Regionalisation of nitrogen balances with the CAPRI model (RegNiBal) : pilot project in support of the Eurostat working gGroup on agri-environmental indicators.
Date: 2015-10-09
Institutions: Institute for Environment and Sustainability
Subjects: EU environmental policy, France, Germany, Hungary, Italy, common agricultural policy, environmental impact, environmental indicator, environmental monitoring, nitrogen, pollution from agricultural sources, research report

Regionalisation of Nitrogen Balances 
with the CAPRI Model (RegNiBal) 

Pilot project in support of the 
Eurostat Working Group on  
Agri-Environmental Indicators 

Fethi Şaban Özbek, Adrian Leip, Franz Weiss, 
Gema Carmona Garcia         

Co-authors (listed in alphabetical order): Filiberto 
Altobelli, Laurence Grassart, Maximilian Hofmeier, 
Miroslav Kukucka, Annalisa Pallotti, Agnes Patay, 
Thorsten Thuen 

2 0 1 5  

Report EUR 27339 EN 

 
 
European Commission 
Joint Research Centre 
Institute for Environment and Sustainability  

Contact information 
Adrian Leip 
Address: Joint Research Centre, Via E. Fermi, 2749 I-21027 Ispra, Italy  
E-mail: adrian.leip@jrc.ec.europa.eu  
Tel.: +39 0332 786327 

JRC Science Hub 
https://ec.europa.eu/jrc 

Legal Notice 
This publication is a Technical Report by the Joint Research Centre, the European Commission’s in-house 
science service.  
It aims to provide evidence-based scientific support to the European policy-making process. The scientific output 
expressed does not imply a policy position of the European Commission. Neither the European Commission nor 
any person acting on behalf of the Commission is responsible for the use which might be made of this publication. 

Photo © Adrian Leip 2015 

JRC96482 

EUR 27339 EN 

ISBN 978-92-79-49416-1 (PDF) 

ISSN 1831-9424 (online) 

doi:10.2788/078406 

Luxembourg: Publications Office of the European Union, 2015 

© European Union, 2015 

Reproduction is authorised provided the source is acknowledged. 

Abstract 

The report presents the results of the pilot project “Regionalisation of Gross Nitrogen Balances with the 
CAPRI model” (RegNiBal), which was carried out by Joint Research Centre in cooperation with Eurostat 
and delegates from the volunteering countries (France, Germany, Hungary and Italy). The objective of 
the  pilot  project  was  to  evaluate  the  differences  between  national  Eurostat/OECD  Gross  Nitrogen 
Balances  (GNB)  and  the  GNB  calculated  using  the  CAPRI  model,  with  the  overall  goal  of  using  the 
CAPRI model to (operationally) provide regional GNB data to complement the national GNBs. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXECUTIVE SUMMARY 

The  recent  developments  in  the  Common  Agricultural  Policy  (CAP)  call  for  the 
monitoring  of  the  environmental  impacts  of  agricultural  activities  at  regional  level  (EC 
2006). Environmental assessments at regional level are also needed according to the EC 
Rural  Development  Policy  (2007-2013),  and  to  the  EU  Nitrate  and  Water  Framework 
Directives.  In  Europe,  the  Gross  Nutrient  Balance  (GNB)  is  considered  to  be  a  priority 
agri-environmental  indicator  (EC  2006)  of  relevance  for  both  water-  and  air-quality 
policies.  Regional  GNB  estimations  produce  more  accurate  results  than  the  national 
estimations, especially for countries that experience different climates or have regionally 
differing agricultural production systems (Özbek 2014, Özbek and Leip 2015).  

The  Eurostat  Working  Group  on  agri-environmental  indicators  (AEI)  therefore 
explored  the  possibility  of  refining  the  spatial  resolution  of  reported  GNBs1,  going  from 
national to (at least) NUTS2 level, and possibly also beyond2. The development of such 
methods,  however,  is  challenging  and  requires  approaches  to  ‘disaggregate’  national 
data which are unavailable at higher resolution to smaller spatial units.  

The  “Common  Agricultural  Policy  Regional  Impact”  (CAPRI)  model  could  potentially 
provide  such  data.  CAPRI  is  already  used  to  calculate  the  GNB  at  the  NUTS2  level,  as 
well as on a high resolution map for scientific projects and concept development (Leip et 
al 2011b, 2011d, 2008, Britz and Leip 2009, Leip et al 2011a).  

The  “Regionalisation  of  Gross  Nitrogen  Balances  with  the  CAPRI  model”  (RegNiBal) 
pilot project was carried out by the JRC in cooperation with Eurostat and delegates from 
four  volunteering  countries:  Italy,  France,  Germany  and  Hungary.  The  objective  of  the 
pilot  project  was  to  evaluate  differences  between  national  Eurostat/OECD  GNB  figures 
and  the  GNB  figures  calculated  using  CAPRI,  and  to  assess  the  feasibility  of  using  the 
CAPRI  model  to  (operationally)  provide  regional  GNB  data  to  complement  the  national 
GNBs.  

The RegNiBal project aimed to: 

  Perform a detailed analysis of GNB methodology in Eurostat/OECD GNB common 

guidelines (Eurostat, 2013) and in the CAPRI model 

  Assess methodological differences between the methods used by the countries in 
the  project,  CAPRI,  and  the  Eurostat/OECD  GNB  common  guidelines  (Eurostat 
2013)  

  Assess  the  numeric  differences  between  the  datasets  of  the  countries  in  the 

project, CAPRI and the Eurostat databases  

 

Identify the causes of the differences between datasets, and suggest solutions for 
selected issues to the countries taking part in the project, CAPRI and Eurostat 

This report describes modifications that were implemented in the national and CAPRI 
methodologies  since  the  start  of  RegNiBal  (partly  based  on  results  of  the  project),  and 
makes  recommendations  for  actions  to  be  taken  in  order  to  improve  the  comparability 
and quality of the data.  

1   The national GNBs of 12 EU Member States presented in the Eurostat GNB database were calculated by the 

countries. The GNBs for other countries were not available, and have been estimated by Eurostat. 

2   Working Group on agri-environmental indicators meeting in February 2012 

1 

                                           
Summary of the results, conclusions and further research 

How many issues could/could not/could partly be solved? 

We  identified  33  “issues”  that  were  related  to  major  discrepancies  between  the 
methods and warranted further assessment: 28 of these concerned both CAPRI  and the 
national (DE, FR, HU, IT) methodologies in the project, three related to general aspects 
of the CAPRI model, and two related to EUROSTAT data. At the end of the project, 12 of 
the identified issues were solved, three were partially solved and 18 could not be solved. 
However, but some progress and concrete recommendations were made for almost all of 
the issues identified. 

For  the  unsolved  issues:  can  we  say  that  national  data  are  generally  better/worse 

than CAPRI? 

At  the  beginning  of  the  project,  the  differences  between  the  surplus  calculated  with 
CAPRI  data  and  the  national  methodologies  were  high.  Some  national  data  were 
updated/corrected (partly on the basis of RegNiBal observations and recommendations), 
which  led  to  a  reduction  in  the  differences  with  CAPRI.  At  the  end  of  the  project,  the 
absolute differences between the CAPRI and national data were 31% for France, 26% for 
Hungary, 13% for Italy and 3% for Germany.  

At the start of the RegNiBal project, we found that CAPRI was generally more reliable 
than  the  national  methodologies,  but  this  situation  changed  with  the  improvements 
made,  as  described  above.  Further  analysis  is  currently  needed  to  evaluate  whether 
CAPRI or national data (NAT) are ‘better’ with regard to the remaining unsolved issues. 

Overall, which unsolved topics are most important? 

Nitrogen  (N)  excretion  by  swine  and  N  removal  by  grass  are  considered  to  be  the 
most important unsolved issues because of their considerable impact on N-input and N-
output levels.  

Estimating  the  N  excretion  by  swine  was  found  to  be  problematic  for  all  countries 
participating in  the  project.  CAPRI  estimates  for  feed  intake  by  swine  were  higher  than 
the NAT figures of DE and FR. Further animal budget analysis for IT and HU is needed to 
understand  the  reason  for  differences  in  N-excretion  coefficients.  Countries  are  not 
always  sufficiently  accurate  in    estimating  and/or  using  the  average  number  of  animals 
and N-excretion coefficients in N-manure-excretion estimations. 

Estimating  the  N  removal  by  grass  was  found  to  be  problematic  for  three  of  four 
project countries. For estimates of the dry matter yields of grassland, the differentiation 
of permanent grassland according to the proposal of the GRASSDATE project (Velthof et 
al  2014)  would  likely  help:  (i)  grassland  out  of  production  but  maintained,  (ii) 
unimproved  grassland  (including  both  sole  use  or  private  land  and  common  land),  (iii) 
improved  grassland  (by  N-input  levels  <50,  50-100,  >100  kg  N/ha/yr,  sole  use  and 
common land).  

2 

 
 
 
 
In which major areas is CAPRI is relatively weak? 

The major areas of difficulties for CAPRI are the following:  

  Grassland yield and grass N content. 

  Feed intake, partly linked to inaccurate statistical data (Leip et al 2014). Detailed 

animal budgets from all countries are not available for comparison. 

  Seed and planting materials should be explicit in the CAPRI GNB. 

  N  from  organic  fertilisers  (other  than  manure)  and  manure  withdrawal,  stocks, 

and import estimations are not considered in the CAPRI model. 

Can  the  CAPRI  model  be  used  to  provide  national  GNB  (at  NUTS2  level),  and  if  not, 

what needs to be done? 

  The  CAPRI  model  is  very  strong  in  making  GNB  calculations,  and  the  RegNiBal 
project enabled us to identify several possible improvements in national data and 
methods. 

  The  use  of  the  animal  budget  to  estimate  N  excretion  is  a  major  asset  in  the 
CAPRI  methodology,  but  runs  the  risk  of  including  outliers  if  the  use  of  feed  is 
overestimated in the statistical sources.  

  There  is  a  large  level  of  uncertainty  in  the  yields  of  grass  and  other  (non 
marketable)  fodder,  as  well  as  their  N  content.  This  also  affects  the  accuracy  of 
national data.  

  Generally,  RegNiBal  showed  that  the  CAPRI  model  could  provide  adequate 
national (and later on regional and spatially explicit) GNBs. However, for the four 
countries assessed, further work is needed to in order to understand the residual 
disparities in the data. 

  The  CAPRI  model  can  calculate  Land  N  Budgets  (corresponding  to  GNBs)  and 
Farm  N  Budgets  (FNB).  A  comparison  of  the  N-surplus  calculated  using  both 
method  helps  to  constrain  the  calculations.  In  the  FNB  method,  feed  and  fodder 
produced  in  the  country  (or  region)  and  manure  applied  within  the  country  (or 
region) are considered as ‘internal flows’ and thus do not need to be estimated to 
quantify the N surplus; on the other hand, data on ‘imported’ feed and ‘exported’ 
animal products are needed (for details on the comparison of the two approaches, 
see Leip et al 2011b). In the CAPRI model, data on animal products and imported 
feedstuffs  are  available  from  statistical  sources,  and  are  more  reliable  than  the 
data on fodder N intake and manure excretion. 

  Regional-level  data  were  available  only  for  FR  and  IT.  While  the  focus  of  the 
project  was  on  comparisons  at  the  national  scale,  RegNiBal  showed  that  some 
improvements were also beneficial for the regional comparisons. 

3 

 
 
 
 
How can the process be simplified/improved? 

One  observation  of  RegNiBal  was  that  some  ‘mistakes’  are  made  due  to  different 
interpretations of the Eurostat/OECD  Handbook on Nutrient Budgets  (Eurostat 2013) or 
by inaccurate use of data.  Differences in  the statistical data used for the  national GNBs 
and data reported to Eurostat in other statistical tables also led to inconsistencies.    

The  process  can  be  simplified  by  making  use  of  data  tables  already  available  from 
Eurostat for GNB calculation at national and  (in principle) NUTS2 levels. This could help 
to considerably reduce the reporting burden on countries. 

What should the next steps be? 

There are three different options: 

1.  Regional GNBs that are fully consistent with the national GNBs submitted cannot 
currently  be  generated.  However,  national  estimates  are  not  necessarily  better 
than  CAPRI  estimates,  so  regional  and  spatially  explicit  data  generated  by  the 
CAPRI model can be regarded as suitable for providing advice to policy. Such data 
will be useful proxy data, that is independent of the national official GNBs.  

2.  CAPRI  GNBs  can  be  converged  with  official  national  GNBs.  This  would  help 
improve  both  CAPRI  and  national  estimates,  but  requires  further  investment. 
Such investment would, however, also have added value for other areas, such as 
improved CAPRI scenarios for climate change mitigation assessments, the use of 
CAPRI  as  an  independent  estimate  of  GHG  emissions,  harmonisation  of  national 
methodologies, and improved consistency between various datasets. 

3.  Another route could be the abovementioned ‘simplified process’ which makes use 
of existing information as much as possible and generates national (and regional) 
GNBs that are fully consistent with the Eurostat/OECD common guidelines (which 
is  also  suitable  for  producing  regional  GNBs).  Such  a  system  would  probably  be 
most cost-efficient in the short term and would efficiently provide good estimates. 

Any  combination  of  the  three  abovementioned  options  is  possible,  as  are  ‘hybrid’ 
solutions. For example, a Eurostat algorithm using existing data tables could be informed 
by data from both countries and the CAPRI model.  

4 

 
CONTENTS 

List of Tables ............................................................................................. 7 

List of Figures ............................................................................................ 7 

List of Abbreviations and Symbols ................................................................ 8 

Glossary .................................................................................................. 10 

1  Introduction .......................................................................................... 13 

2  Method ................................................................................................ 14 

2.1  Overview ................................................................................................. 14 
2.2  CAPRI ..................................................................................................... 14 
2.2.1 Atmospheric N deposition ................................................................... 15 
2.2.2 Biological N fixation ............................................................................ 16 
2.2.3 N removal BY OFAR and other crop products ......................................... 16 
2.2.4 N removal BY GRAS ........................................................................... 16 
2.2.5 N excretion BY swine and ‘other cattle’ ................................................. 17 
2.2.6 N from mineral fertiliSer ..................................................................... 18 
2.2.7 Regional data availability .................................................................... 18 
2.3  National Methods...................................................................................... 18 

3  Results ................................................................................................. 19 

3.1 
3.2 

Identification of major differences .............................................................. 22 
Initial recommendations to improve the quality and consistency of the 
estimates ................................................................................................ 24 

4  Discussion ............................................................................................ 25 

4.1  N-input by Atmospheric N deposition (DE and FR) ........................................ 25 
4.2  N-input by Biological N fixation (DE, FR, IT) ................................................. 27 
4.3  N-input by mineral fertiliSerS (HU and IT) ................................................... 30 
4.4  N excretion BY swine (DE, FR, HU, IT) ........................................................ 32 
4.5  N excretion BY other cattle (IT) .................................................................. 37 
4.6  N excretion BY sheep and goats (FR) .......................................................... 38 
4.7  N removal BY GRAS (FR, HU, IT) ................................................................ 39 
4.8  N removal BY OFAR (DE and FR) ................................................................ 43 
4.9  N removal BY other cereals except for wheat and barley (DE and FR) ............. 45 
4.10  N removal BY olives and Other fruit (IT) ...................................................... 46 
4.11  N removal BY maize and wheat (HU) .......................................................... 47 
4.12  Comparison at the regional level ................................................................ 49 

5  Conclusions .......................................................................................... 52 

6  References ........................................................................................... 54 

Annexes................................................................................................... 61 

5 

 
 
Annex 1.   N contents of all crops in CAPRI* 

Annex 2.   CAPRI calculation method for the herd size of animals in places per 

year 

ANNEX 3.   Regionalisation of the coefficients used in NAT & CAPRI 

Annex 4.   Nitrogen balance legends in the CAPRI MODEL 

Annex 5 

Annex 6 

ANNEX 7 

ANNEX 8 

N-excretion coefficients of selected animal groups in the CAPRI and i 
country datasets* 

Comparison of animal N-budgets for fattening pigs in the CAPRI and 
DE-nat data 

Comparison of animal N-budgets for swine in the CAPRI and FR-nat 
data 

Comparison of animal N-budgets for fattening pigs in the CAPRI AND 
FR-nat data 

Annex 9 

Comparison of animal N-budgets for pigs in the CAPRI and IT-nat data 

Annex 10  N contents (kg N/tonne) from selected crops in the CAPRI and in 

country datasets 

Annex 11.  Mineral fertiliser data in different Eurostat databases (tonnes) 

Annex 12 

Summary, results & achievements of the RegNiBaL project 

Annex 13  Data needs and data sources for regionalisation of Gross Nitrogen 

Balances of EU Member States 

Annex 14  Data on area, yield, and N content of permanent grassland in the 

CAPRI and in countries’ databases 

62 

63 

64 

66 

67 

68 

70 

72 

74 

76 

77 

78 

86 

88 

6 

 
 
 
 
 
LIST OF TABLES  

Table 2.1 

References of the data sets used in the report 

Table 3.1 

The main GNB data components of the CAPRI model and countries at 
the beginning of the RegNiBal project 

Table 3.2   The main GNB data components of the CAPRI model and countries at 

the end of the RegNiBal project 

Table 4.1   Wet, dry, and total deposition fluxes on the agricultural area in the 

German models and CAPRI model results for 2004 

Table 4.2   N fixation by clover and GRAS in CAPRI and national data 

Table 4.3 

The biological N-fixation data for OFAR 

Table 4.4   Consumption of mineral fertilisers in Italy (kt) 

Table 4.1   N excretions in the CAPRI and in the country datasets 

Table 4.6  N excretions in the CAPRI and in the country datasets 

Table 4.7   N excretions in the CAPRI and in the country datasets 

Table 4.8  N excretions in the CAPRI and in the country datasets 

Table 4.9  N excretions in the CAPRI and in the country datasets 

Table 4.10  N excretions in the CAPRI and in the country dataset 

Table 4.11  N removal with GRAS in CAPRI and in the country dataset 

Table 4.12  N removal by GRAS (permanent grassland) in the HUcapri and HUnat 

Table 4.13  N removal by GRAS in the CAPRI and in the country datasets 

Table 4.14  N removal by OFAR in the CAPRI and in the country datasets 

Table 4.15  N removal by other cereals in the CAPRI and in the country datasets 

Table 4.16  N removal by olives and fruit in the CAPRI and in the country datasets 

Table 4.17  N removal by wheat and maize in the CAPRI and in the country dataset 

LIST OF FIGURES 

Figure 1 

Figure 2 

Figure 3 

Animal categories selected for in-depth analysis 

Crop categories selected for in-depth analysis 

Scatter plots for the regional nitrogen surplus data of FRcapri and 
FRnat 

Figure 4 

Scatter plots for the regional nitrogen surplus data of ITcapri and ITnat 

14 

20 

21 

26 

28 

30 

31 

32 

34 

35 

37 

38 

39 

41 

42 

43 

44 

45 

47 

48 

23 

23 

50 

51 

7 

 
 
 
 
 
LIST OF ABBREVIATIONS AND SYMBOLS 

AEI 

Agri-environmental indicators 

ARVALIS 

French Agricultural Applied Research Organisation 

ATMOSD 

Atmospheric deposition 

BIOFIX 

Biological fixation 

BNF 

CAPRI 

Biological nitrogen fixation 

Common Agricultural Policy Regionalised Impact  

CITEPA  

Interprofessional Technical Centre for Studies on Air Pollution 

CORPEN 

French Steering Committee for Environmentally Friendly Agricultural 
Practices  

DEnat 

National data sent by Germany 

CRESID 

Crop residues (input) 

DM 

DüV 

EFMA 

EMEP 

Dry matter 

German Fertilization Ordinance  

European Fertilizer Manufacturers Association 

European Monitoring and Evaluation Programme (EMEP) under the 
Convention on Long-range Transboundary Air Pollution (CLRTAP) for 
international co-operation to solve transboundary air pollution 
problems.  

