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CAP's environmental policy and land use in arable farms: An impacts assessment of greening practices changes in Italy
Science of the Total Environment 647 (2019) 516–524
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
CAP's environmental policy and land use in arable farms: An impacts assessment of greening practices changes in Italy Raffaele Cortignani ⁎, Gabriele Dono DAFNE – Università degli Studi della Tuscia, Viterbo, Italy
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• We evaluate the impacts of new greening practices and of CAP first pillar reform. • Net income increases in particular in the mountain areas of Northern Italy. • The pesticide use increases in the intensive areas of the plain of Northern Italy. • Employment decreases especially in the most depressed areas of Southern Italy. • The future CAP should adapt more to the needs of the various territories.
a r t i c l e
i n f o
Article history: Received 4 April 2018 Received in revised form 30 July 2018 Accepted 30 July 2018 Available online 02 August 2018 Editor: D. Barcelo Keywords: Land use Environmental policy Greening practices First pillar reform Positive mathematical programming
a b s t r a c t The study assesses the possible impact of first pillar reform of the Common Agricultural Policy by focusing on the new greening rules defined by the recent Omnibus regulation. The analysis was carried out on a Farm Accountancy Data Network sample of Italian farms using by a Positive Mathematical Programming model. Moreover, our analysis is stratified by geographical area and altimetric level and uses some additional environmental and social indicators beyond those economic. The results indicate that the new greening rules generate positive but limited environmental impacts, which reinforce those already determined by the previous CAP reform, for example the use of chemical fertilizers is further reduced. These additional positive environmental impacts are obtained with very limited income reductions. Yet, the impacts on the various geographical areas and their altimetric levels are different, and sometimes controversial. For example, there is a growth in the use of pesticides in the northern plains, due to the increase in rice and soybean areas. On the other hand, agricultural employment in Southern Italy decreases, where agriculture is an important source of employment for the rural population. This reduction also affects the mountain areas, thus accentuating the already high risk of abandonment. All this suggests that more targeted measures will have to be implemented in the future planning of agricultural policy, so as to shape the various actions according to the characteristics of the various rural areas, as well as to the specific priorities of the Member State. © 2018 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding author at: Via San Camillo de Lellis snc, 01100 Viterbo, Italy. E-mail address: [email protected] (R. Cortignani).
https://doi.org/10.1016/j.scitotenv.2018.07.443 0048-9697/© 2018 Elsevier B.V. All rights reserved.
Over the last decades, the Common Agricultural Policy (CAP) has shifted emphasis from price and market intervention to direct payments, partly conditional on environmental requirements (Matthews, 2013;
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Alons, 2017). In 2015, the CAP reform introduced the greening criteria, which conditioned 30% of direct payments to meet three environmental requirements: crop diversification, maintaining permanent grassland and Ecological Focus Area (EFA). From February to May 2017, the European Commission organized a public consultation of all interested EU organizations and citizens on issues and modalities to modernize and simplify the CAP (European Commission, 2017a): its results demonstrated the important role attributed to the CAP in maintaining and improving environmental conditions in rural areas by intervening on agricultural activities. Pending on the CAP post 2020, the mid-term review of the current CAP (Omnibus regulation, entered into force on 1 January 2018) has also changed the first pillar of CAP, including the greening practices (De Castro, 2017). The review has eased the constraints on diversification and EFA, trying to simplify some commitments that have proved difficult to satisfy for farms, and for administration and control systems (European Commission, 2017a, 2017b). In a context such as this, of frequent adjustments and ongoing changes to agricultural policy, ex-ante evaluation of their impact on the agricultural sector can enable policymakers to make more informed decisions about future policies (Reidsma et al., 2018). Many studies and evaluations of changes in agricultural policy have used mathematical programming models focused on the impact on production choices and on economic, social and environmental performance (Buysse et al., 2007; van Ittersum et al., 2008; Britz et al., 2012; Pelikan et al., 2014; de Frahan et al., 2016). Numerous studies have been conducted to evaluate the impacts of the 2015 reform of the direct payment system, in particular using Positive Mathematical Programming (PMP) models. Among the most recent, Solazzo et al. (2016), Cortignani et al. (2017), Gocht et al. (2017) and Louhichi et al. (2018) show that the previous greening rules have limited impacts, that acting on coupled payments is more effective in generating environmental benefits, and that convergence has the highest economic impact among all the components of the reform. The main objective of this paper is to evaluate the effects of greening practices defined in the recent Omnibus regulation on the arable crops sector which includes the cultivation systems most affected by the greening practices (Cimino et al., 2015). Furthermore, the analysis considers the impacts of the changes to the first pillar of the CAP in force since 2015. This allows understanding if the recent changes (Omnibus regulation) continue or reverse what started with the 2015 reform. Compared to the existing literature, the paper presents some innovative elements. The analysis refers to the entire national territory as opposed to other recent papers, where the study areas are specific regions (Solazzo and Pierangeli, 2016; Solazzo et al., 2016) or territories (Cortignani et al., 2017; Cortignani and Dono, 2018). Moreover, compared to other studies dedicated to the entire agriculture of various European countries (Gocht et al., 2017; Louhichi et al., 2017; Louhichi et al., 2018) our analysis is structured by geographical area and altimetric level and uses some additional environmental and social indicators. In this way we aim to highlight how greening practices, defined in a uniform way for all the territories, affect areas with very different characteristics of the agricultural activity. In other words we want to analyze how the various greening practices affect the intensive plain areas and the other less intensive areas (hill and mountain) considering also the different characteristics for geographical area (north, centre, south). Finally, the focus on the use of pesticides and employment is due to the growing interest in the environmental and occupational effects of the various CAP measures. 2. Materials and methods 2.1. Data and characteristics of farms sample The analysis was been carried out using by the Farm Accountancy Data Network1 (FADN) that provide data for evaluating the income of 1
http://ec.europa.eu/agriculture/rica/
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agricultural holdings and the impacts of the Common Agricultural Policy. The data refer to physical and structural characteristic (such as location, crop areas, labour force, uses of chemical input and water, etc.) and economic data (such as the revenue of the different crops, production costs, CAP payments, etc.). These data were used to elaborate the PMP model, being explained later on. The analysis was conducted on the 2798 Italian arable farms and classified as specialist cereals, oilseeds and protein crops, general field cropping, specialist vegetables outdoor. This sample represents 147,603 Italian farms and 3591,000 ha of Utilised Agricultural Area (UAA; Table 1). Therefore, a prominent part of the 7,009,000 ha of arable land detected by the VI Italian Agricultural Census is covered. The choice of these farms types has been carried out considering that, given the characteristics of cultivation systems,2 these farm types are the most influenced by the greening practices (Cimino et al., 2015) and the changes made in the Omnibus regulation. Table 1 shows in detail the data on land use, on the use of inputs and on the main economic variables for the total number of farms represented by the FADN sample, and distinguished by geographical areas and altimetric levels. These values represent the reference scenario (baseline, year 2014) for the simulations of the CAP scenarios shown in the results section. 2.2. Representation of economic analysis: PMP model, input data and simulations Farm-level mathematical programming models are important and widely used analytical tools in agricultural economics because they are able to represent farmer responses to changes in policy and market conditions. In the second half of the 1990s researchers moved from the classical linear or quadratic programming to PMP. This latter approach requires a relatively limited amount of data and can be perfectly calibrated to the reference period. It recovers additional information from observed activity levels, allowing researchers to specify a quadratic objective function so that the resulting nonlinear model exactly reproduces the observed behaviour of farmers and can be used for simulation analyses (Arfini and Paris, 1995; Howitt, 1995; Paris and Howitt, 1998; Heckelei and Wolff, 2003). This method not only automatically and exactly calibrates the model to observed activity levels, but also avoids adding ad-hoc constraints and over-specialised responses of the model to policy changes (de Frahan et al., 2016). The following figure (Fig. 1) summarizes the relevant aspects of the economic analysis. The inputs of PMP model refer to crops and farms. The objective function maximizes the farms gross margin and considers for each crop the revenue and production costs, the areas and the CAP coupled payments. The constraints concern the availability of land and farm labour. The model also considers the possibility of hiring external labour at a relative price (in the objective function). As for the use of inputs, labour needs of crops were used to consider the total requirement in the labour constraint and to evaluate its overall use in the various scenarios. On the other hand, the needs of the other inputs (water, nitrogen, phosphorus, potassium, pesticides) were not explicitly modelled in the objective function and in the constraints, but have been used to quantify the overall use of production factors based on land use in the different scenarios. The results also concern the economic results of the farms. The model was calibrated to the scenario observed in 2014 year and then used to carry out two types of simulations: “A” is based on the first pillar reform of the CAP 2014–2020; “B” is based on recent changes in greening practices defined by the Omnibus regulation.
2 Specialised farming in the cultivation of durum wheat, maize, soft wheat, barley, rice, grain legumes, forage legumes, processed tomato.
