Simulating the yields of bioenergy and food crops with the crop modeling software BioSTAR: the carbon-based growth engine and the BioSTAR ET0 method

Background

With a growing production and use of agricultural substrates in biogas facilities, the competition between food and energy production, environmental issues, and sustainability goals has seen an increase in the last decade and poses a challenge to policy makers. Statistical yield data has a low spatial resolution and only covers standard crops and makes no statement in regard to yields under climate change. To support policy makers and regional planners in an improved allocation of agricultural land use, a new crop model (BioSTAR) has been developed.

Results

Simulations with weather and yield data from 7 years and four regions in Lower Saxony have rendered overall good modeling results with prediction errors (RMSE and percentage) ranging from 1.6 t and 9.8% for winter wheat to 2.1 t and 11.9% for maize. The model-generated ET₀ and ET_a values (mean of four locations) are lower than ET₀/ET_a values calculated with the Penman-Monteith method but appear more realistic when compared to field trial data from northern and eastern Germany.

Conclusions

The model has proven to be a functioning tool for modeling site-specific biomass potentials at the farm level, and because of its Access® database interface, the model can also be used for calculating biomass yields of larger areas, like administration districts or states. Out of the seven crops modeled in this study, only limited yield and test site data was available for winter barley, winter rye, sorghum, and sunflower. For further improvement of model performance and model calibration, more trial data and data testing are required for these crops.