Location-specific modelling of N dynamics to reduce gaseous N emissions and further N losses in crop production (SmartMaN2agement) 2023-2026

The focus of the project is on the reduction of N2O and NH3 emissions as well as N leaching through anticipatory, informed and site-adapted management in crop production. The specific objectives are, among others, the improvement of the understanding of the nitrogen cycle and N losses under different site and management conditions (crop rotations, catch crops, fertilizer types, amounts, and application times) as well as the experimental and simulation-based quantification of N losses (gaseous N emissions, nitrate inputs to groundwater) through a process-based mapping of N dynamics in the soil, N losses, and N uptake by crops. Furthermore, a site-differentiated modeling of the N cycle, N mineralization, N losses (gaseous, leaching), N uptake and yield of selected long-term experiments will be carried out. The consistent focus of the project on modeling, using extensive data sets from different long-term experiments for the validation of the models, ensures that the ambitious goal of a model-based and comprehensive evaluation will succeed. From this evaluation, well-founded statements about the influences of the different farming systems, crop rotations, soil tillage and fertilization events adapted to the respective location as well as the respective farming system are to be derived within the project duration, regionalized to the federal territory and processed for agricultural practice and consulting.

The data required for the modeling will be compiled from four long-term trials in Germany (see map) by the respective trial organizers from preliminary studies or newly collected and transmitted to the modelers at the University of Bonn. In order to obtain a comprehensive picture of the N-cycle including gaseous N-losses, N-uptake and yield, the data are processed and compiled from previous years as well as collected during the project period and supplemented by additional sampling beyond the standard measurements. These data form the basis for the modeling.