Climate-induced rice yield anomalies linked to large-scale atmospheric circulation in Bangladesh using multi-statistical modeling

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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.

Section: Introductionmentioning

confidence: 99%

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

Ghose

Islam

Salam

et al. 2021

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“…Where, Ut,T=∑ ∑ 𝑠𝑔𝑛(𝑋𝑖 − 𝑋𝑗) 𝑇 𝑗=𝑡+1 𝑡 𝑖=1 (9) KT is the location point where the abrupt change point of time series data occurred. The probability of significance level of KTis approximately lied on p≤0.05 followed by equation (10). following by the equation (11).…”

Section: Pettitt's Testmentioning

confidence: 91%

Assessing and forecasting of groundwater level fluctuation in Joypurhat district, northwest Bangladesh, using wavelet analysis and ARIMA modeling

Rashid¹,

Alamgir

Ahmed³

et al. 2022

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Groundwater resource plays a crucial role for agricultural crop production and socio-economic development in some parts of the world including Bangladesh. Joypurhat district, the northwest part of Bangladesh, a crop production hub, is entirely dependent on groundwater irrigation. A precise assessment and prediction of groundwater level (GWL) can assist long-term GWR management, especially in drought-prone agricultural regions. Therefore, this study was carried out to identify trends and magnitude of GWL fluctuation (1980-2019) using the Modified Mann-Kendall test, Pettitt's Test, and Sen Slope estimators in the drought-prone Joypurhat district, northwest Bangladesh. Time-series data analysis was performed to forecast GWL from 2020 to 2050 using the Auto-Regressive Integrated Moving Average (ARIMA) model. The findings of the MMK test revealed a significant declining trend of GWL, and the trend turning points were identified in the years 1991, 1993, 1997, and 2004, respectively. Results also indicate that the declining rate of GWL varied from 0.104 m/yr to 0.159 m/yr and the average rate of GWL declination was 0.136 m/yr during 1980-2019. The outcomes of wavelet spectrum analysis depicted two significant periods of the declining trend in Khetlal and Akkelpur Upazilas. The results obtained from the optimal identified model ARIMA (2,1,0), indicating that GWL will decline at a depth of 13.76 m in 2050, and the average declination rate of GWL will be 0.143 m/yr in the study area. The predicted results showed a similar declining tendency of GWL from 2020 to 2050, suggesting a disquieting condition, particularly for Khetlal Upazila. This research would provide a practical approach for GWL assessment and prediction that could help decisionmakers implement long-term GWR management in the study area.

“…On the other aspect, excessive rain during the rainy monsoon season can result in soil erosion or wash-out and nutrient depletion at the soil surface due to fast runoff. Similarly, farmers may experience rice production losses because of inadequate and unpredictable rainfall during the monsoon season (Ghose et al 2021a).…”

Section: Multiple Linear Regression Modelingmentioning

confidence: 99%

“…The northwest (NW) region is vital for the rice production hub. This region has a complex hydro-geographic setting and considerable spatial and temporal variations in environmental and rice formative elements (Sarker et (Ghose et al 2021a). Rice production and yield mainly depend on climatic variability, and hazardous events like oods and droughts can harm rice yield.…”

Section: Introductionmentioning

confidence: 99%

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

Islam

Nabila

Hasanuzzaman

et al. 2022

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Because of evident climatic variations and signi cant 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 modi ed 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 coe cient of variation. The results show that observed temperature and humidity trends were bene cial 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 in uence 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.