Study of the adoption of greenhouse gas mitigation technologies by EU livestock farmers.
Date: 2024-07-19
Institutions: Joint Research Centre
Subjects: EU agricultural market, adaptation to climate change, agricultural production, animal nutrition, cost analysis, database, food chain, human nutrition, livestock farming, reduction of gas emissions, report, simulation

ISSN 1831-9424 

Study of the adoption of greenhouse gas 
mitigation technologies by EU livestock 
farmers 

Eory, V., Begho, T., Macleod, M., Martinez, M.A., 
Castellanos, V., Gomez Barbero, M. 

2024 

 EUR 31710 EN 

 
 
  
 
This  document  is  a  publication  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science  and  knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
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concerning the delimitation of its frontiers or boundaries. 

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JRC130981 

EUR 31710 EN 

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ISBN 978-92-68-08481-6   ISSN 1831-9424   doi:10.2760/74512 

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© European Union, 2024 

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How to cite this report: European Commission, Joint Research Centre, Eory, V., Begho, T., Macleod, M., Martinez, M.A., Castellanos, V. and 
Gomez Barbero, M., Study of the adoption of greenhouse gas mitigation technologies by EU livestock farmers, Publications Office of the 
European Union, Luxembourg, 2024, https://data.europa.eu/doi/10.2760/74512, JRC130981. 

 
 
 
 
 
 
 
Contents 

Abstract ....................................................................................................................................................................................................................................................................... 5 

Executive summary .......................................................................................................................................................................................................................................... 6 

1 

Introduction ................................................................................................................................................................................................................................................. 11 

2  Case study selection ........................................................................................................................................................................................................................... 13 

2.1  Selection of systems and locations ........................................................................................................................................................................ 13 

2.2  Selection of mitigation measures ............................................................................................................................................................................ 15 

2.2.1 

Literature review of current adoption of selected measures ...................................................................................... 19 

2.2.2 

Adoption rates ........................................................................................................................................................................................................... 20 

2.2.3 

Factors influencing adoption....................................................................................................................................................................... 24 

2.2.3.1 

Farm characteristics .............................................................................................................................................................................. 24 

2.2.3.2 

Farmer socio-economic characteristics .............................................................................................................................. 25 

2.2.3.3 

Farmer attitudes ....................................................................................................................................................................................... 26 

2.2.3.4 

Characteristics of the technology ............................................................................................................................................. 27 

2.2.3.5 

Policy .................................................................................................................................................................................................................... 27 

2.2.4 

Selected case studies......................................................................................................................................................................................... 28 

2.3  Survey methodology ............................................................................................................................................................................................................. 28 

2.3.1 

Definition of practice and adoption ...................................................................................................................................................... 28 

2.3.1.1 

Use of breeding index in dairy production in Poland.............................................................................................. 28 

2.3.1.2 

Use of multiphase feeding in pig production in France ....................................................................................... 29 

2.3.2 

Target population and sampling ............................................................................................................................................................. 29 

2.3.2.1 

Use of breeding index in dairy production in Poland.............................................................................................. 29 

2.3.2.2 

Use of multi-phase feeding in pig production in France .................................................................................... 30 

2.3.3 

Data cleaning ............................................................................................................................................................................................................. 31 

2.3.3.1 

Use of breeding index in dairy production in Poland.............................................................................................. 31 

2.3.3.2 

Use of multi-phase feeding in pig production in France .................................................................................... 32 

2.4  Statistical methods................................................................................................................................................................................................................. 32 

2.5  Greenhouse gas modelling ............................................................................................................................................................................................. 32 

3  Results .............................................................................................................................................................................................................................................................. 34 

3.1  Use of breeding index in dairy production in Poland ............................................................................................................................. 34 

3.1.1 

Uptake of breeding index ............................................................................................................................................................................... 34 

3.1.2 

Productivity, herd parameters and feed intake......................................................................................................................... 34 

3.1.2.1 

Productivity and key herd parameters ................................................................................................................................. 34 

3.1.2.2  Dairy cow feed ............................................................................................................................................................................................ 36 

3.1.3 

Greenhouse gas emission and financial modelling............................................................................................................... 37 

3.1.3.1 

Input assumptions ................................................................................................................................................................................... 37 

1 

 
 
3.1.3.2 

Results of greenhouse gas emissions modelling ...................................................................................................... 37 

3.1.3.3 

Partial budget calculation ................................................................................................................................................................ 38 

3.1.4 

Socio-economic analysis and farmers’ views ............................................................................................................................. 39 

3.1.4.1 

Farm and farmer characteristics of adopters and non-adopters .............................................................. 39 

3.1.4.2 

Farm management ................................................................................................................................................................................. 44 

3.1.4.3 

Awareness and extent of use of the breeding index ............................................................................................. 45 

3.1.4.4 

Environmental views ............................................................................................................................................................................. 46 

3.1.4.5 

Adoption of other greenhouse gas reducing management practices .................................................... 46 

3.1.4.6 

Reasons for adoption............................................................................................................................................................................ 47 

3.1.4.7 

Perceived effects of adoption ...................................................................................................................................................... 48 

3.1.4.8  Opinions of non-adopters ................................................................................................................................................................. 49 