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Table 1 Characteristics of the farms sample: number, land use and environmental, social and economic indicators. Absolute value for total Italy and each zone (year 2014). Total
Number of Sample farms Representative farms Land use (kha) Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Others UAA Use of main inputs (MM) Water - m3 Nitrogen - kg Phosphorus - kg Potassium - kg Pesticides - kg Labour - h Economic results (MM €) Gross margin Revenues Direct payments Costs
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
2798 147,603
966 49,689
200 10,164
89 2921
71 2176
503 18,404
58 2639
282 21,320
412 27,226
217 13,065
807 472 326 235 202 196 159 154 117 95 83 80 666 3591
46 405 226 114 198 3 32 139 23 5 0 34 196 1420
6 45 34 39 0 1 36 14 8 1 0 0 39 222
0 2 2 11 0 0 10 0 2 0 0 0 5 31
28 0 1 3 0 4 6 0 1 5 3 5 16 72
166 11 39 41 0 30 6 1 20 69 10 3 95 490
20 2 6 12 0 3 5 0 4 3 1 0 20 75
150 2 0 2 4 16 7 0 15 2 22 27 140 387
306 5 10 9 0 79 24 0 36 8 38 7 113 635
86 1 9 3 0 60 34 0 9 3 9 2 43 258
2877.8 327.1 131.0 101.8 6.9 214.9
1855.7 166.2 49.8 57.1 4.2 55.4
81.3 14.7 8.0 6.8 0.3 8.7
9.6 1.1 1.1 1.1 0.0 3.1
16.2 4.3 2.4 1.2 0.0 3.9
43.6 52.4 14.2 7.7 0.6 26.2
1.3 3.5 1.3 1.9 0.0 2.8
712.7 39.4 24.4 16.8 1.2 58.0
124.2 33.8 23.7 5.4 0.3 41.8
33.3 11.6 6.0 3.7 0.2 14.9
5023 7080 1608 3666
2208 2982 724 1498
251 388 63 200
49 79 4 34
112 182 30 100
533 714 212 392
52 64 21 33
855 1450 212 807
706 866 271 432
256 354 72 170
Fig. 1. Summary diagram of the economic analysis.
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Following, a detailed description of greening practices (including recent changes) and of the simulation scenarios are shown.
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• in greening omnibus scenario, application of greening practices in force since January 2018.
2.3. Greening practices In 2015, the CAP reform introduced the greening criteria, which made the payment of 30% of the direct payments dependent on compliance with three environmental requirements: 1. diversifying cultivation by growing at least two crops on farms where the arable land exceeds 10 ha (and at least three crops where arable land exceeds 30 ha), and by limiting the main crop to 75% of the arable land (and the two main crops to 95% of the arable land where arable land exceeds 30 ha); 2. maintaining permanent grassland at the national, regional, or farm level; 3. maintaining at least 5% of the arable land of farms larger than 15 ha as Ecological Focus Areas (EFA); these areas may take the form of fallow land, terraces, landscape features, buffer strips, hectares of agroforestry, strips of eligible hectares along forest edges, areas with short-rotation coppice, afforested areas, areas with catch crops or green cover, or areas with nitrogen-fixing crops.3 The mid-term review of the CAP 2014–2020 (Omnibus regulation) came into force on 1th January 2018, also with the interesting contribution of the European Parliament that dealt with simplifying some commitments for farms, and for administration and control systems. The Omnibus regulation also determines some changes to the first pillar of CAP, including the greening practices (De Castro, 2017). The main changes concern: a. the paragraph 2 of article 44 of Reg. 1307/2013, which allows farms with more than 75% of the arable land area occupied by rice to maintain the area unchanged, provided that the main crop not occupy more than 75% of the remaining area; b. the inclusion of leguminous in letter a) of paragraph 3 of article 44 of Reg. 1307/2013, which makes it possible to consider alfalfa and other leguminous among crops that can be used for exemption from the obligation; c. the elimination of the 30-hectare limit provided for in letter a) and letter b) of paragraph 3 of article 44 of Reg. 1307/2013 (diversification practice); d. the possibility that mixtures of nitrogen-fixing crops and other plants may be qualified as EFA, provided that the predominance of the nitrogen-fixing crops in the mixtures is ensured; e. the conversion factor of the nitrogen fixing crops for EFAs is raised from 0.7 to 1; f. the elimination the 30-hectare limit set out in letters a) and b), paragraph 4 of article 46 of Reg. 1307/2013 (EFA practice) g. the ban of pesticide use in nitrogen-fixing crops recognized as EFA from 1 January 2018. 2.4. Simulated scenarios Two post-reform scenarios, greening 2017 and greening omnibus, were simulated on the baseline calibrated on 2014 pre-reform year. Both scenarios consider the convergence of the basic payment that will take place by 2019, the greening payments and the coupled payments; the differences between the post-reform simulation scenarios are: • in greening 2017 scenario, application of greening practices in force until December 2017;
3 The mathematical formulation of greening constraints and of two (basic and greening) payments has been widely dealt with in the recent literature. For more details refer to Cortignani and Dono (2015), Solazzo and Pierangeli (2016), Cortignani et al. (2017).
The comparison between the post-reform scenarios allows highlighting the effect of new rules of greening practices. The effects of the convergence of the basic payment and coupled payments are observed in the greening 2017 scenario. In particular, our simulations consider that the convergence of the basic payment does not influence the production choices, but only the level of direct payments, therefore of income.4 On the other hand, both simulations maintain at about 50% the level of greening over the basic payments.5 Finally, the structure and levels of coupled payments remain those assigned to the region based on the latest available data.6 3. Results The results refer to land use and the main inputs, and to economic values; they concern both Italy and its northern, central and southern areas, with the plains, hills and mountains zones. The following tables show first the percentage changes in the 2017 greening scenario compared to the absolute baseline values shown in the previous Table 1. Then, show the percentage changes in the greening omnibus scenario compared to the 2017 greening. The description of the results concerns in particular the greening omnibus scenario to highlight the impacts of greening practices changes; this will be done by highlighting the trends already in place caused by the modifications to the first pillar from 2015. To facilitate the description of the results and isolate the individual impacts, the greening practices changes described above (a.–g.) are grouped and called as follows7: • diversification (a, b and c) • EFA – environmental value (d, e and f) • EFA – pesticides (g)
It should be noted that the diversification (leguminous), EFA – environmental value and EFA – pesticides, are interlinked through impacts on various types of leguminous. In these cases there may be different impacts. In fact, the reform reduces the diversification constraints on fodder legumes, and also increases their environmental value (simplification and less impact on non-EFA crops). On the other hand, it negatively affects their cultivation through banning pesticide use in the EFA. 3.1. Land use The results on land use (Table 2) show that the diversification (rice) determines an increase in rice area, particularly in larger and more extensive farms in northern Italy (+1.6%), while the diversification (leguminous) causes a total increase in alfalfa (+4.6%) and leguminous herbage (+1.7%). As regards the alfalfa, the increase is observed in
4 More specifically, Italy adopted the Irish convergence model that creates a single region at a national level in which gradually, and until 2019, basic payments are modified from the current levels to more homogeneous values, yet, without achieving a uniform value. Both simulations apply the level that will be reached at 2019. 5 Solazzo and Pierangeli (2016) and Cortignani et al. (2017) show that most Italian farms should continue to comply with greening practices even with the possible reduction of the related payment, given the application of administrative sanctions in case of noncompliance. 6 Italy has introduced coupled payments for sugar beets, processed tomatoes and rice. In Northern Italy, a soya payment has also been introduced, and now other payments for cereals (durum wheat), oilseeds (rape and sunflower) and legumes (cereals and fodder). 7 The results section shows the overall impacts of all changes. The appendix report the impacts of each single group.
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Table 2 Land use in the simulated scenarios (percentage changes over baseline and greening 2017 [%Δ]). Total
Greening 2017 (%Δ over baseline)
Greening omnibus (%Δ over 2017)
Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops
North
−4.0 −11.5 −9.5 −8.3 5.3 −8.7 1.3 19.7 −13.5 9.5 10.1 11.4 −5.6 −0.4 −1.8 −2.0 4.6 1.6 1.7 −0.4 −4.2 −1.3 −0.9 −5.0 0.0 −0.3
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−9.5 −11.8 −9.1 −6.8 5.2 −20.0 6.2 19.7 −16.3 −15.9 0.0 16.6 −1.7 −0.9 −1.9 −2.7 6.5 1.6 0.0 −1.8 −4.4 −2.6 −3.9 0.2 0.0 −0.8
−11.8 −11.6 −5.0 −1.1 0.0 −2.0 2.2 21.3 −5.4 −12.2 0.0 9.2 3.6 −1.3 −1.2 −0.4 1.0 0.0 0.0 0.1 −2.3 −0.2 0.3 0.0 0.0 −0.8
0.0 −5.9 −5.4 −12.3 0.0 0.0 2.7 0.0 −15.4 0.0 0.0 0.0 0.0 0.0 0.0 −1.4 14.0 0.0 0.0 −1.4 0.0 −2.0 0.0 0.0 0.0 0.0
−4.3 0.0 −38.8 −3.2 0.0 0.6 −1.8 0.0 −9.8 11.8 19.3 2.6 −11.6 −0.2 0.0 0.0 0.1 0.0 −0.2 0.4 0.0 0.0 −0.1 −4.3 0.0 2.3
−2.9 −8.6 −16.6 −15.5 0.0 2.0 −3.7 −24.6 −13.8 16.7 29.5 18.2 −5.9 −0.8 −1.3 −0.9 1.8 0.0 3.6 0.0 −10.2 −1.4 −0.8 −4.5 −0.2 −0.9
6.9 −0.4 −11.2 −17.3 0.0 6.3 0.2 0.0 −5.1 31.8 59.3 0.0 −7.4 0.3 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3
−6.2 −7.9 0.0 −9.8 8.3 −6.7 −3.7 0.0 −18.7 −14.0 2.6 8.3 −9.7 −0.2 0.2 0.0 −0.2 0.0 0.1 0.0 0.0 −2.5 −8.6 −6.6 0.0 0.1
−4.8 −5.1 −5.4 −10.3 0.0 −13.4 −1.0 0.0 −14.2 −24.8 8.6 7.0 −8.7 −0.4 −3.4 −0.2 7.4 0.0 1.2 0.0 0.0 −0.8 0.3 −5.7 −0.1 −0.8
1.4 −0.5 −6.6 −5.5 0.0 −9.3 −0.5 0.0 −4.2 −25.3 3.8 −1.4 −6.9 0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 2.4
particular in the northern plain (+6.5%), norther mountain (+14.0%) and southern hill (+7.4%); while leguminous herbage increase in the central hill (+3.6%). The EFA – pesticides causes a decrease of soybean (−4.2%) mainly in northern Italy (−4.4%), and in the faba bean (−5.0%), especially in the central hill (−4.5%), in the southern plain (−6.6%) and in the southern hill (−5.7%). Finally, the EFA – environmental value increases the environmental value of the nitrogen fixing crops and, consequently, generates a greater simplification for farms to respect the EFA practice. In terms of land use, the impact of reduction on non-EFA crops is small. Compared to the greening 2017 scenario, the overall impacts observed in greening omnibus scenario show that: - rice further increases (+1.6% in the greening omnibus over +5.3% in the greening 2017); - forage legumes have only a partial compensation of the reduction observed in the greening 2017 (for example: alfalfa, +4.6% vs −8.3%); grain legumes partially reduce over the increase observed in the greening 2017 (e.g. for soybean, −4.2% vs +19.7%);
- grain cereals (durum wheat, maize, soft wheat, barley) further decrease (e.g. for maize, −1.8% and −11.5%).