EXCRET 

Nitrogen in excretion of animals 

EXPPRD 

Nitrogen export with harvested material and crop residues or animal 
products 

FLEG 

FRnat 

GNB 

GRAE 

GRAI 

GRAS 

ELBA 

HUnat 

IDELE 

The share of leguminous crops in OFAR 

National data sent by France 

Gross Nitrogen Balance 

Extensively managed permanent grassland  

Intensively managed permanent grassland  

Total permanent grassland   

A sectorial model for economic and environmental analysis 

National data sent by Hungary 

French Livestock Institute   

IMAGE 

Integrated Model to Assess the Global Environment 

INEA 

IPCC 

Italian National Institute of Agricultural Economics 

Intergovernmental Panel on Climate Change 

ISTAT 

Italian Statistical Office 

8 

ITnat 

JKI 

KSH 

LEVL 

National data sent by Italy 

Federal Research Centre for Cultivated Plants of Germany 

Hungarian Statistical Office 

Activity level of crop (1000 ha) or animal (1000 heads) production 
activities  

MAAF 

French Ministry of Agriculture, Food and Forestry 

MINFER 

Mineral fertiliser applied, including some parts lost in runoff or 
emissions 

MITERRA 

A tool for integrated assessment of N emissions from agriculture  

NAT 

NITF 

NUTS 

OCER 

OECD 

OFAR 

National data 

Nitrogen requirement in above-ground biomass (harvested and crop 
residues)  

Nomenclature of territorial units for statistics 

Other cereals aggregate in the CAPRI model 

Organisation for Economic Co-operation and Development 

Other fodder from arable land in the CAPRI model 

RegNiBal 

Regionalisation of Nitrogen Balances with the CAPRI model 

SURTOT 

Total surplus nutrient input net of exports in products 

fBNF 

UAA 

The share of fixed N in NITF 

Utilised Agricultural Area 

UNFCCC 

United Nations Framework Convention on Climate Change 

∆act 

∆abs 

∆perc 

Actual difference between CAPRI and NAT 

Absolute difference between CAPRI and NAT 

Percentage difference between CAPRI and NAT 

∆absperc 

Absolute percentage difference between CAPRI and NAT 

9 

 
 
GLOSSARY3 

Atmospheric nitrogen deposition - the process by which N airborne particles and 
gases are deposited to soils, vegetation, water bodies, and other surfaces  

Biological N fixation (BNF) - the action carried out by a range of bacteria that are 
either living in loose associations with plants, in symbiotic associations with 
plants (Rhizobium and Actinomycetes), or free-living in soils or water 

BNF by free living organisms – the nitrogen fixation by free-living organisms 

BNF by grass-legume mixtures – the nitrogen fixation through the area under 

temporary and permanent grassland mixed with leguminous forage crops  

BNF by leguminous crops – the nitrogen fixation through the action of bacteria 

which live symbiotically in root nodules of leguminous crops 

Free-living organisms - cyanobacteria (most prominent in rice fields), heterophic 

diazotrophic bacteria and autotrophic bacteria in agricultural systems 

Change in manure stock - the manure stock at beginning of the year minus the 

manure stock at the end of the year 

Consumption of fertiliser - the sum of the consumption of mineral fertilisers and 

other organic fertilisers (excluding manure) 

Crop product - all crop products harvested or grazed on the reference area 

Crop residues - the residues of (i) plants which remain after harvesting of the 

(fodder) crop (e.g. stalks and stubble); (ii) harvested as a by-product of the 
fodder (crop) (e.g. straw) and can be used as feed for animals, bedding material 
or other purposes; (iii) plants which are burned on the field 

Crop residues removed from the field - the residues which are removed completely 
from agriculture, which may include straw, head leaves and stems and other crop 
residuals removed from the field 

Crop yield - mean harvested production per unit of harvested area for crop products 

Dry atmospheric nitrogen deposition – the atmospheric nitrogen deposition as a 
result of complex atmospheric processes such as settling, impaction, and 
adsorption 

Excretion coefficient – factor representing the average amount of nutrients excreted 
per animal head per year (animal place or average annual animal number for 
animals with multiple production cycles) 

3   Prepared using the following sources:  

(i)  Eurostat,  2013.  Nutrient  Budgets,  EU-27,  NO, CH.  Methodology  and  Handbook. Version  1.02.  Eurostat 

and OECD, Luxembourg 

(ii) RAMON - Eurostat's Concepts and Definitions Database  

http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC    

(iii) Gross Nutrient Balance (aei_pr_gnb) - Reference Metadata 

http://ec.europa.eu/eurostat/en/web/products-catalogues/-/KS-FM-13-004   

(iv)  Regulation  (EC)  No  543/2009  of  the  European  Parliament  and  of  the  Council  of  18  June  2009 
concerning crop statistics and repealing Council Regulations (EEC) No 837/90 and (EEC) No 959/93 

10 

                                           
 
Fodder from arable land - lucerne, green maize and cereals for silage or green 

fodder, grass in rotation and other green fodder; not included the grass from 
permanent grassland, fodder beets and other roots for fodder 

Harvested area - the part of the crop area that is harvested 

Leguminous crops - leguminous plants, beans, soya bean, and pulses 

Manure input - the sum of manure production by livestock minus manure withdrawals 

plus change in stocks plus manure imports 

Manure withdrawal - the withdrawal of manure from agriculture, for instance exports 

of manure to other regions, non-agricultural use, processing of manure by 
industry, etc. 

Mineral fertilisers - the fertilisers prepared using inorganic materials manufactured 

through an industrial process 

N content – the coefficient which represents the N content of harvested or grazed 

crop products (kg N per tonne of biomass) 

Nitrogen inputs – the nitrogen in mineral fertilisers and organic manure applied to 
agricultural land, seed and planting materials, biological fixation by leguminous 
crops and clover, and wet and dry deposition from the atmosphere  

Nitrogen outputs - the nitrogen in biomass harvested from arable and permanent 

crops and crops used for fodder 

Nitrogen surplus - the potential surplus of nitrogen on agricultural land, calculated as 
the difference between nitrogen inputs to an agricultural system and nitrogen 
removed (outputs) from the system per hectare of agricultural land  

Organic fertilisers (excluding manure) - sewage sludge, urban compost, industrial 
waste products and other organic products which are used as fertilisers on 
agricultural soils 

Permanent grassland - the land used permanently (for five years or more) to grow 
herbaceous forage crops, through cultivation (sown) or naturally (self-seeded), 
and that is not included in the crop rotation on the holding 

Reference area - the Utilised Agricultural Area (UAA) 

Temporary grassland – the land used for grazing, hay or silage included as a part of 

a normal crop rotation, lasting at least one crop year and less than five years, 
sown with grass or grass mixtures 

Utilised agricultural area - the total area taken up by arable land, permanent 
pasture and meadow, land used for permanent crops and kitchen gardens 
included in the crop rotation on the holding 

Wet atmospheric nitrogen deposition – the atmospheric nitrogen deposited 

through precipitation (rain, snow, clouds, and fog) 

11 

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1 

INTRODUCTION 

Nitrogen  (N)  is  an  essential  element  for  plant,  animal  and  human  nutrition.  On  the 
other  hand,  N  losses  can  negatively  impact  environmental  quality  and  human  welfare 
(Sutton  et  al.,  2011a,b,  2013).  These  impacts  include  negative  effects  on  biodiversity, 
eutrophication,  nitrate  accumulation  in  waters,  acidification,  nitrous  oxide  emissions 
(with effects on global warming and the depletion of the stratospheric ozone layer), and 
risks  to  human  health  due  to  exposure  to  ozone  and  particulate  matter  (Sutton  et  al 
2011a, Smil 2011).  

Agricultural  production  is  an  important  source  of  the  N  that  ends  up  in  ground-  and 
surface waters and the atmosphere (Sutton et al., 2011a,b; Erisman et al., 2013; Fowler 
et  al.,  2013).  N  deficiency,  N  surplus  (NS)  and  N-use  efficiency  (NUE)  in  agricultural 
production are estimated on the basis of agricultural N budgets (OECD 2001, Oenema et 
al 2003, Leip et al 2011b, Eurostat 2013, Britz and Witzke 2012).  

The  Gross  Nutrient  Balance  is  considered  to  be  one  of  the  key  agri-environmental 
indicators (EC 2006, 2004, DG AGRI 2006, EC 2014, EU 2013), as it provides a measure 
of  potential  water  pollution  through  the  loss  of  nutrients  from  agricultural  sources,  as 
well as of potential air pollution, mainly through the emission of ammonia (NH3). Recent 
developments  in  the  Common  Agricultural  Policy  (CAP)  require  the  monitoring  of 
environmental impacts of agricultural activities at regional level (EC 2006). Furthermore, 
N  surplus  and  NUE  are  important  to  identifying  the  options  for  achieving  a  resource-
efficient  agri-food  chain  (e.g.  European  Commission  2011,  2014).  Based  on  recent 
methodological  development  of  the  Gross  Nitrogen  Budget  (GNB)  or  ‘Land  Nitrogen 
Budget’4  (Leip  et  al.,  2011c;  Özbek  and  Leip,  2015),  the  Eurostat/OECD  handbook  for 
estimating national GNB was updated in 2013. 

Regional  estimations  of  nutrient  budgets  produce  more  accurate  results  than  the 
national  estimations,  especially  for  the  countries  where  different  climates  are  observed 
or with regionally differing agricultural production systems (Özbek 2014, Özbek and Leip 
2015). They are also more suitable as a basis for policy advice,  as most  environmental 
problems occur at the level of smaller regional (and non-administrative) units (De Vries 
et al., 2011a; Leip et al., 2011a; Grizzetti et al., 2012). 

The  Eurostat  Working  Group  on  agri-environmental  indicators  (AEI)  therefore 
explored  the  possibility  of  refining  the  spatial  resolution  of  reported  GNBs,  going  from 
national to NUTS2 level, and possibly also beyond (Eurostat, 2012). The development of 
such  methods,  however,  is  challenging  and  requires  approaches  to  ‘disaggregate’ 
national data which are unavailable at higher resolution to smaller spatial units.  

The  “Common  Agricultural  Policy  Regionalised  Impact”  model  (CAPRI)  could 
potentially provide  such data. CAPRI is already calculating GNB at the NUTS2 level and 
on  spatial  units  at  high  resolution  (Homogeneous  Spatial  Mapping  Units,  HSMU)  for 
scientific  projects  and  concept  development  (Leip  et  al  2011b,  2011d,  2008,  Britz  and 
Leip 2009).  

4   The  term  ‘Land  Nitrogen  Budget’  had  been  used  to  differentiate  N  surplus  calculations  that  quantify  the 
balance at the soil surface (Soil N Budgets) from those which also include losses from livestock housing and 
manure storage and management systems (Land Nitrogen Budgets). While the latter give similar results to 
the Farm N Budgets, they differ in data requirements (Leip et al 2011b). 

13 

                                           
Therefore,  the  “Regionalisation  of  Gross  Nitrogen  Balances  with  the  CAPRI  model” 
(RegNiBal)  pilot project  was  launched,  to  be  carried  out  by  the  JRC in  cooperation  with 
Eurostat  and  delegates  from  volunteering  countries.  The  objective  of  the  pilot  project 
was  to  evaluate  differences  between  national  GNB  and  the  GNB  calculated  using  the 
CAPRI model, with the overall goal of using the CAPRI model to (operationally) provide 
regional  GNB  data  to  complement  the  national  Eurostat/OECD  GNBs.  Italy,  France, 
Germany and Hungary volunteered to participate in this pilot project. 

This  report  presents  the  results  of  the  RegNiBal  pilot  project  and  makes 
recommendations  for  actions  which  would  facilitate  the  use  of  the  CAPRI  model  for 
complementing national GNB estimates with data at the regional scale. 

2  METHOD 

2.1  OVERVIEW 

We  compared  the  estimates  of  national  GNBs  with  CAPRI  data  for  four  countries: 
France, Germany, Hungary and Italy. For each country, differences between the ‘national 
methodology’  (as  they  are  submitted  to  Eurostat)  and  the  CAPRI  methodology  (see 
below)  were  identified  and  discussed.  Based  on  this  analysis,  the  causes  of  major 
differences  were  highlighted  and  solutions  or  improvements  were  suggested  to  one  or 
both  of  the  methodologies.  In  some  cases,  these  ‘solutions’  were  already  implemented 
during  the  course  of  the  project,  leading  to  a  decrease  in  the  number  of  observed 
differences. 

In the following we will refer to the datasets as given in Table 2.1: 

Table 2.1 

References of the data sets used in the report 

Country 

Germany 

France 

Hungary 

Italy 

2.2  CAPRI 

CAPRI 

DEcapri 

FRcapri 

HUcapri 

ITcapri 

National methodology (NAT)  

DEnat 

FRnat 

HUnat 

ITnat  

The “Common Agricultural Policy Regionalised Impact” (CAPRI5) model (CAPRI, 2012) 
is  a  multi-purpose  system  for  modelling  the  EU’s  agriculture,  which  helps  analyse  the 
socio-economic  and  environmental  effects  of  agricultural  policies  and  trade  agreements 
in 
(international/EU/member  state/NUTS2),  environmental  policies,  and  changes 
exogenous drivers (population/GDP, etc.). It is an economic model for agriculture, which 
integrates  an  environmental  accounting  model  (Weiss  and  Leip  2012,  Leip  et  al  2014, 
2011b).  It  reports  Nitrogen  Balances  at  the  spatial  resolution  of  NUTS26.  CAPRI  further 

5   http://www.capri-model.org     

6   NUTS : Nomenclature of Territorial Units for Statistics 

14 

  
 
 
                                           
comprises  a  disaggregation  module  which  estimates  land  use  shares,  farm  input  and 
environmental indicators at the pixel level (1 km by 1 km) (Leip et al 2008, 2011c, Leip 
2011). 

A detailed description of the CAPRI model is given in CAPRI (2012), and a description 
of  the  accounting  for  GHG  emissions  is  given  in  Leip  et  al.  (2010)  and  Weiss  and  Leip 
(2012).  The  calculation  of  nitrogen  flows  and  budget  is  explained in  Leip  et  al.  (2011c, 
2014b). 

Nitrogen surplus is defined in the CAPRI model as the difference between N input and 
output (Nsurplus = Ninputs - Noutputs). Differences between the calculation made using CAPRI 
and  the  Eurostat  (2013)  ‘ideal  approach’  include  missing  data  in  CAPRI  on  organic 
fertilisers other than manure, seed & planting material not considered in the input term, 
no  estimation  of  biological  N  fixation  by  free-living  organisms,  and  no  data  on  manure 
imports and exports. 

In this  section, the  CAPRI  methodology  for the  quantification  of  the  major terms in 
the GNB (atmospheric deposition, biological fixation, N removal in permanent grasslands 
(GRAS), in other fodder on arable land (OFAR) and in other crop production, N excretion 
for  swine  and  other  cattle,  mineral  fertiliser)  is  presented.  For  some  of  the  terms,  the 
method  has  been  slightly  changed  or  updated  during  the  course  of  the  project,  as  a 
follow-up from the interim recommendations. In this case, both the original method and 
the method of the latest CAPRI version are given. 

CAPRI  results were extracted at the beginning and end of the project. The final data 

are based on CAPRI rev. 130 from 24 July 2014.  

2.2.1  ATMOSPHERIC N DEPOSITION 

  Original method 

Atmospheric  N  deposition  is  derived  from  the  European  Monitoring  and  Evaluation 
Programme  (EMEP)  database,  in  which  the  data  is  obtained  from  EMEP  measurement 
-  as 
stations.  CAPRI  contains  wet  deposition  measurement  data  for  NH4
compiled  by  Mulligan  (2006):  “EMEP  data  have  been  obtained  by  multiplying  the 
weighted  mean  concentration  by  the  total  amount  of  precipitation  in  the  period.  The 
concentrations for days with missing precipitation data have consequently been assumed 
equal to the weighted average of the period”. 

+  and  NO3

  Updated method 

Atmospheric  (wet  and  dry)  deposition  for  different  ecosystem  types  has  been 
obtained from the EMEP MSC-W chemical transport model (version rv4.5, Simpson et al 
2012,  2014).  Atmospheric  deposition  is  calculated  in  the  model  on  a  sub-grid  for 
different  land  cover  categories,  taking  into  account  vegetation  cover,  phenology  and 
surface  characteristics.  These  runs  use  emission  data  and  meteorological  data  for  the 
years  2003-2005.  These  data  will  be  input  into  the  CAPRI  modelling  framework  at  the 
pixel level. 

15 

 
2.2.2  BIOLOGICAL N FIXATION 

  Original method 

Biological N fixation is estimated by assuming that a fixed share (fBNF) of the N need in 
above  ground  biomass  is  covered  by  biological  N  fixation  (Leip  et  al  2011b).  These 
shares are 75% for soybeans and pulses, and 5% for GRAS. A weighted average of the 
share  of  legumes  and  other  fodders  are  used  for  OFAR.  The  formula  used  for  the 
calculation is given below: 

Nbiofix=NITF*LEVL*fBNF 

(1) 

Nbiofix 

NITF  

LEVL 

fBNF 

Biologically fixed N (kg N/yr) 

Nitrogen requirement in aboveground biomass (harvested and crop residues) (kg N/ha/yr) 

Cropped area (ha) 

The share of fixed N in NITF (%) 

  Updated method 

CAPRI rev.130 uses new fBNF values for N fixation for GRAS by using country-specific 
shares for fixed N on the basis of the LegumeFutures project (Helming et al 2014). GRAS 
fBNF values for DE, FR, HU, and IT are 14%, 7%, 17%, and 16%, respectively.  

2.2.3  N REMOVAL BY OFAR AND OTHER CROP PRODUCTS 

Other  fodder  on  arable  land  (OFAR)  consists  of  annual  green  fodder  including  clover 
and  mixtures,  lucerne,  other  perennial  green  fodder  (usually  legumes)  and  temporary 
grassland. The removal of N by crop products is estimated by multiplying the N contents 
of the crops (kg N per kg of product)7 by crop area and by crop yield. Crop yields in the 
CAPRI  database  are  derived  from  crop  production  statistics  in  Eurostat.  The  N  contents 
of  crop  outputs  are  based  on  the  data  used  in  the  MITERRA  model.  The  N  contents  in 
this  study  have  been  adjusted  using  a  factor  which  is  dependent  on  the  fraction  of  N 
available to the plant (Velthof et al 2007). 

2.2.4  N REMOVAL BY GRAS 

N  removal  by  GRAS  production  is  estimated  by  multiplying  the  N  content  of  GRAS 
(from the MITERRA model) by the GRAS area and yield. CAPRI uses expert data for 2002 
as the key information for GRAS yields. In order to convert these data into time series, 
the data are linked to the aggregated cereal yields, assuming that long run yield growth 
and  yearly  fluctuations  for  grass  can  be  approximated  by  cereal  yields.  The  yields  for 
pasture,  meadows  and  other  fodder  on  arable  land  are  adjusted  accordingly  (Britz  and 
Witzke 2012). 

In  CAPRI,  the  yield  of  intensively  managed  grassland  (GRAI)  and  the  yield  of 

7   See Annex 1 for N contents of all crops in CAPRI 

16 

 
 
 
 
                                           
extensively managed grassland (GRAE) were artificially created from the yield of GRAS. 
The  area  of  GRAE  and  GRAI  was  arbitrarily  set  at  50%  of  the  area  of  GRAS  for  all 
countries.  The  N  contents  of  GRAS  were  17.5,  17.2,  17.6  and  15.2  kg  N/tonne  for  DE, 
FR, IT and HU, respectively. 

2.2.5  N EXCRETION BY SWINE AND ‘OTHER CATTLE’ 

N  excretion  coefficients  are  calculated  in  CAPRI  on  the  basis  of  an  animal  budget:  N 
exported  in  animal  products  (retention)  is  subtracted  from  the  N  intake  through  feed 
(Eq. 2.) to obtain N excretion. The N intake is derived from crude protein intake, which 
depends, among others, on the average live body weight, the daily weight gains and the 
length  of  the  fattening  period  and,  depending  on  the  animal  type,  on  the  number  of 
piglets born, eggs produced, milk yield. Crude protein intake is converted to N intake by 
applying a N:protein ratio of 1:6. 

N retention is the sum  of N in livestock products like eggs and milk and N in animal 

biomass (including meat), applying corresponding N contents. 

N excretion = N intake – N retention 

(2)

  Swine 

The total swine population in the CAPRI model includes fattening pigs and sows. The 
CAPRI  model  uses  the  herd  size  of  pigs,  obtained  by  Eurostat  through  a  maximum  of 
four  annual  surveys  (April,  May  or  June,  August,  and  December).  The  herd  size  of  the 
sows  is  the  average  number  emerging  from  these  surveys  (April,  August,  and 
December).  The  herd  of  fattening  pigs  is  calculated  by  multiplying  the  number  of 
slaughtered  fattening  pigs  (= slaughtered  pigs  -  slaughtered  sows)  by  the  average 
fattening  period  (Annex  2).  The  number  of  sows  is  directly  used  since  they  live  for 
more than one year. Piglets up to a weight of 20 kg are included in the sows activity. 
The  input  for  sows  is  one  piglet  of  20  kg  and  feed  for  the  sow  and  piglets  up  to  a 
weight  of  20  kg;  the  output  of  the  sows  are  piglets  of  20  kg  each,  meat  from  the  old 
slaughtered sow, and the manure from the sow and the young output piglets until they 
weigh 20 kg.  

Since excretion of N by piglets is included in the CAPRI model for sows but not in the 
national data, we recalculated the N excretion coefficients for sows (with piglets) for NAT 
to  make  them  comparable  with  those  of  the  CAPRI  model.  In  order  to  achieve  this,  we 
divided  total  N  excretion  of  sows  and  piglets  in  NAT  by  the  number  of  sows  in  NAT.  In 
order  to  compare  the  number  of  swine  in  the  CAPRI  model  and  NAT,  the  CAPRI  piglet 
number  was  calculated  by  multiplying  the  number  of  sows  in  the  CAPRI  model  by  the 
ratio  [(number  of  piglets)/(number  of  sows)],  obtained  from  the  Eurostat  livestock 
database8.  

8   http://epp.eurostat.ec.europa.eu/portal/page/portal/agriculture/data/database  

17 

 
 
  
 
                                           
  Cattle 

The activity level of heifers and bulls for fattening equals the number of slaughtered 
heads  plus  net  exports  of  fattened live  animals.  If  the  numbers  of  slaughtered  heifers 
and bulls are  unavailable, 45%  of  total cattle  slaughtering  (excluding cows  and  calves 
if available) are used as a default value (Britz and Witzke 2012). 

2.2.6  N FROM MINERAL FERTILISER 

The total amount of N applied with mineral fertilisers is based on Member State data 
of the European Fertilizer  Manufacturers Association (EFMA), as published by FAOSTAT, 
and from data gathered by IFA/FAO through an expert questionnaire, reporting average 
mineral fertiliser application rates per crop and Member State. 

