the eu cattle sector: challenges and opportunities - milk and meat

DIRECTORATE-GENERAL FOR INTERNAL POLICIES

POLICY DEPARTMENT B: STRUCTURAL AND COHESION POLICIES

AGRICULTURE AND RURAL DEVELOPMENT

RESEARCH FOR AGRI COMMITTEE - THE EU CATTLE SECTOR: CHALLENGES

AND OPPORTUNITIES - MILK AND MEAT

ANNEXES

This document was requested by the European Parliament's Committee on Agriculture and Rural Development. AUTHORS Wageningen University, The Netherlands: Rico Ihle, Liesbeth Dries, Roel Jongeneel, Thomas Venus, Justus Wesseler RESPONSIBLE ADMINISTRATOR Guillaume Ragonnaud / Albert Massot Policy Department B: Structural and Cohesion Policies European Parliament B-1047 Brussels E-mail: [email protected] EDITORIAL ASSISTANCE Lyna Pärt LINGUISTIC VERSIONS Original: EN ABOUT THE PUBLISHER To contact the Policy Department or to subscribe to its monthly newsletter please write to: [email protected] Manuscript completed in February 2017 © European Union, 2017 Print ISBN 978-92-846-0601-6 doi:10.2861/886610 QA-01-17-073-EN-C PDF ISBN 978-92-846-0600-9 doi:10.2861/85585 QA-01-17-073-EN-N This document is available on the Internet at: http://www.europarl.europa.eu/supporting-analyses DISCLAIMER The opinions expressed in this document are the sole responsibility of the author and do not necessarily represent the official position of the European Parliament. Reproduction and translation for non-commercial purposes are authorized, provided the source is acknowledged and the publisher is given prior notice and sent a copy.

http://www.europarl.europa.eu/supporting-analyses

DIRECTORATE-GENERAL FOR INTERNAL POLICIES

POLICY DEPARTMENT B: STRUCTURAL AND COHESION POLICIES

AGRICULTURE AND RURAL DEVELOPMENT

RESEARCH FOR AGRI COMMITTEE - THE EU CATTLE SECTOR: CHALLENGES

AND OPPORTUNITIES - MILK AND MEAT

ANNEXES

Abstract The cattle sector is of great economic importance within the EU agricultural sector. Productivity of the sector is very heterogeneous. In the near future, a further increase in milk and bovine meat supply can be expected. To avoid a decline in farm gate prices, further product differentiation at the EU level, an increase in export opportunities as well as compensation for environmental services to support extensification will be needed.

IP/B/AGRI/IC/2016-014 February 2017 PE 585.911 EN

ANNEXES: The EU cattle sector: challenges and opportunities ____________________________________________________________________________________________

161

CONTENTS

ANNEXES 171

Annex to chapter 1: Role of the EU cattle sector 178

Annex to chapter 2: Current Situation in the EU Dairy Sector 212

Annex to chapter 3: Current Situation in the EU Bovine Meat Sector 249

Annex to chapter 4: Challenges and Opportunities 259

Annex to chapter 5: Policy Framework of the EU Cattle Sector 272

Policy Department B: Structural and Cohesion Policies ____________________________________________________________________________________________

162

LIST OF ABBREVIATIONS

AWU Annual work unit, see Table A.1 for definition

bln Billion(s)

CAP Common Agricultural Policy of the EU

DPs Direct payments (European Commission, 2015c)

EU-S Southern MS of the EU, see Table A.1 for definition

Extra-EU

trade Trade between a MS and a country which is not member of the EU

FADN Farm Accountancy Data Network of the European Commission

FSS Farm Structure Survey of Eurostat

ha Hectare

Intra-EU

trade Trade which takes place only between EU MS

LFA Less favoured area, see Table A.1 for definition

LSU Livestock unit, see glossary (Table A.1) for definition

m Million

MENA Middle East and North Africa

MS Member State(s) of the EU

mt Million tonnes

PPS Purchasing power standard

RDP Rural development programme

SO Standard Output as defined in European Commission (2008a)

SSA Sub-Saharan Africa

TF14 Type of Farm classification as defined in European Commission

(2008a), see Table A1.2 in the Appendix


163

TP Transitional Payment (European Commission, 2015c)

TSO Total Standard Output as defined in European Commission (2008a)

UAA Utilized agricultural area

UTPs Unfair trading practices

VCS Voluntary coupled support


164

LIST OF TABLES Table A.1: Glossary in alphabetical order 171 Table A1.1: General and principal types of farms belonging to the EU cattle sector 179 Table A1.2: General and principal types of EU farms (TF14 grouping) 179 Table A1.3: Production values of bovine raw products in the EU 180 Table A1.4: Economic size thresholds for selecting farms 180 Table A1.5: Number of commercial and non-commercial farms per TF14 category 182 Table A1.6: Standard output of commercial and non-commercial farms per TF14 category 183 Table A1.7: Econometric results on MS contributions to the SO of the EU cattle sector 185 Table A1.8: Contributions of the EU13 and EU-S to the SO of the EU cattle sector 186 Table A1.9: The importance of the cattle sector in MS’s national agriculture in 2013 189 Table A1.10: Correlations between the MS’s national cattle sector characteristics 190 Table A1.11: Econometric results underlying Figure 7 191 Table A1.12: Regions with the highest and lowest shares of cattle-keeping farms 192 Table A1.13: Regions with the highest and lowest labour income per FWU 193 Table A1.14: Quintile borders of the regional distribution of labour income per AWU 194 Table A1.15: Quintile borders of the regional distribution of labour income per FWU 195 Table A1.16 Categories of cattle-based products in EU agri-food trade 197 Table A1.17: Cattle-based products and EU agri-food trade categories 200 Table A1.18: EU trade in bovine products in 2015 201 Table A1.19: Development of EU exports of bovine products 203 Table A1.20: Development of EU imports of bovine products 204 Table A1.21: Development of the share of bovine products 206 Table A1.22: Shares in global bovine products trade by continent 207 Table A1.23: Development of the share of bovine products in intra-EU agri-food trade 210 Table A1.24: Trading partners of the largest net exports and net imports 211 Table A2.1: Macroeconomic characteristics and structure of milk production of MS 212 Table A2.2 Cattle-based products and EU agri-food trade categories 215 Table A2.3: Changes in milk deliveries and cow numbers per MS 216 Table A2.4: Regions with the highest and lowest numbers of mixed livestock farms 218 Table A2.5: Regions with the highest and lowest numbers of crops and cattle farms 219 Table A2.6: Regions with the highest and lowest average dairy herd per farm 220 Table A2.7: Regions with the highest and lowest total cattle kept by specialist dairying farms 221 Table A2.8: Regions with the highest and lowest total cattle kept by other dairying farms 222 Table A2.9: Highest and lowest stocking densities of dairying and meat farms 223 Table A2.10: Highest and lowest stocking densities of mixed livestock farms 223


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Table A2.11: Highest and lowest stocking densities of crops and cattle farms 224 Table A2.12: Regions with the highest and lowest milk yield of dairying and meat farms 225 Table A2.13: Regions with the highest and lowest milk yield of mixed livestock farms 226 Table A2.14: Regions of highest and lowest milk yield of crops and cattle farms 227 Table A2.15: Regions of highest and lowest economic size of dairying and meat farms 228 Table A2.16: Regions with the highest and lowest economic size of mixed livestock farms 229 Table A2.17: Regions with the highest and lowest economic size of crops and cattle farms 230 Table A2.18: Regions with the highest and lowest economic size of dairying and meat farms 231 Table A2.19: Regions with the highest and lowest farm income of mixed livestock farms 232 Table A2.20: Regions with the highest and lowest farm income of crops and cattle farms 233 Table A2.21: Regions with the highest and lowest labour income of dairying and meat farms 234 Table A2.22: Regions with the highest and lowest labour income of mixed livestock farms 235 Table A2.23: Regions with the highest and lowest labour income of crops and cattle farms 236 Table A2.24: Highest and lowest shares of labour 237 Table A2.25: Highest and lowest shares of labour 238 Table A2.26: Highest and lowest shares of labour 239 Table A2.27: Highest and lowest shares of labour income 240 Table A2.28: Highest and lowest shares of labour income 241 Table A2.29: Highest and lowest shares of labour income 242 Table A2.30: Structure of the dairy processing industry 243 Table A2.31: Main dairy processors on the EU market, 2008-2013 244 Table A2.32: Market shares of main dairy processors per MSa 245 Table A3.1: Macroeconomic characteristics and bovine meat production of MS 249 Table A3.2: Regions with the highest and lowest average cattle numbers per farm 251 Table A3.3: Regions with the highest and lowest cattle numbers 252 Table A4.1: Investment cost in dairy cow stables 259 Table A4.2: Stakeholders interviewed 260 Table A4.3: Self-assessment of the personal expertise of the interviewees 260 Table A4.4: Interview guidelines of the qualitative interviews 261 Table A5.1: List of RDP submeasures 272 Table A5.2: Calculations of key variables 276 Table A5.3: Regions of highest and lowest share of cattle-related payments in farm income under new CAP for farm type (46) 282 Table A5.4: Regions of highest and lowest share of cattle-related payments in


166

farm income under new CAP for farm type (47) 282 Table A5.5: Regions of highest and lowest share of cattle-related payments in farm income under new CAP for farm type (73) 283 Table A5.6: Regions of highest and lowest share of cattle-related payments in farm income under new CAP for farm type (83) 284 Table A5.7: Regions of highest and lowest share in benchmark income for farm type (45) under new CAP 285 Table A5.8: Regions of highest and lowest share in benchmark income for farm type (47) under new CAP 286 Table A5.9: Regions of highest and lowest share in benchmark income for farm type (73) under new CAP 287 Table A5.10: Regions of highest and lowest share in benchmark income for farm type (83) under new CAP 288 Table A5.11: Regions of highest and lowest change in importance of cattle-related CAP payments in farm income of farm type (45) 289 Table A5.12: Regions of highest and lowest change in importance of cattle-related CAP payments in farm income of farm type (46) 290 Table A5.13: Regions of highest and lowest change in importance of cattle-related CAP payments in farm income of farm type (47) 291 Table A5.14: Regions of highest and lowest change in importance of cattle-related CAP payments in farm income of farm type (73) 292 Table A5.15: Regions of highest and lowest change in importance of cattle-related CAP payments in farm income of farm type (83) 293 Table A5.16: Impact of CAP direct payment measures on the income 294 Table A5.17: Impact of CAP direct payment measures on the income 295 Table A5.18: Impact of CAP direct payment measures on the income 296 Table A5.19: Impact of CAP direct payment measures on the income 297

LIST OF MAPS Map A.1: FADN regions 175 Map A.2: Correspondence of NUTS2 and FADN regions 176 Map A.3: Regional distribution of commercial farms across the EU 177 Map A1.1: Regional distribution of the share of cattle-keeping farms 192 Map A1.2: Regional distribution of labour income per FWU in the EU cattle sector 193 Map A1.3: Quintiles of the regional distribution of labour income per AWU 194 Map A1.4: Quintiles of the regional distribution of labour income per FWU 195 Map A2.1: Regional distribution of mixed livestock farms 217 Map A2.2: Regional distribution of crops and cattle farms 218 Map A2.3: Regional distribution of average dairy herd per farm 219 Map A2.4: Regional distribution of total cattle kept by specialist dairying farms 220 Map A2.5: Regional distribution of total cattle kept by other dairying farms 221


167

Map A2.6: Stocking density of dairying and meat farms 222 Map A2.7: Stocking density of mixed livestock farms 223 Map A2.8: Stocking density of crops and cattle farms 224 Map A2.9: Regional distribution of the milk yield of dairying and meat farms 225 Map A2.10: Regional distribution of the milk yield of mixed livestock farms 226 Map A2.11: Regional distribution of the milk yield of crops and cattle farms 227 Map A2.12: Regional distribution of economic size of dairying and meat farms 228 Map A2.13: Regional distribution of economic size of mixed livestock farms 229 Map A2.14: Regional distribution of economic size of crops and cattle farms 230 Map A2.15: Regional distribution of farm income of dairying and meat farms 231 Map A2.16: Regional distribution of farm income of mixed livestock farms 232 Map A2.17: Regional distribution of farm income of crops and cattle farms 233 Map A2.18: Regional distribution of labour income of dairying and meat farms 234 Map A2.19: Regional distribution of labour income of mixed livestock farms 235 Map A2.20: Regional distribution of labour income of crops and cattle farms 236 Map A2.21: Share of labour employed by dairying and meat farms 237 Map A2.22: Share of labour employed by mixed livestock farms in total regional labour 238 Map A2.23: Share of labour employed by crops and cattle farms in total regional labour 239 Map A2.24: Labour income of dairying and meat farms as share of regional income 240 Map A2.25: Labour income of mixed livestock farms as share of regional income 241 Map A2.26: Labour income of crops and cattle farms as share of regional income 242 Map A2.27: Distribution of main dairy production sites across the Netherlands 246 Map A2.28: Percentage change in dairies numbers per MS between 2006 and 2015 247 Map A2.29: Annual milk production per MS in 2015 248 Map A2.30: Percentage change in milk production per MS between 2006 and 2015 248 Map A3.1: Regional distribution of average cattle numbers per farm 251 Map A3.2: Distribution of total cattle numbers kept by specialist fattening farms 252 Map A3.3: Share of main beef and veal companies in national production volume 255 Map A3.4: Share of main beef and veal companies in French production volume 256 Map A3.5: Slaughterings of bovine animals per MS in 2015 257 Map A3.6: Slaughterings of bovine animals per MS in 2015 257 Map A3.7: Percentage change of cattle slaughterings per MS 2006 - 2015 258 Map A5.1: Distribution of total CAP payments received by all EU cattle sector farms per region 277 Map A5.2: Distribution of total coupled CAP payments for dairy cows received by all EU cattle sector farms per region 277 Map A5.3: Distribution of total coupled CAP payments for non-dairying cattle received by all EU cattle sector farms per region 278 Map A5.4: Distribution of total decoupled CAP payments received per by all EU cattle sector farms per region 278


168

Map A5.5: Ratio of total cattle-related CAP payments in total EU cattle sector farm income per region 279 Map A5.6: Distribution of total net income of all EU cattle sector farms per region 279 Map A5.7: Distribution of total agricultural labour employed by all EU cattle sector farms per region 280 Map A5.8: Distribution of the sum of payments other than the cattle-related ones received by all cattle sector farms per region 280 Map A5.9: Distribution of labour income/ AWU per average EU cattle sector farm 281 Map A5.10: Cattle-related CAP payments in farm income under the new CAP for farm type (46) 281 Map A5.11: Cattle-related CAP payments in farm income under the new CAP for farm type (47) 282 Map A5.12: Cattle-related CAP payments in farm income under the new CAP for farm type (73) 283 Map A5.13: Cattle-related CAP payments in farm income under the new CAP for farm type (83) 284 Map A5.14: Distribution of relation of labour income of farm type (45) to benchmark income under the new CAP 285 Map A5.15: Distribution of relation of labour income of farm type (47) to benchmark income under the new CAP 286 Map A5.16: Distribution of relation of labour income of farm type (73) to benchmark income under the new CAP 287 Map A5.17: Distribution of relation of labour income of farm type (83) to benchmark income under the new CAP 288 Map A5.18: Distribution of change in importance of cattle-related CAP payments in farm income of farm type (45) between old and new CAP 289 Map A5.19: Distribution of change in importance of cattle-related CAP payments in farm income of farm type (46) between old and new CAP 290 Map A5.20: Distribution of change in importance of cattle-related CAP payments in farm income of farm type (47) between old and new CAP 291 Map A5.21: Distribution of change in importance of cattle-related CAP payments in farm income of farm type (73) between old and new CAP 292 Map A5.22: Distribution of change in importance of cattle-related CAP payments in farm income of farm type (83) between old and new CAP 293


169

LIST OF FIGURES Figure A1.1: National averages of the production value shares of bovine products 186 Figure A1.2: Share of the value of bovine raw products produced in the EU 187 Figure A1.3: Rankings of the cattle sectors of the MS in national agriculture 188 Figure A1.4: Distribution of area used by cattle-keeping farms across MS in 2013 196 Figure A1.5: Development of the share of the export value of bovine products 205 Figure A1.6: Development of value shares of extra-EU trade in total EU trade 207 Figure A1.7: Market shares in global bovine product exports 208 Figure A1.8: Market shares in global bovine product imports 208 Figure A1.9: Trade balances in global bovine product trade 209 Figure A2.1 Distribution of the average total area per farm (ha) per MS 214 Figure A2.2 Development of the share of EU13 dairy production in EU total 214 Figure A2.3: Dairy farmers’ share in selected dairy products 247 Figure A3.1: Development of annual EU bovine and pig slaughter prices 253 Figure A3.2: Development of EU bovine meat production and carcass weight 253 Figure A3.3: Development of the EU non-dairying cattle herd 254 Figure A3.4: Development of the share of the EU13 in EU total bovine meat production 254

LIST OF STAKEHOLDER VIEWS Stakeholders' View 4: Key determinants of the current milk price crisis 262 Stakeholders' View 5: Main opportunities of the EU cattle sector in next 10 years 263 Stakeholders' View 6: Main challenges of the EU cattle sector in next 10 years 264 Stakeholders' View 7: Currently most beneficial policies for the EU cattle sector 266 Stakeholders' View 8: Currently most challenging policies for the EU cattle sector 267 Stakeholders' View 9: Do EU cattle-keeping farmers need more CAP support? 268 Stakeholders' View 10: Potentially most beneficial policies for the EU cattle sector 269 Stakeholders' View 11: Solutions to the current milk price crisis 270 Stakeholders' View 12: Effects of the Brexit on the EU cattle sector 271


170


171

ANNEXES Table A.1: Glossary in alphabetical order

TERM/ SYNONYM DEFINITION SOURCE

Agricultural holding/ farm

«a single unit, both technically and economically, which has a single management and which undertakes agricultural activities […] within the economic territory of the European Union, either as its primary or secondary activity»

European Commission (2008b), art. 2(a); Eurostat (2016b)

Agri-food trade

All products as defined by the European Commission Agricultural Trade Statistics Nomenclature.

European Commission (2016f, 2016g)

AWU

Annual work unit: «the work performed by one person […] on a full-time basis. […] 1,800 hours […]: equivalent to 225 working days of eight hours each», see also Eurostat (2016i) and European Parliament (2015a).

Eurostat (2016f)

Bovine products

All products that are based on cattle farming and traded inside the EU as well as internationally as defined by the HS 6-digit level codes in Table A1.16.

Authors

Bovine raw products

The cows' milk, milk products, beef and veal as produced as raw products on the farms. Authors

Cattle-keeping farms

Farms classified to belong to the production category “EU cattle sector” are called in this report cattle-keeping farms.

Authors

Commercial farm

«a farm which is large enough to provide a main activity for the farmer and a level of income sufficient to support his or her family» «the set of farms which constitute the FADN field of observation in a given country is represented by those agricultural holdings surveyed by the FSS, with an economic size exceeding the threshold set for that country», see Table A1.4 for the thresholds

European Commission (2016d)

DPs Direct payments are support for farmers granted independently of quantities produced, that is, decoupled from production.

European Parliament (2015b, section 1 and table 1.1), European Parliament (2015c)

Economic size Measured in SO in 1000 euros/year. European

Commission (2016c)

EU cattle sector

Consisting of the EU bovine meat and the EU dairy sector. Authors


172

EU bovine meat sector

All economic activities which focus on the production of food commodities made of beef or veal, that is, all farms which engage in the production of meat from cattle as well as all firms which produce a range of food products from this meat which encompasses both beef as well as veal. For the purpose of this analysis, the EU dairy sector is defined to consist of the principal farming type (46). See footnote 7, p. 31, and Table A1.3, p. 179, for an explanation of this grouping.

Authors

EU dairy sector

All economic activities which focus on the production of milk-based food commodities, that is, all farms which engage in cow milk production as well as all firms which produce a range of food products from the cow milk. Milk in the EU is also produced by sheep and goats and buffalos; however, they account for only 3% of total EU milk production and are therefore neglected in this analysis (Eurostat, 2016c). Milk is therefore used as a synonym for “cow milk”. For the purpose of this analysis, the EU dairy sector is defined to consist of the principal farming types (45), (47), (73) and (83). See footnote 7, p. 31, and Table A1.3, p. 179, for an explanation of this grouping.

Authors

EU15

States which were already EU members by 30 April 2004: Belgium (BE), Denmark (DK), France (FR), Germany (DE), Greece (EL), Ireland (IE), Italy (IT), Luxembourg (LU), Netherlands (NL), Portugal (PT), Spain (ES), United Kingdom (UK), Austria (AT), Finland (FI) and Sweden (SE)

Eurostat (2016e)

EU13

All states which accessed the EU after 30 April 2004: Cyprus (CY), Czech Republic (CZ), Estonia (EE), Hungary (HU), Latvia (LV), Lithuania (LT), Malta (MT), Poland (PL), Slovakia (SK), Slovenia (SI), Bulgaria (BG), Romania (RO) and Croatia (HR)

Eurostat (2016e)

EU25

EU15 and all MS which accessed the EU on 1 May 2004: Cyprus (CY), Czech Republic (CZ), Estonia (EE), Hungary (HU), Latvia (LV), Lithuania (LT), Malta (MT), Poland (PL), Slovakia (SK) and Slovenia (SI)

Eurostat (2016e)

EU27 The EU25 and all MS which accessed the EU on 1 January 2007: Bulgaria (BG) and Romania (RO) Eurostat (2016e)

EU28 All states which are member since 1 July 2013: EU15 and EU13, that is, EU27 plus Croatia (HR) Eurostat (2016e)

EU-N Northern MS of the EU defined as those MS which are not part of the group of EU-S as defined below. Authors

EU-S Southern MS of the EU defined as those MS which are at least partly located south of 46th degree of latitude and Authors


173

directly border the Mediterranean Sea, Black Sea or Atlantic Ocean. These are the following eleven MS: BG, CY, EL, ES, FR, HR, IT, MT, PT, RO and SI

Farm net income

«Remuneration to fixed factors of production of the farm (work, land and capital) and remuneration to the entrepreneurs risks (loss/profit) in the accounting year» calculated as Farm Net Value Added (SE415) - Total external factors(SE365) + Balance subsidies & taxes on investments (SE405), measured in €/ farm

European Commission (2016c)

FWU Family Working Unit: «An FWU is an AWU of unpaid (family) labour», see also Eurostat (2016i) and European Parliament (2015a).

