This title appears in the Scientific Report : 2013 

Crop growth patterns at the field scale: Detection, understanding and modeling
Stadler, A (Corresponding author)
Rudolph, Sebastian / Kupisch, M / Langensiepen, M / Ewert, F
Agrosphäre; IBG-3
TR32-HOBE International Symposium, Bonn (Germany), 2013-03-11 - 2013-03-14
Modelling and Monitoring Terrestrial Systems: Methods and Technologies
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.