3.2. Use of main inputs The land use changes just described, determine a variation in the use of inputs (Table 3). This variation is overall limited. However in some areas is more evident and in most cases is additional respect to the major changes observed in the greening 2017 scenario. The use of water is reduced in particular in the northern plain (−0.9%) and increases in the southern hill (+1.6%); both changes accentuate what was observed in the greening 2017 scenario (respectively equal to −2.0% and +2.1%); The use of nitrogen is reduced in particular in the northern plain (−1.1%), in the northern hill (−1.3%) and in the central hill (−0.8%); the northern plain and the central hill are two intensive areas in terms of nitrogen, so the effect is positive and reinforce the reduction observed in the greening 2017 scenario (respectively equal to −3.4% and −2.8%);
Table 3 Use of main inputs in the simulated scenarios (percentage changes over baseline and greening 2017 [%Δ]). Total
Greening 2017 (%Δ over baseline)
Greening omnibus (%Δ over 2017)
Water Nitrogen Phosphorus Potassium Pesticides Labour Water Nitrogen Phosphorus Potassium Pesticides Labour
−4.1 −3.7 −1.7 −2.3 1.1 −2.4 −0.6 −0.8 −0.6 −0.4 0.3 −0.3
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−2.0 −3.4 −1.8 −3.1 3.1 −1.9 −0.9 −1.1 −1.3 −0.6 0.7 −0.7
−2.2 −5.9 −5.6 0.2 −1.2 −0.3 −0.3 −1.3 −0.7 −0.7 −0.3 −0.4
−0.1 −1.4 −1.2 −1.2 −5.9 −1.2 0.0 0.9 1.0 0.9 −0.3 0.4
0.3 −3.2 −2.0 −2.3 −0.6 −0.4 0.0 0.6 −0.2 0.0 −1.2 −0.5
−3.5 −2.8 −1.4 −3.7 0.0 −1.7 −0.2 −0.8 −0.2 −0.2 −0.5 −0.3
0.6 4.8 1.9 0.8 −15.1 −1.4 0.0 0.4 0.5 0.4 0.0 0.5
−11.3 −5.1 −2.6 −0.9 −2.1 −3.2 −0.1 −0.2 −0.2 −0.1 −0.1 −0.1
2.1 −6.3 0.0 −1.2 −6.0 −2.8 1.6 −0.4 −0.4 −0.3 −0.1 −0.2
1.0 −0.7 −0.9 −1.4 −2.7 −3.5 0.0 0.1 0.4 0.0 0.0 0.5
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Table 4 Economic results in the simulated scenarios (percentage changes over baseline and greening 2017 [%Δ]). Total
Greening 2017 (%Δ over baseline)
Greening omnibus (%Δ over 2017)
Gross margin Revenues Direct payments Costs Gross margin Revenues Direct payments Costs
−5.9 −2.7 −10.7 −1.9 −0.1 −0.3 0.0 −0.5
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−7.2 −1.8 −15.6 −0.6 −0.2 −0.6 0.0 −0.9
−1.0 −2.1 4.1 −1.5 −0.1 −0.4 −0.1 −0.7
3.3 −2.2 70.8 −1.2 1.9 1.6 0.0 0.7
−3.0 −1.8 −4.4 −1.3 0.1 0.0 −0.1 −0.1
−4.6 −2.9 −7.6 −3.1 −0.1 −0.4 −0.1 −0.6
1.6 −0.2 7.8 2.1 0.2 0.3 0.0 0.3
−6.9 −4.1 −14.3 −3.8 0.0 −0.1 0.0 −0.1
−6.7 −4.0 −8.7 −2.6 −0.1 −0.3 −0.1 −0.5
−1.1 −2.7 7.6 −0.7 0.4 0.5 0.0 0.4
in some areas there is a slight increase in the use of nitrogen (mountain of the three areas and the central plain). Analogous variations are observed in the use of phosphorus and potassium; The overall use of pesticides slightly increases (+0.3%), accentuating what observed in the greening 2017 scenario (+1.1%); this is due to the increase that occurs in the northern plain (+0.7% and +3.1%); Labour hours slightly decrease (−0.3%), adding to what was observed in the greening 2017 scenario (−2.4%); considering the two simulation scenarios, the reduction is particularly relevant in southern Italy (−3.3% in the plain, −3.0% in the hill, −3.0% in the mountain) where the agricultural sector is an important source for employment. The slight increase that occurs in the three mountain areas is not sufficient to counteract the reductions observed in the greening 2017 scenario. 3.3. Economic results The economic results (Table 4) show that the overall economic impact on gross margin is limited (−0.1%). Regarding the individual areas, the gross margin of farms located in mountain increases, particularly in northern Italy where there is an increase in revenues (+1.6%) due to the major alfalfa surface; in the northern and central Italy this adds to the increase observed in the greening 2017 scenario, which was due to the convergence of the basic payment (higher direct payments). In the other areas, gross margin is subject to small variations; however, note that in the greening 2017 scenario the gross margin reduction is large due to the decrease in revenues from sales and the convergence of the basic payment. 4. Discussion These results of this study offer interesting insights on the link between CAP reform and land use, on socio-economic impacts, on input use. The greening 2017 scenario (that include the convergence of basic payments, the coupled payments and the greening practices in force until December 2017) modifies the land use, mainly reducing the cereals cultivation and forage legumes, instead the cultivation of grain legumes grows. In this context, greening practices have limited impacts, while coupled payments are more incisive in determining changes and environmental benefits. Furthermore, the economic impact is mainly due to the convergence as already noted by Cortignani et al. (2017), Gocht et al. (2017) and Louhichi et al. (2017). As a main innovative contribution, our results indicate a growth in the use of pesticides in northern plain, due to the increase in rice and soya areas, and a decrease in the agricultural employment, especially in southern Italy and including the mountain areas, thus accentuating the already high risk of abandonment. The Omnibus scenario (greening practices in force since January 2018) involves positive, even if limited, environmental impacts accentuate what observed in the greening 2017 scenario with reductions in the use of fertilizers. Globally, the cultivation of cereals decreases further, while
the impacts on grain and forage legumes partially compensate the changes of the previous scenario, with a reduction of the grain legumes and an increase of the forage legumes. Land use changes depend on the specificities of the considered areas. In northern Italy, the increase in rice cultivation is due to the presence of larger and larger farms benefiting from the new diversification rule. Alfalfa increases in the plains and in the mountains where it generates appreciable income, being widely used for feeding the dairy cattle in these areas. The increase in soybean cultivation, inter alia noted in recent years, is due to the coupled payment provided for northern Italy and the recognition of this crop as EFA. Yet, the ban of pesticides use in the EFA could result in a reduction of this cultivation. Despite this, the use of pesticides further increases in northern Italy as the effect of the ban of pesticide use in the EFA (which reduces soybean cultivation) is cancelled by the increase in rice area. In central Italy, the major changes concern the leguminous herbage and faba bean. The former are mainly cultivated in hilly areas, included in many crop rotations, and mainly used to feed the flocks. Faba bean cultivation has greatly increased in recent years due to coupled payment provided for central (and southern) Italy and the recognition of this crop as EFA. Yet, as for soybean, the ban of pesticides use in the EFA could limit this crop and other grain legumes. Similar results are obtained for southern Italy. All these effects are associated with very limited income reductions. It is interesting to examine the effects of the individual components of the new greening rules. Modifying the diversification rules limitedly modifies the use of inputs, while increases income in the northern mountain farms due to the elimination of the constraint that affected the alfalfa in the previous formulation. The increase in the environmental value of nitrogen-fixing crops does not reduce their extension since coupled payments make them very profitable in comparison to alternative crops, albeit more intensive. The positive environmental impacts are mainly generated by the ban of pesticides use in the EFA, which limits the more intensive crops (e.g., soybean). In conclusion, according to our results, the greening practices, also in the new version, continue to exhibit limited and controversial impacts. To overcome the limits of greening, the European Commission proposes replacing greening payments with mandatory conditionality of direct payments as a basis for more ambitious voluntary practices that improve eco-efficiency (European Commission, 2017b). Moreover, measures to support eco-efficiency would also be more easily acceptable by policy makers than the imposition of radical measures that reduce farm profitability (Coderoni and Esposti, 2018). Our results on the Italian geographic areas and their altimetric levels suggest that, although attractive, the idea of a system based on simple and uniform mechanisms for the whole territory, which offers appreciable environmental benefits, is not plausible (Brady et al., 2017). Due to the vast diversity of agronomic, environmental and economic conditions across the UE areas, it is impossible to design a set of simple rules that are universally applicable to the whole territory. Efficient environmental management requires place-specific adjustment, or spatial targeting, which is the diametric opposite to universal rules (Brady et al., 2017).
