Assessment cap reform 2014 2020 in Emilia-Romagna Region

324 views

Published on

Assessment of CAP reform 2014-2020 in Emilia Romagna Region - Published on Agricultural Cooperative Management and Policy, Cooperative Management, DOI: 10.1007/978-3-319-06635-6_20, Springer International Publishing Switzerland 2014

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
324
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Assessment cap reform 2014 2020 in Emilia-Romagna Region

  1. 1. Chapter 20 Assessment of CAP Reform 2014–2020 in the Emilia-Romagna Region R. Gigante, F. Arfini and M. Donati Abstract The aim of this contribution is to evaluate the impacts of the European Commission proposals on rural areas of the Emilia-Romagna Region in Italy. The model considers the three main characteristics of the CAP 2014–2020 reform, and in particular measures the impact of greening criteria on land allocation in different farm systems and economic effects on rural areas. It will show which rural systems and types of farm will be favored or penalized by the reform. The model will also provide results on dynamics in land use. The assessment is made using an ‘‘integrated’’ regional model based on Positive Mathematical Programming (PMP). 20.1 Introduction The debate on the Common Agriculture Policy (CAP) for the years 2014–2020 started in 2011, and at the time of writing is nearing conclusion. The reform was approved by the final European plenary session on 13th March 2013 by 25 of the 27 Ministers of Agriculture. On April 11th, it was discussed by the Trilogue in informal tripartite meetings of representatives of the European Parliament, the Council and the Commission. The goal was to reach a common political agreement by the end of June. But on the other hand, the European budget, including agriculture funding, is at present still undefined. In this uncertain context, what remains of the European Commission’s official proposal of 2011 (European Commission 2011a) is that future CAP will continue its main focus on environmental measures directing European farms toward a model of sustainable agriculture responsible for the management of environmental resources and attentive to public financial resources and well-being of consumers (Matthews 2013). The aim of this contribution is to R. Gigante (&) Á F. Arfini Á M. Donati Department of Economics, University of Parma, Parma, Italy e-mail: gigante@inea.it C. Zopounidis et al. (eds.), Agricultural Cooperative Management and Policy, Cooperative Management, DOI: 10.1007/978-3-319-06635-6_20, Ó Springer International Publishing Switzerland 2014 371
  2. 2. evaluate the impacts of the European Commission proposals (European Commis- sion 2011b) on rural areas in the Emilia-Romagna Region in terms of province, altitude, and farm type. The study will consider the impact of greening criteria, capping and the regionalization scheme on land allocation, and the relative economic effects on different regional farm systems. It will focus on the regional effect of the CAP reform, considering the nonhomogeneity of the Emilia-Romagna Region, and show the main consequences in terms of land use and income distribution among different farm types and rural systems across one of the most important Italian farming regions. The evaluation will be made by a regional model based on the use of Positive Mathematical Programming (PMP). Data are extracted from an integrated regional database that matches FADN Data with IACS information for the year 2010. The chapter is organized as follows; Sect. 20.1 describes the evolution of CAP in environmental measures, changes in distribution of aid, and the system of trans- ferring resources from Pillar 1 to Pillar 2; Sect. 20.2 focuses on the methodology and describes the AGRISP model and its implications; Sect. 20.3 describes the new measures taken into account by the model, detailing amounts and restrictions imposed by the reform; Sect. 20.4 shows results from the model and their implications for changes in land use and economic indicators. Results are shown at regional and province level and also by altimetry. The last section discusses the new agriculture policy and the possible effects on rural areas in Emilia-Romagna. 20.2 The Policy Setting This section discusses the main innovations in the CAP 2014 reform, and how these have evolved from recent policy reforms. The new CAP in fact fulfills many medium- and long-term expectations both within and outside the EU. The Health Check of 2008 enforced a series of provisions of the 2003 reform, and the new reform will continue to enhance intervention on environmental protection (Anania 2008). The main concerns are the new greening measures, agricultural development and modulation methods, with new proposals oriented toward rural development and setting CAP as a more equal policy between Member States (MS) with a new single payment scheme. Reform paths can be viewed from either an economic point of view or as a requirement to renew environmental strategies at a higher level. Economically, the necessity to reduce CAP expenditure and the eradication of the old system of direct support will cut the EU budget, bringing down both surplus production and international tensions (De Filippis and Frascarelli 2012). Envi- ronmentally, the measures reflect institutional concerns and widespread awareness of limits to availability of natural resources, and are a prerequisite for acceptance of expenditure by European taxpayers (European Commission (a) and (b)). 372 R. Gigante et al.
