Analysis of indicator value development according to farm types

Development of economic indicator values

The economic indicators are significantly impacted by the modified direct payments. Table 3.2-5 presents the results of the subsidy reduction scenarios SUBred60% and SUBshift70%

for the farm types in comparison to the baseline scenario CAP2003. The scenarios SUBred60% and SUBshift70% result in a subsidy volume which is for the complete study region by 63% and 62% smaller than in the baseline scenario CAP2003. This reflects a positive effect for the objective to reduce public expenditures. Grassland farm types (GL-IG and GL-EG) are affected most, with reductions of subsidies of more than 60% to 70% in comparison to the CAP2003 scenario. Due to the scenario assumptions, the reduction of the payments from Pillar 1 is in the SUBshift70% scenario at least 10pp larger than in the SUBred60% scenario. The SUBred60% scenario results also show slight reduction of payments from Pillar 2; this is attributable to abandoning of UAA. In SUBshift70% payments from Pillar 2 are increased by the money deducted from Pillar 1 and shifted to Pillar 2. From this shift of payments in SUBshift70% arable farm types show higher benefits than grassland

counties, with relative increases of payments from Pillar 2 being between 30% and 40%

higher in arable counties than in grassland counties.

Both subsidy reduction scenarios result in an average decrease in total gross margin of -11pp in the complete study region in comparison to the CAP2003 scenario. The losses in TGM are with 20pp higher in GL-EG than in the other farm types with 12pp and 13pp. TGM excluding subsidies is not significantly changed, i.e. it is almost equal to the one in the CAP2003 baseline scenario (cf. Subsection 3.3.3).

The modified direct payments result also in a change of distribution of subsidies and income.

Table 3.2-4 presents the average values of subsidies and income as well as the relative and absolute deviation from the value of the complete region. The ranges of absolute deviations are smaller in both subsidy reduction scenarios (e.g. -29 EUR ha^-1 to +21 EUR ha^-1 in SUBshift70%) than in the CAP2003 scenario (e.g. -40 EUR ha^-1 to +35 EUR ha^-1). However, the ranges of total gross margin deviations are larger in the subsidy reduction scenarios (e.g.

-527 EUR ha^-1 to +43 EUR ha^-1 in SUBred60%) than in the CAP2003 scenario, whereas the relative deviations are similar in both subsidy reduction scenarios to the deviations in CAP2003.

Table 3.2-4: Average subsidy volume and total gross margin in CAP2003, SUBred60% and SUBshift70% scenarios and deviations of farm types from the average value.

AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg

Baseline scenario (CAP203)

EUR ha^-1 % absolute differences from BW [EUR ha^-1]

SUBvol^m 364 407 351 405 426 391 93 104 90 103 109 100 -27 16 -40 14 35 0

SUBvol^m Pillar 1 275 300 273 297 302 289 95 104 94 103 104 100 -14 11 -16 8 13 0

SUBvol^m Pillar 2 90 106 78 108 124 102 88 104 76 105 122 100 -12 4 -24 6 22 0

TGMvolⁿ incl. SUB 1832 1748 1864 1936 1283 1796 102 97 104 108 71 100 36 -48 68 140 -513 0 TGMvolⁿ excl. SUB 1468 1341 1513 1531 858 1404 105 96 108 109 61 100 64 -63 109 127 -546 0

AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg

SBUred60%

EUR ha^-1 % absolute differences from BW

SUBvol^m 196 221 187 222 226 212 92 104 88 105 106 100 -16 9 -25 10 14 0

SUBvol^m Pillar 1 108 117 109 116 111 112 96 105 97 104 99 100 -4 5 -3 4 -1 0

SUBvol^m Pillar 2 88 104 78 106 115 99 89 105 79 107 116 100 -11 5 -21 7 16 0

TGMvolⁿ incl. SUB 1662 1553 1695 1749 1089 1616 103 96 105 108 67 100 46 -63 79 133 -527 0 TGMvolⁿ excl. SUB 1466 1332 1508 1527 863 1404 104 95 107 109 61 100 62 -72 104 123 -541 0

AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe BWg

SUBshift70%

EUR ha^-1 % absolute differences from BW

SUBvol^m 195 220 184 226 234 213 92 103 86 106 110 100 -18 7 -29 13 21 0

SUBvol^m Pillar 1 81 87 81 87 84 84 96 103 97 104 100 100 -3 3 -3 3 0 0

SUBvol^m Pillar 2 114 133 102 138 149 129 89 103 79 107 116 100 -15 4 -27 9 20 0

TGMvolⁿ incl. SUB 1655 1544 1693 1753 1098 1611 103 96 105 109 68 100 44 -67 82 142 -513 0 TGMvolⁿ excl. SUB 1460 1324 1509 1527 865 1398 104 95 108 109 62 100 62 -74 111 129 -533 0 a to d: Clustered counties with high shares of … a: … arable land and cash crops; b: … arable land and fodder crops; c: … intensive grassland; d: … extensive grassland and fodder crops;

e: … extensive grassland. f: Average of all counties. g: All counties aggregated.

Development of supply and environmental indicator values

In both subsidy reduction scenarios UAA fall abandoned in almost all counties. However, the redistributed payments from Pillar 1 to Pillar 2 allow in SUBshift70% a slightly lower share of abandoned UAA in the farm types GL-FC and GL-EG. On the other hand, more UAA falls abandoned in AL-FC in SUBshift70% because the increased payments for AEM have only a small impact here. Most of the payments in AL-FC are payments for intercropping on arable land. Payments for AEM on grassland are higher, but due to small shares of arable land the payments for AEM in AL-FC are in total small. In both scenarios land abandoning appears most pronounced in GL-EG, which are the problematic farm types for supply objectives due to high extensification of agricultural production. In GL-EG agricultural productivity is that small, that high reductions of subsidies result in the abandoning of marginal arable land. Thus, it seems as if specific policy measures would need to be applied to avoid such an abandoning of UAA.

In both subsidy reduction scenarios intensive crop area tends to decrease due to reduction of cash crop area. Animal production does not change, because it is not affected by the modelled changes of subsidies. Environmental indicator values and potential AEM area change slightly due to the abandoned UAA, which reduces the potential AEM and the related area for the average nitrogen input.

Table 3.2-5: Development of indicators values in SUBred60% and SUBshift70%.

SUBred60% SUBshift70%

AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe Allg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe Allg

SUB^m volume [%] -58 -59 -66 -71 -73 -63 -58 -59 -67 -69 -70 -62

SUB volume Pillar 1 [%] -69 -74 -100 -119 -109 -82 -81 -86 -117 -138 -125 -95

SUB volume Pillar 2 [%] -2 -3 0 -2 -7 -3 39 29 21 22 19 27

TGMⁿ volume incl.

SUB [%] -12 -13 -11 -12 -19 -11 -13 -14 -11 -12 -18 -11

TGM volume excl.

SUB [%] 0 -1 0 0 1 0 -1 -2 0 0 2 0

Cereals [pp]^o -1 -2 -1 -2 -4 -1 -1 -4 0 -1 -4 -2

Maize [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Fodder crops [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Others^p [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Root crops [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Oil seeds and legumes [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Set-aside area [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Conv. of grassland^q [pp]^o 0 0 0 -1 0 0 -1 0 -1 -1 0 0

