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belonging crop rotation information with its annual crop type
coverage is available. The annual change of the land use pattern
can now be addressed for the Rur-Watershed. Related to this
annual alteration, is the change of crop dependent matter fluxes
and management practices like N- and C-inputs, N- and C-
removals etc. Consequently, the mapping of annually varying
management patterns is only possible by using such a land use
data base.
4. DISCUSSION & CONCLUSION
Different approaches of land use dependent regionalization of
agricultural management are presented in this contribution. In
contrast to many other published approaches, the MDA-based
case studies focus on the integration of available related data
from multiple sources and data integration technologies within a
GIS-environment. These data are the base for the spatial
distribution of agricultural management. Besides GIS, the
multitemporal and multisensoral remote sensing classification
for multiannual crop type mapping is the core and key of the
presented approach. Furthermore, the distribution of e.g.
calculated N-input is carried out by using knowledge-based
production rules which are developed for each crop type and a
set of spatial settings (e.g. protection areas).
In this contribution, we presented in three case studies the
development and improvement of the regionalization of
agricultural management based on the MDA. From the
beginnings in the late nineties, a step-wise improvement is
documented which finally results in crop rotation maps. From
our knowledge, the presented approach is the only method to
provide the spatial input data which are needed for regional
agro-ecosystem modelling and which were identified by
Kersebaum et al. (2007) as the most limiting parameter in
regional agro-ecosystem modelling. As an example, a
screenshot of one of the latest DNDC versions is shown in
Fig. 11. For each year of a long-term simulation, the crop type
or crop types (in case of a cropping index > 1) have to be
provided with all related management information. Finally, the
MDA derived land use data can provide crop cover with the
related soil information for large regions.
Ciop | Tilage | Fertiization | Manure Amendment | Weeding | Flooding | Irrigation | Grazing or cutting |
Crop parameters
tis crucial for modeling soil biogeochemistry to correctly simulate crop growth/yield. Please push this button ta
review and modify the crop parameters to ensure they are as close as possible to observations.
Number of new crops consecutively planted in this year
Crop H = i <- Last | Next > |
Crop type: n Com x
Default maximum biomass production (kg diy matter/ha]:
Grain | 1500 Leaf+siem [1540 ^ Root
Planting month: [ 5 days L |
Harvest month: I 10 day= | 1
Harvest mode 1: in this year; 2: in next year
fo |lse empirical crop growth sub-model
#" Use physiology/phenoloay sub-model
* Additional parameters for physiology/phenology sub-model ss
Initial biomass [kg div matter/ha]
Initial photosynthesis efficiency I EE
Maxirnum photasynthesis rate. ka CO2/ha/hr
Development rate in vegetative stage
Accept |
[7
Is it a cover crop? i Mo 7” Yes Development rate in reproductive stage
Fraction of leaves and stems left in field after [ 04 us
harvest 04
CroplD | CropTupe | Planting 1 | Harvest | Mode | Residue | Yield |
1st crop 1 5 1 10 1 1 0400000 1500.00...
| DK. | Cancel | Apr | Help
Figure 11. Screenshot of the DNDC model (9.1) (http://www.dnde.sr.unh.edu/)
5. REFERENCES
Bareth, G., 2009. GIS- and RS-based spatial decision support:
structure of a spatial environmental information system (SEIS).
International Journal of Digital Earth, 2(2), pp. 134-154.
Bareth, G., 2008. Multi-Data Approach (MDA) for enhanced
land use and land cover mapping. Proc. XXI ISPRS Congress,
3-11 July 2008, Beijing, China.
Bareth, G., 2001. Integration of an IRS-1C land use
classification in the official topographical information system
(ATKIS) to enhance the quality of the information of arable
