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Use of national data to improve the localisation of livestock. Marta Pérez-Soba Han Naeff Janneke Roos Wim Nieuwenhuizen Alterra, The Netherlands Haarlem, 22nd March 2002. Where are the different livestock types geographically located?. Biophysical features. Land cover: CORINE 43. - PowerPoint PPT Presentation
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Marta Pérez-Soba Han Naeff
Janneke RoosWim Nieuwenhuizen
Alterra, The Netherlands
Haarlem, 22nd March 2002
Use of national data to improve the localisation of livestock
Where are the different livestock types geographically located?
Where are the livestock systems located?
Biophysical features
Land cover: CORINE 43 Others: altitude, soils, climate
Selection land cover classes
Allocation maps (European approaches)
Validation: geo-referenced national/regional databases
Conclusions for the ELPEN system
CORINE Land cover
Scale 1:100.000, 43 classesMinimum mapping element: 25 ha
Validation ofEuropean 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%)
For the validation we distinguished 4 land-dependent livestock categoriesLivestock category Livestock Units (LU)Dairy cows 1.0
Other cows (suckler) 0.8
Other bovine 0.4; 0.7; 0.8; 1.0
Sheep & Goats 0.1
Example: The Netherlands
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
Validation (in)accuracy regional levelGeo-referenced Giab-2000 farm data
Data aggregated grid 5x5
according to ELPEN algorythm
Giab-2000
Grid 5x5 km2
Our reference
Data aggregated grid 5x5 Data aggregated at three regional levels
Municipalities (500)
Agricultural regions (66)
Province (12)
Levels we used for validation
Provincial levelAgricultural regionsMunicipality level
Our reference maps: GIAB 2000Geo-referenced Giab-2000 farm data : aggregated at 5x5 km grid
Nr. of livestock farms:1 - 1011 - 5051 - 100101 - 212no data
Nr. of dairy cows LU:1 - 1010 - 3030 - 6060 - 100100- 200200- 350350- 600600-25002500-5000
Inaccuracy caused by regional level: dairy cows
Province (12)
Data aggregated 5x5 km grid
according to ELPEN algorithm
Data aggregated at regional levelsGeo-referenced
Giab-2000 farm data
Giab-2000
Grid 5x5 km2
Our reference Municipalities (500)
Nr. of dairy cows LU:1 - 1010 - 3030 - 6060 - 100100- 200200- 350350- 600600-25002500-5000
Results comparison GIAB - Elpen algorithm
Green: too many dairy cows in ELPEN approach (-2000 - -50 LU) Gray : approximately correctRed : too few dairy cows in ELPEN approach (50 - 2000 LU)
Dairy: aggregated / Province Dairy: aggregated / Municipality
Results comparison GIAB - Elpen algorithm
Green: too many sheep in ELPEN approach (-400 - -10 LU)Gray : approximately correctRed : too few sheep in ELPEN approach (10 - 400 LU)
Sheep/goat: aggregated / Province Sheep/goat: aggregated / municipality
Validation of Corine classesProcedure:
• 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
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
Example of error due to lack of data on location of farm land:
In Groningen is only onesmall area of Annual crops.Most of the farm land of the3 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
Livestock density (LU/ha) per Corine class per province
Livestock type Corine class Groningen Friesland Drenthe Overijssel Flevoland Gelderland Utrecht N-Holland Z-Holland Zeeland N-Brabant LimburgDairy cows Pastures 0.7 0.9 0.6 0.9 0.6 0.6 0.8 0.5 0.7 0.2 0.6 0.3
Land pp occ agric 0.3 0.7 0.7 0.4Complex cultivation 0.4 0.6 0.7 0.8 0.5 0.2 0.2 0.6 0.3Non-irrigated arable 0.2 0.3 0.2 0.6 0.1 0.5 0.2 0.1 0.1 0.2 0.2