Conv. of arable land^r [pp]^o 0 0 0 1 -2 0 0 0 0 1 -1 0

Intensive grassland [pp]^o 0 1 1 1 0 0 0 0 0 1 0 0

Extensive grassland [pp]^o -1 0 0 -1 -3 -1 0 1 1 0 -2 0

Abandoned UAA^s [pp]^o 1 2 0 2 8 2 1 4 0 1 7 3

Dairy cows [%] 0 0 0 0 0 0 0 0 0 0 0 0

Bulls [%] 0 0 0 0 -1 0 0 0 0 0 -1 -1

Fattening pigs [%] 0 0 0 0 0 0 0 0 0 0 0 0

Intensive crop area [pp]^o -2 -3 -1 -2 -5 -2 -2 -5 -1 -2 -4 -2

Intensive variant area [pp]^o -1 2 1 1 0 1 -2 2 0 0 0 1

Nitrogen total [%] 1 1 0 2 5 1 0 1 0 1 4 1

Nitrogen total (weight.)^t [%] 1 1 0 2 7 2 1 3 0 1 5 2

Nitrogen organic [%] 0 0 0 0 -1 0 0 0 0 0 -1 0

Nitrogen demand [%] 0 -1 0 1 3 1 -1 -1 0 0 2 -1

Erosion potential [pp]^u -1 0 0 -2 -3 0 -1 -1 -1 -2 -4 0

Erosion pot.(weight.)^t [pp]^u 0 1 0 0 -2 1 -1 1 -1 -2 -2 1

GHG^v emissions [%] 0 -1 0 0 -2 -1 -1 -1 0 0 -2 -1

Potential AEM area^w [pp]^o -10 -6 -5 -10 -18 -8 -9 -13 -3 -6 -13 -8

Notes: a to d: Clustered counties with high shares of … a: … arable land and cash crops; b: … arable land and fodder crops; c: … intensive grassland; d: … extensive grassland and fodder crops; e: … extensive grassland. f: Average of all counties. g: All counties aggregated. h: Minimum value of all counties. i: 25 percent quartile. j: 50 percent quartile. k: 75 percent quartile. l:

Maximum value of all counties. m: Subsidy. n: Total gross margin. o: Percent share of UAA/percentage points of UAA compared to the share in reference situation. p: Aggregated area of root crops, oil seeds, legumes, set-aside area and special crops. q: Conversion of grassland into arable land. r: Conversion of arable land into grassland. s: Utilized agricultural area not agriculturally used and not entitled for subsidy payments. t: heads per hectare/difference in percent. u: Weighted by acreage of intercrops reducing the impact of nitrate leaching and erosion. v: Potential in percent of uncovered arable land/difference in percent. w: Green house gas. x: Area where potentially agri-environmental measures (AEM) can be applied to, but which are not necessarily implied in optimization process and thus not simulated as activity.

3.2.6 Analysis of results according to achievement of policy objectives

Due to their specific agricultural structure single counties and the farm types are affected differently by the modified subsidies with respect to the developments in economic, production and environmental indicator values.

The simulated policy affects the farm types mostly with respect to changes in the values of the economic indicators subsidies and total gross margin. Agricultural production is less affected by the scenarios and the environmental indicators are only partially affected. Table 3.2-6 summarizes the observed development of the indicator values and the impact on the policy objectives in comparison to the CAP2003 situation

The decreased subsidy volume indicates a positive development with respect to the objective of subsidy reduction for all farm types in both scenarios. In SUBshift70% the payments from Pillar 2 are increased due to shifting the money from Pillar 1 to Pillar 2. However, the total subsidy volume still shows a decrease. In SUBred60% subsidy payments from Pillar 2 are decreased in the farm type GL-EG.

Both scenarios result in a decrease of the TGM. In comparison to the CAP2003 scenario the negative distributional effect is less for subsidies but it is similar or even more negative for the distribution of the income.

The supply objective is not significantly influenced with respect to production. Changes in productions are small, however all farm types show a negative development of retaining UAA and the potential AEM area also decreases, i.e. shows a negative development.

Table 3.2-6: Impact on policy objectives in SUBred60% and SUBshift70%.

SUBred60% SUBshift70%

AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe Allg AL-CCa AL-FCb GL-IGc GL-FCd GL-EGe Allg

SUB^m volume [%] ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++

SUB volume Pillar 1 [%] ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++

SUB volume Pillar 2 [%] 0 0 0 0 + 0 - - - - - - - - - - - -

TGMⁿ volume incl.

