Journal of Policy Modeling 31 (2009) 225–238

Available online at www.sciencedirect.com

Evaluation of tobacco cultivation alternatives underthe EU common agricultural policy (CAP)

Basil Manos a,∗, Thomas Bournaris a, Jason Papathanasiou b,Parthena Chatzinikolaou a

a Aristotle University of Thessaloniki, Department of Agricultural Economics, 54124 Thessaloniki, Greeceb University of Macedonia, Thessaloniki, Greece

Received 1 October 2007; received in revised form 1 May 2008; accepted 1 July 2008Available online 10 September 2008

Abstract

A multicriteria model was formulated in order to study the impacts of different tobacco diversificationalternatives on income, employment and environment. It was developed in the context of the research projectDIVTOB (Diversification for Tobacco Growing Regions in the Southern European Union), a European FP6funded project. The model estimates the farmers’ utility function taking in account various conflicting criteriathat can explain the farmers’ behaviour (e.g. maximisation of farm income, risk minimisation, labour, etc.).The model is used to simulate alternative scenarios and policies and to take alternative farm plans that achievedifferent levels of income, labour and environmental impacts.© 2008 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

Keywords: CAP; Decoupling; Evaluation; Multicriteria analysis

1. Introduction

In 2003–2004, the European Union (EU) introduced direct payments to EU farmers based solelyon historical payments. The direct payments, to be implemented in 2005–2007 at the discretionof its member states, greatly enhance ongoing reforms of the EU’s Common Agricultural Policy(CAP). Such payments, by being up to 100% decoupled from current production, allow farmersto make production decisions based more on market signals than on policy interventions.

∗ Corresponding author. Tel.: +30 2310 998805.E-mail addresses: manosb@agro.auth.gr (B. Manos), jasonp@gen.auth.gr (J. Papathanasiou).

0161-8938/$ – see front matter © 2008 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.doi:10.1016/j.jpolmod.2008.07.001

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Following the decoupling of the subsidies of this reform, the producer receives a part of thesubsidies (e.g. 60%) obtained during the reference period as a fixed payment and the rest (e.g.40%) as area payment. The concept of decoupling has become one of the key issues in agriculturalpolicy design (OECD, 2002). Several definitions have been put forward, all of them to the extent ofthe production effects of farm support. In particular, a policy scheme is defined as fully decoupledif it does not influence production decisions of farmers receiving payments and that it permitsfree market determination of prices. Finally, a policy scheme is defined as partial decoupling ifit results in production that exceeds the level that would exist without it but does not exceed thatwhich would exist if the scheme was fully coupled to production (Cahill, 1997). Such payments,by being up to 100% decoupled from current production, allow farmers to make productiondecisions based more on market signals than on policy interventions. In order to receive this areapayment, the producer does not need to harvest; the only requirement is to reach the open capsulestage. This requirement would make it more profitable for most producers to semi-abandonmenttobacco production, which would involve a drastic reduction in input usage (fertilizers, pesticidesand irrigation water) and no harvest (Arriaza & Gomez-Limon, 2006; Gomez-Limon & Atance,2004; Rizov, 2004; Osterburg, Nitsch, & Kristensen, 2005; Osterburg & von Horn, 2006).

In order to study the impacts of tobacco decupling and the different tobacco diversificationalternatives on income, employment and environment we developed a multi-criteria model. Themodel utilises the weighted goal-programming approach and estimates the farmers’ utility func-tion taking in account various conflicting criteria that can explain the farmers’ behaviour (e.g.maximisation of farm income, risk minimisation, labour, etc.).

The model is used to simulate alternative tobacco decoupling scenarios and to take alternativefarm plans that achieve different levels of income, labour and environmental impacts.

The model applied to two case studies in Greece and specifically to the region of Toumba KilkisTobacco Cooperative in Central Macedonia and the region of Agricultural Cooperative of CigarsTobacco in Thessaly, using tobacco alternatives and their technical and economic data resultedfrom field research in these regions (Manos, Bournaris, Chatzinikolaou, Terzi, & Kiomourtzi ,2008).

