Calculation of Agricultural Nitrogen Quantity for EU River Basins

Transcription

1 Calculation of Agricultural Nitrogen Quantity for EU River Basins Final Report: EUR EN EUROPEAN COMMISSION DIRECTORATE GENERAL JOINT RESEARCH CENTRE ISPRA Institute for Environment & Sustainability Land Management

2 The mission of the JRC is to provide customer- driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy- making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national. Calculation of Agricultural Nitrogen Quantity for EU River Basins Final Report: EUR EN Framework contract: F3ED ISP BE on the provision of expertise in the field of Agri-Environment Authors JM Terres P Campling S Vandewall J VanOrshoven JRC IES Land Management Spatial Application Division KU Leuven Research& Development

3 TABLE OF CONTENTS Table of contents... 1 List of figures... 3 List of Tables... 4 Executive summary Introduction Background and scope Outline of the report Methods Database preparation NUTS 2 and NUTS 3 farm statistical census data Technical coefficients Nitrogen balance calculations NOPOLU software NOPOLU soil surface nitrogen balance accounting procedures Overlay analysis to generate the hydrosol CORINE land cover to disaggregate N balance estimates Preliminary processing of data Nitrogen balance calculation in NOPOLU Sensitivity analysis Inclusion of atmospheric deposition in N balance Results Soil Surface Nitrogen balance for Europe (EU15) using NUTS 2 data for Soil Surface Nitrogen Balance for Europe (EU15) using NUTS 3 data for Time series nitrogen balance simulations using NUTS 2 census data Estimation of the effect of using CORINE Land Cover in nitrogen balance estimations N balance simulation including atmospheric deposition Relative importance of nitrogen balance components contributing to N balance Comparison of nitrogen balance simulations with EUROSTAT nitrogen balance estimates Results of sensitivity analysis Sensitivity of N balance to yield values Sensitivity of N balance to manure coefficients (organic fertiliser) Sensitivity of N balance to fertilisation coefficients Sensitivity of N balance to crop exportation coefficients Conclusions sensitivity analysis Discussion Data quality Methodology Results - N balance estimates at NUTS2 and NUTS 3 level for Europe Results - N balance estimates at the catchment level Results - Time series analysis

4 4.1.5 Results - Sensitivity analysis Encountered problems and suggested solutions Conclusions Refences Annex Overview of agricultural census data used in N balance calculations for France and Spain (representative for other EU countries) Correspondence table between Farm Structure Survey variables and CORINE Land Cover Nitrogen fertilizer amounts for EU Animal rejection rates for EU Crop yields (100 kg/ha) for EU N surplus results for EU-15 based on FSS NUTS N surplus results for European countries based on FARM STRUCTURE SURVEY (FSS) NUTS Comparison of NUTS 2 and NUTS 3 administrative level census datasets - France Comparison of NUTS 2 and NUTS 3 administrative level census datasets for Spain Results of time series N balance estimates (kg/ha) at NUTS 2 level Results estimation effect CORINE Land Cover on N surplus calculation Estimation of effect of CLC: differences between datasets Nitrogen input and output for Europe, NUTS N surplus results: analysis of extreme values in selected areas Extreme results on NUTS level Extreme results on catchment level

5 LIST OF FIGURES Figure 1 The terms of the soil surface nitrogen balance...12 Figure 2: Spatialisation of N balance estimates using the CORINE Land Cover as a co-variable...14 Figure 3: N Balance in Kg/ha for Europe (EU 15) calculated at NUTS 2 level...19 Figure 4: N Balance in Kg/ha for Europe (EU 15) calculated at catchment level (1:1Million scale)...20 Figure 5: Histogram of N balance estimates (kg/ha)...21 Figure 6: N Balance in Kg/ha for Europe (EU 15) calculated at NUTS level using the NUTS 3 census database...22 Figure 7: N Balance in Kg/ha for Europe (EU 15) calculated at catchments level using the NUTS 3 census database...24 Figure 8: Evolution of N balance differences (Kg N/ha) between 1990 and 1997 for Europe (EU 15)...26 Figure 9 The change in the average N Balance (kg N/ha) between 1990 and 1997 for each EU member state...27 Figure 10 The change in the average N Balance (kg N/ha) between 1990 and 1997 for selected NUTS 2 regions...27 Figure 11: Scheme summarizing the methodology for the assessment N balance calculations with and without the use of CLC to spatialise data...28 Figure 12 N balance results for Spain with and without the use of CLC to spatialise data for administrative units...29 Figure 13: Scatter plots regression analysis of the CLC effect on nitrogen estimation (tons)...30 Figure 14: Scatter plots of residuals of the regression analysis to assess the CLC effect on Nitrogen estimation (tons)...31 Figure 15: Input of N atmospheric deposition (kg/ha) for Europe...32 Figure 16: Percentage breakdown of fertiliser, organic fertiliser (manure) and atmospheric deposition at the EU level...33 Figure 17: Percentage distributions of input components to the N balance per EU member state Figure 18: Nitrogen balance at NUTS2 level for EU, incorporating atmospheric deposition of N...35 Figure 19: Nitrogen input by mineral fertilizer (kg N/ha), based on FARM NUTS 2 data...36 Figure 20: Nitrogen input/ livestock manure (kg N/ha), based on FARM NUTS 2 data...37 Figure 21: Nitrogen output/ exportation values (kg N/ha), based on FARM NUTS 2 data...39 Figure 22 EUROSTAT N balance simulations (1993)

6 LIST OF TABLES Table 1 NUTS levels used for NUTS 2 and NUTS 3 N estimates per country...10 Table 2: Summary statistics of N balance estimates for NUTS 2 and Table 3: Re-calculated summary statistics of N balance estimates for NUTS Table 4: Summary statistics simple linear regression analysis...30 Table 5: Absolute and relative contribution of N atmospheric deposition to N surplus...31 Table 6: Total N (tons) and percentage breakdown of fertiliser, organic fertiliser (manure) and atmospheric deposition to the N balance per EU member state...33 Table 7: Summary of nitrogen input and output estimates at country level, based on 1990 NUTS 2 FSS data and 1997 technical coefficients...40 Table 8: Absolute changes (tons N) in the nitrogen balance estimates according to changing yield values for Europe...42 Table 9 Relative changes (%) in Nitrogen to the reference tonnage (EU wide NUTS2 results) according to changing yield values for Europe...42 Table 10: Absolute and relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing yield values for selected NUTS 2 regions...43 Table 11: Absolute changes (tons N) in nitrogen balance according to changing manure coefficients for Europe...43 Table 12: Relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing manure coefficients for Europe...44 Table 13: Absolute and relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing manure coefficients for selected nuts regions...44 Table 14: Absolute changes (tons N) in nitrogen balance according to changing fertilisation coefficients for Europe...45 Table 15: Relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing fertilisation coefficients for Europe...45 Table 16: Absolute and relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing fertilisation coefficients for selected nuts regions...45 Table 17: Absolute changes (tons N) in nitrogen balance according to changing exportation coefficients for Europe...46 Table 18: Relative changes (%)to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing exportation coefficients for Europe...46 Table 19: Relative changes (%) in Nitrogen to the reference tonnage (EU wide NUTS2 results) according to changing yield values for Europe

7 EXECUTIVE SUMMARY 1. The primary objective of the study was the implementation of a new methodology to calculate soil surface nitrogen balances for European Union (EU) catchments. This methodology encompasses the use of CORINE Land Cover data to spatialise statistical information and the use of readily available geographical and agricultural census datasets from the European Commission (EC). A relational database program, NOPOLU, was used to calculate soil surface Nitrogen balances. 2. Geographical Information System (GIS) techniques were used to prepare data for input into NOPOLU and to map out results. Prior to the nitrogen balance calculation an overlay analysis was executed between the administrative areas (NUTS), river basins (1:1Million European catchment boundaries) and CORINE Land Cover. The resulting database, called Hydrosol, is used in NOPOLU to link data and simulation results to the administrative, geographical and hydrographical boundaries. 3. Agricultural census data was provided from the Farm Structure Survey (FSS) datasets at NUTS 2 and NUTS 3 administrative level. Significant differences were found between the two datasets, in relation to the aggregation of agricultural census variable categories, with NUTS 3 data providing more detail than NUTS2. The aggregation of census variables not only had implications for the input data used in the N balance calculations, but also caused differences in the application of technical coefficients of N content. 4. EUROSTAT provided national technical coefficients of the N content of fertilizer rates, harvested crops and manure, together with excretion rates. In addition, crop yields were provided at NUTS 2 and 3 levels. Necessary precautions need to be taken into account regarding the technical coefficients provided at the national level, as these values do not reflect regional differences in climate, farming practices, varieties and breed. 5. Calculations were carried out at NUTS2 and NUTS3 administrative level and at catchment level. The results on NUTS 2 level show an average surplus of 25 kg N/ha, and a median of 27 kg N/ha. The distribution of the balances shows high surplus amounts in regions of intensive livestock farming (Flanders (B), the Netherlands, Brittany (FR), Pohjois (FI)), and low or deficit values in the central areas of Spain, France and Italy. The low average surpluses for certain regions of France and especially Italy, put into doubt the validity of the technical coefficients provided for grass and pasturelands. The output at catchment level shows a more differentiated distribution of the N balance than the administrative level. 6. The availability of more detailed data for a number of European countries enables the production of results on NUTS 3 administrative level for The results on NUTS 3 level show an average surplus of 20 kg N/ha, and a median of 12 kg N/ha. The pattern of results is comparable to the NUTS 2 results; however absolute values can differ for certain NUTS regions, in particular for Spain and Ireland, due to the use of average technical coefficients (as a result of aggregated agricultural census variable categories at NUTS 2 level). This observation highlights the importance of regrouping census variables and their corresponding coefficients. 7. Analysis of the input and output components shows a high importance of mineral fertilizer in the nitrogen input (EU average 75 %, country values between 54% (BE) and 91% (IR)), 5

