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Variability of climate-induced rice yields in northwest Bangladesh using multiple statistical modeling

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Abstract

Because of evident climatic variations and significant contribution to national food production, Bangladesh is a climate extreme hotspot region of examination for climatic consequences for rice (Oryza Sativa) crop production. This study intends to explore the variability of climatic variables (e.g., variations in mean temperature, rainfall, relative humidity, and sunshine duration) with rice yields (e.g., Aus, Aman, and Boro rice varieties) in northwest Bangladesh. The modified Mann–Kendall test, Theil–Sen slope estimator, and multiple linear regression (MLR) modeling were used to estimate the association among these factors. Heteroskedasticity and autocorrelation constant standard error (HAC) and feasible generalized least square (FGLS) technique were adopted to measure the climate-rice crop nexus using the regional level dataset for 1976–2015. Furthermore, the spatiotemporal variation of rice yield trends with climatic variables was mapped and assessed by the coefficient of variation. The results show that observed temperature and humidity trends were beneficial for Aus and Aman yields but not Boro yields. In contrast, observed rainfall and sunshine trends were negative for all three rice seasons. The outcomes of the MLR model explained 67%, 92%, and 83% of the variability in Aus, Aman, and Boro rice yields in the study region. The model outcomes showed that humidity and rainfall have negatively affected Aus and Aman rice crops, while temperature and rainfall positively influence Boro rice yield. Regarding the climate change issues and safeguarding food safety at the regional level, the concerned authorities should provide substantial attention to improving heat and drought-tolerance high-yielding varieties against climate effects on Aus and Aman rice varieties.

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Acknowledgement

The authors would like to express their gratefulness to the Bangladesh Meteorological Department (BMD) and the Bangladesh Bureau of Statistics (BBS) for sharing data for this research. We would also like to extend our thanks to the Department of Disaster Management, Begum Rokeya University, Rangpur, for providing valuable support during this study.

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Authors and Affiliations

  1. Faculty of Environmental Management, Prince of Songkla University, Songkhla, Thailand

    Abu Reza Md. Towfiqul Islam & Kuaanan Techato

  2. Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Songkhla, 90110, Thailand

    Abu Reza Md. Towfiqul Islam & Kuaanan Techato

  3. Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

    Abu Reza Md. Towfiqul Islam, Md. Hasanuzzaman & Md. Bozlar Rahman

  4. Department of Disaster and Human Security Management, Bangladesh University of Professionals, Dhaka, Bangladesh

    Irtifa Alam Nabila & Md. Mostafizur Rahman

  5. Agricultural Engineering Dept, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

  6. Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia

    Javed Mallick

  7. Department of Geography, The University of Burdwan, West Bengal, India

    Subodh Chandra Pal

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  1. Abu Reza Md. Towfiqul Islam
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  9. Md. Mostafizur Rahman
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Contributions

A.R.M.T.I., J.M., and M.B.R. designed, planned, conceptualized, drafted the original manuscript; M.H. and I.A.N were involved in statistical analysis and interpretation; M.B.R., A.E., and J.M. contributed to instrumental setup, data analysis, and validation; K.T., A.E., I.A.N., and M.H. contributed to editing the manuscript, literature review, and proofreading; J. M., K.T., S.C.P., M.M.R., and A.R.M. T.I. were involved in software, mapping, and proofreading during the manuscript drafting stage.

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Correspondence to Javed Mallick or Kuaanan Techato.

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Islam, A.R.M.T., Nabila, I.A., Hasanuzzaman, M. et al. Variability of climate-induced rice yields in northwest Bangladesh using multiple statistical modeling. Theor Appl Climatol 147, 1263–1276 (2022). https://doi.org/10.1007/s00704-021-03909-1

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