2. Multicriteria model definition

In order to analyze how decoupled payments may influence farm production decisions weextend Sumpsi, Amador, and Romero (1993, 1997) and Amador, Sumpsi, and Romero (1998)methodologies for the analysis and simulation of agricultural systems based upon multicri-teria techniques. These authors propose weighted goal programming as a methodology forthe analysis of decision making. This methodology has been successfully implemented onreal agricultural systems (Berbel & Rodriguez, 1998; Gomez-Limon & Berbel, 2000; Manos,Bournaris, Kamruzzaman, Nakou, & Tziaka, 2004; Manos, Bournaris, Kamruzzaman, Begum,& Papathanasiou, 2006; Manos, Begum, Kamruzzaman, Nakou, & Papathanasiou, 2007).

We employ this methodology to estimate a surrogate utility function in order to simulatefarmers’ decision-making processes. This surrogate utility function is then used to estimate thevalue of decoupled payments in crop production.

The first step consists of defining a tentative set of objectives f1(X), . . . fi(X), . . . fn(X) whichseeks to represent the real objectives of the farmers (e.g. profit maximisation, risk minimisation,nitrogen balance, etc.). Then we determine the pay-off matrix of the above set of objectives. Theelements of the matrix are calculated by optimising one objective in each row. Thus, fij is thevalue of the i attribute when the j-th objective is optimised.

B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238 227

Using this pay-off matrix we estimate a set of weights wj that optimally reflect farmers’preferences. For this purpose we solve the following weighted goal-programming model (Romero,1991):

Minq∑

i=1

ni + pi

fi

subject to:

q∑

j=1

wjfij + ni − pi = fi, i = 1, 2, ..., q andq∑

j=1

wj = 1

where pi is the positive deviational variable (i.e. the measurement of the over-achievement of thei-th objective respect to a given target), and ni is a negative deviational variable that measures thedifference between real value and model solution for the i-th objective).

The surrogate utility function resulted by this process will be used to estimate the new cropplans with the following adaptations:

1. The income includes the decoupled payments.2. We introduce crops alternative crops in the area in order to allow the farmers to adapt new

products.

2.1. Variables

Each farmer has a set of variables Xi (crops). These are the decision variables that can assumeany value belonging to the feasible set.

2.2. Objectives

Three objectives have been regarded as belonging to the farmer’s decision-making process.

2.2.1. Profit maximisationFarmers wish to maximize profits. Gross margin (GM) is a good estimator of profit, and

maximisation of profit is equivalent in the short run to maximisation of gross margin.

Max GM =∑

GMi × Xi

where GMi is the gross margin of the crop Xi.

2.2.2. Risk minimisationAgricultural production is subject to price and yield fluctuations, and risk is therefore always

present in any agricultural system.

total risk = X̄′i[Cov]X̄i

where [Cov] is the variance/covariance matrix of gross margins during the 5-year period, and Xi

is the crop decision vector.

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2.2.3. Minimisation of labour inputsThis objective implies not only a reduction in the cost of this input but also an increase in leisure

time and the reduction of managerial involvement. Labour is computed as the sum of labour forall farming activities (TLi), and its objective function will be as follows:

TL =∑

TLi × Xi

Fertiliser minimisation is not taken into account in the model because it is only a publicobjective. For this reason it is not considered in the decision process by farmers (Zekri andRomero, 1993). As a result, no other objectives are proposed in advance. We will assume that thethree objectives mentioned above are enough to explain farmers’ behaviour.

2.3. Constraints

Constraints are concerning total cultivation area, CAP, market and other constraints, rotationaland agronomic considerations.

2.4. Attributes

Attributes are values of interest for the analysts that are deduced as functions of decisionvariables. In this sense, we have considered attributes that are relevant to policy makers. Themodel used in this study has been developed in order to estimate the values of these attributes (notrelevant to the decision maker) at the same time as the decision variables. The analysed attributesare:

Economic impact: we measure the economic impact of changes in policy by measuring agricul-tural income in euro/ha.Social impact: since agriculture is one of the main sources of employment any change in policywill significantly affect the social structure of rural areas. This attribute is measured in labour-hours per hectare (hours/ha).The environmental impact: we estimate the fertilizer use since this is a criterion relevant forpolicy analysis. It represents the environmental impact (non-point pollution caused by nitrogenfertilization).