8 whereas the share of organic fertilizer is lower (average 13%, country values between 5% (LU) and 34% (NL)). The average contribution of atmospheric deposition for Europe is 6 kg N/ha (average 12% of the total input, country values between 4% (IR) and 21% (FI)). The lower share of organic fertilizer can be explained by differences in calculation methodology between the present study and the EUROSTAT calculations. In our case an abatement factor is generally included to reduce animal manure input, counting for internal changes of fodder that occur inside areas larger than the elementary unit. A second difference is that the use of feedstuffs as fodder is not taken into account in this study, all fodder is presumed to come from high-grade (or, if necessary low-grade) pasture. 8. Time series analysis of soil-surface N balances was carried out at NUTS 2 census level for 1990, 1993, 1995, and 1997, keeping technical coefficients (from 1997) constant. The overall tendency was a slight decrease of the overall N surplus, with sharp reductions over this time period in the Netherlands and Greece. 9. The effect of the CORINE Land Cover in nitrogen balance calculations was examined by aggregating NUTS 3 census data to NUTS 2 and NUTS1 level and then disaggregating the data in NOPOLU back to NUTS 3, respectively with and without the use of CORINE Land Cover. These simulations indicated a slight improvement in estimation when the CORINE Land Cover was used. 10. The sensitivity analysis of the technical coefficients showed a higher sensitivity of crop related coefficients than manure coefficients. The overall sensitivity analysis revealed the need to improve the quality of the technical coefficients, requiring more consistency and reflecting regional differences. 11. The model structure of NOPOLU could be improved by integrating N input from atmospheric deposition into the accounting module of the program, as opposed to just adding on N values from atmospheric deposition to the balance calculations. In addition, shifting between the use of the manure abatement factor for intensive livestock farming regions and introducing imported fodder (feedstuff) amounts into the accounting procedure should be improved (as well of availability of information on technical coefficients for intensive livestock farming, use of feedstuff and its characteristics). 12. The main discussion items of the study related to: - Data quality: considerable differences between NUTS 2 and NUTS 3 FARM census data underline the need of more regional technical coefficients, reflecting regional agronomic practices. - Methodology: the CORINE Land Cover can be used as a co-variable to spatialise and distribute agricultural statistical data to different geographical units, but the correspondence table between CLC and FSS variables is likely to vary according to climate and region, therefore an optimisation and adaptation of this table of correspondence to regional conditions is desirable. There is a net positive effect of using CLC for calculations at administrative level. The main advantage is being able to spatialise calculations to catchment areas. - Results: The main difference between the obtained results and the EUROSTAT surplus figures is the lower share of organic fertilizer, due to differences in calculation hypothesis. 13. The main conclusions that can be drawn from the study are: - The use of NUTS 2 FARM census based N balance simulations is appropriate for monitoring the trends in soil surface N balance estimates. 6

9 - There is still an important work of validation, homogenisation of the technical coefficients available for estimating soil-surface N balances for countries and regions in EU15, at all the reported scales of this study. - Once the model is up and running NOPOLU has proven to be an efficient tool for conducting scenario analysis. A number of further improvements and refinements to the model have been carried out during the project, and further improvements are indicated (point 11). - The inclusion of CORINE land cover in the modelling process improves the results at the administrative level. Its main strength is its use as a co-variable to distribute and reaggregate N balance estimates to geographical units. Acknolegement: The authors of this publication are grateful to all those who contributed in this work for providing information, expertise and review. In particular to M. Pau-Vall for providing some data and P. Crouzet (IFEN) for his comments and availability in steering the study. 7

10 1 INTRODUCTION 1.1 Background and scope Excessive nitrogen poses a threat to environment leading to pollution of water and eutrophication. Following policy objectives for the integration of environmental concerns into agricultural practices, aimed at reducing current and potential pollution, the monitoring of nitrogen surpluses via the elaboration of nitrogen surplus indicator (as recommended in Commission Communications COM(2000)20 and COM(2001)144) is a required tool to highlight vulnerable areas for nutrient pollution (also required for the implementation of the Water Framework Directive). Although there is not a linear relationship between N surplus and nitrate in water, the risk of N leaching is greater when local surpluses are larger. Hence, local assessment of N surplus is a prerequisite to any risk assessment with respect to water bodies and environmental protection. The calculation of Nitrogen balance estimates represents an indicator of agriculture sustainability and the potential for "background", or non-point source contamination of waters as well. Therefore, for environmental assessment to support EU policies, there is a need to establish the most accurate and homogeneous estimate across Europe of the spatial distribution of both the nitrogen quantities used and release d by agricultural activities. The Final Report: Calculation of Agricultural Nitrogen Quantity for EU River Basins presents the results of the project to estimate nitrogen balances of agricultural origin for catchments within the EU15 member states. The project is the first study carried out under the framework contract: F3ED ISP BE on the provision of expertise in the field of Agri-Environment, which is funded by the Joint Research Centre Ispra. The study s aim is to provide spatially georeferenced nitrogen balance estimates for use as policy-relevant agri-environmental pressure indicators and as potential input into hydrological nitrogen-fate models. The study is a continuation of work carried out by Crouzet (IFEN, 2000), who used a relational database program, NOPOLU developed by Beture-Cerec, to estimate nitrogen balances for case study river basins in France (Brittany-Loire) and Germany-Czech Republic (Elbe). The objectives of the study were to: - Test the feasibility of calculating Agricultural Nitrogen Quantity for all EU River Basins using readily available datasets for the European Commission (EC) services. - Assess the effect of using CORINE Land cover (CLC) to spatialise Nitrogen balance estimations The results presented in the Interim Report (Campling et al., 2001) provided an indication of Nitrogen balance calculations at NUTS and catchment level for France and Spain. This Final Report broadens and deepens the scope of the initial research work done by including the following aspects: - Full explanation of database development procedures, including the means undertaken to fill in census data gaps. - Estimates of Nitrogen balance for the entire European Union for Time series analysis of Nitrogen balance estimates for the European Union using Farm Structure Survey (FSS) NUTS 2 data. - A test on the effect of using CLC in the estimations of Nitrogen balance. 8

11 - A comparison of Nitrogen balance calculations at administrative unit level with estimates derived by EUROSTAT. - The inclusion of Nitrogen from atmospheric deposition into the Nitrogen balance model. - A sensitivity analysis of technical coefficients, and - A detailed analysis of selected Nitrogen balance hot spot areas. 1.2 Outline of the report In Chapter 3 - Methods, the methods and procedures used to prepare the EU wide databases for input into the soil surface nitrogen balance module and the variation in the technical coefficient values provided by EUROSTAT is presented. An overview is provided of the accounting procedures, which are behind the soil surface nitrogen balance procedures. In addition, the approach for assessing the effect of using CLC in soil surface nitrogen balance estimates is presented, along with the sensitivity analysis of technical coefficients. In Chapter 4 Results, the results of the Nitrogen balance calculations for Europe during 1990 are presented for both NUTS 2 and NUTS 3 data levels. In addition, time series of nitrogen balance estimates were calculated based on NUTS2 data; the effect of using CLC was tested and compared to EUROSTAT calculations; and, the sensitivity of the different model parameters was analysed. In Chapter 5 Discussion, the results are evaluated in terms of assessing the appropriateness of the approach to provide indicators of N surplus hotspots across Europe. Finally, in Chapter 6 Conclusions, the concluding points of the Final Report are listed. 9

12 2 METHODS 2.1 Database preparation The geographical data sets that have been supplied by EUROSTAT-GISCO, EC-JRC, and EMEP for use in this study include: - NUTS 2 and 3 administrative boundaries at 1:1M scale (NUEC1MV7), EUROSTAT GISCO - CORINE database land cover layer at 100 m grid cell resolution (LCEUGR100), EUROSTAT GISCO; - Provisional European catchments boundaries at 1:1M scale (sub1k412.shp), JRC; and, - EMEP N-Atmospheric deposition at 50-x 50 km grid cell resolution. - EUROSTAT provided the farm structure survey data sets and technical coefficients via the New Cronos databases: - Farm Structure Survey (FSS) data at NUTS 3 and NUTS 2 level providing data of crop types and cultivation areas, crop yields, types and numbers of livestock. The data were provided in dft format and a special program (CubX) was used to read the data NUTS 2 and NUTS 3 farm statistical census data EUROSTAT provided two census datasets: one mainly at NUTS 2 level (NUTS 1 for some countries) and the other mainly at NUTS 3 level (NUTS 2 for some countries). Due to differences in NUTS level scale between member states, some NUTS 1 regions are combined with NUTS 2 and some NUTS 2 regions are combined with NUTS 3 regions, so that the scale of information across the EU is maintained (Table 1). Country NUTS 2 N estimates NUTS 3 N estimates AT NUTS 1 No data BE NUTS 1 NUTS 2 DE NUTS 1 NUTS 2 DK NUTS 0* NUTS 3 ES NUTS 2 NUTS 3 FI NUTS 2 No data FR NUTS 2 NUTS 3 GR NUTS 2 NUTS 3 IE NUTS 0* NUTS 3 IT NUTS 2 NUTS 3 LU NUTS 0 NUTS 0 NL NUTS 1 NUTS 2 PT NUTS 2 NUTS 3 SE NUTS 2 No data UK NUTS 1 No data * For Multiple level regions the highest administrative level has been taken Table 1 NUTS levels used for NUTS 2 and NUTS 3 N estimates per country 10

13 The data at NUTS 2 level are available for 1990, 1993, 1995 and 1997, so they can be used to carry out a time series analysis. The data at NUTS 3 level are only available for A detailed comparison of the census variables between the two datasets showed considerable differences between the datasets (Annex 8.1). The main difference is the fact that in the NUTS 2 dataset variable categories are less detailed because of the aggregation of the following variables: Industrial crops (D/13), fresh outdoor vegetables (D/14-D/15), flowers and ornamental plants (D/16-D/17), forage plants (D/18), permanent pasture and meadows (F), permanent crops (G), sheep (J/09), goats (J/10), pigs (J/11 - J/13), poultry (J/14 J/16). The aggregation of census variables not only has implications for the input data used in the N Balance calculations, but also causes differences in the technical coefficients used. Also a minor difference between the two data sets is that there are three census categories missing in the NUTS 2 data set: fallow land (D/21); equidea (J/01); rabbits, breeding females (J/17) Technical coefficients The technical coefficients are provided by EUROSTAT. A distinction is made between the crop and livestock coefficients on the one hand and the CORINE land cover and agricultural uses, on the other. For the crop and livestock coefficients the following data were provided: - For crops: N fertilisation rates for each crop type, the N content of harvested crops (N exportation value) and the crop yields. A field fodder was activated if the cultivation is used for fodder, requiring the inclusion of the dry matter rate. - For livestock: excretion rates and N content of manure, and fodder needs for each livestock type. All technical coefficients were provided for Yield values were provided for , except for UK, ES, IT and GR (data for 1996) and NL (data for 1995). Necessary precautions need to be taken into account regarding the technical coefficients provided at the European level. First, national rather than regional technical coefficients have been used for Nitrogen balance calculations for the European Union because they were the only ones available on an operational basis. In the case of livestock fodder needs, French data has been used for the other European Union member states however, although in reality, large regional variations occur in farming systems and agricultural practises. Second, EUROSTAT technical coefficient data are the result of a harmonized methodology, which may not in all cases reflect country-specific particularities. In other words, the use of a standardised European nomenclature requires the amalgamation of regionally important crops or livestock categories. Finally, the N coefficients supplied by the member states also differ remarkably between countries (e.g. fertilisation coefficients) or are exactly equal for a set of countries (e.g. manure coefficients), which suggests that these are not national coefficients supplied by member states, but are values that have been assigned to the countries in question. Values for technical coefficients can be imported at different scale levels, but in this study technical coefficients are provided at the national scale level. 2.2 Nitrogen balance calculations NOPOLU software NOPOLU System 2 is a relational database programme developed in Microsoft Access 2000 by Beture-Cerec (LeGall, G), which enables data processing and accounting procedures for integrated emissions and impact assessments to be carried out for large data sets. The program is 11

