In this section the simulation of the Soil Organic Carbon (SOC) by EPIC and the integration of the simulation results to EU-EFEM will be described. The EPIC simulations feature site-specificity, with respect to HRU-specificity, accounting thus for (natural) production conditions like soil type and weather. EPIC simulations are done for three alternative tillage managements (1 conventional and 2 conservational tillage schemes) and two alternative managements of plant residues. All management alternatives matter as to SOC accumulation. Apart from SOC simulation results, also simulated yields are in part transferred to EU-EFEM (see section 3.3.3).
The concept of the HRU as modelling unit for the biophysical EPIC (see section 3.2.1) is based on the four data components soil, climate, technology, and management. For the simulation of SOC in EPIC, a fifth component is added: crop rotation. Incorporating crop rotation as a parameter in the SOC simulation better depicts soil organic matter dynamics. As an alternative to these SOC simulations, crop unspecific values on a per hectare basis could be taken, or single crop specific values like those provided by the Cross-Compliance regulations amended with the 2003 reforms of AGENDA 2000 (see section 3.1.2). But both only paint a static picture independently of preceding crops, i.e. cultivation history or crop rotation.
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The data transfer from EPIC to EU-EFEM is via an ad hoc conceived interface which intersects EPIC’s spatially explicit data with data on its regional representation per NUTS-II regional unit (according to Figure 4, previous section). In order to achieve a manageable data level and to limit the number of necessary EPIC simulations, the soil parameters in the wider sense were classified into: three slope classes (0 - 6%, 6 - 15%, >15%), two altitude zones (0 - 600 m, >600 m), two stoniness classes (0 - 25%, >25%), one soil depth class, and five soil types (coarse, medium, medium fine, fine, very fine)27.
With the integration of the rotation specific SOC values into EU-EFEM, EU-EFEM also had to simulate crop rotations. Integration is through a binary variable which is the only practicable way, although it carries the disadvantages of longer solving time, dual solution, and computational performance. Each rotation is defined by a certain upper and lower share of the total farmed area which a certain crop or crop group may occupy. Normally, in linear programming models (like EU-EFEM) only rather wide rotational limits are integrated as constraints within which the model freely combines crops. Here, in contrast, a number of sets of different rotational limits are integrated. These sets of rotational limits have to be mutually exclusive. This mutual exclusivity is necessary for a binary variable, i.e. the model has to decide for one crop rotation what the upper and lower limits for each crop or crop group will be that have to be kept.
Mentioned in section 3.2.2, only a finite number of crop rotations per HRU (deduced from empiric cultivation history) were simulated by EPIC. This means that for a change of crop rotation that goes beyond the historic variation in crop rotations, no EPIC simulation results are available. For the production of the EU-EFEM reference situation this suffices, since both models start from the same land use scheme. For the scenario analyses, however, scenario assumptions can shift the competitiveness of production activities beyond the historic crop rotations. This means that simulations should cover the whole range of possible crop rotations at least as far as noticeable differences between rotations as to SOC values manifest. Since no additional EPIC simulations were available, an alternative approach was chosen. The alternative approach reduces the specificity of crop rotations to HRUs by applying several aggregations of original HRU specific EPIC simulation results.
27 According to sand and clay contents (see S
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Within EPIC, crop rotation was represented by a sequence of single crops during a ten year period. The range of crops in EPIC is the same as in EU-EFEM. Now, the crop rotations for EU-EFEM had to be defined in such way that they could be built on EPIC simulations. This was the starting point for a first aggregation of EPIC simulations. For the definition of rotations in EU-EFEM, single crops were grouped according to their impact on SOC, which is the parameter of interest for this modelling exercise. The main differences with respect to SOC development, which falls together with soil humus development, are assumed for the four groups: (1) cereals, (2) hoed tuber crops (e.g. beet or potato), (3) maize, and (4) humus accumulating crops (including set-aside). A resolution of 20%-steps for crop shares (i.e. discrete variable)28 was contented by the applicants of EPIC and EU-EFEM in the current framework. Because of this classification of rotations according to crop groups, the same rotation could be found in several HRUs.