European Commission (2012a)

ha Hectare. One hectare equals 10 000 m2 or 0.01 km2. -

HS code Harmonized System Code is a common categorization of commodities traded internationally.

UN Trade Statistics (2016a)

LFA

Less favoured area: «areas affected by significant natural handicaps, notably a low soil productivity or poor climate conditions, and where maintaining extensive farming activity is important for the management of the land»

European Commission (2009b)

LSU

Livestock unit: «reference unit which facilitates the aggregation of livestock from various species and age as per convention [...] The reference unit used for the calculation of livestock units (=1 LSU) is the grazing equivalent of one adult dairy cow producing 3 000 kg of milk annually, without additional concentrated foodstuffs [...]»

Eurostat (2016g)

PPS Purchasing power standard: «artificial currency unit [...] Theoretically, one PPS can buy the same amount of goods and services in each country.»

Eurostat (2016k)

RDP

Rural development programme: «Each Member State should prepare either a national rural development programme for its entire territory or a set of regional programmes or both a national programme and a set of regional programmes. Each programme should identify a strategy for meeting targets in relation to the Union priorities for rural development and a selection of measures.»

European Commission (2013c), art. 7

Standard output/ SO

«standard value of gross production [...] determined for each region [...] and for each crop and livestock characteristics of the farm structure survey», that is, «the average monetary value of the agricultural output at farm-gate price of each agricultural product (crop or livestock) in a given region»

European Commission (2008a), art. 5 And European Commission (2016c)


174

Stocking density

«Density of ruminant grazing livestock: average number of bovine L[S]U (except calves for fattening) and sheep/goat L[S]U per hectare of forage UAA. Forage area includes fodder crops, agricultural fallows and land withdrawn from production (except when non-food crops are cultivated), permanent pasture and rough grazing. Stocking density is calculated only for holdings with corresponding animals and with forage area.»

European Commission (2016c)

TSO Total standard output: «sum of the values obtained for each characteristic by multiplying the standard outputs per unit by the number of corresponding units»

European Commission (2008a), art. 5, and European Commission (2016a)

UAA

Utilized agricultural area: «[...] the area used for farming. It includes the land categories arable land, permanent grassland, permanent crops and other agricultural land such as kitchen gardens (even if they only represent small areas of total UAA). The term does not include unused agricultural land, woodland and land occupied by buildings, farmyards, tracks, ponds, etc.»

Eurostat (2016h)

UTPs

Unfair trading practices: «[...] practices that grossly deviate from good commercial conduct and are contrary to principles of good face and fair dealing, and are uni-laterally imposed by one trading partner on another.»

European Commission (2014b)

VCS Voluntary coupled support: «[...] Member States may decide to grant farmers support coupled to production. Coupled support may only be granted to those sectors and regions of the Member State in which specific types of farming, or specific agricultural sectors, play a particularly important economic, social or environmental role.»

European Parliament (2015b, section 1.2.4.10), European Parliament (2015c)

Source: Authors


175

Map A.1: FADN regions

Source: European Commission (2016l)


176

Map A.2: Correspondence of NUTS2 and FADN regions

Source: Authors based on European Commission (2016p) and Eurostat (2016j) Note: FADN regions have dark green borders. NUTS2 regions have light green borders. In 2013, there were 136 FADN regions and 273 NUTS2 regions. Especially in the north-eastern MS and the UK often more than 2 NUTS2 regions belong to one FADN regions. The colour categories of the plots with eight categories are based on the categorization of the data into eight quantiles, that is, each colour category contains the same number of regions. Hence, the same numbers of regions fall into each interval given in the legend. Similarly, the maps with quintiles have been designed.


177

Map A.3: Regional distribution of commercial farms across the EU

Source: Authors based on European Commission (2016j) Note: The plot shows the number of all commercial farms per FADN region. In some MS, FADN regions and NUTS2 regions coincide, in others they do not. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2010. The colour categories are based on the categorization of the data into eight quantiles, that is, each colour category contains the same number of regions. Hence, the same numbers of regions fall into each interval given in the legend.


178

Annex to chapter 1: Role of the EU cattle sector The system of EU farm classification consists of:

• 9 general types of farming, • 22 principal types of farming and • 62 particular types of farming.

European Commission (2008a) contains the exact definitions of all categories and sub-categories. The Farm Accountancy Data Network (FADN) considers various of these aggregation levels while the Farm Structure Survey of Eurostat only uses the 22 principal farming types (Eurostat, 2016a). In this classification only commercial farms are considered, that is, farms that exceed a minimum economic size (for details, see Annex I of European Commission, 2015a, or European Commission, 2009a). For the purpose of this study, three general types of farming are relevant:

• (4) Specialist grazing livestock o (45) Specialist milk o (49) Specialist cattle

• (7) Mixed livestock holdings o (70) Mixed livestock

• (8) Mixed crops — livestock o (80) Mixed crops and livestock.

We perform the analysis at the level of the principal farming types. Relevant for this study are therefore the following five principal farming type categories:

• (45) Specialist dairying, • (46) Specialist cattle — rearing and fattening (“specialist fattening”), • (47) Cattle — dairying, rearing and fattening combined (“dairying and meat”), • (73) Mixed livestock, mainly grazing livestock (“mixed livestock”) and • (83) Field crops — grazing livestock combined (“crops and cattle”).

The principal farming types

• (74) Mixed livestock, mainly granivores and • (84) Various crops and livestock combined

contain just to some minor extent cattle farming and are, therefore, neglected in this analysis if the analysis is carried out at the principal farming type level because they are barely relevant for the EU cattle sector. That is, if the analysis is carried out at the principal farming type level, then the five principal farming type categories mentioned in Table A1.1 are considered. However, if the analysis is carried out at the general farm type level, then the four general farm type categories mentioned in Table A1.1, including principal types (74) and (83), are considered.


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Table A1.1: General and principal types of farms belonging to the EU cattle sector

Principal farming type acc. to TF14

grouping

General farm type acc. to TF14 grouping

FADN code acc. to Eur.

Com. (2008a)

Eurostat code

Sub-sector of EU cattle

sector

Specialist dairying (45) Specialist milk 45 FT45_SO EU dairy sector

Specialist fattening (49) Specialist cattle 46 FT46_SO EU bovine meat sector

Dairying and meat (49) Specialist cattle 47 FT47_SO EU dairy sector

Mixed livestock (70) Mixed livestock 73 FT73_SO EU dairy sector

Crops and cattle (80) Mixed crops and livestock 83 FT83_SO EU dairy

sector

Source: Authors Note: For an explication of which TF14 group belongs to which sub-sector of the EU cattle sector (milk vs. bovine meat), see Table 3 and footnote 7, p. 33 et seq.. Table A1.2: General and principal types of EU farms (TF14 grouping)

General type of farming (TF14) Principal type of farming Code Description Abbreviation Code Description 15. Specialist COP (15) S COP 15. Specialist cereals, oilseeds and protein crops 16. Specialist other field crops (16) S o fc 16. General field cropping 20. Specialist horticulture (20) S hort

21. Specialist horticulture indoor 22. Specialist horticulture outdoor 23. Other horticulture

35. Specialist wine (35) S wine 35. Specialist vineyards 36. Specialist orchards - fruits (36) S fruits 36. Specialist fruit and citrus fruit 37. Specialist olives (37) S olives 37. Specialist olives 38. Permanent crops comb. (38) Per cro 38. Various permanent crops combined 45. Specialist milk (45) S milk 45. Specialist dairying 48. Specialist sheep and goats (48) S sheep 48. Sheep, goats and other grazing livestock 49. Specialist cattle (49) S cattle

46. Specialist cattle - rearing and fattening

47. Cattle - dairying, rearing and fattening combined

50. Specialist granivores (50) S gran

51. Specialist pigs 52. Specialist poultry 53. Various granivores combined

60. Mixed crops (60) M cro 61. Mixed cropping 70. Mixed livestock (70) M liv 73. Mixed livestock, mainly grazing livestock

74. Mixed livestock, mainly granivores 80. Mixed crops and livestock (80) M cro &

liv

83. Field crops - grazing livestock combined

84. Various crops and livestock combined

Source: European Commission (2016b)/ European Commission (2008a) Note: The farm classification has changed 5 times so far. This is the currently valid classification according to European Commission (2008a).


180

Table A1.3: Production values of bovine raw products in the EU at the principal farming type level

(A) Principal farming type

(B) Total

(C) Milk & milk

products

(D) Beef &

veal

(E) Share milk

products

(F) Share beef & veal

(G) Sub-

sector

(45) Specialist dairying 68.1 48.2 7.4 71% 11% Dairy

(46) Specialist fattening 15.8 <0.1 10.0 <1% 64% Meat

(47) Dairying and meat 6.2 2.7 1.8 44% 29% Dairy

(73) Mixed livestock 6.6 2.1 0.8 31% 13% Dairy

(83) Crops and cattle 19.9 4.4 3.1 22% 16% Dairy

EU cattle sector 116.6 57.4 23.2 49% 20%

Source: Authors based on European Commission (2016j) Note: Data only for commercial farms grouped by principal farming types. At the principal farming type level, the EU cattle sector is defined as the sum of the farm types 45, 46, 47, 73 and 83. Data for 2013, values in columns (B) to (D) in bln euros. Column (A) contains the principal farms types belonging to the EU cattle sector. Column (B) shows the total production value of each type in the entire EU. Columns (C) and (D) contain the production value of each farm type of milk/milk products and beef/veal, respectively. Column (E) contains the share of column (C) in (B). Column (F) shows the share of column (D) in (B). Column (G) determines which principal farming type belongs to which of the two sub-sectors of the EU cattle sector. This column is determined based on which of the two major commodities (milk vs. beef) accounts for the largest share in the total output of each principal farming type. This definition at the principal farming type level is in line with the common analyses of parts of the EU cattle sector such as European Commission (2013b). Table A1.4: Economic size thresholds for selecting farms of the FSS into the FADN

sample according to European Commission (2008a, in 1000 EUR/farm)

MS/ REGION 2010 2012 2013

Belgium 25 25 25

Bulgaria 2 2 2

Czech Republic 8 8 8

Denmark 15 15 15

Germany 25 25 25

Estonia 4 4 4

Ireland 4 8 8

Greece 4 4 4

Spain 4 4 4

France 25 25 25

France (Guadeloupe) 15 15


181

France (Martinique) 15 15

France (La Réunion) 15 15

Croatia 4

Italy 4 4 4

Cyprus 4 4 4

Latvia 4 4 4

Lithuania 4 4 4

Luxembourg 25 25 25

Hungary 4 4 4

Malta 4 4 4

Netherlands 25 25 25

Austria 8 8 8

Poland 4 4 4

Portugal 4 4 4

Romania 2 2 2

Slovenia 4 4 4

Slovakia 15 15 25

Finland 8 8 8

Sweden 15 15 15

United Kingdom 25 25 25

United Kingdom (Northern Ireland) 15 15 15

Source: European Commission (2016a)


182

Table A1.5: Number of commercial and non-commercial farms per TF14 category

(A) Farm cate-gory

(B) General

farm type

(C) Com. farms

(D) Share in total com. farms

(E) Non-com. farms

(F) Share in total

non-com. farms

(G) Total

(H) Share

in total

(I) Share com. farms

in total

EU total 4,859 100% 7,149 100% 12,008

100% 40%

EU livestock sector 1,941 40% 2,212 31% 4,153 35% 47%

(45) 572 12% 86 1% 658 5% 87%

(48) 448 9% 286 4% 733 6% 61%

(49) 367 8% 173 2% 540 5% 68%

(50) 162 3% 1,262 18% 1,424 12% 11%

(70) 393 8% 406 6% 798 7% 49%

EU cropping sector 2,226 46% 4,057 57% 6,283 52% 35%

(15) 523 11% 849 12% 1,371 11% 38%

(16) 392 8% 1,283 18% 1,675 14% 23%

(20) 179 4% 64 1% 243 2% 74%

(35) 267 5% 325 5% 592 5% 45%

(36) 299 6% 314 4% 613 5% 49%

(37) 245 5% 738 10% 983 8% 25%

(38) 122 3% 153 2% 275 2% 44%

(60) 200 4% 331 5% 532 4% 38%

Mixed cropping & livestock 691 14% 880 12% 1,571 13% 44%

(80) 691 14% 880 12% 1,571 13% 44%

EU cattle sector 2,023 42% 1,545 22% 3,567 30% 57%

Source: Authors based on Eurostat dataset ef_kvftreg and European Commission (2016c) Note: The data is for the year 2010 (most recent data available). Farm numbers are expressed in thousands. At the general farm type level, the EU cattle sector is defined as the sum of farm types 45, 49, 70 and 80. Note that this definition is broader, that is, more imprecise than the definition used in Table 2 since not all farm belonging to these four general farm types keep cattle (see Table A1.2 for details). Thus, the numbers for the EU cattle sector are overestimated in comparison to Table 2. The columns have the following meaning:

http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ef_kvftreg&lang=en


183

Column (A): The overarching category of the TF14 general farm types. Column (B): The category number of the general farm type as defined in Table A1.2 on page 179. Column (C): The number of commercial farms of each TF14 category based on European Commission (2016c). Column (D): The share of the number of commercial farms in each TF14 category in the EU total of commercial

farms. Column (E): The number of non-commercial farms of each TF14 category (calculated as the difference between

the total farm number and the number of commercial farms). Column (F): The share of the number of non-commercial farms in each TF14 category in the EU total of non-

commercial farms. Column (G): The total number of farms in each TF14 category in the EU based on ef_kvftreg. Column (H): The share of the total number of farms in each TF14 category in the EU total. Column (I): The share of the number of commercial farms in each TF14 category in column (C) in the EU total

farm number per TF14 category in column (G). Table A1.6: Standard output of commercial and non-commercial farms per TF14

category

(A) Farm cate-gory

(B) General

farm type

(C) Com. farms

(D) Share in total com. farms

(E) Non-com. farms

(F) Share in total

non-com. farms

(G) Total

(H) Share

in total

(I) Share com. farms

in total

EU total 281 100% 50.3 100% 331 100% 85%

EU livestock sector 138 49% 17.5 35% 155 47% 89%

(45) 50 18% 3.2 6% 53 16% 94%

(48) 12 4% 3.1 6% 15 4% 79%

(49) 18 7% 3.2 6% 21 6% 85%

(50) 48 17% 8.5 17% 56 17% 85%

(70) 10 4% -0.5 -1% 10 3% 105%

EU cropping sector 118 42% 28.6 57% 146 44% 80%

(15) 26 9% 11.1 22% 37 11% 70%

(16) 24 9% 6.7 13% 31 9% 78%

(20) 24 9% 5.3 11% 30 9% 82%

(35) 19 7% 1.4 3% 20 6% 93%

(36) 10 4% 1.4 3% 12 4% 88%

(37) 4 1% 0.2 0% 4 1% 94%

(38) 3 1% 0.3 1% 4 1% 92%

(60) 7 2% 2.2 4% 9 3% 75%



184

Mixed cropping & livestock 25 9% 4.1 8% 29 9% 86%

(80) 25 9% 4.1 8% 29 9% 86%

EU cattle sector 104 37% 10.1 20% 114 34% 91%

Source: Authors based on Eurostat dataset ef_kvftreg and European Commission (2016c) Note: The data is for the year 2010 (most recent data available). The SO numbers are expressed in bln euros. At the general farm type level, the EU cattle sector is defined as the sum of farm types 45, 49, 70 and 80. Note that this definition is broader, that is, more imprecise than the definition used in Table 2 since not all farm belonging to these four general farm types keep cattle (see Table A1.2 for details). Thus, the numbers for the EU cattle sector are overestimated in comparison to Table 2. The columns have the following meaning: Column (A): The overarching category of the TF14 general farm types. Column (B): The category number of the general farm type as defined in Table A1.2 on page 179. Column (C): The standard output of commercial farms of each TF14 category based on European Commission

(2016c). Column (D): The share of the standard output of commercial farms in each TF14 category in the EU total of

commercial farms. Column (E): The standard output of non-commercial farms of each TF14 category (calculated as the difference

between the total standard output and the standard output of commercial farms). Column (F): The share of the standard output of non-commercial farms in each TF14 category in the EU total of

non- commercial farms. Column (G): The total standard output of farms in each TF14 category in the EU based on ef_kvftreg. Column (H): The share of the total standard output of farms in each TF14 category in the EU total. Column (I): The share of the standard output of commercial farms in each TF14 category in column (C) in the EU

total standard output per TF14 category in column (G).




185

Table A1.7: Econometric results on MS contributions to the SO of the EU cattle sector

(A) Reference category (B) Figure (C) Intercept

(D) DEU13

(E) DEU-S

(F) p-value

Total SO of the EU cattle sector

Figure 3 5.34* -4.07* 0.02

Total SO of specialized dairy farms in the EU

Figure 16 3.07* -2.53* <0.01

Total SO of EU specialized cattle farms

Figure 29 1.31* -1.17* <0.01

Total SO of EU mixed farms with cattle

Figure 17 0.96* -0.37 0.45

Total SO of the EU cattle sector

Figure 3 3.59* -0.37 0.84

Total SO of specialized dairy farms in the EU

Figure 16 2.17* -0.70 0.50

Total SO of EU specialized cattle farms

Figure 29 0.65* 0.30 0.48

Total SO of EU mixed farms with cattle

Figure 17 0.76* 0.04 0.93

Source: Authors based on Eurostat dataset ef_kvftreg Note: The basis for the regressions is the data displayed in the Figures mentioned in column (B). The following two models have been estimated for each of the reference categories mentioned in column (A): 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝐷𝐷𝐸𝐸𝐸𝐸13 + 𝜀𝜀𝑖𝑖 and 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝐷𝐷𝐸𝐸𝐸𝐸−𝑆𝑆 + 𝜀𝜀𝑖𝑖 where 𝐷𝐷𝐸𝐸𝐸𝐸13 (𝐷𝐷𝐸𝐸𝐸𝐸−𝑆𝑆) takes the value 1 if the MS belongs to the EU13 (EU-S) and 0 otherwise. 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑖𝑖 is the contribution of the national cattle sector of the i-th MS in bln € to the reference categories mentioned in column (A), i.e. either the EU cattle sector or one of its sub-sectors. Hence, it describes the importance the respective national cattle (sub-) sector plays in the cattle (Sub-) sector of the entire EU. For the first four rows, the column (C) denotes the average national SO of the cattle sector of the respective (sub-) sector of the EU15. Column (D) denotes the difference between the average in the EU13 and the average in the EU15. The average national SO of the cattle sector of the respective (sub-) sector of the EU13 can therefore be calculated line by line, specifically for a given reference category as (C)-(D). For the last four rows, the column (C) denotes the average national SO of the cattle sector of the respective (sub-) sector of the EU-N. Column (E) denotes the difference between the average of the EU-S and the average of the EU-N. The average national SO of the cattle sector of the respective (sub-) sector of the EU-S can therefore be calculated line by line, specifically for a given variable as (C)-(E). Column (F) displays the p-values of the coefficients in (D) and (E). If a value in (D) or (E) carries an asterisk (*), then the difference between both groups (EU15 vs EU13 or EU-N vs. EU-S, respectively) is statistically significant at the 5% level of significance, that is, both groups differ structurally from each other in regard to the reference category in the same row in column (A) and should be dealt with separately for policy purposes.



186

Table A1.8: Contributions of the EU13 and EU-S to the SO of the EU cattle sector

(A) Reference category (B) Figure (C) EU total

(D) Share

(E) EU13

(F) EU-S

SO of the EU cattle sector Figure 3 96.6 100% 17% 37%

SO of specialized dairy farms Figure 16 53.1 55% 13% 30%

SO of EU specialized cattle farms

Figure 29 21.5 22% 8% 49%

SO of EU mixed farms with cattle

Figure 17 22.0 23% 35% 40%

Source: Authors based on Eurostat dataset ef_kvftreg Note: Data for 2013 in bln euros. Column (A) contains the reference category. Column (B) displays the figure in which the structure of the contributions of the MS is shown. Column (C) contains the total SO in bln € for the respective (sub-) sector of the EU cattle sector. Column (D) displays the shares of the three subsectors in the total SO of the EU cattle sector. Column (E) contains the contribution of the EU13 to the numbers in column (C). Column (F) contains the contribution of the EU-S to the numbers in column (C). Figure A1.1: National averages of the production value shares of bovine products

in total production values in 2013

Source: Authors based on European Commission (2016c) Note: For each farm type, the diagram shows the distribution of the 28 national averages of the production value shares of bovine products in the total production values in 2013. It elaborates on footnote 6, page 29. The bold vertical line denotes the median of the national shares. The dot denotes the weighted average share of each farm type at the EU level as displayed in column (F) of Table 3.



