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Influencing Factors on Bioavailability and Spatial Distribution of Soil Selenium in Dry Semi-Arid Area

Author

Listed:
  • Muhammad Raza Farooq

    (School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
    Jiangsu Bio-Engineering Research Center for Selenium, Suzhou 215123, China)

  • Zezhou Zhang

    (College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
    Yangtze River Delta Functional Agricultural Research Institute, Anhui Science and Technology University, Chuzhou 239000, China)

  • Linxi Yuan

    (Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Xiaodong Liu

    (School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China)

  • Abdul Rehman

    (CAS-Key Laboratory of Crust-Mantle Materials and the Environments, School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China)

  • Gary S. Bañuelos

    (USDA, Agricultural Research Service, San Joaquin Valley Agricultural Sciences Center, 9611 S. Riverbend Ave., Parlier, CA 93648, USA)

  • Xuebin Yin

    (School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
    Jiangsu Bio-Engineering Research Center for Selenium, Suzhou 215123, China
    Institute of Functional Agriculture Science and Technology (iFAST) at Yangtze River Delta, Anhui Science and Technology University, Chuzhou 239000, China)

Abstract

The chemical transformation of selenium (Se) in the topsoil, especially when regarded as low to sufficient Se (with high bioavailability) in dry arid environments, has great importance in the alkaline soils to yield Se-enriched food regionally. The Se content in the highly alkaline soil of the northwest region of China has inordinate agriculture economic potential, and such soil distribution is likely to produce Se-enriched crops with distinct features. One such large area of Zhongwei was investigated for the distribution of soil Se and its bioavailability, and the influencing chemical factors of soil total Se (T-Se) and bioavailable Se (B-Se) in the agroecosystem. The results suggested that the T-Se in Zhongwei soils (mg/kg) ranged from 0.01 to 0.55 with a mean of 0.2 ± 0.08, which was lower than the average Se distribution of both China (0.29 mg/kg) and the world (0.40 mg/kg). However, the overall B-Se proportion (16%) in T-Se was adequately higher than in other Se-rich soils. Spatial distribution depicted that the T-Se was specified as deficient in 42.6% and sufficient in 55.5% of the studied area, while Zhongning county was prominent with a higher B-Se proportion (22%) in the T-Se of Zhongwei. The influencing factors, such as pH and organic matter (OM), showed significant association with B-Se, as suggested by Pearson’s correlation and multiple linear regression (MLR). Furthermore, the vertical distribution of T-Se and B-Se was higher in agricultural soil (AS) than in natural soil (NS) and can be justified in the context of their association with OM. Based on these results, the Se-fortified crops can be yielded by practices to improve corresponding influencing chemical factors of soil, especially in dry areas.

Suggested Citation

  • Muhammad Raza Farooq & Zezhou Zhang & Linxi Yuan & Xiaodong Liu & Abdul Rehman & Gary S. Bañuelos & Xuebin Yin, 2023. "Influencing Factors on Bioavailability and Spatial Distribution of Soil Selenium in Dry Semi-Arid Area," Agriculture, MDPI, vol. 13(3), pages 1-17, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:576-:d:1081943
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    References listed on IDEAS

    as
    1. Hsiao,Cheng, 2022. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781009060752, October.
    2. Hsiao,Cheng, 2022. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781316512104, October.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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