We review the recent development of Conservation Agriculture (CA) for rice-based smallholder farms in the Eastern Gangetic Plain (EGP) and the underpinning research on agronomy, weed control, soil properties and greenhouse gas emissions being tested to accelerate its adoption in Bangladesh. The studies are based mostly on minimum soil disturbance planting in strip planting (SP) mode, using the Versatile Multi-crop Planter (VMP), powered by a two-wheel tractor (2WT). One-pass SP with the VMP decreased fuel costs for crop establishment by up to 85% and labour requirements by up to 50%. We developed strip-based non-puddled rice (Oryza sativa) transplanting (NPT) in minimally-disturbed soil and found that rice grain yield increased (by up to 12%) in longer-term practice of CA. On farms, 75% of NPT crops increased gross margin. For non-rice crops, relative yield increases ranged from 28% for lentil (Lens culinaris) to 6% for wheat (Triticum aestivum) on farms that adopted CA planting. Equivalent profit increases were from 47% for lentil to 560% for mustard (Brassica juncea). Moreover, VMP and CA adopting farms saved 34% of labour costs and lowered total cost by up to 10% for production of lentil, mustard, maize (Zea mays) and wheat. Effective weed control was obtained from the use of a range of pre-emergent and post-emergence herbicides and retention of increased crop residue. In summary, a substantial body of research has demonstrated the benefits of CA and mechanized planting for cost savings, yield increases in many cases, increased profit in most cases and substantial labour saving. Improvement in soil quality has been demonstrated in long-term experiments together with reduced greenhouse gas emissions.

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.

Conservation agriculture (CA) based practices are gaining popularity in smallholder farming systems recently because they ensure soil quality and soil health, and they also reduce the total cultivation costs. However, weeds are a major hinderance to achieving the desired yield of crops in cereal-based cropping systems under CA-based practices. Proper weed management is a big challenge for crop growers to reduce the labor demand and cost of production. Considering these burning issues, a field study was done with a CA-based rice–wheat–mung bean (Vigna radiata L.) cropping pattern in two consecutive years (2017–2018 and 2018–2019). The study aimed to understand the types and distribution of weeds in non-puddled rice fields and also in strip-planted wheat fields in sandy loam textured soil with low organic matter (0.87%) content. Furthermore, we also tried to discover the most economic and effective ways to manage weeds in both cereals of the cropping pattern. The performance of two herbicides—pendimethalin (as pre-emergence) and carfentrazone–ethyl + isoproturon (as post-emergence)—for strip-planted wheat and three herbicides—two pre-emergence herbicides pretilachlor and pyrazosulfuron–ethyl as well as one post-emergence herbicide, bispyribac–sodium—for non-puddled rainy season rice were evaluated, where the ‘weedy check’ and ‘weed free’ treatments were considered as a control. In a two-year field experiment, eight weed species in the strip-planted wheat field during the first year and fourteen species in the second year were identified. Among them, three grasses such as Cynodon dactylon (L.) Pers., Digitaria sanguinalis (L.) Scop. and Echinochloa colona (L.) Link and one broadleaf weed Physalis heterophylla (L.) Nees, were found to the dominant in both years. During the first season, the dominant weed species in strip-planted wheat was Physalis heterophylla (L.); Digitaria sanguinalis (L.) Scop was dominant in the second year. In a non-puddled field of rainy season rice, eleven weed species were found in the first year and twelve species were found in the second year. Among the weed species, four types of grasses (Cynodon dactylon (L.) Pers., Leptochloa chinensis (L.) Nees, Echinochloa colona (L.) Link and Leersia hexandra (Sw.), one sedge (Fimbristylis miliacea (L.) Vahl) and four broadleaf weed species (Ludwigia decurrens (Walter), Jussiaea repens (L.), Enhydra fluctuans (Lour) and Alternanthera sessilis (L.) R.Br. ex DC.) were found the common in both years. The most dominant weed in non-puddled rainy season rice was Leptochloa chinensis (L.) for the first year and Alternanthera sessilis (L.) for the second year. The study revealed that the sequential application of pendimethalin as pre-emergence treatment followed by carfentrazone–ethyl + isoproturon as post-emergence treatment were most effective and economically viable for weed control in strip-till wheat because they achieved the highest grain and straw yields. For weed management in non-puddled rainy season rice, the herbicide pyrazosulfuron–ethyl applied as pre-emergence treatment and bispyribac–sodium as the post-emergence application were found to be the most effective combination to obtain a desirable yield.