Multi method change point detection of seasonal climate shifts for agricultural calendar revision in Rangpur Bangladesh

This study investigates climate-induced seasonal shifts and their implications for agricultural calendars in Rangpur, Bangladesh, using a multi-method statistical framework. Analyzing 34 years (1990–2023) of meteorological data from four stations, the research combines Mann–Kendall trend analysis, Sen’s slope estimation, and change-point detection (Pettitt test and PELT algorithm). Results reveal significant warming trends (~ 1.0 °C) and declining humidity, alongside a critical climatic shift around 2006, marked by a + 0.50 °C temperature increase and a 333.6 mm reduction in rainfall. Seasonal analysis shows intensified warming and drying during monsoon and post-monsoon periods, disrupting traditional crop calendars. Crop-specific assessments indicate increased climatic stress, while yield data reveal slowed growth rates despite overall production gains, suggesting technological adaptation masking climate impacts. The findings highlight a growing mismatch between climate patterns and agricultural practices, emphasizing the need for dynamic calendar revision and climate-informed adaptation strategies to sustain agricultural productivity.