14 built on a modular base, and the Nitrogen balance computation system is one module. The algorithms underpinning the Nitrogen balance calculations are based on the model developed by C.Vidal and F.Kozak for the SCEES (Service Central des Enquêtes et Etudes Statistiques) and adapted to the EU wide census classification system NOPOLU soil surface nitrogen balance accounting procedures Figure 1 The terms of the soil surface nitrogen balance The soil surface nitrogen balance calculates the difference between the total quantity of nitrogen inputs entering the soil and the quantity of nitrogen outputs leaving the soil annually (Figure 1). The terms of the soil surface nitrogen balance are as follows: - Nitrogen Inputs: o Inorganic or chemical fertiliser: Nitrogen fertiliser amount applied to crops (unit kg nitrogen per t of fertiliser). o Livestock manure nitrogen production: number of live animals (cattle, pigs, sheep, goats, poultry, horses and other livestock) distinguished in terms of general species (e.g. chicken, turkey), gender, age, purpose, multiplied by an equivalence coefficient. A presence coefficient can be introduced to account for the length of time animals are on the land and the density coefficient to replace the surfaces or confined areas by the number of individuals. The equivalent number of animals is multiplied by the N rejection rate per equivalent animal. o Biological nitrogen fixation: due to the difficulty in estimating the amount of symbiotic fixation by leguminous crops, NOPOLU has a default fixation of 90 kg per ha input in planted areas of legume crops or pasture, which can be adjusted if suitable data are available 1. o Atmospheric deposition: total agricultural land area multiplied by a single coefficient of nitrogen deposited from the atmosphere (kg/ha). 1 OECD does attempt to use nitrogen fixation coefficients from crops and free-living soil organisms. 12

15 - Nitrogen ouputs: o Harvested crops: quantity of harvested crop production (T/ha) multiplied by the respective coefficients of nitrogen uptake. o Harvested forage crops: quantity of harvested forage crop production and grass consumption from pasture (T/ha) multiplied by respective coefficients of nitrogen uptake. The soil surface nitrogen balance equals the sum of N inputs minus the sum of N outputs. If the balance is negative there is a N deficit indication, and if the balance is positive, then there is a N surplus indication. An abatement factor, x is introduced to reduce the input of animal manure into the N balance. The abatement factor is computed locally, at a larger scale than the NUTS level, to account for the internal changes of fodder that occur inside areas larger than the elementary unit. In a first stage the total demand for livestock fodder is computed based on the fodder needs data. Then the total production of fodder (maize, fodder beetroots, grasses etc.) is calculated. If fodder production exceeds fodder demand, it means that part of the grass from meadows has not been grazed; hence the export by meadows is limited to the quantity consumed. In the opposite case, if fodder demand is higher than fodder production, it is assumed that grass from low-grade meadows, containing less nitrogen, is used as fodder, which implies that the amount of nitrogen in fodder will be lower. In this case, the abatement factor is applied to reduce the nitrogen input by manure (Crouzet, 1999). General assumptions: The following assumptions are adopted by NOPOLU either because the variables are too difficult to estimate or because the net effect is negligible: - Calculations are for agricultural surfaces and assume that there is no fallow land or heather. - N fixation by non-symbiotic microorganisms is not accounted for. - De-nitrification is not accounted for. - Volatilisation of ammonia from applied urea is not considered, but losses by volatilisation during the storage of animal wastes are assumed to be 20 % - Mineralisation of organic N in the soil is considered to be in equilibrium with the reorganisation of mineral N and the humification of new organic material (as a permanent land use regime is assumed). - On heather grounds ( landes ) and alpine meadows (les alpages ) it is assumed that there is an equilibrium between the N content of animal manure production and the N content of consumed grasses. These surfaces are not taken into account for the calculation of the N balance Overlay analysis to generate the hydrosol The tabular database resulting from the geographic overlay between the administrative areas, river basins and CORINE Land Cover is called Hydrosol. The Hydrosol database is the link between the administrative, geographical and hydrographical boundaries. Considering each elementary intersection between the lowest level watershed and the most detailed administrative code, the hydrosol database contains the area of each of the 44 CORINE Land Cover classes contained at the intersection. The hydrosol file was created in the following steps: - Intersection of NUTS and river basin coverages; 13

16 - Cross tabulation between the intersection and the CORINE Land Cover grid; - A join of the intersection and the cross tabulation files; and, - Database processing to ensure that the combination of the NUTS code and river basin code has a unique record in the hydrosol CORINE land cover to disaggregate N balance estimates National statistics are generally collected for administrative units, although for agri-environmental applications such as nitrogen fate modelling, river basin units are more appropriate. Therefore, statistical data need to be allocated to catchment units. CORINE Land Cover database provides EU wide geo-referenced data, and can be used to reallocate statistical data, enabling a spatial distribution of agricultural census data (Kayadjanian and Vidal, 2001) or a re-allocation of nitrogen balance estimates to river basins (Figure 2). Statistical data crop area Livestock N Balance estimates NUTS 3 CORINE Spatialise N Balance estimates Catchment N Balance estimates reported at catchment level Figure 2: Spatialisation of N balance estimates using the CORINE Land Cover as a co-variable 14

17 The CORINE land cover is used, on the basis of the hydrosol layer, to spatialise N balance estimates calculated at the administrative level. The re-allocation procedure uses the results of: - The overlay analysis between the administrative areas, river basins and CORINE Land Cover (CLC), which provides the land cover area for each hydrosol polygons, and - The correspondence table between CORINE Land Cover classes and Farm Structure Survey variables (provided in Annex 8.2). The N balance estimates are apportioned to the hydrosol polygons according to the percentage area of the CLC classes occurring in the hydrosol entities. The hydrosol therefore allows the N Balance estimates to be aggregated to both lower administrative levels or to catchments. The correspondence table provides the upper limit (as a percentage of the area) for each CLC class that could be used for FSS variables. For example, CLC class (non-irrigated arable land) could be cultivated up to 95% of its total area with the FSS variable D/01 (common wheat), while CLC class (mainly agriculture area but with significant natural vegetation) could receive only up to 60% of its total area with common wheat. This results in pooling together all CLC areas available to receive the adequate census areas 2. The correspondence table was elaborated for a French case-study by I Forge (CORPEN) according to expertise provided by agronomists and environmentalists. Being the only correspondence table available, it was used for the present study but necessary adaptation/optimisation would be necessary to apply it at European level. In NOPOLU it is possible to set other coefficient values for each CLC class. The nitrogen balance estimates are then apportioned to the elementary intersection polygons according to the percentage area of the CLC classes occurring in it. It therefore allows the nitrogen balance estimates to be aggregated to both lower administrative levels or to catchments. The following factors have to be considered when using CLC to spatialise data calculated on the basis of the FSS (Kayadjanian and Vidal, 2001): - Semantics certain CLC classes are not clearly defined, for example, CLC land principally occupied by agriculture with significant areas of natural vegetation. Additionally, the relation between the FSS variables and CLC classes is qualitative, meaning that for a defined CLC class it is only possible to indicate which crops and livestock categories occur in that CLC class. - Regionalisation - one correspondence table (between statistical and CLC classes) has been applied for the whole of Europe, while variations in agricultural practices suggest that the correspondence table should be optimised at regional level. - Spatial scale the minimum size of 25 ha of CLC s mapping units results in some inaccuracy in assigning CLC classes, for example, non-agricultural polygons may in fact contain small agricultural areas. - Temporal scale the acquisition period for the CLC is over a 10-year period ( ), whereas the farm structure survey is over a 2-year period. - The differences between the crop and livestock areas based on the CORINE Land Cover information and based on the Farm Structure Survey results in an underestimation of N Balance estimates when FSS agricultural area is lower than the CLC agricultural area and an 2 Note that similarly, the area allowed to receive manure is expressed as a percentage of the area of each crop type (ex FSS D14- Fresh vegetable, melons, strawberries will never receive manure, while a fraction of maize which may cover a certain proportion of arable land + a certain proportion of heterogeneous agricultural land may receive manure). 15

18 overestimation of the surplus when FSS agricultural area is higher than the CLC agricultural area. This effect is likely to be a severe over or under estimation for very small catchments and where sliver polygons occur (often in coastal areas) as a result of the NUTS-CLC-River Basin overlay analysis. An additional remark is that the relation between census variables and CLC classes is qualitative, meaning that for a defined CLC class it is only possible to indicate which crops and livestock categories occur in that CLC class 2.3 Preliminary processing of data To automate the importation of agricultural census data into NOPOLU a function in Access VBA was developed. This function converts dbf tables for each crop and livestock category, containing crop and animal values per NUTS level, which are exported from New Cronos to one excel table with crop and animal types in the columns and the NUTS codes in the rows. This excel file has the appropriate data structure to be imported into NOPOLU. 2.4 Nitrogen balance calculation in NOPOLU A calculation in NOPOLU can be defined as a combination of a scenario and instructions defining the chosen output scale level. Each scenario consists of four groups of technical coefficients (crop, cultivation independent of parameter (yield values), manure and distribution), a census file, a hydrosol cover and optionally a file with atmospheric deposition data. The sets of technical coefficients are stored independently of the scenarios so they can be used in several scenarios very easily. Census data are available on NUTS 2 level for 1990, 1993, 1995 and The technical coefficients for fertilisation, exportation and breeding are only available for 1997, whereas yield values are available for 1990 to 1997 for some countries (France, Portugal, Finland, Sweden), and only for 1997 or 1996 for the others. So the time series analysis has been executed with constant technical coefficients (1997 values) and time series census values. 2.5 Sensitivity analysis The scenario and sensitivity analysis is carried out to assess the sensitivity of the nitrogen balance model. The aim is to identify the most sensitive parameters, which influence the nitrogen balance at the country and regional level (NUTS2). A sensitivity analysis is relevant because the correctness of the calculated nitrogen balance depends mainly on the correctness of the three main components of the model, being the quantity of supplied fertiliser (mineral and organic), the crop yields and the nitrogen content of harvested crops. The sensitivity analysis has been done by examining variations in technical coefficients and using the ability of NOPOLU to undertake scenario analysis. Parameter values have been increased and decreased by 5, 10, 15 and 20 percent increments. Other scenarios could also be carried out, such as selecting one value for all crops or livestock for all entities (Crouzet, 2000), focusing on outlier detection in technical coefficients, or evaluating the contribution of certain crops in selected NUTS regions (for example for maize). The methodology chosen, however, offers a good basic analysis of the sensitivity of the surplus parameters all over Europe, and represents an approach of exploring the sensitivity of the main components of the soil surface Nitrogen balance model. 16

