Erstellung eines integrierten Modells zur Vorhersage des Stickstoffbedarfes von intensivem Grünland zur Optimierung der Düngung und Reduzierung von Stickstoffverlusten (MULNV, 2018-2019)
Nitrogen fertilization of intensively used grassland aims at maximising yield and optimizing quality. However, future fertilization strategies have to take limited resources and the aim of reducing negative side effects, such as gaseous (N2O, NH3, NOx) and liquid N losses (NO3-) into consideration. To reach this aim and apply the right amount of fertilizer, it is necessary to be able to predict the biomass development and N uptake in grassland. Besides the uptake of N by plants, also the available soil N as released from inorganic and organic fertilizer as well as mineralised from soil organic matter has to be taken into account. Based on this, the present project aims at evaluating existing models of plant growth and N mineralisation, to integrate the relevant approaches allowing a prediction of biomass development and N uptake by plants in grassland. Model adaptation and sensitivity analyses will be done using existing data from various experiments. Additionally, data will be collected in established experiments. A remote sensing platform with various sensors will be used to assess the biomass and N content of the grassland at a given time. In combination with the developed prediction model, the remote sensing data shall be used to facilitate a non-destructive assessment and prediction of the N uptake. This will then define the amount of N to be applied. Overall, the project aims at improving N fertilization in grassland, while at the same time minimising N losses and reducing the negative environmental effect of N.