IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i5p731-d814334.html
   My bibliography  Save this article

Determinants of Soil Bacterial Diversity in a Black Soil Region in a Large-Scale Area

Author

Listed:
  • Jiacheng Niu

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Huaizhi Tang

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Qi Liu

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Feng Cheng

    (China Land Surveying and Planning Institute, Beijing 100032, China)

  • Leina Zhang

    (China Land Surveying and Planning Institute, Beijing 100032, China)

  • Lingling Sang

    (China Land Surveying and Planning Institute, Beijing 100032, China)

  • Yuanfang Huang

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Chongyang Shen

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Bingbo Gao

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Zibing Niu

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

Abstract

Soils in black soil areas are high in organic matter and rich in nutrients. Soil microorganisms are particularly critical to cultivated land. The objective of this study was to explore the influencing factors of soil bacterial diversity under special regional conditions in a black soil region. In this study, the cultivated land in a black soil area was used as the study area and a random forest was used to map the bacterial abundance in the black soil area based on 1810 sample points. DbMEM analysis was used to quantify the spatial effect of the black soil area and to identify the influencing factors of soil bacterial abundance in the black soil area in combination with soil properties, terrain, and climate. Results of a variation division showed that broad (8.336%), AT (accumulated temperature, 5.520%), and pH (4.184%) were the main factors affecting soil bacterial diversity. The broad effect was more significant in the spatial effect, which may be related to the local landscape configuration. Overall, our research showed that the influencing factors of soil bacteria will be affected by regional characteristics.

Suggested Citation

  • Jiacheng Niu & Huaizhi Tang & Qi Liu & Feng Cheng & Leina Zhang & Lingling Sang & Yuanfang Huang & Chongyang Shen & Bingbo Gao & Zibing Niu, 2022. "Determinants of Soil Bacterial Diversity in a Black Soil Region in a Large-Scale Area," Land, MDPI, vol. 11(5), pages 1-16, May.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:731-:d:814334
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/5/731/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/5/731/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ye, Sijing & Song, Changqing & Shen, Shi & Gao, Peichao & Cheng, Changxiu & Cheng, Feng & Wan, Changjun & Zhu, Dehai, 2020. "Spatial pattern of arable land-use intensity in China," Land Use Policy, Elsevier, vol. 99(C).
    2. Peters, Jan & Baets, Bernard De & Verhoest, Niko E.C. & Samson, Roeland & Degroeve, Sven & Becker, Piet De & Huybrechts, Willy, 2007. "Random forests as a tool for ecohydrological distribution modelling," Ecological Modelling, Elsevier, vol. 207(2), pages 304-318.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huaizhi Tang & Jiacheng Niu & Zibing Niu & Qi Liu & Yuanfang Huang & Wenju Yun & Chongyang Shen & Zejun Huo, 2023. "System Cognition and Analytic Technology of Cultivated Land Quality from a Data Perspective," Land, MDPI, vol. 12(1), pages 1-17, January.