2.2.7  REGIONAL DATA AVAILABILITY  

The  CAPRI  model  calculates  all  GNB  components  at  the  NUTS2  level.  Activity  data 
(acreage, animal numbers) are obtained from regional databases or disaggregated to the 
NUTS2 level in the CoCo module, providing a Consistent and Complete database at this 
level. Mineral fertiliser  application rates by crop and NUTS2 region are estimated in the 
fertiliser module taking into account the variation of yield and the available N input from 
manure,  biological  N  fixation,  crop  residues  and  atmospheric  deposition.  N-excretion 
values are also region-specific. Atmospheric deposition is taken from gridded EMEP data 
and varies at the NUTS2 level. All other coefficients are constant at national (or even EU) 
level (Annex 3). 

2.3  NATIONAL METHODS 

GNB calculations of the countries are based on Eurostat/OECD common methodology. 
In  the  practical  implementation  of  this  methodology,  Nsurplus  is  calculated  by 
subtracting  Noutput  (harvested  crops,  forage,  crop  residues  in  the  field)  from  Ninput 
(fertilisers,  gross  input of  manure,  atmospheric  deposition,  biological  fixation,  seed  and 
planting materials), and the result is divided by the reference area  𝐴𝑟𝑒𝑓 (Eurostat 2013). 
𝐴𝑟𝑒𝑓  is  the  total  UAA,  comprising  arable  land,  permanent  crop  land,  and  utilised 
permanent grassland.  

Some  small  differences  were  observed  at  the  beginning  of  the  RegNiBal  project 

between the methodologies used by the four countries:  

  FRnat does not include organic fertilisers and seed & planting materials. 

 

ITnat  estimates  withdrawals  of  manure  and  manure  imports,  but  does  not 
estimate crop residues exported9. 

  HUnat estimates manure withdrawals10. 

  DEnat,  FRnat,  and  HUnat  do  not  estimate  manure  import  as  the  data  is  not 

available or negligible.  

9   Same as above footnote. 

10  The country estimated manure withdrawals at the beginning of the RegNiBal project, but this component is 

not included in the recalculated GNBs of HU-NAT. 

18 

                                           
With regard to data sources for the coefficients: 

 

ITnat  uses  country-specific  N  contents  of  all  crops  &  forage,  except  for  fodder 
root  crops  (OECD  estimate),  and  country-specific  N-excretion  coefficients  for  all 
livestock except for male cattle, buffaloes, donkeys and mules (OECD estimate). 

  FRnat  uses  country-specific  N  contents  of  all  crops  &  forage,  and  uses  country-
specific  N-excretion  coefficients  from  their  national  GHG  inventory  (which  is 
submitted  to  the  United  Nations  Framework  Convention  on  Climate  Change  - 
UNFCCC).  

  DEnat uses country-specific data for all coefficients used in GNB calculations.  

  HUnat  uses  expert  estimations  and  values  from  the  Hungarian nitrate  regulation 

for N content of crops & forage and for N-excretion coefficients. 

With regard to regional data, only FR and IT provided the regional balances at NUTS2 
level. FR provided the regional balances for 2003, 2004 and 2005.  IT provided them for 
2006.  Both  countries  used  the  same  coefficients  for  all  NUTS2  regions  for  all  GNB 
components (except for regional average deposition rate of FR), and the same N animal 
excretion coefficients (except for pigs and heifers of IT (Annex 3). 

3  RESULTS 

The  main  GNB  data  components  of  the  CAPRI  model  and  countries,  and  the 
comparisons  between  them,  are  presented  in  Tables  3.1  and  3.2.  The  two  tables  show 
the GNBs estimated by the countries and by the CAPRI model at the beginning and end 
of the project, respectively. The following metrics were used in the analysis: 

  Actual Difference 

𝜟𝒂𝒄𝒕 =  𝑪𝑨𝑷𝑹𝑰 − 𝑵𝑨𝑻                                                   

  Absolute Difference     

𝜟𝒂𝒃𝒔 =   | 𝑪𝑨𝑷𝑹𝑰  −  𝑵𝑨𝑻 | 

  Percentage Difference 

𝚫𝒑𝒆𝒓𝒄 =  

𝑪𝑨𝑷𝑹𝑰−𝑵𝑨𝑻

𝑵𝑨𝑻

⋅ 𝟏𝟎𝟎  

  Absolute Percentage Difference 

𝚫𝒂𝒃𝒔𝒑𝒆𝒓𝒄 = | 

𝑪𝑨𝑷𝑹𝑰−𝑵𝑨𝑻

𝑵𝑨𝑻

| ⋅ 𝟏𝟎𝟎  

According  to  Tables  3.1  and  3.2  (see  also  Annex  12),  the  differences  in  N  surplus 
(SURTOT) between NAT and the CAPRI model were significantly lower at the end of the 
project for the changes implemented by the CAPRI model and NAT since the start of the 
RegNiBal project). 

19 

 
 
 
 
 
 
 
 
 
Table 3.1 

The main GNB data components of the CAPRI model and countries at the 

beginning of the RegNiBal project 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Planting 
material 
EXPPRD-CRESID 

SURTOT 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

FR - Components* 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

814 

704 

NE 

125 

185 

NE 

859 

969 

680 

866 

9 

233 

180 

9 

1 370 

606 

134 

-162 

NA 

-108 

5 

NA 

-511 

363 

20 

-19 

NA 

-46 

3 

NA 

-37 

60 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

195 

339 

NE 

23 

55 

NE 

354 

258 

124 

281 

1 

36 

66 

11 

518 

0 

71 

58 

NA 

-13 

-10 

NA 

-164 

258 

57 

21 

NA 

-36 

-16 

NA 

-32 

1 412  410 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

1 357 

1 772 

NE 

103 

189 

NE 

1 891 

1 531 

1 263 

1 798 

60 

222 

399 

25 

2 087 

1 681 

93 

-26 

NA 

-119 

-209 

NA 

-196 

-150 

7 

-1 

NA 

-53 

-53 

NA 

-9 

-9 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

1 610 

2 280 

NE 

322 

502 

NE 

2 579 

2 135 

1 820 

2  286 

NE 

130 

622 

NE 

3 589 

1 268 

-210 

-7 

NA 

193 

-120 

NA 

-1 010 

867 

-12 

0 

NA 

149 

-19 

NA 

-28 

68 

NE: Not estimated; NA: Not applicable. Note: CAPRI data was extracted in May 2013. 
* See Annex 4 for GNB components` legends. ** See Section 3. for the definitions. 

20 

 
 
 
 
 
 
Table 3.2  

The main GNB data components of the CAPRI model and countries at the 

end of the RegNiBal project 

IT***- Components* 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

773 

704 

NE 

139 

185 

NE 

806 

995 

832 

742 

8 

297 

216 

9 

1 220 

883 

-58 

-38 

NA 

-158 

-31 

NA 

-414 

112 

-7 

-5 

NA 

-53 

-14 

NA 

-34 

13 

HU***- Components* 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

164 

339 

NE 

28 

58 

NE 

342 

247 

151 

281 

1 

20 

67 

9 

331 

197 

13 

58 

NA 

8 

-9 

NA 

11 

50 

9 

21 

NA 

42 

-13 

NA 

3 

26 

DE***- Components* 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

1 305 

1 772 

NE 

124 

184 

NE 

1 784 

1 602 

1 263 

1 798 

60 

154 

349 

25 

2 087 

1 562 

42 

-26 

NA 

-30 

-165 

NA 

-303 

40 

3 

-1 

NA 

-19 

-47 

NA 

-15 

3 

FR***- Components* 

CAPRI 
(Gg N/yr) 

NAT 
(Gg N/yr) 

∆act** 
(Gg N/yr) 

∆perc ** 
(%) 

EXCRET 

MINFER 

Organic Fertilisers 

BIOFIX 

ATMOSD 

Seed & Plant. mat. 

EXPPRD-CRESID 

SURTOT 

1 634 

2 280 

NE 

416 

513 

NE 

2 486 

2 357 

1 809 

2 326 

21 

320 

320 

34 

3 037 

1 793 

-175 

-46 

NA 

96 

193 

NA 

-551 

564 

-10 

-2 

NA 

30 

60 

NA 

-18 

31 

NE: Not estimated; NA: Not applicable. Note: CAPRI data was extracted on July 2014. * See Annex 4 for 
GNB components` legends. ** See Section 3. for the definitions. *** IT national GNB is Eurostat estimation. 
(http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=aei_pr_gnb&lang=en). Extracted on 17.11.2014. 

21 

 
 
 
3.1  IDENTIFICATION OF MAJOR DIFFERENCES 

Major differences between NAT and CAPRI data were identified for livestock (based on 
the  N  animal  excretion)  and  for  crops  (based  on  N  in  exported  crop  products  and  crop 
output excluding crop residues) as shown in Figure 1 and Figure 2, respectively. In each 
case, two criteria were combined (x and y axes in the Figures):  

 

 

(x) the absolute percentage difference between estimates (of animal excretion 
or N crop output) and  
(y) relative contribution of the individual animal/crop categories to total animal 
N excretion or N removal with crops in the country.  

As  the  importance  of  a  flow  term  to  the  total  flow  (N  excretion  or  crop  N  output) 
depends  on  the  methodology,  the  average  of  the  CAPRI  model  and  NAT  shares  was 
used.  

We applied a threshold of (x * y > 0.05, see lines in Figure 1 and Figure 2) to select 
those  flows  where  both  differences  between  the  methodologies  and  significance  for  the 
GNB were high and which thus deserved further attention (see bold markers in Figure 1 
and Figure 2). This method provided an initial list of selected flows for each country. For 
these initially selected flows, other  terms of the GNB (biological N fixation, atmospheric 
deposition,  and  mineral  fertiliser  application)  were  analysed  in  a  similar  way,  and  were 
added to the list of non-matching flows if a large difference was observed. The following 
flows where identified: 

  Germany:  atmospheric  deposition,  N  removal  by  other  fodder  on  arable  land 
(OFAR),  permanent  grassland  (GRAS)  and  other  cereals  (OCER),  biological 
fixation, N excretion by swine. 

  France: N removal by OFAR, GRAS and OCER, N excretion by swine, sheep and 

goats, biological fixation, and atmospheric deposition. 

  Hungary:  N  removal  by  GRAS,  maize  and  wheat,  N  excretion  by  swine,  and 

mineral fertiliser. 

  Italy:  N  removal  by  GRAS,  olives  and  fruits,  N  excretion  by  swine  and  other 

cattle, mineral fertiliser, and biological fixation. 

22 

Note: The average of CAPRI and country contributions is used in the y axis. The figure shows animal 
categories selected for in-depth analysis by countries. The larger symbols indicated the selected animal 
categories. 

Figure 1 

Animal categories selected for in-depth analysis 

Note:  The  average  of  CAPRI  and  country  contributions  is  used  in  the  y  axis.  The  figure  shows  crop 
categories  selected  for  in-depth  analysis  by  countries.  The  larger  symbols  indicated  the  selected  crop 
categories. *OFAR=’Other fodder from arable land’; **Other crops=NURS,FLOW,OCRO 

Figure 2 

Crop categories selected for in-depth analysis 

23 

 
 
 
3.2  INITIAL RECOMMENDATIONS TO IMPROVE THE QUALITY AND 

CONSISTENCY OF THE ESTIMATES 

A  first  screening  of  the  differences  gave  rise  to  some  initial  ‘recommendations’  that 
could  help  to  improve  the  quality  and  consistency  of  the  GNB  estimates.  These 
recommendations are listed below.  

  CAPRI 

  Assess  the  possibility  to  include  N  input  from  organic  fertilisers  other  than 
manure (sewage sludge, compost, etc.), seed & planting materials, and manure 
imports or exports.  

  Update deposition flows, including both wet and dry deposition.  

  Assess  the  possibility  to  update  or  use  country-specific  N  contents  of  the  crop 

aggregate ‘other cereals’ (OCER). 

  Check  and  possibly  update  the  N  content  of  GRAS  for  FR  (e.g.  on  the  basis  of 

GRASSDATE results) 

  Revise  the  methodology  to  estimate  biological  nitrogen  fixation  (BNF),  which 

was found to be lower compared to national and some literature data. 

  Germany 

  Exclude  BNF  from  free-living  organisms  unless  new  and  better  evidence  for 

crops under national conditions are available.  

  Check  the  model  used  to  estimate  atmospheric  deposition  and/or  justify  the 

absence of net atmospheric transport (export) of nitrogen. 

  There  are  large  differences  between  the  DEnat  and  DEcapri  N-excretion 
coefficients  for  fattening  pigs.  A  comparison  of  detailed  animal  budgets  is 
needed. 

  France 

  Assess  the  possibility  to  include  the  N  input  from  organic  fertilisers,  seeds  and 

planting. 

  Check yield and N content of OFAR. 

  Assess  the  possibility  to  use  the  results  of  the  GRASSDATE  project  for 

estimating N removal by GRAS.  

  There  are  large  differences  between  the  FRnat  and  FRcapri  N-excretion 
coefficients  for  swine,  sheep  and  goats.  A  comparison  of  detailed  animal 
budgets should be made. 

  Check N-deposition values. 

  Hungary 

  Check  yield  of  GRAS.  More  information  is  required  to  make  a  conclusion  as  to 
the cause of discrepancy in grassland areas. Hungary should check why the GNB 

24 

of  grassland  areas  differs  from  the  value  reported  to  the  EUROSTAT  Crops 
products - annual data (apro_cpp_crop) database. 

  Report the N content of crops and crop residues separately. 

  One  possible  reason  for  the  difference  in  the  animal  numbers  between  the 
CAPRI  model  and  NAT  could  be  that  animals  are  slaughtered  without  entering 
the official statistics, which we recommend be checked.  

  Additional  and  more  detailed  comparisons  of  the  statistical  data  should  also  be 
made with respect to the differences observed in the N-excretion coefficients. A 
comparison  of  the  animal  budget  for  fattening  pigs  and  sows  in  the  CAPRI  and 
NAT data would possibly be required. 

  A  more  detailed  comparison  of  fertiliser  data  of  the  HUcapri  and  HUnat  is 

required. 

Italy 

 

Include N crop residues removed from GNB estimations. 

  Both  ITnat  and  ITcapri  should  check  the  yields  of  GRAS;  it  may  be  that  more 

information is required. 

 

Justify  or  revise  the  high  N-content  values  used  for  olives  and  fruits.  We  also 
recommend that the ITcapri checks its data regarding olives. 

  There  are  large  differences  between  the  ITnat  and  ITcapri  N-excretion 
coefficients for swine and other cattle. A comparison of detailed animal budgets 
is needed. 

 

Include BNF by grass–legume mixtures in GNB estimations. 

  Exclude  BNF  by  free-living  organisms  unless  new  and better  evidence for  crops 

under national conditions are available.  

Some  of  these  recommendations  were  subsequently  implemented,  and  issues  were 
partly resolved. However, some were still unresolved at the end of the project. Section 4 
discusses each of the identified flows.  

4  DISCUSSION 

4.1  N-INPUT BY ATMOSPHERIC N DEPOSITION (DE AND FR) 

Atmospheric deposition was identified as an issue for two countries, with the following 

observations: 

  Germany 

  ∆abs:  209 Gg N/yr (Table 3.1) 

  ∆absperc: 53% 

25 

 
 
 
 
National method: Initially, DEnat used a methodology developed at UBA (Gauger et 
al  2008)  for  atmospheric  deposition,  which  does  not  differentiate  between  arable  land 
and  permanent  grassland.  This  was  updated  with  data  from  a  new  study  which 
differentiated  between  arable  land  and  grassland  (Gauger  2010,  see  Table  3.1).  Data 
from  Gauger  (2010)  is  based  on  detailed  gridded  emission  data  from  wet  deposition 
measurements,  high-resolution  land-use  maps  for  the  years  2004-2007,  and  chemical 
transport  modelling.  Total  deposition  loads  of  N  are  calculated  for  a  1x1  km²  grid  by 
combining information of dry, wet and cloud /occult deposition fluxes (Gauger 2010).  

Assessment: Even though estimates of atmospheric deposition decreased during this 
update from a total of  399 Gg N/yr to 349  Gg N/yr, the estimate was still considerably 
higher than that of DEcapri, at 189 Gg N/yr (2003-2005). The difference could be partly 
explained by the fact that DEcapri takes only wet deposition into consideration. Indeed, 
dry deposition, which was estimated by the updated DEcapri method (see Section 2.2.1) 
to be 72 Gg N/yr, would explain most of the difference (see Table 4.1).  

Table 4.1   Wet, dry, and total deposition fluxes on the agricultural area in the 

German models and CAPRI model results for 2004 

2004 data comparison 

Atmospheric deposition on the 
agricultural area 

NOy-N 

NHx-N 

N 

Deposition  
DEnat Model (2010) 

CAPRI Model* (updated 
method), Germany 

CAPRI-updated /  
DEnat model  (2010) 

CAPRI-updated /  
DEnat model  (2010) 

Wet deposition [Gg/yr  ] 

Dry deposition [Gg/yr  ] 

71 

61 

99 

170 

132 

193 

Total deposition [Gg/yr  ] 

132 

231 

363 

Wet deposition [Gg/yr  ] 

Dry deposition [Gg/yr  ] 

90 

26 

104 

194 

46 

72 

Total deposition [Gg/yr  ] 

116 

150 

266 

∆abs in total deposition [Gg/yr  ] 

16 

81 

97 

∆absperc in total deposition [%] 

12 

35 

27 

* See Section 2.2.1 for the methodology 

Recommendation and Conclusion: The CAPRI model needs to incorporate the new 
atmospheric deposition data, including both wet and dry deposition. DEnat was updated. 
∆abs of total atmospheric deposition between  the updated CAPRI and DEnat data is now 
93  Gg  N/yr,  compared  to  the  original  ∆abs  of  209  Gg  N/yr  (2003-2005).  Moreover,  the 
approach for the model estimation of dry deposition was revised within a recent national 
project  (Wichink  Kruit  et  al  2014).  Implementing  the  results  of  the  project  will  further 
decrease the N loads of atmospheric N deposition. Therefore, the issue is considered as 

26 

 
 
resolved.  

  France 

  ∆abs: 120 Gg N/yr (Table 3.1) 

  ∆absperc: 19% 

National  method:  FRnat  calculated  atmospheric  N  deposition  by  multiplying  N-

deposition flux rates from EMEP by total area covered.  

Assessment:  A  mistake  in  the  value  of  the  area  used  in  the  estimations  of  FRnat 
atmospheric deposition was identified (total area instead of utilized agricultural area) and 
corrected. This reduced the calculated value of atmospheric deposition from 622 Gg N/yr 
to 320 Gg N/yr, which was significantly lower than the FRcapri estimate of 502 Gg N/yr. 
The new EMEP data that will be incorporated in the CAPRI model (see section 2.2.1) will 
reduce ∆abs of total atmospheric deposition between  the updated  CAPRI and FRnat data 
to 23 Gg N/yr (2003-2005). 

Recommendation & Conclusion: After updating the atmospheric N-deposition data 
of both FRcapri and FRnat, ∆absperc will decrease to an acceptable value of 7%. The issue 
is  considered  as  resolved.  However,  FRnat  could  consider  using  the  updated  CAPRI 
method (see section 2.1.1), to differentiate between land uses. 

4.2  N-INPUT BY BIOLOGICAL N FIXATION (DE, FR, IT) 

Biological N fixation (BNF) was identified as an issue for three countries:  

  Germany 

  ∆abs: 119 Gg N/yr (Table 3.1) 

  ∆absperc: 53% 

The share of BNF in total N input was low for both the CAPRI model (3%) and DEnat 

(6%). 

National  method:  BNF  added  up  to  a  total  of  222  Gg  N/yr,  and  it  contains:  N-
fixation  of  plant  populations  that  can  fix  N,  such  as  peas,  beans,  clover  and  mixtures, 
lucerne (64 Gg N/yr); N-fixation by legumes in permanent pasture (90 Gg N/yr) and N-
fixation by free-living organisms (68 Gg N/yr). DEnat used default values from literature 
for the  estimation of  the BNF  of legumes and free-living organisms. BNF by legumes in 
permanent pastures came from expert estimations.  

Assessment of N-fixation in grasslands: Most of the difference in total N fixation 
between  DEcapri  and  DEnat  was  explained  by  the  difference  in  the  BNF  by  free-living 
organisms  (68  Gg  N/yr),  and  GRAS  (58  Gg  N/yr).  Now,  the  CAPRI  model  has 
implemented  a  new  method  for  N  fixation  by  GRAS,  using  country-specific  shares  for 
fixed  N  on  the  basis  of  the  LegumeFutures  project  (Helming  et  al  2014).  Following this 
methodology,  the  ∆abs  in  symbiotic  N  fixation  by  GRAS  between  the  CAPRI  and  the 
country datasets had decreased from 58 to 38 Gg N/yr. 

27 

 
 
 
Table  4.2  shows  that  the  N-fixation  coefficient  for  GRAS  was  lower  in  CAPRI  than  in 
DEnat data, and that the clover share in the GRAS of the country is closer to the average 
shares  in  the  literature  studies  compared  to  the  CAPRI  model.  We  therefore 
recommended that the methodology of the CAPRI model should be revised.  

Conclusion: The difference in symbiotic N fixation  by GRAS between the CAPRI and 
the country datasets decreased after the implementation of a new method for N fixation 
by GRAS in the CAPRI model,  using country-specific fBNF. However, in order to close the 
issue, the legume share in GRAS must be defined for the country. 

