3. Modelling energy use in agricultural production
3.3 Estimating direct energy use .1 Estimating diesel-fuel energy use
Diesel fuel is one of the most important direct-energy sources used in agricultural pro-duction. Moerschner (2000) estimated diesel-fuel use as 20 to 50 per cent of total prima-ry-energy use. Outlaw et al. (2005) attested its importance, calculating that diesel and min-eral fertiliser together account for over 55 per cent of energy use in US agriculture. For a European comparison, it is important to estimate diesel use realistically and on a regional basis. Models estimating diesel-fuel use as a function of the most important influencing factors such as type of soil preparation, preparation depth, soil type and plot size (see Corti jo, 2000) have been developed e.g. by KTBL (2004). Other models use physical
para-meters and data on specific diesel or machinery use (such as Moerschner, 2000). Due to the lack of EU-wide data for calculations of this sort, diesel use is calculated here using the KTBL model (KTBL, 2004). Regional shares of conservation soil-preparation methods, acti-vity-specific soil quality (Britz et al., 2005) and regional plot-size data are taken into account . Diesel use for grassland is calculated (by means of underlying predefined shares of differ-ent work processes) as a function of regional data on grass yield, cutting behaviour and pasture share. CAPRI is thus combined with activity-based data on pasture which is in turn based on UNFCCC (2000).
In order to achieve the required level of detail in the activity-based and regionalised CAPRI approach, a wide range of factors determining diesel use is considered. A database established by KTBL (2004) serves as the basis for the calculation procedure. In it, diesel consumption values are given for a broad range of production activities under specific struc-tural and mechanisation conditions, based on a uniform methodological approach. There is a strong focus on soil preparation, since it accounts for the majority of overall diesel con-sumption, as shown in Diepenbrock et al. (1995) and Moerschner (2000). The latter has stated that fuel consumption for tillage (soil preparation) is determined by a number of fac-tors, such as machining depth, type of machinery, soil type, slope inclination, plot size, etc.
Following Cortijo (2000) and as cited in Moerschner (2000), the driving forces for diesel consumption can be classified according to their importance as shown in Tab. 3.
Tab. 3: Share of different parameters affecting diesel-fuel consumption for crop activities
Parameter Maximum variation
Number of passes and type of work process (main parameter:
machining depth) 30 %
Soil quality 25 %
Influence of the machinery operator 15 %
Suitability of the machine for the work process (incl. differences
between machinery manufacturers) 10 %
Plot size 5 %
Slip 2 %
Source: Cortijo (2000).
Kalk et al. (1997) and VDLUFA (1997) illustrate the effect of both plot size and machi-nery type on diesel requirements for soil preparation. Figure 8 shows the declining diesel requirements with increasing plot size and decreasing engine power. Even so, plot size is more important than engine-power class.
The CAPRI model design allows us to consider soil type and plot size on an activity ba-sis. Machinery type and machining depth cannot be displayed, owing to a lack of statisti-cal data. Nevertheless, by integrating machinery-stock data into CAPRI, as shown in Chap-ter 3.4.2, an indication about the machine used can be arrived at via exclusion (where mainly large tractors are used, large trailed machinery is very likely). Secondly, since soil quality is considered, soil-quality classes are established on the basis of the soil-quality me-thodology described in Chapter 3.2.3. Fuel consumption can be determined on the levels light/medium/heavy soil. Thirdly, plot-size information is used to consider important scale effects of fuel consumption. Accordingly, both national statistical data such as BMVEL (2001) or Pitlik (2006), which accurately give the activity-specific plot-size data on a NUTS-I or NUTS-II level, and less-precise data such as EUROSTAT (1995), which encompass a wi-der definition of plot size, were used. Fourthly, average percentages of conservation tillage
Fig. 8. Diesel requirement for soil preparation for different tractor-engine classes; Source: based on Kalk et al. (1997).
and no-till are considered on a national level. Such data was provided by ECAF (2006) on a NUTS-0 level. Finally, the production process is divided into a non-harvest and a harvest part, allowing us to take precise account of region-specific machinery stock in the non-har-vest part. Equation 14 shows the calculation mechanism for diesel-fuel energy use [EFUL].
