Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield.

High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets.

Multi-year aboveground data of minirhizotron facilities in Selhausen.

Improved understanding of crops' response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO and HO gas fluxes, and crop growth processes are rare.

Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity.

Agricultural Biodiversity dynamics has been evaluated by social metabolism or by landscape structure-function analysis. In this study, by using ELIA modeling, we used both methods in combination to understand how the interplay between social metabolism and landscape structure-function can affect biodiversity pattern distribution. We used energy reinvestment (E) as an indicator of social metabolism and landscape heterogeneity (Le) as an indicator of landscape structure-function.