Table 4.2  

N fixation by clover and GRAS in CAPRI and national data 

CAPRI 

National Data 

N fixation coefficient for clover (kg 
N/ton) 

19.1 (=N fixation for clover / 
average yield) 

18.6 (=N fixation for clover / 
average yield) 

N fixation for clover (kg N/ha ) 

157*  

153  

Other data (kg N/ha ) 

N fixation coefficient for GRAS (kg 
N/ton) 

47-235 in Denmark (Carter 2005) 
74-280 in UK (Cowling 1982) 
83-283 in Switzerland (Boiler and Nosberger 1987) 
83-286 in Ireland (Masterton and Murphy 1976) 

0.9 (CAPRI old method) 
1.4 (CAPRI new method) 
(=Symbiotic N fixation from 
GRAS / GRAS area / average 
yield) 

3.1 (=N fixation (kg N/ha) / 
average yield) 

The clover share in the GRAS (%)(=N 
fixation coefficient for GRAS / N 
fixation coefficient for clover*100) 

5 (CAPRI old method) 
7(CAPRI new method)*** 

17 

Other data (%) 

5-40 (Bachinger)** 
1-75 in Finland Arja (2005) 

* Calculated by multiplying N need for OFAR by fBNF (CAPRI data) 
**http://www.zalf.de/de/forschung/institute/lse/downloads/Documents/oekolandbau/schaetztrainer/progra
m/en_explain.pdf 
*** See Section 2.2.2 on the new and old CAPRI methods.  

Assessment of N-fixation by free-living organisms: According to Wagner (2012) 
the  contribution  of  free-living  organisms  to  global  nitrogen-fixation  rates  is  generally 
considered  to  be  minor  because  of  the  scarcity  of  suitable  carbon  and  energy  sources. 
Heterotrophic  free-living  N2  fixers  that  use  plant  residues  such  as  straw  and  leaf  litter 
appear to contribute only small amounts of N to dry land agriculture, mostly <5 kg N/ha 
per  year  (Unkovich  et  al  2008).  However,  some  studies  measured  BNF  by  free-living 
organisms  as  being  higher  than  5  kg  N/ha  per  year,  giving  an  excess  of  20  kg  N/ha 
during the growing season in cereal fields in humid environments (Neyra and Dobereiner 
1977). A study in Australia of an intensive wheat rotation farming system demonstrated 
that  free-living  microorganisms  contributed  20  kg  N/ha  per  year  to  the  long-term 
nitrogen needs of this crop system (30-50% of the total needs, Vadakattu and Peterson 

28 

 
 
 
 
2006). Also, free-living N-fixation in flooded rice production systems has been shown to 
be  up  to  30  kg  N/ha  (Firth  et  al  1973),  and  in  tropical  crops  such  as  sugarcane  in  the 
order  of  10–65  kg  N/ha  per  year  (e.g.  Boddey  et  al  1995),  and  in  the  order  of  160  kg 
N/ha (Bohlool et al 1992). 

Thus, the amount of N fixed by free-living soil bacteria is generally small, i.e. < 5 kg 
N per ha per year (Paul and Clark 1996, Unkovich  et al 2008, Beriner and Hirsch 1984, 
Vitousek  et  al  2002),  with  the  exception  of  high  values  found  mainly  in  humid  tropical 
regions.  We  recommend  that  BNF  from  free-living  organisms  should  not  be  included  in 
the  GNB  unless  new  and  better  evidence  for  crops  under  national  conditions  are 
available.  

Conclusion:  Germany  will  exclude  BNF  by  free  living  organisms  as  soon  as  the 
methodology  for  the  sustainability  indicator  ‘N-surplus  (Nachhaltigkeitsindikator  N-
Überschuss)’ is revised (resolved). 

  France 

  ∆abs: 193 Gg N/yr (Table 3.1) 

  ∆absperc: 149% 

National  method:  The  biological  fixation  methodology  was  updated  in  the  last 
French National Working Group. Fixed coefficients in kg per ha were replaced with data 
on  N  removal  with  legumes  (kg  N/tonne/yr),  the  share  of  fixed  N  (%),  and  legume 
shares in grassland (%) on the basis of expert knowledge.  

Assessment:  Biological  fixation  for  FR  increased  from  130  Gg  N/yr  to  294  Gg  N/yr, 
and the ∆perc in biological fixation between the CAPRI and FR data was minimised (from 
149% to 10%).  

Conclusion: The issue has been solved. 

Italy 

  ∆abs: 108 Gg N/yr (Table 3.1) 

  ∆absperc: 46% 

National  method:  ITnat  used  OECD  coefficients  of  biological  fixation,  which  are 

multiplied by the area of legume crops. 

IT included N fixation by free-living organisms. 

IT did not estimate N fixation by leguminous crops in grasslands.  

Assessment:  The  biggest  ∆abs  in  biological  fixation  between  CAPRI  and  the  country 
results were from OFAR (47%), followed by N fixation from free living organisms (36%), 
soybean (11%) and N-fixation in permanent grassland (6%). ITnat estimated that fixed 
nitrogen was 13  Gg N/yr for clover  and 142 Gg  N/yr for alfalfa. The N fixation of these 
crops was 78 Gg N/yr higher than that of the OFAR group in the CAPRI model. Biological 
N fixation by free-living organisms came closely behind the OFAR group. 

29 

 
 
 
 
Table 4.3 

The biological N-fixation data for OFAR 

CAPRI 

IT 

Other Data 

N content (kg N/tonne) 

13.1 for all OFAR 
crops (=NITF / 
Eurostat yield) 

26.5 for alfalfa  

% of N fixed in the 
amount of N exporteda 

75% for all OFAR 
crops 

86% for alfalfab 

22.0 (Macolino et al 2013) 
29.3 
http://www.feedipedia.org/node/275 

100% for leguminous crops (MITERRA) 
200% for pulses, soybeans and legumes 
(IPCC, IMAGEc) 

The amount of biological 
N fixation of alfalfa (kg 
N/ha/yr) 

76.6d 

180  

174 (Kelner et al 1997)  
200 (Smil 1999) 

Note: CAPRI data was extracted in May 2013. 
a The source of other data is de Vries et al. (2011) 
b Calculated by using alfalfa yields of IT in the Eurostat crop products database and the data reported by the 
country 
c The Integrated Model to Assess the Global Environment 
d Calculated by multiplying the N need of OFAR by fBNF (CAPRI data) 

Table 4.3 shows that the method for calculating BNF used in the CAPRI model yields 
relatively  low  N-fixation  values  compared  to  national  and  literature  data.  We  therefore 
recommended  that  the  methodology  of  the  CAPRI  model  be  revised.  Now,  the  CAPRI 
model has implemented a new method for calculating N fixation by OFAR, using country-
specific  shares  for  fixed  N  on  the  basis  of  the  LegumeFutures  project  (Helming  et  al 
2014). But the ∆abs in symbiotic N fixation by OFAR between the new CAPRI method and 
the country dataset is still high (decreased from 79 to 69 Gg N/yr). The main reason for 
this is that the NITF in the CAPRI data is lower than in NAT and in the literature (Table 
4.3).  

Recommendation & Conclusion:  

  Differences in symbiotic N fixation by OFAR between the new CAPRI method and 
the  country  are  still  high.  We  recommend  that  CAPRI  check  the  NITF  value  for 
OFAR.  

  We  recommend  that  BNF  by  free-living  organisms  should  not  be  included  in  the 
GNB  unless  new  and  better  evidence  for  crops  under  national  conditions  is 
available (see above).  
Italy should include the biological N fixation from grass–legume mixtures. 

 

4.3  N-INPUT BY MINERAL FERTILISERS (HU AND IT) 

Mineral  fertilisers  were  identified  as  an  issue  for  two  countries,  with  the  following 

observations: 

  Hungary 

  ∆abs: 58 Gg N/yr (Table 3.1) 

  ∆absperc: 21% 

30 

 
 
 
 
This  component  has  a  high  importance  in  HUcapri  and  HUnat.  The  share  of  this 

component in total N input was 55% and 54% for HUcapri and HUnat.  

National method: HU mineral fertiliser data were based on fertiliser sales’ data.  

Assessment:  The  country  data  reported  to  both  Eurostat  and  the  FAO  (281  kt/yr) 
was lower than the CAPRI data (339 kt/yr). The country expert recommended using the 
official  mineral  fertiliser  for  HU  value  in  the  CAPRI.  However,  HUcapri  data  also  come 
from official sources. 

Recommendation & Conclusion: The issue is ‘open’. There is the need to check the 

HUnat and HUcapri data against the information from EFMA.  

Italy 

  ∆abs: 162 Gg N/yr (Table 3.1) 

  ∆absperc: 19% 

This  component  has  a  high  importance  in  ITcapri  and  ITnat.  The  share  of  this 

component in total N input was 38% and 44% for ITcapri and ITnat.  

National  method:  ITnat  used  mineral  fertiliser  sales  data  of  the  Italian  Statistical 

Office (ISTAT).  

Assessment:  The  ITcapri  mineral  fertiliser  data  were  lower  than  the  ITnat  and  FAO 
data. However, data in Eurostat databases were of the same magnitude as that used by 
the  CAPRI  model.  There  are  two  different  sets  of  data  for  the  country  in  two  different 
Eurostat databases (Table 4.4).  

Table 4.4  

Consumption of mineral fertilisers in Italy (kt) 

CAPRI* 

Data reported 
by the 
country** 

FAO Fertilisers 
database*** 

Eurostat GNB 
Database**** 

Eurostat 
Consumption 
of Mineral 
Fertilisers 
Database**** 

704 

866 

838 

686 

742 

Consumption 
of mineral 
fertilisers 

(kt) 

* Extracted in May 2013 
** Two-year average (2003, 2004); the other data are three-year averages (2003, 2004, 2005) 
*** http://faostat3.fao.org/download/R/RF/E (Extracted on 20.11.2014) 
**** http://epp.eurostat.ec.europa.eu/portal/page/portal/agri_environmental_indicators/data/database 
(Extracted on 17.11.2014) 

Recommendation  &  Conclusion:  We  recommend  that  Italy  clarifies  the  values  to 
be  used  and  the  reason  for  different  data  in  different  databases.  The  CAPRI  model 
should be checked to determine the cause of the large difference with the FAO database. 
The issue is not resolved. 

31 

 
 
 
 
 
4.4  N EXCRETION BY SWINE (DE, FR, HU, IT) 

N excretion by swine was identified as an issue for four countries: 

  Germany 

  The highest ∆abs in N excretion of animal categories between national and CAPRI data 

  ∆act: 122 Gg N/yr 
  A significant share (130%) of the ∆act of total N excretion by animals (94 Gg N/yr) 

National  method:  N  excretion  coefficients  for  DE  are  derived  from  the  German 
Fertilization  Ordinance  (DüV  2007)  and  calculated  on  the  basis  of  an  animal  budget: 
exported N in animal products (retention) is subtracted from the N intake by feed  (DLG 
2005). 

Assessment:  Table  4.5  shows  that  the  total  difference  in  N  excretion  for  swine 
between  the  country  and  CAPRI  data  (40%)  resulted  mostly  from  the  difference  in  N 
excretion  for  fattening  pigs  (63%)  (Annex  7).  The  CAPRI  coefficient  for  N  excretion  by 
fattening  pigs  was  higher  than  the  coefficient  values  of  the  country  and  those  found  in 
the  literature.  In  addition,  the  CAPRI  model  estimated  much  higher  N-excretion 
coefficients for fattening pigs for Germany than for HU and IT (Annex 5). 

Table 4.1  

N excretions in the CAPRI and in the country datasets 

CAPRI 

DE 

Fattening 
pigs 

Sows  
(with 
piglets) 

N excretion coefficient  
(kg N/place/yr) 

18.7 

33.8 

Total 

15.3 
*** 

Fattening 
pigs 

Sows  
(with 
piglets) 

Total 

11.3 

34.5 

10.6 
*** 

N swine excretion  
(Gg N/yr) 

N animal excretion  
(Gg N/yr) 

N input  
(Gg N/yr) 

318 

87 

405 

195 

88 

283 

1,357 

3,422 

1,263 

3,768 

32 

 
  
  
 
  
 
  
 
 
  
 
 
 
Literature values: 

Fattening pigs 

Sows 

N-excretion coefficient from the 
literature (kg N/place/yr) 

12-15** (a) 
11 (b) 
11 (c)  

11.7 (d) 
14.8 (e) 
11.9 (f) 

32-38** (a)  
26 (b) 
33 (c) 

Note: CAPRI data was extracted in May 2013. 
*a: Ketelaars and van der Meer (1999) for EC Member States; b: OECD (1997) for OECD countries; c: 
Levington Agriculture (1997) for Europe; d: 2013 German GHG Inventory; e: GAINS model (Asman et al 
2011) ; f: RAINS model (the predecessor of GAINS (http://gains.iiasa.ac.at/models/)) (Oenema et al 2009). 
**N-excretion coefficients’ range depending on N-conversion coefficient. 
*** Calculated for the country by using weighted average of N-excretion coefficients for piglets, fattening 
pigs, sows and boars, and for the CAPRI model by using corrected animal numbers (see Section 2.2.5). 

To  calculate  the  total  N-excretion  coefficient  of  fattening  pigs  (e),  DEnat  used  the 
weighted  average  of  N-excretion  coefficients  of  standard  (a  and  b)  and  low  feeding  (c 
and d) fattening pigs, using two daily gain rates, i.e. 700 g/d (a and c) 800 g/d (b and d) 
according to (Eq. 3) (Bach et al 2011).  

𝒆 =

(𝒂×𝟐+𝒃×𝟐+𝒄+𝒅)

𝟔

(3) 

DEcapri feed intake was higher than DEnat by 83%, and N intake was higher by 56% 
(see Annex 6). DEcapri live weight, and consequently retention, was higher by 23% and 
20%, respectively. This resulted in 75% more N excretion in DEcapri.  

DEnat  (DLG  2005)  used  the  December  survey  for  the  animal  numbers,  but  the  N-
excretion  coefficient  used  was  kg  N/place/yr.  An  update  is  required  from  animals’  herd 
size to places, but is likely to result in only minor changes for N excretion (from 195 Gg 
N/yr to 192 Gg N/yr).  

Recommendation  &  Conclusion:  Further  analysis  is  required  to  understand  the 
reason  for  the  higher  feed  intake  in  CAPRI.  DEnat  should  report  animal  numbers  per 
place instead of per head. 

  France 

  ∆abs: 65 Gg N/yr (Table 3.1) 

  A  significant  share  (-31%)  of  the  difference  of  total  N  animal  excretion  (-210  Gg 

N/yr) 

National  method:  FR  used  N-excretion  coefficients  from  the  national  GHG 
inventories. These coefficients were estimated by  the Interprofessional Technical Centre 
for Studies on Air Pollution (CITEPA). CITEPA uses a model called PACRETE11 to combine 
and harmonise the different data sources of livestock size, feeding, type of buildings etc. 
in  order  to  calculate  these  coefficients.  In  2012,  the  method  was  improved  using 

11  This  system provides  an  integrated  approach  to estimate  livestock  emissions  at  regional  level.  It  includes 
livestock  nitrogen  excretion,  mass  flow  approach  for  nitrogen,  updated  emission  factors  for  NH3  and 
particles as well as updated data on breeding practices. 

33 

 
 
 
 
 
 
   
 
           
 
 
 
                                           
country-specific data based on national studies (CORPEN) for every species. N-excretion 
data now integrates time dependent data on feed and production level. 

Assessment:  Table  4.6  shows  that  there  is  a  large  difference  in  the  N-excretion 
coefficient for swine between that CAPRI and the country data; literature data are closer 
to the CAPRI than the country data. 

Table 4.6 

N excretions in the CAPRI and in the country datasets 

N-excretion coefficient for swine (kg 
N/place/yr) 

11.6 

CAPRI 

FR 

7.2 

Other data (kg N/place/yr) 

12 (Asman et al 2011) 

N-excretion coefficient for fattening 
pigs (kg N/place/yr) 

12.7 

7.7 (CORPEN 2003) 

Other data* (kg N/place/yr) 

12-15** (a) 
11 (b) 
11 (c) 

N-excretion coefficient for sows (with 
piglets) kg N/place(=head)/yr  

36.4 

24.6 (CORPEN 2003) 

Other data* (kg N/place/yr) 

32-38** (a) 
26 (b) 
33 (c) 

N-excretion by swine (Gg N/yr) 

172 

N-excretion by animals (Gg N/yr) 

1,610 

N input (Gg N/yr) 

4,714 

107 

1,820 

4,857 

Note: CAPRI data was extracted in May 2013. 
*a: Ketelaars and van der Meer (1999) for EC Member States; b: OECD (1997) for OECD countries; c: 
Levington Agriculture (1997) for Europe 
**N excretion coefficients’ range depending on the N-conversion coefficient. 

Comparison  of the animal N-budgets for swine in FRnat  (CORPEN 2003) and FRcapri 
showed higher N intake and N retention by both fattening pigs and sows in CAPRI data. 
The ∆absperc in N intake and N retention were 166% and 68% for fattening pigs, and 95% 
and  62%  for  sows,  respectively  (Annex  7).  For  fattening  pigs  in  standard  feeding,  we 
found  higher  N  intake  (113%)  in  CAPRI  data,  mainly  resulting  from  higher  feed  intake 
(166%), and higher N retention (68%) resulting from higher implied live weight (126%); 
this lead to 54% more N excretion in the CAPRI model (Annex 8). 

In  the  Eurostat/OECD  GNB  common  guidelines  (Eurostat  2013),  the  N-excretion 
coefficient  is  defined  as  kg  N/head/yr  with  an  explanation  that  it  should  be  used  as  kg 
N/place/yr by the countries. The N-excretion coefficient is kg N/place/year according to 
the  country  source  document  (CORPEN  2003).  But  the  country  reported  it  as  kg 
N/head/year because of the definition in the common guidelines.  

34 

 
  
 
Recommendation & Conclusion: Further analysis to understand the reason for the 
higher  feed  intake  and  higher  implied  live  weight  in  CAPRI  is  needed.  FRnat  should 
report the N-excretion coefficient for swine per place instead of per head. 

  Hungary 

  ∆abs: 39 Gg N/yr (Table 3.1) 

  A significant share (96%) of the ∆act of total N animal excretion (40 Gg N/yr).  

National  method:  Statistics  on  numbers  of  national  animals  were  based  on  an 
annual  data  collection,  with  1st  December  as  the  reference  day.  HU  N-excretion 
coefficients  were  based  on  the  national  nitrate  regulation,  and  values  were  determined 
on  the  basis  of  domestic  and  international  literature  sources,  including  institutes  in 
Hungary.  

Assessment: Table 4.7 shows that the CAPRI N-excretion coefficients for swine were 

higher than those in the national and literature data (cf Annex 5). 

Table 4.7  

N excretions in the CAPRI and in the country datasets 

CAPRI 

Fattening 
pigs 

Sows  
(with 
piglets) 

HU 

Total 

Fattening 
pigs 

Sows (with 
piglets) 

Total 

24.8 

43.3 

20.5* 

9.24 

34.4 

9.6* 

60 

19 

80 

27 

14 

41 

195 

612 

154 

518 

N-excretion coefficient  
(kg N/place/yr) 

N-excretion by swine 
(Gg N/yr) 

N-excretion by animals 
(Gg N/yr) 

N input (Gg N/yr) 

Literature values:  

Fattening pigs 

Sows 

Other data (kg 
N/place/yr) 

· 12-15** (a) 
· 11 (b) 
· 11 (c) 

· 32-38** (a)  
· 26 (b) 
· 33 (c) 

Note: CAPRI data was extracted in May 2013. 
* Calculated for the country by using a weighted average of N-excretion coefficients for piglets, fattening 
pigs, sows and boars, and for the CAPRI model by using corrected animal numbers (see Section 2.2.5). 
**a: Ketelaars and van der Meer (1999) for EC Member States; b: OECD (1997) for OECD countries; c: 
Levington Agriculture (1997) for Europe  
***N-excretion coefficients’ range depending on N-conversion coefficient. 

35 

 
 
  
 
  
 
 
  
 
  
 
 
  
 
 
 
 
No detailed national data  was available to allow  for the comparison  of the animal N-

budgets for swine.  

The  number  of  swine  in  the  CAPRI  dataset  (3 894  (1  000  heads))  was  notably  lower 
than  that  of  the  country  data  4 275  (1 000  heads).  HUnat  used  the  end-of-year  survey 
data  for  the  number  of  pigs,  whereas  CAPRI  used  the  herd  size  for  pigs  obtained  from 
Eurostat  data  in  maximum  four  annual  surveys  (April,  May  or  June,  August,  and 
December).  HUnat data were corrected by using CAPRI methodology in Annex 2,  which 
eliminated the difference in animal numbers. 

HUnat now uses updated methodology and the weighted average  of mid-year survey 
data (50% for the year t) and end-of-year survey data (25% for the years t and t-1) for 
the number of swine for  each group.  However, this new methodology  slightly increases 
HUnat  swine  numbers,  and  consequently  ∆abs  between  HUnat  and  HUcapri  swine 
numbers.  The  country  also  updated  N-excretion  coefficients  based  on  the  country  GHG 
reporting, and ∆absperc in total N animal excretion between HUnat and HUcapri decreased 
from 57% to 9%.  