In order to cover diesel energy use for animal-production systems, such as basic feed mixing or feed transport, the quantity consumed is calculated via the quantity of feed component applied, as shown in Equation 28.
Equation 14 Mechanism for calculating diesel-fuel consumption
a
Production activity [ha/animals/1000 animals]
Plot size [1/2/5/10/20/40/80 ha]
Soil quality [light/medium/heavy]
Work-process steps [soil preparation/seed preparation/seedbed preparation/
fertiliser application/plant protection/harvesting/transport]
Pasture share [% of grassland]
Quantity of diesel fuel [l/ha]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
3.3.2 Estimating electricity and heating-gas-energy use in animal production
Although substantial use is made of electricity in all production activities, its main im-portance can be seen in animal production. Coefficients for electricity use in animal hus-bandry have been developed by Boxberger et al. (1997). These are used for the current study, with a distinction being made between EU-wide uniform basic values including light-ing, ventilation, and manure management on the one hand, and yield-based (e.g. milk-cooling) or feed-specific (e.g. concentrate-preparation) components on the other. As shown in Equation 15, the uniform basic values for electricity use [ELEC] are adjusted according to herd size, building type, and manure-management system. This is done to take account of degression effects dependent upon the herd size, as well as the different electricity require-ments of manure-storage and daily-spread systems. Furthermore, animal-specific space requi rements are taken into consideration. For feedstuff electricity requirements, the share
10 Diesel-fuel consumption for soil preparation according to tractor-engine class
Diesel-fuel
of farm-specific feedstuff is calculated and the relevant electricity requirements for trans-port, milling and preparation are applied. Concentrate production is analysed separately, as shown in Chapter 3.4.4.
Equation 15 Electricity requirements in animal production (excluding feedstuffs and milk-cooling)
Production activity [ha/animals/1000 animals]
Herd size [cattle:<10/11-50/51-100/>100];[pigs/poultry/other:
<5/5-50/51-100/100-399/>399]
Building type [per animal activity; Northern-/Central-/Southern-European type]
Manure-management system [manure storage/daily spread]
Space unit [1m2]
Space requirements of animal activity [m2/animal]
Electricity use [kWh/m2]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
In the Scandinavian and Baltic regions, where stable heating is required, energy use for heating gas [EGAS] in MJ/animal is quantified according to Equation 16.
Equation 16 Heating-gas requirements in animal production (excluding feedstuffs)
a
Production activity [ha/animals/1000 animals]
Herd size [cattle:<10/11-50/51-100/>100];[pigs/poultry/other:
<5/5-50/51-100/100-399/>399]
Building type [per animal activity;Northern-/Central-/Southern-European type]
Space requirements of animal activity [m2/animal]
Heating-gas use [m3/m2]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
3.3.3 Complementary direct-energy use
A range of production processes displayed in the current CAPRI approach show com-plementary direct-energy use covering different direct-energy sources. These are valid for a number of different cases: firstly, where a production process has an equal need for two different energy sources: this can be seen in greenhouse production, where electricity is required for lighting whilst heating gas provides temperature-balancing between the out-side and the required optimal indoor temperature; and secondly, where a range of produc-tion processes can be performed with either of the direct-energy sources, e.g. pumping for irrigation can be performed by electric pumps or diesel pumps. In order to simplify mat-ters, the relevant usage shares within the EU regions are included in the calculation proce-dure, so the equation in question covers two direct-energy sources. A similar situation is considered by covering heating oil and electricity in the grain-drying-process formula, as both of these direct-energy sources are used for grain drying in the EU. A third case is where fuel substitution is observed in production processes. With grassland harvesting, for exam-ple, petrol-driven mowers are as common in a number of regions as diesel-driven ones.
The same holds true for irrigation pumping, where petrol-driven pumps are as common as diesel-driven pumps. In order to simplify the estimation of energy use, all such complemen-tary energy sources are processed below in diesel-fuel equivalents.