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5. Conclusions The conducted analysis can contribute to the current debate on future structure of CAP both on the new rules on greening practices defined in the recent Omnibus regulation, and in general on the whole of the first pillar structure. Regarding the new rules, our paper highlights their ability to further reduce the use of chemical fertilizers, however at a relatively low cost (loss of income) for farmers. On the other hand, the new rules are not effective in reducing the use of pesticides, especially in more intensive agricultural areas, such as the northern plain, where the wide choice of intensive crops allows farms to nullify the environmental benefits. Our conclusion is that in order to intervene on this type of pollutants it could be more effective to resort to coupled payments provided for less intensive crops rather than to tighten up environmental rules. The other controversial aspect concerns the use of agricultural labour that, in fact, decreases with the reduction of the intensification of the cultivation systems: this effect is particularly serious in areas where agriculture is the main source of employment for the rural population, as in Southern Italy. In these cases the environmental instruments of agricultural policy must be integrated by developing alternative employment opportunities that the European Commission identifies in the sectors of tourism, renewable energies, the circular economy (European Commission, 2017b). The differentiation by altimetric level clearly shows that the CAP reform, since 2015, is shifting financial resources from the agricultural areas that in the past were more intensive to more extensive areas. Our analysis shows an increase in agricultural incomes in mountain areas, due to the convergence of the basic payment and the application of the new rules on the diversification of alfalfa, which reduce the constraints for this crop. On the contrary, farms operating in more intensive areas, especially in the plains, suffer significant income reductions. Sustainable intensification strategies in these areas seem necessary to limit income losses and to have positive impacts on the environment. Various aspects of our results seem to be in line with the proposed post-2020 regulation, recently formulated by the European Commission (European Commission, 2018). According to this proposal, farm income for all farmers should be supported with a basic support of income.
The targeting of this support will be improved by differentiating the amounts of these payments by groups of territories, by socioeconomic or agronomic conditions. The differentiation of income support will have to be specified by the Member States, which will thus be able to consider the differences in the socio-economic or agronomic conditions of the various territories, in accordance with their national, regional or local needs. In addition, in order to avoid adverse effects on employment, the effects on the use of labour should be taken into account when applying the mechanism. Some limits of the conducted analysis must be highlighted as well as possible developments and improvements. The used PMP approach does not consider crops and production techniques not observed in the reference period. This means that in the simulation scenarios farms adapt to the new policy by only changing the observed mix of crops and techniques and not by adopting new productive solutions. The model uses the production function of Leontief, which is the most widespread in mathematical programming models. However, other functions could be used that, by modifying production levels with the use of inputs, can cause changes in techniques and not in land use. The model is risk neutral. Yet, changing the levels of direct payments could influence farmers' choices based on their risk attitude. Therefore, the use of utility functions with explicit risk terms could generate different results. The CAP cross-compliance and agro-climatic-environmental measures should be also considered since a their greater integration with the greening is foreseen in the next version of the CAP. Finally, other indicators may be added in order to consider the impact on various environmental components, such as biodiversity, CO2 emissions, landscape, and the nitrogen cycle. Acknowledgments The study was carried out under the MACSUR 1 and 2 projects (D.M. 2660/7303/2012 - www.MACSUR.eu) funded by the Italian Ministry of Agricultural, Food and Forestry Policies, Italy (MiPAAF). MACSUR is funded as part of the JPI FACCE. Financial contribution was also obtained in the frame of the MIUR (MInistry for education, University and Research) initiative “Department of excellence” (Law 232/2016).
Appendix A. Results for each single group of changes of greening practices Table A.1 Land use in the greening omnibus scenario for each single group of changes (percentage changes over greening 2017 [%Δ]). Total
Diversification (%Δ over 2017)
EFA environmental value (%Δ over 2017)
Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Total UAA Durum wheat Maize Soft wheat Alfalfa Rice
−0.1 −0.6 −0.9 4.7 1.6 1.9 −0.4 −0.5 −0.1 0.0 0.0 0.0 0.2 0.3 0.0 0.1 0.1 −0.4 0.0
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−0.2 −0.7 −1.3 6.6 1.6 0.0 −1.6 −0.5 0.0 0.0 0.0 0.0 −0.4 0.2 0.1 0.1 0.1 −0.3 0.0
0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.4 0.3 0.0 0.1 0.1 −0.2 0.0
0.0 0.0 −1.4 14.0 0.0 0.0 −1.4 0.0 −2.0 0.0 0.0 0.0 0.0 3.8 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.6 0.0 0.0 0.0 0.1 0.0
0.0 0.0 0.0 2.3 0.0 3.7 0.0 0.0 −0.2 0.0 0.0 0.0 0.0 0.4 0.1 0.1 0.1 −1.1 0.0
0.3 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.5 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 −0.8 0.0 0.0 0.0 0.3 0.1 0.0 0.2 0.0 −0.3 0.0
−0.1 0.0 0.0 7.4 0.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 2.4 0.9 0.0 0.0 0.0 0.0 0.0
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Table A.1 (continued) Total
EFA pesticides (%Δ over 2017)
Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Total UAA Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Total UAA
North
0.0 0.1 −0.1 0.1 0.5 −0.2 0.0 0.0 0.0 −0.4 −1.2 −1.2 0.4 −0.2 −0.1 −0.1 −4.0 −1.3 −1.3 −5.2 0.0 −0.5 −0.9
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
0.0 0.2 −0.1 0.1 1.4 0.0 0.1 0.0 0.0 −0.7 −1.2 −1.5 0.4 −0.2 0.0 −0.3 −4.1 −2.8 −5.4 0.0 −0.1 −0.4 −1.2
0.0 0.2 0.1 0.0 0.3 6.6 0.0 0.0 0.0 −1.3 −1.3 −0.6 0.5 0.0 0.0 −0.1 −2.4 −1.1 0.0 0.0 0.0 −1.0 −0.7
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
−0.2 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −4.3 0.0 −0.6 −0.4
0.0 0.0 0.0 0.2 0.5 −0.7 0.0 −0.1 0.0 −0.9 −1.4 −1.0 0.7 0.0 −0.1 0.0 −10.2 −1.4 −1.3 −4.9 −0.2 −1.0 −0.9
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.1 3.4 −0.4 0.0 0.0 0.0 −0.2 0.0 0.0 0.1 0.0 −0.4 0.0 0.0 −1.8 −8.6 −6.7 0.0 −0.2 −0.7
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.3 −3.4 −0.2 0.3 0.0 −0.2 0.0 0.0 −0.8 0.3 −5.9 −0.1 −0.8 −0.8
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Table A.2 Use of main inputs in the greening omnibus scenario for each single group of changes (percentage changes over greening 2017 [%Δ]). Total
Diversification (%Δ over 2017)
EFA environmental value (%Δ over 2017)
EFA pesticides (%Δ over 2017)
Water Nitrogen Phosphorus Potassium Pesticides Labour Water Nitrogen Phosphorus Potassium Pesticides Labour Water Nitrogen Phosphorus Potassium Pesticides Labour
0.0 −0.1 0.0 0.1 0.7 0.2 −0.1 0.0 0.0 0.0 0.0 0.0 −0.6 −0.8 −0.7 −0.6 −0.4 −0.4
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−0.1 −0.2 −0.1 0.1 1.1 0.2 −0.1 0.0 0.0 0.0 0.0 0.0 −0.8 −1.0 −1.2 −0.8 −0.5 −0.9
0.1 0.0 0.2 0.2 0.0 0.2 0.0 0.0 0.0 0.1 0.0 0.1 −0.5 −1.3 −0.9 −0.9 −0.3 −0.6
0.0 0.9 1.0 0.9 −0.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −0.2 0.0 −1.2 −0.5
0.0 0.1 0.1 0.1 0.0 0.2 0.0 0.1 0.1 0.0 0.1 0.0 −0.2 −1.0 −0.5 −0.4 −0.7 −0.4
0.0 0.4 0.5 0.4 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −0.2 −0.2 −0.1 −0.1 −0.1
1.9 0.0 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 −0.3 −0.4 −0.5 −0.3 −0.1 −0.4
0.0 0.1 0.4 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Table A.3 Economic results in the greening omnibus scenario for each single group of changes (percentage changes over greening 2017 [%Δ]). Total
Diversification (%Δ over 2017)
EFA environmental value (%Δ over 2017)
EFA pesticides (%Δ over 2017)
Gross margin Revenues Direct payments Costs Gross margin Revenues Direct payments Costs Gross margin Revenues Direct payments Costs
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 −0.2 −0.5 −0.1 −0.7
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 −0.4 −0.8 −0.1 −1.1
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 −0.2 −0.5 −0.1 −0.8
1.9 1.6 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 −0.1 −0.1 −0.1
0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.1 −0.3 −0.6 −0.1 −0.8
0.2 0.3 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −0.1 0.0 −0.2
0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 −0.2 −0.4 −0.1 −0.5
0.4 0.5 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
CAP's environmental policy and land use in arable farms: An impacts assessment of greening practices changes in Italy Raffaele Cortignani ⁎, Gabriele Dono DAFNE – Università degli Studi della Tuscia, Viterbo, Italy
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• We evaluate the impacts of new greening practices and of CAP first pillar reform. • Net income increases in particular in the mountain areas of Northern Italy. • The pesticide use increases in the intensive areas of the plain of Northern Italy. • Employment decreases especially in the most depressed areas of Southern Italy. • The future CAP should adapt more to the needs of the various territories.