3. Scenario definition

The CAP reform in tobacco sector has been adopted by the Council Regulation 1782/2003 andthe Commission Regulation 1973/2004. The main points of it are:

• Full decoupling will be carried out after a 4-year transition period, starting in 2006. Duringthese 4 years, at least 40% of the tobacco premiums have to be included in the decoupled singlepayment for farmers.

• After the 4-year transition period, from 2010, tobacco aid will be completely de-linked fromproduction. Some 50% will be transferred to the SPS and the remaining 50% will be used forrestructuring programmes in tobacco producing regions under the rural development policy.

• According to the results of negotiations between the member states and the European Commis-sion, the rates of tobacco decoupling vary across the different European tobacco regions from

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40% to 100%. The rate of tobacco decoupling in all tobacco regions will be changed from theyear 2010 and fixed to a 100%.

It is obvious that now and after the year 2013 when all direct subsidies to tobacco will bestopped the farmers should adjust their crop plans to the new conditions. New crops should becultivated instead of tobacco.

To this end we have chosen five scenarios to be implemented in the model. These are:

1. Decoupling 0% baseline.2. Decoupling 0% baseline under alternative crops.3. Decoupling 40% under alternative crops.4. Decoupling 50% under alternative crops.5. Decoupling 100% under alternative crops.

Scenario 1 represents the baseline used as represents the baseline used as reference to asses theimpact of decoupling through comparison with scenarios. Scenarios 2–5 represent the adoptionof alternative crops by the farmers under different scales of tobacco decoupling.

4. Study regions

We have applied the methodology explained above in two regions in Greece that are brieflydescribed in this section.

Toumba agricultural area in Kilkis Prefecture is located in the North part of Greece in CentralMacedonia. Large expanses of the region are covered by forests of willows, oaks and pines, aswell as rarer species of plants and flowers. The climate is continental with cold winters and hotsummers. The economy is based primarily on agriculture, the most important crops being tobacco,wheat and cotton, and to a lesser extent maize. Fields of alfalfa, barley, rye and sunflowers arealso common.

Elassona agricultural area in Larisa Prefecture is located in the Central part of Greece inThessaly. Its geography includes farmlands in the valley areas, the mountains to the west and eastand forests in the west and east as well as grasslands, ledges are to be founded in some areas

Fig. 1. Distribution of utilized agricultural in Toumba.

230 B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238

and barren area in the higher elevations. The climate is more continental than any other part ofGreece. It has a Mediterranean climate with dry hot summers and mild to cool winters, except inthe mountain areas which get warm summers and cold winters. In the summer, Larissa is oftenthe warmest area in Greece and often the coldest in winter. The most important crops in this areaare wheat, maize, tobacco, alfalfa, barley, and rye.

4.1. Land use of utilized agricultural area

The utilized agricultural area (UAA) in Toumba covers an area of 1589 ha. Arable crops arethe main cultivation for the majority of the agricultural holdings. In arable crops are includedcereals, cotton, tobacco, maize, alfalfa, aromatic crops and industrial crops.

Fig. 1 presents the distribution of utilized agricultural in Toumba agricultural area. It is coveredby arable crops especially hard wheat (29.0%), soft wheat (6.2%), cotton (29.8%), maize (2.5%)and tobacco (26.7%). As we can see tobacco has a major part in the existing crop plan.

The utilized agricultural area (UAA) in Elassona covers an area of 4880.3 ha. Arable crops arethe main cultivation for the majority of the agricultural holdings. In arable crops cereals, cotton,tobacco, maize alfalfa and industrial crops are included. As we can see tobacco is not a majorcrop in the area but is one of the most important one.

Fig. 2 presents the distribution of utilized agricultural in Elassona agricultural area. It is coveredby arable crops especially hard wheat (38.7%), soft wheat (14.4%), rye (9.1%), maize (12.3%),tobacco (7.6%) and alfalfa (8.3%).