  3. 3. 20.2.1 Evolution of Environmental Focus in the CAP From the start, the CAP has been characterized by reforms that have tried to align the varying objectives of the Community in agriculture in European society. In the beginning, at the time of the Treaty of Rome (1957), CAP focused on agricultural policy with the priority of food security. In subsequent years, the emphasis moved to a new concept, linking agriculture to environmental issues. The first concrete attempt at this was in the MacSharry reform (1992) which gave EU farm policy a new role in an attempt to re-establish a proper relationship between agriculture and the environment and also to revise CAP expenditure limits. The MacSharry reform introduced the concept of ‘‘green’’ to the CAP, and its ‘‘accompanying measures’’ (Reg. EC no. 2078/92) contained predominantly agri-environment schemes, such as the adoption of environmentally friendly farming methods (e.g., organic farming), reducing the use of chemical products and processes and the extensifi- cation of farming. This led to the 1999 reform, Agenda 2000, which aimed to strengthen public intervention in agriculture for sustainability, especially envi- ronmental sustainability, and then to the Fischler Reform of 2003 (Reg. EC no. 1782/2003). The Fischler Reform introduced the principle of decoupling aid and the cross-compliance mechanism: the farmer is obliged to comply with minimum environmental standards in order to obtain agricultural support (Sorrentino et.al. 2011). In 2008, a further review of the CAP (Health Check or Fisher-Boel Reform) completed the decoupling process and amended specific additional intervention, reinforcing its environmental objectives. Among other measures, the controversial Article 68 of EC Regulation No. 73/2009 provides annual additional payments for forms of agricultural management of the environment, such as extensive animal farming. The last step in this evolution on environmental issues will be imple- mented in programming period 2014–2020 with the new greening measures. Although at the time of writing, the European Commission has yet to take final decisions on the new CAP, this contribution attempts to make a preliminary evaluation of the effects on the agriculture sector in the Emilia-Romagna region. 20.2.2 A More Equal CAP Since the early 1990s, CAP policy has been gradually reformed toward market orientation in the two reform packages of 2003 and 2007. These replaced a large share of the price support by direct payments per hectare of land and per head of livestock. These direct payments were only paid to certain crops and certain types of livestock. In 2003, the Fischler reform substantially changed European policies for supporting farmers with decoupling of direct payments. The new ‘‘Single Payment Scheme’’ introduced payment per hectare of agricultural land, indepen- dently of the individual farmer’s output. It is paid regardless of whether the farmer 20 Assessment of CAP Reform 2014–2020 373
  4. 4. produces or not, as long as the land is kept in good agricultural and environmental condition. However, there are exceptions to the general principle of decoupling, since individual member states are currently allowed to keep limited coupled payments for some products (partial decoupling). The reform was intended to make European agriculture more competitive and market-oriented as required by WTO, and at the same time to provide support to farmers with less distortion of production and trade. Decoupled payments allow farmers to respond better to signals from the market, to supply the food sector, and to create a basis for providing public goods. The scheme was amended slightly in 2007 and has been in force until today. CAP 2014–2020 is expected to provide for a fully direct payment system for all MS and by the beginning of 2019, all MS will move to a uniform payment per hectare scheme, applied at the national or regional level. In Italy as in some other MS, and consequently in Emilia-Romagna, this reform will be a big challenge for farmers accustomed to the concept of acquired rights. The change is to be accompanied by other measures; cross-compliance is maintained and greater modulation is intro- duced to the new coupled scheme. It could also lead to redistribution between agrarian regions and farms, and between production sectors, which could affect the competitiveness of different farm activities and sectors. There will very likely be variations in the competitiveness of farms and sectors. An example of the effect of aid redistribution at the local level is shown in Fig. 20.1. Using administrative regional boundaries, it shows the present situation and the effects of the new regionalization, with the future single payment scheme in the different provinces of Emilia-Romagna (Gigante 2013). The shrinkage of financial resources and decoupled payments reduce the average aid level in most provinces. Because of the type of production (cereal, tomato, milk), many prov- inces lose resources in favor of areas producing fruits and wine, which were not eligible for payments in the past. The effect of aid redistribution can also be differentiated by altimetry (Fig. 20.2). The effect is much stronger on plain areas, where resources are cut for the benefit of mountain areas. At altimetry levels too, the type of production is important: plain areas used to receive aid for almost all hectares, so the redistri- bution operates a linear reduction. Mountain and some hill areas gain advantage thanks to smaller cultivated surface area and because most farms specialize in milk production. In fact, despite the milk quota system, mountain areas have until now been less favored. 20.2.3 New Resources to Pillar 2 The new capping mechanism appears to be very different from the scheme introduced by the Health Check in 2007 and in force today. The current scheme applies the mechanism of modulation only to beneficiaries receiving more than €300,000 in direct payments, which are subject to an additional cut of 14 %. But under the new scheme, the upper limit of direct payments to farmers will be 374 R. Gigante et al.
