Advertisement

Characteristics of organic dairy major farm types in seven European countries

  • A. Wallenbeck
  • T. Rousing
  • J. T. Sørensen
  • A. Bieber
  • A. Spengler Neff
  • B. Fuerst-Waltl
  • C. Winckler
  • C. Peiffer
  • F. Steininger
  • C. Simantke
  • S. March
  • J. Brinkmann
  • J. Walczak
  • P. Wójcik
  • V. Ribikauskas
  • S. Wilhelmsson
  • T. Skjerve
  • S. Ivemeyer
Open Access
Article
  • 395 Downloads

Abstract

This study aimed to identify organic dairy major farm types (MFTs) in seven European countries, describe these MFTs in an open research database and assess central characteristics of the MFTs. This was conducted in a three-step procedure including (1) Identification of organic MFTs in seven European countries: Austria, Switzerland, Germany, Denmark, Lithuania, Poland and Sweden, based on existing data from dairy databases and consultations with experts within the respective fields of knowledge; (2) Collection of data on farm characteristics, management procedures, production level and herd health from at least 10 farms per MFT and country and (3) Creating an open research database on MFT characteristics, description of essential characteristics of MFTs and assessment of similarities and differences between farms within and across MFTs. The results indicate variations in herd characteristics such as production level, herd size, farm size, housing system, milking system and cow health status between organic dairy farms in these seven European countries. It also indicates variations in management strategies such as feeding, animal health management and recruitment strategies across the organic dairy sector in Europe. These variations seem to be associated with differences between regions and countries in the conditions for organic dairy production, such as topography, land availability and regulations.

Keywords

Organic dairy production Farm size Housing Production level Health Feeding Breeding 

Introduction

Consumer demand for organic products is increasing (European Commission 2014; Eurostat 2016) and this, together with the premium price for organic products, has prompted an increase in organic production (Rosati and Aumaitre 2004; Escribano et al. 2015). Moreover, organic production contributes public goods such as biodiversity and human and animal health and welfare (Jespersen et al. 2017). In Europe, the organic dairy sector has seen a steady increase, reaching a market share of between 2 and 11% in 2016 depending on country, with the main markets being in Western Europe (Sørensen et al. 2006). Consequently, organic dairy production in the EU has almost doubled since 2007, and by 2016 constituted more than 2.8% (4.1 million metric tons) of total EU milk production (Willer and Lernoud 2018). Organic dairy production is characterised by high-roughage diets based on home-grown feedstuffs, outdoor access on pasture and restrictive use of antibiotics (EC 2007). In organic dairy production, animal health and welfare is promoted by preventative actions such as using suitable breeds, feed, management and housing system. Accordingly, compared with conventional farms, organic dairy cattle farms have a higher amount of pasture-based feeding systems and a lower amount of concentrate feeding (Kristensen and Kristensen 1998; Valle et al. 2007; Thomassen et al. 2008) and on average a lower use of medicines (Richert et al. 2013; Van Wagenberg et al. 2016). Some of the major challenges within European organic dairy production are preventative handling of mastitis and metabolic diseases (Bennedsgaard et al. 2010; Ivemeyer et al. 2012), improvement of production efficiency based on local feed resources and development of suitable breeding strategies (Röös et al. 2018).

The organic dairy cattle sector must adapt to differences in topography, infrastructure, national and regional organic regulations, traditions and typical management procedures. Consequently, organic dairy farm types vary across Europe. These differences arise within individual countries (e.g. Ivemeyer et al. 2017), but can be expected to be of even greater magnitude between European countries. Thus, there is not one, but many, organic dairy production systems in Europe. However, no systematic mapping of organic dairy production environments in Europe is available at present. Thus, an essential step in the development process in order to enhance knowledge exchange within the European organic dairy sector, and also to provide evidence-based information for advisors, policymakers and regulation developers, is to identify and map major organic dairy farm types in Europe.

Aim

The aim of this study was to identify organic dairy major farm types (MFTs) in seven European countries, describe these MFTs in an open research database and assess central characteristics of the MFTs.

Material and methods

This study was conducted in a three-step procedure:
  1. (1)

    Identification of organic MFTs in seven European countries: Austria (AT), Switzerland (CH), Germany (DE), Denmark (DK), Lithuania (LT), Poland (PL) and Sweden (SE), based on existing data from dairy databases and consultations with experts (production advisors (including veterinarians) and researchers) within the respective fields of knowledge.

     
  2. (2)

    Collection of data on farm characteristics (herd size and structure, housing and milking system), management procedures (related to feed, health and breeding), production level and herd health from at least 10 farms per MFT (Table 2).

     
  3. (3)

    Creation of an open research database on MFT characteristics, description of essential characteristics of MFTs and assessment of similarities and differences between farms within and across MFTs.

     

This three-step procedure was chosen in order to identify and describe some major variations and patterns in the European organic dairy sector, and was not intended to give a representative quantification of characteristics of European organic dairy farms.

Identification of MFTs in seven European countries

The MFTs in each country were identified according to a standardised protocol that covered four criteria: geographical location, herd size, housing type and production level. The protocol was completed using data from different national data sources and expert panel interviews, although methods for MFT identification varied to some extent between countries (Table 1). The relevance of the criteria considered also varied between countries, e.g. there is greater variation in geographical location and milk production level in Switzerland and Austria than in Denmark. Thus, the main criteria included in MFT identification also varied between countries (Table 1).
Table 1

Information source, identification method and main criteria included in identification of major farm types (MFTs) of organic dairy farms in the seven countries included in the analysis

Country

Information source

MFT identification method

Main criteria included in MFT identification

Austria

National cattle database and data from an on-farm research project

Expert opinion (researchers and representatives of breeding organisations) based on collected information

Herd size, housing type, location/level of intensity of farming

Switzerland

National cattle database and expert panel interview

Expert opinion (researchers and advisors) based on collected information

Location, production level

Germany

National agricultural statistical database, data from on-farm research projects and expert panel interview

Cluster analysis, expert opinion based on results from cluster analysis (Ivemeyer et al. 2017)

