The total N-fertilizer input map for the “Kraichgau” is given in 
Fig. 8. It represents the sum of N-input from animal waste and 
mineral fertilizer. Astonishingly, the MDA-based 
regionalization of N-management does not show a strong 
administrative footprint anymore even so the data used to 
calculate the amount of N-input are for administrative units. 
The distribution of the N-amount within each administrative 
unit according to complex spatial rules and land use classes is 
very strong and gives a more realistic pattern. 
  
   
Legend 
[LL] Tovetship Border 
Residential ! ladustriat 
Forest 
  
Totat N-Fertilization 
SES very fow 
z tow 
  
medion 
  
5 Kilom dd 
5 18 i iu 
er CI MN ers [ ED 
  
  
  
Figure 8. Total-N input for the *Kraichgau" (Rohierse, 2004) 
3.3. The Rur-Watershed 
The catchment of the river Rur is situated in Western Germany, 
with small parts in The Netherlands and Belgium. The study 
area is characterised by a rather flat terrain in the northern part, 
which is dominated by intensive agriculture, whereas the 
southern part consists of low mountain ranges with forest areas 
and grassland (Fig. 9) (Waldhoff et al., 2011). 
For the MDA, numerous satellite imagery of various sensors 
(ASTER, RapidEye a.m.m.) for the years 2007-2011 were 
purchased within the framework of the interdisciplinary 
“Transregional Collaborative Research Centre 32 (CRC/TR32): 
Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, 
Modelling and Data Assimilation” (www.tr32.de). As a result, 
the classification of crop types is possible (Fig. 9). In contrast to 
the before mentioned case studies, the ambitious objective of 
this case study is the MDA-based production of crop rotation 
maps serving as a data rich environment for the regionalization 
of agricultural management. 
Multitemporal land use classifications are carried out for each 
year, yielding annual crop type maps. By overlaying the annual 
crop type maps in a GIS-environment, regional crop rotation 
patterns can be produced in a spatial context. As an example, 
the annual crop type data for the years 2007 to 2009 are 
summarized in crop rotations in Fig. 10. The results of the crop 
rotation mapping are very promising due to its spatial 
resolution. The data/map scale and the quality of the 
classification enable the spatial identification of field or 
management units. For these spatial units, the 
  
  
     
      
  
  
  
   
  
     
  
  
  
     
         
   
   
  
Figure 9. Crop type 
  
  
{ [7] Rur Catchment 
$93 [71 Subregions 
$54 [7] Country Border 
$ Sugar Beet 
Winter Wheat 
Winter Barley 
] ^ Urban Green Area 
4 BE Bare Ground 
; Pea 
3 Summer Wheat 
3 BE Oat 
  
  
map for the Rur-Watershed 
(Waldhoff et al., 2011) 
  
[3 Rer Catchment 
[.] Subregions 
LU/Crop Rotation 
1.] Other 
Bii Coniferous Trees 
Deciduous Trees 
8 SB - Ww - wer » 
i] Pasture 
£5 58 - WW - WB* 
"| Road, Settlement 
9 58 - SB - WW 
-R 
SB - WW - SB 
] Urban Green Area | 
  
  
  
       
  
    
Abbreviations 
CR « Crop Rotation 
SB = Sugar Beet 
WB = Winter Barlcy 
WW = Winter Wheat 
R = Rapeseed 
M = Maize 
RY = Rye 
* = Rhenish CR 
** a likely Rhenish CR 
  
Figure 10. Crop rotation map 2007-2009 for the Rur- 
Watershed (Waldhoff et al., 2011) 
        
        
      
      
      
     
   
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