19 2.6 Inclusion of atmospheric deposition in N balance The NOPOLU model was adapted to allow the inclusion of the atmospheric deposition of N as an input into the nitrogen balance. Atmospheric deposition values were derived from 50 by 50 km grid cells, published by the Co-operative programme for monitoring and evaluation of the long range transmission of air pollutants in Europe (EMEP). The inclusion of the input of N by atmospheric deposition allows a more realistic calculation of the nitrogen balance and enables a comparison to be made with EUROSTAT nitrogen estimates, which include atmospheric deposition. Presently the N from atmospheric deposition is added to the general surplus calculation, since the atmospheric deposition data are for the total NUTS area, whereas the other input are related to the agricultural area only. 17

20 3 RESULTS 3.1 Soil Surface Nitrogen balance for Europe (EU15) using NUTS 2 data for 1990 The 115 NUTS 2 balance results have an average surplus of 25 kg N/ha and a median of 27 kg N/ha. The minimum value is 423 kg N/ha (IT12) and the maximum value is 324 kg N/ha (NL4). The standard deviation is The distribution of the balances shows high surplus amounts in regions of intensive farming (Flanders (B), the Netherlands, Brittany (FR), Pohjois (FI)). Lower values occur in the central areas of Italy, Spain and France. The unexpectedly high N estimates in Finland are due to a high N fertiliser input of 160 kg/ha for fodder crops (D18), indicated in Annex 8.3. Fodder crops account for ha out of an agricultural area of ha in FI15, and ha out of an agricultural area of ha in FI13. 18

21 Figure 3: N Balance in Kg/ha for Europe (EU 15) calculated at NUTS 2 level The results at the catchment level show a similar Nitrogen balance distribution as the estimates at the NUTS level, but with some important differences (Figure 3). The high surpluses in Finland and the Western UK are more accentuated; also some catchments with higher values in Northern Italy become visible. The average balance on catchment level is 60 kg N/ha (out of 4118 entities) and the median is 33 kg N/ha, with higher values occurring in smaller catchments where intensive agricultural is practiced. The standard deviation is 243 kg N/ha. The distribution of nitrogen balance values shows that 89 % of the values lie within the (-100, +100 kg N/ha) range (Figure 4). 19

22 For both Figure 3 and Figure 4, the low average surpluses for France and especially Italy are due to unrealistically high exportation values for pastures and meadows (F) (see Annex 8.14, which provides a detailed analysis of extreme results occurring in Italy (IT13)). Kg/ ha Figure 4: N Balance in Kg/ha for Europe (EU 15) calculated at catchment level (1:1Million scale) 20

23 Number of catchments Kg N/ ha Figure 5: Histogram of N balance estimates (kg/ha) 3.2 Soil Surface Nitrogen Balance for Europe (EU15) using NUTS 3 data for 1990 The availability of more detailed data for a number of European countries enables the production of results on NUTS 3 administrative level for Data were not available for Austria, Sweden and Finland. Additionally the most recent NUTS nomenclature, established in 1998 does not correspond to the previous versions used in the census files, in particularly NUTS regions in Germany (creation of new NUTS 3 regions and transfer of communes between different NUTS 3 in Sachsen), Sweden (merging of counties and transfer of communes), Finland (split of NUTS 3 regions and transfer of communes), Ireland (introduction of NUTS 2 code) and the United Kingdom (completely new Nuts division on all levels). On NUTS 2 level census data were corrected, but this was not done for NUTS 3 regions. 21

24 Kg/ha Figure 6: N Balance in Kg/ha for Europe (EU 15) calculated at NUTS level using the NUTS 3 census database The pattern of results is comparable to the NUTS 2 results, however, absolute values can differ for certain NUTS regions (especially for Spain and Ireland), due to the use of average technical coefficients applied to aggregated variable categories at the NUTS2 level. Average technical coefficients are not weighted to the distribution of the composing variables (e.g. for pigs), because this would need to be done for each NUTS region. The effect of working with aggregated variables and non-weighted average technical coefficients causes the discrepancies between the two datasets. In Table 2 the summary statistics of the two sets of results are shown. 22

25 Summary measures for FARM NUTS 2 results (n = 115) Balance (kg/ha) Mean 25 Median 27 Standard deviation 65 Minimum -423 Maximum 324 Range 747 Summary measures for FARM NUTS 3 results (n = 383) Balance (kg/ha) Mean 20 Median 12 Standard deviation 46 Minimum -111 Maximum 273 Range 384 Table 2: Summary statistics of N balance estimates for NUTS 2 and 3 Calculation of Agricultural Nitrogen Quantity for EU River Basins The mean and median balance value is slightly higher in the FARM NUTS 2 results, due to differences in technical coefficients used. The higher range in the FARM NUTS 2 result set is due to two extremely negative balances in Italy (IT13 (Liguria) and IT31 (Trentino), see Annex 8.13). The high negative values suggest severe data uncertainty. For this reason, distribution statistics are re-calculated on series that exclude these outliers for NUTS 2 (Table 3). Summary measures for FARM NUTS 2 results (n = 113) Surplus (kg/ha) Mean 31 Median 27 Standard deviation 47 Minimum -54 Maximum 324 Range 378 Table 3: Re-calculated summary statistics of N balance estimates for NUTS 2 23

26 Kg/ha Figure 7: N Balance in Kg/ha for Europe (EU 15) calculated at catchments level using the NUTS 3 census database The output at catchment level (Figure 7) shows a similar pattern to the output at NUTS level, except for some differences in Ireland and Spain, which were already detected in the maps at NUTS level. The smaller catchment units allow for a more differentiated picture of the balance within a NUTS region, for example in ES51, ES12 and FR52, where within the NUTS region, catchments with very high balances (> 80 kg N/ha) are detected. The results on catchment level derived from NUTS 2 data do not generate the same differentiation. For example in the region 24

27 ES12 no catchment with a higher surplus is detected, whereas in FR52 and ES51 all catchments are assigned a very high surplus (> 80 kg N/ha). 3.3 Time series nitrogen balance simulations using NUTS 2 census data Census data are available on NUTS 2 level for 1990, 1992, 1995 and The technical coefficients for fertilisation, exportation and breeding are only available for 1997, whereas yield values are available for 1990 to 1997 for some countries (France, Portugal, Finland, Sweden), and only for 1996 or 1997 for the others. So the time series analyses have been carried out with constant technical coefficients and yield values (1997 values) with time series census values. The full results are provided in Annex In 20 NUTS regions out of the 114 there is an increase of the N surplus between 1990 and regions have a negative evolution and in 10 regions no differences have been detected. In the regions with an increase in N balance estimates, the mean increase is 6 kg N/ha, and in the regions with a decrease in N balance estimates the mean decrease is -9 kg N/ha. The geographical spread of the evolution is presented in Figure 8. Between 1990 and 1997 balance values have sharply reduced in the Netherlands (Figure 9 and Figure 10), in particular Limburg Brabant (NL4) and Gelderland Overrijsel Flevoland (NL2). In Greece, N balance estimates in several NUTS regions have decreased considerably (Figure 9). Other areas of high N balance estimates are characterized by a slight decrease (Flanders (BE1), (DEA), (UKE) or a slight increase (Brittany (FR52), Denmark (DK)) in N. 25

28 Figure 8: Evolution of N balance differences (Kg N/ha) between 1990 and 1997 for Europe (EU 15) 26

29 AT BE DE DK ES FI FR GR IE IT LU NL PT UK -20 average N balance 1990 (1993 for Austria and Finland) average N balance 1997 Figure 9 The change in the average N Balance (kg N/ha) between 1990 and 1997 for each EU member state NL2 NL4 FR52 IE BE1_2 DK ES51 average N balance 1990 average N balance 1997 Figure 10 The change in the average N Balance (kg N/ha) between 1990 and 1997 for selected NUTS 2 regions 3.4 Estimation of the effect of using CORINE Land Cover in nitrogen balance estimations CORINE Land Cover Grid is used to proportionally assign data and results of nitrogen balance calculations to catchments or administrative regions according to the land use characteristics. In particular, the total agricultural area, and the relative distribution of the different CLC agricultural subclasses are taken into account. The distribution of crop and animal types of the CLC classes and the usable area of each CLC class is imported in NOPOLU as a scenario (see Annex 8.2 for distribution model) (Forge, 1998). 27

30 The effect of spatialisation with CLC was tested by the creation of a number of scenarios presented in the scheme below (Figure 11). The aggregation of NUTS 3 census data to NUTS 2 and NUTS 1 levels was carried out to evaluate to what extent the use of CLC can overcome the use of agricultural census data supplied at higher administrative level. In a previous study, Crouzet (2000) demonstrated for a catchment in France with detailed statistical data (NUTS5 level) that using CLC resulted in a gain in agricultural census detail from NUTS3 to NUTS4. In that study, computation from NUTS2 to NUTS3 was also done, but results were not conclusive. Figure 11: Scheme summarizing the methodology for the assessment N balance calculations with and without the use of CLC to spatialise data Since there are considerable differences between the two available censuses, only the FARM census on NUTS 3 level was used, as this is the most complete dataset. This data set was used to elaborate the reference data, which was used for results comparison. Out of this census database, the censuses at NUTS 2 (2) and NUTS 1 (3) level were aggregated. Six calculations were carried out. The reference NUTS 3 N balance (1) is the result of a balance calculation without disaggregating and reaggregating data. The two CLC NUTS 3 N balance calculations use the CLC for redistribution of results (4 and 6). In the two basic NUTS 3 N Balance calculations (5 and 7) reaggregation has been done without using the information from the CORINE Land Cover to disaggregate results. N balance results (in tons N) are illustrated for Spain (Figure 12), with simulation results for Spain provided in Annex A first observation from Figure 12 is the considerable difference between the reference results and the results obtained from data aggregated to NUTS 1 level. The reaggregation from NUTS 3 to NUTS 1 level does not seem to produce realistic results. Especially for those NUTS 1 areas with large differences among the constituent NUTS 3 regions (e.g.es2, ES6), where both strongly negative and positive estimates are apparently neutralized by carrying out the calculation at NUTS 1 level. This outcome confirms previous findings that using aggregated statistics over large areas 28