Herd size, housing type, location, production level

Denmark

National cattle database and expert panel interview

Expert opinion (researchers and advisors) based on collected information

Housing type, production level

Lithuania

Data from the organic certification organisation Ekoagros

Expert opinion (researchers) based on collected information

Herd size, housing type

Poland

National cattle database and data from Agricultural and Food Quality Main Inspection

Principal components analysis1

Housing type, location

Sweden

National cattle database (Växa 2014) and data from organic certification organisation KRAV

Expert opinion (researchers and production advisors from Växa) based on collected information

Herd size, production level (nested within milking equipment type)

1Poland: MFTs were defined using data from 463 organic dairy farms obtained through the national cattle database. Using PROC FACTOR method = prin in SAS 9.4® software (SAS Institute Inc. 2017), principal component analysis (PCA) was used to identify variables that explained the largest degree of variance. Input variables were the continuous variables ‘herd size’ and ‘milk yield’, and the binominal variables ‘location mountain’, ‘location lowland’, ‘loose housing’ and ‘tie-stall’. Number of components to be retained was decided using Kaiser’s criterion. On the basis of the PCA, location mountain, location lowland, loose housing and tie-stall were chosen as defining variables for MFTs. However, this created a farm type with too few observations (location mountain, loose housing; n = 6). The two mountain farm types were therefore merged into one, and three final MFTs were defined (lowland, loose housing; lowland, tie-stall; mountain, loose housing and tie-stall)

Collecting data on farm characteristics for each MFT

The characteristics of each farm type were investigated in an in-depth farm survey that included detailed questions on approximately 90 items, such as farm location, housing and milking system, herd size and structure, production level, herd health status and preventive health management, feeding including ratio formulation, pasture management, fodder production and breeding strategies, including breeds kept on the farm and reproduction techniques used. For details of the survey, see OrganicDairyHealth database (2018). The survey protocol was developed jointly by all partners, translated into native languages and used in all countries. Data collection according to the survey protocol was performed as interviews, questionnaires, data collection from databases or a combination of these methods. Methods for identification of farms for inclusion in data collection and methods for data collection varied between countries (Table 2). Protocol information was collected from at least 10 farms per farm type. A survey was considered complete when critical information on housing and milking system, herd size and structure, production level and herd health status was recorded. In total, 715 completed surveys were obtained. All data collected referred to production year 2014, except for Denmark, for which information for production year 2015 was recorded.
Table 2

Methods used for data collection and for farm identification and number of farms with a completed survey in each of the seven countries included in the analysis

Country

Methods for data collection

Methods for identification of farms

Number of farms with complete surveys

Austria

Interviews by phone (57 farms), in combination with data from the national cattle database Rinderdatenverbund (RDV) and another research project1 (27 farms)

Farms included in another research project and farms identified by performance testing and organic organisations that best fitted the major farm type (MFT) criteria

60

Switzerland

Interviews by main or phone in combination with data from the national cattle database

Organic dairy farms with completed records for 2014 were selected from a FiBL database. Of 142 farms fulfilling MFT criteria, 10 farms per MFT were randomly selected within geographical regions

20

Germany

Interviews on farms (13 farms) and interviews by phone (28 farms), in combination with data collected in other research projects

Farms included in other research projects and farms in the research database that best fitted the MFT criteria

41

Denmark

Interviews on farms in combination with data from the national cattle database

Farms included in other research studies. All fitting the DK MFT-profiles, thus including farms with robot milking

10

Lithuania

Interviews on farms (9 farms), interviews by phone (12 farms), paper questionnaire (19 farms) and data from the organic certification organisation Ekoagros

Farms that best fitted the MFT criteria and then randomly selected within geographical regions

40

Poland

National cattle database and data from Agricultural and Food Quality Main Inspection

All farms with sufficient data in the Polish databases and fulfilling the MFT criteria

463

Sweden

Internet-based questionnaire

The questionnaire was e-mailed to 400 randomly selected KRAV-certified organic dairy farms in Sweden. Of these, 108 entered and 81 completed the survey

81

1Records collected within the project ‘Analysis and optimization of production efficiency and environmental impact within the Austrian cattle production’ (Efficient Cow), Project No. 100861, were used

Building a database on organic dairy farm characteristics

Data obtained in the data collection process were compiled into a common database with information on farm level to be used as input to other work packages in the OrganicDairyHealth project (OrganicDairyHealth database 2018). Database information summarised on MFT level is publicly available and can be accessed at http://projects.au.dk/coreorganicplus/research-projects/organicdairyhealth/database-summary/. It is intended for use in future research projects. The database is extensive and includes more than 250 variables describing detailed characteristics of the 715 farms surveyed. In this paper, we do not aim to provide a complete description of all recorded characteristics, but focus on describing critical farm, herd and management characteristics.

Data processing

All data were edited in Microsoft Excel 2013 (Microsoft Inc., 2013) and SAS® 9.4 (SAS Institute Inc., 2013). Descriptive statistics on variations within and between MFTs were calculated using SAS® 9.4 (SAS Institute Inc., 2013). Graphical illustrations were created in RStudio ver1.0.143 (RStudio Team, 2016). The final dataset analysed comprised 22 variables and 715 farm observations from the seven countries included in the study.

Results

Identified MFTs

The number of MFTs identified varied between countries. Definitions of the MFTs identified in each partner country are presented in Table 3.
Table 3

Definition and name of the major farm types identified in the survey in each of the seven countries included in the analysis

Country and code structure

Definitions of major farm types (MFT)

Name MFT

Austria AT_Barn type_Area

Loose housing, herd size small-medium to large; extensive/higher/alpine regions

AT_Loose_Alpine

Loose housing, herd size small-medium to large; intensive/favourable production area

AT_Loose_Favourable

Loose housing, herd size small-medium to large; medium production area

AT_Loose_Medium

Tie-stall, herd size small to medium; higher/alpine regions

AT_Tie_Alpine

Tie-stall, herd size small to medium; favourable or medium production area

AT_Tie_Fav_Medium

Switzerland CH_Yield

Lowland (valley), high input (≥ 6500 kg milk yield)

CH_High

Mountain region, high input (≥ 6000 kg milk yield)

Lowland (valley), low input (< 6500 kg milk yield)

CH_Low

Mountain region, low input (< 6000 kg milk yield)