a r t i c l e
i n f o
Article history: Received 4 April 2018 Received in revised form 30 July 2018 Accepted 30 July 2018 Available online 02 August 2018 Editor: D. Barcelo Keywords: Land use Environmental policy Greening practices First pillar reform Positive mathematical programming
a b s t r a c t The study assesses the possible impact of first pillar reform of the Common Agricultural Policy by focusing on the new greening rules defined by the recent Omnibus regulation. The analysis was carried out on a Farm Accountancy Data Network sample of Italian farms using by a Positive Mathematical Programming model. Moreover, our analysis is stratified by geographical area and altimetric level and uses some additional environmental and social indicators beyond those economic. The results indicate that the new greening rules generate positive but limited environmental impacts, which reinforce those already determined by the previous CAP reform, for example the use of chemical fertilizers is further reduced. These additional positive environmental impacts are obtained with very limited income reductions. Yet, the impacts on the various geographical areas and their altimetric levels are different, and sometimes controversial. For example, there is a growth in the use of pesticides in the northern plains, due to the increase in rice and soybean areas. On the other hand, agricultural employment in Southern Italy decreases, where agriculture is an important source of employment for the rural population. This reduction also affects the mountain areas, thus accentuating the already high risk of abandonment. All this suggests that more targeted measures will have to be implemented in the future planning of agricultural policy, so as to shape the various actions according to the characteristics of the various rural areas, as well as to the specific priorities of the Member State. © 2018 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding author at: Via San Camillo de Lellis snc, 01100 Viterbo, Italy. E-mail address: [email protected] (R. Cortignani).
https://doi.org/10.1016/j.scitotenv.2018.07.443 0048-9697/© 2018 Elsevier B.V. All rights reserved.
Over the last decades, the Common Agricultural Policy (CAP) has shifted emphasis from price and market intervention to direct payments, partly conditional on environmental requirements (Matthews, 2013;
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Alons, 2017). In 2015, the CAP reform introduced the greening criteria, which conditioned 30% of direct payments to meet three environmental requirements: crop diversification, maintaining permanent grassland and Ecological Focus Area (EFA). From February to May 2017, the European Commission organized a public consultation of all interested EU organizations and citizens on issues and modalities to modernize and simplify the CAP (European Commission, 2017a): its results demonstrated the important role attributed to the CAP in maintaining and improving environmental conditions in rural areas by intervening on agricultural activities. Pending on the CAP post 2020, the mid-term review of the current CAP (Omnibus regulation, entered into force on 1 January 2018) has also changed the first pillar of CAP, including the greening practices (De Castro, 2017). The review has eased the constraints on diversification and EFA, trying to simplify some commitments that have proved difficult to satisfy for farms, and for administration and control systems (European Commission, 2017a, 2017b). In a context such as this, of frequent adjustments and ongoing changes to agricultural policy, ex-ante evaluation of their impact on the agricultural sector can enable policymakers to make more informed decisions about future policies (Reidsma et al., 2018). Many studies and evaluations of changes in agricultural policy have used mathematical programming models focused on the impact on production choices and on economic, social and environmental performance (Buysse et al., 2007; van Ittersum et al., 2008; Britz et al., 2012; Pelikan et al., 2014; de Frahan et al., 2016). Numerous studies have been conducted to evaluate the impacts of the 2015 reform of the direct payment system, in particular using Positive Mathematical Programming (PMP) models. Among the most recent, Solazzo et al. (2016), Cortignani et al. (2017), Gocht et al. (2017) and Louhichi et al. (2018) show that the previous greening rules have limited impacts, that acting on coupled payments is more effective in generating environmental benefits, and that convergence has the highest economic impact among all the components of the reform. The main objective of this paper is to evaluate the effects of greening practices defined in the recent Omnibus regulation on the arable crops sector which includes the cultivation systems most affected by the greening practices (Cimino et al., 2015). Furthermore, the analysis considers the impacts of the changes to the first pillar of the CAP in force since 2015. This allows understanding if the recent changes (Omnibus regulation) continue or reverse what started with the 2015 reform. Compared to the existing literature, the paper presents some innovative elements. The analysis refers to the entire national territory as opposed to other recent papers, where the study areas are specific regions (Solazzo and Pierangeli, 2016; Solazzo et al., 2016) or territories (Cortignani et al., 2017; Cortignani and Dono, 2018). Moreover, compared to other studies dedicated to the entire agriculture of various European countries (Gocht et al., 2017; Louhichi et al., 2017; Louhichi et al., 2018) our analysis is structured by geographical area and altimetric level and uses some additional environmental and social indicators. In this way we aim to highlight how greening practices, defined in a uniform way for all the territories, affect areas with very different characteristics of the agricultural activity. In other words we want to analyze how the various greening practices affect the intensive plain areas and the other less intensive areas (hill and mountain) considering also the different characteristics for geographical area (north, centre, south). Finally, the focus on the use of pesticides and employment is due to the growing interest in the environmental and occupational effects of the various CAP measures. 2. Materials and methods 2.1. Data and characteristics of farms sample The analysis was been carried out using by the Farm Accountancy Data Network1 (FADN) that provide data for evaluating the income of 1
http://ec.europa.eu/agriculture/rica/
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agricultural holdings and the impacts of the Common Agricultural Policy. The data refer to physical and structural characteristic (such as location, crop areas, labour force, uses of chemical input and water, etc.) and economic data (such as the revenue of the different crops, production costs, CAP payments, etc.). These data were used to elaborate the PMP model, being explained later on. The analysis was conducted on the 2798 Italian arable farms and classified as specialist cereals, oilseeds and protein crops, general field cropping, specialist vegetables outdoor. This sample represents 147,603 Italian farms and 3591,000 ha of Utilised Agricultural Area (UAA; Table 1). Therefore, a prominent part of the 7,009,000 ha of arable land detected by the VI Italian Agricultural Census is covered. The choice of these farms types has been carried out considering that, given the characteristics of cultivation systems,2 these farm types are the most influenced by the greening practices (Cimino et al., 2015) and the changes made in the Omnibus regulation. Table 1 shows in detail the data on land use, on the use of inputs and on the main economic variables for the total number of farms represented by the FADN sample, and distinguished by geographical areas and altimetric levels. These values represent the reference scenario (baseline, year 2014) for the simulations of the CAP scenarios shown in the results section. 2.2. Representation of economic analysis: PMP model, input data and simulations Farm-level mathematical programming models are important and widely used analytical tools in agricultural economics because they are able to represent farmer responses to changes in policy and market conditions. In the second half of the 1990s researchers moved from the classical linear or quadratic programming to PMP. This latter approach requires a relatively limited amount of data and can be perfectly calibrated to the reference period. It recovers additional information from observed activity levels, allowing researchers to specify a quadratic objective function so that the resulting nonlinear model exactly reproduces the observed behaviour of farmers and can be used for simulation analyses (Arfini and Paris, 1995; Howitt, 1995; Paris and Howitt, 1998; Heckelei and Wolff, 2003). This method not only automatically and exactly calibrates the model to observed activity levels, but also avoids adding ad-hoc constraints and over-specialised responses of the model to policy changes (de Frahan et al., 2016). The following figure (Fig. 1) summarizes the relevant aspects of the economic analysis. The inputs of PMP model refer to crops and farms. The objective function maximizes the farms gross margin and considers for each crop the revenue and production costs, the areas and the CAP coupled payments. The constraints concern the availability of land and farm labour. The model also considers the possibility of hiring external labour at a relative price (in the objective function). As for the use of inputs, labour needs of crops were used to consider the total requirement in the labour constraint and to evaluate its overall use in the various scenarios. On the other hand, the needs of the other inputs (water, nitrogen, phosphorus, potassium, pesticides) were not explicitly modelled in the objective function and in the constraints, but have been used to quantify the overall use of production factors based on land use in the different scenarios. The results also concern the economic results of the farms. The model was calibrated to the scenario observed in 2014 year and then used to carry out two types of simulations: “A” is based on the first pillar reform of the CAP 2014–2020; “B” is based on recent changes in greening practices defined by the Omnibus regulation.
2 Specialised farming in the cultivation of durum wheat, maize, soft wheat, barley, rice, grain legumes, forage legumes, processed tomato.