3.1.5 

COVID-19 impact ................................................................................................................................................................................................... 51 

3.2  Use of multi-phase feeding in pig production in France .................................................................................................................... 51 

3.2.1 

Uptake of multi-phase feeding ................................................................................................................................................................ 51 

3.2.2 

Productivity, herd parameters and feed intake......................................................................................................................... 52 

3.2.2.1 

Performance of growing pigs during fattening ........................................................................................................... 52 

3.2.2.2 

Reported change in performance with adoption of additional phase ................................................... 52 

3.2.3 

Greenhouse gas emissions ........................................................................................................................................................................... 54 

3.2.3.1 

Input assumptions ................................................................................................................................................................................... 54 

3.2.3.2 

Results of greenhouse gas emissions modelling ...................................................................................................... 55 

3.2.4 

Socio-economic analysis and farmers’ views ............................................................................................................................. 55 

3.2.4.1 

Farm and farmer characteristics of adopters and non-adopters .............................................................. 55 

3.2.4.2 

Farm management ................................................................................................................................................................................. 59 

Source: Own elaboration. ............................................................................................................................................................................................. 61 

3.2.4.3 

Awareness and extent of use of multi-phase feeding ......................................................................................... 61 

3.2.4.4 

Environmental views ............................................................................................................................................................................. 62 

3.2.4.5 

Adoption of other greehouse gas reducing management practices ....................................................... 62 

3.2.4.6 

Reasons for increasing the number of feeding phases ....................................................................................... 63 

3.2.4.7 

Perceived effects of increasing the number of feeding phases ................................................................. 64 

3.2.4.8  Opinions of non-adopters ................................................................................................................................................................. 64 

3.2.5 

COVID-19 impact ................................................................................................................................................................................................... 66 

4  Conclusions .................................................................................................................................................................................................................................................. 68 

4.1  Use of breeding index in dairy production in Poland ............................................................................................................................. 68 

4.2  Use of multi-phase feeding in pig production in France .................................................................................................................... 69 

4.3  Overall conclusion.................................................................................................................................................................................................................... 69 

References ............................................................................................................................................................................................................................................................. 71 

List of figures ..................................................................................................................................................................................................................................................... 77 

List of tables........................................................................................................................................................................................................................................................ 78 

2 

 
 
Annexes .................................................................................................................................................................................................................................................................... 80 

3 

 
 
Authors 

V. Eory (SRUC) 

T. Begho (SRUC) 

M. MacLeod (SRUC) 

M.A. Martinez (IPSOS) 

V. Castellanos (IPSOS) 

M. Gómez-Barbero (JRC, European Commission) 

4 

 
 
 
 
 
Abstract 

5 

 
 
Executive summary 

6 

 
 
 
 
Breeding index use on dairy farms in Poland 

Figure 1. Reasons for adopting the breeding index 

7 

 
 
Figure 2. Emissions intensity by farm system for breeding index adoption 

8 

 
 
Multi-phase feeding on pig farms in France 

9 

 
 
Figure 3. Emissions intensity by farm system for multi-phase feeding adoption 

10 

 
 
 
 
1 

Introduction 

Agriculture  is  facing  the  double  challenge  of  continuing  to  provide  healthy  and  nutritious  food 
while substantially reducing its environmental impact. The sector currently produces 13% of the 
total  greenhouse  gas  (GHG)  emissions  in  the  EU  (EEA  2021),  and  it  is  the  main  source  of 
ammonia  emissions  and  nitrate  and  phosphorous  leaching  (Sutton  ed.  2011).  Livestock 
production  is  associated  with  nitrous  oxide  (N2O)  emissions  from  the  feed  produced  (including 
grazing  land  and  cropland  for  feed  production)  and  from  manure  storage,  with  methane  (CH4) 
production from enteric fermentation and manure management, and with carbon dioxide (CO2) 
emissions  from  land  use  and  land  use  changes  associated  with  feed  production  as  well  as 
electricity and fuel use in farm operations. In addition, EU agriculture uses imports (notably feed 
and  fertiliser),  which  induces  emissions  outside  the  EU  that  are  not  captured  in  the  EU’s  GHG 
inventory. In 2021 enteric CH4  was the biggest  agricultural GHG  source, accounting for 48% of 
total  domestic  agricultural  emissions,  followed  by  N2O  from  soils  (31%)  and  manure 
management (17%) (UNFCCC 2023). Cattle counted for 85% of enteric methane, with a roughly 
equal  contribution  from  dairy  cattle  and  non-dairy  cattle.  Sheep  contributed  9%  of  enteric 
methane, and other livestock the remaining 6%. 

Total EU domestic agriculture emissions declined by 22% between 1990 and 2010, but only by 
2% between 2010 and 2020. While improvements in productivity (e.g. in terms of dairy cow milk 
yield and fertility) have reduced the emission intensity (EI) (the kg of CO2e per unit of output) of 
some commodities, production (including of milk, poultry meat, pork and oilseeds) has increased, 
offsetting these improvements in the absolute emissions (German et al. 2021). The trend in total 
agricultural  emissions  presents  a  challenge,  as  the  EU  has  recently  strengthened  its  GHG 
reduction  targets.  The  European  Climate  Law  (adopted  in  2021)  sets  out  a  legally  binding  net 
zero greenhouse gas emission goal to be achieved by 2050, with an intermediate target of 55% 
reduction by 2030 (compared to 1990 levels) (EU, 2021).  

