A study was conducted to assess the effects of organic amendments on soil aggregates, carbon (C) sequestration, and energy use efficiency (EUE) during five consecutive Boro and Transplanted Aman rice seasons in Bangladesh during 2018–2020. Five treatments (viz., control (only inorganic fertilizers), cow dung (CD), vermicompost (VC), rice straw (RS), and poultry manure (PM)) were used. The organic materials were applied at 2 t C ha⁻¹ season⁻¹ to all the plots, except in the control treatment. Inorganic fertilizers were applied in all treatments in both seasons following integrated nutrient management (INM). The data reveal that PM was found to be more efficient at increasing the water-stable soil aggregates (WSA), followed by the RS, CD, and VC. The WSA in smaller-sized soil aggregates were found to be higher than those in larger-sized soil aggregates. VC was found to be the most effective in terms of C sequestration (29%), followed by PM (26%), CD (22%), and RS (20%). The highest EUE was attributed to the control treatment (9.77), followed by the CD (8.67), VC (8.04), RS (2.10), and PM (1.18), which showed energy wastage in the organic treatments. The system productivity (SP) followed the opposite trend of the EUE. The INM is a better approach to improve the soil health, the C sequestration, and the SP, but it appeared as an energy-inefficient strategy, which suggests that a balanced application of organic and inorganic nutrients is needed in order to achieve yield sustainability and EUE.

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.

Field experiment on sowing dates was carried out with BARI Mung-6 during pre-monsoon (kharif-I) season of 2021 for the evaluation of Agricultural Production Systems Simulator (APSIM) crop model. The APSIM model was parameterized using data from March 10 sowing, while validation was done with other sowing dates and data from literatures. Simulation was done with elevated temperatures (1-, 2- and 3-°C) to find out the adaptation option against future temperature stress situations. The model was run for different sowing dates using long-term (1981–2021) historical weather data. The evaluations showed that the model performance was satisfactory in predicting crop phenology, total biomass and grain yields of BARI Mung-6. Simulated grain yields during March 10 to March 25 sowings were very similar to attainable grain yields while, very early or late sowing gave comparatively lower grain yields. The best simulated planting window was from 15 to 25 March having the highest mean grain yields with less variability over the years. Increase in temperature by 1°C increase exhibited no significant influence on grain yields across the sowing dates, but significant yield reductions were observed with the rise of temperatures by 2 and 3°C on March 20, March 30 and April 10 sowings. Elevated temperatures showed positive impact on grain yield of March 10 sowing only. Results revealed that optimum sowing window for mungbean is from 15 to 25 March with existing weather conditions. In future temperature rises situations, sowing of seeds by the first week of March would be one of the options to combat climate change impact on mungbean grain yield in Bangladesh.

The Agricultural Production Systems Simulator (APSIM) model was calibrated and validated and used to optimize the sowing window for mungbean (var. BARI Mung-6) at Gazipur, the South-central climatic zone of Bangladesh. Simulation was also done with elevated temperatures (1, 2 and 3 ºC) to find out the adaptation option against future temperature stress situations. The model was run for eight sowing dates viz., February 20, March 05, March 10, March 15, March 20, March 25, March 30 and April 10 using long-term (41 years) historical weather data. A field experiment was carried out with BARI Mung-6 under four sowing dates (March 10, March 20, March 30, and April 10) during 2021 for model evaluation. The APSIM model was calibrated with the data from March 10 sowing, while validation was done with other sowing dates along with long-term (1981 to 2021) weather data. The evaluations with the experimental data showed that the model performance was satisfactory to predict crop phenology, total biomass and grain yields for BARI Mung-6. Simulated yields during March 10 to March 25 sowing was very similar to attainable seed yields while, very early or late sowing gave comparatively lower seed yields with higher variability over the years. The best planting window was from March 15 to March 25 which simulated the highest mean seed yield with less variability over the years. Climate change scenario analyses at 1, 2 and 3 ºC rises in temperature revealed that 1°C increase in temperature has no significant influence on seed yields across the sowing dates but significant yield reductions were observed with the rise of temperatures by 2 and 3 °C on March 20, March 30 and April 10 sowings. Elevated temperatures showed positive impact on seed yield of March 10 sowing only. Results revealed that optimum sowing window for mungbean is from March 15 to March 25 under existing weather conditions but in future, sowing mungbean seeds in March 10 would be the option to combat temperature rise stress situations for sustained productivity.