Drought and salinity are two of the most frequently co-occurring abiotic stresses. Despite recent advances in the elucidation of the effects of these stresses individually during the vegetative stage of plants, significant gaps exist in our understanding of the combined effects of these two frequently co-occurring stresses. Here, Tibetan wild barley XZ5 (drought tolerant), XZ16 (salt tolerant), and cultivated barley cv. CM72 (salt tolerant) were subjected to drought (D), salinity (S), or a combination of both treatments (D+S). Protein synthesis is one of the primary activities of the green part of the plant. Therefore, leaf tissue is an important parameter to evaluate drought and salinity stress conditions. Sixty differentially expressed proteins were identified by mass spectrometry (MALDI-TOF/TOF) and classified into 9 biological processes based on Gene Ontology annotation. Among them, 21 proteins were found to be expressed under drought or salinity alone; however, under D+S, 7 proteins, including S-adenosylmethionine synthetase 3 (SAMS3), were exclusively upregulated in drought-tolerant XZ5 but not in CM72. HvSAMS3 carries both N-terminal and central domains compared with Arabidopsis and activates the expression of several ethylene (ET)-responsive transcription factors. HvSAMS3 is mainly expressed in the roots and stems, and HvSAMS3 is a secretory protein located in the cell membrane and cytoplasm. Barley stripe mosaic virus-based virus-induced gene silencing (BSMV-VIGS) of HvSAMS3 in XZ5 severely compromised its tolerance to D+S and significantly reduced plant growth and K⁺ uptake. The reduced tolerance to the combined stress was associated with the inhibition of polyamines such as spermidine and spermine, polyamine oxidase, ethylene, biotin, and antioxidant enzyme activities. Furthermore, the exogenous application of ethylene and biotin improved the tolerance to D+S in BSMV-VIGS:HvSAMS3-inoculated plants. Our findings highlight the significance of HvSAMS3 in the tolerance to D+S in XZ5.

Aluminum (Al) toxicity and drought are two major constraints on plant growth in acidic soils, negatively affecting crop performance and yield. Genotypic differences in the effects of Al/low pH and polyethyleneglycol (PEG) induced drought stress, applied either individually or in combination, were studied in Tibetan wild (XZ5, drought-tolerant; XZ29, Al-tolerant) and cultivated barley (Al-tolerant Dayton; drought-tolerant Tadmor). Tibetan wild barley XZ5 and XZ29 had significantly higher H⁺-ATPase, Ca²⁺Mg²⁺-ATPase, and Na⁺K⁺-ATPase activities at pH 4.0+Al+PEG than Dayton and Tadmor. Moreover, XZ5 and XZ29 possessed increased levels in reduced ascorbate and glutathione under these conditions, and antioxidant enzyme activities were largely stimulated by exposure to pH 4.0+PEG, pH 4.0+Al, and pH 4.0+Al+PEG, compared to a control and to Dayton and Tadmor. The activity of methylglyoxal (MG) was negatively correlated with increased levels of glyoxalase (Gly) I and Gly II in wild barley. Microscopic imaging of each genotype revealed DNA damage and obvious ultrastructural alterations in leaf cells treated with drought or Al alone, and combined pH 4.0+Al+PEG stress; however, XZ29 and XZ5 were less affected than Dayton and Tadmor. Collectively, the authors findings indicated that the higher tolerance of the wild barley to combined pH 4.0+Al+PEG stress is associated with improved ATPase activities, increased glyoxalase activities, reduced MG, and lower reactive oxygen species levels (like O₂⁻ and H₂O₂) due to increased antioxidant enzyme activities. These results offer a broad comprehension of the mechanisms implicated in barley’s tolerance to the combined stress of Al/low pH and drought, and may provide novel insights into the potential utilization of genetic resources, thereby facilitating the development of barley varieties tolerant to drought and Al/low pH stress.

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.

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.