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.

Soil carbon (C) mineralization was studied in an incubation experiment comprised of two factors having six organic materials and three nitrogen (N) rates. Cow dung (CD), rice straw (RS), wood ash (WA), cow dung biochar (CB), rice straw biochar (RB) and wood biochar (WB) considering 2.5 g C kg⁻¹ soil along with three levels of N, i.e., 0, 0.05 and 0.10 g N kg⁻¹ soil were mixed with 400 g of soil used in each pot. The pots were placed for 0, 30, 60, 90, 120, 150 and 180 days of incubation, and soils were collected after each incubation and analyzed for C and N. Irrespective of treatment factors, C decreased in an irregular fashion until 180 days of incubation. From the initial level of 1.91%, C contents decreased to 1.08, 1.10, 1.06, 1.23, 1.17 and 1.12% in soil mixed with CD, RS, WA, CB, RB and WB, respectively, and to 1.28, 1.11 and 0.99% in 0, 0.05 and 0.10 g N kg⁻¹ soil, respectively, at 180 days of incubation. The mineralization followed the order of WA > CD > RS > WB > RB > CB. Biochars could supply stable C in soil, while N enhances mineralization; optimization of N is therefore essential to ensure soil C accretion.

With rapid industrialization in Gazipur areas of Bangladesh, untreated industrial effluents have been polluting rice soils which could exert potential ecological risk. Therefore, four different types of industries including chemical (SL), textile and paints (MIX), dyeing (CK), and sweater and dyeing (RD) were selected to monitor the intensity of heavy metal pollution in rice soils and ecological risk assessment. The di-acid digestion method was used for the determination of Pd, Cd, and Ni, and the DTPA extraction method was used for Fe, Zn, and Cu. ArcGIS was used to visualize the spatial patterns of heavy metal pollution, and different pollution indices were calculated to assess the ecological risk. The highest concentration (mg kg⁻¹) of Cd (0.72), Pb (104.20), and Ni (5.02) was found in soils of the MIX industrial area. The highest concentration (mg kg⁻¹) of Fe (147.65) and Zn (11.27) was found in the SL industry, while the highest Cu (7.67) was found in the CK industry. It was evident from the spatial distribution that the soils of paddy fields adjacent to the different industries are more contaminated than background soil. Although the potential ecological risk of heavy metal was low, different pollution indices indicated low to high pollution. Thus, the adjacent rice field soil of different industries is being contaminated by different heavy metals which may raise ecological risk.

Soil texture is an independent and innate soil property and other dynamic soil properties such as electrical conductivity (EC), organic carbon (OC) content and cation exchange capacity (CEC) are mostly dependent on it. An attempt was made to develop a model for numerically simulating soil texture and also to construct relationships of the developed model with other soil properties. Hypothetical data of particle size distribution and our data were used to justify and validate the newly defined indices. Scatter diagrams showed good association between the indices and hypothetical data of soil separates. Moreover, similar trends were observed between the line charts of USDA soil textural class codes and the indices. Strong correlations (r = 0.78–0.96) were found between the indices and soil separates (sand, silt and clay) for our data. However, the indices demonstrated moderate correlations (r = –0.34 to –0.55) with EC and OC of the soils. Strong nonlinear relationships were found between CEC and the three indices (R2 = 0.699, R2 = 0.732 and R2 = 0.672 (all P < 0.001). Furthermore, the variability of EC, OC and CEC within a single USDA textural class and customised textural index groups were described using the developed model. The developed indices showed excellent fitness for simulation of soil texture and demonstrated an extended applicability in terms of their relationships with soil properties related to soil texture, which will help in constructing digital soil maps.