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Trends in Agroforestry Research from 1993 to 2022: A Topic Model Using Latent Dirichlet Allocation and HJ-Biplot

Author

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  • Karime Montes-Escobar

    (Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador
    Department of Statistics, University of Salamanca, 37008 Salamanca, Spain)

  • Javier De la Hoz-M

    (Department of Statistics, University of Salamanca, 37008 Salamanca, Spain
    Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia)

  • Mónica Daniela Barreiro-Linzán

    (Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador)

  • Carolina Fonseca-Restrepo

    (Departamento de Veterinaria, Facultad de Ciencias Veterinarias, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador)

  • Miguel Ángel Lapo-Palacios

    (Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador)

  • Douglas Andrés Verduga-Alcívar

    (Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador)

  • Carlos Alfredo Salas-Macias

    (Carrera de Agronomía, Facultad de Ingeniería Agronómica, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador)

Abstract

Background: There is an immense debate about the factors that could limit the adoption of agroforestry systems. However, one of the most important is the generation of scientific information that supports the viability and benefits of the proposed techniques. Statistical analysis: This work used the Latent Dirichlet Allocation (LDA) modeling method to identify and interpret scientific information on topics in relation to existing categories in a set of documents. It also used the HJ-Biplot method to determine the relationship between the analyzed topics, taking into consideration the years under study. Results: A review of the literature was conducted in this study and a total of 9794 abstracts of scientific articles published between 1993 and 2022 were obtained. The United States, India, Brazil, the United Kingdom, and Germany were the five countries that published the largest number of studies about agroforestry, particularly soil organic carbon, which was the most studied case. The five more frequently studied topics were: soil organic carbon, adoption of agroforestry practices, biodiversity, climatic change global policies, and carbon and climatic change. Conclusion: the LDA and HJ-Biplot statistical methods are useful tools for determining topicality in text analysis in agroforestry and related topics.

Suggested Citation

  • Karime Montes-Escobar & Javier De la Hoz-M & Mónica Daniela Barreiro-Linzán & Carolina Fonseca-Restrepo & Miguel Ángel Lapo-Palacios & Douglas Andrés Verduga-Alcívar & Carlos Alfredo Salas-Macias, 2023. "Trends in Agroforestry Research from 1993 to 2022: A Topic Model Using Latent Dirichlet Allocation and HJ-Biplot," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2250-:d:1144552
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    References listed on IDEAS

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    1. Müting, Luisa & Mußhoff, Oliver, 2025. "Money doesn't grow on trees – Or does it? How agroforestry system design makes agroforestry more attractive to smallholders in Senegal," Agricultural Systems, Elsevier, vol. 224(C).

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