Calibrating the dynamic vegetation model LPJ-GUESS for crop yield simulation in southern Sweden using observed crop yield and satellite-based evapotranspiration data

Process-based crop models are extensively used to assess the impacts of climate change, environmental variations, and management practices on crop yields. However, parameters sourced from the literature are often not universally applicable, necessitating calibration to enhance the model performance. The in-situ observed crop yield (hereafter referred to as “observed crop yield”) is commonly used for model calibration. Satellite-based data, such as evapotranspiration (ET), offers additional insights into plant growth and holds significant potential for enhancing calibration efforts. The LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) model has been applied extensively to simulate crop yields across various scales, but it has not been calibrated for the regional-scale yield simulation. This study aims to enhance the performance of LPJ-GUESS in simulating crop yields in southern Sweden through calibration using observed crop yield and satellite-based ET data. Results demonstrated that calibrating with observed crop yield substantially improved simulation accuracy for both spring barley and winter wheat, reducing the normalized root mean square error (nRMSE) from 50.3% to 12.8% and from 15.5% to 12.2%, respectively. Sensitivity analysis identified four key parameters influencing yield simulations: minimum C:N ratio (CNmin), N demand reduction by leaves (Ndred), and the retranslocation of nitrogen and carbon (Nret and Cret). Calibration using the Penman-Monteith-Leuning Version 2 (PML-V2) ET product moderately enhanced yield simulation accuracy, particularly for winter wheat, achieving an nRMSE of 14.2%, demonstrating its potential as an alternative when especially when the long-term and continuous observed crop yield is not available. The calibrated LPJ-GUESS model effectively simulated crop yield for both crop types under drought and normal conditions, highlighting its robustness across varying environmental scenarios.