Recommendation  &  Conclusion:  (i)  ∆abs  between  HUnat  and  HUcapri  swine 
numbers  is  higher  than  before,  so  the  issue  is  ‘open’.  We  recommend  that  HUnat 
evaluates the CAPRI methodology for calculating herd sizes of fattening pigs (see Annex 
2).  (ii)  Even  though  the  difference  in  total  N–excretion  by  animals  has  been  reduced 
considerably, further comparison of the detailed animal budget is required to assess the 
differences in the N-excretion coefficients. 

Italy 

  ∆abs: 100 Gg N/yr (Table 3.1) 

  A significant share (75%) of the ∆act of total N-excretion by animals (134 Gg N/yr). 

The  share  of  N-excretion  values  for  swine  in  the  total  N  excretion  was  high  for  both 

the CAPRI and the country data, at 23% and 12% respectively.  

National method: The source of the N-excretion coefficient for swine was the ELBA 
Model - University of Bologna. The ELBA Model is an economic model developed for the 
analysis of the environmental sustainability of Italian agriculture. 

Assessment:  The  reason  for  the  difference  between  N-excretion  by  swine  in  the 
CAPRI  and  the  country  data  was  the  difference  in  N-excretion  coefficients.  The  N-
excretion  coefficient  for  fattening  pigs  for  ITcapri  is  higher  than  in  ITnat,  FRnat  and 
DEnat  (Annex  5),  and  higher  than  some  literature  values  (Table  4.8).  However,  the 
literature  data  of  the  animal  N-budgets  for  swine  for  IT,  including  the  N-excretion 
coefficient for fattening pigs, are similar to ITcapri (see Annex 9).  

36 

 
 
 
Table 4.8 

N excretions in the CAPRI and in the country datasets 

CAPRI 

IT 

Fattening 
pigs 

Sows 
(with 
piglets) 

Total 

Fattening 
pigs 

Sows 
(with 
piglets) 

Total 

23.2 

37.6 

20.1*** 

9.3 

46.7 

9.3*** 

156 

29 

184 

62 

23 

84 

814 

1 828 

680 

1 976 

Fattening pigs 

Sows 

12-15** (a) 
11 (b) 
11 (c) 
22.1 (d)  

32-38** (a)  
26 (b) 
33 (c) 
36.6 (d) 

N-excretion coefficient for 
swine (kg N/place/yr) 

N-excretion by swine  
(Gg N/yr) 

N-excretion by animals  
(Gg N/yr) 

N input (Gg N/yr) 

Literature values: 

Other Data* 

Note: CAPRI data was extracted in May 2013. 
*a: Ketelaars and van der Meer (1999) for EC Member States; b: OECD (1997) for OECD countries; c: 
Levington Agriculture (1997) for Europe; d: Oenema et al. (2014) for Italy 
**N-excretion coefficients’ range depends on the N-conversion coefficient. 
*** Calculated for the country by using a weighted average of N-excretion coefficients for piglets, fattening 
pigs, sows and boars, and for the CAPRI model by using corrected animal numbers (see Section 2.2.5). 

Recommendation  &  Conclusion:  Further  analysis  of  the  detailed  animal  budget  is 
required  to  assess  the  differences  in  the  N-excretion  coefficients  between  ITnat  and 
ITcapri. 

4.5  N EXCRETION BY OTHER CATTLE (IT) 

N excretion by other cattle was only identified for Italy: 

Italy 

  ∆abs: 52 Gg N/yr (Table 3.1) 

  A significant share (39%) of the ∆act of total N-excretion by animals (134 Gg N/yr). 

The  share  of  N-excretion values for  other  cattle in the total N  excretion amount was 

high in both ITcapri (28%) and ITnat (26%).  

National method: The N-excretion coefficient for male cattle older than 2 years and 
for  buffaloes  was  from  the  OECD.  The  N-excretion  coefficient  for  other  cattle  was  from 
the ELBA Model - University of Bologna.  

37 

  
  
  
  
  
 
 
 
 
 
Assessment:  The  N-excretion  coefficients  are  the  main  reason  for  the  difference 
between  the  N  excretion  from  other  cattle  of  CAPRI  and  the  country  data.  Table  4.9 
shows that the country N-excretion coefficient was lower than the data of CAPRI and the 
literature. 

Table 4.9 

N excretions in the CAPRI and in the country datasets 

N-excretion coefficient for other cattle 
(kg N/place/yr) 

48.5 

CAPRI 

IT* 

36.0 

Other Data (kg N/place/yr) 

46.9 (Asman et al 2011) for IT 
51 (IPCC 2006)* 
45.5 (small breed) - 61.4 (large breed) Ketelaars and van der 
Meer(1999) for EC Member States)** 

N-excretion by other cattle (Gg N/yr) 

229.1 

N-excretion by swine (Gg N/yr) 

184 

N-excretion by animals (Gg N/yr) 

814 

N input (Gg N/yr) 

1 828 

177.4 

84 

680 

1 976 

Note: CAPRI data was extracted in May 2013. 
* This ratio was calculated from default values for Western Europe in IPCC 2006 as N excretion [(kg N/1  000 
kg animal mass/day) * Animal mass (kg) / 1 000*365] 
** The weighted average values of N-excretion coefficients of suckling cattle and growing cattle calculated by 
using CAPRI animal numbers. 

Recommendation & Conclusion: Further comparison of the detailed animal budget 

is required. 

4.6  N EXCRETION BY SHEEP AND GOATS (FR) 

N excretion by sheep and goats was only identified as an issue for France: 

  France 

  ∆abs: 91 Gg N/yr (Table 3.1) 

  A  significant  share  (-43%)  of  the  difference  of  total  N  animal  excretion  (-210  Gg 

N/yr) 

National  method:  The  same  approach  as  for  swine  (see  Section  4.4)  was  used  for 

the estimation of N-excretion coefficients. 

Assessment:  According  to  Table  4.10,  the  N  excretion  coefficients  for  sheep  and 
goats were lower in the CAPRI model than in the country and the literature data. CAPRI 
N-excretion  coefficients  were  also  significantly  lower  than  the  NAT  values  for  Germany 

38 

 
 
 
 
 
and Hungary, and slightly lower for Italy (Annex 5).  

Table 4.10  N excretions in the CAPRI and in the country dataset 

N-excretion coefficient of sheep & goats 
(kg N/place/yr) 

8.1 

CAPRI 

FR 

16.5 

Other Data (kg N/place/yr) 

11.3 for sheep (OECD 1997) for OECD countries  
13.5 for goats (OECD 1997) for OECD countries 
20 for sheep and goats Levington Agriculture (1997) for Europe 
12 for sheep and goats for FR in GAINS database (Asman et al 2011) 
15 for sheep and 18 kg for goats (IPCC, 2006)* 
15.5 for sheep and goats (EEA 2009) (default value) 

N-excretion coefficient of sheep and 
goats for fattening activity (kg 
N/place/yr) 

N-excretion coefficient of sheep and 
goats for milk production activity (kg 
N/place(=head)/yr) 

6.9 

8.4 

Not reported 

Not reported 

Other Data (kg N/place/yr) 

12.3 for ewes (DEFRA 2007) for UK 
20.6 for female goats (DEFRA 2007) for UK 
22.8 for ewes (Ketelaars and van der Meer 1999)** 
20.6 for female goats ewes (Ketelaars and van der Meer 1999) 

N-excretion by sheep & goats (Gg N/yr) 

80 

N-excretion by animals (Gg N/yr) 

1 610 

N input (Gg N/yr) 

4 714 

171 

1 820 

4 857 

Note: CAPRI data was extracted in May 2013. 
* This ratio was calculated from default values for Western Europe in IPCC 2006 as “N excretion (kg N/1 000 
kg animal mass/day) * Animal mass (kg) / 1 000 *365”   
**The values are for EU Member States. 

No detailed data is available for the country animal N-budgets for sheep and goats. 

Recommendation & Conclusion: To understand the reason behind the different N-
excretion  values,  it  is  recommended  to  make  an  in-depth  assessment  of  the  animal  N-
budgets for sheep and goats in the FRnat and FRcapri.  

4.7  N REMOVAL BY GRAS (FR, HU, IT) 

N removal by GRAS was identified as an issue for three countries: 

39 

 
  
 
 
 
  France 

  Almost one third (31%) of total N removal by crop products of the country 

  ∆abs: 307 Gg N/yr (Table 3.1) 
  A  significant  share  (121%)  of  the  ∆act  of  total  estimates  in  N  exports  in  crops  and 

forage  
(-253 Gg N/yr) 

National method: To calculate pasture consumption, FR used a methodology based 
on animal requirements. Pasture consumption in FR was estimated by subtracting fodder 
crop production other than pasture from animal requirements. Animal requirements were 
estimated  by  multiplying  numbers  of  livestock  by  a  coefficient  defined  by  animal 
category.  

The N  contents in  the CAPRI  model are based on adjusted literature  values. FR uses 
country-specific  coefficients  from  COMIFER  2013  (National  expert  group  regarding 
fertilisation) who compiled data from the Institut de l'élevage (IDELE) and the Institut du 
végétal (ARVALIS).  

Assessment: Table 4.11 shows that the N content of GRAS in the CAPRI values was 
lower than in the country data. The N content of GRAS for FR is 2.25% according to the 
MITERRA  model,  which  was  the  weighted  average  of  the  N  content  for  intensively 
managed  grassland  (3%  dry  matter)  and  for  extensively  managed  grassland  (2% 
(d.m)).  Shares  of  GRAI  and  GRAE  in  GRAS  for  FR  were  assumed  to  be  25%  and  75%, 
respectively  (Velthof  et  al  2007).  The  share  of  GRAE  (extensive  permanent  grasslands, 
permanent  grasslands  with  traditional  orchards,  and  summer  grasslands)  in  total 
permanent  grassland  was  19%  according  to  French  TERUTI  2000  data,  and  23% 
according to French Annual Agricultural Statistics 1999 (Pointereau et al 2007). 

Using MITERRA N contents and the average shares of these two national studies of FR 
for  GRAE  and  GRAI  gave  the  N  content  of  GRAS  for  FR  at  2.8%.  This  percentage  is 
higher  than  the  current  CAPRI  and  MITERRA  values,  and  it  is  closer  to  FR  country-
specific value.  

40 

 
 
Table 4.11  N removal with GRAS in CAPRI and in the country dataset  

CAPRI 

FR 

Other data 

Area (kha) 

9 494 

9 581 

Dry Matter (DM) Yield (100 kg/ha) 

43.7 

38.4 

DM Crop Production (kt) 

41 447 

36 782 

DM N content (kg N/tonme) 

N removal by GRAS (Gg N/yr) 

N removal by crops and forage  
(Gg N/yr) 

Note: CAPRI data was extracted in May 2013. 

17.2 

714 

27.8 

1 021 

3 066 

3 319 

10 013      

(Eurostat) 

43.8  
(MITERRA) 

22.5  
(MITERRA) 

Recommendation  &  Conclusion:  CAPRI  should  check  the  GRAS  N  content  and 
possibly  update it  using  a  combination  of literature  (Velthof  et  al  2007,  e.g.  Pointereau 
et al 2007)and country estimations. The possibility of using results from the GRASSDATE 
project (Velthof et al 2014) in FRnat and FRcapri should also be evaluated. For estimates 
of  dry  matter  yields  of  grassland,  the  differentiation  of  grassland  according  to  the 
proposal  of  the  GRASSDATE  project  would  be  important  (permanent  grassland: 
grassland  out  of  production  but  maintained,  unimproved  grassland  (sole  use,  common 
land),  improved  grassland  (by  N-input  levels  <50,  50-100,  >100  kg  N/ha/yr,  sole  use 
and common land)). Thus, the issue is still open. 

  Hungary 

  ∆abs: 37 Gg N/yr (Table 3.1) 

  A  significant  share  (-36%)  of  the  ∆act  of  total  estimates  of  N  exports  in  crops  and 

forage (-102 Gg N/yr) 

National method: HU data for the area and the yield were compiled from farmers for 
the  national  annual  survey  on  agricultural  activities.  The  scope  of  data  collection  for 
agricultural  enterprises  was  full  scale.  Two groups  were  established  for  collected  data 
from  private  farms.  The  large  private  farms  were  covered  completely;  the  smaller 
private  farms  were  selected  by  using  a  sampling  procedure  in  line  with  the  regulation: 
543/2009/EC.  Approximately  8 000  agricultural  enterprises  and  30 000  private  farms 
reported the data to the Hungarian Central Statistical Office for each year.  

Assessment: Table 4.12 shows that HUcapri contained significantly higher values for 
the  GRAS  area  and  the  yield  than  HUnat.  CAPRI  data  were  close  to  the  permanent 
grassland area reported to Eurostat by the country. 

The  main  reason  for  the  difference  in  grassland  yield  is  that  the  proportion  of  GRAI 
was  assumed  as  25%  in  the  MITERRA  model,  which  is  the  source  of  the  CAPRI  GRAS 
yield, whereas the country assumed almost zero intensive grassland.  

41 

  
 
 
 
 
 
Table 4.12  N removal by GRAS (permanent grassland) in the HUcapri and HUnat 

CAPRI 

HU 

Other data 

Area (kha) 

949 

503 

1 058 (Eurostat) 

Dry Matter (DM) Yield (100 kg/ha) 

35.2 

14.9 

DM Crop Production (kt) 

3 339 

751 

39 (MITERRA) 
15 (Nagy 2006) for HU 
15-25 (Vinczeffy 1993) for HU 

DM N content (kg N/tonne) 

15.2 

18.5 

22.5 (MITERRA) 

N removal by GRAS (Gg N/yr) 

51 

14 

N removal by crops and forage (Gg N/yr)  396 

498 

Note: CAPRI data was extracted in May 2013. 

Recommendation & Conclusion: (i) The CAPRI yield value for permanent grassland 
in Hungary should be checked; (ii) the HUnat area for permanent grassland used and/or 
in  the  Eurostat  database  (crop  products  -  annual  data (apro_cpp_crop))  should  be 
checked.  The  grassland  area  in  the  Eurostat  database  for  Hungary  before  2010  also 
includes  unused  grassland  areas.  The  grassland  area  should  only  contain  the  utilsed 
agricultural area, according to Eurostat definitions. 

Italy 

  ∆abs: 190 Gg N/yr (Table 3.1) 

  A  significant  share  (69%)  of  the  ∆act  of  total  estimates  in  N  exports  in  crops  and 

forage (-276 Gg N/yr) 

National  method:  Italy  used  a  literature  value  (Schleef  and  Kleinhanb  1994)  for 
GRAS  yield.  GRAS  area  values  were  taken  from  ISTAT.  ITnat  used  OECD  estimates  for 
GRAS consumption, considering that 70% of the produce was consumed. The N content 
of GRAS was taken from the ELBA Model - University of Bologna.  

Assessment. Table 4.13 shows that the difference between CAPRI and national GRAS 

dry matter yield values was high; Eurostat and literature data were inconclusive. 

42 

 
 
 
 
 
 
 
 
 
Table 4.13  N removal by GRAS in the CAPRI and in the country datasets 

CAPRI* 

IT** 

Other data 

Area (kha) 

3 895 

4 350 

4 381 (Eurostat) 

Dry Matter (DM) Yield (100 kg/ha) 

26.6 

48.8 

35 (MITERRA) 
12.53 (Eurostat) 

DM Crop Production (kt) 

10 366 

21 238*** 

DM N content (kg N/tonne) 

17.6 

25.0 

22.6 (MITERRA) 

N removal by GRAS (Gg N/yr) 

182 

372 

N removal by crops and forage (Gg N/yr) 

1 094 

1 370 

Note: CAPRI data was extracted in May 2013. 
* Three-year average (2003, 2004, 2005) 
** Two-year average (2003, 2004) 
*** In GNB calculations, 70% of DM crop produce is consumed. 

Recommendation & Conclusion: We recommend that the values for GRAS yield of 

both Italy and the CAPRI model be checked; more information may be required. 

4.8  N REMOVAL BY OFAR (DE AND FR) 

The N removal by OFAR was identified as an issue for two countries: 

  Germany 

OFAR  is  defined  in  the  CAPRI  model  as  a  group  consisting  of  other  annual  green 
fodder, clover and mixtures, lucerne, and other perennial green fodder, usually legumes 
and temporary grassland. National crop data were grouped to match this group as  well 
as  possible.  Only  cereals  harvested  while  they  were  green  and  other  annual  green 
fodder  data  were  available  for  grouping  in  OFAR  in  the  dataset  reported  by  the  DEnat. 
However,  DEnat  included  clover  and  mixtures,  lucerne,  and  temporary  grassland  in 
“permanent meadows/grasses: consumption”. By re-grouping these crops, the difference 
between the values for N-removal by OFAR of the DEcapri and DEnat datasets fell below 
the defined threshold. 

Recommendation & Conclusion: The issue arose due to inconsistent data grouping, 

and has been resolved. The aggregation of these crops should be revised in DEnat. 

  France 

  ∆abs: -159 Gg N/yr (Table 3.1) 

  A  significant  share  (63%)  of  the  ∆act  of  total  estimates  in  N  exported  in  crops  and 

forage (-253 Gg N/yr). 

43 

  
 
 
 
 
 
For  comparison,  FRnat  data  for  other  annual  green  fodder,  leguminous  plants  and 
temporary  grasslands  were  aggregated  into  an  OFAR  group.  The  N  contents  in  the 
CAPRI  model  were  based  on  adjusted  literature  values,  while  FR  uses  country-specific 
data. 

Assessment:  Table  4.14  shows  that  the  N  contents  of  the  crops  in  the  OFAR  group 

for FR were high. 

Table 4.14  N removal by OFAR in the CAPRI and in the country datasets 

N content of other annual green fodder (kg 
N/tonne) 

Other Data (kg N/tonne) 

FR 

35.0 

CAPRI 

3.0 (ITnat) 
3.5 (HUnat) 
4.5 (DEnat) 
5.8 (MITERRA) 

N content of leguminous plants (kg N/tonne) 

39.0 

Other Data (kg N/tonne) 

27.0 (ITnat) 
27.0 (HUnat) 
26.0 (DEnat) 
31.5 (red clover*) 
http://www.feedipedia.org/node/1250 

N content of temporary grassland (kg N/tonne) 
kg N/ton 

27.8 

Other Data (kg N/tonne) 

18.0 (HUnat) 

N content of OFAR (kg N/tonne) 

17.0 

29.7 

Other Data (kg N/tonne) 

17.0 (ITcapri)  
15.0 (HUcapri)  
17.0 (DEcapri) 

N removal by OFAR (Gg N/yr) 

479 

N removal by crops and forage (Gg N/yr) 

3 066 

Note: CAPRI data was extracted in May 2013. 
*Clover has the highest crop share (85%) in leguminous products in the FR dataset. 

638 

3 319 

According  to  FRcapri,  OFAR  production  was  28 726  kt  (d.m.)  whereas  FRnat  puts 
OFAR production data  at 21 635 kt (d.m.). The country OFAR production data is 26 605 
kt (d.m.) in the Eurostat crop products database.  

Recommendation: We recommend that France checks the N contents of the crops in 
the  OFAR  group.  With  regard  to  the  difference  in  OFAR  dry  matter  production,  the 
country  should  ensure  that  the  data  is  consistent  with  the  Eurostat  crop  products 
database.  

Conclusion: Current FRnat N content data have been updated very recently and are 

44 

 
  
 
 
 
 
  
 
unlikely  to  be  updated  again  in  the  near  future.  The  difference  in  the  OFAR  dry  matter 
production  figures  of  the  CAPRI  and  the  country  datasets  can  be  reduced  in  the  short 
term by ensuring that the NAT data is consistent with the Eurostat database. 

4.9  N REMOVAL BY OTHER CEREALS EXCEPT FOR WHEAT AND BARLEY (DE 
AND FR) 

N removal by cereals other  than wheat and barley was identified as an issue for two 

countries: 

  Germany 

  ∆abs: 48 Gg N/yr (Table 3.1) 

  A  significant  share  (26%)  of  the  ∆act  of  total  estimates  in  N  exports  in  crops  and 

forage (184 Gg N/yr) 

National  method:  The  coefficients  for  other  cereals  used  in  DE  GNB  tables  were 
taken  from    the  DüV  (2007)  (Fertiliser  Ordinance  of  DE).  These  coefficients  were 
calculated as the mean of the coefficients of corn with different crude protein ratios in 
dry matter. 

Assessment:  ∆absperc  in  production  of  other  cereals  except  for  wheat  and  barley 
between the CAPRI and the country data was not very high. Therefore, the main reason 
for the high ∆absperc in this group was the high difference in  the N contents. Most of the 
∆abs  in  other  cereals  between  the  CAPRI  model  and  DE  was  accounted  for  by  triticale 
(65%), followed by grain maze (20%), oats (12%) and rye and meslin(3%).  

Table 4.15 shows that the OCER N content of FRcapri is significantly higher than that 

of the DEnat and literature data.  

Table 4.15  N removal by other cereals in the CAPRI and in the country datasets 

CAPRI 

DE 

Other data 

OCER* 

Triticale 

Triticale - N content  
(kg N/tonne) 

N content (kg N/tonne) 

28.3 

17.2 

N removal by other cereals (Gg 
N/yr) 

79 

49 

N removal by crops and forage 
(Gg N/yr) 

2 239 

2 055 

15 (MITERRA) 
18.6 (Lassaletta et al 2014) 
18.7 (www.feedipedia.org/node/6476) 
14.5-15.5 (Akgün et al 2011) for Turkey 
(TR) 

Note: CAPRI data was extracted in May 2013. 
*Cereals other than wheat, maize, barley, rice, rye, maslin and oats 

45 

 
 
 
  
 
Recommendation  &  Conclusion:  The  possibility  of  using  country-specific  N 
contents  for  other  cereals  (OCER  aggregate)  in  CAPRI  should  be  evaluated.  The  same 
applies for the N contents of other crop aggregates in the CAPRI database. Note that the 
DEnat N content of OCER, at 17.2 kg N/tonne, is close to the average N contents of the 
cereals  other  than  OCER  in  CAPRI,  at  16.7  kg  N/tonne,  and  higher  than  the    OCER  N-
content values in the literature and other countries (Annex 9). 