  5. 5. €300,000. However, in order to preserve and stimulate the application of envi- ronmental measures and practices, the cost of greening will not be considered in the budget. In order to maintain employment in the sector, the capping mechanism will be mitigated for farms employing waged labor. The EU Commission draft states that the direct payments scheme will take into account the employment level on farms, and the amount of wages actually paid and declared by farmers for the previous year, including social security contributions and employment taxes, will be added to the total amount of direct payments due. As the result of capping, more resources will be available for transfer to Pillar 2 (Rural Development). Moreover, given that each MS has a different agriculture system, each MS is given the option of transferring up to 10 % of national financial resources assigned for direct payments (Pillar 1) to Rural Development (Pillar 2). In addition, MS which receive less than 90 % of the EU average direct payments can now transfer up to 5 % of the funds assigned for Rural Development to the Direct Payment System. Fig. 20.1 Redistribution of aid in Emilia-Romagna provinces Fig. 20.2 Redistribution of aid in Emilia-Romagna provinces by altimetry 20 Assessment of CAP Reform 2014–2020 375
  6. 6. 20.3 Description of the Model: PMP and Database Positive Mathematical Programming (PMP) included in the Agricultural Regional Integrated Simulation Package (AGRISP) model (Arfini et al. 2005) was used to assess the impact of CAP 2014–2020 on Emilia-Romagna. This model is one of the possible applications of the PMP across Europe (Heckelei et al. 2012). In this research, AGRISP was used to reproduce the effects of the regionalized single payment system, greening measures, and the new capping mechanism on farm behavior and farm economic performance. As noted in the introduction, the simulations are based on the draft by the European Commission, so rather than certain consequences they indicate possible potential consequences. The simula- tions demonstrate a capability differential of farms in reacting to new policy and market scenarios, and show how the reform will affect the production and eco- nomic levels of the farms investigated. 20.3.1 Evolution of PMP Methodology As is well known, PMP has a long history (Heckelei and Britz 2005; Heckelei et al. 2012). After early Linear Programming models, which showed the optimum combination for production according to the technological matrix, the next step was positive models where the optimum is considered at the observed production level, which reveals farm cost structures. The main aim here is to give as true a picture as possible of the current situation, then simulate the behavior of farm producers as agricultural policy intervention is shifted (Arfini and Donati 2013). The versatility of PMP means it can be fitted to valuation models with different levels of detail, so it can be applied to a single farm (business model) or for simulations of the dynamics of a territory (regional model) or a production sector (sectorial model). It is also possible to structure a mixed model using an integrated database including regional and sector aspects. Despite their differences, these models have a common matrix deriving from their microeconomic formulation and embodied in the use of information collected at the enterprise level, regardless of the ‘‘scale’’ (corporate, regional, or sectorial) of the simulation. In business models the scope is limited to the single firm, but the models used here provide regional or sectorial results for aggregate geographical areas or entire productive sectors. Sectorial models are usually used by decision-makers in assessing agri- cultural policy for a single and specific production. But although their primary objective is to analyze supply changes of products subject to intervention, the models can also have a regional significance to the extent they quantify the impact of the policy at the regional, national, or international level (Arfini and Donati 2011). The element that distinguishes the sectorial from the regional model is the aggregation criteria of the farms; for sectorial models, the aggregation criteria is the farm type. 376 R. Gigante et al.
