∆E of -1.22×105 J mol-1 is used.
• The emission rates, in kg CH4 kg-1 VS per day, found by the different authors are
sometimes rather different. Béline (2003) and Husted (1994) have emission rates that are about up to a factor of 10 higher than that of Amon et al. (2003) and Sommer et al. (2000).
The main conclusion that can be drawn from these data that both the emission rate at a reference temperature (e.g. 15° C) and the temperature dependence are highly uncertain.
However, the methodology implied by eqn (6.2) is applied, and a standard value of the rate parameter km of 0.005 is used. This parameter is defined in the parameter file via the Conversion parameter.
The emission rates were reduced by 5% if a cover is present. This factor is derived from the winter and summer measurements of Amon et al. (2003).
Solid manure
The methane emissions is assumed to depend on storage time:
( )
4 0exp
ECH =E −a t (7.1)