Germany DE_Scale_Yield1

Large-scale herds, high milk yield, typically cubicles, typically northern Germany

DE_Large_High

Medium-scale herds, low-medium milk yield, typically eastern Germany

DE_Medium_Low/Medium

Medium-scale herds in southern Germany, medium milk yield

DE_Medium_Medium_South

Small-scale herds, low milk yield, typically straw yards, typically western Germany

DE_Small_Low

Denmark DK_Barn type_Yield

Danish organic, loose-housed, high production

DK_Loose_High

Lithuania LT_Barn type_Herdsize

Loose housing, small (< 80 cows), milking parlour

LT_Loose_Small

Loose housing, large (≥ 80 cows), milking parlour

LT_Loose_Large

Tie-stall, large (≥ 50 cows), mobile single milking machine connected to milkline/bucket

LT_Tie_Large2

Tie-stall, small (< 50 cows), mobile single milking machine connected to milkline/bucket

LT_Tie_Small

Poland PL_Location_Barn type

Mountain regions, tie-stall and loose housing

PL_Mountain_Loosetie

Lowland regions, loose housing

PL_Lowland_Loose

Lowland regions, tie-stall

PL_Lowland_Tie

Sweden SE_MilkingEquip

Tie-stall, mobile single milking machine connected to milkline

SE_Machine_Milkline

Loose housing, milking parlour

SE_Parlour

Loose housing, milking robot

SE_Robot

1All German MFT had loose housing systems. For more details of German MFT definitions, see Ivemeyer et al. (2017) MFT names: DE_Medium_Low/Medium = MFT A, DE_Small_Low = MFT B, DE_Large_High = MFT C; DE_Medium_Medium_South = MFT D)

2As this MFT had only three observations, it was combined with LT_Loose_Large for statistical analysis

Farm size and structure

Farm size, in terms of number of cows and area of arable land, varied between MFTs (median 11 to 143 lactating cows per herd, median 28 to 385 ha per farm). Size showed skewed distributions within MFTs, as indicated by the interquartile ranges (IQRs) and the relationships between medians and IQRs (Table 4). The availability of arable land for pasture and access to semi-natural pasture varied between regions, and thus between MFTs (Table 4). The age distribution of the cows in the herd varied between a MFT median of 16.7 and 35.8% for first-parity cows in the herd. Age distribution also varied between countries, with a higher proportion of first- and second-parity cows in MFTs in Austria, Denmark and Sweden and a larger proportion of cows in parity 3 or older in MFTs in Germany, Lithuania and Switzerland (Table 4). Variation was also observed in average age at first calving, which was higher and showed greater variation in MFTs in Austria, Switzerland and Germany compared with MFTs in Denmark, Lithuania and Sweden (Table 4).
Table 4

Farm size and structure, housing and milking system, feeding, production level, herd health and use of breeds for the major farm types (MFTs) in different countries. For MFT definitions, see Table 3. IQR, interquartile range

MFT1

AT_Loose_Alpine

AT_Loose_Favourable

AT_Loose_Medium

AT_Tie_Alpine

AT_Tie_Fav_Medium

CH_High

CH_Low

DE_Large_High

DE_Medium_Low_Medium

DE_Medium_Medium_South

DE_Small_Low

DK_Loose_High

LT_Loose_Small_Large

LT_Loosetie

LT_Tie_Small

PL_Mount_Loosetie

PL_Lowland_Loose

PL_Lowland_Tie

SE_Machine_Milkline

SE_Parlour

SE_Robot

Farm size and structure

 Number of lactating cows

  N

11

9

16

9

12

10

9

11

10

10

10

10

10

10

18

143

48

270

17

16

30

  Median

21

19

26

13

14

28

19

78

40

40

24

143

58

106

28

11

33

12

30

70

70

  IQR

10.9

8.1

12.4

6.8

3.7

27.7

7.5

56.7

15.1

11.8

10.1

67

22

17

10

6

23

7

20

90

55

 Parity distribution of cows (%)

  N

11

9

16

9

12

9

10

11

10

10

10

10

9

9

16

0

0

0

16

13

26

  1st parity

35.7

29.6

25.5

32.2

28.2

24.6

23.5

27.7

22.6

22.8

17.6

35.8

26.3

16.7

17.7

27.2

30.8

32.2

  2nd parity

26.5

23.4

23.0

21.7

22.7

21.8

17.7

17.2

18.4

19.5

17.9

21.7

21.3

24.5

17.0

26.4

24.5

27.6

  3rd or higher parity

37.8

47.0

51.5

46.1

49.1

53.6

58.8

55.1

59.0

57.7

64.5

42.5

52.4

58.8

65.3

46.4

44.7

40.2

 Total land (ha)

  N

6

5

13

6

8

10

9

11

10

10

10

9

11

11

18

143

48

269

12

12

18

  Median

44

55

47

38

28

36

30

170

176

56

77

201

223

385

71

38

216

37

148

214

239

  IQR

33

42.5

35

20.5

10.7

19.2

13.8

230

76

18

38

79

141

335

23

96.9

389

69.3

194

246

168

 Total pasture land (ha)

  N

6

5

13

6

8

10

9

11

10

10

10

9

11

11

18

143

48

269

12

12

18

  Median

37

20

34

25

20

26

28

66

71

45

40

60

135

185

54

20

68

18

21

50

36

  IQR

42.0

24.0

14.5

27.5

17.8

13.0

8.0

55.0

37.7

19.0

27.0

68.0

43.1

100.0

19.0

63.0

217.0

43.8

33.2

76.0

43.5

 Area of semi-natural pasture (ha)

  N

11

7

16

7

10

10

10

11

10

10

10

9

0

0

0

143

48

269

12

12

19

  Median

36

40

24

14

24

26

62

65

45

37

7

18

52

16

5

23

15

  IQR

20.9

42.0

21.5

15.7

7.2

4.2

53

25.7

22.0

27.0

21.0

56.7

167.0

36.0

9.5

33.5

38.0

 Housing and milking system (%)

  N

11

10

17

10

12

7

9

11

10

10

10

10

11

11

18

143

48

270

20

20

40

  Loose-housed

100

100

100

0

0

85.7

55.6

100

100

100

100

100

100

72.7

5.56

4.2

100

0

15

100

100

  Tie

0

0

0

100

100

14.3

44.4

0

0

0

0

0

0

27.3

94.4

95.8

0

100

85

0

0

 Lying area in loose housing (%)