Agricultural emissions belong under the Effort Sharing Regulation (ESR) (with emissions from the 
production  of  some  inputs,  e.g.  power  and  fertiliser,  covered  by  the  EU  Emissions  Trading 
System). Alongside agriculture, ESR covers road transport, buildings, waste and small industries. 
The ESR target was revised in 2023 in light of the European Climate Law: the sectors covered by 
the ESR have to achieve a collective reduction of 40% in their emissions by 2030 compared to 
2005  (Yougova  2023).  Though  a  sectoral  target  for  agriculture  across  the  EU  does  not  exist, 
some  Member  States  have  set  goals  or  legal  targets  for  agricultural  emissions  (for  example 
Denmark and Ireland). So far, the key policy instrument supporting agricultural transition in the 
EU  has  been  the  Common  Agricultural  Policy,  with  little  achievement  in  GHG  mitigation  until 
recently  (European  Court of  Auditors,  2021).  As  part  of  the  European  Green  Deal,  the  Farm  to 
Fork  Strategy  aims  to  transform  the  European  food  system  (EU,  2020)  and  in  line  with  these 
goals  the  new  Common  Agricultural  Policy  (2023-2027)  strengthened  the  ambition  towards 
reduced  GHG  emissions  from  agriculture.  Though  Member  States  have  much  freedom  in  the 
implementation,  their  progress  is  regularly  monitored  through  their  CAP  Strategic  Plans 
(European Commission, 2023).  

Given the difficulties of agricultural GHG mitigation whilst providing healthy and affordable food, 
it  is  imperative  to  mitigate  GHG  emissions  and  other  pollution  through  improved  technologies 
and  reduced  resource  use  in  food  systems  (Conijn  et  al.  2018;  Liu  et  al.  2016).  Increasing 
efficiency  of  the  livestock  production  comes  with  the  benefits  of  productivity,  financial  and 
environmental  gains, while further technologies are also available to reduce emissions with no 

11 

 
 
negative  impact  on  productivity.  The  emission,  productivity  and  financial  impacts  of  such 
technologies have been assessed in numerous projects across the EU (Eory et al. 2018; Moran et 
al. 2011; Pellerin et al. 2017; Schulte et al. 2012). However, there is less understanding about to 
what  extent  these  technologies  are  used  on  livestock  farms  and  about  the  factors  influencing 
farmers’  decisions  on  the  uptake  of  them.  While  some  information  on  the  uptake  of  certain 
technologies  is  available,  the  uptake  of  most  technologies  are  mainly  unknown,  with  studies 
scattered between countries, livestock species and technologies. 

The purpose of this study is to support the policy goal of reducing GHG emissions from livestock 
production  via  improving  the  understanding  of  adoption  and  potential  impacts  of  animal 
husbandry practices. This report explores the extent and nature of the uptake of greenhouse gas 
mitigation  practices  in  two  case  studies  in  the  livestock  sector,  analysing  in  detail  i)  how  the 
mitigation  measures  are  implemented  on  farms,  ii)  what  are  the  drivers  and  barriers  in  their 
adoption and iii) assessing possible greenhouse gas impacts of them. 

12 

 
 
2  Case study selection 

To analyse contrasting situations of practice adoption, two case studies were selected, where a 
case study is a combination of a commodity (e.g. pig meat), location (e.g. north of Italy) and GHG 
mitigation measure (e.g. covering slurry tanks). 

2.1  Selection of systems and locations 

Previous studies have quantified the GHG emissions arising from livestock production within the 
EU  (Leip  et  al.  2010;  Lesschen  et  al.  2011)  with  estimates  for  the  EU27  for  the  year  2015 
produced by the FAO using GLEAM, the Global Livestock Environmental Assessment Model (FAO 
2022). These studies highlight  the importance of cattle  and pigs as sources of GHG  emissions 
within  Europe,  in  terms  of  total  protein  and  GHG  production  (Figure  4  and  5).  Given  the 
differences  between  ruminants  and  monogastrics  (regarding  physiology,  farm  management, 
economics,  emissions  profile  and  mitigation  options),  we  decided  that  the  case  studies  should 
cover both groups. While beef is important economically, and has a large impact on land use and 
habitat  management,  cattle  milk  is  the  single  biggest  source  of  livestock  protein  and  GHG 
emissions within Europe. For the monogastrics, pig emissions are greater than poultry, and pigs 
represent the biggest source of meat within Europe.  

Figure 4. Production and GHG emissions in the EU27 in 2015 (based on FAO 2022) 

Source: Own elaboration.  

13 

 
 
 
 
 
Figure 5. GHG emissions by category, EU27 (based on FAO 2022) 

Source: Own elaboration.  

Table 1. Top ten producers of milk and pigmeat (by mass) within the EU in 2018.  

Source: FAOstat, extrated 17/3/2020.  

14 

 
 
 
 
 
Figure 6. Livestock density within the European Union in 2016 for: (a) dairy cattle on specialized 
dairy farms, (b) other cattle, (c) pigs and (d) poultry. Estimated by dividing the number of livestock 

units by the utilised agricultural area (UAA) within each NUT. 

