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

• Published in: Theoretical and applied climatology 2021-10, Vol.146 (1-2), p.29-44
• Main Authors: Ghose, Bonosri, Islam, Abu Reza Md. Towfiqul, Salam, Roquia, Shahid, Shamsuddin, Kamruzzaman, Mohammad, Das, Samiran, Elbeltagi, Ahmed, Salam, Mohammed Abdus, Mallick, Javed
• Format: Article
• Language: English
• Subjects: Analysis; Anomalies; Aquatic Pollution; Atmospheric circulation; Atmospheric oscillations; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Circulation indexes; Climate change; Climate science; Climatic indexes; Climatology; Crop yield; Crop yields; Dipoles; Earth and Environmental Science; Earth Sciences; Fluctuations; Food security; Food supply; Original Paper; Principal components analysis; Regions; Rice; Rice yield; Sea surface; Surface temperature; Tropical climate; Waste Water Technology; Water Management; Water Pollution Control; Yield forecasting
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-021-03725-7

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.