4.2. Technical and economic coefficients

As for construction of the mathematical programming model, both primary and secondary dataare used. The primary data were collected by field research using a questionnaire and the secondary

B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238 231

Fig. 2. Distribution of utilized agricultural in Elassona.

data from the Regional Government of Central Macedonia and the Regional Government ofThessaly. The data are referred to the period 2001–2005 (5 years). We also used additional dataprovided by the Department of Agricultural Economics of Aristotle University.

5. Results

The pay-off matrix for the Toumba agricultural area is shown in Table 1 and for Elassona inTable 2.

The last column in Tables 1 and 2 show the real values in the study region. It gives the valuesof the three objectives for the actual crop distribution, in a 100 ha farm. In this way we can seehow far the real situation (2005) is from any single optimum (column). This induces us to try acombination of the three objectives, as a better simulation of farmers’ behaviour.

Table 1Pay-off matrix for Toumba agricultural area.

Values Optimum Real (observed values)

GM VAR TL

GM 157,576 29,773 30,194 148,919VAR 157,539,762 5,591,798 5,705,454 139,112,028TL 79,489 11,046 11,000 78,915

Table 2Pay-off matrix for Elassona agricultural area.

Values Optimum Real (observed values)

GM VAR TL

GM 59,605 27,982 29,556 55,368VAR 21,672,030 4,712,046 5,513,486 18,992,057TL 22,051 7,350 7,250 23,901

232 B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238

Table 3Comparison between observed values and MCDM model in Toumba agricultural area.

Observed values MCDM model

Mod. values % deviation

GM 148,919 157,576 5.8VAR 139,112,028 157,539,762 13.2TL 78,915 79,489 0.7Soft wheat 6.2 15.0 140.8Hard wheat 29.0 9.2 −68.3Barley 0.5 2.0 297.3Rye 0.4 0 −100.0Maize 2.5 0 −100.0Tobacco 26.7 26.8 0.2Cotton 29.8 33.0 10.6Sunflower 0.5 2.0 297.3Alfalfa 0.6 2.0 217.8Set aside 3.6 10.0 178.8

Total 100.0 100.0

Applying the weighted goal-programming model described above to the values ofTables 1 and 2 we take the set of weights that best reflects farmers’ preferences. These are:

Toumba Elassona

w1 = (maximize GM) = 0.8787 w1 = (maximize GM) = 0.8420w2 = (minimize VAR) = −0.1213 w2 = (minimize VAR) = −0.1580w3 = (minimize TL) = 0.000 w3 = (minimize TL) = 0.000

These weights show a type of farmers’ behaviour that combines profit maximisation andrisk minimisation. Minimisation of risk is an important criterion, since it has weight 12.13%in Toumba and 15.80% in Elassona agricultural area. This is combined with the criterion ofprofit maximisation that has a great weight (87.87% in Toumba and 84.20% in Elassona). Onthe contrary, total labour is not considered as a relevant criterion in these particular agriculturalsystems.

The estimation of these weights was based on the current situation (2005). In this sense it isimportant to note that the set of weights can be considered as a structural factor. As these weightscorrespond to the psychological attitudes of the producers it is reasonable to assume that theywill be kept at the same level in the short and the medium run, and this is actually an importantpre-assumption in our simulation.

In order to simulate decoupling scenarios, we will use the above weights to represent thefarmers’ utility function. The utility functions will be:

Toumba : U = 87.87% GM − 12.13 VAR (1)

Elassona : U = 84.20% GM − 15.80 VAR (2)

These functions that the MCDM model will attempt to maximize will be applied to the subse-quent simulation. Tables 3 and 4 show the comparison between the real situation and the predictedsituation with the help of MCDM model, which has as objective function the maximisation of theutility function.

B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238 233

Table 4Comparison between observed values and MCDM model in Elassona agricultural area.