  7. 7. The application of mathematical programming to agricultural policy entails defining from the outset a reference to a regional area (in the case of a regional model) or to a specific productive sector (sectorial model). Regional and sectorial models are not necessarily alternatives; in general, a single productive segment is analyzed individually with reference to a territorial area, in contemporaneous regional and sectorial studies. However, the variables of interest and the PMP model need to be defined a priori. So in a complex scenario like local rural development the model needs to cover different farm types and truthfully represent land use and productivity levels of the farmers in the area. It is useful to minimize the amount of data, although there needs to be enough detail to describe both individual farmer behavior and tech- nologies and production decisions at the farm level. AGRISP (Arfini et al. 2005), which shows in detail the use of land, farm specialization, and different farm classes by size, is an appropriate compromise. AGRISP in fact represents a fusion of two databases: the AGEA database on land use of each farm, and the FADN database of the profitability of each production process activated. So rather than a ‘‘model,’’ AGRISP is a tool of analysis that overcomes limitations of other similar tools used to simulate agricultural policies at the regional level. The combined use of AGRISP data and PMP methodology means that all output models can be calculated precisely. In Europe as in Italy, at present, the main source of statistical data on structural characteristics of farm production and economics is the Agricultural Accountancy Data Network (FADN). Data are obtained by survey and the database is structured as a statistical sample of all farms. It can be considered ‘‘ideal’’ for coefficients on farm production techniques and farm economic characteristics. FADN has, how- ever, three major limitations: (i) a lack of technical information on the amount of input used for each process; (ii) the representativeness of data is based on the standard gross margin, while the presence of a specific process reflects the land use; (iii) the level of representativeness of farms decreases significantly from regional level to provincial level. In particular, the first aspect (the lack of quantitative data on inputs) is an essential element to allow simulation models (especially those based on mathe- matical programming) in order to define the technology used by that of single farms. Consequently, the lack of such data excludes the use of FADN for the purposes of analysis of agricultural policy through the use of mathematical pro- gramming. The limits on the representativeness of FADN data also make it difficult to represent production systems in areas smaller than NUTS3 and perform statis- tical inferences to the statistical universe. To overcome this limitation, AGRISP integrates FADN with the Italian administrative database AGEA.1 The integration of FADN and AGEA thus makes it possible to measure the exact dimension of agricultural production systems, gross marketable output, subsidies distributed, the 1 AGEA is the Italian official body entitled to pay farmers eligible for CAP payments. Farmers have to provide AGEA with all the information related to agricultural activities including land use. 20 Assessment of CAP Reform 2014–2020 377
  8. 8. volume of variable costs attributable to each process, and the gross income for each type of activity. The two databases combined in a single database FADN-AGEA give a com- plete dataset of land use and technical and economic parameters for production processes of all the farms included in the analysis. The aggregation of information is performed at the level of macro-farm (farms in the AGEA database grouped by size) and farm specialization by each agricultural area (a homogeneous altitude area belonging to the same province). More precisely, in each province three altitude levels, seven size classes (0–10 ha, 10–20 ha, 20–30 ha, 30–50 ha, 50–100 ha, 100–300 ha, [300 ha) and three economic sectors (Fruit and vegeta- bles, Animal production, and Others) were considered, where each class represents the minimum farm type reference. Naturally each macro-farm considers all agri- cultural activity present in the territory as they are registered in the AGEA data- base. The integration of the two databases was effected by specific software able to perform statistical analysis and yield information on farmer choices on production and economic indicators. 20.3.2 AGRISP Model Overview The AGRISP model is able to estimate for each province and at the overall regional level the effects of CAP measures on farmer production plans and farmer income. It gives insight into production decisions for the current observed situation (baseline) and into future decisions after the CAP reform, and thus models farmer strategy. The AGRISP model consists of three main phases: (i) Extraction of data on farms in the sample; (ii) PMP estimation of cost functions at macro-farms level, calibration to observe reality and simulations; and (iii) Analysis of results. AGRISP can be defined as a regional tool, because in a single resolution it can simulate the effects of agricultural policies on different homogeneous areas (agricultural regions) constituting the administrative regions (provinces). It can also be defined as ‘‘integrated’’ because it includes modules that manage the flow of information for the functional analysis of agricultural policy. The process of organizing information is probably the most innovative element. AGRISP provides information on the production choices made by individual farmers, capturing their strategies in the prereform situation and in projected results organized by agrarian regions and at the regional level. The use of a single database covering land use (from AGEA) and the profitability of single processes (from FADN) combined with PMP methodology allows analysis of the impact of agricultural policies both at the micro- and macro-level. It is useful for both setting rural policies and estimating changes in supply at the regional level. In simulating the effects of agricultural policy at the regional level, AGRISP aggregates cost functions into a single regional model, and constructs a set of constraints able to simulate the policies for the whole region (Fig. 20.3). 378 R. Gigante et al.