Yet the number of rotations per region including several HRUs was reduced. Still higher flexibility was aspired to for EU-EFEM, which required a second aggregation. This second aggregation does not aggregate rotations, but HRUs. Similarities between the characteristics defining a HRU were considered a joining criterion for aggregation. How the EPIC characteristics of HRUs got carried through in newly- defined EU-EFEM classes can be seen in Table 12. It illustrates which characteristics are merged and opposes the number of EPIC classes to merged EU-EFEM classes (e.g. 7 EPIC slope classes were merged to 3 EU-EFEM classes).
Table 12: Aggregation of EPIC Classes for EU-EFEM
HRU Criterion EPIC Classes Aggregation EU-EFEM Classes
(Number) (Identure Numbers) (Number)
soil texture 5 1, 2, 3, 4, 5 5
altitudes 4 1+2, 3+4 2
stoniness 3 1+2, 3 2
soil depth 4 1+2+3+4 1
Slope 7 1+2, 3+4, 5+6+7 3
With the two aggregations (first: original EPIC crop rotations; second: HRU characteristics), the major rotation and site specific conditions are still captured, while
28
Example: Rotation 1): 0% cereals, 0% maize, 20% tubers, 80% humus crops, rotation 2) 0% cereals, 0% maize, 40% tubers, 60% humus crops. Deviations are allowed up to ±10 percentage points.
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the accuracy of EU-EFEM is increased by offering several and not only one possibility for site-specific crop rotations with correspondent EPIC SOC values.
In addition to the current crop, the crop rotation (cultivation history), and the site- specific conditions, management is also considered to be an important factor for SOC development. EPIC considers three types of tillage as management options. But additionally the treatment of plant residues is understood as an important factor, since it is the main driver for organic matter input into soils if residues are left on a field. For reasons of computational feasibility, only two extreme cases are simulated by EPIC: (1) all residues are left on the field, or (2) all residues are taken from the field. In this study a linear trend is assumed for the SOC development between both points. Although desirable, the organic matter input from organic fertilisation was not simulated by EPIC for different scenarios. Only the amounts calculated by EU-EFEM for its reference situation were assumed in EPIC. Although EU-EFEM is not calibrated as to crop specific fertilisation (the nitrogen cycling is simulated on a plot level, see section 3.3.3), this crop specific attribution and the direct link to regional animal capacities makes this data the best available.
The high specificity of EPIC’s simulations and the integration of delivered SOC- values into the economic surrounding of EU-EFEM is a powerful means in terms of accuracy and computational feasibility, as well as in terms of political and economic scenario analyses. However, there are cases which speak for the application of less detailed general SOC-values. For example, policy programmes is such a case. Typically these are designed to treat (groups of) farmers equally, independent of soil types found in their fields or other uncontrollable factors.
A policy programme which aims at SOC-accumulation among other and generally means to promote sustainable agriculture by binding subsidies to environment services is the Cross-Compliance programme (as already mentioned above, the programme was imposed by the 2003 reforms of the CAP). SOC accumulation is addressed in the form of humus accumulation. In this context fixed factors that account for humus formation are formulated. They express the change of humus stocks per hectare due to the cultivation of a certain crop. Yet the cultivation alone affects humus stocks, but also crop residues in case left on field for decomposition and not withdrawn. The latter correlation is not per hectare but per weight units of residues left on field.
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Table 13: Humus Accumulation Factors in Cross-Compliance Regulation
Crop Related to Cultivation Related to Residues Left on Field Manure Related to Manure Spread on Field (kg/ha) (kg/dt DM) (kg/dt DM) cereals -280.0 10.0 liquid 12.0
non cereals* -280.0 10.0 solid 15.0
maize -560.0 10.0 sugar beet -760.0 0.8 potato -760.0 0.8 green clover 600.0 0.0 (fall) fallow29 180.0 -.- catch crop 120.0 -.-
*non-cereals: includes legumes, clover-like fodder plants, and hoed crops (like maize or beets, except grain maize).
Source: BMVEL (2006), German version of the Cross Compliance Regulation.
In the Cross-Compliance regulation the cultivation of crops either entails humus consumption or accumulation, according to the type of crop. Residues left in the field, in contrast, always contribute to humus accumulation (compare Table 13). Farmers participating in the cross-compliance programme have to calculate the humus balance for their farms by means of a simple calculation. They multiply the number of hectares under each crop with the crop specific humus factor. This procedure is for the main crop. For the by-product (crop residues), the quantity of by-products in decitons is multiplied with the residue specific humus factor. The cross-compliance programme stipulates maximal deviation from an equilibrated balance at ±0 kg per farm.