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An accurate, cost-efficient, and early crop yield projection is a national and global necessity. This study is aimed to achieve the national demands by proposing crop yield estimation models on the Google Earth Engine (GEE) platform. This study directly used dynamic crop phenology metrics to model soybean crop yield by considering climatic regions in the USA (e.g., Central, East, Northeast, South, Southeast, and West North Central regions). We modeled soybean yields with vegetative growth metrics (VGMs) of NDVI narrated as VGM70 (avg. NDVI of 70 days from the emergence), VGM85 (avg. NDVI of 85 days from the emergence), VGM98T (total NDVI of 98 days from the emergence), VGM120 (avg. NDVI of 120 days from the emergence), VGM mean (avg. NDVI of the growth season), VGM max (maximum NDVI of the growth season), and climatic factors (i.e., daytime surface temperature: DST, night-time surface temperature: NST, and precipitation) from 2000 to 2019. This study further explored individual predictors and a combination of predictors in modeling crop yield for diverse climatic regions. Therefore, we proposed six crop yield linear models for each of the climatic divisions, and these models are then compared with support vector machine (SVM) models. All models showed reliable predictability based on adjusted R-square, normalized root mean square error (NRMSE), normalized mean prediction error (NMPE) parameters, and a p-value of less than 0.001.The impact of the independent predictor in the best crop yield models is discussed based on the regression weights (beta weight: β), and, the VGM max is identified as the significant predictor in crop yield modeling for different climatic regions. Overall, this study will help the national agricultural management system for better monitoring and forecasting of soybean yield to support and manage soybean production., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

Background: The ready-made garment (RMG) sector is a significant contributor to the economic growth of Bangladesh, accounting for 10% of the country's GDP and more than 80% of its foreign exchange earnings. The workforce in this sector is predominantly made up of women, with 2.5 million women working in the industry. However, these women face numerous challenges in carrying out their culturally-expected household responsibilities, including childcare, due to severe resource constraints. As a result, the children of these working women have a higher incidence of malnutrition, particularly stunted growth. This study aims to identify the factors that contribute to stunting in children under the age of five whose mothers work in the RMG sector in Bangladesh., Methods: The study collected data from 267 female RMG workers in the Gazipur district of Bangladesh using a simple random sampling technique. Chi-square tests were used to determine the associations between the factors influencing child stunting, and Multinomial Logit Models were used to estimate the prevalence of these factors., Results: The study found that the prevalence of moderate and severe stunting among the children of RMG workers living in the Gazipur RMG hub was 19% and 20%, respectively. The study identified several significant predictors of child stunting, including the mother's education level, nutritional knowledge, control over resources, receipt of antenatal care, household size, sanitation facilities, and childbirth weight. The study found that improving the mother's education level, increasing household size, and receiving antenatal care during pregnancy were important factors in reducing the likelihood of child stunting. For example, if a mother's education level increased from no education to primary or secondary level, the child would be 0.211 (0.071-0.627) and 0.384 (0.138-1.065) times more likely to have a normal weight and height, respectively, than to be moderately stunted., Conclusion: The study highlights the challenges faced by working women in the RMG sector, who often receive minimal wages and have limited access to antenatal care services. To address these challenges, the study recommends policies that support antenatal care for working-class mothers, provide daycare facilities for their children, and implement a comprehensive social safety net program that targets child nutrition. Improving the socioeconomic status of mothers is also critical to reducing child malnutrition in this population., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Haque et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Unplanned industrialization and improper management of wastes and gases into open surfaces are affecting the agricultural lands causing heavy metal pollution. This study monitored a suburban industrial zone located beside the Dhaka Export Processing Zone (DEPZ) at the Dhaka district in Bangladesh. We studied the heavy metal (Fe, Mn, Cr, Cu, Ni, Co, Zn, Pb, and Cd) concentration in the agricultural soils, plants, and in the atmospheric particulate matter (PM). The soils were found moderately contaminated with Fe, Cu, Ni, Co, and Zn and less contamination with Mn, Cr, Pb, and Cd. The enrichment factor (EF) and pollution load index (PLI) concluded a moderate level of soil pollution in this region. Besides, the plant samples showed an excess level of Cr and a similar level of Cu, Ni, Co, Zn, Pb, and Cd compared with the levels of industrial polluted sites of Bangladesh. The atmospheric PM analysis showed the presence of Fe, Mn, Cu, Ni, Zn, and Pb metals. The EF showed the anthropogenic origin of Mn and Ni in the atmospheric PM. The statistical correlation (r < 0.0001) of soil and plant heavy metals showed the possibility of transfer of metals from soil to plant which will cause the increase of pollution intensity. Overall, this agricultural region became an intermediate pollution zone. This study will help the decision-maker become conscious of heavy metal pollution in the suburban regions to monitor agricultural lands from anthropogenic pollution.