  9. 9. 20.4 The Policy Scenario In this evaluation of the CAP reform, three main aspects have been considered based on the regulation proposal of the European Commission n. 625-COM2011. These are basic payments, greening measures, and capping. Direct Payments will follow the new Basic Payment Scheme. Until today, the EU-15 was covered by a Single Payments Scheme allowing for historical refer- ences, or a payment per hectare, or a ‘‘hybrid’’ combination of the two, and most of the EU-12 was covered by the Single Area Payments Scheme (SAPS). From 2013, a single new ‘‘Basic Payment Scheme’’ applies. The aim is to significantly reduce discrepancies between the levels of payments between farmers, between regions (internally) and between MS through full implementation of current legislation. All MS will be obliged to move toward a uniform payment per hectare at the national or regional level by the start of 2019. In line with the Commission proposals in the Multi-Annual Financial Framework, the national envelopes for direct payments will be adjusted so that those who receive less than 90 % of the EU-27 average payment per hectare will receive more. The gap between current payments and 90 % of the EU-27 average is reduced by one-third. The Com- mission is committed to discussing a longer term objective of achieving ‘‘complete convergence’’ through equal distribution of direct support across the European Union in the next Financial Perspectives after 2020. In addition to the Basic Payment, each farm will receive a payment per hectare for following farm practices beneficial for the climate and the environment. MS will use 30 % of the national envelope in order to pay for this. The draft proposal Fig. 20.3 Data structure in the AGRISP model 20 Assessment of CAP Reform 2014–2020 379
  10. 10. of the European Commission states that this payment will be mandatory, and will not be subject to capping. The three practices eligible for payment are: (i) main- taining permanent pasture, (ii) implementing crop diversification (at least 3 crops on arable land, none of which account for more than 70 % of the land, and the third crop at least 5 % of the arable area); and (iii) maintaining an ‘‘ecological focus area’’ of at least 7 % of farmland (excluding permanent grassland). This area may include field margins, hedges, trees, fallow land, landscape features, biotopes, buffer strips, and wooded areas. The capping mechanism will define the amount of support that any individual farm can receive from the Basic Payment Scheme. The sum will be limited to €300,000 per year. Current payment levels will be reduced by 70 % for the part from €250,000–300,000; by 40 % for the part from €200,000–250,000, and by 20 % for the part from €150,000–200,000. However, in order to take employment into account, the farm can deduct the costs of salaries, including taxes and social security contributions, declared the previous year, before these reductions are applied. The AGRISP model is now applied to Emilia-Romagna to evaluate the effects of the new EU support measures in the agricultural sector. Results are detailed at the regional and provincial level, and by altimetry. The following scenarios are identified: 1. Baseline The base scenario on which the comparison is carried out is the situation recorded in 2010, obtained by updating the 2007 calibrated solution with the market price variation for 2007–2010. 2. Greening This scenario simulates full application of the CAP reform. All constraints and the new policy aids are activated (new Basic Payment Scheme with distribution to all Utilized Agricultural Areas (UAA), mandatory greening scheme, and new capping mechanism). For the value of aid per hectare for the Emilia-Romagna region, the amount calculated by the National Institute of Agricultural Economics is a basic region- alized payment of 148.4€/ha, while for the greening component they calculate 89.4€/ha. These figures are used in our simulations, which also consider the reduction of total aid under the mechanism of gradual reduction according to the scale described above. 20.5 The Impact of Policy 20.5.1 Variation in Land Use Results of land use by single process simulations show changes between the current baseline scenario and after reform in the greening scenario. Figure 20.4 reports the results in hectares, and Table 20.1 reports the results in percentages. 380 R. Gigante et al.