187

Figure A1.2: Share of the value of bovine raw products produced in the EU in total EU annual agricultural production value per general farm type

Source: Authors based on European Commission (2016c)

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

30,0%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

(60) Mixed crops

(38) Permanent crops combined

(37) Specialist olives

(36) Specialist orchards fruits

(35) Specialist wine

(20) Specialist horticulture

(16) Specialist other fieldcrops

(15) Specialist COP

(50) Specialist granivores

(48) Specialist sheep and goats

(70) Mixed livestock

(80) Mixed crops and livestock

(49) Specialist cattle

(45) Specialist milk


188

Figure A1.3: Rankings of the cattle sectors of the MS in national agriculture

Source: Authors based on Eurostat dataset ef_kvftreg Note: The diagram shows the ranking of the national cattle sectors of each MS and the entire EU cattle sector in the respective total agricultural sector with respect to each of the six variables of area, farm number, livestock units, labour force, standard output and mainly subsistence (for further explanation of the variables see Figure 6, page 42). The larger the width and the more intense the colour of a line is, the higher is the importance of the national cattle sector for the entire EU cattle sector as defined by the magnitude of the national standard output in 2013. For each of the six variables, the vertical point columns visualise the ranking of the MS’s cattle sectors and the EU cattle sector, displaying the MS cattle sector with the largest share for the respective variable at the top and the one with the smallest share at the bottom. The shares are defined as the percentage the MS’s cattle sector accounts for in the total size of the respective variable in the total agricultural sector of the MS. For example, Luxembourg in the left-upper corner is ranked first for area. This means, that the cattle sector of Luxembourg occupies the largest share (84%) of the total area used in agriculture of this MS in comparison to all other MS and the EU cattle sector etc. The shares underlying this ranking are shown in Table A1.9 below in alphabetic order of the MS names. Underlying data for 2013. The fat black solid line denotes the ranking of the (farm-number) weighted average the entire EU cattle sector relative to the averages of the cattle sectors of the MS. The dashed medium-fat lines denote the largest producers in 2013 defined as the MS whose cattle sector had a standard output of at least 1 bln euros. The intensity of the colours of these dashed lines indicate the EU-wide importance ranking of the cattle sector among these MS defined again via their standard output in 2013. The dark green dashed lines denote the three largest national cattle sectors in the EU that account together for 45% of the total output of the EU cattle sector. The medium light green dashed lines denote the 5 MS that follow the largest 3. These 5 countries account together for 31% of the total standard output, such that these 8 countries together account for 76% of the total. The dotted very thin green lines connecting the dark green points denote the smallest national players in the EU cattle market defined as the MS with a standard output of less than 1 bln € in 2013. Q1, Q2, Q3 and Q4 denote the quartiles of the ordering. That is, Q3 and Q4 denote the 50% smallest MS according to each variable whereby Q4 refers to the lower half (the 7 MS with the lowest shares among all) of this group and Q3 to the upper half. Q2 and Q1 denote the 50% largest MS according to each variable whereby Q2 refers to the lower half of this group and Q1 to the upper half (the 7 MS with the highest shares among all). The line between Q2 and Q3 denotes the median of the 28 MS (neglecting the EU average). The countries ranking directly above and below this line can be considered “typical” for the EU cattle sector among the 28 national cattle sectors concerning this

Q1

Q2

Q3

Q4



189

characteristic. EU average denotes the weighted average of each variable, that is, it reflects the importance of the cattle sector at EU level neglecting any MS borders. For example, the share of the cattle sector in total agricultural area of Ireland ranks second among all MS. The share of cattle keeping farms among all Irish farms ranks first. The share of the Irish cattle sector in its total livestock units, agricultural labour force and TSO ranks second, first and second, respectively. Table A1.9: The importance of the cattle sector in MS’s national agriculture in 2013

MS Farm number

Livestock units

Labour force

Standard output Area Mainly

subsistence

Austria 44% 57% 48% 41% 37% 0%

Belgium 48% 44% 42% 34% 58% 0%

Bulgaria 27% 46% 28% 15% 6% 33%

Croatia 19% 44% 26% 37% 23% 16%

Cyprus 1% 23% 5% 18% 9% 0%

Czech Republic 33% 57% 53% 47% 41% 29%

Denmark 26% 28% 28% 28% 27% 0%

Estonia 19% 63% 47% 55% 38% 14%

Finland 25% 56% 44% 48% 33% 0%

France 30% 56% 31% 29% 42% 10%

Germany 41% 49% 44% 40% 47% 0%

Greece 3% 21% 5% 8% 4% 1%

Hungary 4% 25% 9% 17% 15% 2%

Ireland 71% 80% 74% 73% 66% 0%

Italy 7% 40% 14% 16% 17% 4%

Latvia 31% 64% 47% 42% 33% 22%

Lithuania 34% 65% 43% 41% 37% 34%

Luxembourg 60% 84% 63% 80% 84% 0%

Malta 4% 33% 7% 22% 6% 2%

Netherlands 39% 43% 30% 33% 54% 0%

Poland 22% 46% 29% 31% 31% 22%

Portugal 14% 47% 18% 32% 30% 8%

Romania 13% 34% 24% 21% 12% 13%

Slovakia 32% 51% 50% 39% 31% 37%

Slovenia 39% 65% 51% 53% 55% 30%


190

Spain 9% 29% 14% 16% 19% 1%

Sweden 26% 60% 41% 47% 39% 0%

United Kingdom 31% 49% 35% 38% 27% 0%

EU cattle sector 17% 47% 26% 29% 30% 14%

Source: Authors based on Eurostat dataset ef_kvftreg Note: For each characteristic, the MS that come closest to the median value are underlined. They can be interpreted as a “typical” MS in regard to the role of the cattle sector in their entire national agricultural sector. Table A1.10: Correlations between the MS’s national cattle sector characteristics

Farm number

Livestock units

Labour force

Standard output Area Mainly

subsistence

Farm number 0.79 0.89 0.79 0.88 0.05

Livestock units 0.90 0.91 0.79 0.26

Labour force 0.90 0.82 0.24

Standard output 0.85 0.06

Area -0.07

Source: Authors based on Eurostat dataset ef_kvftreg Note: For each characteristic, the MS that come closest to the median value are underlined. They can be interpreted as a “typical” MS in regard to the role of the cattle sector in their entire national agricultural sector.




191

Table A1.11: Econometric results underlying Figure 7

(A) Characteristic

(B) Intercept

(C) DEU13

(D) DEU-S

(E) p-value

Farm number 0.31* -0.10 0.12

Livestock units 0.49* -0.02 0.72

Labour force 0.35* -0.03 0.65

Standard output 0.37* -0.04 0.58

Area 0.39* -0.13 0.07

Mainly subsistence 0.02 0.17* <0.01

Farm number 0.34* -0.19* <0.01

Livestock units 0.54* -0.14* 0.02

Labour force 0.43* -0.22* <0.01

Standard output 0.43* -0.19* <0.01

Area 0.41* -0.20* <0.01

Mainly subsistence 0.09* 0.01 0.79

Source: Authors based on Eurostat dataset ef_kvftreg Note: The basis for the regressions is the data in Table A1.9. The following two models have been estimated: 𝑠𝑠ℎ𝑣𝑣𝑎𝑎𝑣𝑣𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝐷𝐷𝐸𝐸𝐸𝐸13 + 𝜀𝜀𝑖𝑖 and 𝑠𝑠ℎ𝑣𝑣𝑎𝑎𝑣𝑣𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝐷𝐷𝐸𝐸𝐸𝐸−𝑆𝑆 + 𝜀𝜀𝑖𝑖 where 𝐷𝐷𝐸𝐸𝐸𝐸13 (𝐷𝐷𝐸𝐸𝐸𝐸−𝑆𝑆) takes the value 1 if the MS belongs to the EU13 (EU-S) and 0 otherwise. 𝑠𝑠ℎ𝑣𝑣𝑎𝑎𝑣𝑣𝑖𝑖 is the share of the i-th MS as shown in Table A1.9. It can be interpreted as the importance of the national cattle sector in the total national agricultural sector of a MS concerning a given characteristic in (A). For the first six rows, the column (B) denotes the average national share of the cattle sector in the EU15. Column (C) denotes the difference of the average in the EU13 to the average in the EU15. The average importance of the cattle sector in the EU13 can therefore be calculated line by line, that is, for a given variable as (B)-(C). For the last six rows, the column (B) denotes the average national share of the cattle sector in the EU-N. Column (D) denotes the difference between the average in the EU-S and the average in the EU-N. The average importance of the cattle sector in the EU-S can therefore be calculated line by line, that is, for a given variable as (B)-(D). Column (E) displays the p-values of the coefficients in (C) and (D). If a value in (C) or (D) carries an asterisk (*), then the difference between both groups (EU15 vs EU13 or EU-N vs. EU-S, respectively) is statistically significant at the 5% level of significance, that is, both groups differ structurally from each other concerning the characteristic in the same row in (A) and should be dealt with separately for policy purposes.



192

Map A1.1: Regional distribution of the share of cattle-keeping farms

Source: Authors based on European Commission (2016j) Note: The plot shows the share of cattle-keeping farms (defined at the principal type of farming level) in the total number of farms per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2010 (most recent data available). Table A1.12: Regions with the highest and lowest shares of cattle-keeping farms

Region (MS) Share in com. farms Region (MS) Share in com. farms Asturias (ES) 98% Abruzzo (IT) 2%

Cantabria (ES) 82% Ipiros-Peloponissos-Nissi Ioniou (EL) 2%

Luxembourg (LU) 81% Puglia (IT) 2% Franche-Comte (FR) 79% Toscana (IT) 2%

Auvergne (FR) 78% Sterea Ellas-Nissi Egaeou-Kriti (EL) 1%

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions in which the share of the number of cattle-keeping farms (defined at the principal type of farming level) is highest among all commercial farms in the FADN region. The two right-most columns show the five FADN regions in which the share of the number of cattle-keeping farms among all commercial farms in the FADN region is lowest. The reference category “all farms” is in this context all commercial farms in the FADN region. Data for 2010.


193

Map A1.2: Regional distribution of labour income per FWU in the EU cattle sector

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income per FWU of cattle-keeping farms (defined at the principal type of farming level) in euros. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. The data is the average numbers of years 2011 until 2013 in order to smooth out potential exceptional annual effects. Table A1.13: Regions with the highest and lowest labour income per FWU in the

EU cattle sector

Region (MS) Farm income Region (MS) Farm income Lombardia (IT) 67,032 Severoiztochen (BG) 3,066 Emilia-Romagna (IT) 59,565 Malopolska and Pogorze (PL) 3,061 Veneto (IT) 56,734 Sud-Muntenia (RO) 2,861 Mecklenburg-Vorpommern (DE) 52,250 Nord-Est (RO) 2,770

Picardie (FR) 39,186 Yuzhen tsentralen (BG) 1,694 Source: Authors based on European Commission (2016j) Note: Labour income is measured by the farm net income per FWU in euros. The two left-most columns show the five FADN regions in which the average farm net income per FWU in € of cattle-keeping farms (defined at the principal type of farming level) is highest among all 136 FADN regions of the EU. The two right-most columns show the five FADN regions in which the average farm net income per FWU of cattle-keeping farms in € is lowest. The data are the average numbers of years 2011 until 2013 in order to smooth out potential exceptional annual effects.


194

Map A1.3: Quintiles of the regional distribution of labour income per AWU

Source: Authors based on European Commission (2016j) Note: The plot shows the quintiles of the farm net value added per AWU of cattle-keeping farms (defined at the principal type of farming level) in euros. Since there is data for 128 FADN regions, each quintile consists of 25 or 26 regions. White regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. The data are the average numbers of years 2011 until 2013 in order to smooth out potential exceptional annual effects. Table A1.14: Quintile borders of the regional distribution of labour income per

AWU

Quintile Farm income Maximum 65,629 Quintile 1 35,121 Quintile 2 26,531 Quintile 3 20,191 Quintile 4 10,771 Quintile 5 (minimum) 2,367

Source: Authors based on European Commission (2016j) Note: Labour income is measured by the farm net value added per AWU in euros. The income thresholds in the second column denote (except for the maximum) the minimum farm incomes per AWU belonging to each quintile. The data are the average numbers of years 2011 until 2013 in order to smooth out potential exceptional annual effects.


195

Map A1.4: Quintiles of the regional distribution of labour income per FWU

Source: Authors based on European Commission (2016j) Note: The plot shows the quintiles of the average farm net income per FWU of cattle-keeping farms (defined at the principal type of farming level) in euros. Since there is data for 128 FADN regions, each quintile consists of 25 or 26 regions. White regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. The data are the average numbers of years 2011 until 2013 in order to smooth out potential exceptional annual effects. Table A1.15: Quintile borders of the regional distribution of labour income per FWU

Quintile Farm income Maximum 67,032 Quintile 1 28,119-67,031 Quintile 2 22,072-28,118 Quintile 3 16,931-22,071 Quintile 4 9,306-16,930 Quintile 5 (minimum) 1,694-9,305

Source: Authors based on European Commission (2016j) Note: Labour income is measured by the farm net income per FWU in euros. The income thresholds in the second column denote (except for the maximum) the minimum farm incomes per FWU belonging to each quintile. The data are the average numbers of years 2011 until 2013 in order to smooth out potential exceptional annual effects.


196

Figure A1.4: Distribution of area used by cattle-keeping farms across MS in 2013

Source: Authors based on Eurostat dataset ef_kvftreg Note: This figure shows the relationship between farm type, total area used by all farms of a farm type in a MS and average area per farm. This figure shows the area which is farmed by the farms which keep cattle (either for milk or meat production) belonging to each of the five farm types of the EU cattle sector in each MS. MS are distinguished by whether they belong to the EU13 or EU15. The size of the rectangles is relative to the size of the entire figure and is proportional to the share of the area farmed by a specific farm type in a specific country. Interpreted at the MS level, it shows the total area farmed by the entire beef sector of a MS as a share of the area farmed by the entire EU cattle sector. The colour of the rectangles shows the average farm size category measured by agricultural land per farm in ha of a specific farm type in a specific country. The legend for the colours is located below the figure.

Methodology used in the trade analysis The trade analysis is based on the categorization of European Commission (2016f, 2016g). Therefore, total agri-food trade of the EU is the sum of the values in € or the quantities of all HS 6-digit codes specified in European Commission (2016g). For a summary of the EU agri-food trade see also European Commission (2016h). This nomenclature groups the following six classes of agricultural products into the following meta-categories: Agricultural food and feed products

• Commodities • Other primary agricultural products (among others meat and milk) • Agricultural processed products incl. wine

Products of the food and drink industry • Beverages • Food preparations

Non-edible products • Non-edible products

Table A1.16 outlines the subset of the 56 HS 6-digit level categories among these agri-food trade commodities that originate directly or indirectly from cattle farming. Columns (A) and (B) contain the code and description as defined in UN Trade Statistics (2016a). Column (C) displays the agri-food category as defined by European Commission (2016g). Column (D) specifies the four subgroups of these bovine products as used in this study. Table A1.17 shows how these two categories relate to each other.



197

Table A1.16 Categories of cattle-based products in EU agri-food trade

(A) HS6 CODE

(B) COMMODITY (C) AGRI-FOOD CATEGORY

(D) BOVINE PRODUCT CATEGORY

010210 Live animals Other primary agricultural products Animals



021020 Meat preparations Agricultural processed products incl. wine Meat

020110 Bovine meat, fresh, chilled and frozen

Other primary agricultural products Meat











020610 Offal, animal fat and other meats, fresh, chilled and frozen








040610 Cheese Agricultural processed products incl. wine Dairy


198





040210 Milk powders and whey Commodities Dairy







040510 Butter Commodities Dairy



0404S0 Milk powders and whey Commodities Dairy

040110 Fresh milk and cream, buttermilk and yogurt

Other primary agricultural products Dairy













051110 Non-edible animal products Non-edible products other bovine product


199


Other primary agricultural products other bovine product





160250 Meat preparations Agricultural processed products incl. wine other bovine product

190110 Infant food and other cereals, flour, starch or milk preparations

Food preparations other bovine product





350220 Casein, other albuminoidal substances and modified starches

Non-edible products other bovine product

350290 Casein, other albuminoidal substances and modified starches

Non-edible products other bovine product

410110 Raw hides, skins and furskins Non-edible products other bovine product








200



Source: Authors based on European Commission (2016f, 2016g) Table A1.17: Cattle-based products and EU agri-food trade categories

(B) BOVINE PRODUCT CATEGORY

(A) AGRI-FOOD CATEGORY

ANIMALS DAIRY MEAT OTHER BOVINE PRODUCT

SUM

Agricultural processed products incl. wine 5 1 1 7

Commodities 11 11

Food preparations 3 3

Non-edible products 12 12

Other primary agricultural products 3 7 10 3 23

Vertical sum 3 23 11 19 56

Source: Authors based on European Commission (2016f, 2016g)


201

Table A1.18: EU trade in bovine products in 2015

(A) HS6 CODE

(B) HS6 CODE NAME (C) BOVINE PRODUCT CATEGORY

(D) IMPORT VALUE

(E) EXPORT VALUE

(F) TRADE

BALANCE (E)-(D)

040690 CHEESE dairy 47.7 57.5 9.8

040210 MILK AND CREAM IN SOLID FORMS dairy 5.8 13.6 7.8


010229 LIVE CATTLE (EXCL. PURE-BRED FOR BREEDING) animals 8.6 12.8 4.2

040610 FRESH CHEESE "UNRIPENED OR UNCURED CHEESE dairy 15.4 19.4 4.0

040410 WHEY AND MODIFIED WHEY dairy 6.0 9.2 3.2

040510 BUTTER dairy 11.8 14.4 2.6

040120 MILK AND CREAM dairy 14.4 16.6 2.2

020120 FRESH OR CHILLED BOVINE CUTS meat 13.4 15.2 1.8

040291 MILK AND CREAM, CONCENTRATED dairy 2.5 4.3 1.8

010221 PURE-BRED CATTLE FOR BREEDING animals 1.2 2.9 1.7

040150 MILK AND CREAM dairy 5.7 7.3 1.6

040630 PROCESSED CHEESE dairy 5.2 6.7 1.6

040390 BUTTERMILK, CURDLED MILK AND CREAM, KEPHIR etc.

dairy 4.1 5.5 1.3

020629 FROZEN EDIBLE BOVINE OFFAL meat 0.4 1.7 1.3

020610 FRESH OR CHILLED EDIBLE OFFAL OF BOVINE ANIMALS meat 1.1 2.0 0.9

040640 BLUE-VEINED CHEESE dairy 1.8 2.5 0.7

040620 GRATED OR POWDERED CHEESE dairy 3.9 4.5 0.6


202

040490 PRODUCTS CONSISTING OF NATURAL MILK CONSTITUENTS

dairy 1.4 2.0 0.6

040590 FATS AND OILS DERIVED FROM MILK dairy 3.4 3.8 0.5

020110 CARCASES OR HALF-CARCASES OF BOVINE ANIMALS

meat 7.1 7.3 0.3


020220 FROZEN BOVINE CUTS meat 0.6 0.8 0.1

020622 FROZEN EDIBLE BOVINE LIVERS meat 0.1 0.1 0.1

021020 MEAT OF BOVINE ANIMALS, SALTED meat 0.6 0.7 <0.1

020210 FROZEN BOVINE CARCASES AND HALFCARCASES meat 0.1 0.1 <0.1

020621 FROZEN EDIBLE BOVINE TONGUES meat 0.1 0.1 <0.1

0404S0 CONFIDENTIAL TRADE OF SUB-CHAPTER 0404 AND SITC GROUP 0

dairy 0.0 0.0 0.0

040140 MILK AND CREAM dairy 0.5 0.4 >-0.1

040299 MILK AND CREAM dairy 2.2 2.1 -0.1

040110 MILK AND CREAM dairy 2.1 1.9 -0.2

040520 DAIRY SPREADS dairy 0.7 0.5 -0.3

040310 YOGURT dairy 8.1 7.8 -0.3

020230 FROZEN, BONELESS MEAT OF BOVINE ANIMALS meat 8.6 6.0 -2.6

020130 FRESH OR CHILLED BOVINE MEAT, BONELESS meat 28.6 25.0 -3.6

Source: Authors based on Eurostat Comext dataset DS-016893 Note: Data for 2015 in bln euros. Categories are sorted according to a decreasing magnitude of trade balance.

http://epp.eurostat.ec.europa.eu/newxtweb/


203

Table A1.19: Development of EU exports of bovine products

(A) YEAR

(B) ANIMALS

(C) MEAT

(D) DAIRY

(E) OTHER BOVINE PRODUCTS

(F) MEAT AND DAIRY (C)+(D)

2000 0.1 0.6 5.2 1.6 5.8

2001 0.0 0.6 5.3 1.7 5.9

2002 0.0 0.6 4.8 1.6 5.3

2003 0.0 0.3 4.8 1.5 5.1

2004 0.1 0.4 4.9 1.6 5.3

2005 0.1 0.3 4.9 1.7 5.2

2006 0.1 0.3 4.8 1.9 5.1

2007 0.2 0.2 5.9 2.2 6.2

2008 0.1 0.4 6.2 2.4 6.6

2009 0.1 0.3 5.2 2.5 5.4

2010 0.2 0.7 7.1 3.0 7.8

2011 0.3 1.1 8.1 3.7 9.1

2012 0.7 0.8 8.7 4.4 9.5

2013 0.5 0.7 9.3 5.1 10.0

2014 0.5 0.8 10.2 6.2 11.0

2015 0.9 0.9 9.3 6.4 10.2

Source: Authors based on Eurostat Comext dataset DS-016893 Note: Data in bln euros.



204

Table A1.20: Development of EU imports of bovine products

(A) YEAR

(B) ANIMALS

(C) MEAT

(D) DAIRY


(F) MEAT AND DAIRY (C)+(D)

2000 <0.01 0.9 0.8 1.1 1.8

2001 <0.01 0.7 0.9 1.2 1.6

2002 <0.01 0.9 0.7 1.0 1.6

2003 <0.01 0.9 0.7 0.8 1.6

2004 <0.01 1.1 0.7 0.8 1.8

2005 <0.01 1.3 0.6 0.8 1.9

2006 <0.01 1.5 0.6 0.8 2.2

2007 0.01 1.6 0.7 0.8 2.3

2008 0.01 1.3 0.7 0.8 2.0

2009 0.01 1.2 0.6 0.7 1.8

2010 <0.01 1.3 0.6 0.9 1.9

2011 <0.01 1.5 0.6 0.9 2.1

2012 <0.01 1.5 0.6 0.9 2.1

2013 <0.01 1.5 0.7 0.9 2.2

2014 <0.01 1.7 0.7 0.9 2.4

2015 <0.01 1.8 0.6 0.9 2.4

Source: Authors based on Eurostat Comext dataset DS-016893 Note: Data in bln euros.