Observed values MCDM model

Mod. values % deviation

GM 55,368 59,605 7.7VAR 18,992,057 21,672,030 14.1TL 23,901 22,051 −7.7Soft wheat 14.4 17.3 19.9Hard wheat 38.7 14.5 −62.6Barley 6.1 7.3 19.8Rye 9.1 10.9 19.5Maize 12.3 24.5 99.7Tobacco 7.6 5.5 −27.2Alfalfa 8.3 10.0 19.9SA 3.5 10.0 188.8

Total 100.0 100.0

From Tables 3 and 4 we conclude that the methodology we have adopted gives a better approx-imation to observed values at the present (2005). Trying to combine the two objectives, profitmaximisation and total risk minimisation, the MCDM model gives a farm plan that achievesgross margin 5.8% in Toumba and 7.7% in Elassona greater than the existent farm plan. Asregards the total labour, the MCDM model achieves a small increase 0.7% in Toumba and animportant reduction −7.7% in Elassona.

We further applied the MCDM model to the two different cases studies for the policy scenariosunder the following acceptances:

1. Income includes the decoupled payments for tobacco.2. We introduced tobacco alternative crops in the two areas in order to allow the farmers to adapt

new products.

To this end, we used extra questionnaires for farmers and experts living in the study areas,in order to make the choice of production plan crops and the alternative crops as well as tocalculate their necessary technical and economic coefficients. We identified in this way 15 tobaccoalternatives in Toumba area and 14 in Elassona area (Tables 5 and 6).

5.1. Case studies crop plans

We apply our model to Toumba Kilkis tobacco agricultural area which constitutes our firstcase study. Tobacco has an important role in existing crop plan (26.7%). In the second case studyin Elassona, tobacco cultivation is one of the major crops but only with 7.6%. We analyze fivealternative scenarios concerning the three different levels of tobacco decoupling.

By applying our MCDM model and the five scenarios to the two case study areas we get thefollowing crop plans presented in Tables 5 and 6. These tables show the comparison between thepresent situation (Scenario 1) and the predicted situation with the help of the MCDM model, whichhas as objective function the maximisation of utility function under the four tobacco decouplingscenarios.

234 B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238

Table 5Production plan under five tobacco decoupling scenarios in Toumba.

Crops Present Decoupling

Scenario 1, 2005 Scenario 2 Scenario 3 Scenario 4 Scenario 50% 40% 50% 100%

Soft wheat 15.0 5.0 5.0 5.0 5.0Hard wheat 9.2 20.5 20.1 20.0 21.6Barley 2.0 1.0 1.0 1.0 1.0Rye 0.0 0.0 0.0 0.0 0.0Maize 0.0 1.3 6.2 7.4 5.7Tobacco 26.8 17.5 3.7 2.2 0.0Cotton 33.0 33.0 33.0 33.0 33.0Sunflower 2.0 0.0 0.0 0.0 0.0Alfalfa 2.0 4.0 4.0 4.0 4.0SA 10.0 3.8 4.4 4.5 4.7Oregano not irrigated 1.0 1.0 1.0 1.0Basil 0.5 0.5 0.5 0.5Mint 0.5 0.5 0.5 0.5Oil seed rape 0.0 0.0 0.0 2.0Anise irrigated 0.0 0.0 0.0 0.0Wheat organic 1.5 1.5 1.5 1.5Hard wheat organic 6.2 6.0 6.0 6.5Maize organic 0.4 1.9 2.2 1.7Alfalfa organic 1.2 1.2 1.2 1.2Vetch seed organic 1.0 1.0 1.0 1.0Cherries 0.0 1.6 1.6 1.6Plums 0.0 0.3 0.3 0.3Pears 0.0 0.6 0.6 0.6Pomegranates 1.6 1.6 1.6 1.6Stevia 0.0 5.0 5.0 5.0

100.0 100.0 100.0 100.0 100.0

From the results we can summarise that organic crops (organic wheat, hard wheat, maize, andalfalfa) aromatic plants (oregano, mint, and basil) are adopted by the farmers in order to changetheir crop plans as tobacco alternatives. We can conclude that the new CAP will impact seriouslythe production plans. The most interesting results are those related to the tobacco cultivation. Aswe can see farmers will abandon tobacco when decoupling becomes 100%. At least 3.8% of thecultivated area in Toumba and 7.86% in Elassona will set aside. As long as the processing phaseis continued to 100% decoupling, farmers will also adopt fruit trees (cherries, plums, pears andpomegranates) in Toumba and the new stevia cultivation in both case studies (Kienle, 2002).