  11. 11. The results show that at the regional level, greening will lead to a big reduction of almost 38,000 ha in cultivated areas. There will be different impacts on various crops as farmers adjust production choices to market prices. Basically there will be a decrease in almost all crops, except for certain cereals (barley, oats, etc.) which show a big increase (+18 %). But there will be big decreases in surface areas of wheat (-4.3 %) from the current 114,000 ha to about Fig. 20.4 Variation in agricultural land use 20 Assessment of CAP Reform 2014–2020 381
  12. 12. 110,000 ha, maize (-6.0 %) by about 75,500 ha, other seed crops (e.g., sunflower) by 1,000 ha (-10.5 %), and the biggest decrease will be in fodder (-10.6 %), which decreases from the current 264,000 ha to about 236,000 ha. As noted, the decrease in fodder by about 30,000 ha is the largest. It is probably due to two main factors; the steady increase in profits on cereal crops especially in recent years, and low market profits on fodder crops excluding crops used for breeding. These changes are extremely significant for Emilia-Romagna because fodder tends to take place prior to abandonment of the land. Greening measures would further bring down farm profits. The lowering of fodder crop is justified also by the choice of entrepreneur to use this type of crop for environmental purposes as required by greening measures. This type of adjustment entailed by the CAP reform illustrates how the market will now drive production choices for farmers, who will no longer focus on maximizing payments but will have to maximize farming profits instead. Table 20.2 shows the variation in land use by altimetry bands. 20.5.2 Variation in Economic Values Table 20.3 shows the changes in economic components per hectare at regional and altitude levels. Gross Salable Production (GSP) falls by 7.4 % from the current 3,276€/ha to 3,033€/ha. This is mainly because part of the land is taken out of production to meet greening requirements. The values of GSP by altimetry bands present a heterogeneous distribution: contraction on plains is about -5.2 %, while hills and mountains decrease by -10.8 and -20.5 %, respectively. The reduction in variable costs (-7.7 %) is in line with the changes in GSP as farms make the mandatory adjustments required by greening and halt production of certain crops. Table 20.1 Detailed variation in agriculture land use Processes Baseline (ha) Green (ha) Base/green (Variation in %) Wheat 113,935 109,008 -4.3 Corn 80,476 75,665 -6.0 Other cereals 21,665 25,639 +18.3 Rice 7,865 7,576 -3.7 Soybean 21,608 20,732 -4.1 Other oil seeds 9,180 8,214 -10.5 Sugar beet 72,711 71.863 -1.2 Tomato 28,518 27,837 -2.4 Fodder 263,784 235,802 -10.6 Permanent meadows and pastures 11,073 11,473 +3.6 Other crops 39,020 38,397 -1.6 Surface greening 0 37,630 – Total 669,835 669,835 – 382 R. Gigante et al.
  13. 13. These dynamics appear mainly in plain and hill areas, while in mountain areas the structural rigidity of production does not permit a reduction in variable costs proportional to the fall in GSP. The gross margin at the 1st level, calculated by subtracting variable costs from GSP, which is an indicator of business efficiency, is Table 20.2 Variation of agriculture land use by altimetry Plain Hill Mountain Processes Greening (ha) VAR (%) (ha) Greening (ha) VAR (%) (ha) Greening (ha) VAR (%) (ha) Wheat 91,274 82167 -9.9 21,867 22,473 2.8 795 4,367 449.5 Corn 72,678 67.456 -7.2 7,744 8,072 4.2 54 137 154.1 Other cereals 11,556 10,745 -7.0 9,278 10,558 13.8 831 4,336 422.0 Rice 7,865 7,576 -3.7 – – – – – – Soybean 21,354 20,495 -4.0 254 237 -6.9 – – – Other oilseeds 8,882 7,946 -10.5 298 268 -10.1 – – – Sugar beet 70,629 69,759 -1.2 2,082 2,104 1.1 – – – Tomato 23,558 22,837 -3.1 4,928 4,965 0.8 32 34 6.2 Fodder 122,733 115737 -5.7 93,720 83,484 -10.9 47,331 36,580 -22.7 Pastures 1,916 1,966 2.6 5,291 5,485 3.7 3,866 4,022 4.0 Other crops 36,110 35,485 -1.7 2,882 2,852 -1.0 28 60 114.9 Surface greening – 26,385 – – 7,844 – – 3,401 – Table 20.3 Variation of economic indicators at regional and altimetry levels Baseline (€/ Ha) Green (€/ Ha) VAR (%) Region level GSP 3,276 3,033 -7.4 – Total variable costs 2,356 2,176 -7.7 = Gross margin (1° level) 920 857 -6.9 + Total AID 307 237,8 -22.5 = Gross margin (2° level) 1,227 1,095 -10.8 Plain areas GSP 3,416 3,238 -5.2 – Total variable costs 2,494 2,354 -5.6 = Gross margin (1° level) 922 884 -4.1 + Total AID 339 237 -29.9 = Gross margin (2° level) 1,261 1,122 -11.1 Hills areas GSP 2,922 2,606 -10.8 – Total variable costs 2,051 1,801 -12.2 = Gross margin (1° level) 871 804 -7.6 + Total AID 246 237,8 -3.4 = Gross margin (2° level) 1,117 1,042 -6.7 Mountain areas GSP 3,029 2,409 -20.5 – Total variable costs 1,989 1,646 -17.2 = Gross margin (1° level) 1,041 763 -26.7 + Total AID 186 237 27.6 = Gross margin (2° level) 1,227 1,001 -18.4 20 Assessment of CAP Reform 2014–2020 383