205

Figure A1.5: Development of the share of the export value of bovine products in the total extra-EU agri-food export value

Source: Authors based on Eurostat Comext dataset DS-016893 Note: All variables are indexed, that is, the value of 2000 is set to equal 0, the values of the variables for the following years are each divided by the value in 2000 of the corresponding variable and 1 is subtracted from the quotient. Therefore, this graph shows the relative changes of the variables in comparison with 2000. Calculated based on the data in Table A1.19 and Table A1.20.

-200%

-100%

0%

100%

200%

300%

400%

500%

2000 2005 2010 2015

Animals

Meat

Dairy

Other bovine products

Meat and dairy



206

Table A1.21: Development of the share of bovine products in extra-EU agri-food exports

(A) YEAR (B) ANIMALS

(C) MEAT

(D) DAIRY


(F) EU AGRI-FOOD EXPORTS

2000 0.1% 1.2% 9.5% 2.9% 54

2001 0.0% 1.1% 9.6% 3.0% 56

2002 0.0% 1.0% 8.4% 2.8% 57

2003 0.1% 0.6% 8.7% 2.7% 55

2004 0.1% 0.6% 8.8% 2.9% 56

2005 0.1% 0.5% 8.2% 2.8% 60

2006 0.2% 0.5% 7.0% 2.8% 68

2007 0.2% 0.3% 8.4% 3.1% 70

2008 0.1% 0.4% 8.0% 3.1% 78

2009 0.2% 0.4% 7.2% 3.5% 72

2010 0.2% 0.8% 8.1% 3.4% 87

2011 0.3% 1.0% 7.9% 3.6% 102

2012 0.7% 0.7% 7.6% 3.9% 114

2013 0.4% 0.5% 7.7% 4.2% 121

2014 0.4% 0.7% 8.3% 5.1% 123

2015 0.7% 0.7% 7.2% 4.9% 130

Change since 2000 +452% -45% -25% +69% +139%

Source: Authors based on Eurostat Comext dataset DS-016893 Note: Columns (B) to (E) are based on the value shares of the exports in euros. Column (F) in bln euros.



207

Figure A1.6: Development of value shares of extra-EU trade in total EU trade

Source: Authors based on Eurostat Comext dataset DS-016893 Note: EU trade is divided into the groups intra-EU trade (trade that takes place only between EU MS) and extra-EU trade (trade between a MS and a country which is not an EU member) based on the value shares. Total EU trade is the sum of both. The line ‘Animal imports’, for example, shows how the value of animal imports of the EU which originated in countries that are not EU members as a share of the total value of animals imports developed over time. The line “Bovine meat exports” shows what value share bovine meat exports to countries outside the EU had in total EU bovine meat exports, defined as the sum of intra-EU and extra-EU trade in the respective direction and for each product category. Category sums are calculated as the sum of the trade values of all the relevant HS6 commodity categories outlined in Table A1.18. Table A1.22: Shares in global bovine products trade by continent

Trade category Africa Asia Eur-ope

EU of Eur-ope

Extra-EU of

Europe

North Amer-

ica Oce-ania

South Amer-

ica Global dairy imports 6% 34% 51% 97% 2% 6% 1% 1% Global dairy exports 1% 6% 67% 94% 23% 7% 16% 3% Global dairy trade balance 7% 37% 23% 81% 80% 9% 20% 4% Global meat imports 5% 38% 35% 96% 14% 18% 0% 3% Global meat exports 1% 11% 30% 96% 7% 19% 19% 20% Global meat trade balance 5% 43% 6% 61% 41% 5% 19% 22% Global animals imports 3% 29% 35% 97% 0% 33% 0% 0% Global animals exports 3% 1% 45% 98% 20% 29% 16% 6% Global animals trade balance 5% 22% 10% 87% 69% 45% 13% 5% Global bovine products imports 6% 35% 45% 97% 5% 11% 1% 2% Global bovine products exports 1% 7% 53% 94% 20% 12% 17% 9% Global bovine products trade balance 6% 41% 14% 78% 72% 6% 21% 11%

Source: Authors based on Comtrade (2016) Note: All trade categories are expressed in US$. The shares in the global trade balances refer to the absolute value of the trade balances of all countries for which data was available. The trade values of ‘bovine products’ are the sums of the values of dairy, meat and live animals. The column ‘EU of Europe’ denotes the share of the EU in the total trade values for all European countries. The column ‘Extra-EU of Europe’ denotes the share of Extra-EU trade in the total trade values for all European countries. Data for 2014.

0%

10%

20%

30%

40%

50%

60%

2000 2005 2010 2015

Animal imports

Bovine meat imports

Dairy imports

Other bovine productimports

Animal exports

Bovine meat exports



208

Figure A1.7: Market shares in global bovine product exports by country and commodity group

Source: Authors based on Comtrade (2016) Note: This figure gives a visual impression of the market shares of the bovine product categories according to Table A1.18 of single countries and continents in global exports of dairy and bovine meat commodities as well as live bovine animals. Countries are grouped (coloured) according to the continent most of their territory inhabits, e.g. Russia and Turkey are classified as belonging to Asia. These values are the sum of the export values of all the relevant HS6 commodity categories outlined in Table A1.18. The size of a rectangle is relative to the size of the entire figure and is proportional to the share of the country/continent in global exports of these HS6 categories. The value of the EU is the added value of all MS. EU trade is divided into intra-EU trade and extra-EU trade based on the value shares of 2014 in Figure A1.6. Data for 2014. Figure A1.8: Market shares in global bovine product imports by country and

commodity group

Source: Authors based on Comtrade (2016) Note: This figure gives a visual impression of the market shares of the bovine product categories according to Table A1.18 of single countries and continents in global imports of dairy and bovine meat commodities as well as live bovine animals. Countries are grouped (coloured) according to the continent most of their territory inhabits, e.g. Russia and Turkey are classified as belonging to Asia. These values are the sum of the export values of all the relevant HS6 commodity categories outlined in Table A1.18. The size of a rectangle is relative to the size of the entire figure and is proportional to the share of the country/continent in global exports of these HS6 categories. The value of the EU is the added value of all MS. EU trade is divided into intra-EU trade and extra-EU trade based on the value shares of 2014 in Figure A1.6. Data for 2014.


209

Figure A1.9: Trade balances in global bovine product trade

Source: Authors based on Comtrade (2016) Note: Data by country and commodity group. This figure gives a visual impression of the magnitudes of the trade balances of dairy, bovine meat and live bovine animals per product categories according to Table A1.18 of single countries and continents at the global level. The trade balance is defined as the value of imports subtracted from the value of exports of a country. Countries are grouped (coloured) according to the continent most of their territory inhabits, e.g. Russia and Turkey are classified as belonging to Asia. These values are the sum of the export values of all the relevant HS6 commodity categories outlined in Table A1.18. The size of a rectangle is relative to the size of the entire figure and is proportional to the share of the country/continent in global exports of these HS6 categories. The value of the EU is the added value of all MS. EU trade is divided into intra-EU trade and extra-EU trade based on the value shares of 2014 in Figure A1.6. Data for 2014.


210

Table A1.23: Development of the share of bovine products in intra-EU agri-food trade

(A) YEAR (B) ANIMALS

(C) MEAT

(D) DAIRY


(F) INTRA-EU AGRI-FOOD TRADE

2000 0.1% 3.2% 9.5% 2.1% 163

2001 0.1% 2.3% 9.7% 2.1% 173

2002 0.1% 2.8% 8.7% 2.1% 180

2003 0.1% 3.1% 9.3% 2.0% 186

2004 0.1% 3.2% 9.4% 1.9% 194

2005 0.1% 3.3% 9.2% 1.9% 207

2006 0.1% 3.5% 9.1% 1.9% 222

2007 0.1% 3.3% 9.5% 1.8% 247

2008 0.1% 3.4% 8.9% 1.7% 271

2009 0.1% 3.5% 8.3% 1.7% 253

2010 0.1% 3.3% 9.0% 1.8% 274

2011 0.1% 3.3% 9.1% 1.9% 302

2012 0.7% 3.4% 8.5% 1.9% 317

2013 0.7% 3.1% 9.0% 2.0% 331

2014 0.7% 3.1% 9.3% 2.1% 333

2015 0.6% 3.2% 8.3% 2.1% 346

Change since 2000 +698% -1% -13% +1% +112%

Source: Authors based on Eurostat Comext dataset DS-016893 Note: Columns (B) to (E) are based on the value shares of the exports recorded for intra-EU agri-food trade in euros. Column (F) in bln euros. Base on values in nominal terms.



211

Table A1.24: Trading partners of the largest net exports and net importsfor EU bovine meat, dairy and live animal trade

(A) PARTNER

(B) LARGEST

NET EXPORT (C) REGION

(D) PARTNER

(E) LARGEST

NET IMPORT (F) REGION

China 0.92 East Asia Namibia -0.05 SSA

US 0.78 North America New Zealand -0.13 Oceania

Algeria 0.49 MENA Uruguay -0.30 South America

Saudi Arabia 0.48 MENA Argentina -0.37 South America

Hong Kong 0.40 East Asia Brazil -0.45 South America

Source: Authors based on Eurostat Comext dataset DS-016893 Note: Data for 2015 in bln euros. Columns (B) and (E) show the largest surplus/deficit for the aggregated balance of the value of EU exports and imports of bovine meat, dairy and live animals, respectively. Columns (C) and (F) contain the regions the partner country is located in. The table contains the five partner countries with which the trade balance of these three commodity groups for the EU is largest/smallest, respectively. For the ranking, only trade partners are considered with which the EU had a trade balance of at least 10 m € in 2015.



212

Annex to chapter 2: Current Situation in the EU Dairy Sector Table A2.1: Macroeconomic characteristics and structure of milk production of MS

(A)

MS

(B)

AR

EA

(C)

PO

PU

LATI

ON

(D)

GD

P

(E)

AC

CES

SIO

N

YEA

R

(F)

PO

PU

LATI

ON

D

ENS

ITY

(G)

GD

P/C

AP

ITA

(H)

MIL

K

PR

OD

UC

TIO

N

(I)

MIL

K

DEL

IVER

IES

(J)

CO

W N

UM

BER

(K)

MIL

K Y

IELD

AT 83.9 8.6 337 1995 102 39.3 3.1 3.5 534 6.6

BE 30.5 11.3 409 1957 369 36.4 4.0 3.8 529 7.2

BG 110.4 7.2 44 2007 65 6.1 0.5 1.0 283 3.6

CY 9.3 0.8 17 2004 92 20.6 0.2 0.2 26 6.6

CZ 78.9 10.5 164 2004 134 15.6 2.5 3.0 369 8.2

DE 357.4 81.2 3026 1958 227 37.3 31.9 32.5 4285 7.6

DK 42.9 5.7 266 1973 132 47.0 5.3 5.3 570 9.4

EE 45.2 1.3 20 2004 29 15.6 0.7 0.8 91 8.8

EL 132.0 10.9 176 1981 82 16.2 0.6 0.8 111 6.9

ES 505.9 46.4 1081 1986 92 23.3 6.8 6.9 844 8.2

FI 338.4 5.5 207 1995 16 37.9 2.4 2.4 282 8.5

FR 633.2 66.4 2184 1958 105 32.9 25.3 25.9 3661 7.1

HR 56.6 4.2 44 2013 75 10.4 0.5 0.7 152 4.4

HU 93.0 9.9 109 2004 106 11.0 1.5 1.9 251 7.7

IE 69.8 4.6 215 1973 66 46.4 6.6 6.6 1240 5.4

IT 302.1 60.8 1636 1958 201 26.9 11.1 11.4 1826 6.3

LT 65.3 2.9 37 2004 45 12.7 1.4 1.7 301 5.8

LU 2.6 0.6 52 1958 218 92.6 0.3 0.3 49 7.0

LV 64.6 2.0 24 2004 31 12.3 0.8 0.9 162 5.8

MT 0.3 0.4 9 2004 1363 20.5 0.0 0.0 6 6.5


213

NL 41.5 16.9 679 1958 407 40.2 13.3 13.7 1717 8.0

PL 312.7 38.0 428 2004 122 11.3 10.9 10.9 2134 5.1

PT 92.2 10.4 179 1986 112 17.3 1.9 1.8 243 7.5

RO 238.4 19.9 160 2007 83 8.1 0.9 4.0 1191 3.3

SE 438.6 9.7 444 1995 22 45.6 2.9 2.9 337 8.7

SI 20.3 2.1 39 2004 102 18.7 0.6 0.6 113 5.4

SK 49.0 5.4 78 2004 111 14.4 0.9 1.0 139 6.8

UK 248.5 64.9 2569 1973 261 39.6 15.2 15.5 1918 8.1

Source: Authors based on European Commission (2016m) and European Union (2016) Note: Data for 2015. Column (B) in 1000 km2. Column (C) in million inhabitants. Column (E) in bln euros. Column (F) in inhabitants/km2. Column (G) in euros/capita. Column (H) and column (I) in million tonnes. Column (J) in 1000 heads. Column (K) in 1000kg/(cow and year).


214

Figure A2.1 Distribution of the average total area per farm (ha) per MS

Source: Authors based on Eurostat dataset ef_kvftreg Note: Data for 2013. This figure shows the distribution of the total area per farm for each of the five farm types belonging to the EU cattle sector for both commercial and non-commercial farms. Each boxplot summarizes the 28 national average farm areas of the MS belonging to the respective farm type. The boxplots are ordered by decreasing median. The left (right)-most thin vertical line after the dashed line indicates the minimum (maximum) national average farm area among the 28 MS. The left (right)-most border of each coloured box indicates the size of the largest of the 25% (75%) smallest national average farm areas. The dot indicates the average area of all farms in the EU belonging to the respective farm type. The bold vertical line inside each coloured box indicates the EU median area per farm, that is, it indicates the particular national average farm area that is the largest (smallest) of the 50% smallest (largest) national average farm areas all over the EU. The farer the dot is away from the bold vertical line, the more unequal are the average areas per farm within one farm type distributed between the MS, that is, there are few MS in which farms operate on very large areas on average. Figure A2.2 Development of the share of EU13 dairy production in EU total

Source: Authors based on European Commission (2016i)

0%

10%

20%

30%

40%

50%

60%

70%

80%

2000 2005 2010 2015

Dairy cows

Milk yield

Milk production

Milk deliveries



215

Table A2.2 Cattle-based products and EU agri-food trade categories

Year Dai

ry c

ows

(mio

h

ead

s)

of w

hic

h E

U1

5

of w

hic

h E

U1

3

Milk

yie

ld (

kg/d

airy

co

w)

of w

hic

h E

U1

5

of w

hic

h E

U1

3

Del

iver

ed t

o d

airi

es

(mio

t)

of w

hic

h E

U1

5

of w

hic

h E

U1

3

2000 27.2 19.9 7.4 5484.2 6080.2 3875.4 130.9 114.5 16.5

2001 27.1 20.0 7.2 5527.8 6077.6 3997.8 132.1 114.9 17.2

2002 26.6 19.5 7.1 5609.5 6191.7 4016.6 132.1 114.6 17.6

2003 26.1 19.2 6.9 5737.5 6316.6 4126.9 132.9 115.2 17.7

2004 25.4 18.7 6.7 5843.3 6422.3 4234.8 132.6 114.2 18.4

2005 25.0 18.2 6.7 5846.8 6598.0 3817.4 134.1 114.8 19.4

2006 24.4 17.9 6.6 5950.8 6676.4 3975.9 133.3 113.9 19.4

2007 24.3 17.8 6.5 5934.6 6662.0 3944.6 133.8 114.4 19.4

2008 24.4 18.1 6.4 5962.2 6641.1 4033.3 135.3 115.9 19.4

2009 23.9 17.8 6.1 6062.3 6725.1 4126.2 134.2 115.0 19.2

2010 23.3 17.6 5.8 6277.7 6941.0 4257.1 137.3 118.5 18.8

2011 23.1 17.4 5.6 6442.3 7116.7 4361.9 140.3 121.1 19.2

2012 23.0 17.6 5.5 6470.1 7057.1 4593.6 140.6 120.6 20.0

2013 23.3 17.8 5.4 6483.2 7039.7 4659.8 141.6 121.7 19.9

2014 23.3 17.9 5.4 6741.0 7278.1 4950.9 147.9 126.9 21.0

2015 23.4 18.2 5.2 6876.4 7367.8 5162.6 151.6 130.3 21.4

Source: Authors based on European Commission (2016i) Note: This table presents the raw data of Figure 24, p. 74, and Figure A2.2.


216

Table A2.3: Changes in milk deliveries and cow numbers per MS between 2000 and 2015

MS Change in deliveries MS

Change in cow numbers

Latvia 151% Slovakia -60%

Croatia 108% Estonia -51%

Poland 72% Lithuania -49%

Estonia 52% Czech Republic -48%

Slovenia 37% Croatia -45%

EU13 37% Latvia -44%

Austria 35% EU13 -39%

Belgium 34% Poland -38%

Luxembourg 28% Portugal -37%

Portugal 24% Slovenia -36%

Ireland 24% Hungary -36%

Netherlands 23% Romania -35%

Cyprus 21% Spain -34%

Spain 21% Greece -30%

Germany 19% Sweden -30%

Lithuania 18% Finland -30%

Denmark 18% Malta -29%

EU28 17% United Kingdom -26%

EU15 15% EU28 -25%

France 8% Austria -24%

United Kingdom 8% Belgium -23%

Italy 7% France -22%

Bulgaria 5% Denmark -20%

Malta 2% EU15 -19%


217

Finland 1% Bulgaria -19%

Romania 0% Germany -18%

Czech Republic -4% Cyprus -12%

Slovakia -4% Italy -12%

Greece -6% Netherlands -3%

Hungary -6% Ireland 2%

Sweden -10% Luxembourg 3%

Source: Authors based on European Commission (2016i) Note: This table is also based on Table A2.2. The MS are sorted in descending order according to the absolute magnitude of change between 2000 and 2015. Map A2.1: Regional distribution of mixed livestock farms

Source: Authors based on European Commission (2016j) Note: The plot shows the number of farms of principal farming type (73) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2010.


218

Table A2.4: Regions with the highest and lowest numbers of mixed livestock farms

Region (MS) Number of com. farms Region (MS) Number of

com. farms Sud-Muntenia (PO) 41,100 Andalucia (ES) 900 Mazowsze and Podlasie (PL) 32,900 Vlaanderen (BE) 500

Nord-Est (RO) 27,100 Baden-Wurttemberg (DE) 500 Sud-Vest-Oltenia (RO) 26,000 Hessen (DE) 400 Nord-Vest (RO) 22,700 Czech Republic (CZ) 400 Sum (share in total) 149,800 (62%) Sum (share in total) 2,700 (1%)

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest numbers of mixed livestock farms in all farms. The two right-most columns show the five FADN regions which have the smallest numbers of mixed livestock farms. Data for 2010. 98 regions are recorded as not having any mixed livestock farms. Map A2.2: Regional distribution of crops and cattle farms

Source: Authors based on European Commission (2016j) Note: The plot shows the number of farms of principal farming type (83) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2010.


219

Table A2.5: Regions with the highest and lowest numbers of crops and cattle farms

Region (MS) Number of com. farms Region (MS) Number of com.

farms Mazowsze and Podlasie (PL) 41,100 Mecklenburg-

Vorpommern (DE) 300

Malopolska and Pogorze (PL) 26,100 Wales (UK) 300

Wielkopolska and Slask (PL) 15,800 Yugoiztochen (BG) 300

Sud-Vest-Oltenia (RO) 14,600 Thueringen (DE) 200 Nord-Vest (RO) 10,100 Luxembourg (LU) 100 Sum (share in total) 107,700 (46%) Sum (share in total) 1,200 (<1%)

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest numbers of crops and cattle farms in all farms. The two right-most columns show the five FADN regions which have the smallest numbers of crops and cattle farms. Data for 2010. 47 regions are recorded as not having any crops and cattle farms. Map A2.3: Regional distribution of average dairy herd per farm

Source: Authors based on European Commission (2016j) Note: The plot shows the average number of dairy cows kept on farms belonging to the EU cattle sector per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013.


220

Table A2.6: Regions with the highest and lowest average dairy herd per farm

Region (MS) Animals per farm Region (MS) Animals per farm Mecklenburg-Vorpommern (DE) 222 Nord-Vest (RO) 3

Sachsen-Anhalt (DE) 179 Vest (RO) 3 Thueringen (DE) 133 Nord-Est (RO) 3 Sachsen (DE) 131 Sud-Vest-Oltenia (RO) 2 Slovakia (SK) 124 Sud-Muntenia (RO) 2

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average dairy cow herd size per cattle-keeping farm. The two right-most columns show the five FADN regions in which the smallest average dairy cow herd size per cattle-keeping farm. Data for 2013. 10 regions are recorded as not keeping any dairy cows. Map A2.4: Regional distribution of total cattle kept by specialist dairying farms

Source: Authors based on European Commission (2016j) Note: The plot shows the total number of cattle (that is, dairy cows as well as any other cattle) kept on specialist dairying farms (type (45) as defined at the principal type of farming level) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013.


221

Table A2.7: Regions with the highest and lowest total cattle kept by specialist dairying farms

Region (MS) Cattle number Region (MS) Cattle number The Netherlands (NL) 2,088,761 Adriatic Croatia (HR) 17,499 Bayern (DE) 1,921,595 Baleares (ES) 16,821 Ireland (IE) 1,694,848 Umbria (IT) 11,056 Mazowsze and Podlasie (PL) 1,478,916

Malta (MT) 9,597

Niedersachsen (DE) 1,127,357 Liguria (IT) 4,506 Sum (share in total) 8.4m (28%) Sum (share in total) 59,479 (0.2%)

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest total number of cattle (that is, dairy cows as well as any other cattle) kept on specialist dairying farms. The two right-most columns show the five FADN regions in which the smallest total number of cattle (that is, dairy cows as well as any other cattle) kept on specialist dairying farms. Data for 2013. For 15 regions, no data was available. The total refers to the number of cattle kept by all farms of this type throughout the EU. Map A2.5: Regional distribution of total cattle kept by other dairying farms

Source: Authors based on European Commission (2016j) Note: The plot shows the total number of cattle (that is, dairy cows as well as any other cattle) kept on other dairying farms (types (47), (73) and (83) as defined at the principal type of farming level) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013.


