In this study, we attempt to anticipate annual rice production in Bangladesh (1961–2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0, 1, 1) model with drift was chosen based on the findings. The drift parameter value shows that the production of rice positively trends upward. Thus, the ARIMA (0, 1, 1) model with drift was found to be significant. On the other hand, the XGBoost model for time series data was developed by changing the tunning parameters frequently with the greatest result. The four prominent error measures, such as mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE), and mean absolute percentage error (MAPE), were used to assess the predictive performance of each model. We found that the error measures of the XGBoost model in the test set were comparatively lower than those of the ARIMA model. Comparatively, the MAPE value of the test set of the XGBoost model (5.38%) was lower than that of the ARIMA model (7.23%), indicating that XGBoost performs better than ARIMA at predicting the annual rice production in Bangladesh. Hence, the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh. Therefore, based on the better performance, the study forecasted the annual rice production for the next 10 years using the XGBoost model. According to our predictions, the annual rice production in Bangladesh will vary from 57,850,318 tons in 2021 to 82,256,944 tons in 2030. The forecast indicated that the amount of rice produced annually in Bangladesh will increase in the years to come.

As the price of local onions is greatly impacted by the price of imported onions at these two levels of the supply chain, the goal of this study was to ascertain whether onion prices in Bangladesh are transmitted asymmetrically at the wholesale and retail levels. To analyze asymmetry, the study employed the nonlinear autoregressive distributed lag (NARDL) model in the short and long run using monthly time series data from January 2006 to December 2020. The NARDL model captures the effects of both positive and negative shocks in the short as well as in the long run. The empirical results from the NARDL indicate that the wholesale price of local onion shares a short-run relationship with the wholesale price of imported onion while the local retail price of onion shares a long-run relationship with the imported retail price of onion. In addition, the short-run impact of local wholesale and imported wholesale prices is asymmetric. Long-run evidence supports the existence of an asymmetric effect between the local and imported retail onion prices. Using the Pairwise Granger causality test, we examined the causal relationships between wholesale and retail prices. The direction of the casual relationship indicates that the wholesale and retail prices of imported onions lead to the wholesale and retail prices of local onions. A clear understanding of the onion market, how prices move between market actors, and its role in determining market price interaction could be gained by analyzing the asymmetric relationship between the local and imported onion prices. As a result, significant policy recommendations could be made to control the onion price in Bangladesh.

The dairy sector in Bangladesh has shifted from subsistence to commercial and enterprise-driven due to the increasing demand of milk and milk products. In this context, this study was conducted to explore the different participants in dairy value chains and their role in the production, processing and marketing of dairy products. This study addresses the constraints faced by these participants and the factors influencing the value chain efficiency as a case study from Bogura districts of Bangladesh. The Bogura district was selected purposively considering the most potential geographical area of dairy farming. Face-to-face surveys were conducted to collect data during January to April 2019 using two sets of well-designed questionnaires. Multiple regression models were analyzed for the factors affecting the value addition of selected dairy food products. Moreover, Kendall's coefficient of concordance was applied for identifying the constraints faced by dairy products value chain actors. The empirical findings showed that processors of dairy food products added the highest value among all the actors involved in the value chain. Furthermore, the findings revealed that gender, education, purchase price, and sale price significantly influenced the value addition of processed dairy products (yogurt and ghee) value chain. The findings also revealed that the most untiring constraints faced by the market actors were the lack of capital and credit facilities, insufficient storage facilities, lack of processing center, and lack of proper transportation facilities. The findings of this study can help the stakeholders to improve the policy measures towards developing a new market for the dairy food products which might result in high value added, domestic, and export oriented dairy industry.

COVID‐19 is a serious worldwide health emergency that is affecting many nations. The financial standing and mental health of women are negatively impacted by such widespread epidemics. Thus, the purpose of this study was to evaluate, among married women in Bangladesh during the COVID‐19 pandemic, the level of women’s economic empowerment and mental health and related determinants. The author used secondary research from numerous published research articles, review articles, and published international and national reports like the World Health Organization (WHO), UNDP, and United Nations (UN) to examine women’s economic empowerment and mental health during the COVID‐19 pandemic. The COVID‐19 condition made it more common for women to experience loneliness, melancholy, anxiety, and sleep disruption. Women face the greatest risk because they make up 70% of the healthcare workforce and work in caregiving facilities. According to COVID‐19, the primary causes of poor mental health in Bangladesh were being unemployed, being obese, lack of concealing, and having no family. Bangladesh’s patriarchal family system and physical weakness of women leads to increased mental disorders, workplace dangers, abuse, exploitation, harassment, and physical harm during crises and quarantine. This will promote women’s economic empowerment and improve mental health conditions. For this reason, more research about these vulnerable populations is required.