Groundwater is a crucial natural resource that varies in quality and quantity across Bangladesh. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Gazipur district, Bangladesh, by integrating eleven thematic layers. Data and information were gathered from Landsat 8, the digital elevation model, the google earth engine, and several ancillary sources. A multi-criterion decision-making (MCDM) based analytical hierarchy process (AHP) was used in a GIS platform to estimate the groundwater potential index. The potential index values were finally classified into five sub-groups: very low, low, moderate, high, and very high to generate a groundwater water potential zone (GWPZ) map. The results show that groundwater potential in about 0.002% (0.026 km²) of the area is very low, 3.83% (63.18 km²) of the area is low, 56.2% (927.05 km²) of the area is medium, 39.25% (647.46 km²) of the area is high, and the rest 0.72% (11.82 km²) of the area is very high. The validation of GWPZ maps based on the groundwater level data at 20 observation wells showed an overall accuracy of 80%. In addition, the ROC curve showed 84% accuracy of GWPZ maps when validated with water inventory points across the study region. Overall, this study presents an easy and practical approach for identifying groundwater potential zones, which may help improve planning and sustainable groundwater resource management.

In the changing climatic condition, temperature is the most vulnerable parameter and is projecting a trend of increase in the future. Crop growth and development process depend largely on air temperature. This study aims to determine the role of increasing air temperature in yield, crop water requirement (CWR), and other agronomic parameters of irrigated rice. Ceres-rice model associated in the Decision Support System for Agrotechnology Transfer (DSSAT) was used in 15 different locations of Bangladesh. Grain yield, growth duration, and crop water requirement of widely cultivated irrigated rice (Boro rice) variety BRRI dhan28 were analysed in normal temperature and elevated air temperature by 1°C, 2°C, 3°C, and 4°C. The result revealed detrimental effect of elevated temperature on growth duration and grain yield. The estimated highest growth duration reduction of 30 days was found in Moulvibazar for 4°C temperature rise. The grain yield reduction was projected by 0–17%, 16–35%, 31–49%, and 39–61% from the normal condition if the seasonal mean temperature increased by 1°C, 2°C, 3°C, and 4°C, respectively. The country average crop water requirement was found to be 405 mm of which the highest 445 mm and the lowest 358 mm were recorded in Moulvibazar and Chandpur, respectively. The study revealed that the country average rice CWR reduced by 5%, 8%, 12%, and 17% over the normal condition for 1°C, 2°C, 3°C, and 4°C rising temperature, respectively. For 1°C temperature rise, BRRI dhan28 life span shortened by 6.4 days, grain yield reduced by 695 kg, and estimated CWR decreased by 14 mm. The projected declining CWR indicated that irrigated rice will require less irrigation water, but it will cause considerable yield loss under elevated temperature. Though elevated temperature will save huge irrigation water used in country-wide Boro rice cultivation, the crop developers need to introduce new heat-tolerant cultivar to minimize yield loss.

Rationale: Greenhouse gas (GHG) emissions from crop agriculture are of great concern in the context of changing climatic conditions; however, in most cases, data based on lifecycle assessments are not available for grain yield variations or the carbon footprint of maize. The current study aimed to determine net carbon emissions and sequestration for maize grown in Bangladesh. Methods: The static closed-chamber technique was used to determine total GHG emissions using data on GHG emissions from maize fields and secondary sources for inputs. A secondary source for regional yield data was used in the current study. GHG emission intensity is defined as the ratio of total emissions to grain yield. The net GHG emission/carbon sequestration was determined by subtracting total GHG emissions (CO₂ eq.) from net primary production (NPP). Results: Grain yields varied from 1590 to 9300 kg ha⁻¹ in the wet season and from 680 to 11,820 kg ha⁻¹ in the dry season. GHG emission intensities were 0.53–2.21 and 0.37–1.70 kg CO₂ eq. kg⁻¹ grain in the wet and dry seasons, respectively. In Bangladesh, the total estimated GHG emissions were 1.66–4.09 million tonnes (MT) CO₂ eq. from 2015 to 2020, whereas the net total CO₂ sequestration was 1.51–3.91 MT. The net CO₂ sequestration rates were 984.3–5757.4 kg ha⁻¹ in the wet season and 1188.62–5757.39 kg ha⁻¹ in the dry season. This study observed spatial variations in carbon emissions and sequestration depending on growing seasons. In the rice–maize pattern, maize sequestered about 1.23 MT CO₂ eq. per year⁻¹, but rice emitted about 0.16 MT CO₂ eq. per year⁻¹. This study showed potential spatiotemporal variations in carbon footprints. Recommendation: Special care is needed to improve maize grain yields in the wet season. Fertiliser and water use efficiencies need to be improved to minimise GHG emissions under changing climatic conditions. Efforts to increase the area under cultivation with rice–maize or other non-rice crop-based cropping systems are needed to augment CO₂ sequestration. The generation of a regional data bank on carbon footprints would be beneficial for combating the impact of climate change.