222

Table A2.8: Regions with the highest and lowest total cattle kept by other dairying farms

Region (MS) Cattle number Region (MS) Cattle number Wielkopolska and Slask (PL) 647,014 Castilla-La Mancha (ES) 3,110

Czech Republic (CZ) 578,337 Molise (IT) 2,949 Mazowsze and Podlasie (PL) 564,805 Sterea Ellas-Nissi

Egaeou-Kriti (EL) 1,425

Pays de la Loire (FR) 446,678 Ipiros-Peloponissos-Nissi Ioniou (EL) 397

Wallonie (BE) 396,547 Thessalia (EL) 100 Sum (share in total) 2.6m (25%) Sum (share in total) 7,980 (<0.1%)

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the total number of cattle (that is, dairy cows as well as any other cattle) kept on other dairying farms. The two right-most columns show the five FADN regions in which the smallest total number of cattle (that is, dairy cows as well as any other cattle) kept on other dairying farms. Data for 2013. For 43 regions, no data was available. The total refers to the number of cattle kept by all farms of this type throughout the EU. Map A2.6: Stocking density of dairying and meat farms

Source: Authors based on European Commission (2016j) Note: The plot shows the stocking density of dairying and meat farms (principal farming type (47)) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013.


223

Table A2.9: Highest and lowest stocking densities of dairying and meat farms

Region (MS) Stocking density Region (MS) Stocking density

Vlaanderen (BE) 3.1 Austria (AT) 0.5 Sud-Est (RO) 2.9 Latvia (LV) 0.5 Nordrhein-Westfalen (DE) 2.3 Lithuania (LT) 0.4 The Netherlands (NL) 2.3 Estonia (EE) 0.4 Niedersachsen (DE) 2.1 Slovakia (SK) 0.4

Source: Authors based on European Commission (2016j) Note: Stocking density measured in LSU per ha UAA, see Table A.1, p. 171, for an exact definition. The two left-most columns show the five FADN regions with the highest stocking density of dairying and meat farms. The two right-most columns show the five FADN regions with the smallest stocking density of dairying and meat farms. Data for 2013. For 89 regions, no data was available. Map A2.7: Stocking density of mixed livestock farms

Source: Authors based on European Commission (2016j) Note: The plot shows the stocking density of mixed livestock farms (principal farming type (73)) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A2.10: Highest and lowest stocking densities of mixed livestock farms


Sud-Vest-Oltenia (RO) 3.3 Austria (AT) 1.0 Vlaanderen (BE) 3.0 Centru (RO) 1.0 Sud-Muntenia (RO)

2.8 Continental Croatia (HR) 0.9

The Netherlands (NL) 2.3 Andalucia ES) 0.5 Niedersachsen (DE) 2.3 Latvia (LV) 0.3

Source: Authors based on European Commission (2016j) Note: Stocking density measured in LSU per ha UAA, see Table A.1, p. 171, for an exact definition. The two left-most columns show the five FADN regions with the highest stocking density of mixed livestock farms. The two right-most columns show the five FADN regions with the smallest stocking density of mixed livestock farms. Data for 2013. For 98 regions, no data was available.


224

Map A2.8: Stocking density of crops and cattle farms

Source: Authors based on European Commission (2016j) Note: The plot shows the stocking density of crops and cattle farms (principal farming type (83)) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A2.11: Highest and lowest stocking densities of crops and cattle farms


Castilla-La Mancha (ES) 8.0

Extremadura (ES) 0.6

Thessalia (EL) 7.5 Estonia (EE) 0.5 Sud-Vest-Oltenia (RO) 3.4 Aragon (ES) 0.5 Makedonia-Thraki (EL) 3.2 Andalucia (ES) 0.4 Wallonie (BE) 2.8 Alentejo e do Algarve (PT) 0.3

Source: Authors based on European Commission (2016j) Note: Stocking density measured in LSU per ha UAA, see Table A.1, p. 171, for an exact definition. The two left-most columns show the five FADN regions with the highest stocking density of crops and cattle farms. The two right-most columns show the five FADN regions with the smallest stocking density of crops and cattle farms. Data for 2013. For 47 regions, no data was available.


225

Map A2.9: Regional distribution of the milk yield of dairying and meat farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average milk yield in kg/ cow of farms of principal farming type (47) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A2.12: Regions with the highest and lowest milk yield of dairying and meat

farms

Region (MS) Average milk yield Region (MS) Average milk

yield The Netherlands (NL) 7,855 Acores e Madeira (PT) 3,597

Nordrhein-Westfalen (DE) 7,424 Malopolska and Pogorze (PL) 3,508

Niedersachsen (DE) 7,189 Centru (RO) 3,480 Vlaanderen (BE) 7,128 Nord-Est (RO) 3,113 Champagne-Ardenne (FR) 7,128 Aosta (IT) 2,340

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average milk yield in kg/cow of principal farming type (47). The two right-most columns show the five FADN regions which have the smallest average milk yield in kg/cow of principal farming type (47). Data for 2013. For 89 regions, no data was available.


226

Map A2.10: Regional distribution of the milk yield of mixed livestock farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average milk yield in kg/ cow of farms of principal farming type (73) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A2.13: Regions with the highest and lowest milk yield of mixed livestock

farms


yield The Netherlands (NL) 8,307 Malopolska and Pogorze (PL) 3,247 Niedersachsen (DE) 8,202 Sud-Est (RO) 3,150 Nordrhein-Westfalen (DE) 8,044 Centru (RO) 3,009 Vlaanderen (BE) 7,801 Continental Croatia (HR) 2,859 Bretagne (FR) 7,716 Sud-Vest-Oltenia (RO) 2,824



227

Map A2.11: Regional distribution of the milk yield of crops and cattle farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average milk yield in kg/ cow of farms of principal farming type (83) per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A2.14: Regions of highest and lowest milk yield of crops and cattle farms


yield England-East (UK) 9,515 Sud-Est (RO) 3,426 Piemonte (IT) 9,291 Sud-Vest-Oltenia (RO) 3,212 Thüringen (DE) 9,099 Yugoiztochen (BG) 3,181 Mecklenburg-Vorpommern (DE) 9,047 Campania (IT) 2,716

Brandenburg (DE) 8,972 Sterea Ellas-Nissi Egaeou-Kriti (EL) 1,300



228

Map A2.12: Regional distribution of economic size of dairying and meat farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average economic size of dairying and meat farms (principal farming type (47)) measured in SO in terms of 1000 € per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A2.15: Regions of highest and lowest economic size of dairying and meat

farms Region (MS) Economic size Region (MS) Economic size Slovakia (SK) 574 Centru (RO) 10 Czech Republic (CZ) 284 Sud-Est (RO) 9 The Netherlands (NL) 245 Malopolska and Pogorze (RO) 8 Luxembourg (LU) 239 Nord-Vest (RO) 7 Schleswig-Holstein (DE) 233 Nord-Est (RO) 6

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average economic size of dairying and meat farms (principal farming type (47)). The two right-most columns show the five FADN regions which have the smallest economic size of dairying and meat farms (principal farming type (47)). Data for 2013. For 90 regions, no data was available.


229

Map A2.13: Regional distribution of economic size of mixed livestock farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average economic size of mixed livestock farms (principal farming type (73)) measured in SO in terms of 1000 € per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Table A2.16: Regions with the highest and lowest economic size of mixed

livestock farms Region (MS) Economic size Region (MS) Economic size Czech Republic (CZ) 679 Sud-Vest-Oltenia (RO) 6 The Netherlands (NL) 417 Sud-Est (RO) 6 Vlaanderen (BE) 307 Nord-Vest (RO) 6 Bretagne (FR) 289 Sud-Muntenia (RO) 5 Niedersachsen (DE) 269 Nord-Est (RO) 5

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average economic size of mixed livestock farms (principal farming type (73)). The two right-most columns show the five FADN regions which have the smallest economic size of mixed livestock farms (principal farming type (73)). Data for 2013. For 102 regions, no data was available.


230

Map A2.14: Regional distribution of economic size of crops and cattle farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average economic size of crops and cattle farms (principal farming type (83)) measured in SO in terms of 1000 € per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Table A2.17: Regions with the highest and lowest economic size of crops and cattle

farms Region (MS) Economic size Region (MS) Economic size Thüringen (DE) 1674 Nord-Est (RO) 8 Mecklenburg-Vorpommern (DE) 1240 Nord-Vest (RO) 8

Sachsen-Anhalt (DE) 1020 Sud-Est (RO) 7 Slovakia (SK) 904 Vest (RO) 6 Sachsen (DE) 834 Sud-Vest-Oltenia (RO) 6

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average economic size of crops and cattle farms (principal farming type (83)). The two right-most columns show the five FADN regions which have the smallest economic size of crops and cattle farms (principal farming type (83)). Data for 2013. For 46 regions, no data was available.


231

Map A2.15: Regional distribution of farm income of dairying and meat farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income of dairying and meat farms (principal farming type (47)) measured in € per year and FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data is the average of net farm income of years 2011, 2012 and 2013. Table A2.18: Regions with the highest and lowest economic size of dairying and

meat farms

Region (MS) Net farm income Region (MS) Net farm

income Emilia-Romagna (IT) 55,722 Centru (RO) 6,284 Wallonie (BE) 54,642 Malopolska and Pogorze (PL) 6,276 Lorraine (FR) 52,385 Mazowsze and Podlasie (PL) 5,490 Czech Republic (CZ) 51,052 Sud-Est (RO) 3,303 Schleswig-Holstein (DE) 49,076 Nord-Est (RO) 2,568

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average farm net income in euros/year of dairying and meat farms (principal farming type (47)). The two right-most columns show the five FADN regions which have the smallest farm net income of dairying and meat farms (principal farming type (47)). Data is the average of net farm income of years 2011, 2012 and 2013. For 90 regions, no data was available. Slovakia is not taken into account, since the average farm income was negative.


232

Map A2.16: Regional distribution of farm income of mixed livestock farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income of mixed livestock farms (principal farming type (73)) measured in € per year and FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data is the average of net farm income of years 2011, 2012 and 2013. Table A2.19: Regions with the highest and lowest farm income of mixed livestock

farms


income Czech Republic (CZ) 84,194 Sud-Muntenia (RO) 3,105 Bretagne (FR) 70,037 Malopolska and Pogorze (PL) 2,851 Vlaanderen (BE) 66,059 Lithuania (LT) 2,722 The Netherlands (NL) 52,896 Nord-Est (RO) 1,741 Niedersachsen (DE) 39,819 Slovenia (SI) 683

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average farm net income in euros/year of mixed livestock farms (principal farming type (73)). The two right-most columns show the five FADN regions which have the smallest farm net income of mixed livestock farms (principal farming type (73)). Data is the average of net farm income of years 2011, 2012 and 2013. For 102 regions, no data was available.


233

Map A2.17: Regional distribution of farm income of crops and cattle farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income of crops and cattle farms (principal farming type (83)) measured in € per year and FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data is the average of years 2011, 2012 and 2013. Table A2.20: Regions with the highest and lowest farm income of crops and cattle

farms


income Thüringen (DE) 153,275 Vest (RO) 4,463 Sachsen-Anhalt (DE) 128,215 Sud-Muntenia (RO) 4,231 Mecklenburg-Vorpommern (DE) 108,010 Malopolska and Pogorze (PL) 3,772

Sachsen (DE) 88,380 Denmark (DK) 3,430 Vlaanderen (BE) 84,323 Nord-Est (RO) 3,346

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average farm net income in euros/year of crops and cattle farms (principal farming type (83)). The two right-most columns show the five FADN regions which have the smallest farm net income of crops and cattle farms (principal farming type (83)). Data is the average of net farm income of years 2011, 2012 and 2013. For 46 regions, no data was available. Slovakia is taken into account, since the average farm income was negative.


234

Map A2.18: Regional distribution of labour income of dairying and meat farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income/AWU of dairying and meat farms (principal farming type (47)) measured in € per year and FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data is the average of years 2011, 2012 and 2013. Table A2.21: Regions with the highest and lowest labour income of dairying and

meat farms Region (MS) Labour income Region (MS) Labour income Schleswig-Holstein (DE) 29,657 Centru (RO) 4,533 Vlaanderen (BE) 27,546 Pomorze and Mazury (PL) 4,302 Wallonie (BE) 26,227 Mazowsze and Podlasie (PL) 3,774 Ireland (IE) 26,057 Nord-Est (RO) 2,786 Emilia-Romagna (IT) 25,258 Sud-Est (RO) 2,708

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average labour income (farm net income/AWU) in euros/year of dairying and meat farms (principal farming type (47)). The two right-most columns show the five FADN regions which have the smallest labour income of specialist dairying farms. Data is the average of years 2011, 2012 and 2013. For 90 regions, no data was available. Slovakia is not taken into account, since the average farm income was negative.


235

Map A2.19: Regional distribution of labour income of mixed livestock farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income/AWU of mixed livestock farms (principal farming type (73)) measured in € per year and FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data is the average of years 2011, 2012 and 2013. Table A2.22: Regions with the highest and lowest labour income of mixed

livestock farms Region (MS) Labour income Region (MS) Labour income Netherlands (NL) 34,326 Sud-Est (RO) 2,287 Bretagne (FR) 34,022 Lithuania (LT) 2,169 Vlaanderen (BE) 32,765 Malopolska and Pogorze (PL) 2,096 Niedersachsen (DE) 19,169 Nord-Est (RO) 1,687 Bayern (DE) 15,890 Slovenia (SI) 714

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average labour income (farm net income/AWU) in euros/year of mixed livestock farms (principal farming type (73)). The two right-most columns show the five FADN regions which have the smallest labour income of specialist dairying farms. Data is the average of years 2011, 2012 and 2013. For 102 regions, no data was available.


236

Map A2.20: Regional distribution of labour income of crops and cattle farms

Source: Authors based on European Commission (2016j) Note: The plot shows the average farm net income/AWU of crops and cattle farms (principal farming type (83)) measured in € per year and FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data is the average of years 2011, 2012 and 2013. Table A2.23: Regions with the highest and lowest labour income of crops and

cattle farms Region (MS) Labour income Region (MS) Labour income Vlaanderen (BE) 45,250 Sud-Vest-Oltenia (RO) 3,187 Picardie (FR) 42,045 Nord-Est (RO) 3,065 Wallonie (BE) 38,505 Continental Croatia (HR) 3,016 Poitou-Charentes (FR) 32,048 Malopolska and Pogorze (PL) 2,643 Niedersachsen (DE) 31,029 Severoiztochen (BG) 2,052

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average labour income (farm net income/AWU) in euros/year of crops and cattle farms (principal farming type (83)). The two right-most columns show the five FADN regions which have the smallest labour income of specialist dairying farms. Data is the average of years 2011, 2012 and 2013. For 46 regions, no data was available. Slovakia is not taken into account, since the average farm income was negative.


237

Map A2.21: Share of labour employed by dairying and meat farms in total regional labour

Source: Authors based on European Commission (2016j) and European Commission (2015b, table C.05) Note: The plot shows the share of the total labour in AWU per year employed by dairying and meat farms (principal farming type (47)) as a share in the total labour force employed in the FADN region. Total regional labour is defined as the number of total employed persons aged 20-64 in the FADN region based on suitable aggregation of the NUTS2-level data in European Commission (2015b, table C.05 – Employment rate). Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Agricultural labour for 2013, total regional labour for 2014. Table A2.24: Highest and lowest shares of labour employed by dairying and meat

farms in regional labour

Region (MS) Share in regional labour Region (MS) Share in

regional labour Aosta (IT) 1.44% Czech Republic (CZ) 0.04% Nord-Vest (RO) 1.43% Makedonia-Thraki (EL) 0.04% Centru (RO) 0.96% Ipiros-Peloponissos-Nissi Ioniou (EL) 0.02% Nord-Est (RO) 0.83% Thessalia (EL) 0.01% Sud-Est (RO) 0.68% Sterea Ellas-Nissi Egaeou-Kriti (EL) 0.01%

Source: Authors based on European Commission (2016j) and and European Commission (2015b, table C.05) Note: The two left-most columns show the five FADN regions in which dairying and meat farms (principal farming type (47)) employ the highest share of regional labour. The two right-most columns show the five FADN regions in which this share is lowest among all FADN regions. Agricultural labour for 2013, total regional labour for 2014.


238

Map A2.22: Share of labour employed by mixed livestock farms in total regional labour

Source: Authors based on European Commission (2016j) and European Commission (2015b, table C.05) Note: The plot shows the share of the total labour in AWU per year employed by mixed livestock farms (principal farming type (73)) as a share in the total labour force employed in the FADN region. Total regional labour is defined as the number of total employed persons aged 20-64 in the FADN region based on suitable aggregation of the NUTS2-level data in the European Commission (2015b, table C.05 – Employment rate). Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Agricultural labour for 2013, total regional labour for 2014. Table A2.25: Highest and lowest shares of labour employed by mixed livestock



regional labour Sud-Vest-Oltenia (RO) 5.3% Bayern (DE) 0.02% Sud-Muntenia (RO) 4.1% Nordrhein-Westfalen (DE) 0.02% Nord-Est (RO) 3.5% Hessen (DE) 0.02% Nord-Vest (RO) 3.2% Netherlands (NL) 0.02% Centru (RO) 2.4% Baden-Wurttemberg (DE) 0.01%

Source: Authors based on European Commission (2016j) and and European Commission (2015b, table C.05) Note: The two left-most columns show the five FADN regions in which mixed livestock farms (principal farming type (73)) employ the highest share of regional labour. The two right-most columns show the five FADN regions in which this share is lowest among all FADN regions. Agricultural labour for 2013, total regional labour for 2014.


239

Map A2.23: Share of labour employed by crops and cattle farms in total regional labour

Source: Authors based on European Commission (2016j) and European Commission (2015b, table C.05) Note: The plot shows the share of the total labour in AWU per year employed by crops and cattle farms (principal farming type (83)) as a share in the total labour force employed in the FADN region. Total regional labour is defined as the number of total employed persons aged 20-64 in the FADN region based on suitable aggregation of the NUTS2-level data in European Commission (2015b, table C.05 – Employment rate). Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Agricultural labour for 2013, total regional labour for 2014. Table A2.26: Highest and lowest shares of labour employed by crops and cattle



regional labour Sud-Vest-Oltenia (RO) 2.6% Wales (UK) 0.04% Nord-Vest (RO) 1.5% Saarland (DE) 0.04% Del-Alfold (HU) 1.2% Scotland (UK) 0.03% Mazowsze and Podlasie (PL) 1.2% Nordrhein-Westfalen (DE) 0.03% Eszak-Alfold (HU) 1.1% England-East (UK) 0.02%

Source: Authors based on European Commission (2016j) and and European Commission (2015b, table C.05) Note: The two left-most columns show the five FADN regions in which crops and cattle farms (principal farming type (83)) employ the highest share of regional labour. The two right-most columns show the five FADN regions in which this share is lowest among all FADN regions. Agricultural labour for 2013, total regional labour for 2014.


240

Map A2.24: Labour income of dairying and meat farms as share of regional income

Source: Authors based on European Commission (2016j) and and European Commission (2015b, tables CCI 1 - Population and C.08 - GDP per capita) Note: The plot shows the average income per AWU of dairying and meat farms (principal farming type (47)) as a share in the average income per FADN region. Average income per FADN region is defined as GDP/capita calculated from European Commission (2015b, tables CCI 1 - Population and C.08 - GDP per capita). Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Agricultural income is the average of years 2011, 2012 and 2013, regional income for 2014. Table A2.27: Highest and lowest shares of labour income of dairying and meat

farms in regional average income

Region (MS) Share in av. income Region (MS) Share in av.

income Acores e Madeira (PT) 122% Nordrhein-Westfalen (DE) 21% Franche-Comte (FR) 103% Centru (RO) 20% Champagne-Ardenne (FR) 91% Sud-Est (RO) 13% Sicilia (IT) 87% Estonia (EE) 11% Piemonte (IT) 74% Baden-Wurttemberg (DE) 6%

Source: Authors based on European Commission (2016j) and European Commission (2015b, tables CCI 1 and C.08) Note: The two left-most columns show the five FADN regions in which the share of income per AWU of dairying and meat farms (principal farming type (47)) is highest in the average income of the FADN region defined as GDP per capita. The two right-most columns show the five FADN regions in which this share is lowest among all FADN regions. Agricultural income is the average of years 2011, 2012 and 2013, regional income for 2014.


241

Map A2.25: Labour income of mixed livestock farms as share of regional income

Source: Authors based on European Commission (2016j) Note: The plot shows the average income per AWU of mixed livestock farms (principal farming type (73)) as a share in the average income per FADN region. Average income per FADN region is defined as GDP/capita calculated from European Commission (2015b, tables CCI 1 - Population and C.08 - GDP per capita). Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Agricultural income is the average of years 2011, 2012 and 2013, regional income for 2014. Table A2.28: Highest and lowest shares of labour income of mixed livestock farms

in regional average income


income Bretagne (FR) 127% Mazowsze and Podlasie (PL) 29% Vlaanderen (BE) 90% Baden-Wurttemberg (DE) 29% Andalucia (ES) 89% Malopolska and Pogorze (PL) 22% Netherlands (NL) 88% Lithuania (LT) 18% Niedersachsen (DE) 59% Slovenia (SI) 4%

Source: Authors based on European Commission (2016j) and European Commission (2015b, tables CCI 1 and C.08) Note: The two left-most columns show the five FADN regions in which the share of income per AWU of mixed livestock farms (principal farming type (73)) is highest in the average income of the FADN region defined as GDP per capita. The two right-most columns show the five FADN regions in which this share is lowest among all FADN regions. Agricultural income is the average of years 2011, 2012 and 2013, regional income for 2014.