31 reduces the spatial variability. The use of CLC allows some spatialisation of the N balance estimates statistics, but results are limited by the resolution of the input data. ES2 ES6 Figure 12 N balance results for Spain with and without the use of CLC to spatialise data for administrative units For the NUTS 2 aggregated results, a simple linear regression was used to compare the results. The objective is to check the distance between the reference and the aggregated data with or without use of CORINE Land Cover as disaggregation layer. Table 2 and Figure 13 present the results. 29

32 Results of simple linear regression for BASIC2 (5) Summary measures Multiple R 0,78 R-Square 0,61 StErr of Est 5348,55 Regression coefficients Coefficient Std Err t-value p-value Lower limit Upper limit Constant 240,39 772,13 0,31 0, , ,61 Reference 0,62 0,07 8,48 0,00 0,47 0,77 Results of simple linear regression for CLC 2 (4) Summary measures Multiple R 0,84 R-Square 0,70 StErr of Est 5922,54 Regression coefficients Coefficient Std Err t-value p-value Lower limit Upper limit Constant 158,28 855,00 0,19 0, , ,30 Reference 0,85 0,08 10,48 0,00 0,68 1,01 Table 4: Summary statistics simple linear regression analysis Two results are to be considered: First, the R - square, also called the coefficient of determination, can be interpreted as the fraction of the variation of the response variable explained by the regression line. The table indicates a better value for the CLC scenario, explaining 70% of the variation (against 61% in the first case, therefore a gain of 9/40 ~ 25% of the un-explained variance). The square root of R² (Multiple R) is the correlation between the fitted values and the observed values of the response variable. Both simulations turn out to give acceptable correlations near 80 %, which is illustrated by the scatter plots on Figure Fitted CLC BASIC2 Figure 13: Scatter plots regression analysis of the CLC effect on nitrogen estimation (tons) Second, the slope (0.85 against 0.62) is closer to 1 (identity) and better estimated when CORINE Land Cover is used. This is an important point since the objective is to obtain the consistent surplus value despite basic statistics are aggregated at a higher rank. The standard error of estimate, which is the standard deviation of the residuals, is slightly higher in the CLC simulation, indicating that the basic simulation is a slightly better simulation. However in the plots of residuals (Figure 14), no evidence for structural higher residuals is found, neither a positive or negative bias. The total sum of differences (Annex 8.12) between the surplus of the reference data set (1) and the surplus calculated with CLC (2) or without CLC (3) indicates for both scenarios a slight negative deviation from the reference results (-6120 tons nitrogen for the 30

33 scenario with CLC and tons nitrogen for the scenario without CLC). We can conclude, that the simulation using the CORINE Land Cover provides better results because the regression model of (4) explains more accurately the N (tonnage) than regression model (5) Residuals CLC simulation Residuals "basic" simulation reference reference Figure 14: Scatter plots of residuals of the regression analysis to assess the CLC effect on Nitrogen estimation (tons) 3.5 N balance simulation including atmospheric deposition The average contribution of atmospheric deposition in Europe is 6 kg/ha, with maximum values for Belgium (13,3 kg/ha), the Netherlands (13,8 kg/ha) and to a lesser extent Germany (10,3kg/ha) and Luxemburg (10,4 kg/ha) (Table 3 and Figure 15). Note that the surface for atmospheric deposition is the total NUTS area. The Nitrogen contribution from atmospheric deposition is similar to the results published by Hansen (2000). The average contribution of atmospheric deposition to the nitrogen input in agricultural areas is 12% (Table 3). The share is the highest in Finland (21%), Austria (19%) and Italy (19%). Atm Depositon (kg) N surplus (without atmospheric deposition) Surface Atm input atmospheric TOTAL Entity Depositon (ha) deposition (kg/ha) surplus AT ,4-4,6 1,9 BE ,3 96,9 110,2 DE 3,63E ,3 43,1 53,4 DK ,9 71,8 77,6 ES 2,19E ,5 32,8 37,3 FI ,4 72,1 74,5 FR 3,7E ,8 14,4 21,1 GR ,9 29,1 34,0 IE ,2 30,4 33,7 IT 2,33E ,8 44,6 52,3 LU ,4-14,7-4,4 NL ,8 158,1 171,9 PT ,4 40,9 46,3 UK 1,65E ,8 39,2 46,0 Europe 1,71E ,6 36,1 Table 5: Absolute and relative contribution of N atmospheric deposition to N surplus 31

34 Kg/ha Figure 15: Input of N atmospheric deposition (kg/ha) for Europe Across the European Union, the average % contribution to the agricultural soil surface nitrogen balance of mineral fertiliser, organic fertiliser (manure) and atmospheric deposition is 75%, 13% and 12%, respectively (Table 4 and Figure 16). The range in % mineral fertiliser contribution is 54% (B) to 91% (IR). In countries where intensive livestock farming is taking place, the % contribution of manure to the nitrogen balance is close to 30 % (B, NL, and DK). The range in % contribution of atmospheric deposition to the nitrogen balance is 4 % (IR) to 21 %(FI). 32

35 Country Agricultural Area (ha) Fertilization (tons) Breeding emission (tons) atmospheric deposition (tons) Total input (tons) AT , ,1684 BE , ,2802 DE , ,016 DK , ,06 ES , ,806 FI , ,578 FR , ,667 GR , ,2826 IR , ,5433 LU , ,80062 IT , ,136 NL , ,9965 PT , ,4471 UK , ,663 TOTAL , ,44 % fertiliser % organic fertiliser % atmospheric deposition AT 56% 24% 19% 100% BE 54% 33% 12% 100% DE 67% 19% 15% 100% DK 67% 29% 5% 100% ES 80% 7% 13% 100% FI 72% 7% 21% 100% FR 82% 7% 12% 100% GR 71% 12% 17% 100% IR 91% 5% 4% 100% LU 82% 5% 14% 100% IT 70% 12% 19% 100% NL 58% 34% 8% 100% PT 66% 19% 15% 100% UK 85% 8% 7% 100% TOTAL 75% 13% 12% 100% Table 6: Total N (tons) and percentage breakdown of fertiliser, organic fertiliser (manure) and atmospheric deposition to the N balance per EU member state Europe N surplus 12% 13% 75% % fertiliser % organic fertiliser % atmospheric deposition Figure 16: Percentage breakdown of fertiliser, organic fertiliser (manure) and atmospheric deposition at the EU level. 33

36 100% 90% 80% 70% 60% 50% % atmospheric deposition % organic fertiliser % fertiliser 40% 30% 20% 10% 0% AT BE DE DK ES FI FR GR IR LU IT NL PT UK TOTAL Figure 17: Percentage distributions of input components to the N balance per EU member state. The distribution of total N surplus (kg/ha) at the NUTS 2 and NUTS 3 level, with the contribution of atmospheric deposition included, indicates particular N Surplus hot-spot regions, such as Brittany, Catalonia, Benelux and Denmark (Figure 18). 34

37 Kg/ha Figure 18: Nitrogen balance at NUTS2 level for EU, incorporating atmospheric deposition of N 3.6 Relative importance of nitrogen balance components contributing to N balance The geographical distribution of the main constituting factors of the nitrogen balance (input from mineral fertilisation, livestock manure input and exportation of nitrogen by crops) provides additional information about nitrogen balance hot spots. 35

38 Kg/ha Figure 19: Nitrogen input by mineral fertilizer (kg N/ha), based on FARM NUTS 2 data Figure 19 shows the nitrogen input by mineral fertilizer, based on FARM NUTS 2 census data and 1997 EUROSTAT technical coefficients. The nitrogen input from mineral fertiliser for each NUTS 2 region is provided in Annex The pattern of nitrogen input by mineral fertilizer is mainly consistent with the findings by Hansen (Hansen, 2000). Regions with high fertilisation input are the Benelux, Western France, the UK, Finland and some regions in Spain, Italy, Greece and Finland. Values for the UK, Ireland and some regions in Finland seem to be somewhat higher than in the Hansen study. Focusing on NUTS2 level peak regions are noted in the Netherlands 36

39 (162 kg/ha for NL and NL2), the UK (143 kg/ha for UKE and 280 kg/ha for UKM and Finland (152 kg/ha for FI14). Kg/ha Figure 20: Nitrogen input/ livestock manure (kg N/ha), based on FARM NUTS 2 data The values of nitrogen input from livestock manure are in general substantially lower than the input values from mineral fertilizer (Figure 20). Values are also by and large lower than the values presented by Hansen (2000), despite using the same manure technical coefficients. Hot spots are the Benelux (in particular NL2 and NL4 and BE 1-2) and to a lesser extent Brittany (FR52) and Denmark, which are regions with high livestock densities. An explanation for the lower values in comparison to results from Hansen (2000) is the use of a local abatement factor, reducing the input by livestock manure. 37

40 The abatement factor is computed locally, at a larger scale than the result NUTS level, to account for the internal changes of fodder that occur inside areas larger than the elementary unit. In a first stage the total demand for livestock fodder is computed based on the fodder needs data. Then the total production of fodder (maize, fodder beetroots, grasses etc.) is calculated. If fodder production exceeds fodder demand, it means that part of the grass from meadows has not been grazed; hence the export by meadows is limited to the quantity consumed. In the opposite case, if fodder demand is higher than fodder production, it is assumed that grass from low-grade meadows, containing less nitrogen, is used as fodder, which implies that the amount of nitrogen in fodder will be lower. In this case, the abatement factor is applied to reduce the nitrogen input by manure (Crouzet, 1999). The abatement factor is an adjustment of fodder sources in the case that available fodder is not sufficient for livestock needs. However another possibility within livestock management practices would be to use feedstuff for the livestock, with the consequence being that the amount of nitrogen content in animal manure will be higher. This is particularly true in intensive breeding areas and this would also increase the proportion of the organic fertiliser in the N surplus balance calculation. The use of abatement factor therefore promotes the use of grass as fodder as much as possible, and the use of fodder from low grade pastures when demand is too high. This may not always correspond to agricultural practises and would therefore result in an underestimation of the content of N in manure (and therefore the use of organic fertiliser in this study). In our case, the abatement factor was applied for the entire EU, and no distinction was made between intensive and extensive livestock farming systems. This point was already raised by Crouzet (2000) who concludes that applying the abatement factor, was not appropriate for regions where intensive livestock is practised. An improvement in the modelling approach would therefore be to introduce a criterion based on livestock density, which allows the abatement factor to be suppressed, and whereby realistic supplies of imported feedstuffs into the N balance accounting procedure are included (provided information on the use of feedstuff and related N content in manure are available). 38