5.2. Economic impact

The impacts from the crop plan changes to farmer’s income, labour and fertilizers use whenwe solve the model by optimising the utility function are shown in Figs. 3 and 4. Specifically,Figs. 3 and 4 show three typical curves one for income, one for labour and one for fertilizers usethat reflects how the farmer adapts the affects of decoupling. The indexes in the two figures arecompared with the present Scenario 1 that corresponds to index 100.

B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238 235

Table 6Production plan under five tobacco decoupling scenarios in Elassona.

Crops Present Decoupling

Scenario 1, 2005 Scenario 2 Scenario 3 Scenario 4 Scenario 50% 40% 50% 100%

Soft wheat 17.3 11.15 11.15 11.15 11.15Hard wheat 14.5 30.00 30.00 29.00 29.00Barley 7.3 4.62 4.62 4.62 4.62Rye 9.0 3.50 5.55 8.00 9.00Maize 24.5 10.38 10.38 10.38 10.38Tobacco 7.4 4.95 1.50 0.45 0.00Alfalfa 10.0 6.54 6.54 6.54 6.00SA 10.0 8.05 7.95 7.86 8.00Oregano 0.50 0.50 0.50 0.50Tea 0.50 0.50 0.50 0.35Basil 0.00 0.00 0.00 0.00Mint irrigated 0.00 0.00 0.00 0.15Sunflower 0.00 0.00 0.00 0.00Oil seed rape 0.00 0.00 0.00 0.00Anise 0.00 0.00 1.00 0.00Wheat organic 3.35 3.35 3.35 3.35Hard wheat organic 9.00 9.00 7.29 7.29Barley organic 1.38 1.38 1.38 1.38Maize organic 3.12 3.12 3.12 3.12Alfalfa organic 1.96 1.96 1.96 1.96Vetch seed organic 1.00 1.00 1.00 1.00Stevia 0.00 1.50 2.00 2.75

100.00 100.00 100.10 100.00

As tobacco decoupling changes from 0% to 40% to 50% and finally 100%, farmers adapt bychanging their crop plans in order to obtain the best results. The different slopes of the demandcurves are due to changes in the crop plan, as an adaptation to tobacco decoupling.

Tobacco decoupling has similar effects in the farmer’s income in both case studies. In the secondscenario, we observe that with 0% decoupling we will have an increase of farmer’s income 0.9%in Toumba and 1.5% in Elassona. We can conclude that the farmers, adopting in their crop plans

Fig. 3. Economic, social and environmental impact under five tobacco decoupling scenarios in Toumba.

236 B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238

Fig. 4. Economic, social and environmental impact under five tobacco decoupling scenarios in Elassona.

alternative cultivations such as aromatic, energy or organic crops, can achieve an increase in theirincome in comparison with the existent crop plan. On the other hand when tobacco decouplingstarts we observe a decrease in farmer’s income from 7.4% in Scenario 2 to 11.9% in Scenario5 for the first case study and from 1.5% in Scenario 2 to 2.3% in Scenario 5 in the second one.The difference between the two case studies depends on the different participation of the tobaccocultivation in the existent crop plans.

In other words the total tobacco decoupling will decrease significantly the income of the farmersin the two case study areas by 11.9% in Toumba and 2.3% in Elassona.

5.3. Social impact

Tobacco decoupling brings about a severe reduction in farm labour inputs as a result ofresponses through changes in crop plans, introducing less profitable crops as substitutes forhigher-value/higher labour-intensive crops such as tobacco. This implies that intensive cropswill be replaced by less demanding and more mechanised crops. This circumstance, in relationto labour, can be observed in Fig. 3, where we can see farmers’ behaviour when demand is basedon the multiattribute utility model.

Figs. 3 and 4 show evidently that we have high reduction in the labour demand. In first scenario(tobacco decoupling 0%) we can see a decrease in labour and in both two case studies. Tobaccodecoupling 40% has as result farmers abandoning the cultivation of tobacco that is impressed inFigs. 3 and 4 as labour reduction 23.9% in Toumba and 26.2% in Elassona. When the decouplingreaches 100%, labour reduction reaches 69.6% in Toumba and 56.6% on Elassona.