Determination of the optimum sowing window not only can improve maize yield significantly but also can fit maize in the existing cropping pattern. To get the advantages of sowing maize at the optimum time, a study was designed and carried out at the research field of Bangladesh Agricultural Research Institute, Rangpur, Bangladesh during 2015–2017. Another aim of the study was to forecast the yield of maize for the northern and western regions of Bangladesh using the CERES-Maize model. The study considered 5 November, 20 November, 5 December, 20 December, and 5 January as sowing dates for maize to identify the optimum sowing window. Three hybrid maize varieties, viz., BARI Hybrid Maize-9 (BHM-9), NK-40, and Pioneer30V92 were used. The study was laid out in a split-plot design, assigning the sowing dates in the main plot and the varieties in the sub-plot. To forecast the yield, the daily weather data of 2017 were subjected to run the model along with thirty years (1986–2015) of weather data. The genetic coefficients of the tested maize varieties were obtained through calibration of the model by using the observed field data of 2015–2016 and through validation by using the data of 2016–2017. The seasonal analysis was done using the DSSAT CERES-Maize model to confirm the experimental findings for optimizing the sowing window for maize at the northern region (Rangpur) of the country and subsequently adjusted the model for the western region (Jashore). The model performances were satisfactory for crop phenology, biomass, and grain yield. The NRMSE for anthesis was 0.66% to 1.39%, 0.67% to 0.89% for maturity date, 1.78% to 3.89% for grain yield, and 1.73% to 3.17% for biomass yield. The optimum sowing window for maize at the Rangpur region was 5 November to 5 December and 5 to 20 November for the Jashore region. The CERES-Maize model was promising for yield forecasting of the tested maize varieties. It gave a realistic yield forecast at approximately 45 days prior to the harvest of all the tested varieties. The study results are expected to be useful for both the farmers and the policy planners to meet up the future maize demands.

Background: Rainfall is the key contributor to provide soil moisture for wet season rice (T. Aman) cultivation. Erratic rainfall often causes water shortage resulting negative impact on plant growth and grain yield. The study aimed to determine suitable transplanting window that utilize maximum rainfall for T. Aman rice. Firstly, three years field experiment were conducted in Kushtia, Bangladesh from T. Aman, 2013 to 2015, and then the findings were implemented for another two adjacent locations, Panba and Rajshahi. The field experiment considered six transplanting dates of popular cultivar BR11 (growth duration 145 days) at 7 days interval starting from 10 July to 14 August. The CROPWAT 8.0 model was used to calculate crop water requirement (CWR), effective rainfall and irrigation demand (ID) from collected weather data in each growth phase of rice. Results: In all locations T. Aman rice received enormous rainfall up to vegetative phase resulting no irrigation demand in all three tested years. The early transplanting received more rainfall in reproductive phase than late planting. Thus, Irrigation demand increased at reproductive phase with delay transplanting in moderate drought prone Kushtia, Pabna and Rajshahi. A significant relationship (R² = 0.71) observed between reproductive phase ID and grain yield, while grain yield responded weakly with the ID at ripening phase. Based on yield performance 10–24 July found suitable transplanting window for BR11 in Kushtia. Considering the relationship between ID and grain yield, 10–17 July and 10–24 July considered the best transplanting window in Pabna and Rajshahi, respectively. Conclusions: Location specific suitable transplanting windows were selected considering minimum ID at reproductive phase and the maximum grain yield. Delay in transplanting demanded more irrigation and reduced grain yield. Whereas, early transplanting utilized maximum rainfall, reduced ID in reproductive stage and ensured desired grain yield.