1. Marta Pérez-Soba Han Naeff Janneke Roos Wim Nieuwenhuizen Alterra, The Netherlands Haarlem, 22nd March 2002 Use of national data to improve the localisation of livestock

2. Where are the different livestock types geographically located?

3. Where are the livestock systems located? Biophysical features Land cover: CORINE 43Others: altitude, soils, climate Selection land cover classes Allocation maps (European approaches) Validation: geo-referenced national/regional databases Conclusions for the ELPEN system

4. CORINE Land cover Scale 1:100.000, 43 classes Minimum mapping element: 25 ha

5. Validation of European approach 5 From the 43 CORINE classes we selected 8 classes: –pastures –land principally occupied by agriculture –annual crops associated with permanent crops –complex cultivation patterns –moors and heath lands –natural grasslands –agro-forestry areas –non-irrigated arable land (10%)

6. For the validation we distinguished 4 land-dependent livestock categories Example: The Netherlands

7. Questions to be answered How (in)accurate is the allocation procedure of approach 5: Inaccuracy caused by chosen regional level? Inaccuracy caused by chosen land cover classes? By applying the ELPEN algorythm of approach 5 to the same Dutch data, we ruled out inaccuracies caused by different data sets

8. Validation (in)accuracy regional level Geo-referenced Giab-2000 farm data Data aggregated grid 5x5 according to ELPEN algorythm Giab-2000 Grid 5x5 km 2 Our reference Data aggregated grid 5x5 Data aggregated at three regional levels Municipalities (500) Agricultural regions (66) Province (12)

9. Levels we used for validation Provincial levelAgricultural regionsMunicipality level

10. Our reference maps: GIAB 2000 Geo-referenced Giab-2000 farm data : aggregated at 5x5 km grid Nr. of livestock farms: 1 - 10 11 - 50 51 - 100 101 - 212 no data Nr. of dairy cows LU: 1 - 10 10 - 30 30 - 60 60 - 100 100- 200 200- 350 350- 600 600-2500 2500-5000

11. Inaccuracy caused by regional level: dairy cows Province (12) Data aggregated 5x5 km grid according to ELPEN algorithm Data aggregated at regional levels Geo-referenced Giab-2000 farm data Giab-2000 Grid 5x5 km 2 Our reference Municipalities (500) Nr. of dairy cows LU: 1 - 10 10 - 30 30 - 60 60 - 100 100- 200 200- 350 350- 600 600-2500 2500-5000

12. Results comparison GIAB - Elpen algorithm Green: too many dairy cows in ELPEN approach (-2000 - -50 LU) Gray : approximately correct Red : too few dairy cows in ELPEN approach (50 - 2000 LU) Dairy: aggregated / ProvinceDairy: aggregated / Municipality

13. Results comparison GIAB - Elpen algorithm Green: too many sheep in ELPEN approach (-400 - -10 LU) Gray : approximately correct Red : too few sheep in ELPEN approach (10 - 400 LU) Sheep/goat: aggregated / ProvinceSheep/goat: aggregated / municipality

14. Validation of Corine classes Procedure: Overlay GIAB farm data with Corine 100 x 100m (derived from polygon map with smallest area of 25 ha) Analyse which Corine classes are relevant Define a methodology to improve localisation: Calculate livestock density / Corine class / province Derive relative attractivity for the different livestock types of each Corine class per province Derive better rules for localisation in ELPEN

15. Overlay GIAB farm data with CORINE 100 x 100m Farm 203 nr of dairy cows: 40 nr of sheeps: 100 fodder area: 25 ha pasture area: 30 ha cadastral area: 60 ha etc

16. Example of error due to lack of data on location of farm land: In Groningen is only one small area of Annual crops. Most of the farm land of the 3 farms within this area might be outside this Corine class, but is computed as being inside. Overlay GIAB farm data with CORINE 100 x 100m Error due to use of farm address instead of location of farm land (not available yet): Many (post) addresses of farms are in urban areas

17. Livestock density (LU/ha) per Corine class per province Conclusion: Pastures: mostly highest density per livestock type per province Land pp occ agr: in most provinces too small area, density in between Complex cult pt: In some provinces high density, in others low Non-irr ar land: mostly lowest density per livestock type per province

18. Livestock density (LU/ha) per Corine class per province Dairy cows and Sheep area Dairy/ Bovine area

19. Livestock density (LU/ha) per Corine class per province Other cows area Bovine area Livestock pasture area Dairy + sheep area

20. What can we do with these results? From the overlay of national data (2000) with Corine we know: The approximate livestock density of each corine class per livestock type, per province From this we can derive: The relative attractivity of each corine class per livestock type, per province Restrictions on the nr of LU/ha to be allocated per Corine class per livestock type, per province Attractivity and Restrictions are input for the new allocation procedure: approach 6, that has been implemented in the ELPEN system

21. New allocation procedure: Approach 6: Competition for grassland

22. Allocation procedure Approach 6 Climate map Altitude map Corine land use map Attractivity map bovine Attractivity map sheep Attractivity map dairy National statistical data on livestock restriction map bovine restriction map sheep restriction map dairy Knowledge rules European statistical data on livestock Allocation map bovine Allocation map sheep Allocation map dairy Allocation proc. 6