  14. 14. thus affected in different ways. Overall in the region it falls by 6.9 %; but while in plain areas it falls -4.1 %, and in hill areas it falls -7.6 %, in mountain areas it falls by -26.7 %. As noted above, total payments, now consisting of regionalized basic pay- ment + greening payments, are reduced across the region by 22.5 %, from €307/ha to €237.8/ha on average. Taking this decrease into account, the new distribution of aid over plain and hill areas leads to a contraction which poorly affects what we might call the 2nd level gross margin or 1st level gross margin + aid. In mountain areas, the new distribution of payments is advantageous given that currently average aid per hectare stands are only 190€/ha. Overall, however, across the region, the reduction of 2nd level gross margin falls by nearly 11 %. The reorga- nization imposed by regionalization and greening measures will clearly have a negative impact on the overall agricultural sector of the Emilia-Romagna region. Data disaggregated by province shows differing situations across the region. At the province level, without considering altimetry: GSP/ha decreases by -11 % in the provinces of Bologna (BO), Modena (MO), Parma (PR), and by -14.3 % in Reggio-Emilia (RE), but there is a smaller contraction of between 5 and 7 % in the remaining provinces. The bigger reduction in the first group of provinces is closely linked to the decline in livestock at each altimetry. Figures 20.5, 20.6, 20.7, and 20.8 report a more detailed analysis of reform effects, and show values specified for different altitude levels and provinces. The GSP (Fig. 20.5) is affected by a big reduction in the mountainous areas of Parma, Reggio-Emilia, Modena and Bologna, with decreases in values that range from -15 to -25 %, while in hill areas (except for Bologna) show decreases ranging Fig. 20.5 Variation in GSP 384 R. Gigante et al.
  15. 15. from -15 to -20 % approximately. The second graph (Fig. 20.6) reports dis- similarity in total variable costs. In general these are aligned with the decreases in GSP as processes are adjusted. But in some areas, such as mountain areas of Parma and Modena, where farms show more structural rigidity, the realignment is less Fig. 20.6 Variation in variable costs Fig. 20.7 Variation in 1st level gross margin 20 Assessment of CAP Reform 2014–2020 385
  16. 16. proportional. For this reason, 1st level gross margin (Fig. 20.8) downsizes in mountainous areas: Parma -45 %, Reggio-Emilia -25 %, Modena -30 %. Finally, Fig. 20.8 shows the changes in the 2nd level gross margin. This margin includes the effect of aid redistribution where mountain areas receive the most advantage. But mountain areas and some hilly areas confirm the biggest falls: Parma (-33.4 %), Reggio-Emilia (-21.1 %) and Modena (-23.4 %), and all show values well below the regional average of 11.9 %. 20.6 Conclusion and Policy Recommendations The model shows that greening measures combined with the regionalized distri- bution of basic payments will lead to substantial reductions in terms of GSP and farm income in Emilia-Romagna, assuming constant prices. The biggest conse- quences at farm level will be covering fixed investment costs. Greening generates a double effect: a contraction of harvested surfaces (e.g., forage, wheat, and maize) and a big shift in land use and resources toward higher price crops (e.g., cereals and tomatoes). The extension of regionalized aid to almost all UAAs with a single payment level in all regions, accompanied by a reduction in the amount of aid, will lower average aid per hectare for the plain areas, where at present farmers are accustomed to higher levels of support, in favor of mountain areas. But despite the increase in direct payments for mountain areas, these are the most badly hit by the reform. Ongoing discussion between the DG-AGRI Committee and the Trilogue is currently focusing on adjusting some of the greening criteria, both in terms of Fig. 20.8 Variation in 2nd level gross margin 386 R. Gigante et al.
