Performance of current canola (Brassica napus) hybrids under future rainfed production management

Canola is vulnerable to the current changing weather conditions, mainly due to moisture and temperature-related stresses. Adaptation strategies such as shifting planting dates allow producers to improve canola's response to environmental conditions. This study aims to explore the optimal setting to increase canola productivity within the Canadian Prairies under future scenarios from the Shared Socioeconomic Pathways. Hence, we define the optimal planting period to avoid water and temperature stresses as well as the optimal nitrogen (N) concentration in fertilization to maximize canola productivity. We used DSSAT-Pythia to simulate four canola hybrids, 24 planting dates, five nitrogen concentrations, and four future climate scenarios, with a spatial resolution of 0.25° × 0.25° in the Canadian Prairies. The model's performance showed satisfactory predictions of canola phenology and grain yield for all hybrids. On spatial and temporal averages, the second hybrid showed highest yield values, with most values between 2500 and 3000 kg ha −1 . In addition, spatial analysis shows that the first hybrid can complete the crop cycle in all growing zones when planted early (April), and the second and third hybrids completed the cycle when planted later (June and July). Nitrogen uptake was affected by weather conditions. The higher the temperature, especially during the bolting stage, the less nitrogen uptake from the plant. Fertilization with high N concentration (200 kg ha −1 ) is expected to be more effective before May 19 under very hot scenarios and before June 08 under mild temperatures. Overall, canola yield increased with an increase in N concentration. • Shifting planting dates avoid weather variability impacts on canola production. • Analyses included fertilization and seeding date management to four canola hybrids. • Lowest yield values were mostly found in the dryest area of the Canadian Prairie. • Current canola hybrids may well-adapt to future weather.