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Table 1 Characteristics of the farms sample: number, land use and environmental, social and economic indicators. Absolute value for total Italy and each zone (year 2014). Total
Number of Sample farms Representative farms Land use (kha) Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Others UAA Use of main inputs (MM) Water - m3 Nitrogen - kg Phosphorus - kg Potassium - kg Pesticides - kg Labour - h Economic results (MM €) Gross margin Revenues Direct payments Costs
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
2798 147,603
966 49,689
200 10,164
89 2921
71 2176
503 18,404
58 2639
282 21,320
412 27,226
217 13,065
807 472 326 235 202 196 159 154 117 95 83 80 666 3591
46 405 226 114 198 3 32 139 23 5 0 34 196 1420
6 45 34 39 0 1 36 14 8 1 0 0 39 222
0 2 2 11 0 0 10 0 2 0 0 0 5 31
28 0 1 3 0 4 6 0 1 5 3 5 16 72
166 11 39 41 0 30 6 1 20 69 10 3 95 490
20 2 6 12 0 3 5 0 4 3 1 0 20 75
150 2 0 2 4 16 7 0 15 2 22 27 140 387
306 5 10 9 0 79 24 0 36 8 38 7 113 635
86 1 9 3 0 60 34 0 9 3 9 2 43 258
2877.8 327.1 131.0 101.8 6.9 214.9
1855.7 166.2 49.8 57.1 4.2 55.4
81.3 14.7 8.0 6.8 0.3 8.7
9.6 1.1 1.1 1.1 0.0 3.1
16.2 4.3 2.4 1.2 0.0 3.9
43.6 52.4 14.2 7.7 0.6 26.2
1.3 3.5 1.3 1.9 0.0 2.8
712.7 39.4 24.4 16.8 1.2 58.0
124.2 33.8 23.7 5.4 0.3 41.8
33.3 11.6 6.0 3.7 0.2 14.9
5023 7080 1608 3666
2208 2982 724 1498
251 388 63 200
49 79 4 34
112 182 30 100
533 714 212 392
52 64 21 33
855 1450 212 807
706 866 271 432
256 354 72 170
Fig. 1. Summary diagram of the economic analysis.
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Following, a detailed description of greening practices (including recent changes) and of the simulation scenarios are shown.
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• in greening omnibus scenario, application of greening practices in force since January 2018.
2.3. Greening practices In 2015, the CAP reform introduced the greening criteria, which made the payment of 30% of the direct payments dependent on compliance with three environmental requirements: 1. diversifying cultivation by growing at least two crops on farms where the arable land exceeds 10 ha (and at least three crops where arable land exceeds 30 ha), and by limiting the main crop to 75% of the arable land (and the two main crops to 95% of the arable land where arable land exceeds 30 ha); 2. maintaining permanent grassland at the national, regional, or farm level; 3. maintaining at least 5% of the arable land of farms larger than 15 ha as Ecological Focus Areas (EFA); these areas may take the form of fallow land, terraces, landscape features, buffer strips, hectares of agroforestry, strips of eligible hectares along forest edges, areas with short-rotation coppice, afforested areas, areas with catch crops or green cover, or areas with nitrogen-fixing crops.3 The mid-term review of the CAP 2014–2020 (Omnibus regulation) came into force on 1th January 2018, also with the interesting contribution of the European Parliament that dealt with simplifying some commitments for farms, and for administration and control systems. The Omnibus regulation also determines some changes to the first pillar of CAP, including the greening practices (De Castro, 2017). The main changes concern: a. the paragraph 2 of article 44 of Reg. 1307/2013, which allows farms with more than 75% of the arable land area occupied by rice to maintain the area unchanged, provided that the main crop not occupy more than 75% of the remaining area; b. the inclusion of leguminous in letter a) of paragraph 3 of article 44 of Reg. 1307/2013, which makes it possible to consider alfalfa and other leguminous among crops that can be used for exemption from the obligation; c. the elimination of the 30-hectare limit provided for in letter a) and letter b) of paragraph 3 of article 44 of Reg. 1307/2013 (diversification practice); d. the possibility that mixtures of nitrogen-fixing crops and other plants may be qualified as EFA, provided that the predominance of the nitrogen-fixing crops in the mixtures is ensured; e. the conversion factor of the nitrogen fixing crops for EFAs is raised from 0.7 to 1; f. the elimination the 30-hectare limit set out in letters a) and b), paragraph 4 of article 46 of Reg. 1307/2013 (EFA practice) g. the ban of pesticide use in nitrogen-fixing crops recognized as EFA from 1 January 2018. 2.4. Simulated scenarios Two post-reform scenarios, greening 2017 and greening omnibus, were simulated on the baseline calibrated on 2014 pre-reform year. Both scenarios consider the convergence of the basic payment that will take place by 2019, the greening payments and the coupled payments; the differences between the post-reform simulation scenarios are: • in greening 2017 scenario, application of greening practices in force until December 2017;
3 The mathematical formulation of greening constraints and of two (basic and greening) payments has been widely dealt with in the recent literature. For more details refer to Cortignani and Dono (2015), Solazzo and Pierangeli (2016), Cortignani et al. (2017).
The comparison between the post-reform scenarios allows highlighting the effect of new rules of greening practices. The effects of the convergence of the basic payment and coupled payments are observed in the greening 2017 scenario. In particular, our simulations consider that the convergence of the basic payment does not influence the production choices, but only the level of direct payments, therefore of income.4 On the other hand, both simulations maintain at about 50% the level of greening over the basic payments.5 Finally, the structure and levels of coupled payments remain those assigned to the region based on the latest available data.6 3. Results The results refer to land use and the main inputs, and to economic values; they concern both Italy and its northern, central and southern areas, with the plains, hills and mountains zones. The following tables show first the percentage changes in the 2017 greening scenario compared to the absolute baseline values shown in the previous Table 1. Then, show the percentage changes in the greening omnibus scenario compared to the 2017 greening. The description of the results concerns in particular the greening omnibus scenario to highlight the impacts of greening practices changes; this will be done by highlighting the trends already in place caused by the modifications to the first pillar from 2015. To facilitate the description of the results and isolate the individual impacts, the greening practices changes described above (a.–g.) are grouped and called as follows7: • diversification (a, b and c) • EFA – environmental value (d, e and f) • EFA – pesticides (g)
It should be noted that the diversification (leguminous), EFA – environmental value and EFA – pesticides, are interlinked through impacts on various types of leguminous. In these cases there may be different impacts. In fact, the reform reduces the diversification constraints on fodder legumes, and also increases their environmental value (simplification and less impact on non-EFA crops). On the other hand, it negatively affects their cultivation through banning pesticide use in the EFA. 3.1. Land use The results on land use (Table 2) show that the diversification (rice) determines an increase in rice area, particularly in larger and more extensive farms in northern Italy (+1.6%), while the diversification (leguminous) causes a total increase in alfalfa (+4.6%) and leguminous herbage (+1.7%). As regards the alfalfa, the increase is observed in
4 More specifically, Italy adopted the Irish convergence model that creates a single region at a national level in which gradually, and until 2019, basic payments are modified from the current levels to more homogeneous values, yet, without achieving a uniform value. Both simulations apply the level that will be reached at 2019. 5 Solazzo and Pierangeli (2016) and Cortignani et al. (2017) show that most Italian farms should continue to comply with greening practices even with the possible reduction of the related payment, given the application of administrative sanctions in case of noncompliance. 6 Italy has introduced coupled payments for sugar beets, processed tomatoes and rice. In Northern Italy, a soya payment has also been introduced, and now other payments for cereals (durum wheat), oilseeds (rape and sunflower) and legumes (cereals and fodder). 7 The results section shows the overall impacts of all changes. The appendix report the impacts of each single group.
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Table 2 Land use in the simulated scenarios (percentage changes over baseline and greening 2017 [%Δ]). Total
Greening 2017 (%Δ over baseline)
Greening omnibus (%Δ over 2017)
Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops
North
−4.0 −11.5 −9.5 −8.3 5.3 −8.7 1.3 19.7 −13.5 9.5 10.1 11.4 −5.6 −0.4 −1.8 −2.0 4.6 1.6 1.7 −0.4 −4.2 −1.3 −0.9 −5.0 0.0 −0.3
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−9.5 −11.8 −9.1 −6.8 5.2 −20.0 6.2 19.7 −16.3 −15.9 0.0 16.6 −1.7 −0.9 −1.9 −2.7 6.5 1.6 0.0 −1.8 −4.4 −2.6 −3.9 0.2 0.0 −0.8
−11.8 −11.6 −5.0 −1.1 0.0 −2.0 2.2 21.3 −5.4 −12.2 0.0 9.2 3.6 −1.3 −1.2 −0.4 1.0 0.0 0.0 0.1 −2.3 −0.2 0.3 0.0 0.0 −0.8
0.0 −5.9 −5.4 −12.3 0.0 0.0 2.7 0.0 −15.4 0.0 0.0 0.0 0.0 0.0 0.0 −1.4 14.0 0.0 0.0 −1.4 0.0 −2.0 0.0 0.0 0.0 0.0
−4.3 0.0 −38.8 −3.2 0.0 0.6 −1.8 0.0 −9.8 11.8 19.3 2.6 −11.6 −0.2 0.0 0.0 0.1 0.0 −0.2 0.4 0.0 0.0 −0.1 −4.3 0.0 2.3
−2.9 −8.6 −16.6 −15.5 0.0 2.0 −3.7 −24.6 −13.8 16.7 29.5 18.2 −5.9 −0.8 −1.3 −0.9 1.8 0.0 3.6 0.0 −10.2 −1.4 −0.8 −4.5 −0.2 −0.9
6.9 −0.4 −11.2 −17.3 0.0 6.3 0.2 0.0 −5.1 31.8 59.3 0.0 −7.4 0.3 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3
−6.2 −7.9 0.0 −9.8 8.3 −6.7 −3.7 0.0 −18.7 −14.0 2.6 8.3 −9.7 −0.2 0.2 0.0 −0.2 0.0 0.1 0.0 0.0 −2.5 −8.6 −6.6 0.0 0.1
−4.8 −5.1 −5.4 −10.3 0.0 −13.4 −1.0 0.0 −14.2 −24.8 8.6 7.0 −8.7 −0.4 −3.4 −0.2 7.4 0.0 1.2 0.0 0.0 −0.8 0.3 −5.7 −0.1 −0.8
1.4 −0.5 −6.6 −5.5 0.0 −9.3 −0.5 0.0 −4.2 −25.3 3.8 −1.4 −6.9 0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 2.4
particular in the northern plain (+6.5%), norther mountain (+14.0%) and southern hill (+7.4%); while leguminous herbage increase in the central hill (+3.6%). The EFA – pesticides causes a decrease of soybean (−4.2%) mainly in northern Italy (−4.4%), and in the faba bean (−5.0%), especially in the central hill (−4.5%), in the southern plain (−6.6%) and in the southern hill (−5.7%). Finally, the EFA – environmental value increases the environmental value of the nitrogen fixing crops and, consequently, generates a greater simplification for farms to respect the EFA practice. In terms of land use, the impact of reduction on non-EFA crops is small. Compared to the greening 2017 scenario, the overall impacts observed in greening omnibus scenario show that: - rice further increases (+1.6% in the greening omnibus over +5.3% in the greening 2017); - forage legumes have only a partial compensation of the reduction observed in the greening 2017 (for example: alfalfa, +4.6% vs −8.3%); grain legumes partially reduce over the increase observed in the greening 2017 (e.g. for soybean, −4.2% vs +19.7%);
- grain cereals (durum wheat, maize, soft wheat, barley) further decrease (e.g. for maize, −1.8% and −11.5%).