SUB [%] - - - - - - - - - - - - - - - - - - - - - - - -

TGM volume excl. SUB [%] 0 0 0 0 0 0 0 0 0 0 0 0

Cereals [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Maize [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Fodder crops [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Others^p [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Root crops [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Oil seeds and legumes [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Set-aside area [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Conv. of grassland^q [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Conv. of arable land^r [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Intensive grassland [pp]^o 0 0 0 0 0 0 0 0 0 1 0 0

Extensive grassland [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Abandoned UAA^s [pp]^o 0 0 0 0 - 0 0 0 0 0 - 0

Dairy cows [%] 0 0 0 0 0 0 0 0 0 0 0 0

Bulls [%] 0 0 0 0 0 0 0 0 0 0 0 0

Fattening pigs [%] 0 0 0 0 0 0 0 0 0 0 0 0

Intensive crop area [pp]^o 0 0 0 0 + 0 0 + 0 0 0 0

Intensive variant area [pp]^o 0 0 0 0 0 0 0 0 0 0 0 0

Nitrogen total [%] 0 0 0 0 - 0 0 0 0 0 0 0

Nitrogen total (weight.)^t [%] 0 0 0 0 - 0 0 0 0 0 - 0

Nitrogen organic [%] 0 0 0 0 0 0 0 0 0 0 0 0

Nitrogen demand [%] 0 0 0 0 0 0 0 0 0 0 0 0

Erosion potential [pp]^u 0 0 0 0 0 0 0 0 0 0 0 0

Erosion pot.(weight.)^t [pp]^u 0 0 0 0 0 0 0 0 0 0 0 0

GHG^v emissions [%] 0 0 0 0 0 0 0 0 0 0 0 0

Potential AEM area^w [pp]^o - - + 0 - - - - 0 - - 0 0 0 - - -

Notes: a to d: Clustered counties with high shares of … a: … arable land and cash crops; b: … arable land and fodder crops; c: … intensive grassland; d: … extensive grassland and fodder crops; e: … extensive grassland. f: Average of all counties. g: All counties aggregated. h: Minimum value of all counties. i: 25 percent quartile. j: 50 percent quartile. k: 75 percent quartile. l: Maximum value of all counties. m: Subsidy. n: Total gross margin. o: Percentage points of utilized agricultural area compared to the share in reference situation. p: Aggregated area of root crops, oil seeds, legumes, set-aside area and special crops. q: Conversion of grassland into arable land. r: Conversion of arable land into grassland. s: Utilized agricultural area not agriculturally used and not entitled for subsidy payments. t: Weighted by acreage of intercrops reducing the impact of nitrate leaching and erosion. u: Percentage points difference from reference situation. v: Green house gas. w: Area where potentially agri-environmental measures (AEM) can be applied to, but which are not necessarily implied in optimization process and thus not simulated as activity.

+ small positive impact on objective, ++ medium positive impact on objective, +++ highest positive impact on objective

- small negative impact on objective, - - medium positive impact on objective, - - - highest positive impact on objective, 0: no impact on objective

With respect to the single policy objectives the analysis of results allow the following conclusions.

Economic objectives

Reduction of subsidies due to high budgetary costs and the impact of reduced direct payments on income stabilisation

The policy objective of subsidy reduction is achieved as expected. Due to scenario definition the complete study region retains the same income as in the reference year. However, income compensating effects result from the assumptions of increasing yields and agricultural prices.

Adaption of direct payments due to negative distributional effects

As can be seen in the distribution of income and subsidies (cf. Subsection 3.2.4 and 3.2.5) the negative distributional effect remains in both subsidy reduction scenarios. While the negative distributional effect is reduced for subsidies, it still appears for the income. The modified direct payments are simulated in a relatively simple way, which does not fully address the objective of reducing the negative distributional effects. Thus, the simple reduction or the shifting of payments from Pillar 1 to Pillar 2 might not be appropriate to aim at the objective of reducing the negative distributional effects of subsidies and income which appears between intensive farming and extensive farming counties.