    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. Yikalo H. Araya & Tarmo K. Remmel & Ajith H. Perera, 2016. "What governs the presence of residual vegetation in boreal wildfires?," Journal of Geographical Systems, Springer, vol. 18(2), pages 159-181, April.
    2. Yu, Zhenning & She, Shuoqi & Xia, Chuyu & Luo, Jiaojiao, 2023. "How to solve the dilemma of China’s land fallow policy: Application of voluntary bidding mode in the Yangtze River Delta of China," Land Use Policy, Elsevier, vol. 125(C).
    3. Sarah Mittlefehldt & Erin Bunting & Emily Huff & Joseph Welsh & Robert Goodwin, 2021. "New Methods for Assessing Sustainability of Wood-Burning Energy Facilities: Combining Historical and Spatial Approaches," Energies, MDPI, vol. 14(23), pages 1-18, November.
    4. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    5. Jianhui Dong & Wenju Yun & Kening Wu & Shaoshuai Li & Bingrui Liu & Qiaoyuan Lu, 2023. "Spatio-Temporal Analysis of Cultivated Land from 2010 to 2020 in Long’an County, Karst Region, China," Land, MDPI, vol. 12(2), pages 1-22, February.
    6. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
    7. Bemah Ibrahim & Isaac Ahenkorah & Anthony Ewusi, 2022. "Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    8. Dthenifer Cordeiro Santana & Regimar Garcia dos Santos & Pedro Henrique Neves da Silva & Hemerson Pistori & Larissa Pereira Ribeiro Teodoro & Nerison Luis Poersch & Gileno Brito de Azevedo & Glauce Ta, 2023. "Machine Learning Methods for Woody Volume Prediction in Eucalyptus," Sustainability, MDPI, vol. 15(14), pages 1-11, July.
    9. Sun, Xueqing & Xiang, Pengcheng & Cong, Kexin, 2023. "Research on early warning and control measures for arable land resource security," Land Use Policy, Elsevier, vol. 128(C).
    10. Li, Xiaoliang & Wu, Kening & Yang, Qijun & Hao, Shiheng & Feng, Zhe & Ma, Jinliang, 2023. "Quantitative assessment of cultivated land use intensity in Heilongjiang Province, China, 2001–2015," Land Use Policy, Elsevier, vol. 125(C).
    11. Yirui Han & Qinqin Pan & Yuee Cao & Jianhong Zhang & Jiaxuan Yuan & Borui Li & Saiqiang Li & Renfeng Ma & Xu Luo & Longbin Sha & Xiaodong Yang, 2022. "Estimation of Grain Crop Yields after Returning the Illegal Nurseries and Orchards to Cultivated Land in the Yangtze River Delta Region," Land, MDPI, vol. 11(11), pages 1-19, November.
    12. Vanesa Mateo-Pérez & Marina Corral-Bobadilla & Francisco Ortega-Fernández & Vicente Rodríguez-Montequín, 2021. "Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-22, April.
    13. Saeid SHABANI, 2017. "Modelling and mapping of soil damage caused by harvesting in Caspian forests (Iran) using CART and RF data mining techniques," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 63(9), pages 425-432.
    14. Mengyao Han & Shuchang Li, 2021. "Transfer Patterns and Drivers of Embodied Agricultural Land within China: Based on Multi-Regional Decomposition Analysis," Land, MDPI, vol. 10(2), pages 1-16, February.
    15. Jun-Mao Liao & Ming-Jui Chang & Luh-Maan Chang, 2020. "Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques," Energies, MDPI, vol. 13(7), pages 1-22, April.
    16. Ke, Xinli & Chen, Jing & Zuo, Chengchao & Wang, Xiaoqian, 2024. "The cropland intensive utilisation transition in China: An induced factor substitution perspective," Land Use Policy, Elsevier, vol. 141(C).
    17. Long Kang & Rui Zhao & Kening Wu & Zhe Feng & Huafu Zhao & Sicheng Zhang, 2023. "Distribution Characteristics and Influencing Factors of Soil Biological Indicators in Typical Farmland Soils," Land, MDPI, vol. 12(4), pages 1-14, March.
    18. Nan Ke & Xupeng Zhang & Xinhai Lu & Bing Kuang & Bin Jiang, 2022. "Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China," Land, MDPI, vol. 11(2), pages 1-17, February.
    19. Ting Du & Chao Li & Zhaolin Wang, 2023. "Spatial Differentiation and Influencing Mechanisms of Farmland Transfer Rents in Mountainous Areas: Evidence from Chongqing and Its Surrounding Areas," Land, MDPI, vol. 12(3), pages 1-19, March.
    20. Xu, Feng & Chi, Guangqing & Zhang, Zhexi & Yang, Jianxin, 2023. "How does quality regional growth affect land resources dependence in China? Evidence based on spatial Durbin panel models," Resources Policy, Elsevier, vol. 81(C).

    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:jlands:v:11:y:2022:i:5:p:731-:d:814334. 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.