Optimizing Sunflower Yield Under Water Constraints: A Comparative Analysis of Irrigation Strategies, Seed Varieties, and Simulation Models

This study explored the impact of increasing water restrictions, driven by climate change, on sunflower yield and assessed the adaptability of different seed varieties to these challenging conditions. Three irrigation strategies were applied: S0 (no irrigation), S1 (semi-irrigation), and S2 (full irrigation). Two sunflower cultivars, Sanay and Pioneer, were tested to evaluate how irrigation and seed variety influence key parameters such as leaf area index (LAI), normalized difference vegetation index (NDVI), plant height, and yield. The results indicated that the Pioneer variety generally exhibited higher LAI and yield values compared to Sanay. The maximum LAI value of 4.43 was observed in July under full irrigation (S2). NDVI results further supported these findings, showing that fully irrigated Pioneer plants had higher photosynthetic activity, indicating better adaptation to water stress. While Sanay produced taller plants and relatively high biomass even without irrigation (S0), Pioneer achieved superior yield performance under optimal water conditions. Both cultivars recorded their highest yields under the S2 treatment, highlighting the significance of effective water management for sunflower production. To complement the experimental data, yield predictions were performed using three crop simulation models: AquaCrop, DSSAT, and WOFOST. Among these, AquaCrop provided the most accurate predictions, especially under full irrigation, showing strong alignment with observed yield values. However, the variability observed in model predictions emphasizes the need for careful assessment of these tools across different environmental conditions. In conclusion, selecting appropriate seed varieties and developing optimized irrigation strategies are critical for maintaining agricultural sustainability under water-limited conditions driven by climate change. The accurate simulation of crop yields under different irrigation regimes can further support decision-making processes, but variability in model outputs highlights the importance of model validation based on environmental factors.