Limitations in modelling are given for the number of AEM, which could be simulated as regional activity at regional level. The few modelled AEM for arable land and grassland might not be sufficient to represent the entire mix of policy measures of Pillar 2, and modelling more measures of Pillar 2 might reflect the requirements of counties more adequately.

Supply objective: adaption of direct payments to retain UAA

The reduction of subsidy volume is reached under keeping most of the UAA in production, indicating that the simulated modified direct payments might be justified with regard to the objective of retaining UAA under production. However, it is projected that in some regions UAA is abandoned when direct payments are reduced and this can also not be avoided in the SUBshift70% scenario, where some payments are shifted from Pillar 1 to Pillar 2. Therefore a justification for direct payments in order to keep agricultural land is not fully given within these simulated policy scenarios. As for the negative distributional effects, the problem of

land abandoning requires a better adapted and targeted policy measure in Pillar 2 than modelled here.

Environmental objective: Reduction of production intensity and environmental pressure Compared to the CAP2003 scenario agricultural production does not change significantly in the scenarios assuming modified direct payments. However, the trend that reduced subsidies might result in regional extensification and regionally focused intensification of agricultural production can be observed for extensive NUTS3 counties and farm types. Environmental pressure shows slightly increasing tendency and is not repressed by the policy instruments.

3.2.7 Scenario discussion

In this Subsection two modelling assumptions are discussed: the assumptions of high price increases and the effect of abandoning of UAA under the limitation that a land market is not considered in the modelling approach. In addition, some policy recommendations are given based on the scenario results.

Public expenditures and agricultural income

The decrease of subsidy volume is simulated by the modification of the parameter direct payments from Pillar 1 and Pillar 2 in the scenarios SUBred60% and SUBshift70%. These parameter modifications are modelled in a simplified way and might not reflect the complexity needed, especially with regard to modifications of Pillar 2 payments.

With the regional production model used in this study payments from Pillar 2 can be only attributed to AEM which are modelled as production activities. AEM activities are defined only for intensive and extensive grassland farming as well as for intercropping⁴¹. Thus, Pillar 2 payments are attributed only to three AEM. Consequently, an extension of the number of AEM activities in the model and the attribution of Pillar 2 to more than three AEM activities could provoke different results for the scenario SUBshift70%.

The results of the scenarios SUBred60% and SUBshift70% are compared with the baseline scenario CAP2003, in order to describe the impact of the modified payments. In comparison to the baseline scenario CAP2003 the scenarios SUBred60% and SUBshift70% result in a decrease of subsidies by 63% and 62% and income decreased by 11%. However, in comparison to the reference year REF, in the scenarios SUBred60% and SUBshift70% the

41 For more details on the modelling of AEM activities see Section 3.5.

subsidy volume is decreased by 11% and 12% while agricultural income is kept stable on average of the study region (cf. Subsection 3.2.3). This maintenance of the agricultural income level can be attributed to the scenario assumption of an increase in prices and yields, as this increase compensates for the monetary losses due to reduced subsidies. As mentioned in Section 3.1 the price assumptions from the year 2007 reflect extremely high prices and the scenario results would be different under assumption of smaller price increases. However, it has also to be kept in mind that due to increasing competition between food and energy production on agricultural area as well as due to increased demand for agricultural products, agricultural prices are generally expected to increase in the future.⁴²

The effect of abandoning UAA

Both subsidy reduction scenarios result in shares of abandoned UAA which’s extent for the total study region is assumed to be in an acceptable range. However, the UAA falling abandoned is projected to be regionally quite large, for example in fodder crop counties (e.g.

SH, AA) and extensive grassland counties (e.g. FDS, BL).

