Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh.

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm.

Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981-2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.

Systems thinking on the gendered impacts of COVID-19 in Bangladesh: A systematic review.

The COVID-19 pandemic has disproportionately affected women and threatens to overturn four decades of progress in Sustainable Development Goal (SDG) 5: Gender Equality and Women's Empowerment. To better grasp the key areas of concern that gender inequality exists, gender studies and sex-disaggregated evidence are required. Using the PRISMA technique, this review paper is the first attempt to present a comprehensive and current picture of the gendered dimensions of the COVID-19 pandemic in Bangladesh regarding economic well-being, resource endowments, and agency. This study found that women were more likely to face hardship as widows, mothers, or sole breadwinners after the loss of husbands and male household members because of the pandemic. The evidence suggests that the advancement of women during this pandemic was hampered by poor reproductive health outcomes; girls' dropping out of school; job loss; less income; a comparable wage gap; a lack of social security; unpaid work burnout; increased emotional, physical, and sexual abuse; an increase in child marriages; and less participation in leadership and decision-making. Our study found inadequate sex-disaggregated data and gender studies on COVID-19 in Bangladesh. However, our research concludes that policies must account for gender disparities and male and female vulnerability across multiple dimensions to achieve inclusive and effective pandemic prevention and recovery.

Labor market and unpaid works implications of COVID-19 for Bangladeshi women.

Crisis impacts are never gender-neutral, and COVID-19 is no exception. The pandemic has further exacerbated the gender and socioeconomic inequalities, therefore, crucial to undertake a gender impact analysis of COVID-19. This perspective paper highlights women's vulnerability in the labor market and focused on the increasing unpaid workloads in the response to the COVID-19 outbreak. Focusing on various surveys, feminized sectors such as agriculture, garments have been hardest hit by the pandemic. Female workers have been rapidly lost their means to earn income and confined to homes. Beyond lost jobs and reduced working hours, the pandemic has also increased the time poverty of women. While pre-pandemic unpaid work burdens are well established as strong, the study indicates that burdens are escalated after-pandemic. Women balanced intensified unpaid care and domestic works simultaneously or make a tradeoff, without or minimal help from men. Such results suggest a gender-inclusive policy to minimize the effects of the pandemic, placing women at the center of focus.