Dataset Information

Metagenome dataset of lateritic soil microbiota from Sadaipur, Birbhum, West Bengal, India.

ABSTRACT: The data represents the bacterial community profile obtained through metagenomic sequencing of soil sample, collected from the 'Rarh' region of West Bengal, which is characterized by the lateritic badlands dating back to the late Pleistocene. Taxonomic binning and operational taxonomic unit (OTU) prediction of the Illumina sequencing data indicated the abundance Proteobacteria (61%) followed closely by Bacterioidetes (35%). The top two most abundant genera identified, were Sphingobacterium and Acinetobacter respectively. Chemical properties of soil, such as pH, organic carbon content, available nitrogen, phosphorus, and potassium were also analyzed for enabling future researchers to correlate the abundance of microbial taxa with the prevalent conditions. These findings can be effectively used to formulate strategic microbiome engineering through bioaugmentation for a sustainable agricultural system.

SUBMITTER: Mukhopadhyay M

PROVIDER: S-EPMC8086014 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Hybrid rice requires adequate nitrogen (N) management in order to achieve good yields from its vegetative and reproductive development. With this backdrop, a field experiment was conducted at Regional Research Station (Coastal Saline Zone), Bidhan Chandra Krishi Viswavidyalaya, Kakdwip, West Bengal (India) to record growth and yield performance of hybrid rice (cv. PAN 2423) under varied N-fertilizer doses. A modelling approach was adopted for the first time in hybrid rice production system under coastal ecosystem of West Bengal (India). In the present study, the Agricultural Production Systems Simulator (APSIM) model was calibrated and validated for simulating a hybrid rice production system with different N rates. The APSIM based crop simulation model was found to capture the physiological changes of hybrid rice under varied N rates effectively. While studying the relationship between simulated and observed yield data, we observed that the equations developed by APSIM were significant with higher R2 values (≥0.812). However, APSIM caused an over-estimation for calibrate data but it was rectified for validated data. The RMSE of models for all the cases was less than respective SD values and the normalized RMSE values were ≤20%. Hence, it was proved to be a good rationalized modelling and the performance of APSIM was robust. On the contrary, APSIM underestimated the calibrated amount of N (kg ha−1) in storage organ of hybrid rice, which was later rectified in case of validated data. A strong correlation existed between the observed and APSIM-simulated amounts of N in storage organ of hybrid rice (R2 = 0.94** and 0.96** for the calibration and validation data sets, respectively), which indicates the robustness of the APSIM simulation study. Scenario analysis also suggests that the optimal N rate will increase from 160 to 200 kg N ha−1 for the greatest hybrid rice production in coming years under elevated CO2 levels in the atmosphere. The APSIM-Oryza crop model had successfully predicted the variation in aboveground biomass and grain yield of hybrid rice under different climatic conditions.

Dataset Information

Metagenome dataset of lateritic soil microbiota from Sadaipur, Birbhum, West Bengal, India.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets