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

Bonosri Ghose,Shamsuddin Shahid,Abu Reza Md Towfiqul Islam,Roquia Salam,Mohammed Abdus Salam,Samiran Das,Ahmed Elbeltagi,Javed Mallick,Mohammad Kamruzzaman

doi:10.1007/s00704-021-03725-7

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.

Highlights

Understanding the profound impacts of climate instability on agricultural systems is vital for developing appropriate adaptation strategies (Challinor et al 2014; Deryng et al 2014; Moore and Lobell 2014; Siebert and Ewert 2014; Ghose et al 2021a)
Adopting assumptions after Fang (2011), the present study considered three conditions, which are: i) during Aus rice-growing season, the trend of actual Aus rice yield must be equivalents to the management-induced Aus rice yield found from simulation curve; ii) study area plays the main role for changing the factors of the management-induced Aus rice yield, and the variation of actual Aus rice yield varies with climatic variability of the respective area; and iii) the impacts of the key climatic variables (KCVs) on the Aus rice growth must be analogous to the feature of Aus crop growth and the response of Aus crop to the
The results showed that the first three principal components (PC) were adequate to elaborate on the interannual variations of climate-derived yield index (CDYI) for the study period of 1980 – 2017

Summary

Introduction

Understanding the profound impacts of climate instability on agricultural systems is vital for developing appropriate adaptation strategies (Challinor et al 2014; Deryng et al 2014; Moore and Lobell 2014; Siebert and Ewert 2014; Ghose et al 2021a). Many studies have examined the effects of ENSO on rice production in different Asian countries (e.g., (Zubair 2002; Selvaraju 2003; Falcon et al 2004). Many recent studies showed the adverse effects of El-Niño derived climate variation on agriculture of Malaysia, Thailand and Cambodia (Al-Amin and Alam 2016; Girmay Reda and Tripathi 2016; Vimean Pheakdey et al 2017). Studies showed El Niño effects on Bangladesh's agriculture through water shortage, soil depletion, and planting season disruption (Chowdhury 2003). Drought, flood, soil salinity, and cyclone have been described as the ENSO-driven main climatic extremes unfavorably influencing agricultural crop production in Bangladesh (Harun-ur-Rashid and Islam 2007; Islam et al 2017)

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Conclusion