Long–term effects of surface drainage depth on sunflower yield and salinity dynamics in Coastal Bangladesh: An APSIM assessment

Context Coastal saline regions of the Ganges Delta often contain excess soil water after the monsoon season, delaying Rabi crop establishment. Subsequent unseasonal heavy rainfall further exacerbates waterlogging, reducing crop productivity. Although surface drainage can alleviate waterlogging, the optimum surface drainage depth for sunflower remains unclear. Objective This study aimed to identify the optimal surface drainage depth for sunflower cultivation and assess long–term (LT, 1975–2024) yield variability under different seasonal rainfall conditions. Methods Field trials in Dacope, Khulna, tested three surface drainage depths (no drain, 15 cm, and 30 cm, the depth of the open channel) from 2022 to 2024. Data on crop, soil water and soil salinity were used to calibrate the APSIM crop simulation model for local conditions. Model performance was evaluated using R 2 , Root Mean Square Error (RMSE), normalised RMSE, and Student’s t–test. Subsequent scenario analyses then compared LT yields across three dry season rainfall ranges: low (0–100 mm), medium (100–300 mm), and high (300–637 mm) based on 50 years of records. Results Model calibration and parameter adjustment helped address previously observed shortcomings of the APSIM-Sunflower module and enhanced its simulation accuracy for the variables of interest. High R 2 values (0.79–0.98) and acceptable RMSE values between observed and simulated data (7.4 & 6.4 day, 106 & 153 kg/ha, 543 & 549 kg/ha, 0.026 & 0.036 cm 3 /cm 3 , and 46 & 119 mg/kg) were achieved for both calibration and validation across phenology, yield, biomass, soil water, and chloride content (CL), respectively. From LT yield prediction, a 30 cm drainage depth was optimal for all seasonal rainfall categories, resulting in yields 1.1–1.8 times higher than those with 15 cm or no drainage. The LT yields showed a significant ( p < 0.001) positive correlation with seasonal rainfall and solute potential, and a significant ( p < 0.001) negative correlation with CL. All results were within the range of experimental error, confirming the model's reliability. Conclusion The APSIM model demonstrated strong performance in simulating daily phenology, yield, biomass, soil water, salinity, and LT yield for surface drainage depths in coastal soils, and 30 cm was shown to be the best depth. Implications With further improvement by adding a surface drainage module, APSIM could serve as a valuable decision–support tool for researchers and policymakers for managing LT risks in dry season cropping and increasing smallholders’ farm income through cropping systems intensification in the saline coastal zone of the Ganges Delta and similar environments.