  France 

  High ∆abs between NAT and CAPRI data (Table 3.1) 

  ∆absperc: 94% 

The  production  of  other  cereals  except  for  wheat  and  barley  was  updated  by  the 
country during bilateral discussions from 10 012 kt to 16 650 kt. The N removal by other 
cereals was then calculated at 259 Gg N/yr, while the previous value was 151 Gg N/yr. 
In  this  situation,  the  ∆perc  in  N  contents  of  other  cereals  of  CAPRI  and  FR  became 
significantly smaller (from 94% to 20%). 

Conclusion: The issue was resolved. 

4.10 N REMOVAL BY OLIVES AND OTHER FRUIT (IT) 

N removal by olives and fruit was identified as an issue for Italy: 

Italy 

  ∆abs: 234 Gg N/yr (Table 3.1) 

  A  significant  share  (85%)  of  the  ∆act  of  total  estimates  in  N  exported  in  crops  and 

forage (-276 Gg N/yr) 

National  method:  IT  used  the  result  of  the  ELBA  Model  -  University  of  Bologna  for 

the estimates of the N content of olives and fruit.  

Assessment: For these crops, the ∆absperc in crop production between the CAPRI and 
the country data was not very high. Therefore, the main reason for the high ∆absperc was 
the  high  differences  in  N  contents.  The  national  N  content  of  olives  and  other  fruit  was 
much  higher  than  CAPRI    data  and  literature  data  from  other  countries  (Table  4.16  & 
Annex 10).  

46 

 
 
 
 
Table 4.16  N removal by olives and fruit in the CAPRI and in the country datasets 

N content of olives (kg N/tonne) 

4.8 

CAPRI* 

IT** 

43.3 

Other data for N content of olives (kg 
N/tonne) 

8.8*** (Eurostat) 
3.8 (Lassaletta et al 2014)              
9.7 (Therios 2009) 

N content of fruit (kg N/tonne) 

2.1 for fruit 
1.1 for apples, pears and 
peaches 
2.0 for other fruit 

6.6 for fruit 
7.7 for all fruit except for citrus 
fruit 

Other data for N content of fruit (kg 
N/tonne) 

0.8 for apples, pears and peaches (Lassaletta et al 2014) 

N removal by olives (Gg N/yr) 

19 

N removal by fruits (Gg N/yr) 

32 

N removal by crops and forage (Gg 
N/yr) 

1 094 

175 

110 

1 370 

Note: CAPRI data was extracted in May 2013. 
* Three-year average (2003, 2004, 2005) 
** Two-year average (2003, 2004) 
*** The average of N contents of olives for Spain, Malta and Portugal for the year 2008 

Recommendation & Conclusion: We recommend that Italy should justify or revise 
the  high  N  content  values  used  for  olives  and  other  fruit.  We  also  recommend  that  the 
CAPRI  values  for  olives  be  checked,  and  that  the  possibility  of  using  country-specific  N 
content  values  for  aggregates  in  the  CAPRI  model  be  evaluated.  The  issue  can  be 
considered being unresolved. 

4.11 N REMOVAL BY MAIZE AND WHEAT (HU) 

N removal by maize and wheat was identified as an issue for Hungary: 

  Hungary 

  ∆abs: 96 Gg N/yr (Table 3.1) 

  A  significant  share  (94%)  of  the  ∆act  of  total  estimates  for  N  exported  in  crops  and 

forage (-102 Gg N/yr) 

National  method:  HUnat  used  the  N  contents  of  wheat  and  maize  in  the  Nitrate 

Regulation, which includes the N content of crop residues. 

Assessment:  Table  4.17  shows  that  the  country  data  was  higher  than  the  data  of 

other countries, the CAPRI model and the literature. 

47 

  
 
 
 
Table 4.17  N removal by wheat and maize in the CAPRI and in the country dataset 

CAPRI 

N content of wheat (kg N/tonne) 

20.0 

HU 

27.0 

Other data for N content of wheat (kg 
N/tonne) 

19.9 (ITnat) 
21.1 (DEnat) 
19.2 (FRnat) 
20.0-21.8 (The range of N content in the CAPRI model) 
19.5 (Lassaletta et al 2014) 
20 (MITERRA) 
20.2 (http://www.feedipedia.org/node/223) 

N content of maize (kg N/tonne) 

16.7 

25.0 

Other data for N content of maize (kg 
N/tonne) 

15.0 (ITnat) 
14.5 (DEnat) 
15.0 (FRnat) 
16.7 (CAPRI for 4 countries) 
15.2 (Lassaletta et al 2014) 
13.9 (MITERRA) 
14.1 (http://www.feedipedia.org/node/713) 

N removal by wheat (Gg N/yr) 

93 

N removal by maize (Gg N/yr) 

120 

N removal by crops and forage (Gg 
N/yr) 

396 

Note: CAPRI data was extracted in May 2013. 

126 

183 

498 

Conclusion:  We  recommended  that  Hungary  should  report  the  N  content  of  crops 
and crop  residues separately.  As this recommendation was implemented, the issue was 
resolved. 

48 

  
 
 
 
 
 
 
 
 
4.12 COMPARISON AT THE REGIONAL LEVEL 

A comparison of data at the regional level was only possible for FR and IT.  

Scatter plots  were  used  to  determine  whether  or not   there  was  a  fit  between  the 
regional  N  surplus    from  CAPRI  and  from  NAT  data.  There  was  a  poor  fit  between 
FRcapri-old  and  FRnat-old  (in  the  beginning  of  RegNiBal),  and  FRcapri-new  and  FRnat-
new  (at  the  end  of  RegNiBal)  (Figure  3a  and  b)  (the  values  from  the  chi-squared  (χ2) 
distribution (<0.05)  indicate  a  significant  difference  between  FRnat  and  FRcapri). 
However, a slight increase in the p value of χ2 of FRcapri-new and FRnat-new compared 
to  old  values  indicates  that  a  slight  improvement  on  goodness  of  fit  between  NAT  and 
CAPRI  was achieved  at  the regional level  by the end  of the project. The same situation 
was observed for IT (Figure 4a and b).  

Some changes occurred also to the regional N surplus between in the NAT and CAPRI 

data (Figure 3c and d, Figure 4c). 

When we compared all regional data of FRnat-new with FRcapri-new we observed the 

following: 

-  Generally higher CAPRI N surplus 
-  High regional deviations in mineral N-fertiliser usage 
-  Generally lower CAPRI N-excretion levels 
- 
Low regional deviations in N-excretion levels 
-  Generally higher CAPRI atmospheric N deposition 
-  High regional deviations in biological N fixation 
-  High  regional  deviations  in  N  removed  by  crops  and  fodder,  and  generally  lower 

CAPRI N removed by crops and fodder 

Detailed  results  and  assessments  of  the  differences  are  summarised  in  the 

supplementary file (REGNIBAL-SUPPL.FILE.xls). 

49 

 
 
 
 
(a) 

Nitrogen Surplus (kg N ha-1 yr-1) 

(b) 

Nitrogen Surplus (kg N ha-1 yr-1) 

l

d
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100

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150

(c) 

Nitrogen Surplus (kg N ha-1 yr-1) 

(d) 

Nitrogen Surplus (kg N ha-1 yr-1) 

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150

-50

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FRnat-old 

100

150

* FRcapri-old/FRnat-old: the data at the beginning of the RegNiBal project; FRcapri-new/FRnat-new: the 
data at the end of the RegNiBal project 

Figure 3 

Scatter plots for the regional nitrogen surplus data of FRcapri and FRnat 

50 

 
 
 
 
 
 
 
 
(a) 

Nitrogen Surplus (kg N ha-1 yr-1) 

(b) 

Nitrogen Surplus (kg N ha-1 yr-1) 

l

d
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ITnat 

ITnat 

(c) 

CAPRI-OLD VS. CAPRI-NEW 

w
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ITcapri-old 

150

200

* ITcapri-old/ITnat: the data at the beginning of the RegNiBal project; ITcapri-new: the data at the end of 
the RegNiBal project; the assessment for IT is for the year of 2006. 

Figure 4 

Scatter plots for the regional nitrogen surplus data of ITcapri and ITnat  

51 

 
 
 
 
 
 
 
 
 
 
5  CONCLUSIONS 

A  total  of  31  “issues”  were  identified  that  were  related  to  major  discrepancies 
between the methods and warranted further assessment. At the end of the project, 12 of 
the  identified  issues  were  solved,  one  was  partially  solved  and  18  could  not  be  solved, 
but  some  progress  was  achieved  and  concrete  recommendations  were  made  for  almost 
all of them. The results and achievements of RegNiBal are summarised in Annex 12. 

At  the  start  of  the  RegNiBal  project  CAPRI  data  was  generally  judged  to  be  more 
reliable  than  NAT  data.  The  situation  has  changed  with  the  improvements  described 
above;  at  present,  further  analysis  is  needed  to  see  whether  CAPRI  or  NAT  data  is 
‘better’ with regard to the remaining unresolved issues. 

Overall,  N  excretion  by  swine  and  N  removal  by  grass  are  considered  the  most 
important  unresolved  issues  because  of  their  considerable  impact  on  N-input  and  N-
output. The animal budget analysis for swine of DE and FR shows that CAPRI estimates 
higher feed intake than NAT. Countries are not always sufficiently accurate in estimating 
and/or  using  the  average  number  of  animals  and  N-excretion  coefficients  in  N  manure 
excretion  estimations.  For  the  estimates  of  dry  matter  yields  of  grassland,  the 
differentiation  of  permanent  grassland  according  to  the  proposal  of  the  GRASSDATE 
project  (Velthof  et  al  2014)  would  likely  help  (grassland  out  of  production  but 
maintained,  unimproved  grassland  (including  both  sole  use  and    common  land)  and 
improved  grassland  (by  N-input  levels  <50,  50-100,  >100  kg  N/ha/yr,  sole  use  and 
common land).  

The CAPRI model is very strong in several parts of GNB calculations, and the RegNiBal 
project  enabled  us  to  identify  several  possible  improvements  in  national  data  and 
methods. The use of the animal budget to estimate N  excretion is a major asset in the 
CAPRI  methodology,  but  runs  the  risk  of  outliers  if  the  use  of  feed  in  the  statistical 
sources  is  overestimated.  There  is  large  uncertainty  in  grass  yield  and  other  (non 
marketable) fodder yield and their N content.  This affects the accuracy of national data 
as  well.  The  other  major  areas  of  difficulties  for  the  CAPRI  model  are  the  following:  (i) 
Seed  and  planting  materials  should  be  explicit  in  the  CAPRI  GNB;  (ii)  N  from  organic 
fertilisers  (other  than  manure)  and  manure  withdrawal,  stocks,  and  import  estimations 
are not considered in the CAPRI model. 

The  CAPRI  model  can  be  used  to  calculate  both  land  N  budgets  (GNB)  and  farm  N 
budgets. The possibility of comparing the GNB with the farm N-budget helps to constrain 
the  N-surplus  results.  For  the  farm  N-budget,  feed  and  fodder  produced  in  the  country 
(or  region)  and  manure  excreted  and  applied  within  the  country  (or  region)  are 
considered  as  ‘internal  flows’  and  thus  do  not  need  to  be  estimated  to  quantify  the  N-
surplus; data on ‘imported’ feed and ‘exported’ animal products are needed instead (for 
details  on  the  comparison  of  the  two  approaches,  see  Leip  et  al  2011b).  In  the  CAPRI 
model, data on animal products and imported feeds are available from statistical sources 
and  are  thus  more  reliable  than  the  data  on  the  N  intake  of  fodder  and  manure 
excretion, which would not be required.  

Regional-level  data  were  available  only  for  FR  and  IT.  While  the  focus  of  the  project 
was  make  a  comparison  at  the  national  scale,  the  RegNiBal  project  showed  that  some 
improvements could also be achieved at the regional level. 

52 

Generally,  the  RegNiBal  project  showed  that  the  CAPRI  model  could  be  adequate  to 
provide  national  (and  later  regional  and  spatially  explicit)  GNBs.  However,  for  the  four 
countries  assessed,  additional  work  needs  to  be  carried  out  to  understand  residual 
disagreements in the data. 

One  observation  that  emerged  from  the  RegNiBal  project  was  that  some  ‘mistakes’ 
are  made  due  to  different  interpretations  of  the  Eurostat/OECD  handbook  on  Nutrient 
Budgets (Eurostat, 2013) or due to inaccurate use of data. Differences in statistical data 
used  for  the  GNBs  and  data  reported  to  Eurostat  in  other  statistical  tables  also  lead  to 
inconsistencies.  Many  data  required  for  the  regional  GNBs  are  already  available  in 
Eurostat  tables  (e.g.  crop  production,  livestock  number,  fertiliser  use12,  land  use).  In 
order to solve the abovementioned problems, and to produce regional GNB estimations, 
Eurostat  should  evaluate  the  possibility  to  use  those  tables  in  combination  with  a 
(reduced) reporting burden on the country (for example the N content of crops, etc., see 
Annex  13).  NUTS2&3  GNBs  could  be  calculated  using  an  automatic  algorithm/tool. 
Automatic  processing  would  also  reduce  any  errors  that  arise  from  differences  in 
interpretation.  This  would  improve  comparability.  Annex  13  lists  data  and  coefficients 
needed for GNBs, and indicates which Eurostat table would provide the relevant value, or 
if this needs to be delivered by the countries. 

Three  different  options  for  the  next  steps  are  proposed.  Any  combination  of  these 
options  or  ‘hybrid’  solutions  are  possible.  For  example,  a  Eurostat  algorithm  using 
existing data tables could be implemented, both informed by data provided by countries 
and complementarily from the CAPRI model when needed. 

1.  Regional  GNBs  cannot  immediately  be  generated  that  are  fully  consistent  with 
national  GNBs.  However,  the  superiority  of  national  over  CAPRI  estimates  is  not 
always obvious, so regional and spatially explicit CAPRI data can be regarded as a 
suitable  dataset  for  policy  advice.  Such  data  will  be  useful  proxy  data  that  are 
independent of the national official GNBs.  

2.  Convergence of CAPRI with national official GNBs would help improve both CAPRI 
and  national  estimates,  but  this  option  requires  further  investment.  Such 
investment  would,  however,  have  added  value  also  for  other  areas,  such  as 
improved  CAPRI  scenarios  for  climate  change  mitigation  assessments;  use  of 
CAPRI  data  as 
inventories; 
harmonisation  of  national  methodologies    developed  in  the  process;  improved 
consistency between various datasets. 

independent  estimates  of  GHG  emission 

3.  Another route could be the abovementioned ‘simplified process’ which makes use 
of existing information as much as possible and generates national (and regional) 
GNBs that are fully consistent with the Eurostat/OECD common guidelines (which 
are  also    suitable  for  producing  regional  GNBs)  and  using  a  (new)  automatic 
algorithm/tool  that is running at Eurostat. Such a system would probably be the 
most  cost-efficient  in  the  short  term,  and  would  provide  good  estimates 
efficiently. 

12  Use  of  inorganic  fertiliser  data  is  available  at  the  regional  level  for  some  countries  in  the  Eurostat  ‘use  of 

inorganic fertilisers’ database (aei_fm_usefert). 

53 

 
                                           
 
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60 

  
ANNEXES

61 

Annex 1.  

N contents of all crops in CAPRI*  

N content  (kg N/tonne) 

FR 

DE 

HU 

IT 

N content  (kg N/tonne) 

FR 

SWHE 

Soft wheat 

20.0 

20.0 

20.0 

20.0 

OFAR 

Other fodder from arable land 

6.0 

DE 

6.1 

HU 

5.3 

IT 

6.1 

DWHE 

Durum wheat 

23.0 

23.0 

23.0 

23.0 

PARI 

Paddy rice 

22.0 

 NA 

22.0 

22.0 

RYEM 

Rye and meslin 

16.7 

16.7 

16.7 

16.7 

OLIV 

Olive oil 

4.5 

 NA 

 NA 

4.5 

BARL 

Barley 

16.7 

16.7 

16.7 

16.7 

PULS 

Pulses 

41.0 

41.0 

41.0 

41.0 

OATS 

Oats 

18.3 

18.3 

18.3 

18.3 

POTA 

Potatoes 

MAIZ 

Grain maize 

16.7 

16.7 

16.7 

16.7 

SUGB 

Sugar beet 

OCER 

Other cereals 

28.3 

28.3 

28.3 

28.3 

TEXT 

Flax and hemp 

3.5 

1.8 

3.0 

3.5 

1.8 

 NA 

3.5 

1.8 

3.0 

3.5 

1.8 

3.0 

RAPE 

Rape seed 

33.0 

33.0 

33.0 

33.0 

TOBA 

Tobacco 

30.0 

30.0 

30.0 

30.0 

SUNF 

Sunflower seed 

28.0 

28.0 

28.0 

28.0 

TOMA 

Tomatoes 

SOYA 

Soya seed 

58.0 

 NA 

58.0 

58.0 

OVEG 

Other vegetables 

OOIL 

Other oil 

30.0 

30.0 

30.0 

30.0 

APPL 

Apples  pears and peaches 

OIND 

Other industrial crops 

15.5 

15.5 

15.5 

15.5 

OFRU 

Other fruit 

NURS 

Nurseries 

15.5 

15.5 

15.5 

15.5 

CITR 

Citrus fruit 

FLOW 

Flowers 

65.0 

65.0 

65.0 

65.0 

TAGR 

Table grapes 

OCRO 

Other crops 

15.5 

15.5 

15.5 

15.5 

TABO 

Table olives 

3.3 

1.8 

3.3 

1.8 

3.3 

1.8 

3.3 

1.8 

TWIN 

Table wine 

GRAS 

Grass 

MAIF 

Fodder maize 

ROOF 

Fodder root crops 

NA: Not applicable 
* Extracted in May 2013

62 

2.0 

2.0 

1.1 

2.0 

2.0 

1.9 

22.5 

2.9 

3.8 

2.0 

2.0 

1.1 

2.0 

 NA 

 NA 

 NA 

2.9 

3.9 

2.0 

2.0 

1.1 

2.0 

 NA 

1.9 

 NA 

2.9 

3.4 

2.0 

2.0 

1.1 

2.0 

2.0 

1.9 

22.5 

2.9 

3.9 

Annex 2.  

CAPRI  calculation  method  for  the  herd  size  of  animals  in 

places per year 

where 

Hpigf 

Spigs 

isows 

Hsows 

ha 

da 

where 

Hsowsy 

Nsows 

∆tNa 

Herd size of animals in places per year [1000 plpigf/yr] 

Number of animals slaughtered during the year [1000 hda/yr]; Spigs is obtained from 
Eurostat database ZPA1 

Input coefficient (replacement rate) of young sows/sow [hd sowsy/hdsows] 

Herd size of sows in places per year [1 000 plsows/yr] 

Number of days a herd location is defined for animal a [days/pla]; a{pigf,sows} 
h=365 days/pla @ a{pigf,sows} 

Production days of a living animal a [days/hdpigf]; a{pigf,sows,sowsy} 
dsowsy=240 days/hdsowsy  

The herd size of non-mated sows is obtained from Eurostat in maximum four annual 
surveys (April, May or June, August, and December). Hsowsy is the average number 
obtained from these surveys. 

Number of sows alive during the year [1 000 hda/yr] 

Change in animal stock from year t to year t+1  

63 

pigfpigfsowspigfdhiHsowspigsHSsowssowstsowsyNNΔHsowsysowsdhisowsy 
 
 
 
 
 
 
 
ANNEX 3.  

Regionalisation of the coefficients used in NAT & CAPRI 

Inputs & 
Outputs in N 
Balance 

1) Mineral 
fertiliser 

Data needed by 
NUTS 2 level 

The amount of 
mineral fertiliser 

Number of animals 

2) Manure 
production 

N-excretion 
coefficients 

ITnat 

FRnat 

CAPRI 

Different 
coefficients are 
used at NUTS2 
level except for 
pigs and 
heifers. 

The same 
coefficients are 
used at NUTS2 
level. 

Model output; 
varies at NUTS2 
level. 

3) Manure trade, 
withdrawal, and 
treatment 

4) Other organic 
fertiliser  

The amount of N 
from manure trade, 
withdrawal, and 
treatment 

The amount of 
organic fertilisers  

N content of organic 
fertiliser  

Not estimated 

The same 
coefficients are 
used at NUTS2 
level. 

Not estimated 

The area of 
leguminous crops  

Inputs 

The fixation 
coefficient of 
leguminous crops 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

5) Biological N 
fixation 

The area of 
temporary and 
permanent 
grassland 

The fixation 
coefficient of 
grass/legume 
mixtures 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

Utilised agricultural 
area (UAA) 

6) Atmospheric N 
deposition  

Regional average 
deposition rate 

The same 
coefficients are 
used at NUTS2 
level. 

Not the same 
coefficients are 
used at NUTS2 
level. 

7) Seed and 
planting materials 

Cropped areas of 
crops  

The shares of 
fixed N in N 
requirement in 
aboveground 
biomass is used 
(same for all 
regions). 