  N

11

10

17

0

0

9

6

11

10

10

10

10

10

8

1

6

48

0

12

19

40

  Straw yard or sloped floors

0

0

0

0

0

0

50

0

70

0

0

0

0

0

6.25

8.33

21

2.5

  Cubicles

100

100

100

100

100

100

50

100

30

100

100

100

100

100

93.8

91.7

78.9

97.5

  N

11

10

17

10

12

10

10

11

10

10

10

10

11

11

18

7

48

2

20

21

40

  Straw material in lying area (% of farms answering yes)

100

80

88.2

0

0

90

60

81.8

50

100

40

50

45.5

18.2

5.56

85.7

87.5

0

20

9.52

17.5

  N

11

10

17

10

12

10

10

11

10

10

10

7

11

11

18

20

21

39

  Rubber mattress in lying area (% of farms answering yes)

9.09

40

0

0

0

10

0

18.2

0

10

0

100

45.5

54.5

0

25

71.4

97.4

  N

11

10

17

10

12

10

10

11

10

10

10

7

20

21

39

  Concrete floor in lying area (% of farms answering yes)

0

0

17.6

0

0

60

60

9.09

0

10

10

0

5

4.76

2.56

 Milking system (%)

  N

11

10

17

10

12

10

9

11

10

10

10

10

11

11

18

143

48

270

19

20

40

  Robot

9.09

0

0

0

0

0

0

36.36

0

0

0

60

0

0

0

0

0

0

0

0

95

  Parlour

90.91

100

100

0

0

80

66.67

63.64

100

100

100

40

100

72.73

0

0.7

29.17

0

0

100

5

  Single milking machine

0

0

0

100

100

20

33.33

0

0

0

0

0

0

27.27

33.33

4.2

70.83

0.74

100

0

0

  Bucket

0

0

0

0

0

0

0

0

0

0

0

0

0

0

66.67

95.1

0

99.26

0

0

0

Feeding

 Roughage in winter ratio (%)

  N

10

9

13

7

10

4

7

7

7

5

8

8

4

6

7

10

6

14

  Median

75

77

75

79

87

94

95

82

90

76

74

69

75

84

77

66

56

64

  IQR

12.7

13.4

13.7

5.82

10.1

7.43

10.5

4.09

24.4

24.5

23.8

11.6

13

6.28

15.7

18.2

20.6

15.6

  Concentrates [kg/cow&yr] Median

1071

1003

1200

600

915

440

0

1600

860

908

750

2100

1500

2250

  IQR

395

619

1082

698

490

250

75

600

400

540

745

1460

889

608

  N

10

9

15

10

10

10

10

11

10

10

9

9

10

11

17

10

8

19

  Only hay as roughage in winter (% of farms)

10

11.1

0

20

20

30

20

0

40

30

22.2

0

0

0

5.88

0

0

5.26

Production level

 Milk (ECM)

  N

11

9

16

9

12

10

10

11

10

10

10

10

9

10

9

143

48

270

16

14

26

  Median

6707

6435

7643

6073

6489

6834

5370

7732

6058

6301

5370

9505

6073

6150

5800

3000

3493

3000

9443

8250

9200

  IQR

2339

1846

1736

1281

1627

625

869

1305

1530

861

710

1496

1052

650

400

1596

1996

1578

1552

3045

1300

 Protein %

  N

11

9

16

9

12

10

10

11

10

10

10

10

9

9

12

143

48

270

16

12

26

  Median

3.28

3.24

3.35

3.27

3.34

3.38

3.29

3.24

3.45

3.4

3.42

3.46

3.3

3.3

3.25

3.24

3.19

3.24

3.40

3.50

3.40

  IQR

0.19

0.14

0.11

0.08

0.13

0.15

0.13

0.16

0.18

0.18

0.37

0.71

0.09

0.1

0.34

0.19

0.2

0.17

0.1

0.23

0.2

 Fat %

  N

11

9

16

9

12

10

10

11

10

10

10

10

9

9

12

143

48

270

16

12

26

  Median

4.10

4.00

4.12

3.97

4.09

4.08

3.97

4.13

4.30

3.95

4.10

4.21

4.28

4.23

4.25

4.04

3.96

4.07

4.28

4.25

4.18

  IQR

0.24

0.22

0.26

0.17

0.14

0.23

0.21

0.30

0.53

0.24

0.26

1.59

0.22

0.20

0.48

0.41

0.46

0.32

0.30

0.35

0.30

 Average age at first calving (month)

  N

11

10

16

10

11

10

10

11

10

10

10

9

8

11

10

143

48

270

9

13

13

  Median

29.6

27.5

29.0

29.6

27.4

28.5

32.0

27.0

30.2

28.8

31.2

25.5

24.0

24.0

24.5

20.0.

20.0

20.0

24.4

26.0

25.0.

  IQR

6.32

4.91

2.52

3.61

25.9

7.00

3.00

2.37

7.61

6.48

1.52

2.10

3.00

1.00

2.00

0.00

2.00

2.00

2.68

2.00

3.00

 Breeds (% of herds)

  N

11

10

17

10

11

10

10

11

10

10

10

10

10

11

14

12

13

15

  One major breed

45.4

30.0

64.7

70.0

36.4

60.0

20.0

45.5

50.0

70.0

50.0

50.0

30.0

54.5

57.1

16.7

23.1

13.3

  Two major breeds

27.3

40.0

24.9

20.0

36.4

20.0

70.0

18.2

20.0

30.0

30.0

50.0

60.0

27.3

28.6

50.0

30.8

60.0

  More than two major breeds

27.2

30.0

5.9

10.0

27.2

20.0

10.0

36.3

30.0

0.0

20.0

0.0

10.0

18.2

14.3

33.3

46.1

26.7

  N

10

9

13

7

10

10

10

11

10

10

10

10

10

11

16

10

14

17

  Cross bred (dairy breeds) cows in the herd (yes/no)

