Soil erosion modelling: A bibliometric analysis

    Bezak, N and Mikoš, M and Borrelli, P and Alewell, C and Alvarez, P and Anache, J A J and Baartman, J and Ballabio, C and Biddoccu, M and Cerdà, A and Chalise, D and Chen, S and Chen, W and De Girolamo, A M and Gessesse, G D and Deumlich, D and Diodato, N and Efthimiou, N and Erpul, G and Fiener, P and Freppaz, M and Gentile, F and Gericke, A and Haregeweyn, N and Hu, B and Jeanneau, A and Kaffas, K and Kiani-Harchegani, M and Villuendas, I L and Li, C and Lombardo, L and López-Vicente, M and Lucas-Borja, M E and Maerker, M and Miao, C and Modugno, S and Möller, M and Naipal, V and Nearing, M and Owusu, S and Panday, D and Patault, E and Patriche, C V and Poggio, L and Portes, R and Quijano, L and Rahdari, M R and Renima, M and Ricci, G F and Rodrigo-Comino, J and Saia, S and Samani, A N and Schillaci, C and Syrris, V and Kim, H S and Spinola, D N and Oliveira, Paulo Tarso and Teng, H and Thapa, R and Vantas, K and Vieira, D and Yang, J E and Yin, S and Zema, D A and Zhao, G and Panagos, P (2021) Soil erosion modelling: A bibliometric analysis. Environmental Research (TSI), 197. pp. 1-16. ISSN 0013-9351

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    Abstract

    Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication’s CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

    Item Type: Article
    Divisions: Research Program : East & Southern Africa
    CRP: UNSPECIFIED
    Uncontrolled Keywords: Soil erosion modelling, Research impact, Participatory network, Systematic literature review, Citation analysis
    Subjects: Others > Participatory Modeling
    Others > Soil Science
    Depositing User: Mr Arun S
    Date Deposited: 19 May 2021 05:19
    Last Modified: 19 May 2021 05:19
    URI: http://oar.icrisat.org/id/eprint/11808
    Official URL: https://doi.org/10.1016/j.envres.2021.111087
    Projects: UNSPECIFIED
    Funders: UNSPECIFIED
    Acknowledgement: UNSPECIFIED
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