41 Kg/ha Figure 21: Nitrogen output/ exportation values (kg N/ha), based on FARM NUTS 2 data The NOPOLU exportation values represent exportation by harvested crop material and output by meadows. Exportation values are in general lower than the values presented in the study by Hansen (2000). This can be explained by missing exportation coefficients in EUROSTAT data set for some crops (for example, flowers and ornamental plants, forage plants, hops, cotton and soya for all or most countries). A second explanation can be found in the difference in the calculation methodology with regards to the output by meadows. It is difficult to determine the amount of grass harvested and/or grazed on a regional basis. Statistics on harvested grass are only available for a few countries and it is not always clear if existing data refer to actual harvested production or to the potential production of grass (Hansen, 2000). As described above, estimations are based on the fodder needs of livestock. If the total amount of livestock fodder consisted of grass, the grass removed would correspond to the total fodder needed. The quantity 39

42 supplied by fodder other than grass can be calculated in different ways. It can be calculated by using a fixed rate between grass as fodder, and fodder other than grass, by using data about feedstuffs bought from outside the region. Alternatively, it can be assumed that the export by meadows is equal to the quantity consumed and, if the fodder need exceeds the available quantity, additional fodder comes from low-grade pastures. This approach would have an impact on exportation values, but as described above would not affect the N content of manure. In the study by Hansen (2000) feedstuffs bought from outside agriculture have been taken into account to overcome the tendency to overestimate the removal via grass. Table 7 summarizes the nitrogen input and output on country level. The table shows that mineral fertilizers are the major source of nitrogen in the EU. The relative importance varies from about 60 % (Belgium, Netherlands, Denmark) to more than 90 % (Luxemburg, Spain, France, Ireland, Finland). The share of biological fixation is included in the fertilisation share. Breeding Agricultural Fertilisatio Exportatio emission Atmos Depos Total Surplus Fertilisatio Exportatio Breeding emission Atmos Depos Total surplus Country Area (ha) n (tons) n (tons) (tons) (tons) (tons) n (kg/ha) n (kg/ha) (kg/ha) (kg/ha) (kg/ha) AT BE DE DK ES FI FR GR IR LU IT NL PT UK TOTAL Table 7: Summary of nitrogen input and output estimates at country level, based on 1990 NUTS 2 FSS data and 1997 technical coefficients 3.7 Comparison of nitrogen balance simulations with EUROSTAT nitrogen balance estimates EUROSTAT N balance data were available for us for 1993, 1995 and results were used to compare with the results calculated with NOPOLU ( Figure 22), since the detailed NOPOLU calculations have been carried out with data from The overall pattern is similar to the results obtained by EUROSTAT, although absolute values differ. For most countries the EUROSTAT values are somewhat higher, in particular for Italy and Germany. For other countries values are quite similar (Spain, United Kingdom). Explanations for the differences can be found in the limited availability of country specific technical coefficients, which allows for differences in the calculation. The NOPOLU model uses default values for missing values. French technical coefficients were used as default values, except when the French value itself was missing. The use of average technical coefficients due to aggregated census variable categories was already mentioned before and also seems to have a considerable impact. Besides the quality of technical coefficients there is the input by symbiotic fixing. In NOPOLU symbiotic is taken into account by considering nitrogen fixation as a special fertilisation type. 40

43 However, technical coefficients used to calculate the nitrogen content of leguminous crops are not accurate, so differences with the EUROSTAT results are likely. Kg/ha Figure 22 EUROSTAT N balance simulations (1993) 3.8 Results of sensitivity analysis Sensitivity of N balance to yield values To test the sensitivity of the model to changes in yield, yield values were increased and decreased by 5, 10, 15 and 20%. These variations are introduced purely to test the sensitivity of the model, because increased crop yields are to a certain level related to the N fertilisation rate. 41

44 The overall trend is lower balances when yield values are decreased and higher balances when yield values are increased (Table 8). However, the magnitude of variation is quite large between countries with strong fluctuations in the balance, in particular for Italy (89 %) and France (107 %). Finland and the Netherlands have the least variation (11%). At the regional level (NUTS 2), regions characterised by intensive livestock farming (BE1_2, FR52, ES51) show less variation in the balance, whereas regions with intensive cereal production (FR42, UKL) show very high variations. % of change in yield values Entity reference tons BE DE DK ES FI FR GR IE IT LU NL PT UK Table 8: Absolute changes (tons N) in the nitrogen balance estimates according to changing yield values for Europe % of change in yield values Entity reference tons BE 18% 14% 9% 5% -4% -9% -13% -18% DE 29% 22% 15% 7% -7% -15% -22% -29% DK 28% 21% 14% 7% -7% -14% -21% -28% ES 28% 21% 14% 7% -7% -14% -21% -28% FI 11% 8% 6% 3% -3% -5% -8% -11% FR 107% 80% 53% 27% -26% -53% -80% -107% GR 22% 16% 11% 6% -5% -11% -16% -22% IE 62% 47% 31% 16% -15% -31% -46% -62% IT 89% 67% 45% 23% -22% -45% -67% -89% LU 43% 33% 22% 11% -11% -22% -32% -43% NL 11% 8% 5% 3% -3% -5% -8% -11% PT 15% 11% 8% 4% -4% -8% -11% -15% UK 62% 47% 31% 16% -15% -31% -46% -62% Table 9 Relative changes (%) in Nitrogen to the reference tonnage (EU wide NUTS2 results) according to changing yield values for Europe 42

45 % of change in yield values Entity reference tons BE1_ ES ES FR FR GR UKL BE1_2 9% 7% 5% 2% -2% -5% -7% -9% ES43 49% 37% 24% 12% -12% -24% -36% -49% ES51 11% 9% 6% 3% -3% -6% -9% -11% FR42 148% 112% 74% 37% -37% -74% -111% -148% FR52 9% 7% 4% 2% -2% -4% -7% -9% GR13 23% 17% 11% 6% -6% -11% -17% -22% UKL 142% 108% 71% 36% -35% -71% -106% -142% Table 10: Absolute and relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing yield values for selected NUTS 2 regions Sensitivity of N balance to manure coefficients (organic fertiliser) The absolute values of variations in the balance due to increment changes in the manure coefficients are lower than for the other technical coefficients. Italy (16%), Belgium (15%) and Denmark (14%) show the highest variation, however, differences with the other countries are low. On NUTS level regions with intensive livestock activities show higher variation in the surplus, due to the higher share of manure in the total balance. GR13 is on the other side of the spectrum showing little or no variance. % of change in manure values reference Entity tons BE DE DK ES FI FR GR IE IT LU NL PT UK Table 11: Absolute changes (tons N) in nitrogen balance according to changing manure coefficients for Europe 43

46 % of change in manure values Entity BE -15% -11% -8% -3% 3% 8% 10% 15% DE -11% -9% -6% -3% 2% 5% 8% 10% DK -14% -11% -8% -3% 3% 7% 11% 14% ES -3% -2% -2% -1% 1% 3% 4% 5% FI -2% -2% -1% -1% 1% 1% 2% 3% FR -7% -8% -4% -2% 2% 9% 11% 13% GR -7% -6% -3% -3% 0% 1% 4% 4% IE -5% -3% -2% -2% 1% 2% 3% 4% IT -12% -9% -6% -3% 3% 10% 13% 16% LU -3% -3% -2% -1% 1% 2% 2% 3% NL -12% -8% -6% -2% 4% 6% 11% 12% PT -8% -7% -4% -3% 1% 2% 5% 6% UK -8% -8% -4% -3% 1% 2% 5% 6% Table 12: Relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing manure coefficients for Europe Entity % of change in manure values reference tons BE1_ ES ES FR FR GR UKL BE1_2-17% -12% -9% -4% 3% 9% 12% 17% ES43-2% -2% -1% -1% 1% 1% 2% 3% ES51-9% -7% -4% -2% 2% 10% 12% 14% FR42-4% -3% -2% -1% 1% 7% 8% 9% FR52-9% -7% -4% -2% 2% 9% 11% 13% GR13-2% -2% -1% -1% 0% 0% 1% 1% UKL -6% -6% -3% -3% 0% 1% 3% 4% Table 13: Absolute and relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing manure coefficients for selected nuts regions Sensitivity of N balance to fertilisation coefficients The variation in the balance due to changing fertilisation coefficients is high, especially in Italy (110%) and France (117%). Only in the Netherlands, a variation in the fertilisation coefficient of 20% generates a variation in surplus of less than 20% (19%). These are country averages and peak values in agricultural regions reach values up to 160% (FR42), whereas for Flanders and Brussels the variation is only 12%. 44

47 % of change in fertilisation coefficients reference Entity tons BE DE DK ES FI FR GR IE IT LU NL PT UK , , , , , ,906 Table 14: Absolute changes (tons N) in nitrogen balance according to changing fertilisation coefficients % of change in fertilisation coefficients reference Entity tons BE -24% -18% -12% -6% 6% 12% 18% 24% DE -38% -29% -19% -10% 10% 19% 29% 38% DK -34% -25% -17% -8% 8% 17% 25% 34% ES -45% -34% -22% -11% 11% 22% 33% 45% FI -28% -21% -14% -7% 7% 14% 21% 28% FR -117% -88% -59% -29% 29% 59% 88% 117% GR -36% -27% -18% -9% 9% 18% 27% 36% IE -78% -58% -39% -19% 19% 39% 58% 78% IT -110% -83% -55% -28% 28% 55% 83% 110% LU -60% -45% -30% -15% 15% 30% 45% 60% NL -19% -15% -10% -5% 5% 10% 15% 19% PT -27% -21% -14% -7% 7% 14% 21% 27% UK -75% -56% -37% -24% 12% 29% 47% 65% Table 15: Relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing fertilisation coefficients for Europe % of change in fertilisation coefficients reference Entity tons BE1_ ES ES FR FR GR UKL BE1_2-12% -9% -6% -3% 3% 6% 9% 12% ES43-66% -50% -33% -17% 16% 33% 50% 66% ES51-20% -15% -10% -5% 5% 10% 15% 20% FR42-162% -122% -81% -41% 40% 81% 121% 162% FR52-18% -14% -9% -5% 5% 9% 14% 18% GR13-41% -31% -20% -10% 10% 20% 31% 41% UKL -157% -118% -78% -39% 39% 78% 118% 157% Table 16: Absolute and relative changes (%) to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing fertilisation coefficients for selected nuts regions 45