Source: Livestock populations and UAA taken from Eurostat, March 2020. Maps 
created by Matteo Sposato, SRUC. 

Any of the top five countries in terms of production would have made interesting case studies in 
each sector (Table 1), in particular regions with a high concentration of production (Figure 6). We 
chose North-West France for the pig study and Poland for the dairy study. This way the two case 
studies are also contrasting in economic development (GDP per capita), climate (Atlantic versus 
continental) and length of EU membership.  

2.2  Selection of mitigation measures 

From  a  list  of  77  greenhouse  gas  mitigation  measures  for  agricultural  production,  applicable 
across crop production, grassland management and livestock management, we identified eleven 

15 

 
 
 
and  nine  measures  respectively  for  dairy  and  pig  production  (in  total  15  measures)  which  are 
most  relevant  for  GHG  reduction  in  current  production  in  Europe  (Table  2).  The  selection  was 
based on national level studies on agricultural GHG mitigation published in the past ten years in 
Europe  (Eory  et  al.  2015;  Frelih-Larsen  et  al.  2014;  MacLeod  et  al.  2010;  Pellerin  et  al.  2013; 
Schulte  et  al.  2012).  The  shortlisting  considered  mitigation  measures  both  directly  related  to 
livestock  management  and  also  measures  which  are  applicable  on  the  land  area  supporting 
livestock  production  (focusing  on  measures  relevant  both  on  grasslands  and  arable  land).  The 
selection criteria were: 1) high GHG mitigation potential per livestock unit or land area, 2) high 
applicability, 3) immedate availability and 4) low risk of negative co-effects. Another aim was for 
the measures together to cover the key emission sources from livestock farms (emissions from 
soils, rumen and manure). 

Table 2. Shortlisting mitigation measures for dairy and pig systems 

16 

 
 
17 

 
 
Source: Own elaboration.  

For the purpose of this study it is important to select mitigation measures which are likely to be 
adopted  to  some  extent  in  the  study  regions  (not  completely  novel  there),  but  not  yet  fully 
adopted, in order to be  able  to find both adopters and non-adopters. The mitigation measures 
have to relate to farm practices which are easily described so that in the questionnaire current 
adoption can be clearly measured. Based on these additional criteria eight and seven mitigation 
measures were selected as a potential candidate for the case studies, respectively, for dairy and 
pig systems (Table 3), and their current uptake was assessed in the next step. 

Table  3.  Final  short  list  of mitigation measures  for the  case  studies, with  measures  selected  for 
further analysis noted 

Source: Own elaboration. 

18 

 
 
2.2.1  Literature review of current adoption of selected measures 

Table 4. Search terms for greenhouse gas mitigation practices 

19 

 
 
Source: Own elaboration.  

2.2.2  Adoption rates 

20 

 
 
Table 5. Adoption rates of GHG mitigation practices 

21 

 
 
22 

 
 
Source: Own elaboration.  

23 

 
 
2.2.3  Factors influencing adoption  

2.2.3.1  Farm characteristics  

24 

 
 
2.2.3.2  Farmer socio-economic characteristics 

25 

 
 
2.2.3.3  Farmer attitudes  

26 

 
 
2.2.3.4  Characteristics of the technology 

2.2.3.5  Policy 

27 

 
 
2.2.4  Selected case studies 

2.3  Survey methodology  

2.3.1  Definition of practice and adoption 

2.3.1.1  Use of breeding index in dairy production in Poland 

28 

 
 
 
 
2.3.1.2  Use of multiphase feeding in pig production in France 

2.3.2  Target population and sampling 

2.3.2.1  Use of breeding index in dairy production in Poland 

29 

 
 
Table 6. Dairy cow holders and number of head (Eurostat, 2013) 

*Segment 5-19 in fact includes 3–19 data. There is not available data for the segment 5-19. 

Source: Eurostat, 2013.  

Table 7. Adoption rate in the first 100 randomly selected interviews of the sample 

Source: Own elaboration.  

Table 8. Adoption rate in the full sample 

Source: Own elaboration.  

2.3.2.2  Use of multi-phase feeding in pig production in France 

30 

 
 
 
Table 9. Universe distribution of pig farmers in the selected regions  

Source: SCEES,2010.  

Table 10. Adoption rate in the first 100 randomly selected interviews of the sample 

Source: Own elaboration.  

Table 11. Adoption rate in the full sample 

Source: Own elaboration.  

2.3.3  Data cleaning  

2.3.3.1  Use of breeding index in dairy production in Poland 

31 

 
 
2.3.3.2  Use of multi-phase feeding in pig production in France 

2.4  Statistical methods 

2.5  Greenhouse gas modelling 

Table 12. Summary of the GHG categories included in the calculations 

32 

 
 
Source: Own elaboration.  

33 

 
 
3  Results 

3.1  Use of breeding index in dairy production in Poland 

3.1.1  Uptake of breeding index 

Table 13. Number of farms by herd size and adoption status 

Source: Own elaboration.  

3.1.2  Productivity, herd parameters and feed intake 

3.1.2.1  Productivity and key herd parameters 

34 

 
 
 
 
Table 14. Milk production and yield by herd size and breeding index adoption status 

Source: Own elaboration.  

