Predictions of soybean harvest index evolution and evapotranspiration using STICS crop model. (27th July 2021) - Crépeau, Marianne; Jégo, Guillaume; Morissette, René; Pattey, Elizabeth; Morrison, Malcolm J.
Abstract: In the last century, soybean [ Glycine max (L.) Merr.] genetic improvements have resulted in increased yield partly due to an increase in harvest index (HI). To account for these genetic improvements, an update of the soybean calibration of the STICS soil‐crop model was carried out. The model was calibrated and evaluated for two sets of soybean plant parameters using datasets from the Ottawa region (ON, Canada); a low HI cultivar calibrated using datasets (1993–2008) of cultivars of maturity groups (MGs) 00 and 0 and a high HI cultivar calibrated with more recent datasets (2016–2017) of cultivars of MGs 0 and I. The model succeeded in reproducing the HI increase. Leaf area index (LAI), shoot biomass, and yield were also well predicted for the high HI cultivars with a normalized root mean square error (NRMSE) of 34, 10, and 14%, respectively, which was a great improvement compared to the default parametrization proposed in STICS for soybean. Under rainfed conditions, accurate simulation of evapotranspiration is a critical point to achieve good model performance. A comparison of the two crop evapotranspiration approaches available in STICS was also carried out. It showed that the resistive approach (NRMSE of 36%) was more efficient than the crop coefficient approach (NRMSE of 67%). This good performance of the model in predicting evapotranspiration allowed the model to perform equally well under water stress and non‐water stressed conditions. The model could therefore be used in future studies to simulate the impact of water stress on soybean growth in eastern Canada. Core Ideas: The STICS model was able to simulate the evolution of the soybean harvest index. The use of two cultivars to account for harvest index evolution improved the model performance. Evapotranspiration was better predicted by the resistive than by the crop coefficient approach. The model performed equally well under water stress and non‐water stressed conditions.