Other cows Pastures 0.03 0.03 0.06 0.07 0.01 0.06 0.05 0.05 0.04 0.01 0.06 0.06Land pp occ agric 0.04 0.07 0.06 0.00Complex cultivation 0.03 0.03 0.08 0.05 0.02 0.01 0.07 0.07 0.06Non-irrigated arable 0.01 0.01 0.02 0.05 0.00 0.05 0.02 0.01 0.02 0.03 0.03 0.04
Other bovine Pastures 0.4 0.5 0.4 0.6 0.3 0.7 0.6 0.3 0.4 0.1 0.5 0.3Land pp occ agric 0.2 0.6 0.6 0.4Complex cultivation 0.3 0.5 0.9 0.6 0.3 0.1 0.2 0.6 0.3Non-irrigated arable 0.1 0.2 0.1 0.5 0.1 0.5 0.1 0.1 0.1 0.2 0.3
Sheep&goats Pastures 0.08 0.09 0.03 0.04 0.02 0.05 0.08 0.15 0.08 0.03 0.05 0.02Land pp occ agric 0.02 0.03 0.04 0.06Complex cultivation 0.03 0.04 0.05 0.06 0.19 0.04 0.04 0.04 0.03Non-irrigated arable 0.03 0.08 0.01 0.02 0.01 0.04 0.06 0.03 0.02 0.03 0.03
Legend bold characters large enough area to be representative (> 10.000 ha)normal characters indicative (area <10.000 ha)
highest density per livestock type per provincelowest density per livestock type 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
<------ Provinces ----->
Livestock density (LU/ha) per Corine class per province
Livestock type Corine class Groningen Friesland Drenthe Overijssel Flevoland GelderlandDairy cows Pastures 0.7 0.9 0.6 0.9 0.6 0.6
Land pp occ agric 0.3 0.7 0.7Complex cultivation 0.4 0.6 0.7Non-irrigated arable 0.2 0.3 0.2 0.6 0.1 0.5
Other cows Pastures 0.03 0.03 0.06 0.07 0.01 0.06Land pp occ agric 0.04 0.07 0.06Complex cultivation 0.03 0.03 0.08Non-irrigated arable 0.01 0.01 0.02 0.05 0.00 0.05
Other bovine Pastures 0.4 0.5 0.4 0.6 0.3 0.7Land pp occ agric 0.2 0.6 0.6Complex cultivation 0.3 0.5 0.9Non-irrigated arable 0.1 0.2 0.1 0.5 0.1 0.5
Sheep&goats Pastures 0.08 0.09 0.03 0.04 0.02 0.05Land pp occ agric 0.02 0.03 0.04Complex cultivation 0.03 0.04 0.05Non-irrigated arable 0.03 0.08 0.01 0.02 0.01 0.04
Legend bold characters large enough area to be representative (> 10.000 ha)normal characters indicative (area <10.000 ha)
relative high densityrelative low density
Dairy cowsand
Sheep area
Dairy/Bovine
area
Dairy/Bovine
area
Livestock type Corine class Utrecht N-Holland Z-Holland Zeeland N-Brabant LimburgDairy cows Pastures 0.8 0.5 0.7 0.2 0.6 0.3
Land pp occ agric 0.4Complex cultivation 0.8 0.5 0.2 0.2 0.6 0.3Non-irrigated arable 0.2 0.1 0.1 0.2 0.2
Other cows Pastures 0.05 0.05 0.04 0.01 0.06 0.06Land pp occ agric 0.00Complex cultivation 0.05 0.02 0.01 0.07 0.07 0.06Non-irrigated arable 0.02 0.01 0.02 0.03 0.03 0.04
Other bovine Pastures 0.6 0.3 0.4 0.1 0.5 0.3Land pp occ agric 0.4Complex cultivation 0.6 0.3 0.1 0.2 0.6 0.3Non-irrigated arable 0.1 0.1 0.1 0.2 0.3
Sheep&goats Pastures 0.08 0.15 0.08 0.03 0.05 0.02Land pp occ agric 0.06Complex cultivation 0.06 0.19 0.04 0.04 0.04 0.03Non-irrigated arable 0.06 0.03 0.02 0.03 0.03
Legend bold characters large enough area to be representative (> 10.000 ha)normal characters indicative (area <10.000 ha)
relative high densityrelative low density
Livestock density (LU/ha) per Corine class per province
Other cowsarea
Bovinearea
Livestockpasture
area
Dairy +sheeparea
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
New allocation procedure: Approach 6: Competition for grassland
Allocation procedure Approach 6
Climate mapAltitude map
Corine land use map Attractivity map bovineAttractivity map sheepAttractivity map dairy
National statisticaldata on livestock
restriction map bovinerestriction map sheeprestriction map dairy
Knowledge rules
European statisticaldata on livestock
Allocation map bovineAllocation map sheepAllocation map dairy
Allocationproc. 6