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.