3.2. Use of main inputs The land use changes just described, determine a variation in the use of inputs (Table 3). This variation is overall limited. However in some areas is more evident and in most cases is additional respect to the major changes observed in the greening 2017 scenario. The use of water is reduced in particular in the northern plain (−0.9%) and increases in the southern hill (+1.6%); both changes accentuate what was observed in the greening 2017 scenario (respectively equal to −2.0% and +2.1%); The use of nitrogen is reduced in particular in the northern plain (−1.1%), in the northern hill (−1.3%) and in the central hill (−0.8%); the northern plain and the central hill are two intensive areas in terms of nitrogen, so the effect is positive and reinforce the reduction observed in the greening 2017 scenario (respectively equal to −3.4% and −2.8%);
Table 3 Use of main inputs in the simulated scenarios (percentage changes over baseline and greening 2017 [%Δ]). Total
Greening 2017 (%Δ over baseline)
Greening omnibus (%Δ over 2017)
Water Nitrogen Phosphorus Potassium Pesticides Labour Water Nitrogen Phosphorus Potassium Pesticides Labour
−4.1 −3.7 −1.7 −2.3 1.1 −2.4 −0.6 −0.8 −0.6 −0.4 0.3 −0.3
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−2.0 −3.4 −1.8 −3.1 3.1 −1.9 −0.9 −1.1 −1.3 −0.6 0.7 −0.7
−2.2 −5.9 −5.6 0.2 −1.2 −0.3 −0.3 −1.3 −0.7 −0.7 −0.3 −0.4
−0.1 −1.4 −1.2 −1.2 −5.9 −1.2 0.0 0.9 1.0 0.9 −0.3 0.4
0.3 −3.2 −2.0 −2.3 −0.6 −0.4 0.0 0.6 −0.2 0.0 −1.2 −0.5
−3.5 −2.8 −1.4 −3.7 0.0 −1.7 −0.2 −0.8 −0.2 −0.2 −0.5 −0.3
0.6 4.8 1.9 0.8 −15.1 −1.4 0.0 0.4 0.5 0.4 0.0 0.5
−11.3 −5.1 −2.6 −0.9 −2.1 −3.2 −0.1 −0.2 −0.2 −0.1 −0.1 −0.1
2.1 −6.3 0.0 −1.2 −6.0 −2.8 1.6 −0.4 −0.4 −0.3 −0.1 −0.2
1.0 −0.7 −0.9 −1.4 −2.7 −3.5 0.0 0.1 0.4 0.0 0.0 0.5
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Table 4 Economic results in the simulated scenarios (percentage changes over baseline and greening 2017 [%Δ]). Total
Greening 2017 (%Δ over baseline)
Greening omnibus (%Δ over 2017)
Gross margin Revenues Direct payments Costs Gross margin Revenues Direct payments Costs
−5.9 −2.7 −10.7 −1.9 −0.1 −0.3 0.0 −0.5
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−7.2 −1.8 −15.6 −0.6 −0.2 −0.6 0.0 −0.9
−1.0 −2.1 4.1 −1.5 −0.1 −0.4 −0.1 −0.7
3.3 −2.2 70.8 −1.2 1.9 1.6 0.0 0.7
−3.0 −1.8 −4.4 −1.3 0.1 0.0 −0.1 −0.1
−4.6 −2.9 −7.6 −3.1 −0.1 −0.4 −0.1 −0.6
1.6 −0.2 7.8 2.1 0.2 0.3 0.0 0.3
−6.9 −4.1 −14.3 −3.8 0.0 −0.1 0.0 −0.1
−6.7 −4.0 −8.7 −2.6 −0.1 −0.3 −0.1 −0.5
−1.1 −2.7 7.6 −0.7 0.4 0.5 0.0 0.4
in some areas there is a slight increase in the use of nitrogen (mountain of the three areas and the central plain). Analogous variations are observed in the use of phosphorus and potassium; The overall use of pesticides slightly increases (+0.3%), accentuating what observed in the greening 2017 scenario (+1.1%); this is due to the increase that occurs in the northern plain (+0.7% and +3.1%); Labour hours slightly decrease (−0.3%), adding to what was observed in the greening 2017 scenario (−2.4%); considering the two simulation scenarios, the reduction is particularly relevant in southern Italy (−3.3% in the plain, −3.0% in the hill, −3.0% in the mountain) where the agricultural sector is an important source for employment. The slight increase that occurs in the three mountain areas is not sufficient to counteract the reductions observed in the greening 2017 scenario. 3.3. Economic results The economic results (Table 4) show that the overall economic impact on gross margin is limited (−0.1%). Regarding the individual areas, the gross margin of farms located in mountain increases, particularly in northern Italy where there is an increase in revenues (+1.6%) due to the major alfalfa surface; in the northern and central Italy this adds to the increase observed in the greening 2017 scenario, which was due to the convergence of the basic payment (higher direct payments). In the other areas, gross margin is subject to small variations; however, note that in the greening 2017 scenario the gross margin reduction is large due to the decrease in revenues from sales and the convergence of the basic payment. 4. Discussion These results of this study offer interesting insights on the link between CAP reform and land use, on socio-economic impacts, on input use. The greening 2017 scenario (that include the convergence of basic payments, the coupled payments and the greening practices in force until December 2017) modifies the land use, mainly reducing the cereals cultivation and forage legumes, instead the cultivation of grain legumes grows. In this context, greening practices have limited impacts, while coupled payments are more incisive in determining changes and environmental benefits. Furthermore, the economic impact is mainly due to the convergence as already noted by Cortignani et al. (2017), Gocht et al. (2017) and Louhichi et al. (2017). As a main innovative contribution, our results indicate a growth in the use of pesticides in northern plain, due to the increase in rice and soya areas, and a decrease in the agricultural employment, especially in southern Italy and including the mountain areas, thus accentuating the already high risk of abandonment. The Omnibus scenario (greening practices in force since January 2018) involves positive, even if limited, environmental impacts accentuate what observed in the greening 2017 scenario with reductions in the use of fertilizers. Globally, the cultivation of cereals decreases further, while
the impacts on grain and forage legumes partially compensate the changes of the previous scenario, with a reduction of the grain legumes and an increase of the forage legumes. Land use changes depend on the specificities of the considered areas. In northern Italy, the increase in rice cultivation is due to the presence of larger and larger farms benefiting from the new diversification rule. Alfalfa increases in the plains and in the mountains where it generates appreciable income, being widely used for feeding the dairy cattle in these areas. The increase in soybean cultivation, inter alia noted in recent years, is due to the coupled payment provided for northern Italy and the recognition of this crop as EFA. Yet, the ban of pesticides use in the EFA could result in a reduction of this cultivation. Despite this, the use of pesticides further increases in northern Italy as the effect of the ban of pesticide use in the EFA (which reduces soybean cultivation) is cancelled by the increase in rice area. In central Italy, the major changes concern the leguminous herbage and faba bean. The former are mainly cultivated in hilly areas, included in many crop rotations, and mainly used to feed the flocks. Faba bean cultivation has greatly increased in recent years due to coupled payment provided for central (and southern) Italy and the recognition of this crop as EFA. Yet, as for soybean, the ban of pesticides use in the EFA could limit this crop and other grain legumes. Similar results are obtained for southern Italy. All these effects are associated with very limited income reductions. It is interesting to examine the effects of the individual components of the new greening rules. Modifying the diversification rules limitedly modifies the use of inputs, while increases income in the northern mountain farms due to the elimination of the constraint that affected the alfalfa in the previous formulation. The increase in the environmental value of nitrogen-fixing crops does not reduce their extension since coupled payments make them very profitable in comparison to alternative crops, albeit more intensive. The positive environmental impacts are mainly generated by the ban of pesticides use in the EFA, which limits the more intensive crops (e.g., soybean). In conclusion, according to our results, the greening practices, also in the new version, continue to exhibit limited and controversial impacts. To overcome the limits of greening, the European Commission proposes replacing greening payments with mandatory conditionality of direct payments as a basis for more ambitious voluntary practices that improve eco-efficiency (European Commission, 2017b). Moreover, measures to support eco-efficiency would also be more easily acceptable by policy makers than the imposition of radical measures that reduce farm profitability (Coderoni and Esposti, 2018). Our results on the Italian geographic areas and their altimetric levels suggest that, although attractive, the idea of a system based on simple and uniform mechanisms for the whole territory, which offers appreciable environmental benefits, is not plausible (Brady et al., 2017). Due to the vast diversity of agronomic, environmental and economic conditions across the UE areas, it is impossible to design a set of simple rules that are universally applicable to the whole territory. Efficient environmental management requires place-specific adjustment, or spatial targeting, which is the diametric opposite to universal rules (Brady et al., 2017).