242

Map A2.26: Labour income of crops and cattle farms as share of regional income

Source: Authors based on European Commission (2016j) Note: The plot shows the average income per AWU of crops and cattle farms (principal farming type (83)) as a share in the average income per FADN region. Average income per FADN region is defined as GDP/capita calculated from European Commission (2015b, tables CCI 1 - Population and C.08 - GDP per capita). Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Agricultural income is the average of years 2011, 2012 and 2013, regional income for 2014. Table A2.29: Highest and lowest shares of labour income of crops and cattle

farms in regional average income


income Picardie (FR) 177% Malopolska and Pogorze (PL) 27% Eszak-Alfold (HU) 165% Slattbygdslan (SE) 22% Wallonie (BE) 148% Slovenia (SI) 22% Del-Alfold (HU) 144% Luxembourg (LU) 17% Alfold (HU) 140% Denmark (DK) 14%

Source: Authors based on European Commission (2016j) and European Commission (2015b, tables CCI 1 and C.08) Note: The two left-most columns show the five FADN regions in which the share of income per AWU of crops and cattle farms (principal farming type (83)) is highest in the average income of the FADN region defined as GDP per capita. The two right-most columns show the five FADN regions in which this share is lowest among all FADN regions. Agricultural income is the average of years 2011, 2012 and 2013, regional income for 2014.


243

Table A2.30: Structure of the dairy processing industry Total volume of milk

processed (tsd ton) % change in volume processed, 2000 – 2012a

Distribution of dairies, share of size classes in total milk volume processed, 2012a

MS 2012a 2000-2006 2006-2012 < 5,000 5,001-20,000

20,001-50,000

50,001-100,000

>100,000

Belgium 3,333 -5.5 4.3 0.3 1.2 5.2 4.8 88.6 Bulgaria 599 - -32.2 37.6 39.4 23.0 0.0 0.0 Czech Republic 2,503 - 0.9 0.1 4.3 14.0 16.6 65.1 Denmark 4,511 -6.3 6.5 0.4 2.1 2.5 5.7 89.3 Germany 29,754 19.1 -25.8 0.2 0.5 3.4 3.6 92.3 Estonia 665 - 22.1 1.6 0.0 21.6 76.8 0.0 Ireland 1,406 -9.1 -70.0 0.6 11.2 7.3 21.6 59.4 Greece 1,385 - -5.7 29.6 21.7 9.8 26.1 12.9 Spain 7,190 16.0 -1.2 5.4 7.9 5.6 14.1 67.1 France 26,000 1.7 6.7 2.2 3.5 5.7 6.9 81.7 Croatia 192 - -22.2 16.7 34.1 49.2 0.0 0.0 Italy 11,164 11.3 -21.0 15.6 24.0 19.9 15.2 25.3 Cyprus 192 - 9.9 16.4 12.9 35.0 35.7 0.0 Latvia - - - - - - - - Lithuania 911 - -21.6 1.7 13.3 24.4 - Luxembourg 185 - 16.3 - - - - - Hungary 1,188 - -11.7 2.0 10.1 13.2 28.6 46.1 Malta 39 - -5.0 - - - - - Netherlands 11,846 -11.7 -7.2 0.2 0.6 1.2 2.6 95.4 Austria 1,422 11.7 -34.5 5.3 9.3 8.1 23.4 53.8 Poland 3,513 - -59.2 4.4 12.4 14.2 16.8 52.2 Portugal 1,823 3.8 -6.2 5.2 7.3 12.5 14.0 61.0 Romania 930 - -14.4 35.9 24.0 34.7 - - Slovenia 368 - -10.3 - - - - - Slovakia 743 - -4.9 2.7 17.5 - - - Finland 2,281 - -2.8 0.0 5.8 8.3 0.0 86.0 Sweden 2,933 -5.2 -6.8 0.3 1.2 2.1 3.3 93.2 United Kingdom 12,701 -4.7 - 1.4 2.4 3.3 6.4 86.5

a 2015 for Czech Republic and Luxembourg; 2009 for Belgium, Bulgaria, Denmark, Greece, Italy, Hungary, Malta, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden; 2006 for United Kingdom Source: Eurostat (2016m)


244

Table A2.31: Main dairy processors on the EU market, 2008-2013

Company Dairy turnover 2008

(billion Euro)

Dairy turnover 2013

(billion Euro) Nestlé S.A., Switzerland 16.9 21.5 Groupe Danone, France 9.2 15.3 Groupe Lactalis, France 9.2 14.7 Royal FrieslandCampina N.V., The Netherlands

9.1 11.4

Arla Foods amba, Denmark 6.4 9.5 Parmalat Finanziaria S.p.A., Italy 3.6 - Unilever, The Netherlands - 5.6 DMK - 5.3 Bongrain, S.A., France 3.4 4.4 Groupe Sodiaal, France 2.8 5.0 Nordmilch AG, Germany 2.3 - Theo Müller GmbH & Co. KG., Germany 2.2 3.8 Humana MilchUnion e.G., Germany 2.2 - Tine BA, Norway 2.0 - Groupe Bel, France 2.0 - Glanbia plc, Ireland 1.8 - EU 138 174.2

Source: Authors based on Baking+Biscuit International (2009), Foodmanufacture (2014a) and European Parliament (2015d)


245

Table A2.32: Market shares of main dairy processors per MSa

Dairy production value (bio euro)

Cheese Drinking milk products

Yoghurt and sour milk products

Other dairy products

Croatia 0.6 Dukat (Lactalis), 30% Vindija, 10% Belje, 8%

Dukat (Lactalis), 31% Vindija, 16% Meggle Hrvatska, 7%

Dukat (Lactalis), 29% Vindija, 15% Danone, 12%

Dukat (Lactalis), 39% Vindija, 14% Meggle Hrvatska, 9%

Czech Republic 1.6 TPK, 18% Madeta, 17% Lactalis, 10%

Madeta, 16% Danone, 18% Mlekarna Kunin, 16%

Denmark 5.7 Arla Foods, 51% Arla Foods, 55% Thise Mejeri Amba, 7%

Arla Foods, 73% Arla Foods, 63% Thise Mejeri Amba, 11%

Finland 2.5 Valio Oy, 51% Arla Oy, 19%

Valio Oy, 27% Arla Oy, 23%

Valio Oy, 52%

Valio Oy, 36%

France 26.1 Lactalis Group, 21% Lactalis Group, 16% Cedilac, 15%e

Danone France, 36% Lactalis Nestlé Fresh Products, 24%

Danone France, 15% Lactalis Nestlé Fresh Products, 13%

Germany 24.4 Private label, 31% Private label, 67% Danone, 14% Private label, 40% Greece 1.9 FrieslandCampina, 7%

Nounou, 3% Delta Foods, 38% Fage, 25%

Delta Foods, 16% Olympos Dairy, 12% Artisanal, 10%

FrieslandCampina, 52% Delta Foods, 11%

Ireland 3.7 Kerry Foods, 19% Glanbia, 29% Danone, 26% Yoplait, 19% Italy 17.6 Lactalis Group, 13%

Mondelez Italia Services, 6% Coop Italia, 3%

Granarolo, 19% Parmalat, 19% Coop Italia, 6% CONAD, 4%

Danone, 26% Granarolo, 18% Molkerei Alois Müller, 13%

Ferrrero, 33% Lactalis Nestlé Fresh Products, 9% Parmalat, 8%

Netherlands 8.8 Private label, 37% FrieslandCampina, 27%

FrieslandCampina, ? Private label, ?

FrieslandCampina, ? Private label, ?

Poland 6.1 Hochland, 17% SM Mlekovita, 11%

SM Mlekpol, 23% Danone, 29% OSM Piatnica, 17%

Sweden 2.6 Arla Foods, 47% Skånemejerier, 9%

Arla Foods, 44% Arla Foods, 48% Arla Foods, 40%

Note: a Selected MS based on Food&Drink Europe: only those MS where the dairy sector is one of the three main sub-sectors in the food industry are selected. e estimate Source: Authors based on Food&Drink Europe (2015) for dairy production values (2012) and Euromonitor (2015) for market shares per product category (2015)


246

Map A2.27: Distribution of main dairy production sites across the Netherlands

Source: ZuivelNL (2015)


247

Figure A2.3: Dairy farmers’ share in selected dairy products

Source: Authors based on MMO (2016), European Commission (2016n) and European Commission (2016o) Note: The farmers’ share is the share of the average milk price of a given year in the price of the respective processed dairy product, that is, the price processors payed to farmers for purchasing raw milk as the base for the production of the dairy product. All prices use for the calculations are EU annual nominal weighted averages in euros/100 kg. Hence, a value of 8% for Edam in 1991 means, for example, that the average milk price of that year amounted to 8% of the average Edam price, that is the EU level farmers received 8% of the final Edam price on average during 1991. Milk prices are also annual averages measured in nominal euros/100 kg by the axis on the right-hand side. Map A2.28: Percentage change in dairies numbers per MS between 2006 and 2015

Source: Authors based on Eurostat (2016m) Note: The data is plotted on MS level in this map, that is, the legend category labels refer to MS level and all FADN regions of a MS have the corresponding identical colour. 100% would mean no chance, that is, for example a value of 95% means that the number of dairy enterprises in the MS was 5% smaller in 2015 than in 2006.

0

5

10

15

20

25

30

35

40

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

1991 1994 1997 2000 2003 2006 2009 2012 2015

Edam

Cheddar

Butter

WMP

SMP

Milk


248

Map A2.29: Annual milk production per MS in 2015

Source: Authors based on Eurostat (2016m) Note: The data is plotted on MS level in this map, that is, the legend category labels refer to MS level and all FADN regions of a MS have the corresponding identical colour. Data in 1000t.

Map A2.30: Percentage change in milk production per MS between 2006 and 2015

Source: Authors based on Eurostat (2016m) Note: The data is plotted on MS level in this map, that is, the legend category labels refer to MS level and all FADN regions of a MS have the corresponding identical colour. Underlying quantities in 1000t. 100% would mean no chance, that is, for example a value of 95% means that total national milk production in 1000t in the MS was 5% smaller in 2015 than in 2006.


249

Annex to chapter 3: Current Situation in the EU Bovine Meat Sector Table A3.1: Macroeconomic characteristics and bovine meat production of MS

(A)

MS

(B)

AR

EA

(C)

PO

PU

LATI

ON

(D)

GD

P

(E)

AC

CES

SIO

N

YEA

R

(F)

PO

PU

LATI

ON

D

ENS

ITY

(G)

GD

P/C

AP

ITA

(H)

NO

N-

DA

IRY

ING

CA

TTLE

(I)

SU

CK

LER

C

OW

S

(J)

BO

VIN

E M

EA

T P

RO

DU

CT

ION

(K)

CA

RC

AS

S

WEI

GH

T

AT 83.9 8.6 337 1995 102 39.3 1,424 219 229 262

BE 30.5 11.3 409 1957 369 36.4 1,974 445 268 307

BG 110.4 7.2 44 2007 65 6.1 278 76 17 148

CY 9.3 0.8 17 2004 92 20.6 33 0 5 332

CZ 78.9 10.5 164 2004 134 15.6 997 197 69 295

DE 357.4 81.2 3026 1958 227 37.3 8,351 681 1,132 319

DK 42.9 5.7 266 1973 132 47.0 996 94 122 260

EE 45.2 1.3 20 2004 29 15.6 166 25 11 240

EL 132.0 10.9 176 1981 82 16.2 471 163 42 240

ES 505.9 46.4 1081 1986 92 23.3 5,339 1,919 634 269

FI 338.4 5.5 207 1995 16 37.9 621 57 86 306

FR 633.2 66.4 2184 1958 105 32.9 15,745 4,211 1,464 309

HR 56.6 4.2 44 2013 75 10.4 303 19 43 229

HU 93.0 9.9 109 2004 106 11.0 570 117 27 253

IE 69.8 4.6 215 1973 66 46.4 5,182 1,053 564 339

IT 302.1 60.8 1636 1958 201 26.9 4,329 329 788 291

LT 65.3 2.9 37 2004 45 12.7 422 43 45 239

LU 2.6 0.6 52 1958 218 92.6 152 28 9 366

LV 64.6 2.0 24 2004 31 12.3 257 39 18 181

MT 0.3 0.4 9 2004 1363 20.5 9 0 1 274


250

NL 41.5 16.9 679 1958 407 40.2 2,598 85 383 195

PL 312.7 38.0 428 2004 122 11.3 3,628 169 479 270

PT 92.2 10.4 179 1986 112 17.3 1,363 476 89 244

RO 238.4 19.9 160 2007 83 8.1 902 17 119 165

SE 438.6 9.7 444 1995 22 45.6 1,092 176 145 305

SI 20.3 2.1 39 2004 102 18.7 371 57 34 300

SK 49.0 5.4 78 2004 111 14.4 318 60 10 252

UK 248.5 64.9 2569 1973 261 39.6 7,898 1,551 883 335

Source: Authors based on European Commission (2016m) and European Union (2016) Note: Data is for 2015. Column (B) in 1000 km2. Column (C) in million inhabitants. Column (E) in bln Euros. Column (F) in inhabitants/km2. Column (G) in euros/capita. Column (H) and column (I) in million tonnes. Column (J) in 1000 heads. Column (K) in 1000kg/(cow and year).


251

Map A3.1: Regional distribution of average cattle numbers per farm

Source: Authors based on European Commission (2016j) Note: The plot shows the average number of cattle (except dairy cows) kept on farms belonging to the EU cattle sector per FADN region. Cattle numbers are measured in LSU in order to ensure comparability. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Table A3.2: Regions with the highest and lowest average cattle numbers per farm

Region (MS) Heads per farm Region (MS) Heads per farm Thüringen (DE) 190 Nord-Vest (RO) 1 Slovakia (SK) 164 Sud-Muntenia (RO) 1 Brandenburg (DE) 150 Sud-Vest-Oltenia (RO) 1 Mecklenburg-Vorpommern (DE)

148 Nord-Est (RO) 1

Scotland (UK) 130 Thessalia (EL) 1 Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions which have the largest average cattle herds (except dairy cows) per cattle-keeping farm. The two right-most columns show the five FADN regions in which the smallest average cattle herds (except dairy cows) per cattle-keeping farm. Data for 2013. Cattle numbers are measured in LSU for comparability.


252

Map A3.2: Distribution of total cattle numbers kept by specialist fattening farms

Source: Authors based on European Commission (2016j) Note: The plot shows the distribution of total cattle numbers kept by all specialized bovine meat farms per FADN region. Dark grey regions indicate either that the number of sample farms of this production type fell below the FADN reporting threshold or an observation is missing due to some other reason. Data for 2013. Table A3.3: Regions with the highest and lowest cattle numbers kept by

specialized bovine meat farms

Region (MS) Cattle number Region (MS) Cattle number Ireland (IE) 1,888,896 Toscana (IT) 16,242 Bourgogne (FR) 567,924 Umbria (IT) 13,939 Limousin (FR)

551,252 Ribatejo e Oeste (PT) 9,747

Scotland (UK) 550,129 Marche (IT) 9,112 Auvergne (FR) 531,463 Liguria (IT) 7,380

Sum (share in total) 4.1m (28%) Sum (share in total) 56,421 (<0.1%)

Source: Authors based on European Commission (2016j) Note: The two left-most columns show the five FADN regions with the highest total cattle numbers kept by all specialized bovine meat farms of the region. The two right-most columns show the five FADN regions with the smallest total cattle numbers kept by all specialized bovine meat farms of the region. Data for 2013. For 37 regions, no data was available. The total refers to the number of cattle kept by all farms of this type throughout the EU.


253

Figure A3.1: Development of annual EU bovine and pig slaughter prices

Source: Authors based on European Commission (2016n) and European Commission (2016o) Note: Nominal annual weighted average prices in euros/100 kg carcass weight at the EU level. Figure A3.2: Development of EU bovine meat production and carcass weight

Source: Authors based on European Commission (2016i) Note: The development of carcass weight is measured by the right-hand axis, the development of the meat production quantity by the left-hand side axis.

0

50

100

150

200

250

300

350

400

450

500

1991 1994 1997 2000 2003 2006 2009 2012 2015

Young bulls

Steers

Heifers

Cows

Pigs

0

50

100

150

200

250

300

350

0,000

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2000 2005 2010 2015

EU bovine meat production (mt) EU average carcass weight (kg/head)


254

Figure A3.3: Development of the EU non-dairying cattle herd

Source: Authors based on European Commission (2016i) Figure A3.4: Development of the share of the EU13 in EU total bovine meat

production

Source: Authors based on European Commission (2016i) Note: Carcass weight is the average carcass weight in relation to the EU average carcass weight. The development of carcass weight is measured by the right-hand axis, the development of the other variables by the left-hand side axis.

0

10

20

30

40

50

60

70

80

2000 2005 2010 2015

EU non-dairying cattle (m heads) EU suckler cows (m heads)

68%

70%

72%

74%

76%

78%

80%

82%

84%

86%

0%

2%

4%

6%

8%

10%

12%

14%

2000 2005 2010 2015

Bovine meat production Non-dairying cattle Suckler cows Carcass weight


255

Map A3.3: Share of main beef and veal companies in national production volume

Source: Gira (2012)


256

Map A3.4: Share of main beef and veal companies in French production volume

Source: Gira (2012) Note: Bigard has extended its presence on the French bovine meat market through a number of consecutive acquisitions: Gallais Viandes (2000); Davy /Codevia (2002); Charal (2008); Socopa (2009); Sol Viandes (2011).


257

Map A3.5: Slaughterings of bovine animals per MS in 2015

Source: Authors based on Eurostat (2016o) Note: The data is plotted on MS level in this map, that is, the legend category labels refer to MS level and all FADN regions of a MS have the corresponding identical colour. Data in 1000t.

Map A3.6: Slaughterings of bovine animals per MS in 2015

Source: Authors based on Eurostat (2016o) Note: The data is plotted on MS level in this map, that is, the legend category labels refer to MS level and all FADN regions of a MS have the corresponding identical colour. Data in 1000 heads.


258

Map A3.7: Percentage change of cattle slaughterings per MS 2006 - 2015

Source: Authors based on Eurostat (2016o) Note: The data is plotted on MS level in this map, that is, the legend category labels refer to MS level and all FADN regions of a MS have the corresponding identical colour. Underlying data in 1000 heads. 100% would mean no chance, that is, for example a value of 95% means that cattle slaughterings in 1000 heads in the MS were 5% smaller in 2015 than in 2006.


259

Annex to chapter 4: Challenges and Opportunities Investment costs An example of investment costs is outlined for Germany in the following. The German association for technology and structures in agriculture (KTBL) provides investment cost estimates for dairy and cattle stables. Investment expenditures in new dairy cow stables can range widely depending mainly on the size of the stable and the milking system. Table A4.1 provides KTBL Online (2016) estimates for different dairy cow stables. The investment costs include costs for the building including manure storage, technical facilities (e.g., milking system), and outside facilities. Costs per cow can range between 4,865 € when building for 624 cows with an outside rotary milking parlour up to 12,347 € when building for 79 cows with a herringbone parlour. Table A4.1: Investment cost in dairy cow stables for different stable sizes and

milking technologies (€/cow space)

Automatic milking parlour

Herringbone parlour

Inside rotary milking parlour

Outside rotary milking

parlour

Outside rotary milking parlour

Number of cows 78 79 237 408 624 Stable building including manure storage

6,747 8,630 5,364 3,873 3,058

Technical facilities

3,435 3,677 2,334 1,756 1,329

Outside facilities 41 40 42 494 478 Total investment 10,223 12,347 7,739 6,123 4,865

Source: KTBL Online (2016) Investment costs for a cattle fattening stable range between 2,094 € per cattle space when building for 128 animals with slatted floor and 2,194 € per cattle space when building for 288 animals with mobile dung removal (KTBL Online, 2016). The live of a bank loan is usually 20 years for the building and 10 years for the milking system. Investment subsidies range also widely. For example, a farmer may get up to 40 percent investment subsidy for a stable with humane animal welfare practices for up to 100 dairy cows in some German federal states (Chamber of agriculture NRW, 2014).


260

Methodology used for the expert interviews Interviewees have been identified and contacted independently of each other in order to cover all aspects of the EU cattle sector as depicted in Figure 2, p. 28. Hence, it was aimed to select interviewees from the dairy as well as the bovine meat subsector as well as experts having their main expertise in primary production as well as processing. This yielded the following set of interviewees as shown in Table A4.2. Interviews have been conducted on a qualitative and individual basis, that is, interviewees were asked to share their individual professional views. They did not know the answers of the other interviewees. Hence, the answers allow a certain insightful evaluation of whether an answer given is of particular importance for the national cattle sector which the interviewee has expertise for (in case they are only mentioned once by this interviewee) or whether the point raised is of general importance for the EU since it was raised by several supply chain stakeholders having expertise in different MS. Some of the outcomes of the interviews are included in the main text. The remaining answers are included on the following pages. Table A4.2: Stakeholders interviewed

Respondent Main expertise Main supply chain level of expertise MS of expertise

R1 Cattle sector Primary production Italy R2 Dairy sector Major international processing company Netherlands R3 Dairy sector Major international processing company Poland R4 Dairy sector Primary production UK R5 Bovine meat sector Primary production UK R6 Bovine meat sector Primary production Germany R7 Bovine meat sector Major national processing company Germany

Source: Authors

Table A4.3: Self-assessment of the personal expertise of the interviewees

Respondent

Beef production

Beef processing

Veal production

Veal processing

Cow milk production

Cow milk processing

Som

e ex

perien

ce

Expe

rt

Som

e ex

perien

ce

Expe

rt

Som

e ex

perien

ce

Expe

rt

Som

e ex

perien

ce

Expe

rt

Som

e ex

perien

ce

Expe

rt

Som

e ex

perien

ce

Expe

rt

R1 X X X R2 X X R3 X X R4 X R5 X X R6 X X R7 X X X X X X

Sum 0 4 1 2 1 1 1 0 2 3 1 2 Total 4 3 2 1 5 3

Source: Authors


261

The experts were asked to share their views on the following twelve questions belonging to five topic areas as shown in Table A4.2: Table A4.4: Interview guidelines of the qualitative interviews Topic area Questions I Conditions of the current situation of the EU cattle sector I.1 What are according to your view the most important factors which

have shaped the current structure EU cattle sector I.2 What are according to your view the main reasons for the current milk

price crisis? II Conditions of the future development of the EU cattle sector II.1 What are according to your view the most important factors which will

shape the future structure EU cattle sector in the next 10 years? II.2 What are according to your view the largest opportunities the EU

cattle sector is likely to profit from in the next 10 years? II.3 What are according to your view the largest challenges the EU cattle

sector faces in the next 10 years? III Effects of EU agricultural policy on the EU cattle sector III.1 How do you evaluate the current effects of the CAP on the EU cattle

sector? Does the CAP rather help or does it rather harm? III.2 From which current policies does the EU cattle sector benefit most? III.3 Which current policies challenge/ pressure the EU cattle sector most? IV Future needs and options for political support of the EU cattle sector IV.1 Do the agricultural producers of the EU cattle sector need more

support from the CAP? IV.2 Which potential policies would aid most beef/ veal or milk producers

in future? IV.3 How could the current milk price crisis be resolved best? V Effects of the Brexit on the EU cattle sector V.1 What effects of the BREXIT do you expect on the EU cattle sector in

the next 10 years?

