Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969–70 to 2019–20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984–85 to 2019–20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.

Effects of soil management on soil characteristics were investigated on the rhizosphere (RPP) and the nonrhizosphere (NRPP) soil of a re-grass vertisol under Digitaria decumbens and in the soil under continuous cultivation (CC). A low energy technique allowed to separate eight size and density fractions, including macro-and microaggregates while preserving soil bacteria. Organic C and N, microbial biomass C and the number of total bacteria (AODC) and of Azospirillum brasilense and their distribution were determined in soil fractions isolated from the CC, NRPP and RPP soils. Soil macroaggregates (>2000 #m) were similarly predominant in the NRPP and RPP soils when the dispersible clay size fraction (<2 #m) represented more than 25% of the CC soil mass. The main increase of C content in RPP originated from the macroaggregates (> 2000 #m) and from the root fraction, not from the finer separates. The proportion of organic C as microbial biomass C revealed the low turnover of microbial C in the PP situations, especially in the clay size fraction of the NRPP soil. A common shift of AODC toward the finer separates from planted soils (CC and RPP) revealed the influence of living plants on the distribution of soil bacteria. The relative abundance of A. brasilense showed the presence of the active roots of Digitaria in the macroaggregates and their contact with the dispersible clay size fraction of the rhizosphere soil.

Arsenic pollution became a great problem in the recent past in different countries including Bangladesh. The microlevel studies were conducted to see the spatial variation of arsenic in soils and plant parts contaminated through ground water irrigation. The study was performed in shallow tube well command areas in Sadar Upazila (subdistrict), Faridpur, Bangladesh, where both soil and irrigation water arsenic are high. Semivariogram models were computed to determine the spatial dependency of soil, water, grain, straw, and husk arsenic (As). An arsenic concentration surface was created spatially to describe the distribution of arsenic in soil, water, grain, straw, and husk. Command area map was digitized using Arcview GIS from the “mouza” map. Both arsenic contaminated irrigation water and the soils were responsible for accumulation of arsenic in rice straw, husk, and grain. The accumulation of arsenic was higher in water followed by soil, straw, husk, and grain. Arsenic concentration varied widely within command areas. The extent and propensity of arsenic concentration were higher in areas where high concentration of arsenic existed in groundwater and soils. Spherical model was a relatively better and appropriate model. Kriging method appeared to be more suitable in creating interpolated surface. The average arsenic content in grain was 0.08–0.45 mg/kg while in groundwater arsenic level it ranged from 138.0 to 191.3 ppb.

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