This means that the total tobacco decoupling will bring an important increase of unemploymentto the case study areas.

5.4. Environmental impact

We use the demand for fertilisers as an indicator of the environmental impact of agriculture,measured in kilograms of nitrogen added per hectare (N/ha). Tobacco decoupling make farmersto adapt by changing their crop plans in order to obtain the best results. This requirement wouldmake it more profitable for most producers to semi-abandonment tobacco production, whichwould involve a drastic reduction in input usage (fertilizers, pesticides and irrigation water) andno harvest.

B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238 237

Figs. 3 and 4 show that fertilizers use is decreased already in the first scenario. The reductionis bigger both in four scenarios of the second case study (Elassona) when farmers cultivatealternative crops friendly to the environment such as aromatic, energy and organic plants. Thereduction reaches the 20.4% when decoupling is 100%. In the first case study (Toumba) we havea small reduction of fertilizers use (up to 4.1%) because of the fruit trees cultivation adopted bythe farmers.

Thus we may conclude that the tobacco decoupling will have a positive impact to the environ-mental pollution.

6. Conclusions

The results have shown changes in the crop plans, the income, the employment and the envi-ronment in the two case studies in Greece due to the impacts of tobacco decoupling. Thesechanges vary in each study region according to the status quo crop plan, the percentage of tobaccocultivated, the rate of tobacco decoupling and the suggested alternative crops.

We observe that tobacco in both study regions is continuously decreased when we proceedfrom Scenario 1 (that corresponds to the optimum crop plan without tobacco alternatives andwithout decoupling) to Scenario 2 (that corresponds to the optimum plan with alternatives butwithout decoupling), then to Scenario 3 (optimum plan with alternatives and 40% decoupling),then to Scenario 4 (optimum plan with alternatives and 50% decoupling) and finally to Scenario5 (optimum plan with alternatives and 100% decoupling). In both study regions the tobaccoarea in the crop plans becomes zero when tobacco decoupling reaches the 100%. In this processtobacco is substituted by the tobacco alternatives from which stevia seems to occupy an importantpercentage of the crop plan (up to 5%–2.75% respectively in the two study regions Toumba andElassona.

The total gross margin independently of whatever are the tobacco alternatives of crop planspresents a continuous decrease from Scenario 1 to Scenario 5 in the two study regions up to11.9%. Specifically, the gross margin from 100% in Scenario 1 becomes 88.1% in Scenario 5 inToumba, 97.7% in Elassona. This means that the economic impact of tobacco decoupling variesin the two study regions and it is greater in Toumba than in Elassona. This is reasonable becauseof the different crop plans and of the different percentage of the tobacco in the crop plan (26.8%in Toumba and 7.4% in Elassona). It is obvious that the negative economic impacts of the tobaccodecoupling would be greater if we did not suggest alternative crops.

The negative impact of tobacco decoupling is greater on employment in the study regions. Thelabour used is decreased from 56.6% (Elassona) up to 69.6% (Toumba) as the tobacco decouplingvaries from 0% to 100%. Specifically, from 100% in Scenario 1, the labour used falls in Scenario5 to 30.4% in Toumba and 43.4% in Elassona.

On the contrary, the impact of tobacco decoupling on the environment seems to be positive inboth study regions. We observe that tobacco decoupling causes an important decrease to environ-mental pollution up to 20.4%. From 100% in Scenario 1, the fertilisers used falls in Scenario 5 to79.6% in Elassona and 95.9% in Toumba.

In spite of the positive impact of tobacco decoupling on the environment, the loss in farmers’income and mainly the increase of unemployment in the tobacco regions of Greece will be veryimportant in short and medium terms as it is obvious that these matters will cause significantproblems in the local rural economies. A further investigation of tobacco alternatives in all Euro-pean tobacco regions and the permission of stevia’s cultivation in combination are needed. Are-examination and a possible revision of policy for tobacco are required.