80.0

55.6

61.5

42.9

80.0

20.0

10.0

27.3

30.0

40.0

30.0

50.0

0.0

9.09

18.8

20.0

42.9

29.4

Herd health

 Treated for mastitis (all treatments)

  N

7

14

8

5

8

10

9

0

0

0

0

10

4

9

6

0

0

0

14

13

15

  Average per 100 cows and yr

11.4

19.6

18.2

16.3

40.2

16.2

10.9

13.0

22.1

10.4

8.4

12.0

6.8

9.8

 Treated for metabolic diseases

  N

7

14

8

5

8

10

9

0

0

0

0

10

4

9

6

0

0

0

14

14

17

  Average per 100 cows and yr

0.64

0.18

5.50

0.00

0.00

5.53

0.00

0.35

2.42

2.67

2.82

2.68

2.81

1.76

 Culling rate

  N

11

9

16

9

11

10

9

9

6

10

7

0

9

10

15

0

0

0

13

12

15

  Average culled cows (%, per yr)

19.8

24.3

17.1

20.5

23.4

19.3

17.9

25.0

19.3

24.4

14.7

13.0

8.0

14.7

33.5

27.3

28.8

 SCC × 103 average/yr

  N

11

10

16

10

11

10

10

11

10

10

10

10

8

9

12

143

48

270

11

14

20

  Median

171

175

184

138

135

185

176

249

302

152

230

213

265

215

250

847

722

858

180

200

205

  IQR

153

113

129

164

76

66

53

105

195

41

100

65

115

50

84

553

469

512

152

55

51

1Code structure for MFT names for each country is given in Table 3

Housing and milking system

Milking and housing systems were interrelated. A milking parlour was the most common milking system in loose housing systems and a mobile single milking machine was the most common in tie-stall housing systems (Table 4). The majority of all farms had cubicles with straw in the lying area (Table 4). Farms with straw yards or sloped floors were identified as relevant housing systems within MFTs only in Germany, Sweden and Poland.

Feeding strategy

The proportion of roughage in the winter ration varied between a median of 56 and 85% on dry matter (DM) basis for the MFTs surveyed. A feeding strategy including only hay as roughage was most common among German, Swiss and Austrian MFTs (Table 4).

Production level and breeds

Median production level varied from 3000 to 9505 kg energy-corrected milk (ECM) per year, with the lowest production levels on Polish MFTs and the highest on MFTs in Denmark and Sweden. Milk protein and fat content varied between MFTs, but with no clear patterns. A majority of all farms had one major breed in the herd, but crossbreeds in the herd were common. The proportion of farms with only one breed was lower in MFTs in Sweden, compared with the other countries (Table 4).

Herd health and treatment routines

Average herd somatic cell count (SCC) over the year varied between a median of 135 and 858 × 103 cells/mL, with lower levels in some Austrian and Swiss MFTs and higher levels in Polish MFTs (Table 4). Most MFTs had an average mastitis prevalence of between 10 and 20% of lactating cows. Average culling rate varied between 8 and 34% of lactating cows (Table 4).

A majority of farmers on all MFTs except one (a German MFT) used antibiotics routinely for treatment of clinical mastitis. Use of the practice of drying off teat quarters and use of homoeopathic treatments varied more between MFTs, both between and within countries. A higher proportion of farmers in Lithuania and Sweden used drying off quarters as a mastitis treatment and a higher proportion of Lithuanian and Swiss farms used homoeopathic treatment (Fig. 1).
Fig. 1

Proportion of herds in different major farm types (MFTs) with routine treatment using antibiotics (N = 191), drying off teat quarters (N = 181) and homoeopathic treatment for mastitis (N = 183). Polish MFTs are not included, as no data were available

Most farms used veterinary treatments to treat metabolic disease. Homoeopathic treatment was more commonly used by farmers in Lithuania and Switzerland and small German farms (Fig. 2).
Fig. 2

Proportion of herds in different major farm types (MFTs) using routine veterinary (N = 131) and homoeopathic (N = 126) treatment for metabolic diseases. Polish and Swedish MFTs are not included, as no data were available

Discussion

The objectives of this study were to identify major farm types (MFT) in seven European countries, describe these MFTs and assess their critical characteristics. We identified 22 MFTs in the countries studied and entered descriptions of these MFTs into a public research database (http://projects.au.dk/coreorganicplus/research-projects/organicdairyhealth/database-summary/), where the variation in farm characteristics is illustrated. The data collected is not, and was not intended to be, a representative sample of the organic herds in each country, as we did not intend to quantify organic dairy production characteristics. This study, and the methodology used, aimed to capture some major variations and patterns in European MFTs and the characteristics of these. The same basic protocol for data collection was used in all seven countries, but the method of data collection varied between countries, from internet-based questionnaires and compiling data from databases to telephone or in-person interviews. Differences in data collection methods, in combination with differences in management and interpretation of questions between countries and farms, may have influenced the accuracy of the answers, which must be taken into account when interpreting the results.

The results from the survey indicated that the highest milk production level and largest herd sizes are found in northern regions of Europe, i.e. Denmark, Sweden and northern Germany. The smallest herd sizes and lowest milk producution levels occur on Polish, Austrian and Swiss organic dairy MFTs, especially in mountain regions, which also displayed relatively larger variation in these characteristics. Moreover, the Lithuanian data on herd size showed great reported variation, varying from relatively small to the largest of all countries included in the survey. These results are in line with Sørensen et al. (2006), who described dairy cattle production in the industrialised world as highly specialised and with high milk production levels. However, that study also reported large differences, with dairy farms in the Netherlands, Germany, Denmark and Sweden typically being characterised as highly intensive. In the present study, we found that the countries reporting the largest variation in MFTs were Austria and Germany. These countries are situated in a region of Europe that has both lowland and mountain dairy systems. Nevertheless, in Germany, the most extreme differences between MFTs were constituted not only by topography (lowlands in the north and mountainous in the south), but more regarding production intensity (partly within the same regions; Ivemeyer et al. 2017). According to Stuaro et al. (2009), in recent decades, the number of traditional extensive farming units has decreased in favour of highly mechanised and intensive production practices and this decrease has been particularly dramatic for the traditional extensive livestock farms of the Alpine region.

