The ability to predict short-rotation coppice (SRC) willow productivity for a given region would be very helpful for large-scale deployment of this crop. The objectives of this study were to calibrate and validate the 3PG model for two commonly used clones (SX64 and SX67) and to provide yield potential estimates for 16 sites across Canada. One dataset for each clone, including leaf area index (LAI) and stem biomass, was used for calibrating parameters controlling leaf and stem growth. All other datasets, coming from eight different willow plantations, were used for model validation. Model performance was good in predicting stem biomass for the SX64 (normalized mean error (NME) = –8%, normalized root mean square error (NRMSE) = 22%) and SX67 (NME = –3%, NRMSE = 16%) clones. Predictions were more scattered for LAI, with NRMSE close to 35% and 33% and NME of 1% and 8% for SX64 and SX67, respectively. The simulation results show that the greatest yields were obtained with the three-year rotation for the SX67 clone, whereas a two-year rotation seemed to be more appropriate for the SX64 clone. The simulation results also show that growing degree-days had a significant impact on yield potential, which varied from 10.5 to 16.5 t DM·ha−1 for SX64 and from 7.5 to 11.5 t DM·ha−1 for SX67.

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Storage time effects on the soluble sugars concentration and pH of sweet pearl millet and sweet sorghum juice.

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.

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