35 

 
 
 
 
Table  15.  Proportion  of  farmers  indicating  the  fate  of  pure  male  dairy  calves  by  herd  size  and 
breeding index adoption status (% of respondents; multiple answers were possible) 

Source: Own elaboration.  

3.1.2.2  Dairy cow feed 

Table 16. Average proportion of geed material in the rations (% mass as fed, i.e. fresh weight) 

Source: Own elaboration.  

36 

 
 
3.1.3  Greenhouse gas emission and financial modelling 

3.1.3.1 

Input assumptions 

Table 17. Values based on the farm survey. Values in italic vary by farm systems. Note that these are 
unweighted averages hence the milk yield is different to weighted average in Table 14. 

Source: Own elaboration.  

3.1.3.2  Results of greenhouse gas emissions modelling 

37 

 
 
 
 
 
Table 18. Production of meat, milk and GHG emission by system 

Source: Own elaboration.  

Figure 7. Emissions intensity by farm system 

Source: Own elaboration. 

3.1.3.3  Partial budget calculation 

38 

 
 
 
 
 
Table 19. Output, variable costs and gross margin for the three farm systems 

*including  non-lactating  adult  females.  **Assuming  revenue  from  calf  and  cow  sales  is  equal  the  cost  of  heifer 
replacement. ***Total feed consumed by adult females and the dairy breeding herd (replacement females, adult males 
and their replacements) divided by the number of adult females. 
Source: Own elaboration.  

3.1.4  Socio-economic analysis and farmers’ views 

3.1.4.1  Farm and farmer characteristics of adopters and non-adopters 

39 

 
 
Figure 8. Adopters and non-adopters by region (full sample) 

Source: Own elaboration. 

40 

 
 
 
 
 
 
 
 
 
 
 Figure 9. Adopters and non-adopters by farm types (full sample)  

Source: Own elaboration.  

Figure 10. Proportion of adopters by dairy farm size 

Source: Own elaboration.  

41 

 
 
 
Table 20. Statistics of adopters and non-adopters of breeding index by farmer characteristics 

**significant at the 5% level; *** significant at the 1% level 
a t-test and chi-square are used for continuous and categorical variables, respectively 
Source: Own elaboration.  

42 

 
 
 
Table 21. Statistics of farm characteristics of adopters and non-adopters of breeding index 

**significant at the 5% level; *** significant at the 1% level 
Source: Own elaboration.  

Table 22. Statistics of contractual obligations, Nitrate Vulnerable Zone area and agricultural systems 
of adopters and non-adopters of the breeding index 

* Significant at the 10% level; **significant at the 5% level; *** significant at the 1% level 
Source: Own elaboration.  

43 

 
 
 
 
3.1.4.2  Farm management 

Table  23.  The  importance  of  selected  factors  in  the  long-term  profitability  of  the  dairy  herd  (%  of 
respondents) 

Source: Own elaboration.  

44 

 
 
 
 
3.1.4.3  Awareness and extent of use of the breeding index 

Table 24. Description of awareness, adoption and use of the breeding index 

Source: Own elaboration.  

Table 25. Opinion on attributes of the breeding index 

45 

 
 
Source: Own elaboration.  

3.1.4.4  Environmental views 

Table 26. Attitudes of farmers towards the environment (% of respondents) 

Source: Own elaboration.  

3.1.4.5  Adoption of other greenhouse gas reducing management practices 

46 

 
 
Table 27. Adoption of related cattle management practices (% of respondents) 

Source: Own elaboration.  

3.1.4.6  Reasons for adoption 

The main reasons for adopting the breeding index were to have better milk yield and productivity,  

Figure 11. Word cloud showing the reasons for adopting the breeding index. Note: text size is 
proportional to frequency of mentions 

Source: Own elaboration.  

improve  herd  genetics,  to  have  better  knowledge  of  the  herd,  better  health  of  the  herd,  sex-
selection, and increase income and the overall economic performance of the farm (Figure 11). 

47 

 
 
 
 
Table 28. Source and extent of influence in decision to adopt breeding index (% of adopters) 

Source: Own elaboration.  

3.1.4.7  Perceived effects of adoption 

Table 29. Perceived effects of adoption on herd performance change 

48 

 
 
 
Source: Own elaboration.  

Table  30.  Resource  use  effects  of  adopting  the  breeding  index  on  annual  cost  and  labour 
requirement 

Source: Own elaboration.  

3.1.4.8  Opinions of non-adopters 

49 

 
 
 
 
Table 31. Perceived barriers to using the breeding index (% of non-adopters) 

Source: Own elaboration.  

Table 32. Incentive to encourage the use of the breeding index 

50 

 
 
Source: Own elaboration.  

3.1.5  COVID-19 impact  

Table 33. Impact of COVID-19 on the farm business (% of respondents) 

Source: Own elaboration. 

3.2  Use of multi-phase feeding in pig production in France 

3.2.1  Uptake of multi-phase feeding 

51 

 
 
 
 
Table 34. Distribution of farmers using different number of feeding phases 

Source: Own elaboration.  

