Spatio-temporal variability of climatic variables and its impacts on rice yield in Bangladesh

Abstract

Bangladesh is a significant contributor to the global food basket but is also one of the most vulnerable countries to climate change. Understanding regional climatic variability helps reduce climate risks and ensure food security. This research examined monthly, seasonal, and yearly temperature, rainfall, and relative humidity in Bangladesh using data from 35 meteorological stations from 1970–2020. The study utilized the Mann–Kendall method to evaluate trends and employed Sen’s slope to quantify their magnitude. Additionally, quantile regression was applied to analyze the impact of climatic variables on rice yield. The findings revealed that maximum (Tmax) and minimum (Tmin) temperatures were steadily increasing, with the southwest experiencing a more rapid rise compared to other regions in Bangladesh. Seasonal Tmax and Tmin rose in most parts of Bangladesh, particularly during the monsoon. In most areas, there was a significant (p < 0.05) increase in Tmax from June to September and Tmin in December. The variability of minimum temperature was considerably affected by warming throughout the country. The eastern area had the most significant (p < 0.05) annual rainfall increase rate, while the northern region had the lowest. There was evidence of inter-seasonal rainfall shipment, with post-monsoon rainfall rising compared to monsoon season. The quantile regression showed that the rice yield of Aus (summer) and Aman (wet) seasons were influenced by maximum temperature and relative humidity, whereas the rice yield of the Boro (dry) season was affected by maximum temperature, rainfall, and relative humidity. Across geographical segmentation, the regression analysis indicated a high level of variability in the northern climate. Overall, the intensity of mean temperature increased throughout the country. So, there is a need to develop heat or drought-resistant rice varieties and modernized irrigation facilities to mitigate these climate risks. However, farmers should employ automated weather-based advisory services for sustaining rice productivity and food security.