Apart from herd size, the amount of farm land also varied between MFTs, indicating differences in availability of arable land for fodder production, pasture and access to semi-natural pasture. Thus, conditions for dairy production differ between regions, explaining part of the variation in dairy production systems observed in the countries and regions included in this study.

Among the farms surveyed in this study, the majority of cows were kept in loose housing systems, but there are still some tie-stall housing systems, typically in Alpine regions (Austria and Switzerland), Sweden and central Europe (Poland and Lithuania). This is in line with previous findings (e.g. Krieger et al. 2017), indicating that the farms and MFTs included in this study reflect the situation in the European dairy sector. These differences are partly explained by different conditions due to topography in mountain and lowland regions, partly by cultural, traditional and socio-economic differences and partly by differences in regulations in different countries and regions. Since 2010, EU regulations on organic production demand that cows be loose-housed, with the exception of small farms (EC 2007). However, it is not permitted to build new tie-stalls even for small farms. The threshold for ‘small dairy herds’ was not defined consistently in the previous version of EUs regulation (before 2018) and is instead defined at national level (Swensson 2008; Barkema et al. 2015). Countries and federal states within countries have defined these cut-offs individually, e.g. 20–35 cows, depending on federal state, in Germany, 35 livestock units in Austria, and 45 cows in Sweden (Swensson 2008). Altogether, this indicates that a considerable proportion of the cows on organic farms in Europe are kept in tie-stalls with access to pasture during the vegetation period and regular exercise during the remainder of the year, but this proportion can be expected to decrease in the future.

Milking and housing system were found to be interrelated. A milking parlour was the most common milking system in loose-house systems and mobile single milking machine was the most common in tie-stall housing systems. Milking robot systems were used in some MFTs and seemed to be more common in regions with higher milk yields, i.e. northern Germany, Denmark and Sweden. This may partly be explained by high investment costs for automated milking systems and the need for high yields for good returns on this investment. However, it may also be attributable to differences in culture and experiences between regions, e.g. regions where automatic milking is common also in conventional dairy production (Svennersten-Sjaunja and Pettersson 2008).

According to the EU regulations on organic agriculture, at least 60% (50% at the beginning of lactation) of the dry feed matter has to be roughage (EC 2007). The results of our survey indicate a wide range in feeding intensity on organic dairy farms. The highest values are more than 90% roughage in the dry matter ration on Swiss MFTs and typically between 80 and 90% on MFTs in Austria, Germany and Lithuania. The lowest shares of roughage in the ration (below 70%) are used especially on all MFTs in high-yielding regions, such as in Sweden and Denmark. This variation reflects differences in national and regional regulations. For example, according to ‘BioSuisse’ standards of organic farming in Switzerland, the proportion of concentrates in the yearly ration for cattle—defined as cereals and grain legumes such as soybeans—may not exceed 10% of dry matter (BioSuisse, 2017). Krieger et al. (2017) observed differences in concentrate feeding levels for organic dairy farms in four European countries, with an average of 616, 1200, 1500 and 2373 kg concentrates per cow and year in France, Germany, Spain and Sweden, respectively. These results support our findings of a wide range between countries and of high average concentrate amounts fed in Sweden of more than 30% in the dry matter ration. Organic dairy farms in the UK are reported to use on average 1800 kg concentrates per year (Langford et al. 2009). Thus, our results support und complement previous findings and reflect typical variations in feeding management within the European organic dairy sector, caused by differences in available resources and differences in regulations between regions.

According to organic principles and standards, disease prevention must be a high priority in organic dairy production (IFOAM 2014). Disease prevention should primarily be ensured by implementing site-adapted breeding, management, nutrition and housing appropriate to the animal species (EC 2007). However, the regulations clearly state that diseased animals should be treated with appropriate methods to prevent animals suffer (e.g. antibiotic treatments in cases of serious infections). Regarding herd health, in the present study, we focused on mastitis and metabolic diseases as two important health impairments in dairy cows in general (reviewed by Martin et al. 2018 and Sundrum 2015) but also in organic dairy cows (Ivemeyer et al. 2012). Udder health was measured as arithmetic mean of somatic cell count (SCC) over 1 year from test day data and, for both udder and metabolic health, treatment incidence was used to assess disease incidence. When interpreting the results, it has to be taken into account that differences in the use of antibiotics in particular, but also other veterinary medicines, may be due not only to infection levels but also to farmers’ attitudes to treatment choice (Vaarst et al. 2006; Bennedsgaard et al. 2010).

The results show a wide range of udder health conditions in the European organic sector, with the lowest SCC in Alpine and northern European regions and the highest in central European regions. While the results have to be carefully interpreted regarding absolute SCC, they are in line with other findings of similar patterns between regions. They indicate that SCC in organic herds in several European countries is on such a high level that improvements are necessary (Ivemeyer et al. 2012; Krieger et al. 2017). Mastitis treatment incidences found in the present survey support findings by Ivemeyer et al. (2012) that mastitis incidences range widely and that mastitis is a major reason for antibiotic treatment of organic dairy cows. Moreover, all treatment incidences on MFTs (average ranging from 7 to 40 treatments due to udder problems per 100 cow-years) were within the range reported in other studies (e.g. Fall and Emanuelson 2009; Bennedsgaard et al. 2010). Besides production method, management intensity has been found to have an impact on medicine use. Intensive production systems are reported to be closely associated with frequent veterinary treatment (Richert et al. 2013). However, the results from our survey did not indicate interrelations between production intensity (e.g. milk yield and feeding ratio) and treatment incidence for mastitis or metabolic diseases within organic production systems.

Interestingly, the results of the survey indicated that non-antibiotic (non-allopathic) treatment regimes varied more between regions and countries than between MFTs. While e.g. drying off single mastitic udder quarters during lactation seems to be a well-known measure in Sweden and Lithuania, and is also used by some organic farms in Switzerland and Denmark, it seems to be uncommon in Germany and Austria. The use of homoeopathic treatment also varies, being quite common in countries like Switzerland and Lithuania and less used in Denmark and Sweden. The results related to treatment strategies indicate differences between regions and countries caused by differences in regulations and culture. This emphasises the importance of cross-country and region knowledge transfer on farmer and advisor level.

