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Rice yield responses in Bangladesh to large-scale atmospheric oscillation using multifactorial model

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Abstract

This paper intends to explore rice yield fluctuations to large-scale atmospheric circulation indices (LACIs) in Bangladesh. The annual dataset of climate-derived yield index (CDYI), estimated using principal component analysis of Aus rice yield data of 23 districts, and five LACIs for the period 1980–2017 were used for this purpose. The key outcomes of the study were as follows: three sub-regions of Bangladesh, northern, northwestern, and northeastern, showed different kinds of CDYI anomalies. The CDYI time series in north and northeastern regions exhibited a substantial 6-year fluctuation, whereas a 2.75- to 3-year fluctuation predominated the northwestern region. Rice yield showed the highest sensitivity of LACIs in the northern region. Indian Ocean dipole (IOD) and East Central Tropical Pacific SST (Nino 3.4) in July and IOD index in March provide the best yield prediction signals for northern, northwestern, and northeastern regions. Wavelet coherence study demonstrated significant in-phase and out-phases coherences between vital climatic variables (KCVs) and CDYI anomalies at various time-frequencies in three sub-regions. The random forest (RF) model revealed the IOD as the crucial contributing factor of rice yield fluctuations in the country. The multifactorial model with different LACIs and year as predictors can predict rice yield, with the mean relative error (MRE) in the range of 4.82 to 5.78% only. The generated knowledge can be used to early assess rice yield and recommend policy directives to ensure food security.

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Fig. 1

Aus rice production in different districts of Bangladesh

Fig. 2
Fig. 3
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Fig. 5

Aus rice yield and climate variables in regions ((a) region I, (b) region II, (c) region III; the number within a solid line frame indicates; the bold fonts denote the key climate variables (KCVs) affecting yield, estimated using multiple stepwise regression analysis)

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Fig. 10

Aus rice yield using multifactorial model

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Data availability

Data are available upon request on the corresponding author.

Code availability

Not applicable.

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Acknowledgements

We greatly acknowledge the Bangladesh Meteorological Department (BMD) and Bangladesh Bureau of Statistics (BBS) for providing data for this work.

Funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Group under grant number (R.G.P.2 /194/42).

Author information

Authors and Affiliations

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

    Bonosri Ghose, Abu Reza Md. Towfiqul Islam & Roquia Salam

  2. Department of Water and Environmental Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor, Malaysia

    Shamsuddin Shahid

  3. FMPHT Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh

    Mohammad Kamruzzaman

  4. School of Hydrology and Water Resources, Nan**g University of Information Science and Technology, Nan**g, 210044, China

    Samiran Das

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

    Ahmed Elbeltagi

  6. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China

    Ahmed Elbeltagi

  7. Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh

    Mohammed Abdus Salam

  8. Department of Civil Engineering, King Khalid University, Abha, 62529, Saudi Arabia

    Javed Mallick

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  1. Bonosri Ghose
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Contributions

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

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Correspondence to Abu Reza Md. Towfiqul Islam or Javed Mallick.

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Ghose, B., Islam, A.R.M.T., Salam, R. et al. Rice yield responses in Bangladesh to large-scale atmospheric oscillation using multifactorial model. Theor Appl Climatol 146, 29–44 (2021). https://doi.org/10.1007/s00704-021-03725-7

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