IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i3p576-d1081943.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/3/576/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/3/576/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hsiao,Cheng, 2022. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781009060752.
    2. Hsiao,Cheng, 2022. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9781316512104.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    3. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    4. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    5. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    6. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    7. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    8. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    9. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    10. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    11. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    12. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
    13. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    14. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    15. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    16. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    17. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
    18. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    19. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
    20. Mohamed Kais Msakni & Anders Risan & Peter Schütz, 2023. "Using machine learning prediction models for quality control: a case study from the automotive industry," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:576-:d:1081943. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.