Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec 13:13:986519.
doi: 10.3389/fmicb.2022.986519. eCollection 2022.

Climate-smart agricultural practices influence the fungal communities and soil properties under major agri-food systems

Affiliations

Climate-smart agricultural practices influence the fungal communities and soil properties under major agri-food systems

Madhu Choudhary et al. Front Microbiol. .

Abstract

Fungal communities in agricultural soils are assumed to be affected by climate, weather, and anthropogenic activities, and magnitude of their effect depends on the agricultural activities. Therefore, a study was conducted to investigate the impact of the portfolio of management practices on fungal communities and soil physical-chemical properties. The study comprised different climate-smart agriculture (CSA)-based management scenarios (Sc) established on the principles of conservation agriculture (CA), namely, ScI is conventional tillage-based rice-wheat rotation, ScII is partial CA-based rice-wheat-mungbean, ScIII is partial CSA-based rice-wheat-mungbean, ScIV is partial CSA-based maize-wheat-mungbean, and ScV and ScVI are CSA-based scenarios and similar to ScIII and ScIV, respectively, except for fertigation method. All the scenarios were flood irrigated except the ScV and ScVI where water and nitrogen were given through subsurface drip irrigation. Soils of these scenarios were collected from 0 to 15 cm depth and analyzed by Illumina paired-end sequencing of Internal Transcribed Spacer regions (ITS1 and ITS2) for the study of fungal community composition. Analysis of 5 million processed sequences showed a higher Shannon diversity index of 1.47 times and a Simpson index of 1.12 times in maize-based CSA scenarios (ScIV and ScVI) compared with rice-based CSA scenarios (ScIII and ScV). Seven phyla were present in all the scenarios, where Ascomycota was the most abundant phyla and it was followed by Basidiomycota and Zygomycota. Ascomycota was found more abundant in rice-based CSA scenarios as compared to maize-based CSA scenarios. Soil organic carbon and nitrogen were found to be 1.62 and 1.25 times higher in CSA scenarios compared with other scenarios. Bulk density was found highest in farmers' practice (Sc1); however, mean weight diameter and water-stable aggregates were found lowest in ScI. Soil physical, chemical, and biological properties were found better under CSA-based practices, which also increased the wheat grain yield by 12.5% and system yield by 18.8%. These results indicate that bundling/layering of smart agricultural practices over farmers' practices has tremendous effects on soil properties, and hence play an important role in sustaining soil quality/health.

Keywords: agriculture management; climate smart agricultural practices; diversity indices; fungal community; soil organic carbon; tillage.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Different cereal-based management scenarios at CSSRI-CIMMYT research platform, Karnal, Harayana, India.
Figure 2
Figure 2
Abundance of phyla in different crop management-based scenarios. a,b,cMeans followed by similar lowercase letters are not significantly different at 0.05 level of probability using Tukey's HSD test.
Figure 3
Figure 3
Biplot obtained from principal components analysis based on the correlation matrix, showing the two first principal components (explaining 48.8 and 28.6%, respectively). Each point represents scenarios (ScI to ScVI), loadings represent fungi classes, soil physical properties, and SOC, N, P, and K contents of the soil.

Similar articles

Cited by

References

    1. Abarenkov K., Nilsson R. H., Larsson K. H., Alexander I. J., Eberhardt U., Erland S., et al. . (2010). The UNITE database for molecular identification of fungi–recent updates and future perspectives. New Phytol. 186, 281–285. 10.1111/j.1469-8137.2009.03160.x - DOI - PubMed
    1. Altschul S. F., Gish W., Miller W., Myers E. W., Lipman D. J. (1990). Basic local alignment search tool. J. Mol. Biol. 215, 403–410. 10.1016/S0022-2836(05)80360-2 - DOI - PubMed
    1. Andrews S. (2010). A Quality Control Tool for High Throughput Sequence Data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed November, 2018).
    1. Aronesty E. (2013). Comparison of sequencing utility programs. Open Bioinform. J. 7, 1–8. 10.2174/1875036201307010001 - DOI
    1. Bender S. F., Wagg C., van der Heijden M. G. (2016). An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452. 10.1016/j.tree.2016.02.016 - DOI - PubMed