3.2.2  Productivity, herd parameters and feed intake 

3.2.2.1  Performance of growing pigs during fattening 

3.2.2.2  Reported change in performance with adoption of additional phase 

52 

 
 
 
 
Table 35. Liveweight at entry to each feeding phase, and at exit from fattening 

Source: Own elaboration. 

Table 36. Ration crude protein content during each feeding phase 

Source: Own elaboration.  

Table 37. Statistics of pig performance between adopters and non-adopters of multi-phase feeding 

**significant at the 5% level; *** significant at the 1% level 

53 

 
 
  
 
Table  38.  Responses to the  question whether the pig performance  changed because starting using 
the current feeding method (adopters only) 

Source: Own elaboration.  

3.2.3  Greenhouse gas emissions 

3.2.3.1 

Input assumptions 

54 

 
 
 
 
 
 
 
 
Table 39. Ration crude protein content and pig growth rates for the two scenarios 

Source: Own elaboration.  

3.2.3.2  Results of greenhouse gas emissions modelling 

Table 40. Results for growing (i.e. non-breeding) pigs 

Source: Own elaboration. 

Figure 12. GHG emission intensity by scenario 

Source: Own elaboration 

3.2.4  Socio-economic analysis and farmers’ views 

3.2.4.1  Farm and farmer characteristics of adopters and non-adopters 

55 

 
 
 
 
Figure 13. Adopters and non-adopters by region (full sample) 

Source: Own elaboration.  

56 

 
 
  
 
 
Figure 14. Adopters and non-adopters in 2020 

57 

 
 
 
 
Figure 15. Adopters and non-adopters by farm types (full sample) 

Table  41.  Statistics  of  adopters  and  non-adopters  of  multi-phase  feeding  by  farmer  and  farm 
characteristics 

58 

 
 
**significant at the 5% level; *** significant at the 1% level 
a t-test and chi-square are used for continuous and categorical variables, respectively 

Table 42. Income sources and income diversification 

**significant at the 5% level 
a t-test and chi-square are used for continuous and categorical variables, respectively 

3.2.4.2  Farm management 

Table 43. Statistics of contractual obligations, Nitrate Vulnerable Zone area and agricultural systems 
of adopters and non-adopters of multi-phase feeding 

59 

 
 
Table 44. Statistics of pig performance between adopters and non-adopters of multi-phase feeding 

**significant at the 5% level 

Table 45. Important factors in the long-term profitability of farmers' pig production 

60 

 
 
 
Source: Own elaboration.  

3.2.4.3  Awareness and extent of use of multi-phase feeding 

The results of farmers awareness and extent of use of multi-phase feeding are presented in Table 
46. Controversially with the adoption results (34% adopters of multi-phase feeding, with no farmers 
reporting  “I  use  precision  feeding,  tailoring  the  feed  composition  to  the  individual  pig  on  a  daily 
basis”  when  asked  “How  many  feeding  phases  do  you  use  during  fattening?”),  about  44%  of  the 
famers said that they were currently using precision feeding (adjusting the feed daily according to 
the requirements of the pigs). A possible explanation of this could be that precision feeding is a very 
novel technology and farmers’ understanding of what it means could be still very limited and they 
might confuse it with non-individualised nutritional planning. Furthermore, about 50% were aware 
but have no intention to use in the future or have dis-adopted precision feeding.  

In terms of farmers’ perception of the effect of adopting precision feeding, over 50% strongly agree 
that  they  know  where to find  information  on  feed  formulation  if  they  want to  use  three  or more 
phases  in  fattener  feeding.  Over  70%  either  strongly  agree  or  agree  that  using  three  or  more 
phases in fattener feeding provides better feed efficiency and that using three or more phases in 
fattener feeding is compatible with their personal beliefs and values (Table 47). 

Table 46. Adoption of related pig management practices (% of respondents) 

Source: Own elaboration.  

Table 47. Opinion on attributes of using multi-phase feeding 

61 

 
 
3.2.4.4  Environmental views 

Table 48. Attitudes of farmers towards the environment (% of respondents) 

3.2.4.5  Adoption of other greehouse gas reducing management practices 

62 

 
 
 
 
Table 49. Adoption of related pig management practices (% of respondents) 

Source: Own elaboration. 

3.2.4.6  Reasons for increasing the number of feeding phases 

Table 50. Reasons for increasing the number of feeding phases 

Note: multiple responses were allowed by individual respondents 

Source: Own elaboration. 

63 

 
 
 
3.2.4.7  Perceived effects of increasing the number of feeding phases 

Table 51 reports whether the average digestible  energy, CP or lysine content of the ration during 
the fattening phase changed due to moving to the current number of phases. The majority reported 
no change in average digestible energy, CP or lysine content of the ration. For those that witnessed 
a  reduction,  average  digestible  energy  and  CP  content  decreased  by  5.1%  and  6.0%  respectively.  
Besides the unit cost of compound feed and the feed cost per unit of output, the majority of farms 
did  not  witness  a  change  in  costs  and  labour  requirement  as  a  result  of  moving  to  the  current 
number  of  phases  among  adopters.  In  the  case  where  changes  were  observed,  these  were  non-
uniform. 

Table 51. Changes ration content and costs and labour requirement when increasing the number of 
feeding phases amongst those who adopted multi-phase feeding 

Source: Own elaboration. 