The age distribution of the cows in the herds varied between MFTs, with higher proportions of first- and second-parity cows on Austrian, Danish and Swedish MFTs and higher proportions of cows in parity 3 or older on German and Lithuanian MFTs. Moreover, there was variation in average age at first calving, which was reported to be higher and to have a larger variation on MFTs in Austria, Switzerland and Germany compared with MFTs in Denmark, Lithuania and Sweden. This might partly be explained by breed differences where dual-purpose breeds, which usually have a higher age at first calving, are more common in Alpine areas. Accordingly, culling rates varied widely between MFTs. MFTs with a long productive lifetime and a low culling rate were found particularly in Lithuania. A higher culling rate than a median of 25% was found on the high-yielding MFTs in Sweden and Germany. Taken together, these results could indicate differences in dairy cow recruitment strategies between regions, but are also clearly linked to production intensity, where a high intensity is connected to reduced longevity and fertility.

More than one-third of the farms surveyed had one major breed in the herd, but also kept some crossbred cows in the herd, indicating that breeding strategies including crossing of dairy breeds are widespread in the European organic dairy sector. The majority of these crosses were between high-producing dairy breeds, but crosses with native dairy breeds also occurred. The IFOAM norms for organic agriculture state that the breeds used in organic production should be adapted to local conditions, and the use of native breeds is promoted (IFOAM 2014). The widespread use of crossbreeds may be an indication that organic dairy farmers across Europe try to take advantage of heterosis and to benefit from both high-producing modern breeds and the functional traits of locally adapted native breeds.

One of the aims of the study was to describe characteristics of the MFTs in an open research database. The primary aim was to provide input to other work packages of the OrganicDairyHealth project, which has been achieved. A secondary aim was to make the information in the database openly available for future research projects interested in farm characteristics of organic dairy farms. Thus, information from the database summarised on MFT level has been made publicly available for anyone interested to use it for research purposes. However, as the project is finalised, there are no plans for further expansion or development of the database.

Conclusions

This survey revealed variations in herd characteristics such as production level, herd size, farm size, housing system, milking system and cow health status between organic dairy farms in seven European countries. There were also variations in management strategies such as feeding, animal health management and recruitment strategies across the organic dairy sector in Europe. These variations seem to be caused by differences between regions and countries in the conditions for organic dairy production, such as topography, land availability and regulations. Future development of organic dairy production in Europe would benefit from exchange of knowledge and experience, on farm and advisory level, across regions and countries.

Notes

Acknowledgements

The authors gratefully acknowledge all farmers, experts and dairy and performance recording organisations for their cooperation and for sharing information and data. The project ORGANICDAIRYHEALTH is funded by ERA-Net CORE Organic Plus Funding Bodies partners of the European Union’s FP7 research and innovation programme under grant agreement no. 618107, and is supported by funds from The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS); the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the Federal Organic Farming Scheme (BÖLN), and grant no. 2814OE003 ‘Improving animal health and welfare on organic cattle milk production through breeding and management’, Federal Ministry for Sustainability and Tourism (BMNT) under project numbers 100861, 101056 and 101060; The Danish Ministry of Environment and Food and the Danish Agricultural Agency; Polish National Centre for Research and Development and Polish Ministry of Agriculture and Rural Development and The Ministry of Agriculture of the Republic of Lithuania.