Source: Authors


262

Stakeholders' View 1: Key determinants of the current milk price crisis Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Abolition of the milk quota

o Several MS (IE, NL, DE) prepared & did not slaughter dairy cows in order to be in the best position to increase milk production after abolition

• Simultaneous decline of the import demand of milk powder from China Dairy sector: R2 (NL) • No supply crisis but due to demand depression

• Combination of factors: o External (world market: Chinese demand decrease, Russian import ban) o Internal (increased supply in NL & IE, but barely increase in total EU)

• Careful use of ‘crisis’: temporary price decline for several months (no crisis, but market volatility) vs. prices decrease for several years (indeed a crisis)

R3 (PL) • Milk market became very unpredictable and volatile • Combination of factors

o End of quota & oversupply o World market (Chinese import slowdown, Russian import ban) o Soft landing schemes not sufficient to prevent huge impacts on farmers

• PL was severely challenged: dependent on cheese exports to Russia - after 20 months alternatives established (e.g. Saudi Arabia)

• Lack of long-term strategy for the market • No policies that can overcome all these factors together

R4 (UK) • Lacking transparency and missing trust in supply chain • No support mechanisms for helping farmers to deal with price volatility • In early 2016 too much supply

o Reasons: Russia, China, milk quota lifted o Cheap dairy products entered the UK so that global situation impacted

domestic prices Bovine meat sector: R5 (UK) • UK beef prices strong in 2016 due to good domestic demand and supplies

from UK and IE are slightly tighter • Exchange rate: imports from IE less competitive, UK exports could compete • UK 60% self-sufficient in beef, rest from IE IE very dependent on exports • UK best beef prices in Europe for a long time because supply just enough,

consumers are very supportive of British beef R6 (DE) • Major problem is the supply chain structure:

o Strong commitment by farmer-members to dairy cooperatives o Cooperatives’ management’s tend to convince members to deliver at lower

cost instead of negotiating price increases with major retail chains o Marketing decisions made by management, members have little say o Cooperatives’ management’s tend to blame the demand side

(supermarket chains) for the low prices R7 (DE) • The mechanisms of supply and demand

• The low level of organization of the dairy farmers • The changing consumer behaviour

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question I.2 as outlined in Table A4.4.


263

Stakeholders' View 2: Main opportunities of the EU cattle sector in next 10 years Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Growing demand for bulk dairy products in Asia and Africa competing with

Australia and New Zealand • EU exports of niche products such as high quality cheeses (FR, IT, ES) • Increasing EU consumer demand: high and certified standards of animal

welfare, environmental sustainability and low use of antibiotics Dairy sector: R2 (NL) • Value added products

• Trade agreements (large potential for creating extra market opportunities, e.g., CETA includes a large volume of cheese to be exported to Canada) o EU market is saturated, growth on the demand side limited o Exception: some growth in cheese exports to Central/Eastern Europe

• Import growth of China o Will invest more in its dairy sector o In coming years still large demand for dairy imports

• Import growth of Africa o Challenge: development of purchasing power & of demand not certain

R3 (PL) • Huge opportunities in third countries: Africa, Asia, US o Changing habits towards more healthy life style

• EU market barely growing as consumption of dairy products stagnating R4 (UK) • Exports of UHT milk and branded cheese from UK mainly by small dairies

o Little space for different cheese brands in retailing as UK retail market is the most competitive in the world

o Joint aim of UK dairy industry: reduce trade deficit (exchange rate) • Export are the only opportunity to increase value in dairy

o More opportunities by replacing low-cost by high-value exports o MENA and Canada want British products due to strong British feel

Bovine meat sector: R5 (UK) • Global population growth will outstrip decline in red meat consumpt./ capita

• Large affluent population broadly supportive of domestically produced beef • UK beef of good quality & image, but room to improve • Satisfy high consumer demands: good health and welfare, limit the risk of

food scares or fraud by traceability • Improve on competitiveness

o Inevitably higher production costs in Europe o Benefit from technology and natural resources o Learn from net-exporting countries: quality & competitiveness improv.

R6 (DE) • Realize export potential in Eastern Europe • Increased product differentiation based on high quality

R7 (DE) • Developing better communication tools to make understand the consumer and the public what farming, milk and meat production is all about

• Building bigger producer organisations and more efficient supply-chain structures

• Improvement of breeding for longevity and health of the cattle • Better veterinarian policies

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question II.2 as outlined in Table A4.4.


264

Stakeholders' View 3: Main challenges of the EU cattle sector in next 10 years Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Declining image of dairy products due to NGO actions

o Campaigns denouncing animal health problems in dairy production o Campaigns denouncing animal fats in general o Campaigns against transports of calves and ‘broutards’

• Rapid development of surrogate milk market (e.g., from soya) causing a decline of cow milk consumption

• Cattle products have the highest emissions in terms of CO2 equivalents improvements of carbon footprint of dairy products and beef needed

• Declining beef demand due to relative high price and inadequate eating quality improvements of eating quality needed

Dairy sector: R2 (NL) • Maintain the still very good image of the sustainability of dairy production

o Goal: to be seen as a contributor to global food security o Avoid to be seen as environmental polluter

• Maintain the good image of dairy protein o Value of dairy protein as a high nutritional product o Avoid replacement with alternative protein sources of fewer beneficial

characteristics R3 (PL) • Current political situation in PL, EU and worldwide (instability)

o Current PL government very Polish-oriented, so supportive of the PL dairy sector & differentiation from general EU market

o Uncertainty & risk about future economic and agricultural policy in PL o Similar developments might happen in other MS o EU is politically not as stable anymore as it used to be o Brexit may create even more instability potentially affecting the dairy

sector R4 (UK) • Supply response to low milk prices (currently 10% down although milk

prices are taking up again) o Possible shortage of heifers of high milk productivity in the future due to

Farmers selling their dairy cows Producing to beef-type or mix dairy-beef calves instead

of dairy-type calves o Supply may go further down because of structural change on the farms

• Image of (work in) agriculture o Few young people willing to join the industry due to

Negative media reports about the viability of the sector Negative media reports about for governmental support

for farmers’ economic survival Bovine meat sector: R5 (UK) • Costs of production

o Regulation o Competitive meat processing (labour)

• World markets access recognising competition is fierce (Australia, New Zealand, South and North America)

• Poor adoption of new technologies as many beef producers are ‘traditional’ and are less willing to adapt


265

• Fragmented industry • Leadership that

o Encourages the whole supply chain to adopt better genetics and o Become more consumer focussed o Allows all supply chain stakeholders to make a sustainable margin and

be profitable so they can continue to invest R6 (DE) none R7 (DE) • Politics will care less about agriculture and farmers

• Influence of NGO´s on politics and consumer behaviour might grow • Finding skilled trainees and improving the education in the meat and

agricultural sector • Marketing and PR: Improving the understanding of the public and of the

consumers of the cattle sector, its characteristics and challenges

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question II.3 as outlined in Table A4.4.


266

Stakeholders' View 4: Currently most beneficial policies for the EU cattle sector Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Rural Development Policy measures

o Enable to improve the sustainability of dairy and beef farms o Improve their market position by adoption of innovations related to

animal welfare and environmental compatibility Dairy sector: R2 (NL) • Pillar 1 support and support for private storage R3 (PL) • Single Area Payment/ EU funds support

• European Agricultural Fund for Rural Development R4 (UK) none Bovine meat sector: R5 (UK) • Free trade

o Other MS have a good trade routes for live exports, the UK does not • Most valuable support to develop new markets

o Moved away from coupled support o Schemes making farmers become more innovative o Data about abattoir grading o Data about diseases o Schemes that support farmers to

Tag and test for Bovine Viral Diarrhea Kill infected animals Antibiotics use would decrease

R6 (DE) • Opening trade with Canada and the US • Support of Business Incubators

R7 (DE) • Cattle market benefits from the abolishing the milk quota • Cattle market benefits also from the milk reduction program in fall 2016

o Promotes more efficient farming structures o Reduces milk supply o Also responsible for less cows and cattle for the meat market,

that might put pressure on the structure of meat producers • Best would be if politics does not interfere strongly with the economy of the

cattle sector

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question III.2 as outlined in Table A4.4.


267

Stakeholders' View 5: Currently most challenging policies for the EU cattle sector Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Incentives to create producers organizations and interbranch organizations

o Able to control milk supply o Improve the bargaining position of dairy farmers towards the dairy

industry • Nitrate and National Emission Ceilings Directive

o Imposing limits to growth in concentration areas o Providing incentives to reduce nitrogen excretion

Dairy sector: R2 (NL) • Manure policy limitations will remain important also in the future for NL

o New phosphate regulation in Jan 2017 capping total phosphate emissions o May lead to a reduction of the number of cows per land and shrinking

dairy herd size • In some EU markets profits very difficult to make

o For example in DE: many suppliers vs. only few retailers price pressure on dairy products

o No direct policy solution, but sector may cooperate more - antitrust rules R3 (PL) none R4 (UK) • Huge price wars of retailers (milk one of the discounted products because

widely consumed as a staple, barely substitutes) o Milk retail price extremely low insuff. margins for farmers & dairies o Cheese prices are always on discount at retail level

• UK market different: milk only fresh - no UHT o Most retail chains contract 'dedicated farmers groups' for liquid milk

(20% of UK farmers) o Pay higher prices, but require add. product characteristics o Remaining 80%: more linked to global market prices & very vulnerable huge divide between farmers

Bovine meat sector: R5 (UK) • Regulation on Transmissible spongiform encephalopathies hinders trade R6 (DE) • Differences in international production standards

• Cheating in markets for high value products such as organic products • Animal welfare standards

o On some aspects the policies went too far o Did not consider the reality with respect to animal welfare

R7 (DE) • The Brexit • A weak CAP and a weak veterinarian policy • Different framework conditions and laws on animal welfare, emission

protection, building rights, etc. in EU MS

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question III.3 as outlined in Table A4.4.


268

Stakeholders' View 6: Do EU cattle-keeping farmers need more CAP support? Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • No need for more support

Dairy sector: R2 (NL) • Dangerous development in EU market

o Introduction of mandatory country of origin labelling o Already introduced in the meat sector because of food safety issues

(BSE) o Erosion of the internal EU market of mandatory for dairy products o Voluntary origin labelling schemes are a good idea

R3 (PL) • Not more support • Support needs to be more targeted

R4 (UK) none Bovine meat sector: R5 (UK) • Labelling is popular as UK consumers tend to support British beef

• Measures dealing with price volatility • Regulation addressing the poor transparency within the supply chain • Measures reducing price uncertainty

o Farmer received only an indication of the base price (plus processors assessment of meat quality) the week before the animal is slaughtered although production cycle takes years

R6 (DE) • Yes, but not in the form of direct subsidies • Support for on-farm research or demonstration farms • More support for innovations by farmers • Less governmental paternalism

R7 (DE) • Most important criterion: uniform policy framework in the whole EU o Also concerning laws on animal welfare etc.

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question IV.1 as outlined in Table A4.4.


269

Stakeholders' View 7: Potentially most beneficial policies for the EU cattle sector Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Current framework already allows for sufficient options

Dairy sector: R2 (NL) • Preserving the common market without mandatory origin labelling

• Should the issue of bargaining power of farmers be tackled through antitrust or through the CAP?

• When referring to the protection of producer organisations, would this be necessary only in times of crisis or permanently?

R3 (PL) • Currently spot market prices higher than farmer prices (very unusual) • Result of short-term contracting: intervention of milk powder storage is full

and milk price is increasing again • Export diversification: when Russian market closed, many PL dairy

companies started to target new markets in Arab countries o Producing halal or other specific certifications for entering such markets

R4 (UK) • No increase funding for farmers or more supply management needed • Increase farmers' competitiveness and efficiency and increase production

scale (replace smaller family farms by larger units) • EU support of traditional EU image not corresponding with interests of UK

dairy: feeding growing population, low carbon footprint etc. • Better volatility management: futures markets, hedging, insurance • Better market intelligence: better forecasts about the future of the market

o Structure similar to the USDA for data collection in dairy o Daily real-time information on trade and prices o On fresh milk and processed dairy products

• Support market information and transparency: o Better info to consumers about benefits of dairy products o Export support in terms of simplifying export processes and opening up

third markets for EU dairy • Maintain safety net, but make more fit for purpose

Bovine meat sector: R5 (UK) • Address supply chain transparency

• Groceries Code Adjudicator type approach to support industry codes, independent checks on the ‘independent’ graders in abattoirs

R6 (DE) • Supporting healthy food consumptions and nutrition (public good character) • Increasing market transparency

o Markets need to be honest and authentic o Animal friendly slaughtering practices not compensated by the market o Can be supported by smart labelling such as Apps etc. – national

governments and EU should experiment with different concepts • Better control of cheating in high value product markets

o In organic product market cheating is a serious problem o Margins are as high as in the market for narcotics o Some regions have become hotspots for cheating o The larger companies are, the less they have to follow rules

R7 (DE) • Most important criterion: uniform policy framework in the whole EU o Also concerning laws on animal welfare etc.



270

Stakeholders' View 8: Solutions to the current milk price crisis Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) • Incentives to reduce milk production

• Risk of moving the problem to other regions which continue to increase production because of their low production costs

• These latter regions have still comparative cost advantages, but may lose this position because of costs related to environmental regulation

Dairy sector: R2 (NL) • Price volatility will be a continuing challenge

o Volatility of butter or milk powder much more severe than of milk price o Cooperatives are smoothing prices for its members o Possible because are active on different markets

• In 2016 NL: special support program for its farmers rewarding a reduction of milk quantities delivered

R3 (PL) • In natural way between demand and supply • Market needs to regulate itself • Part of farmers will quit if the competitive pressure will become too high

R4 (UK) none Bovine meat sector: R5 (UK) none R6 (DE) • Supporting product differentiation

• Removing non-effective animal welfare standards • Supporting market transparency also with respect to meaningful animal

welfare standards, reducing cheating in food markets R7 (DE) • More efficient structures in the agricultural and cattle organizations

• Better risk management of the farmers and of the politics before the next crisis starts

• Dairies need to adapt products stronger to consumer needs & requirements • EU and MS have to improve their veterinary policies to improve the export

possibilities to the world market



271

Stakeholders' View 9: Effects of the Brexit on the EU cattle sector Respon-

dent (MS)

Answer

Cattle sector: R1 (IT) none

Dairy sector: R2 (NL) • Too early to assess consequences because the terms of the exit have not

been negotiated yet • UK is an important importer of dairy products • Brexit is likely to have negative effects for dairy exporting MS

R3 (PL) • GB might turn more to importing from Oceania • PL is not exporting much to GB so PL will not really be affected • Ireland is likely to be strongly affected since highly integrated with UK in

cattle product markets • Uncertainty about the outcome of the BREXIT still very high

R4 (UK) • UK farmers realise that they won’t have access to direct income support anymore

• Uncertainty about how UK dairy industry will compete in future • Uncertainty about what trade deals • Uncertainty about whether there will there be a single market • Uncertainty about dairy exports from UK as a lot of exports through

exporters in the rest of the EU • UK farmers will be competing with EU farmers because

o EU farmers will have access to CAP support mechanisms o UK will remain more market-focused and no access to support

Bovine meat sector: R5 (UK) • Beef and sheep meat trade is very critical in the whole Brexit discussion

o Vulnerable compared to many other sectors o Will be effected most of all sectors

• Danger that tariffs might be imposed which inhibit supply chains crossing MS borders o Substantial imports from IE because UK only 60% self-sufficient,

substantial uncertainty in IE about future o Second biggest export market for beef is NL

Meat processing and re-import to UK o Biggest worry lamb & sheep exports to FR and DE

• UK bovine and ovine meat sectors o Keep continued free access to EU market and access to EU labour

Many eastern Europeans work in British abattoirs o No interest in FTA with Australia, New Zealand or South America

although UK wants to lead the world in free trade o Danger of becoming flooded by high quality beef o Impact assessment for IE market under free trade huge impact

R6 (DE) none R7 (DE) • Continued uncertainty about form (soft vs. hard) and time frame of Brexit

• New tariff agreements between UK and EU crucial • Notable market shifts are unlikely, since WTO membership rules also apply • Continued UK demand for bacon, ham and other meat products • Continued UK appreciation of the high quality of EU supplies • If the pound remains weak

o British exports are likely to become cheaper o Increases of import prices of meat from DK, NL, DE and IE potentially resulting price pressure on EU market

Source: Authors based on stakeholder interviews Note: This is the summary of stakeholders’ statements responding to question V.1 as outlined in Table A4.4.


272

Annex to chapter 5: Policy Framework of the EU Cattle Sector Table A5.1: List of RDP submeasures

Mea

sure

Sub

mea

sure

Description

M1 Knowledge transfer and information actions M1.1 Support for vocational training and skills acquisition actions; M1.2 Support for demonstration activities and information actions;

M1.3 Support for short-term farm and forest management exchange as well as farm and forest visits.

M2 Advisory services, farm management and farm relief services M2.1 Support to help benefiting from the use of advisory services

M2.2 Support for the setting up of farm management, farm relief and farm advisory services as well as forestry advisory services

M2.3 Support for training of advisors M3 Quality schemes for agricultural products, and foodstuffs

M3.1 Support for new participation in quality schemes

M3.2 Support for information and promotion activities implemented by groups of producers in the internal market

M4 Investments in physical assets M4.1 Support for investments in agricultural holdings;

M4.2 Support for investments in processing/marketing and/or development of agricultural products;

M4.3 Support for investments in infrastructure related to development, modernisation or adaptation of agriculture and forestry;

M4.4 Support for non-productive investments linked to the achievement of agri-environment-climate objectives.

M5 Restoring agricultural production potential damaged by natural disasters and catastrophic events and introduction of appropriate preventive actions

M5.1 Support for investments in preventive actions aimed at reducing the consequences of probable natural disasters, adverse climatic events and catastrophic events;

M5.2 Support for investments for the restoration of agricultural land and production potential damaged by natural disasters, adverse climatic events and catastrophic events.

M6 Farm and business development M6.1 Business start-up aid for young farmers; M6.2 Business start-up aid for non-agricultural activities in rural areas; M6.3 Business start-up aid for the development of small farms;

M6.4 Support for investments in creation and development of non-agricultural activities;

M6.5 Payments for farmers eligible for the small farmers scheme who permanently transfer their holding to another farmer.


273

M7 Basic services and village renewal in rural areas

M7.1

Support for drawing up and updating of plans for the development of municipalities and villages in rural areas and their basic services and of protection and management plans relating to Natura 2000 sites and other areas of high nature value;

M7.2 Support for investments in the creation, improvement or expansion of all types of small scale infrastructure, including investments in renewable energy and energy saving;

M7.3 Support for broadband infrastructure, including its creation, improvement and expansion, passive broadband infrastructure and provision of access to broadband and public e-government;

M7.4 Support for investments in the setting-up, improvement or expansion of local basic services for the rural population including leisure and culture, and the related infrastructure;

M7.5 Support for investments for public use in recreational infrastructure, tourist information and small scale tourism infrastructure;

M7.6

Support for studies/investments associated with the maintenance, restoration and upgrading of the cultural and natural heritage of villages, rural landscapes and high nature value sites including related socioeconomic aspects, as well as environmental awareness actions;

M7.7

Support for investments targeting the relocation of activities and conversion of buildings or other facilities located inside or close to rural settlements, with a view to improving the quality of life or increasing the environmental performance of the settlement;

M7.8 Others.

M8 Investments in forest area development and improvement of the viability of forests

M8.1 Support for afforestation/creation of woodland (Article 21);

M8.2 Support of establishment and maintenance of agro-forestry systems (Article 22);

M8.3 Support for prevention of damage to forests from forest fires and natural disasters and catastrophic events (Article 23);

M8.4 Support for restoration of damage to forests from forest fires and natural disasters and catastrophic events (Article 24);

M8.5 Support for investments improving the resilience and environmental value of forest ecosystems (Article 25);

M8.6 Support for investments in forestry technologies and in processing, mobilising and marketing of forest products (Article 26).