3.2.4.8  Opinions of non-adopters 

64 

 
 
 
Table 52. Feeding method influence of adopters 

Source: Own elaboration.  

Table 53. Perceived barriers to using multi-phase feeding (% of non-adopters) 

Source: Own elaboration.  

65 

 
 
 
 
Table 54. Incentives to encourage adoption of precision feeding 

Source: Own elaboration.  

3.2.5  COVID-19 impact 

66 

 
 
 
 
Table 55. Impact of COVID-19 on the farm business (% of respondents) 

Source: Own elaboration. 

67 

 
 
 
4  Conclusions 

4.1  Use of breeding index in dairy production in Poland 

68 

 
 
 
 
 
4.2  Use of multi-phase feeding in pig production in France 

4.3  Overall conclusion 

As the introduction pointed out, livestock GHG emissions need to be considerably reduced in order to 
meet Europe’s GHG targets. There are many farming practices which have been adopted on a small 
number  of  farms  across  Member  States  and  could  help  in  reducing  on-farm  emissions  if 

69 

 
 
implemented on a larger scale in the EU. Investigating farmers’ likely reasons for adopting or not 
adopting these practices can help designing support structures for wider adoption.  

The  two  livestock  case  studies  presented  above  show  mostly  similar  findings  to  other  studies  on 
adoption of other GHG mitigation practices. Firstly, larger farmers with specialised farms tend to be 
more  innovative,  using  more  new  technologies,  which  points  towards  the  need  of  capital  and 
economies  of  scale  for  implementing  such  changes  on  farms  (Knowler  and  Bradshaw  2007, 
Prokopy  et  al.  2008).    Secondly,  the  wider  information  environment  of  the  farmer  can  positively 
influence adoption, in the first place, familiarising farmers with the options and at a later stage of 
adoption  trusted  sources providing  expertise in  implementation  (Liu  et al.  2018).  Thirdly,  farmers’ 
expectations  on  financial  costs  and  benefits  of  the  change  matters  too,  many  farmers  decide 
against  adoption  based  on  perceived  high  costs  of  a  technology  (Niles  et  al.  2019).  Finally,  the 
specific  characteristics  of  the  technology  play  a  role  in  adoption,  as  not  all  technologies  and 
practices  fit  every  farm  (Ranjan  et  al.  2019).  Where  possible,  care  shall  be  taken  to  tailor  the 
technology to at least larger groups of yet non-adopter farms.  

The results provided evidence that on medium and large farms  which have  adopted  the breeding 
index  the  milk  yield  is  higher,  Since  causality  cannot  be  established  due  to  the  study  design,  the 
modelled  9%  improvement  in  emission  intensity  is  hypothetical,  however,  it  is  in  line  with  the 
experienced trends in dairy  breeding. The evidence on differences in productivity outcomes on pig 
farms  between  adopters  and  non-adopters  of  multi-phase  feeding  was  much  less  pronounced, 
leading to a hypothetical, modelled 3% difference in emission intensity between adopters and non-
adopters. Previous modelling studies usually show that GHG mitigation from individual practices is 
low, nevertheless, the dozens of existing practices together can lead to agricultural GHG reductions 
up to 15-20% (Eory et al. 2018; Lanigen et al. 2023; Moran et al. 2011; Pellerin et al. 2017) if they 
are implemented on the majority of the farms.   

70 

 
 
 
 
 
 
References 

71 

 
 
72 

 
 
73 

 
 
74 

 
 
75 

 
 
76 

 
 
List of figures 

Figure 1. Reasons for adopting the breeding index ................................................................. 7 

Figure 2. Emissions intensity by farm system for breeding index adoption ...................................... 8 

Figure 3. Emissions intensity by farm system for multi-phase feeding adoption ...............................10 

Figure 4. Production and GHG emissions in the EU27 in 2015 (based on FAO 2022) ..........................13 

Figure 5. GHG emissions by category, EU27 (based on FAO 2022) ..............................................14 

Figure 6. Livestock density within the European Union in 2016 for: (a) dairy cattle on specialized dairy farms, 
(b) other cattle, (c) pigs and (d) poultry. Estimated by dividing the number of livestock units by the utilised 
agricultural area (UAA) within each NUT. ............................................................................15 

Figure 7. Emissions intensity by farm system......................................................................38 

Figure 8. Adopters and non-adopters by region (full sample).....................................................40 

 Figure 9. Adopters and non-adopters by farm types (full sample) ..............................................41 

Figure 10. Proportion of adopters by dairy farm size .............................................................41 

Figure 11. Word cloud showing the reasons for adopting the breeding index. Note: text size is proportional to 
frequency of mentions ................................................................................................47 

Figure 12. GHG emission intensity by scenario ....................................................................55 

Figure 13. Adopters and non-adopters by region (full sample) ...................................................56 

Figure 14. Adopters and non-adopters in 2020 ....................................................................57 

Figure 15. Adopters and non-adopters by farm types (full sample) .............................................58 

77 

 
 
 
 
List of tables 

Table 1. Top ten producers of milk and pigmeat (by mass) within the EU in 2018. .............................14 

Table 2. Shortlisting mitigation measures for dairy and pig systems ............................................16 

Table 3. Final short list of mitigation measures for the case studies, with measures selected for further 
analysis noted .........................................................................................................18 

