Rice Yield Responses in Bangladesh to Large-scale Atmospheric Oscillation Using Multifactorial Model

Abstract

\n 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: (1) three sub-regions of Bangladesh, northern, northwestern, and northeastern, showed different kinds of CDYI anomalies; (2) the CDYI time series in northern and northeastern regions exhibited a substantial 6-year fluctuation, whereas a 2.75 to 3-year fluctuation predominated the northwestern region; (3) rice yield showed the highest sensitivity of LACIs in the northern region; (4) Indian Ocean dipole (IOD) and East Central Tropical Pacific SST (Nino 3.4) in July, and IOD index in March provide the best yield forecasting signals for northern, northwestern, and northeastern regions, respectively; (5) wavelet coherence study demonstrated noteworthy in-phase and out-phases coherences between key climatic variables (KCVs) and CDYI anomalies at various time-frequencies in three sub-regions; (6) the random forest (RF) model revealed the IOD as the vital contributing factor of rice yield fluctuations in the country; (6) the multi-factorial 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 5.51% only. The generated knowledge can be used for an early assessment of rice yield and recommend policy directives to ensure food security.