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Potential Indicators of Soil Health Degradation in Different Land Use-Based Ecosystems in the Shiwaliks of Northwestern India

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  • Arnab Bhowmik

    (Department of Soil Science, Punjab Agricultural University, Ludhiana 141004, India
    Department of Natural Resources and Environmental Design, North Carolina A&T State University, Greensboro, NC 27411, USA)

  • Surinder Singh Kukal

    (Department of Soil Science, Punjab Agricultural University, Ludhiana 141004, India)

  • Debasish Saha

    (Department of Soil Science, Punjab Agricultural University, Ludhiana 141004, India
    Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA)

  • Harmandeep Sharma

    (Department of Soil Science, Punjab Agricultural University, Ludhiana 141004, India
    Department of Natural Resources and Environmental Design, North Carolina A&T State University, Greensboro, NC 27411, USA)

  • Anu Kalia

    (Department of Soil Science, Punjab Agricultural University, Ludhiana 141004, India)

  • Sandeep Sharma

    (Department of Soil Science, Punjab Agricultural University, Ludhiana 141004, India)

Abstract

Identifying the importance of soil biology in different land use systems is critical to assess the present conditions of declining soil (C) and global land degradation while regulating soil health and biogeochemical nutrient cycling. A study was undertaken in a mixed watershed comprising of different land use systems (agricultural, grassland, agroforestry, and eroded); situated in the Shiwalik region in the foot hills of the lower Himalayas in India, a fragile ecosystem susceptible to land degradation. Soil samples from 0–15 and 15–30 cm depths were collected from these land use systems and analyzed for a suite of different soil health indicators, including physio-chemical soil properties, aggregate stability, soil microflora, and the enzymatic activities that are critical for nutrient cycling. Principal component analysis was used to group different land uses and understand their association with soil microflora, enzyme activities, and soil physio-chemical properties. We found that a greater number of soil microflora and enzymatic activities were associated with grassland and agroforestry land use systems. Aggregate-associated soil C correlated well with the soil microflora under different land use systems studied. The biplots revealed that the fungal:bacterial ratio (2 × 10 3 –0.1 × 10 3 ) was a robust indicator of C accumulation and soil health, and was in greater association with the agroforestry land use system. Random forest, a non-parametric statistical test, on average explained that 68% to 92% of the variability in soil microbial population was due to land use and other soil health properties. Overall, the biological soil health indicators used in this study demonstrated the fact that land use management systems that employ constant crop cover with minimal disturbance have the potential to improve soil sustainability and ecological functioning.

Suggested Citation

  • Arnab Bhowmik & Surinder Singh Kukal & Debasish Saha & Harmandeep Sharma & Anu Kalia & Sandeep Sharma, 2019. "Potential Indicators of Soil Health Degradation in Different Land Use-Based Ecosystems in the Shiwaliks of Northwestern India," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3908-:d:249382
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    References listed on IDEAS

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    1. Abhishek Kumar & Rajiv Rakshit & Arnab Bhowmik & Nintu Mandal & Anupam Das & Samrat Adhikary, 2019. "Nanoparticle-Induced Changes in Resistance and Resilience of Sensitive Microbial Indicators towards Heat Stress in Soil," Sustainability, MDPI, vol. 11(3), pages 1-19, February.
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    1. Usman Zulfiqar & Saddam Hussain & Muhammad Ishfaq & Nauman Ali & Muhammad Ahmad & Fahid Ihsan & Mohamed S. Sheteiwy & Abdur Rauf & Christophe Hano & Mohamed A. El-Esawi, 2021. "Manganese Supply Improves Bread Wheat Productivity, Economic Returns and Grain Biofortification under Conventional and No Tillage Systems," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
    2. Li Wang & Yong Zhou & Qing Li & Tao Xu & Zhengxiang Wu & Jingyi Liu, 2021. "Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China," Agriculture, MDPI, vol. 11(1), pages 1-23, January.
    3. Jadwiga Wyszkowska & Agata Borowik & Jan Kucharski, 2022. "The Role of Grass Compost and Zea Mays in Alleviating Toxic Effects of Tetracycline on the Soil Bacteria Community," IJERPH, MDPI, vol. 19(12), pages 1-26, June.

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