  17. 17. practical application and access to specific aid. As noted previously, this analysis was made on the basis of the European Commission draft proposal, so numerical measurements may be taken as provisional and attention should be focused more on the structural and territorial weaknesses. But the potential impacts of the CAP reform require discussion and debate. As currently formulated, the proposal offers fewer guarantees to specific local and territorial farmers in that it delegates to an individual MS the decision to apply for aid as ‘‘less-favored areas,’’ or to maintain coupled aid for ‘‘productions with a local relevance.’’ This entails direct intervention by MS policy makers in order to activate specific and voluntary schemes. Given that ‘‘the market’’ will be the new driver for production choices by farmers, the strategic choices of European agri- culture will have to take account of the need to protect local farms and entire production sectors in disadvantaged areas, such as diary or livestock farming in hill and mountain areas. A new mindset will be necessary; the lack of competi- tiveness of farms in regional mountain areas is due in most cases to territorial characteristics and economic environment rather the competitiveness of the farm itself. References Anania, G. (2008). Il futuro dei pagamenti diretti nell’health Check della Pac: regionalizzaizone condizionalità e disaccoppiamento, In F. De Filippis (Ed.), L’Health Check della Pac: una valutazione delle prime proposte della Commissione, Quaderni Gruppo 2013, Edizioni Tellus, pp. 29–40. Arfini F., & Donati M. (2011). Impact of the Health Check on structural change and farm efficiency: A comparative assessment of three European agricultural regions—in Disaggre- gated Impacts of CAP Reforms Proceedings of an OECD Workshop OECD Publishing. Arfini, F., & Donati, M. (2013). Organic production and the capacity to respond to market signals and policies: An empirical analysis of a sample of FADN farms. Agroecology and Sustainable Food Systems, 37(2), 149–171. Arfini, F., Donati, M., & Zuppiroli, M. (2005). Un modele quantitatif pour l’evaluation des effets de la riforme de la PAC en Italie. Economie Rurale, 285, 70–87. De Filippis, F., & Frascarelli, A. (2012). Il nuovo regime dei Pagamenti diretti in agricoltura In F. De Filippis (Ed.) (A cura di), La nuova Pac 2014–2020-Un’analisi delle proposte della Commissione, Quaderni gruppo 2013, Edizioni Tellus. European Commission. (2011a). Proposal for a Regulation of the European Parliament and of the Council establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy. Brussels, 19.10.2011 COM(2011) 625 final/ 2 2011/0280 (COD). European Commission. (2011b). Impact Assessment. Common Agricultural Policy towards 2020. Annex 2: Greening the CAP, Commission Staff Working Paper, Brussels, February. Gigante R. (2013). Assessment of Cap reform post 2013 in rural areas of Emilia-Romagna region. Ph.D. thesis. Heckelei, T., Britz, W., & Zhang, Y. (2012). Positive mathematical programming approaches— recent developments in literature and applied modelling. Bio-based and Applied Economics, 1, 109–124. 20 Assessment of CAP Reform 2014–2020 387
  18. 18. Heckelei, T. & Britz, W. (2005), Models based on positive mathematical programming: State of the art and further extensions In F. Arfini (Eds.), Modelling agricultural policies: state of the art and new challenges. Proceedings of the 89th European seminar of the European Association of Agricultural Economics, (pp. 48–73). Monte Università Parma. Matthews, A. (2013). Greening agricultural payments in the EU’s Common Agricultural Policy. Bio Based and Applied Economics, 2(1), 1–27. Sorrentino, A., Henke, R., & Severini, S. (2011). The common agricultural police after the fischler reform, national implementation, impact assessment and the agenda for future reforms. Burlington: Ashgate. 388 R. Gigante et al.

×