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5. Conclusions The conducted analysis can contribute to the current debate on future structure of CAP both on the new rules on greening practices defined in the recent Omnibus regulation, and in general on the whole of the first pillar structure. Regarding the new rules, our paper highlights their ability to further reduce the use of chemical fertilizers, however at a relatively low cost (loss of income) for farmers. On the other hand, the new rules are not effective in reducing the use of pesticides, especially in more intensive agricultural areas, such as the northern plain, where the wide choice of intensive crops allows farms to nullify the environmental benefits. Our conclusion is that in order to intervene on this type of pollutants it could be more effective to resort to coupled payments provided for less intensive crops rather than to tighten up environmental rules. The other controversial aspect concerns the use of agricultural labour that, in fact, decreases with the reduction of the intensification of the cultivation systems: this effect is particularly serious in areas where agriculture is the main source of employment for the rural population, as in Southern Italy. In these cases the environmental instruments of agricultural policy must be integrated by developing alternative employment opportunities that the European Commission identifies in the sectors of tourism, renewable energies, the circular economy (European Commission, 2017b). The differentiation by altimetric level clearly shows that the CAP reform, since 2015, is shifting financial resources from the agricultural areas that in the past were more intensive to more extensive areas. Our analysis shows an increase in agricultural incomes in mountain areas, due to the convergence of the basic payment and the application of the new rules on the diversification of alfalfa, which reduce the constraints for this crop. On the contrary, farms operating in more intensive areas, especially in the plains, suffer significant income reductions. Sustainable intensification strategies in these areas seem necessary to limit income losses and to have positive impacts on the environment. Various aspects of our results seem to be in line with the proposed post-2020 regulation, recently formulated by the European Commission (European Commission, 2018). According to this proposal, farm income for all farmers should be supported with a basic support of income.
The targeting of this support will be improved by differentiating the amounts of these payments by groups of territories, by socioeconomic or agronomic conditions. The differentiation of income support will have to be specified by the Member States, which will thus be able to consider the differences in the socio-economic or agronomic conditions of the various territories, in accordance with their national, regional or local needs. In addition, in order to avoid adverse effects on employment, the effects on the use of labour should be taken into account when applying the mechanism. Some limits of the conducted analysis must be highlighted as well as possible developments and improvements. The used PMP approach does not consider crops and production techniques not observed in the reference period. This means that in the simulation scenarios farms adapt to the new policy by only changing the observed mix of crops and techniques and not by adopting new productive solutions. The model uses the production function of Leontief, which is the most widespread in mathematical programming models. However, other functions could be used that, by modifying production levels with the use of inputs, can cause changes in techniques and not in land use. The model is risk neutral. Yet, changing the levels of direct payments could influence farmers' choices based on their risk attitude. Therefore, the use of utility functions with explicit risk terms could generate different results. The CAP cross-compliance and agro-climatic-environmental measures should be also considered since a their greater integration with the greening is foreseen in the next version of the CAP. Finally, other indicators may be added in order to consider the impact on various environmental components, such as biodiversity, CO2 emissions, landscape, and the nitrogen cycle. Acknowledgments The study was carried out under the MACSUR 1 and 2 projects (D.M. 2660/7303/2012 - www.MACSUR.eu) funded by the Italian Ministry of Agricultural, Food and Forestry Policies, Italy (MiPAAF). MACSUR is funded as part of the JPI FACCE. Financial contribution was also obtained in the frame of the MIUR (MInistry for education, University and Research) initiative “Department of excellence” (Law 232/2016).
Appendix A. Results for each single group of changes of greening practices Table A.1 Land use in the greening omnibus scenario for each single group of changes (percentage changes over greening 2017 [%Δ]). Total
Diversification (%Δ over 2017)
EFA environmental value (%Δ over 2017)
Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Total UAA Durum wheat Maize Soft wheat Alfalfa Rice
−0.1 −0.6 −0.9 4.7 1.6 1.9 −0.4 −0.5 −0.1 0.0 0.0 0.0 0.2 0.3 0.0 0.1 0.1 −0.4 0.0
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−0.2 −0.7 −1.3 6.6 1.6 0.0 −1.6 −0.5 0.0 0.0 0.0 0.0 −0.4 0.2 0.1 0.1 0.1 −0.3 0.0
0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.4 0.3 0.0 0.1 0.1 −0.2 0.0
0.0 0.0 −1.4 14.0 0.0 0.0 −1.4 0.0 −2.0 0.0 0.0 0.0 0.0 3.8 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.6 0.0 0.0 0.0 0.1 0.0
0.0 0.0 0.0 2.3 0.0 3.7 0.0 0.0 −0.2 0.0 0.0 0.0 0.0 0.4 0.1 0.1 0.1 −1.1 0.0
0.3 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.5 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 −0.8 0.0 0.0 0.0 0.3 0.1 0.0 0.2 0.0 −0.3 0.0
−0.1 0.0 0.0 7.4 0.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0 0.0 0.0 0.0 2.4 0.9 0.0 0.0 0.0 0.0 0.0
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Table A.1 (continued) Total
EFA pesticides (%Δ over 2017)
Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Total UAA Durum wheat Maize Soft wheat Alfalfa Rice Leguminous herbage Permanent grassland and pasture Soybean Barley Sunflower Faba bean Processed tomato Other crops Total UAA
North
0.0 0.1 −0.1 0.1 0.5 −0.2 0.0 0.0 0.0 −0.4 −1.2 −1.2 0.4 −0.2 −0.1 −0.1 −4.0 −1.3 −1.3 −5.2 0.0 −0.5 −0.9
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
0.0 0.2 −0.1 0.1 1.4 0.0 0.1 0.0 0.0 −0.7 −1.2 −1.5 0.4 −0.2 0.0 −0.3 −4.1 −2.8 −5.4 0.0 −0.1 −0.4 −1.2
0.0 0.2 0.1 0.0 0.3 6.6 0.0 0.0 0.0 −1.3 −1.3 −0.6 0.5 0.0 0.0 −0.1 −2.4 −1.1 0.0 0.0 0.0 −1.0 −0.7
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
−0.2 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −4.3 0.0 −0.6 −0.4
0.0 0.0 0.0 0.2 0.5 −0.7 0.0 −0.1 0.0 −0.9 −1.4 −1.0 0.7 0.0 −0.1 0.0 −10.2 −1.4 −1.3 −4.9 −0.2 −1.0 −0.9
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.1 3.4 −0.4 0.0 0.0 0.0 −0.2 0.0 0.0 0.1 0.0 −0.4 0.0 0.0 −1.8 −8.6 −6.7 0.0 −0.2 −0.7
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.3 −3.4 −0.2 0.3 0.0 −0.2 0.0 0.0 −0.8 0.3 −5.9 −0.1 −0.8 −0.8
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Table A.2 Use of main inputs in the greening omnibus scenario for each single group of changes (percentage changes over greening 2017 [%Δ]). Total
Diversification (%Δ over 2017)
EFA environmental value (%Δ over 2017)
EFA pesticides (%Δ over 2017)
Water Nitrogen Phosphorus Potassium Pesticides Labour Water Nitrogen Phosphorus Potassium Pesticides Labour Water Nitrogen Phosphorus Potassium Pesticides Labour
0.0 −0.1 0.0 0.1 0.7 0.2 −0.1 0.0 0.0 0.0 0.0 0.0 −0.6 −0.8 −0.7 −0.6 −0.4 −0.4
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
−0.1 −0.2 −0.1 0.1 1.1 0.2 −0.1 0.0 0.0 0.0 0.0 0.0 −0.8 −1.0 −1.2 −0.8 −0.5 −0.9
0.1 0.0 0.2 0.2 0.0 0.2 0.0 0.0 0.0 0.1 0.0 0.1 −0.5 −1.3 −0.9 −0.9 −0.3 −0.6
0.0 0.9 1.0 0.9 −0.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −0.2 0.0 −1.2 −0.5
0.0 0.1 0.1 0.1 0.0 0.2 0.0 0.1 0.1 0.0 0.1 0.0 −0.2 −1.0 −0.5 −0.4 −0.7 −0.4
0.0 0.4 0.5 0.4 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −0.2 −0.2 −0.1 −0.1 −0.1
1.9 0.0 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 −0.3 −0.4 −0.5 −0.3 −0.1 −0.4
0.0 0.1 0.4 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Table A.3 Economic results in the greening omnibus scenario for each single group of changes (percentage changes over greening 2017 [%Δ]). Total
Diversification (%Δ over 2017)
EFA environmental value (%Δ over 2017)
EFA pesticides (%Δ over 2017)
Gross margin Revenues Direct payments Costs Gross margin Revenues Direct payments Costs Gross margin Revenues Direct payments Costs
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 −0.2 −0.5 −0.1 −0.7
North
Central
South
Plain
Hill
Mountain
Plain
Hill
Mountain
Plain
Hill
Mountain
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 −0.4 −0.8 −0.1 −1.1
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 −0.2 −0.5 −0.1 −0.8
1.9 1.6 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 −0.1 −0.1 −0.1
0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.1 −0.3 −0.6 −0.1 −0.8
0.2 0.3 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.1 −0.1 0.0 −0.2
0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 −0.2 −0.4 −0.1 −0.5
0.4 0.5 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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