The shares of 
fixed N in N 
requirement in 
above-ground 
biomass is used 
(same for all 
regions). 

Calculated from 
gridded 
atmospheric 
deposition data; 
varies at NUTS2 
level. 

64 

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Inputs & 
Outputs in N 
Balance 

Data needed by 
NUTS 2 level 

ITnat 

FRnat 

CAPRI 

N-conversion 
coefficient of seed 
of crops  

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at 
national and 
NUTS2 level. 

Not estimated 

8) Total inputs = sum (1,2,3,4,5,6,7) 

The amount of crop 
production  

N content of crops  

The amount of crop 
production of fodder  

9) Crop 
production 

10) Fodder 
production 

Outputs 

N content of fodder  

11) Crop residues 
removed/burnt 

Crop residues 
removed/burnt of 
crops  

N content of crop 
residues 
removed/burnt 

12) Total outputs = sum(9,10,11) 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

Not estimated 

The same 
coefficients are 
used at NUTS2 
level. 

The same 
coefficients are 
used at NUTS2 
level. 

NS 

NS = (8) - (12) 

Note: The assessment for IT is for the year 2006; for the years 2003, 2004 and 2005 for FR and the CAPRI 
model.

65 

  
  
  
  
  
  
  
  
  
  
Annex 4.  

Nitrogen balance legends in the CAPRI MODEL 

EXPPRD 

Nitrogen exported in harvested material and crop residues or animal products 

ATMOSD 

Atmospheric deposition 

CRESID 

Crop residues (input) 

BIOFIX 

Biological fixation 

MINSAT 

Mineralisation from soil organic matter 

MINFER 

Mineral fertiliser applied, including some parts lost in runoff or emissions 

GASMIN 

Losses in gaseous emissions NH3 and N2O and NOX from mineral fertiliser 

RUNMIN 

Losses in runoff from mineral fertiliser 

SURSOI 

Surplus to soil 

SURTOT 

Total surplus is nutrient input net of exports in products 

LEACHI 

Leaching below rooting zone to groundwater or surface waters 

DENITR 

Denitrification below rooting zone 

ACCUMU 

Accumulation of surplus in soil organic matter 

EXCRET 

Nitrogen in excretion of animals 

GASMAN 

N in gaseous losses during manure management as NH3 and N2O and N2 and NOX 

NH3MAN 

N in gaseous losses during manure management as NH3 

NH3GRA 

Ammonia losses from manure on grazing land 

NH3HOU 

Ammonia losses from manure in housing 

NH3STO 

Ammonia losses from manure in storage systems 

NH3APP 

Ammonia losses from manure during application 

NH3MIN 

Ammonia losses from mineral fertiliser 

NH3TOT 

Ammonia losses total 

GASTOT 

Gaseous losses of N total 

RUNTOT 

Runoff losses total 

RUNMAN 

N in runoff from manure management 

66 

Annex 5 

N-excretion coefficients of selected animal groups in the CAPRI and i country datasets* 

N excretion coefficient (kg 
N/place/yr) 

DE 

FR 

HU** 

IT** 

CAPRI 

The country 

CAPRI 

The country 

CAPRI 

The country 

CAPRI 

The country 

Fattening pigs 

18.7 

11.3 

12.7 

NR 

24.8 

9.2 

23.2 

9.3 

Sows (with piglets) 

33.8 

34.5 

36.4 

NR 

43.3 

34.4 

37.6 

46.7 

Swine 

15.3 

10.6 

11.6 

7.2 

20.5 

9.6 

20.1 

9.3 

Sheep & goats 

6.1 

18.2 

8.1 

16.5 

10.3 

15.7 

9.4 

12.2 

Other cattle 

43.9 

25.2 

52.9 

59.3 

57.1 

37.3 

48.5 

36.0 

* Extracted from CAPRI in May 2013; NAT data were reported by the countries in the beginning of the RegNiBal project. 
** The countries reported N-excretion coefficients as kg N/head/yr in GNB tables. However, they were taken as kg N/place/yr here since the countries used the number 
of animals from official statistics in the calculations. 
NR: Not reported 
Note: The country N-excretion coefficients were calculated as the weighted average of the related animal groups. 

67 

Annex 6 

Comparison of animal N-budgets for fattening pigs in the CAPRI and DE-nat data 

Animal Budget Components 

N contents of pig diet - breeding sow (28 kg – 115 kg) (g/kg feed)1 

N contents of pig diet - growing–finishing pig (28 – 117 kg) (g/kg feed) 

N contents of pig diet (g/kg feeds)2 

The amount of feed intake - breeding sow (28 kg – 115 kg) (kg/head) 

The amount of feed intake - growing–finishing pig (28 – 117 kg) (kg/head) 

The amount of feed intake (kg/head)2 

N intake - breeding sow (28 kg – 115 kg) (kgN/place/year) 

N intake - growing–finishing pig (28 – 117 kg) (kgN/place/year) 

N intake (kgN/place/year) 

N content (g/kg LW) 

Implied live weight (kg LW/place/year) 

Pork meat (kg carcass weight / head)  

Slaughtered animals (live weight in 1 000 t) 

N retention - breeding sow (28 kg – 115 kg) (kgN/place/year) 

N retention - growing–finishing pig (28 – 117 kg) (kgN/place/year) 

68 

PIGF 

DE (DLG 2005)5 

CAPRI 

% Difference 

23.2 

27.3 

25.3 

266.0 

264.0 

264.8 

14.5 

17.0 

15.7 

25.6 

207.9 

NR 

NR 

4.6 

5.8 

NA 

NA 

21.0 

NA 

NA 

484.0 

NA 

NA 

24.6 

25.1 

255.0 

97.6 

5155.4 

NA 

NA 

NA 

NA 

17 

NA 

NA 

-83 

NA 

NA 

-56 

2 

-23 

NA 

NA 

NA 

NA 

Animal Budget Components 

N retention (kgN/place/year)2 

N-excretion coefficient - breeding sow (28 kg – 115 kg) (kgN/place/year) 

N-excretion coefficient - growing–finishing pig (28 – 117 kg) (kgN/place/year) 

N excretion coefficient (kgN/place/year)2 

Herd size of animals (HERD) (1 000 places)3  

Production days for a living animal (DAYS)4 

PIGF 

DE (DLG 2005)5 

CAPRI 

% Difference 

5.3 

10.2 

12.0 

10.4 

6.4 

NA 

NA 

18.2 

16 999.6 

16 998.6 

155.3 

150.7 

-20 

NA 

NA 

-75 

0 

3 

-3 

-75 

Number of animals (LEVL) (1 000 heads)  

39 949.0 

41 184.2 

N excretion (Gg/year) 

176.7 

309.4 

1) The value for DE was calculated as N intake (kg N/place/year) / feed intake (kg/head) / fattening period (=2.35) 
2) The values for DE were calculated as the weighted average of breeding sows (28 kg – 115 kg) and growing–finishing pigs (28 – 117 kg). 
3) DE value is the average number of animals in the Eurostat database (1 000 heads)  
4) The value was calculated by using fattening period given in DLG (2005). (=365 / fattening period (=2.35)) 
5) DE uses the weighted average of N-excretion coefficients of fattening pigs of the standard and N-low feeding daily gain with 700 g/d and 800 g/d (Bach et al., 2011). 
NR: Not reported, NA: Not applicable.  
Note: CAPRI data was extracted in May 2014. CAPRI values might be different from the values in the main text because of the different extraction dates. 

69 

 
 
 
 
 
 
ANNEX 7 

Comparison of animal N-budgets for swine in the CAPRI and FR-nat data 

Animal Budget Components 

N contents of pig diet - weaned piglets (8-30 
kg) (g/kg feeds)1 

N contents of pig diet - growing-finishing pig 
(30-112 kg) (g/kg feeds)1 

PIGF 

SOWS 

TOTAL 

FR 
(CORPEN 
2003)8 

CAPRI 

% Diff 

FR 
(CORPEN 
2003)8 

CAPRI 

% Diff 

FR 
(CORPEN 
2003)8 

CAPRI 

% Diff 

30.8 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

28.2 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

N contents of pig diet (g/kg feeds)1 

28.5 

22.9 

20 

26.4 

20.3 

23 

26.6 

20.7 

22 

The amount of feed intake - weaned piglets 
(8-30 kg) (kg/head) 

The amount of feed intake - growing-finishing 
pig (30-112 kg) (kg/head) 

38.0 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

235.0 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

The amount of feed intake (kg/head)2 

150.3 

399.3 

-166 

1200 

2342 

-95 

199.6 

496.1 

-149 

N intake - weaned piglets (8-30 kg) 
(kgN/place/year)3 

N intake - growing-finishing pig (30-112 kg) 
(kgN/place/year)3 

3.1 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

17.7 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

N intake (kgN/place/year)2 

11.4 

24.4 

-113 

31.5 

47.6 

-51 

13.8 

27.2 

-98 

N content (g/kg LW) 

18.5 

25.1 

-36 

18.5 

25.1 

-36 

18.5 

25.1 

-36 

Implied live weight (kg LW/place/year) 

112.0 

253.2 

-126 

373.0 

445.8 

-20 

224.0 

276.9 

-24 

Pork meat (kg carcass weight / head)6  

86.6 

89.6 

-3 

45.8 

50.5 

-10 

NA  

NA  

NA  

Slaughtered animals (live weight in 1000 t)7 

3 010 

2 961 

2 

78.4 

87.6 

-12 

3 089 

3 048 

1 

70 

N retention - weaned piglets (8-30 kg) 
(kgN/place/year)3 

N retention - growing-finishing pig (30-112 
kg) (kgN/place/year)3 

N retention (kgN/place/year)2 

N retention - weaned piglets (8-30 kg) 
(kgN/place/year) 

N retention - growing-finishing pig (30-112 
kg) (kgN/place/year) 

1.5 

5.5 

3.8 

1.7 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

6.4 

-68 

6.9 

11.2 

-62 

4.1 

6.9 

-68 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

12.2 

NA  

NA  

NA  

NA  

NA  

NA  

NA  

NA  

N excretion coefficient (kgN/place/year)9 

7.7 

12.4 

-62 

24.6 

37.5 

-53 

9.6 

15.5 

-61 

Herd size of animals (HERD) (1 000 places)4  

10 150 

9 656 

Production days for a living animal (DAYS)5 

136.7 

136.7 

Number of animals (LEVL) (1 000 heads)  

27 101 

25 783 

5 

0 

5 

1 336 

1 352 

-1 

11 486 

11 008 

4 

365.0 

365.0 

0 

NA  

NA  

NA  

1 336 

1 352 

-1 

28 437 

27 135 

5 

N excretion (Gg/year) 

77.7 

119.6 

-54 

32.9 

50.7 

-54 

110.5 

170.4 

-54 

1) The value for FR was calculated as N intake (kg N/place/year) / feed intake (kg/head) / fattening period (=365/production day of CAPRI) 
2) The values for FR were calculated as the weighted average of weaned piglets (8-30 kg) and growing-finishing pigs (30-112 kg). 
3) CAPRI production days were used to convert country N retention from head to place. 
4) FR value is the average number of animals in the Eurostat database (1 000 heads).  
5) CAPRI production days data were used for FR value. 
6) Pork meat data of the country for SOWS  was calculated by dividing the number of slaughtered animals by the number of sows. 
7) Slaughtered animals of PIGF data for the country was calculated by multiplying number of animals and pork meat. 
8) Standard feeding data in CORPEN 2003 is used for the country. 
9) This value is expected to be equal to N intake – N retention. But there is an error in the CAPRI model. 
CORPEN: Comité d’Orientation pour der Pratiques agricoles respectueses de L’Environnement   
Note: CAPRI data was extracted in May 2014. CAPRI values might be different from the values in the main text because of the d ifferent extraction dates. NR: Not 
reported, NA: Not applicable.  

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ANNEX 8 

Comparison of animal N-budgets for fattening pigs in the CAPRI AND FR-nat data 

Animal Budget Components 

FR (CORPEN 2003) 

CAPRI 

% Difference 

PIGF: Fattening pigs 

2-phase 
feeding 

Standard 
feeding 

2-phase 
feeding - 
CAPRI 

Standard 
feeding - 
CAPRI 

Average 

N contents of pig diet - weaned piglets (8-30 kg)  
(g/kg feeds)1 

N contents of pig diet - growing-finishing pig (30-112 kg) 
(g/kg feeds)1 

N contents of pig diet  
(g/kg feeds)1 

Feed intake - weaned piglets (8-30 kg) (kg/head) 

29.2 

24.9 

25.4 

38.0 

30.8 

28.2 

28.5 

38.0 

Feed intake - growing-finishing pig (30-112 kg) (kg/head) 

235.0 

235.0 

NA  

NA  

22.9 

NA  

NA  

NA   

NA   

10 

NA   

NA   

NA   

NA   

20 

NA   

NA   

NA   

NA   

15 

NA   

NA   

Feed intake  
(kg/head)2 

N intake - weaned piglets (8-30 kg) (kgN/place/year)3 

N intake - growing-finishing pig (30-112 kg) 
(kgN/place/year)3 

N intake (kgN/place/year)2 

N content (g/kg LW) 

150.3 

150.3 

399.3 

-166 

-166 

-166 

3.0 

15.6 

10.2 

18.5 

3.1 

17.7 

11.4 

18.5 

NA  

NA  

24.4 

25.1 

NA   

NA   

NA   

NA   

NA   

NA   

-139 

-113 

-126 

-36 

-36 

-36 

Implied live weight (kg LW/place/year) 

112.0 

112.0 

253.2 

-126 

-126 

-126 

Pork meat (kg carcass weight / head) 

86.6 

86.6 

89.6 

Slaughtered animals (live weight in 1 000 t)6  

3 010.1 

3 010.1 

2 960.7 

-3 

2 

-3 

2 

-3 

2 

72 

N retention - weaned piglets (8-30 kg) (kgN/place/year)3 

N retention - growing-finishing pig (30-112 kg) 
(kgN/place/year)3 

N retention (kgN/place/year)2 

N retention - weaned piglets (8-30 kg) (kgN/place/year) 

1.5 

5.5 

3.8 

1.5 

1.5 

5.5 

3.8 

1.7 

N retention - growing-finishing pig (30-112 kg) 
(kgN/place/year) 

10.1 

12.2 

NA  

NA  

6.4 

NA  

NA  

NA   

NA   

-68 

NA   

NA   

-68 

NA   

NA   

-68 

NA   

NA   

NA   

NA   

NA   

NA   

N excretion coefficient (kgN/place/year)7 

6.4 

7.7 

12.4 

-93 

-62 

-78 

Herd size of animals (HERD) (1 000 places)4  

10 150.0 

10 150.0 

9 656.1 

Production days for a living animal (DAYS)5 

136.7 

136.7 

136.7 

Number of animals (LEVL) (1 000 heads)  

27 101.3 

27 101.3 

25 782.5 

5 

0 

5 

5 

0 

5 

5 

0 

5 

N excretion (Gg/year) 

65.1 

77.7 

119.6 

-84 

-54 

-69 

1) The value for FR was calculated as N intake (kg N/place/year) / feed intake (kg/head) / fattening period (=365/production day of CAPRI) 
2) The values for FR were calculated as the weighted average of weaned piglets (8-30 kg) and growing-finishing pig (30-112 kg). 
3) CAPRI production days were used to convert country N retention from head to place 
4) FR value is the average number of animals in Eurostat database (1 000 heads)  
5) CAPRI production days data were used for FR value. 
6) Slaughtered animals data for the country was calculated by multiplying number of animals and pork meat. 
7) This value is expected to be equal to N intake – N retention. But there is an error in the CAPRI model. 
CORPEN: Comité d’Orientation pour der Pratiques agricoles respectueses de L’Environnement   
Note: CAPRI data was extracted in May 2014. CAPRI values might be different from the values in the main text because of the d ifferent extraction dates.   
NR: Not reported, NA: Not applicable 

73 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 9 

Comparison of animal N-budgets for pigs in the CAPRI and IT-nat data 

Animal Budget Components 

PIGF 

SOWS 

TOTAL 

IT6 

CAPRI 

% 
Difference 

IT6 

CAPRI 

% 
Difference 

IT6 

CAPRI 

% 
Difference 

N contents of pig diet  
(g/kg feeds) 

The amount of feed intake  
(kg/head)1 

N intake  
(kgN/place/year) 

N content  
(g/kg LW)2 

Implied live weight  
(kg LW/place/year) 

Pork meat  
(kg carcass weight / head)3 

Slaughtered animals  
(live weight in 1 000 t)4 

N retention  
(kgN/place/year) 

N excretion coefficient 
(kgN/place/year) 

Herd size of animals (HERD)  
(1 000 places)5 

Production days for a living animal 
(DAYS) 

Number of animals (LEVL)  
(1 000 heads)  

N excretion 
(Gg/year)7 

74 

25.5 

24.1 

5 

25.0 

22.4 

10 

25.1 

22.8 

745.1 

620.8 

17 

2 216.0 

2 059.7 

7 

841.3 

704.0 

30.4 

27.7 

24.0 

25.1 

346.0 

243.8 

NR 

118.5 

NR 

1 885.7 

8.3 

6.1 

22.1 

21.6 

6 717.7 

6 712.5 

9 

-5 

30 

NA 

NA 

26 

2 

0 

55.4 

47.0 

24.0 

25.1 

783.3 

372.5 

15 

-5 

52 

32.9 

29.7 

24.0 

25.1 

390.1 

256.9 

NR 

NR 

36.2 

NA 

134.3 

112.3 

35.4 

NA 

2 005.8 

1 921.2 

18.8 

9.4 

36.6 

37.7 

752.5 

762.1 

50 

-3 

-1 

0 

9.4 

6.4 

23.6 

23.2 

7 470.2 

7 474.6 

NA 

NA 

228.1 

197.4 

13 

365.0 

365.0 

10 748.3 

12 410.4 

-15 

752.5 

762.1 

-1 

11 500.8 

13 172.5 

148.4 

144.8 

2 

27.5 

28.7 

-4 

176.0 

173.5 

9 

16 

10 

-5 

34 

16 

4 

31 

1 

0 

NA 

-15 

1 

 
 
1) The values for IT were calculated as N intake (kg N/head/year) / N contents of pig diet (kg/kg feed)  
2) The value of sows for IT was taken same as the value of PIGF. 
3) Pork meat data of the country was calculated by dividing slaughtered animals by number of animals. 
4) Slaughtered animals data is from Eurostat database for the country (incl. home slaughterings).  0.78 was used for carcass weight / liveweight ratio of the country 
(Source: CAPRI).  
5) IT value is the average number of animals in Eurostat database (1 000 heads)  
6) The source of the country data: Regione Emilia Romagna (2007).  Deliberazione dell’assemblea legislativa della regione Emilia-Romagna 16 gennaio 2007, n. 96. 
Attuazione del decreto del Ministero delle Politiche agricole e forestali 7 aprile 2006. Programma d’azione per le zone vulnerabili ai nitrati da fonte agricola- Criteri e 
norme tecniche generali. Bollettino Ufficiale Emilia-Romagna. 
7) Regione Emilia Romagna (2007) used a live weight at slaughering which is not representative for the Italian pig farming systems. Oenema et al. (2014) therefore 
corrected the N excretion coefficients from 13.81 to 13.61 kg N/head/year (Oenema et al 2014). The resulting total N excretion for PIGF of 146.3 Gg/year is very close 
to ITcapri. 
NR: Not reported, NA: Not applicable 
Note: The country uses in GNB calculations 9.3 kg N/place/year for N excretion coefficient of fattening pigs (62.5 Gg N/year). The value used by the country is so lower 
than CAPRI and the literature. 
Note: CAPRI data was extracted in May 2014. CAPRI values might be different from the values in the main text because of the d ifferent extraction dates.

75 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 10 

N contents (kg N/tonne) from selected crops in the CAPRI and in country datasets  

Wheat 

Maize 

OCER* 

Olives 

Fruits 

GRAS** 

OFAR*** 

Leguminous plants 

Temporary grassland 

DE 

FR 

HU 

IT 

CAPRI 

The country 

CAPRI 

The country 

CAPRI 

The country 

CAPRI 

The country 

20.0 

16.7 

28.3 

NA 

1.4 

17.5 

16.9 

NA 

NA 

21.1 

14.5 

17.2 

NA 

1.3 

17.6 

NA 

26.0**** 

NA 

20.2 

16.7 

28.3 

4.5 

2.3 

17.2 

16.7 

NA 

NA 

19.2 

15.0 

18.6 

0.1 

1.5 

27.8 

29.7 

39.0 

27.8 

20.0 

16.7 

28.3 

NA 

1.8 

15.2 

14.7 

NA 

NA 

27.0 

25.0 

20.7 

NA 

4.0 

18.5 

20.8 

27.0 

18.0 

21.8 

16.7 

28.3 

4.8 

2.1 

17.6 

17.0 

NA 

NA 

19.9 

15.0 

18.1 

43.3 

6.6 

25.0 

10.3 

27.0 

NA 

NA: Not applicable; CAPRI data was extracted in May 2013; NAT data was reported in the beginning of RegNiBal. 
* Other cereals except for wheat, maize, barley, rice, rye, meslin and oats 
** Permanent grassland 
*** Other annual green fodder, clover and mixtures, lucerne, other perennial green fodder usually legumes and temporary grassland 
**** For the clover and mixtures 

76 

 
Annex 11.  Mineral  fertiliser  data  in  different  Eurostat  databases 

(tonnes) 

Eurostat use of inorganic fertilisers 
database 

Eurostat GNB Database: Consumption of 
mineral fertilisers  

GEO/TIME 

2003 

2004 

2005 

2003 

2004 

2005 

France* 

2 237 000 

2 396  000 

2 346  500 

2 278  707 

2 330  844 

2 324 000 

Italy* 

751 500 

684 500 

621 500 

796 000 

751 700 

684 576 

* Extracted on 21.10.2013

77 

  
Annex 12 

Summary, results & achievements of the RegNiBaL project 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

1 

Germany 

N-fixation in 
permanent 
grassland 

Lower N fixation for clover, 
and lower clover share in 
CAPRI than in national 
data 

Invest in revising 
CAPRI methodology 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

CAPRI has a new method 
implemented for N-
fixation by using 
country-specific rates on 
the basis of 
LegumeFutures project 
(Helming et al., 2014) 

Next action 

Conclusion 

S                        
PS  
NS*  

Further effort for 
defining the legume 
share in the GRAS  

The difference in N-fixation 
in permanent grassland 
between the CAPRI and 
the national data 
decreased after the CAPRI 
methodology was updated. 
However, in order to solve 
the issue, the legume 
share in the GRAS must be 
defined. 