238 B. Manos et al. / Journal of Policy Modeling 31 (2009) 225–238

The Berlato report (Berlato) that has recently adopted by the European Parliament is a start tothe right direction. This report restates its demand to prolong the current support system for thetobacco sector until 2013 to give farmers more time to adjust. This decision is against to the policyof the European Commission which favours the abolishment of all coupled tobacco productionsubsidies by 2010.

References

Amador, F., Sumpsi, M. J., & Romero, C. (1998). A non-interactive methodology to assess farmers’ utility functions: Anapplication to large farms in Andalusia, Spain. European Review of Agricultural Economics, 25, 92–109.

Arriaza, M. & Gomez-Limon, A. J. (2006). How decoupling could mean dismantling of the cotton sector in Spain. NewMedit, 1, 4–14.

Berbel, J., & Rodriguez, A. O. (1998). An MCDM approach to production analysis: an application to irrigated farms insouthern Spain. European Journal of Operational Research, 107, 108–118.

Berlato Sergio report A6-0164/2008. Committee on Agriculture and Rural Development of European Parliament, April21, 2008.

Cahill, S. A. (1997). Calculating the rate of decoupling for crops under CAP/oilseeds reform. Journal of AgriculturalEconomics, 48, 349–378.

Gomez-Limon, A. J., & Berbel, J. (2000). Multi-criterion analysis of derived water demand functions: A Spanish casestudy. Agricultural Systems, 63, 49–72.

Gomez-Limon, J. A., & Atance, I. (2004). Identification of public objectives related to agricultural sector support. Journalof Policy Modeling, 26(8), 1045–1071.

Kienle, U., 2002. Optimized production and harvesting technique of the alternative crop Stevia rebaudiana bertoni.FAIR5-CT97-3751, Final Report, University of Hohenheim.

Manos, B., Bournaris, Th., Kamruzzaman, M., Nakou, I., Tziaka, D. (2004). Sustainability of European Irrigated Agri-culture. In Berbel, J., Gutierez, C. (Eds.), The case of Greece in sustainability of European irrigated agriculture underwater framework directive and agenda 2000. European Commission, ISBN 92-894-8005-X.

Manos, B., Bournaris, Th., Kamruzzaman, M., Begum, A. A., & Papathanasiou, J. (2006). The regional impact of irrigationwater pricing in Greece under alternative scenarios of European policy: A multicriteria analysis. Regional Studies,40(9), 1055–1068.

Manos, B., Begum, A. A., Kamruzzaman, Mohd., Nakou, I., & Papathanasiou, J. (2007). Fertilizer Price Policy, theenvironment and farms behaviour. Journal of Policy Modeling, 29(1).

Manos, B., Bournaris, T., Chatzinikolaou, P., Terzi, M., Kiomourtzi, F. (2008). Tobacco alternatives in Greece. A prelim-inary evaluation and classification. New Medit, 3.

OECD (2002). Decoupling: A conceptual overview. Paris: Directorate for Food, Agriculture and Fisheries, Trade Direc-torate. Document COM/AGR/APM/TD/WP(2000)14/FINAL.

Osterburg, B., & von Horn, L. (2006). Assessing the impacts of decoupling EU direct payments from agriculturalproduction and the potential for ‘re-coupling’. Outlook on Agriculture, 35(2), 107–113.

Osterburg, B., Nitsch, H., & Kristensen, L. (2005). Environmental standards and their linkage to support instruments ofthe EU Common Agricultural Policy. In Paper prepared for presentation at the 99th Seminar of the EAAE.

Rizov, M. (2004). Rural development and welfare implications of CAP reforms. Journal of Policy Modeling, 26(2),209–222.

Romero, C. (1991). Handbook of critical issues in goal programming. Oxford: Pergamon Press.Sumpsi, M. J., Amador, F., & Romero, C. (1993). A research on the Andalusia farmers’ objectives: methodological aspects

and policy implications. In Aspects of the common agricultural policy. VIIth EAAE Congress, Stresa, Italy.Sumpsi, M. J., Amador, F., & Romero, C. (1997). On farmers’ objectives: A multi-criteria approach. European Journal

of Operational Research, 96, 64–71.Zekri, S., & Romero, C. (1993). Public and Private Compromises in Agricultural Water Management. Journal of Envi-

ronmental Management, 37, 281–290.