Table 4. Search terms for greenhouse gas mitigation practices ..................................................19 

Table 5. Adoption rates of GHG mitigation practices ..............................................................21 

Table 6. Dairy cow holders and number of head (Eurostat, 2013) ...............................................30 

Table 7. Adoption rate in the first 100 randomly selected interviews of the sample ...........................30 

Table 8. Adoption rate in the full sample ...........................................................................30 

Table 9. Universe distribution of pig farmers in the selected regions ............................................31 

Table 10. Adoption rate in the first 100 randomly selected interviews of the sample .........................31 

Table 11. Adoption rate in the full sample .........................................................................31 

Table 12. Summary of the GHG categories included in the calculations .........................................32 

Table 13. Number of farms by herd size and adoption status ....................................................34 

Table 14. Milk production and yield by herd size and breeding index adoption status ..........................35 

Table 15. Proportion of farmers indicating the fate of pure male dairy calves by herd size and breeding index 
adoption status (% of respondents; multiple answers were possible) .............................................36 

Table 16. Average proportion of geed material in the rations (% mass as fed, i.e. fresh weight) .............36 

Table 17. Values based on the farm survey. Values in italic vary by farm systems. Note that these are 
unweighted averages hence the milk yield is different to weighted average in Table 14. ......................37 

Table 18. Production of meat, milk and GHG emission by system ...............................................38 

Table 19. Output, variable costs and gross margin for the three farm systems ................................39 

Table 20. Statistics of adopters and non-adopters of breeding index by farmer characteristics ..............42 

Table 21. Statistics of farm characteristics of adopters and non-adopters of breeding index .................43 

Table 22. Statistics of contractual obligations, Nitrate Vulnerable Zone area and agricultural systems of 
adopters and non-adopters of the breeding index ..................................................................43 

Table 23. The importance of selected factors in the long-term profitability of the dairy herd (% of 
respondents) ...........................................................................................................44 

Table 24. Description of awareness, adoption and use of the breeding index ...................................45 

Table 25. Opinion on attributes of the breeding index ............................................................45 

Table 26. Attitudes of farmers towards the environment (% of respondents) ..................................46 

Table 27. Adoption of related cattle management practices (% of respondents) ...............................47 

Table 28. Source and extent of influence in decision to adopt breeding index (% of adopters) ...............48 

Table 29. Perceived effects of adoption on herd performance change ..........................................48 

Table 30. Resource use effects of adopting the breeding index on annual cost and labour requirement .....49 

Table 31. Perceived barriers to using the breeding index (% of non-adopters)..................................50 

Table 32. Incentive to encourage the use of the breeding index .................................................50 

78 

 
 
Table 33. Impact of COVID-19 on the farm business (% of respondents) .......................................51 

Table 34. Distribution of farmers using different number of feeding phases ...................................52 

Table 35. Liveweight at entry to each feeding phase, and at exit from fattening...............................53 

Table 36. Ration crude protein content during each feeding phase ..............................................53 

Table 37. Statistics of pig performance between adopters and non-adopters of multi-phase feeding .......53 

Table 38. Responses to the question whether the pig performance changed because starting using the 
current feeding method (adopters only) .............................................................................54 

Table 39. Ration crude protein content and pig growth rates for the two scenarios ............................55 

Table 40. Results for growing (i.e. non-breeding) pigs .............................................................55 

Table 41. Statistics of adopters and non-adopters of multi-phase feeding by farmer and farm characteristics
 ..........................................................................................................................58 

Table 42. Income sources and income diversification .............................................................59 

Table 43. Statistics of contractual obligations, Nitrate Vulnerable Zone area and agricultural systems of 
adopters and non-adopters of multi-phase feeding ................................................................59 

Table 44. Statistics of pig performance between adopters and non-adopters of multi-phase feeding .......60 

Table 45. Important factors in the long-term profitability of farmers' pig production ..........................60 

Table 46. Adoption of related pig management practices (% of respondents) ..................................61 

Table 47. Opinion on attributes of using multi-phase feeding ....................................................61 

Table 48. Attitudes of farmers towards the environment (% of respondents) ..................................62 

Table 49. Adoption of related pig management practices (% of respondents) ..................................63 

Table 50. Reasons for increasing the number of feeding phases ................................................63 

Table 51. Changes ration content and costs and labour requirement when increasing the number of feeding 
phases amongst those who adopted multi-phase feeding .........................................................64 

Table 52. Feeding method influence of adopters ..................................................................65 

Table 53. Perceived barriers to using multi-phase feeding (% of non-adopters) ................................65 

Table 54. Incentives to encourage adoption of precision feeding ................................................66 

Table 55. Impact of COVID-19 on the farm business (% of respondents) .......................................67 

79 

 
 
 
 
Annexes 

Table 
A1. 
(number of dairy cattle) 

Statistics 

of 

farmer 

characteristics 

by 

dairy 

farm 

size 

Source: Own elaboration. 

80 

 
 
 
 
 
Table A2. Statistics of farm characteristics of adopters and non-adopters of breeding index by 
dairy farm size (number of dairy cattle) 

Source: Own elaboration. 

Table A3. Pearson and Phi correlation matrix 

Source: Own elaboration. 

81 

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