LEC
CAPRI feedstuff components used in concentrates are subject to further processing; for example, partial drying is employed when the initial moisture content of the components is above the technically required level. Either electricity or heating oil is used in drying pro-cesses of this type. The complementary nature of energy use in feedstuff processing is de-scribed in several literature sources such as Bockisch (2000), Sauer (1992), Moerschner (2000) and Keiser (1999). Consequently, the current approach uses electricity and heating oil [ELEC/HOIL] for feedstuff preparation, as shown in Equation 17. Feedstuff preparation is modelled at such a level of detail since it is carried out in part at farm level. In addition, farm products are used as an intermediate means of production, and therefore do not leave the farm sector.
Equation 17 Electricity and heating-gas requirements for feedstuff production
a
Production activity [ha/animals/1000 animals]
Feed component [Cereals/Oilseeds/Energy-rich/Protein-rich]
Moisture content of FC [%]
Feed quantity applied Heating-oil use [l/kg]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
Production in heated greenhouses requires direct-energy sources for both heating and lighting. For the sake of simplicity, the current approach distinguishes between heated and non-heated greenhouses. Non-heated greenhouses in Central and Northern European countries are assumed to be without lighting installations, whilst those greenhouses using heating technologies are lit in order to extend growth rates beyond sunlight phases. For Southern European regions, a simplified approach is chosen in which lighting is considered as an all-in quantity. Consequently, for Central and Northern European countries, direct energy used in heated greenhouses [ELEC/EGAS] is calculated as per Equation 18.
Equation 18 Electricity and heating-gas requirements for greenhouses
a
Production activity [ha/animals/1000 animals]
Greenhouse type [heated/non-heated]
Heated greenhouse type
Scope of production [ha/NUTS-II region]
Electricity use [kWh/m2] Heating-gas use [m3/m2]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
In Southern European regions in particular, irrigation accounts for a significant percen-tage of overall energy use in agricultural production. Different systems such as mobile or fixed installations, different water sources (surface or reservoir water) and different pum-ping systems (diesel-driven pumps, electric pumps) are used. The relevant systems and their use are described in Derbala (2003) and Lal (2004). In order to keep the uniform metho-dology, a standardised irrigation system based on Nemecek et al. (2003) is enlarged whilst taking account of the aforementioned studies. Energy use for irrigation can be quantified
(
+)
=∑ [ ( ) (
+) ]
in MJ/ha on the basis of the regional, activity-specific water quantity divided by electricity [ELEC] and diesel fuel [EFUL]. Equation 19 shows the procedure.
Equation 19 Electricity and diesel-fuel requirements for irrigation
a
Production activity [ha/animals/1000 animals]
Irrigation system and water source [mobile/fixed system, surface water/reservoir water]
Water quantity [m3/ha,year]
Electricity [kWh/m3] Diesel-fuel use [l/m3]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
If carried out, grain drying represents significant direct-energy use, with heating oil and electricity usually being used (Nemecek et al., 2003; Moerschner, 2000). Drying is wide-spread across Central and Eastern Europe, with the frequency and intensity depending largely on climate data during the harvest period. Statistical data on both drying itself and on harvest moisture content of the cereals are scanty, and analysis is very spot-specific (Atzema, 1994; Ryniecki, 1993). Consequently, calculation procedures for water removal are also established for specific conditions. Nemecek et al. (2003) nevertheless offer a for-mula for a broader application framework. The forfor-mula expressed therein implies that the amount of water removed is determined by the original moisture content. Assuming an original moisture content of 20 per cent, the weight of the water removed is about 7 per cent higher than with an original moisture content of 16 per cent. On the other hand, the energy used per kilogram of water removed is somewhat lower when drying from a higher original moisture content than from a lower one. By assuming a constant value of water removed per 0.1 per cent of moisture reduction, the water removed from the grain with high original moisture content is underestimated, with the lower energy use offsetting this underestimation. This context allows the use of a constant water-removal coefficient (Zim-mermann, 2006). Calculation of energy use for electricity [ELEC] and heating oil [EOIL] is shown in Equation 20.
Equation 20 Electricity and oil requirement for grain drying
a m LEC OIL E
Production activity [ha/animals/1000 animals]
Moisture content extracted [harvest moisture content less target moisture content in kg/ha]
Electricity [kWh/m3] Heating-oil use [l/kg]
Energy content [MJ/l; MJ/kWh; MJ/m3; MJ/kg]
3.4 Estimating indirect-energy use