References

  1. Barkema HW, Keyserlingk M, von Kastelic JP, Lam TJGM, Luby C, Roy J-P, LeBlanc SJ, Keefe GP, Kelton D (2015) Invited review—changes in the dairy industry affecting dairy cattle health and welfare. J Dairy Sci 98:7426–7445CrossRefGoogle Scholar
  2. Bennedsgaard TW, Klaas IC, Vaarst M (2010) Reducing use of antimicrobials—experiences from an intervention study in organic dairy herds in Denmark. Livest Sci 131:183–192CrossRefGoogle Scholar
  3. 23EC (2007) Council Regulation (EC) No 834/2007 of 28 June 2007 on organic production and labelling of organic products and repealing. Regulation (EEC) No 2092/91 No. No 834/2007, Official Journal of the European Communities, L189/1 (20.07.2007)Google Scholar
  4. Escribano AJ, Gaspar, P, Mesias FJ, Escribano M (2015) The contribution of organic livestock to sustainable rural development in sensitive areas. International Journal of Research Studies in Agricultural Sciences, 1(1), 21–34Google Scholar
  5. European Commission (2014) Special Eurobarometer: Europeans, agriculture and the common agricultural policy. European Commission, BrusselsGoogle Scholar
  6. Eurostat (2016). Statistics on certified organic livestock by type of species. Eurostat Online database. http://ec.europa.eu/eurostat/web/agriculture/data/database. Accessed 12 Feb 2018
  7. Fall N, Emanuelson U (2009) Milk yield, udder health and reproductive performance in Swedish organic and conventional dairy herds. J Dairy Res 76:402–410.  https://doi.org/10.1017/S0022029909990045 CrossRefPubMedGoogle Scholar
  8. IFOAM (2014) The IFOAM norms for organic production and processing https://www.ifoam.bio/sites/default/files/ifoam_norms_july_2014_t.pdf. Accessed June 2018
  9. Ivemeyer S, Smolders G, Brinkmann J, Gratzer E, Hansen B, Henriksen BIF, Huber J, Leeb C, March S, Mejdell C, Nicholas P, Roderick S, Stoeger E, Vaarst M, Whistance LK, Winckler C, Walkenhorst M (2012) Impact of animal health and welfare planning on medicine use, herd health and production in European organic dairy farms. Livest Sci 145(1–3):63–72CrossRefGoogle Scholar
  10. Ivemeyer S, Brinkmann J, March S, Simantke C, Winckler C and Knierim U (2017) Major organic dairy farm types in Germany and their farm, herd, and management characteristics. Org Agric 1–17  https://doi.org/10.1007/s13165-017-0189-3 CrossRefGoogle Scholar
  11. Jespersen L, Baggesen D, Fog E, Halsnæs K, Hermansen JE, Andreasen L, Strandberg B, Sørensen JT, Halberg N (2017) Contribution of organic farming to public goods in Denmark. Org Agric 7:243–266.  https://doi.org/10.1007/s13165-017-0193-7 CrossRefGoogle Scholar
  12. Kristensen T, Kristensen ES (1998) Analysis and simulation modeling of the production in Danish organic and conventional dairy herds. Livest. Prod. Sci. 54, 55–65CrossRefGoogle Scholar
  13. Krieger M, Sjöström K, Blanco-Penedo I, Madouasse A, Duval JE, Bareille N, Fourichon C, Sundrum A, Emanuelson U (2017) Prevalence of production disease related indicators in organic dairy herds in four European countries. Livest Sci 198:104–108CrossRefGoogle Scholar
  14. Langford F, Rutherford K, Jack M, Sherwood L, Lawrence A, Haskell M (2009). A comparison of management practices, farmer-perceived disease incidence and winter housing on organic and nonorganic dairy farms in the UK. Journal of Dairy Research, 76(1), 6–14  https://doi.org/10.1017/S0022029908003622 CrossRefGoogle Scholar
  15. Martin P, Barkema HW, Brito LF, Narayana SG, Migior F (2018) Symposium review: novel strategies to genetically improve mastitis resistance in dairy cattle. J Dairy Sci 101:2724–2736CrossRefGoogle Scholar
  16. Richert RM, Cicconi KM, Gamroth MJ, Schukken YH, Stigbauer KE, Ruegg PL (2013) Management factors associated with veterinary usage by organic and conventional dairy farms. J Am Vet Med Assoc 242(12):1732–1743.  https://doi.org/10.2460/javma.242.12.1732 CrossRefPubMedGoogle Scholar
  17. Röös E, Mie A, Wivstad M, Salomon E, Johansson B, Gunnarsson S, Wallenbeck A, Hoffmann R, Nilsson U, Sundberg C, Watson CA (2018) Risks and opportunities of increasing yields in organic farming. A review. Agron Sustain Dev 38(14).  https://doi.org/10.1007/s13593-018-0489-3
  18. Rosati A, Aumaitre A (2004) Organic dairy farming in Europe. Livest Prod Sci 90(1):41–51.  https://doi.org/10.1016/j.livprodsci.2004.07.005 CrossRefGoogle Scholar
  19. Sørensen JT, Edwards S, Noordhuizen J, Gunnarsson S (2006) Animal production systems in the industrialised world. Rev Sci Tech Off Int Epiz 25(2):493–503CrossRefGoogle Scholar
  20. Sundrum A (2015) Metabolic disorders in the transition period indicate that the dairy cows’ ability to adapt is overstressed. Animals 5(4):978–1020CrossRefGoogle Scholar
  21. Svennersten-Sjaunja KM, Pettersson G (2008) Pros and cons of automatic milking in Europe. J Anim Sci 86:37–46CrossRefGoogle Scholar
  22. Swensson C (2008) Towards loose housing in Swedish organic dairy production. Poster at: Cultivating the Future Based on Science: 2nd Conference of the International Society of Organic Agriculture Research ISOFAR, Modena, Italy, 18–20 June 2008. http://orgprints.org/12534/
  23. Thomassen MA, van Calker KJ, Smits MC, Iepema GL, de Boer IJ (2008) Life cycle assessment of conventional and organic milk production in the Netherlands. Agric Syst 96(1–3):95–107.  https://doi.org/10.1016/j.agsy.2007.06.001 CrossRefGoogle Scholar
  24. Vaarst M, Bennedsgaard TW, Klaas I, Nissen TB, Thamsborg SM, Østergaard S (2006) Development and daily management of an explicit strategy of non-use of antimicrobial drugs in twelve Danish organic dairy herds. J Dairy Sci 89(5):1842–1853CrossRefGoogle Scholar
  25. Valle PS, Lien G, Flaten O, Koesling M, Ebbesvik M (2007) Herd health and health management in organic versus conventional dairy herds in Norway. Livest. Sci. 112, 123–132CrossRefGoogle Scholar
  26. Van Wagenberg CPA, de Haas Y, Hogeveen H, van Krimpen MM, Meuwissen MPM, van Middelaar CE and Rodenburg TB (2016) Sustainability of livestock production systems: comparing conventional and organic livestock husbandry (no. 2016-035). Wageningen University & ResearchGoogle Scholar
  27. Willer H and Lernoud J (2018) The world of organic agriculture—statistics and emerging trends. FiBL & IOFAM – Organics International (2017): Frick and Bonn, 2017-02-20.Google Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • A. Wallenbeck
    • 1
    • 2
  • T. Rousing
    • 3
  • J. T. Sørensen
    • 3
  • A. Bieber
    • 4
  • A. Spengler Neff
    • 4
  • B. Fuerst-Waltl
    • 5
  • C. Winckler
    • 5
  • C. Peiffer
    • 5
  • F. Steininger
    • 6
  • C. Simantke
    • 7
  • S. March
    • 8
  • J. Brinkmann
    • 8
  • J. Walczak
    • 9
  • P. Wójcik
    • 9
  • V. Ribikauskas
    • 10
  • S. Wilhelmsson
    • 2
  • T. Skjerve
    • 1
  • S. Ivemeyer
    • 7
  1. 1.Department of Animal Breeding and GeneticsSwedish University of Agricultural SciencesUppsalaSweden
  2. 2.Department of Animal Environment and HealthSwedish University of Agricultural SciencesSkaraSweden
  3. 3.Department of Animal ScienceAarhus UniversityAarhusDenmark
  4. 4.Research Institute of Organic Agriculture, FiBL, Department of Livestock SciencesFrickSwitzerland
  5. 5.Department of Sustainable Agricultural SystemsUniversity of Natural Resources and Life Sciences (BOKU)ViennaAustria
  6. 6.ZuchtData EDV-Dienstleistungen GmbHViennaAustria
  7. 7.Section of Farm Animal Behaviour and Husbandry, Faculty of Organic Agricultural Sciences, Nordbahnhofstraße 1aUniversity of KasselWitzenhausenGermany
  8. 8.Thuenen-Institute of Organic Farming, Federal Research Institute for Rural Areas, Forestry and FisheriesWesterauGermany
  9. 9.National Research Institute of Animal ProductionBalicePoland
  10. 10.Department of Food Safety and QualityLithuanian University of Health Sciences, Veterinary AcademyKaunasLithuania

Personalised recommendations