NS 

2 

Germany 

N-fixation 
from free-
living 
organisms 

3 

Germany 

N from 
atmospheric 
deposition 

(i) Free-living organisms 
not considered in CAPRI. 
(ii) N-fixation from free-
living organisms is 
included in the country 
GNB although new and 
better evidence for crops 
under national conditions 
are not available. 

(i) CAPRI doesn't include N 
from atmospheric dry 
deposition in GNB 
calculations. (ii) High N 
from atmospheric dry 
deposition in national data 
compared to EMEP; 
atmospheric export of N 
different from EMEP 
estimates 

4 

Germany 

N excretion 
by swine 

Higher N-excretion 
coefficient of fattening pigs 
in CAPRI than in national 
data 

A comparison of the 
animal budget for 
fattening pigs and 
sows in the CAPRI 

- 

78 

Update national 
data 

Country decided to 
exclude N fixation from 
free-living organisms. 

- 

The issue was "resolved".  

S 

Update CAPRI and 
check national data 

The country started to 
use an updated national 
study. 

(i) CAPRI needs to 
update data. (ii) 
Germany should 
check the model with 
regard to 
atmospheric 
transport (export) of 
N. 

The difference in total 
atmospheric deposition 
between CAPRI-updated 
and DEnat-updated is now 
small. The issue can be 
considered "resolved". 

S 

Further analysis to 
understand the 
reason for the higher 
feed intake in the 

The issue can be 
considered "unresolved" as 
further analysis is needed 
to understand the reason 

NS 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

and national data  

CAPRI data 

for the higher feed intake 
in the CAPRI data.  

5 

Germany 

N removal 
by OFAR 

6 

Germany 

7 

France 

8 

France 

N removal 
by other 
cereals 
other than 
wheat and 
barley 

N input from 
organic 
fertilisers, 
seed and 
planting 

N removal 
by other 
cereals 

Germany reports the 
amount of N from the 
clover and mixtures, 
lucerne, and temporary 
grassland in the section on 
“permanent 
meadows/grasses: 
consumption”. 

Higher N content for other 
cereals (OCER aggregate) 
in CAPRI than in national 
data 

N input from organic 
fertilisers, seed and 
planting not included in 
national data 

Lower amount of 
production of other cereals 
in national data than in 
CAPRI  

Update Germany’s 
reporting for this 
crop group 

- 

Update CAPRI 

- 

Germany needs to 
update its reporting 
for this group. This 
won't affect the GNB 
calculations. 

The issue was due to 
inconsistent data grouping 
and can be considered 
"resolved". However, 
Germany needs to update 
its reporting of this group. 

S 

Assessment of 
feasibility of country-
specific N content in 
CAPRI  

CAPRI update is pending. 

S 

Include the data 

The country included the 
data. 

- 

The issue was "resolved".  

S 

Check the data 

The amount of 
production of other 
cereals was updated by 
the country. 

The biological fixation 
coefficients were 
updated in the latest 
French National Working 
Group 

- 

- 

The issue was "resolved".  

S 

The issue was "resolved".  

S 

9 

France 

Biological 
fixation 

Higher N fixation in CAPRI 
than in national data 

Check the data 

10 

France 

N excretion 
by swine  

Lower N swine excretion 
coefficients in  national 
data than in CAPRI 

A comparison of the 
animal budgets for 
fattening pigs and 
sows in the CAPRI 

- 

Further analysis to 
understand the 
reason for the higher 
feed intake and 

The issue can be 
considered "unresolved" as 
further analysis is needed 
to understand the reason 

NS 

79 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

and national data  

higher implied live 
weight 

for the higher feed intake 
and higher implied live 
weight in the CAPRI 
model.  

11 

France 

N excretion 
by sheep 
and goats 

Lower N-excretion 
coefficients in CAPRI than 
in national data 

Make an in-depth 
assessment of the 
animal N-budgets 
for sheep & goats 

- 

A comparison of the 
animal budgets for 
sheep & goats 

The issue is "open" as no 
detailed data is available 
for the country’s animal N-
budgets for sheep & goats. 

NS 

12 

France 

N from 
atmospheric 
deposition 

13 

France 

N removal 
by OFAR 

(i) The CAPRI data doesn't 
include N from 
atmospheric dry deposition 
in GNB calculations. (ii) 
Higher N from atmospheric 
wet deposition in CAPRI 
than national updated data 
& EMEP data  

(i) Higher N contents for 
the crops in the OFAR 
group in national data in 
CAPRI, (ii) Lower OFAR 
production data in national 
data than in CAPRI and in 
Eurostat database 

CAPRI update 

The country updated the 
atmospheric N 
deposition. 

CAPRI update 

(i) France should 
check the N 
contents for the 
crops in the OFAR 
group (ii) France 
should ensure data 
consistency with 
the Eurostat 
database    

- 

France update 

CAPRI update is pending. 
The issue was "resolved". 
However,  we recommend 
that the country use the 
updated CAPRI method 
(see section 4.1 of the 
text) 

The country mentioned 
that they updated the 
present N contents very 
recently, so to update 
them again would be 
difficult for the country. 
Therefore this part of the 
issue is "closed" but not 
"resolved".  

S 

NS 

80 

 
 
Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

14 

France 

N removal 
by GRAS 

Lower N content for GRAS 
in CAPRI than in the 
country data 

The CAPRI GRAS-N 
content values 
should be checked 

- 

15 

Hungary 

N excretion 
by swine 

(i) Higher N-excretion 
coefficients for swine in 
CAPRI than in the national 
& literature data. (ii) 
Lower number of swine in 
national than CAPRI data 

A comparison of the 
animal budgets for 
fattening pigs and 
sows in the CAPRI 
and national data  

Hungary updated N-
excretion coefficients 
and the number of swine 
for each groups  

(i) CAPRI 
evaluation of the 
GRAS-N content 
update by using a 
combination of 
literature (Velthof et 
al 2007, Pointereau 
et al 2007) and 
country 
estimations. (ii) 
The evaluation of 
results from the 
GRASSDATE 
project (Velthof et 
al., 2014) in 
national data and 
the CAPRI 

(i) A comparison of 
the animal budgets 
for swine (ii) The 
evaluation of the 
country to use the 
CAPRI methodology 
for the herd size for 
fattening pigs 

16 

Hungary 

N from 
mineral 
fertiliser 

Higher N from mineral 
fertiliser in CAPRI than in 
national data  

CAPRI update  

- 

Further investigation 

The issue is "open". 

NS 

The issue is "open" as no 
detailed data is available 
for the country’s animal N-
budgets. (ii) The difference 
in swine numbers between 
national and CAPRI data is 
higher than before, so that 
the issue is ‘open’. 

The issue is ‘open’. There 
is the need to check the 
national and CAPRI data 
against the information 
from EFMA. 

NS 

NS 

81 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

17 

Hungary 

N removal 
by GRAS 

(i) Higher area and yield of 
permanent grassland in 
CAPRI than in national 
data  

18 

Hungary 

N removal 
by maize 
and wheat 

Higher N contents of maize 
and wheat in national than 
in CAPRI data 

19 

Italy 

N from 
mineral 
fertiliser 

Lower mineral fertiliser 
data in CAPRI than in 
national data & the FAO 
database 

(i) CAPRI re-
evaluates the GRAS 
yield for Hungary 
(ii) Hungary 
permanent 
grassland area in 
Eurostat database 
should be revised 
for the related 
years 

Hungary should 
report the N 
content of crops 
and crop residues 
separately. 

(i) Italy clarifies the 
values to be used 
and the reason for 
different data in 
different databases. 
(ii) Difference 
between CAPRI and 
database should be 
checked. 

20 

Italy 

N excretion 
by swine 

Higher CAPRI N-excretion 
coefficient for fattening 
pigs than in national data 
and the literature values 

A comparison of the 
animal budgets for 
fattening pigs and 
sows in the CAPRI 
and national data  

82 

- 

(i) CAPRI evaluation 
of the GRAS yield for 
Hungary (ii) Hungary 
update of permanent 
grassland area in 
Eurostat database   

The issue can be 
considered "unresolved". 

NS 

The country updated the 
N contents. 

- 

The country agreed with 
the recommendation, and 
updated N contents. The 
issue can be considered 
"resolved". 

S 

- 

- 

The data of CAPRI 
and Italy need to be 
checked. 

The issue is "unresolved". 

NS 

Further investigation 

The ITcapri N-excretion 
coefficient for fattening 
pigs is higher than in 
ITnat, FRnat and DEnat, 
and some literature 
values. However, the 
literature data of the 
animal N-budgets for 
swine, including the N 
excretion coefficient for 
fattening pigs for IT, are 

NS 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

similar to ITcapri. Further 
analysis of the detailed 
animal budget is required 
to assess the differences in 
the N-excretion 
coefficients between ITnat 
and ITcapri. The issue is 
"open". 

21 

Italy 

N excretion 
by other 
cattle 

Lower N-excretion 
coefficient in national than 
in CAPRI & literature data 

A comparison of the 
animal budgets for 
fattening pigs and 
sows in the CAPRI 
and national data  

- 

A comparison of the 
animal budgets for 
other cattle 

The issue is "open" as no 
detailed data is available 
for the country’s animal N-
budgets. 

22 

Italy 

N fixation by 
OFAR 

Lower N fixation by OFAR 
in CAPRI than in national & 
literature data 

Invest in revising 
CAPRI methodology 

CAPRI has implemented 
a new method for N-
fixation by using 
country-specific rates on 
the basis of 
LegumeFutures project 
(Helming et al., 2014) 

NITF value for OFAR 
of CAPRI needs to be 
checked.  

The issue is “unresolved” 
as the difference in the 
symbiotic N fixation by 
OFAR of the new CAPRI 
method and the country is 
still big.  

NS 

NS 

23 

Italy 

24 

Italy 

(i) Free-living organisms 
not considered in CAPRI. 
(ii) N-fixation from free-
living organisms  is 
included in the country 
GNB, although new and 
better evidence for crop 
under national conditions 
are not available. 

The biological N fixation 
from grass–legume 
mixtures is not considered 
in national data. 

N fixation 
from free-
living 
organisms 

Biological N 
fixation from 
grass-
legume 
mixtures 

Update national 
data 

Italy should include 
the biological N 
fixation from grass–
legume mixtures in 
the GNB 

- 

- 

Italy needs to update 
data.  

The issue can be 
considered "resolved" after 
country update. 

S 

Italy needs to update 
data.  

The issue is "unresolved". 

NS 

83 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

calculations. 

25 

Italy 

N removal 
by GRAS 

High absolute difference in 
N from GRAS between 
national and CAPRI data, 
mostly resulting from the 
difference in GRAS yield  

GRAS yield should 
be checked for both 
Italy and CAPRI 

- 

GRAS yield should be 
checked for both 
Italy and CAPRI 

The issue is "unresolved". 

NS 

26 

Italy 

N removal 
by olives 
and fruit 

Higher N contents of olives 
and fruit in national than 
CAPRI, literature, and 
other countries’ data 

The components of 
temporary grassland and 
crop residues from field is 
not considered in national 
data. 

The following GNB data 
reported by the countries 
are different from the data 
of these countries in 
Eurostat databases: N 
excretion by swine for FR, 
data on permanent 
grassland area for HU, IT 
and FR. 

27 

Italy 

N output  

28 

FR, HU, IT 

Some GNB 
data  

84 

(i) Italy justifies or 
revises N contents 
of olives and fruit 
(ii) CAPRI checks 
the values for olives 
and evaluates the 
possibility to use 
country-specific N-
content values for 
aggregates. 

Italy should include 
the components of 
temporary 
grassland and crop 
residues from fields 
in the GNB 
calculations.  

It is recommended 
to ensure full 
convergence 
between the 
country data used 
in GNB calculations 
and the data in 
Eurostat databases. 

- 

- 

CAPRI and Italy 
needs to check data. 

The issue is "unresolved". 

NS 

Italy needs to update 
data.  

The issue is "unresolved". 

NS 

N excretion by swine for 
FR became convenient. 

Countries' evaluation 

The issue is "partially 
resolved". 

PS 

Nr 

Country/ 
CAPRI/ 
Eurostat 

Flow 

Initial finding summary  Recommendation 

Implemented changes 
since start of RegNiBal 
(CAPRI or country) 

Next action 

Conclusion 

S                        
PS  
NS*  

29 

CAPRI 

N input  

30 

CAPRI 

N excretion 
by fattening 
pigs 

CAPRI should 
include N from 
organic fertilisers 
(excl. manure), 
seed & planting 
materials, manure 
withdrawal, stocks, 
and import 
estimations into the 
GNB calculations.  

CAPRI should 
correct this error. 

The components of N from 
organic fertilisers (excl. 
manure), seed & planting 
materials, manure 
withdrawal, stocks, and 
import estimations are not 
considered in the CAPRI 
model. 

The N-excretion data 
stored in CAPRI (MANN) 
for fattening pigs is higher 
than would be expected 
{= N intake 
(sum(FEEDS*CRPR(FEEDS
))/6) - N retention 
(CRPR_EXPT)}. 

- 

CAPRI evaluation 

The issue is “unresolved". 

NS 

CAPRI corrected. 

- 

The issue is "resolved".  

S 

31 

CAPRI 

N from 
atmospheric 
deposition 

N from dry atmospheric 
deposition was not 
included in CAPRI. 

*S: Solved, PS: Partially solved, NS: Not solved 

CAPRI should 
update deposition 
flows including both 
wet and dry 
deposition.  

CAPRI initiated the 
update. 

CAPRI update 

The issue is "resolved".  

S 

85 

Annex 13 

Data needs and data sources for regionalisation of Gross Nitrogen Balances of EU Member States 

Inputs & Outputs in 
N Balance 

Data needed by NUTS 2 
level 

Data source 

Explanations 

Inputs 

1) Mineral fertiliser 

The amount of mineral 
fertiliser 

Use of inorganic fertilisers (aei_fm_usefert) 
from Eurostat database 

The data on inorganic fertiliser use in agriculture 
are reported by countries at NUTS 0 level. In some 
cases, data have also been provided at NUTS 2 
level. 

2) Manure production 

Number of animals 

(i) Animal populations (December) by NUTS 2 
regions (agr_r_animal) from the Eurostat 
database; (ii) The data of the surveys of other 
periods from country delivery 

N-excretion coefficients 

Country delivery by NUTS 2 regions 

3) Manure trade, 
withdrawal, and 
treatment 

The amount of N from 
manure trade, withdrawal, 
and treatment 

Country delivery by NUTS 2 regions 

(i) Data on manure exports and imports are 
required if significant (net imports or net exports 
>=5% of 
manure nutrient production). 
(ii) Data on manure treatment and non-agricultural 
use are optional. 

4) Other organic 
fertilisers  

The amount of 
organic fertilisers 

Country delivery by NUTS 2 regions 

5) Biological N fixation 

N content of organic 
fertilisers  

The area of leguminous 
crops  

The fixation coefficient of 
leguminous crops 

The area of temporary and 
permanent grassland 

Country delivery by NUTS 2 regions 

Areas harvested, yields, production by NUTS 2 
regions (agr_r_crops) from the Eurostat 
database 

Country delivery by NUTS 2 regions 

Areas harvested, yields, production by NUTS 2 
regions (agr_r_crops) from the Eurostat 
database 

The fixation coefficient of 
grass/legume mixtures 

Country delivery by NUTS 2 regions OR Tool 
estimation* 

* The fixation coefficient of grass/legume mixtures 
by NUTS 2 regions can be provided by the 
countries. If the countries did not have data 
available, the tool can make the estimations by 
using the shares (the shares of fixed N in the 
Nitrogen requirement in aboveground biomass 
(harvested and crop residues)) taken from the 
report of the EU research project Biological 
nitrogen fixation (BNF) in Europe (LegumeFutures). 

86 

  
  
  
Inputs & Outputs in 
N Balance 

Data needed by NUTS 2 
level 

Data source 

Explanations 

National average deposition rate is calculated by 
dividing total N deposition by total area when using 
the EMEP database. In other cases, it is calculated 
by dividing total agricultural deposition of N by UAA.  

In case countries do not have data available, 
Eurostat estimates nutrient seed input by applying 
Eurostat default values to areas of wheat and 
potatoes from crop statistics (Areas harvested, 
yields, production by NUTS 2 regions 
(agr_r_crops)) 

6) Atmospheric N 
deposition  

Utilised agricultural area 
(UAA) 

Land use by NUTS 2 regions (agr_r_landuse)  
from Eurostat database 

Regional average deposition 
rate 

EMEP database OR Country delivery & Land use 
by NUTS 2 regions (agr_r_landuse)  

7) Seed and planting 
materials 

Cropped areas of crops  

Areas harvested, yields, production by NUTS 2 
regions (agr_r_crops) from Eurostat database 

Sowing rates of crops  

Country delivery by NUTS 2 regions 

N content of seed of crops  

Country delivery by NUTS 2 regions 

8) Total inputs = sum 
(1,2,3,4,5,6,7) 

Outputs 

9) Crop production 

The amount of crop 
production of crops 

Areas harvested, yields, production by NUTS 2 
regions (agr_r_crops) from Eurostat database 

N content of crops  

Country delivery 

10) Fodder production 

The amount of crop 
production of fodders 

Areas harvested, yields, production by NUTS 2 
regions (agr_r_crops) from Eurostat database 

N content of fodders  

Country delivery by NUTS 2 regions 

11) Crop residues 
removed/burnt 

Crop residues 
removed/burnt of crops  

Country delivery by NUTS 2 regions 

N content of crop residues 
removed/burnt of crops  

Country delivery by NUTS 2 regions 

12) Total outputs = 
sum(9,10,11) 

N Surplus 

NS = (8) - (12) 

87 

  
  
  
  
  
  
  
  
Annex 14 

Data  on  area,  yield,  and  N  content  of  permanent  grassland  in  the  CAPRI  and  in  countries’ 

databases 

GEO 

Area (kha) 

Dry Matter (DM) Yield (100 kg/ha) 

DM Crop Production 
(kt) 

DM N content (kg 
N/tonne) 

N removal (Gg N/yr) 

A 

B* 

CAPRI 

A 

B* 

CAPRI 

A 

CAPRI 

A 

CAPRI 

A 

CAPRI 

Germany  

NR 

4 937 

4 955 

NR 

70.0 

74.0 

34 541 

36 669 

17.6 

17.5 

608 

642 

France 

Italy 

9 581 

10 013 

9 494 

38.4 

38.4 

43.7 

36 782 

41 447 

27.8 

17.2 

1 021 

714 

4 350 

4 381 

3 895 

48.8 

12.5 

26.6 

21 238 

10 366 

25.0 

17.6 

372** 

182 

Hungary 

503 

1 058 

949 

14.9 

8.1 

35.2 

751 

3 339 

18.5 

15.2 

14 

51 

A: Reported data by the country; B: Eurostat Database    
NR: Not reported; CAPRI data is extracted in May 2013; NAT data was reported in the beginning of the RegNiBal project. 
Note: The data reported by Italy is a two-year average (2003, 2004). 
* http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=apro_cpp_crop&lang=en. Extracted on 19.09.2013.  
**The GRAS consumption calculated as 70% of the production is used in GNB calculation) 

88 

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European Commission 
EUR 27339 EN – Joint Research Centre – Institute for Environment and Sustainability 

Title: Regionalisation of Nitrogen Balances with the CAPRI Model (RegNiBal) 

Authors: Fethi Şaban Özbek, Adrian Leip, Franz Weiss, Gema Carmona Garcia 

Luxembourg: Publications Office of the European Union 

2015 – 88 pp. – 21.0 x 29.7 cm 

EUR – Scientific and Technical Research series – ISSN 1831-9424 (online) 

ISBN 978-92-79-49416-1 (PDF) 

doi:10.2788/078406 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-

L
B
N
A
-
2
7
3
3
9
-
E
N
N

-

JRC Mission 

As the Commission’s  
in-house science service,  
the Joint Research Centre’s  
mission is to provide EU  
policies with independent,  
evidence-based scientific  
and technical support  
throughout the whole  
policy cycle. 

Working in close  
cooperation with policy  
Directorates-General,  
the JRC addresses key  
societal challenges while  
stimulating innovation  
through developing  
new methods, tools  
and standards, and sharing  
its know-how with  
the Member States,  
the scientific community  
and international partners. 

Serving society  
Stimulating innovation  
Supporting legislation 

doi: 10.2788/078406 

ISBN 978-92-79-49416-1