In counties with abandoning of UAA it can be observed that small crop yields and reduced payments result even under assumptions of high prices in a small gross margin, so that it is not profitable for farmers to keep arable land in production. However, it is questionable if the model approach used in this study and the scenario assumptions are really suitable for a realistic simulation of abandoning of UAA. Thus, the following three aspects should be considered when comparing the model results with results expected in reality: (1) the optimization approach, (2) the assumptions of the 'regional farm approach' and (3) the fact that the alternative activity of energy crop production is not simulated.

Optimization approach

The model ACRE used in this study is an optimization model based on PMP, which optimises all agricultural activities by maximizing the total gross margin. The production activities are calibrated first to the reference situation (REF) and in the scenarios SUBred60% and SUBshift70% the extensions of the agricultural activities are changed in order to reach a new adapted maximum of TGM. To maximise TGM over all activities ACRE optimizes the extension of activities by extending those activities with high gross margin and reducing activities with small (or negative) gross margin. The scarce production factor of UAA is

42 For a comparison with the prices used in this study and other baseline projections see Subsection 3.1.2.

allocated to the activities included in the solution. However, the flexibility in changing the extensions of production activities is restricted.

The subsidy reduction in both subsidy reduction scenarios results in an extreme decrease of the gross margins of crop activities and in some counties (e.g. in extensive counties) gross margins become negative due to relatively small yields. ACRE is not able to fill the UAA set free by increasing the extension of profitable activities. This does not seem to be an economic plausible reaction, since the scarce production factor UAA could be used in a profitable production to maximize the total gross margin.

PMP models find their optimum in the extension of activities where the marginal gross margins of the non-linear gross margin functions are equal for all activities (see Howitt 1995).

The behaviour of a PMP model when modelling an extreme decrease in subsidies can be illustrated with an example. The following equations are derived from Umstaetter (1999) and describe the model constructed as a cost sided calibrated PMP model according to Howitt (1995). The corresponding parameters and values are presented in Table 3.2-7.

Linear programming model

Objective function LP model: ⁼

∑ (

^{+ −}

)

i

i i i i

i y SUB c X

p

TGMlp * *

Resource constraint for land:

∑

^≤

∑

i i i

i X

X ˆ

Activity constraint for land: ˆ *1.0001

i

i X

X ≤

Non negativity condition for activity:Xi ≥0 with

Calibration parameters for marginal crop (here oat) Supply elasticity for marginal crop: ε ≥3.5

Slope coefficient:

(

i

)

i

i X

c ε* γ =

Interception coefficient: α_i =c_i−0.5*γ_i*X_i Calibration parameter for the non marginal crop (wheat)

Adjustment term: _i

i

i X

adj ⁼

∑

0.5*γ * Slope coefficient:

i i i

i X

+adj

= λ

γ ^2*

Interception coefficient: α_i =c_i −λ_i +adj_i

Objective function and restrictions for PMP model Objective function PMP model:

( )

∑

^{+ − −}

=

i

i i

i i

i i

i y SUB NX NX

p

TGMpmp * α 0.5*γ * *

Resource constraint for land:

∑

∑

^≤

i i i

i X

NX

Non negativity condition for activity:

≥0 NXi

Interception with Y-axix:

i i

i i i

i

i p y SUB NXzero

intercept = * + −α −2*0.5*γ * with NXzeroi =0

Marginal gross margin in PMP model:

i i

i i i

i

i p y SUB NXopt

MGMpmp = * + −α −2*0.5*γ * with NXopt_wheat =700;NXopt_oat =300 Slope for marginal gross margin functions in PMP model:

(

i

)

slopei =− ^2*0.5*γ

Table 3.2-7: Parameter symbols, production data and model results of the scenario calculations with the example model.

Symbol Unit SCENARIO I SCENARIO II

wheat oats wheat oats

Production data

Reference acreage Xˆ_i ^{[ha] 700 300 700 300}

Crop yield y_i [dt ha^-1] 60 40 60 40

Price p_i [EUR dt^-1] 10 10 10 10

Price p_i [EUR dt^-1] 10 10 10 10

In document Agro-economic policy analysis with the regional production model ACRE : a case study for Baden-Wuerttemberg (Page 174-189)