48 3.8.4 Sensitivity of N balance to crop exportation coefficients Crop exportation coefficients express the average content of nitrogen in harvested crops. The coefficients are imported in kg/quintal (100 kg) in NOPOLU. The effects of variations in the exportation coefficients are highest in Spain (130%) and France (115%), where an increase of exportation values with 20% reduces the total tonnage almost to zero. On regional level agricultural areas with little livestock show the highest variation (FR42, UKL). The variation on the negative side differs from the variation on the positive side. This is most clear in Italy (119% and - 60%). This can be explained by the inclusion of a correction factor (abatement factor) in NOPOLU with regards to the output by meadows in NOPOLU, when the total fodder demand exceeds the total quantity. % of change in exportation coefficients reference Entity tons BE DE DK ES FI FR GR IE IT 22333, , , , , , , ,757 LU NL PT UK Table 17: Absolute changes (tons N) in nitrogen balance according to changing exportation coefficients for Europe % of change in exportation coefficients reference Entity tons BE 39% 30% 20% 11% -2% -12% -23% -33% DE 34% 30% 19% 15% -1% -11% -16% -26% DK 32% 26% 19% 11% -3% -10% -16% -25% ES 130% 107% 77% 51% -11% -41% -68% -99% FI 31% 25% 17% 10% -3% -11% -18% -27% FR 115% 95% 66% 41% -12% -41% -65% -95% GR 24% 21% 16% 12% -2% -7% -11% -20% IE 62% 50% 37% 23% -11% -25% -38% -62% IT 119% 108% 88% 47% -4% 3% -19% -60% LU 47% 34% 26% 12% -5% -19% -27% -40% NL 11% 9% 7% 2% -2% -5% -9% -12% PT 16% 14% 10% 8% -1% -4% -7% -15% UK 66% 47% 35% 21% -12% -31% -43% -63% Table 18: Relative changes (%)to reference tonnage (EU wide NUTS2 results) in nitrogen balance according to changing exportation coefficients for Europe 46

49 % of change in exportation coefficients reference Entity tons BE1_ ES ES FR FR GR UKL BE1_2 10% 8% 6% 2% -1% -4% -7% -10% ES43 53% 49% 29% 25% -1% -21% -25% -45% ES51 14% 12% 9% 7% 0% -4% -6% -9% FR42 157% 138% 95% 56% -10% -52% -92% -136% FR52 9% 7% 5% 3% -1% -3% -5% -8% GR13 25% 21% 15% 9% -2% -8% -14% -20% UKL 156% 111% 88% 45% -23% -67% -89% -134% Table 19: Relative changes (%) in Nitrogen to the reference tonnage (EU wide NUTS2 results) according to changing yield values for Europe Conclusions sensitivity analysis The sensitivity analysis on the four main parameter classes of nitrogen balance model shows a high variation in the total tonnage of nitrogen. The model response is closely related to the predominant farming system. In general, the variation due to increment changes in manure coefficients is lower than for the variation generated by crop related coefficients. This implies that the manure coefficients are less sensitive than the crop related coefficients, which is an important finding with regards to priority setting for detailed nitrogen surplus analysis and deriving more precise technical coefficients. The sensitivity analysis also indicates the effect of detailed technical coefficients. The model is indeed sensitive to the quality of technical coefficients, and there is an obvious need to collect technical coefficients that are related to regional characteristics, to the farming system and to the climatological setting. The current datasets are characterised by a considerable number of missing values, too general values (identical values for different countries) and different methods of data collection lack of harmonisation - (see for example the study of Hansen (Hansen, 2001) on manure coefficients). 47

50 4 DISCUSSION 4.1 Data quality The reliability of the soil surface Nitrogen balance modelling is heavily dependent on the quality of the input data, in particular the census data and technical coefficients. Considerable differences in N balance results for similar regions using NUTS 2 and NUTS 3 FARM census data underline the importance of the quality of data set. Differences in N balance estimates when using NUTS 2 and NUTS 3 FARM occur because NUTS 2 Farm census data use aggregated census categories of NUTS 3 FARM census data. This means by default that aggregated technical coefficients are also used in the NUTS 2 simulations. It is expected that N Balance simulation results would improve if more regional technical coefficients were provided from national institutes that reflect regional agronomic practises. However, until the N balance calculations can be validated, this suggestion remains an assumption Methodology An important consideration at the start of the project was to estimate the effect of using the CORINE Land Cover (CLC) as a basic layer in the spatialisation and reaggregation of census data. The results in section 3.4 when comparing N balance simulations using NUTS 2 with and without CLC with N balance simulations using reference NUTS 3 data show that there is only a slightly better outcome when the CLC is used at the scale NUTS2 NUTS3. Globally this means that at that scale, the CLC effect is not very important for improving simulations, but it is likely that the effect of using CLC will vary across the EU, depending on the diversity of the agricultural systems, and the size of NUTS regions and catchments. However, a positive contribution of CLC is that, used as a co-variable, does permit the spatialisation of N balance estimates calculated at administrative units to geographical units (such as catchments and Nitrate Directive areas). The assignment of census information to CORINE Land Cover classes within a NUTS occurs using a distribution scenario, that indicates which crops occur in which CORINE Land Cover classes. This distribution scenario only contains qualitative information, so, when crops are assigned to a certain CLC class, the census data are distributed proportionally to these classes, not taking into account eventual differences in crop spreading or geographical variation. Further investigations at a regional scale are needed to determine and quantify the effect of incorporating CLC in N balance simulations. In this study the CLC-to-Census data correspondence table of Forge (1998) was used, which is based on the French agricultural system. It is unlikely that this correspondence table is suitable for use at the EU level. Therefore there is a requirement to develop regional CLCto-Census data correspondence tables Results - N balance estimates at NUTS2 and NUTS 3 level for Europe The main differences in NUTS 2 and NUTS 3 based N Balance simulations occur in the regions of Ireland and Spain due to the use of average technical coefficients in cases where census variables are aggregated at NUTS 2 level. 48

51 The comparison between the NUTS 2 based N balance simulations and NUTS 3 based N balance simulations aggregated to NUTS 2 (section 4.4) shows that there is an acceptable correlation of approximately 80 %. This suggests that using NUTS 2 data for N balance simulations for time series analysis is appropriate. Account was taken of atmospheric deposition, so that comparisons could be made with the N Balance simulations of Hansen (2000). The general N balance patterns are similar, but absolute values are slightly lower. The share of organic fertiliser (manure) is lower than in Hansen (2000) study, because no account is taken of imported feedstuffs for livestock Results - N balance estimates at the catchment level Deriving N balance estimates at the catchment level results in a more accentuated pattern (e.g. FR52, ES12 and ES51). There are a few extreme values, which are due to the following factors: - A mismatch between census data and CORINE Land Cover; - Insufficient accuracy of calculations in NOPOLU for very small catchments i.e. the rounding up effect; and - Natural outliers Results - Time series analysis The good comparison between regional N balances based on NUTS 2 and NUTS 3 farm statistical data provided justification for carrying out a times series analysis of trends in N balances using NUTS 2 data. Between 1990 and 1997 there is a reduction in N surplus in most regions, and significant decreases in Greece. It should be pointed out that the census data was changed with time, but not the technical coefficients (apart from yield) Results - Sensitivity analysis The sensitivity analysis shows that absolute values of variation differ greatly between countries and NUTS regions. This sensitivity can be attributed to differences in agricultural characteristics. The sensitivity of manure related coefficients is less than crop related coefficients. The effect of grass exportation from meadows is also sensitive, and is likely to vary considerably across the EU Encountered problems and suggested solutions The aggregation of census variables not only has implications for the input data used in the N Balance calculations, but also causes differences in the technical coefficients used. Use of average technical coefficients does not provide acceptable results. National rather than regional technical coefficients of N content have been used for Nitrogen balance calculations for the European Union, because they were the only ones available on an operational basis. In the case of livestock fodder needs, French data has been used for the other European Union member states however, in reality, large regional variations occur in farming systems and agricultural practises. EUROSTAT technical coefficients of N content do not reflect regionally specific characteristics. In other words the use of a standardized European nomenclature requires the amalgamation of regionally important crops or livestock categories. Although N content coefficients have been harmonised, they differ remarkably between countries (e.g. fertilisation coefficients) or are exactly equal for a set of countries (e.g. manure coefficients), 49

52 which suggests that these are not national coefficients supplied by member states, but are values that have been assigned to the countries in question. Census data are available on NUTS 2 level for 1990, 1993, 1995 and The technical coefficients for fertilisation, exportation and breeding are only available for 1997, whereas yield values are available for 1990 to 1997 for some countries (France, Portugal, Finland, Sweden), and only for 1997 or 1996 for the others. So the time series analysis has been executed with constant technical coefficients (1997 values) and time series census values. Therefore there is a lack of consistency in time for yield data. There were inconsistencies between the NUTS boundary coverages and census data versions before and after The solutions for different countries were as follows: Germany (creation of new NUTS 3 regions and transfer of communes between different NUTS 3 in Sachsen), Sweden (merging of counties and transfer of communes), Finland (split of NUTS 3 regions and transfer of communes), Ireland (introduction of NUTS 2 code) and the United Kingdom (completely new NUTS division at all levels). At the NUTS 2 level boundary inconsistencies were corrected, but this was not done for NUTS 3 regions. The abatement factor, reducing the input by livestock manure when fodder needs exceed fodder availability (option of the model), should be considered with care, in particular in region of intensive breeding where high Nitrogen feedstuffs are used. Recommendations for improvements have been provided in Section 1 and Section 3. The distribution scenario only contains qualitative information, so, when crops are assigned to a certain CLC class, the census data are distributed proportionally to these classes, not taking into account eventual differences in crop spreading or geographical variation. Further investigations at a regional scale are needed to determine and quantify the effect of incorporating CLC in N balance simulations. In this study the CLC-to-Census data correspondence table of Forge (1998) was used, which is based on the French agricultural system. It is unlikely that this correspondence table is suitable for use at the EU level. Therefore there is a requirement to develop regional CLCto-Census data correspondence tables. 50

