This title appears in the Scientific Report :
2013
Crop growth patterns at the field scale: Detection, understanding and modeling
Crop growth patterns at the field scale: Detection, understanding and modeling
Agricultural ecosystems are shaped by environmental factors, weather and soil characteristics in particular. Heterogeneities of these conditions cause spatial variations of biomass, yield and leaf area index in agricultural fields. The effects of varying spatial conditions on...
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Personal Name(s): | Stadler, A (Corresponding author) |
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Rudolph, Sebastian / Kupisch, M / Langensiepen, M / Ewert, F | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Imprint: |
2013
|
Conference: | TR32-HOBE International Symposium, Bonn (Germany), 2013-03-11 - 2013-03-14 |
Document Type: |
Poster |
Research Program: |
Modelling and Monitoring Terrestrial Systems: Methods and Technologies |
Publikationsportal JuSER |
Agricultural ecosystems are shaped by environmental factors, weather and soil characteristics in
particular. Heterogeneities of these conditions cause spatial variations of biomass, yield and leaf area
index in agricultural fields. The effects of varying spatial conditions on crop growth are generally
examined at distinct spatial scales. However, only few address spatial heterogeneity at the field level.
Since crop growth models try to represent reality, they should ideally mimic the effect of variations in
soil conditions on crop growth and development. Some studies showed that the tested models are
able to represent spatial heterogeneity in plant development and growth at regional scale, if
parameters of environmental conditions are adapted. We hypothesize that taking into account the
effects of soil heterogeneity on plant water and nutrient uptake also improves the accuracy of crop
growth model simulations at the field scale. A crop growth model was applied using information
from winter wheat and sugar beet field trials near Jülich, located in the central western part of
Germany, from 2010 to 2012. These fields are characterized by strong spatial variability in soil
conditions and managed according to standard agronomic practice. The crop growth model was
calibrated separately for each winter wheat and sugar beet cultivar grown on these fields by
adjusting the respective parameters with the help of crop physiological measurements carried out at
point level. The soil model was parameterized for different field sample points with measurements of
apparent soil electromagnetic conductivity (ECa) to account for the spatial heterogeneity in soil
conditions within each field. The crop growth model was subsequently tested whether it could
reproduce the observed spatial patterns of crop growth in the selected fields by considering the
spatial variability in soil properties. The analysis of the above mentioned measurements in the winter
wheat and sugar beet fields revealed a distribution of soil properties whose patterns are reflected in
crop growth. When the ECa of the soil was high, the crop produced more leaf area, biomass and yield
as a crop grown in soils with a lower ECa. This relation was far less expressive in more uniform fields.
We therefore assume that the interaction of soil ECa and crop growth strengthens with increasing
soil heterogeneity. Due to the given relationship between the ECa of the soil and crop growth, the
detected field patterns were used to validate the crop growth model GECROS. Since this model
includes a dynamic photosynthesis module, which is directly interacting with atmospheric input and
the coupled soil model SLIM, we validated it regarding its ability to represent our measured crop
data. When SLIM is parameterized by ECa data, the simulated crop data showed a stronger
accordance with the measured crop data than simulation runs without the adaption of the soil
model. |