M9 Setting up of producer groups and organisations M10 Agri-environment-climate

M10.1 Payment for agri-environment-climate commitments

M10.2 Support for conservation and sustainable use and development of genetic resources in agriculture

M11 Organic farming M11.1 Payment to convert to organic farming practices and methods M11.2 Payment to maintain organic farming practices and methods

M12 Natura 2000 and Water Framework Directive payments M12.1 Compensation payment for Natura 2000 agricultural areas M12.2 Compensation payment for Natura 2000 forest areas

M12.3 Compensation payment for agricultural areas included in river basin management plans


274

M13 Payments to areas facing natural or other specific constraints M13.1 Compensation payment in mountain areas M13.2 Compensation payment for other areas facing significant natural constraints M13.3 Compensation payment to other areas affected by specific constraints

M14 Animal Welfare M15 Forest environmental and climate services and forest conservation

15.1 Payment for forest-environmental and climate commitments 15.2 Support for the conservation and promotion of forest genetic resources

M16 Cooperation

M16.1 Support for the establishment and operation of operational groups of the EIP for agricultural productivity and sustainability

M16.2 Support for pilot projects and for the development of new products, practices, processes and technologies

M16.3 Cooperation among small operators in organising joint work processes and sharing facilities and resources, and for developing and marketing tourism

M16.4

Support for horizontal and vertical cooperation among supply chain actors for the establishment and development of short supply chains and local markets and for promotion activities in a local context relating to the development of short supply chains and local markets

M16.5 Support for joint action undertaken with a view to mitigating or adapting to climate change and for joint approaches to environmental projects and ongoing environmental practices. (Article 35 (2) (f) and (g)

M16.6 Support for cooperation among supply chain actors for sustainable provision of biomass for use in food and energy production and industrial processes

M16.7 Support for non-CLLD strategies (non-Community-led Local Development)

M16.8 Support for drawing up of forest management plans or equivalent instruments

M16.9 Support for diversification of farming activities into activities concerning health care, social integration, community-supported agriculture and education about the environment and food

M17 Risk management M17.1 Crop, animal and plant insurance premium

M17.2 Mutual funds for adverse climatic events, animal and plant diseases, pest infestations and environmental incidents

M17.3 Income stabilisation tool M18 Financing of complementary national direct payments for Croatia M19 LEADER and CLLD

M19.1 Preparatory support M19.2 Support for implementation of operations under the CLLD strategy

M19.3 Preparation and implementation of cooperation activities of the local action group

M19.4 Support for running costs and animation M20 Technical assistance to Member States

Source: Authors. Note: For further details see European Commission (2013c).


275

Methodology used for the income estimations Estimation of payments and incomes of new CAP (2014-2020) vs. old CAP (2007-2013):

• CAP payments are split up into the following categories: P1. DPs (€/ha) P2. VCS per dairy cow (€/head) P3. VCS per meat cattle (€/head) P4. DPs (€/farm) P5. VCS for dairying cattle (€/farm) P6. VCS for meat cattle (€/farm) P7. Other payments defined as the difference between FADN variable

(605) and the sum of P1, P2 and P3 (€/farm)

Using European Commission (2016j), the 2011-2013 averages of the sums per MS of Var1. Farms represented (SYS02) Var2. Total Utilised Agricultural Area (SE025) Var3. Dairy cows (SE085) Var4. Other cattle (SE090) Var5. Total subsidies - excluding on investments-EURO (SE605 ) Var6. Subsidies dairying-EURO (SE616) Var7. Subsidies other cattle-EURO (SE617) Var8. Decoupled payments-EURO (SE630) Var9. Family farm income (SE420) Var10. Total output (SE131) Var11. Total intermediate consumption (SE275) Var12. Depreciation (SE360) Var13. External factors (SE365) Var14. Subsidies & Taxes not from current year) (SE405)

were calculated. My multiplying Var1 and Var2, the total UAA of commercial farms per MS is obtained etc. Calculations were then performed as outlined as in Table A5.2. Furthermore:

• Total CAP payments are calculated from these FADN statistics and include total subsidies to crops and livestock (SE610, SE 615) and decoupled payments (SE 630).

• Labour income was obtained by dividing I_in by AWU (annual work unit). • Table A5.16 to Table A5.19: The benchmark incomes are taken from European

Commission (2015b). The per hectare equivalent of the direct payments has been calculated by dividing the Direct Payment Envelope by the agricultural area eligible for these payments.

• Table 51: the percentages are the ratios between P_c of new CAP and P_c of old CAP • Table 52: the percentages are the ratios between I_in of new CAP and I_in of old CAP • Identical calculations have been performed per FADN region for producing the maps

in section 5.5


276

Table A5.2: Calculations of key variables

Variable Value old CAP Value new CAP Comments

P1 Not relevant DPs2015/ Var1*Var2

DPs2015 are the total ceiling per MS in 2015 (European Parliament, 2015b, Table 1.7) – total national VCS (European Commission, 2015d, slide 10)

P2 Not relevant Values for 2015 from source and

authors’ estimates

European Parliament (2015c, sections ‘Voluntary coupled support’)

P3 Not relevant Values for 2015 from source and

authors’ estimates

European Parliament (2015c, sections ‘Voluntary coupled support’)

P4 SE630 P1*SE025 P5 SE616 P2*SE085 P6 SE617 P3*SE090

P7 SE605-(SE616+SE617+SE630) c.p. assumption: nothing else than DPs and cattle-relevant VCS changes

I_ex SE131-SE275-SE360–SE365+SE405

Farm income excluding CAP payments, c.p. assumption: nothing else than DPs and cattle-relevant VCS changes

P_c SE616+ SE617+ SE630

P4+P5+P6 Cattle-related CAP payments

I_in I_ex+SE605 I_ex+P4+P5+P6+P7 Farm income including CAP payments

Source: Authors


277

The role of CAP payments for the EU cattle sector at aggregated level under the old CAP (2007-2013) is shown in the following maps: Map A5.1: Distribution of total CAP payments received by all EU cattle sector farms

per region

Source: Authors based on European Commission (2016j) Note: The plot shows the total CAP payments received by all commercial cattle-keeping farms in each region excluding subsidies on investments in € as average of the years 2011-2013. Map A5.2: Distribution of total coupled CAP payments for dairy cows received by

all EU cattle sector farms per region

Source: Authors based on European Commission (2016j) Note: The plot shows the total coupled CAP payments for dairy cows received by all commercial cattle-keeping farms in € as average of the years 2011-2013.


278

Map A5.3: Distribution of total coupled CAP payments for non-dairying cattle received by all EU cattle sector farms per region

Source: Authors based on European Commission (2016j) Note: The plot shows the total coupled CAP payments for non-dairying cattle received by all commercial cattle-keeping farms in € as average of the years 2011-2013. Map A5.4: Distribution of total decoupled CAP payments received per by all EU

cattle sector farms per region

Source: Authors based on European Commission (2016j) Note: The plot shows the total decoupled CAP payments received by all commercial cattle-keeping farms per region in € as average of the years 2011-2013.


279

Map A5.5: Ratio of total cattle-related CAP payments in total EU cattle sector farm income per region

Source: Authors based on European Commission (2016j) Note: Data based on the averages of the years 2011-2013. Map A5.6: Distribution of total net income of all EU cattle sector farms per region

Source: Authors based on European Commission (2016j) Note: Data based on the averages of the years 2011-2013.


280

Map A5.7: Distribution of total agricultural labour employed by all EU cattle sector farms per region

Source: Authors based on European Commission (2016j) Note: Data based on the averages of the years 2011-2013. Data in AWU. Map A5.8: Distribution of the sum of payments other than the cattle-related ones

received by all cattle sector farms per region

Source: Authors based on European Commission (2016j) Note: Data based on the averages of the years 2011-2013. Cattle-related payments are defined as the decoupled payments plus the VCS for dairy cows plus the VCS for non-darying cattle.


281

Map A5.9: Distribution of labour income/ AWU per average EU cattle sector farm

Source: Authors based on European Commission (2016j) Note: Data based on the averages of the years 2011-2013. Data is the average income of all cattle-keeping farms per region, that is, the average across all specializations weighted by the farm numbers per specialization, see also Methodology used for the income estimations, p. 275. Map A5.10: Cattle-related CAP payments in farm income under the new CAP for

farm type (46)

Source: Authors based on the Methodology used for the income estimations, p. 275 Note: The plot shows the share of DPs and VCS for dairy and non-dairy cattle in total farm income per FADN region for farm type (46). If the share is larger than 100%, then the farm income without CAP payments was negative (the sum of this negative income plus the CAP payment resulted then into a positive income which is smaller in magnitude than the amount of the payment so that the share of the payment in the final income including CAP payments is larger than 100%).


282

Table A5.3: Regions of highest and lowest share of cattle-related payments in farm income under new CAP for farm type (46)

Region (MS) Share Region (MS) Share Slattbygdslan (SE) 2648% Lombardia (IT) 8% Aquitaine (FR) 776% Pohjanmaa (FI) -149% Nordrhein-Westfalen (DE) 630% Pohjois-Suomi (FI) -155% Sachsen (DE) 431% Sisa-Suomi (FI) -168% Northern Ireland (UK) 385% Etela-Suomi (FI) -188%

Source: Authors based on the Methodology used for the income estimations, p. 275 Note: Negative values indicate that the farm income was estimated to be negative despite including all CAP payments (the cattle-specific ones as well as any further payments). Map A5.11: Cattle-related CAP payments in farm income under the new CAP for

farm type (47)

Source: Authors based on the Methodology used for the income estimations, p. 275 Note: The plot shows the share of DPs and VCS for dairy and non-dairy cattle in total farm income per FADN region for farm type (47). If the share is larger than 100%, then the farm income without CAP payments was negative (the sum of this negative income plus the CAP payment resulted then into a positive income which is smaller in magnitude than the amount of the payment so that the share of the payment in the final income including CAP payments is larger than 100%). Table A5.4: Regions of highest and lowest share of cattle-related payments in farm

income under new CAP for farm type (47) Region (MS) Share Region (MS) Share Slovakia (SK) 2014% Malopolska and Pogorze (PL) 31% Franche-Comte (FR) 162% Sicilia (IT) 29% Basse-Normandie (FR) 156% Acores e Madeira (PT) 27% Champagne-Ardenne (FR) 131% Nord-Vest (RO) 20% Czech Republic (CZ) 126% Emilia-Romagna (IT) 19%

Source: Authors based on the Methodology used for the income estimations, p. 275


283

Map A5.12: Cattle-related CAP payments in farm income under the new CAP for farm type (73)


income under new CAP for farm type (73) Region (MS) Share Region (MS) Share Slovenia (SI) 732% Sud-Muntenia (RO) 30% Czech Republic (CZ) 127% Vlaanderen (BE) 29% Hessen (DE) 99% Sud-Est (RO) 29% Baden-Wurttemberg (DE) 82% Austria (AT) 27% Bayern (DE) 71% Continental Croatia (HR) 23%



284

Map A5.13: Cattle-related CAP payments in farm income under the new CAP for farm type (83)


income under new CAP for farm type (83) Region (MS) Share Region (MS) Share Denmark (DK) 635% Ipiros-Peloponissos-Nissi Ioniou (EL) 34% Auvergne (FR) 356% Vest (RO) 34% Slattbygdslan (SE) 329% Nord-Vest (RO) 34% Aquitaine (FR) 244% Sud-Vest-Oltenia (RO) 24% Brandenburg (DE) 219% Thessalia (EL) 22%



285

Map A5.14: Distribution of relation of labour income of farm type (45) to benchmark income under the new CAP

Source: Authors based on the Methodology used for the income estimations, p. 275 Note: The plot shows the size of labour income/ AWU of farm type (45) in relation to the average labour income of the FADN region. If the share is larger than 100%, then the labour income derived from the farm income including all CAP payments was larger than the non-agricultural benchmark income. Negative values indicate that the labour income was estimated to be negative despite including all CAP payments (the cattle-specific ones as well as any further payments). Table A5.7: Regions of highest and lowest share in benchmark income for farm

type (45) under new CAP Region (MS) Share Region (MS) Share Eszak-Alfold (HU) 299% Aquitaine (FR) 19% Andalucia (ES) 247% Denmark (DK) 19% Umbria (IT) 245% Sachsen (DE) 18% Sardegna (IT) 221% Lan i norra (SE) 13% Campania (IT) 210% Adriatic Croatia (HR) 3%



286



type (47) under new CAP Region (MS) Share Region (MS) Share Sicilia (IT) 158% Bayern (DE) 36% Makedonia-Thraki (EL) 121% Mazowsze and Podlasie (PL) 34% Ipiros-Peloponissos-Nissi Ioniou (EL) 101% Slovenia (SI) 26%

Thessalia (EL) 96% Baden-Wurttemberg (DE) 22% Sterea Ellas-Nissi Egaeou-Kriti (EL) 87% Luxembourg (LU) 20%



287



type (73) under new CAP Region (MS) Share Region (MS) Share Andalucia (ES) 145% Mazowsze and Podlasie (PL) 33% Bretagne (FR) 116% Lithuania (LT) 31% Vlaanderen (BE) 83% Baden-Wurttemberg (DE) 30% The Netherlands (NL) 80% Malopolska and Pogorze (PL) 23% Sud-Vest-Oltenia (RO) 63% Slovenia (SI) 1%



288



type (83) under new CAP Region (MS) Share Region (MS) Share Eszak-Alfold (HU) 220% Luxembourg (LU) 18% Del-Alfold (HU) 187% Slovenia (SI) 16% Andalucia (ES) 182% Continental Croatia (HR) 15% Alfold (HU) 163% Slattbygdslan (SE) 13% Nyugat-Dunantul (HU) 148% Denmark (DK) 10%



289

Map A5.18: Distribution of change in importance of cattle-related CAP payments in farm income of farm type (45) between old and new CAP

Source: Authors based on the Methodology used for the income estimations, p. 275 Note: The plot shows the estimated change in percentage points of the share of cattle-related Cap payments (DPs and VCS for dairying and non-dairying cattle) in farm income of farm type (45) of the new CAP in comparison with the old CAP per FADN region. A negative value of, e.g., -10% of a certain region means that under the new CAP the share of cattle-related CAP payments in average income of farms of type (45) in this region is 10 percentage points lower than under the old CAP. That is, for all negative values indicated in the legend the importance of cattle-specific CAP payments for farm income under the new CAP has diminished in comparison with the old CAP for this farm type in this region. Table A5.11: Regions of highest and lowest change in importance of cattle-related

CAP payments in farm income of farm type (45) Region (MS) Share Region (MS) Share Adriatic Croatia (HR) 151% Pais Vasco (ES) -12% Lan i norra (SE) 94% Brandenburg (DE) -13% Denmark (DK) 50% Acores e Madeira (PT) -13% Aosta (IT) 28% Norte e Centro (PT) -16% Aquitaine (FR) 26% Continental Croatia (HR) -20%



290


Source: Authors based on the Methodology used for the income estimations, p. 275 Note: The plot shows the estimated change in percentage points of the share of cattle-related Cap payments (DPs and VCS for dairying and non-dairying cattle) in farm income of farm type (46) of the new CAP in comparison with the old CAP per FADN region. A negative value of, e.g., -10% of a certain region means that under the new CAP the share of cattle-related CAP payments in average income of farms of type (46) in this region is 10 percentage points lower than under the old CAP. That is, for all negative values indicated in the legend the importance of cattle-specific CAP payments for farm income under the new CAP has diminished in comparison with the old CAP for this farm type in this region. Table A5.12: Regions of highest and lowest change in importance of cattle-

related CAP payments in farm income of farm type (46) Region (MS) Share Region (MS) Share Slattbygdslan (SE) 2194% Slovakia (SK) -181% Aquitaine (FR) 532% Pohjanmaa (FI) -334% Nordrhein-Westfalen (DE) 382% Pohjois-Suomi (FI) -343% Northern Ireland (UK) 217% Sisa-Suomi (FI) -364% Makedonia-Thraki (EL) 115% Etela-Suomi (FI) -410%



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CAP payments in farm income of farm type (47) Region (MS) Share Region (MS) Share Aosta (IT) 29% Bretagne (FR) -4% Sud-Est (RO) 24% Wallonie (BE) -7% Nord-Est (RO) 20% Slovenia (SI) -7% Basse-Normandie (FR) 15% Czech Republic (CZ) -15% Centru (RO) 15% Acores e Madeira (PT) -17%



292



CAP payments in farm income of farm type (73) Region (MS) Share Region (MS) Share Lithuania (LT) 25% Bretagne (FR) -5% Nord-Est (RO) 24% Vlaanderen (BE) -6% Sud-Muntenia (RO) 20% The Netherlands (NL) -6% Sud-Vest-Oltenia (RO) 19% Czech Republic (CZ) -8% Sud-Est (RO) 18% Continental Croatia (HR) -8%



293


Source: Authors based on the Methodology used for the income estimations, p. 275 Note: The plot shows the estimated change in percentage points of the share of cattle-related Cap payments (DPs and VCS for dairying and non-dairying cattle) in farm income of farm type (83) of the new CAP in comparison with the old CAP per FADN region. A negative value of, e.g., -10% of a certain region means that under the new CAP the share of cattle-related CAP payments in average income of farms of type (83) in this region is 10 percentage points lower than under the old CAP. That is, for all negative values indicated in the legend the importance of cattle-specific CAP payments for farm income under the new CAP has diminished in comparison with the old CAP for this farm type in this region. Table A5.15: Regions of highest and lowest change in importance of cattle-

related CAP payments in farm income of farm type (83) Region (MS) Share Region (MS) Share Slattbygdslan (SE) 93% Nord-Pas-de-Calais (FR) -5% Aquitaine (FR) 71% Dunantul (HU) -6% Auvergne (FR) 68% Picardie (FR) -7% Midi-Pyrenees (FR) 41% Austria (AT) -8% Sachsen (DE) 14% Brandenburg (DE) -28%



294

Table A5.16: Impact of CAP direct payment measures on the income of specialized dairy and cattle farms I

BE BG CY CZ DK DE EL

Level of income (excl. payments)

(45) 38,384 3,333 -7,879 -30,637 20,909

(49) 7,638 4,874 -4,964 -6,091 -3,665 1,349

Level of income/AWU (excl. payments)

(45) 22,129 1,935 -2,662 -25,462 14,383

(49) 4,716 3,326 -2,383 -9,632 -2,986 1,032

Level of income (incl. payments)

(45) 57,669 5,297 60,647 32,544 44,401

(49) 34,027 7,554 35,414 16,779 18,702 18,691

Share of direct payments in farm income

(45) 50% 59% -870% -206% 112%

(49) 346% 55% -813% -375% -610% 1286%

Level of income/AWU (incl. payments)

(45) 33,247 3,075 20,488 27,047 30,542

(49) 21,009 5,154 17,001 26,531 15,234 14,300

Benchmark income (outside agriculture)

GDP/capita (national economy)

35,900 5,900 20,400 14,700 46,200 36,000 16,200

(45) without dp 62% 33% -18% -55% 40%

(49) without dp 13% 83% -34% -13% -10% 8%

(45) with dp 93% 52% 139% 59% 85%

(49) with dp 59% 87% 116% 57% 42% 88%



295

Table A5.17: Impact of CAP direct payment measures on the income of specialized dairy and cattle farms II

ES EE FR HR HU IE IT


(45) 25,293 16,068 10,321 2,712 3,747 36,803 65,625

(49) 4,745 3,006 -

11,563 173 1,637 1,089 17,437


(45) 16,331 5,799 6,272 1,224 2,451 41,689

(49) 4,117 2,568 -8,347 35 1,320 1,051 15,128


(45) 37,532 35,933 36,346 8,365 29,004 55,120 77,619

(49) 18,183 12,657 22,318 12,624 25,448 14,315 26,989


(45) 48% 124% 252% 208% 674% 50% 18%

(49) 283% 321% -293% 7197% 1454% 1215% 55%


(45) 24,233 12,969 22,087 3,775 18,969 39,707 49,308

(49) 15,776 10,811 16,112 2,525 20,519 13,814 23,415



22,400 15,200 32,200 10,200 10,600 41,000 26,500

(45) without dp 73% 38% 19% 12% 23% 0% 157%

(49) without dp 21% 20% -36% 2% 15% 3% 66%

(45) with dp 108% 85% 69% 37% 179% 97% 186%

(49) with dp 70% 71% 50% 25% 194% 34% 88%



296

Table A5.18: Impact of CAP direct payment measures on the income of specialized dairy and cattle farms III

LT LU LV MT NL AT PL


(45) 9,097 28,569 3,543 17,447 41,729 20,491 11,220

(49) 5,594 5,263 3,640 3,960 -6,648 12,287 3,065


(45) 6,024 2,267 8,653 27,238 13,410 6,289

(49) 3,894 4,001 2,924 2,737 -5,633 8,943 2,134


(45) 13,102 55,044 8,761 36,182 67,116 27,197 15,185

(49) 14,653 28,786 15,260 7,732 15,528 20,388 6,246


(45) 44% 93% 147% 107% 61% 33% 35%

(49) 162% 447% 319% 95% -334% 66% 104%


(45) 8,676 33,083 5,606 17,943 43,809 17,799 8,512

(49) 10,200 21,883 12,258 5,343 13,157 14,839 4,349



12,400 87,600 11,800 19,000 39,300 38,500 10,700

(45) without dp 49% 0% 19% 46% 69% 35% 59%

(49) without dp 45% 6% 31% 21% -17% 32% 29%

(45) with dp 70% 38% 48% 94% 111% 46% 80%

(49) with dp 82% 25% 104% 28% 33% 39% 41%



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Table A5.19: Impact of CAP direct payment measures on the income of specialized dairy and cattle farms IV

PT RO FI SE SK SI UK


(45) 12,721 3,937 3,329 -3,387 -93,883 6,641 49,318

(49) 4,545 3,902 -24,329 -9,686 -98,234 -757 -4,851


(45) 8,795 3,296 1,930 -2,091 -13,988 3,447 30,542

(49) 3,518 3,178 -19,332 -10,064 -

142,200 -561 -3,881


(45) 23,431 4,522 40,102 31,857 100,660 13,456 76,867

(49) 13,483 4,721 22,912 7,612 28,408 2,936 24,487


(45) 84% 15% 1105% -1041% -207% 103% 56%

(49) 197% 21% -194% -179% -129% -488% -605%


(45) 16,199 3,785 23,248 19,666 14,998 6,984 47,603

(49) 10,434 3,845 18,206 7,910 41,122 2,176 19,594



16,700 7,500 37,600 44,400 18,100 13,900 34,900

(45) without dp 53% 44% 5% -5% -77% 25% 88%

(49) without dp 27% 52% -65% -22% -543% -5% -14%

(45) with dp 97% 50% 62% 44% 83% 50% 136%

(49) with dp 62% 51% 48% 18% 227% 16% 56%


the eu cattle sector: challenges and opportunities - milk and meat

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