53 5 CONCLUSIONS A number of conclusions can be drawn from this study: - The N balance estimates carried out are based on proven methodologies, which combine modelling and statistical approaches that require a minimum data set, with GIS and database technology. The methods are a response to a need for a tool to rapidly calculate and monitor N balance estimates for the entire European Union. The method developed could be applied to accession countries. - The method allows for N balance estimates to be reported at the catchment level, enabling estimates to be used as input into hydrologically based N fate models. - Comparison between NUTS 2 FSS survey based N Balance simulations and NUTS 3 Farm census based N Balance simulations aggregated to NUTS 2, indicated that there was a correlation of 80 % between the two simulations across the EU. This suggests that the use of NUTS 2 FSS survey based N Balance simulations is appropriate for monitoring the time series in soil surface N balance estimates. However, NUTS3 data would give more accurate and comparable results. - There is still an important amount of work needed to validate and standardise the technical coefficients between countries of N content for estimating soil-surface N balances. Furthermore, it was shown that in large countries with several form of agriculture (level of intensification), only the use of regional coefficients would provide satisfactory results. The present study has permitted to identify the zones where coefficients calibration is particularly crucial. - Once the model is up and running NOPOLU is an efficient tool for conducting scenario analysis, which can aid decision makers to assess input data and the model parameters. In addition, the spatial dimension of nitrogen balance estimates are easy to obtain. - The scenario analysis carried out in this study was exploratory in nature, but could be focussed on particular regions or census variables. The outcome of the sensitivity analysis carried out showed that manure coefficients were not as sensitive as the crop-based coefficients. This could mean that in fact the manure coefficients, provided at the national level, vary less across the country, whereas crop yield and fertiliser input practices are more variable. - The inclusion of CORINE Land Cover in the modelling process results in improved results for the chosen reference at the administrative level. Its main strength is its use as a co-variable to distribute and reaggregate statistical data to different geographical units. - The use of CLC provides good results in disaggregating statistical data from NUTS4 to NUTS5 (Crouzet, 2000) and slight improvements for NUTS2 to NUTS3. The gain decreases with the degree of aggregation of the source data, thus making erratic the disaggregation from NUTS1 to NUTS2. - The study highlights the critical issue of the quality of the input statistics and the validity of the technical coefficients of N content. 51

54 6 REFERENCES Crouzet P., G. Le Gall, C. Germain (1999). CORINE LC as a basic layer to non point source emission assessment agricultural emissions comparison between the Loire (F) and another European basin. Final Report for JRC. No: F1ED ISP F. IFEN/BETURE-CEREC, Orléans, France Crouzet, P. (2000). Agricultural statistics spatialisation by means of CORINE Land Cover to model nutrient surpluses, European Environmental Agency, 14p. Gallego, J., Kayadjanian, C., Vidal, C. (2001). Geographical use of statistical data: Methodological overview. Towards agri-environmental indicators Integrating statistical and administrative data with land cover information. Topic Report 6/2001. European Environment Agency. Gallego, J. (2001) Using CORINE Land Cover to map population density. Towards agrienvironmental indicators Integrating statistical and administrative data with land cover information. Topic Report 6/2001. European Environment Agency. Hansen, J. (2000. Nitrogen balances in agriculture. Statistics in Focus. Theme 8 16/2000. EUROSTAT. Kayadjanian, M., Vidal, C., (2001) Reassignment of the Réaffectation des données de l Enquête sur la Structure des Exploitations Agricoles, 2001, 17p Ministère de l agriculture, Service Central des Enquêtes et Etudes Statistques: Logiciel de calcul du bilan de l azote à l échelle cantonale, 1995, 24p OECD (2001). Extract from OECD publication: Environmental Indicators for Agriculture Volume 3: Methods and Results, 32p (Doc AGRI-ENV/FEB01/2.1A-EN) 52

55 7 ANNEX 7.1 Overview of agricultural census data used in N balance calculations for France and Spain (representative for other EU countries) Code Code Description Data Status for 1990 Data Solution FRNuts2 FRNuts3 ESNuts2 ESNuts3 FRNuts2 FRNuts3 ESNuts2 ESNuts3 D/01 common wheat and spelt X X X X OK OK OK OK D/02 durum wheat X X X X OK OK OK OK D/03 rye (including meslin) X X X X OK OK OK OK D/04 barley X X X X OK OK OK OK D/05 oats (including summer meslin) X X X X OK OK OK OK D/06 grain maize X X X X OK OK OK OK D/07 rice X X X X OK OK OK OK D/08 other cereals X X X X OK OK OK OK D/09 pulses for harvest as grain (including seed and mixture of pulses and mixtures of pulses and cereals) X X X X OK OK OK OK D/09/a single crops for fodder: field beans, vetches, sweet lipins MD X MD X OK OK OK OK D/09/b others (single or mixed) MD X MD X OK OK OK OK D/10->D/12 Root crops X X X X OK OK OK OK D/10 potatoes (incl. Early potatoes and seed potatoes) X X X X OK OK OK OK D/11 sugar beet (excluding seeds) X X X X OK OK OK OK D/12 forage roots and tubers (excluding seeds) X X X X OK OK OK OK D/13 industrial crops (including only seeds for herbaceous oil seed plants) X MD X MD OK USE_DD OK USE_DD D/13/a Tobacco MD X X X OK OK OK OK D/13/b Hops MD X MD X OK OK OK OK D/13/c Cotton MD X MD X OK OK OK OK D/13/d Other industrial plants MD X MD X OK OK OK OK D/13/d1 oil seeds MD X MD X OK OK OK OK D/13/d11 oil seeds of rape and turnip MD X MD X OK OK OK OK D/13/d12 oil seeds of sunflower MD X MD X OK OK OK OK D/13/d13 oil seeds of soya MD X MD X OK OK OK OK D/13/d2 Aromatic plants, medicinal and culinary plants MD X MD X OK OK OK OK D/13/d3 Other Industrial crops MD X MD X OK OK OK OK D/13/d31 Sugar cane MD MD MD X OK USE_DD OK OK D/14->D/15 Fresh vegetables, melons, strawberries X MD X MD OK OK OK OK D/14 Fresh vegetables, melons, strawberries - outdoor or under low (nonaccessible) cover MD X MD X OK OK OK OK D/14/a Open fields of fresh vegetables, melons, strawberries - outdoor or under low cover MD MD MD MD OK REQ_ES OK REQ_ES D/14/b Market gardening of fresh vegetables, melons, strawberries - outdoor or under low cover MD MD MD MD OK REQ_ES OK REQ_ES D/15 Crops under green house, glass or high (accessible) cover MD X MD X OK OK OK OK D/16->D/17 Flowers and ornamental plants X MD X MD OK OK OK OK D/16 Flowers and ornamental plants outdoor MD X MD X OK OK OK OK D/17 Flowers and ornamental plants under glass MD X MD X OK OK OK OK D/18 Forage plants X MD X MD OK OK OK OK D/18/a Temporary grass meadows MD X MD X OK OK OK OK D/18/b Other forage crops MD X MD X OK OK OK OK D/18/b1 Other forage crops excluding leguminous plants MD X MD X OK OK OK OK D/18/b2 Other forage crops including leguminous plants MD MD MD MD OK OK OK OK D/19 Seeds and seedlings (excluding sereals and pulses) MD MD MD MD REQ_ES REQ_ES REQ_ES REQ_ES D/20 Potatoes and oil seed plants, other arable crops MD X MD X USE_N3 OK USE_N3 OK D/21 Fallows MD X MD X USE_N3 OK USE_N3 OK F Permanent pastures and meadows X X X X OK OK OK OK F/01 Permanent pastures and meadows excluding rough grazing MD X MD X OK OK OK OK F/02 Permanent pastures and meadows including rough grazing MD X MD X OK OK OK OK G Permanent crops X MD X MD OK OK OK OK G/01 Fruits and berries MD MD MD MD OK OK OK OK G/01/a Fresh Fruits and berries of temperate zones MD X MD X OK OK OK OK G/01/b Fresh Fruits and berries of subtropical zones MD X MD X OK OK OK OK G/01/c Nuts and dry fruits MD X MD X OK OK OK OK G/02 citrus plantations MD X MD X OK OK OK OK G/03 olive plantations MD MD MD MD OK USE_DD OK USE_DD G/03/a olive plantations for table olives MD X MD X OK OK OK OK G/03/b olive plantations for olive oil production MD X MD X OK OK OK OK G/04 Vineyards MD MD MD MD OK USE_DD OK USE_DD G/04/a Vineyards for quality wine MD X MD X OK OK OK OK G/04/b Vineyards for other wines MD X MD X OK OK OK OK G/04/c Vineyards for table grapes MD X MD X OK OK OK OK G/04/d Vineyards for raisins MD MD MD MD OK REQ_ES OK REQ_ES G/05 Nurseries MD X MD X OK OK OK OK G/06 Other Permanent crops MD X MD X OK OK OK OK G/07 Permanent crops under glass or high accessible cover MD X MD X OK OK OK OK J/01 Equidae Total MD X MD X OK OK OK OK J/02->J/08 Bovine animals including buffalo X X X X OK OK OK OK J/02 Bovine animals including buffalo under 1 year old X X X X OK OK OK OK J/02/a Bovine animals including buffalo under 1 year old - Male MD MD MD MD OK OK OK OK J/02/b Bovine animals including buffalo under 1 year old - Female MD MD MD MD OK OK OK OK J/03 Bovine animals 1-2 year old, male X X X X OK OK OK OK 53

56 J/04 Bovine animals 1-2 year old, female X X X X OK OK OK OK J/05 Bovine animals >2 years old, male X X X X OK OK OK OK J/06 Bovine animals >2 years old,heifers X X X X OK OK OK OK J/07 Dairy cows X X X X OK OK OK OK J/08 Other cows X X X X OK OK OK OK J/09 Sheep (all ages) X MD X MD OK OK OK OK J/09/a Sheep breeding females MD X MD X OK OK OK OK J/09/b Other sheep MD X MD X OK OK OK OK J/10 Goats (all ages) X X X X OK OK OK OK J/10/a Goats breeding females MD X MD X OK OK OK OK J/10/b Other goats MD X MD X OK OK OK OK J/11->J/13 Pigs, total X X X X OK OK OK OK J/11 Piglets, live weight < 20 kg MD X MD X OK OK OK OK J/12 Breeding sows > 50 kg MD X MD X OK OK OK OK J/13 Other pigs MD X MD X OK OK OK OK J/14->J/16 Poultry X X X X OK OK OK OK J/14 Poultry - broilers MD X MD X OK OK OK OK J/15 Poultry - laying hens MD X MD X OK OK OK OK J/16 Poultry - other poultry (ducks, turkeys, geese, guinea-fowl) MD X MD X OK OK OK OK J/17 Rabbits, breeding females MD X MD X USE_N3 OK USE_N3 OK X Data MD Missing Data USE_N3 use Nuts3 data USE_N2 use Nuts2 data USE_DD Use detailed data REQ_ES Request for Eurostat data 54

57 7.2 Correspondence table between Farm Structure Survey variables and CORINE Land Cover Source: Forge, I, Possible Distribution Scenario determined by I.FORGE on 8/10/1998 for use in NOPOLU 55