Assessing impact of salinity and climate scenarios on dry season field crops in the coastal region of Bangladesh

Soil salinity is the main environmental limiting factor for current agricultural farming systems in the Bangladesh coastal zone. Targeting crop selection to ameliorate its impact in food production is a priority to ensure food security, diversifying crop outputs and reducing smallholders' vulnerabilities. In this context, crop growth modelling emerges as a useful tool for exploring different scenarios for crop selection. Studying spatial and -temporal heterogeneity in salinity and crop productivity under different climate scenarios. A dataset from the Polder 30 located in the Bangladesh southwest coastal zone, including different farming systems (rice-maize, rice-mungbean, rice-sunflower) comprising two years (2017–2019), was utilized, together with polder-level contiguous layers of factors (i.e., salinity, elevation, precipitation), to create clusters representing different yield-limiting conditions. Two salinity scenarios were defined and included in a long term in-silico assessment to explore dry-season field crop options (maize, sunflower, and mungbean). APSIM models for maize, sunflower and mungbean, were selected to perform all the crop simulations. Based on yield, sunflower was the most stable choice under salinity and maize was the best under non-saline conditions. Lastly, yield stability was accounted for different climate conditions, showing a large yield reduction for maize when combining low precipitation and high temperature, whereas mungbean and sunflower presented less sensitivity to different climates. Right crop selection for the right environment, considering both salinity restrictions and climate uncertainties, is critical for long-term yield stability. The present study provides a novel framework to better match the most suitable crop under challenging salinity restrictions and climate uncertainties affecting smallholders in the coastal zone of Bangladesh. [Display omitted] •Targeting crop selection to optimize food production strengthens food security.•Spatial and temporal variability caused by salinity should be considered to aid smallholders to improve crop selection during the dry-season.•In-silico assessment with APSIM model indicates sunflower as a good choice for the dry-season under salinity and the projected climates.•Crop selection for the right environment is critical for long-term yield stability.•A new framework is provided to better match the most suitable crop under salinity restrictions and climate uncertainties.