Agriculture startups (AgTechs): a bibliometric study using SciMAT and VOSviewer software

Startups agrícolas (AgTechs): un estudio bibliométrico utilizando el software SciMAT y VOSviewer

Authors

DOI:

https://doi.org/10.26668/businessreview/2022.v7i2.312

Keywords:

AgTech, Bibliometric Study, SciMAT, VOSviewer

Abstract

Due to the population increase and the problems related to environmental sustainability that this increase will bring to the planet, the agribusiness sector has a great responsibility to fulfill, in the sense of feeding a growing population sustainably. In this context, there is an increase in investment in innovative technologies, many of them developed and marketed by AgTechs, the technological startups in agribusiness, which have emerged with strength in this 4.0 agriculture scenario, where increasingly accurate, connected technologies are desired, and environmentally sustainable. Despite the growing interest from different markets in the subject of AgTechs, studies showing, in general, and in-depth, the development of the subject in the scientific literature was not found. Thus, this work aimed to present a bibliographical study on the theme of agricultural startups (AgTechs). The methodology used was the bibliometric study, using SciMAT and VOSviewer software. The databases used for the study were Web of Science and Scopus. The results showed that the theme is not well consolidated in the literature, but it is in a dizzying growth, with 71.3% of the articles having been published in the last 3 years, in 79 journals, and with publications covering 44 countries. The study brought important contributions to a better understanding of the term AgTech in the literature and the improvement of concepts related to this ecosystem.

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References

AgFunder - Agriculture and Agtech Investment Opportunities. (2021). AgFunderAgriFood Tech Investing Report - Year in Review 2020. Disponível em: https://research.agfunder.com/2021/2021-agfunder-global-report.pdf. Acesso em: 27 de setembro de 2021.

Akbar, I., e Zaim, I. A. (2019). Innovations in Service: Probing the Evidence in Sustainable Tourism. The Asian Journal of Technology Management Vol. 12, No. 2: 132-148

Alonso, Sergio; Cabrerizo, Francisco-Javier; Herrera-Viedma, Enrique; Herrera, Francisco (2009). “h-index: A review focused in its variants, computation and standardization for different scientific fields”. Journal of informetrics, v. 3, n. 4, pp. 273-289.

Alonso, Sergio; Cabrerizo, Francisco-Javier; Herrera-Viedma, Enrique; Herrera, Francisco (2010). “hg-index: A new index to characterize the scientific output of researchers based on the h- and g-indices”. Scientometrics, v. 82, n. 2, pp. 391-400.

Blanco, T. H. M. 2019. AGTECHS: uma análise do ambiente de negócio paranaense. 125 f. Dissertação (Mestrado) - Curso de Mestrado Profissional em Administração, Programa de Pós-graduação em Administração (PPGA), Universidade Estadual do Oeste do Paraná, Cascavel.

Boursianis, A. D.; Papadopoulou, M. S.; Diamantoulakis, P.; Liopa-Tsakalidi, A.; Pantelis, B.; Salahas, G.; Karagiannidis, G. K.; Wan, S.; Goldos, S. (2020). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in Smart Farming: A Comprehensive Review. Internet of Things, p. 100187.

Cabrerizo, Francisco-Javier; Alonso, Sergio; Herrera-Viedma, Enrique; Herrera, Francisco (2010). “q2-Index: Quantitative and qualitative evaluation based on the number and impact of papers in the Hirsch core”. Journal of informetrics, v. 4, n. 1, pp. 23-28.

Carayannis, E. G., Rozakis, S., & Grigoroudis, E. (2018). Agri-science to agri-business: The technology transfer dimension. Journal of Technology Transfer, 43(4), 837–843.

Cobo, Manuel J.; López-Herrera, Antonio G.; Herrera-Viedma, Enrique; Herrera, Francisco (2012). “SciMAT: A new science mapping analysis software tool”. Journal of the American Society for Information Science and Technology, v. 63, n. 8, pp. 1609-1630.

Cobo, Manuel J.; López-Herrera, Antonio G.; Herrera-Viedma, Enrique; Herrera, Francisco (2011b). “An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field”. Journal of informetrics, v. 5, n. 1, pp. 146-166.

Colezea M., Musat G., Pop F., Negru C., Dumitrasco A., e Mocanu M. (2018). CLUeFARM:

Integrated web-service platform for smart farms Comp. Elec. Agri. 154 134-54.

Corallo, A., Latino, ME, Menegoli, M. (2018). Da indústria 4.0 à agricultura 4.0: Uma estrutura para gerenciar dados de produtos na cadeia de abastecimento agroalimentar para rastreabilidade voluntária. International Journal of Nutrition and Food Engineering, 12 (5), 146-150.

Dutia, S. G. 2014. Agtech: Challenges and opportunities for sustainable growth. Innovations: Technology, Governance, Globalization, v. 9, n. 1-2, p. 161-193.

Egghe, Leo (2006). “Theory and practise of the g-index”. Scientometrics, v. 69, pp. 131-152.

Figueiredo, S.S.S.; Jardim, F.; Sakuda, L.O. (Orgs.). Radar AgTech Brasil 2020/2021: Mapeamento das Startups do Setor Agro Brasileiro. Embrapa, SP Ventures e Homo Ludens: Brasília e São Paulo, 2021.

Graff, G. D., Silva, F. F., & Zilberman, D. (2019). Venture capital and the transformation of private R&D for agriculture and food. Economics of research and innovation in agriculture, Cambridge: National Bureau of Economic Research.

Hirsch, Jorge E. (2005). “An index to quantify an individual’s scientific research output”. Proceedings of the National Academy of Sciences of the United States of America, v. 102, n. 46, pp. 16569-16572.

Kakani, V.; Nguyen, V. H.; Kumar, B.P.; Kim, H.; Pasupuleti, V.R. A critical review on computer vision and artificial intelligence in food industry, Journal of Agriculture and Food Research, 2, 2020. ISSN 2666-1543.

Kouadio, L., Deo, R. C., Byrareddy, V., Adamowski, J. F., Mushtaq, S., & Phuong Nguyen, V. (2018). Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Computers and Electronics in Agriculture, 155, 324–338.

Laengle, S.; Modak, N. M.; Merigo, J. M.; Zurita, G. (2018). Twenty-Five Years of Group Decision and Negotiation: A Bibliometric Overview. Group. Decis. Negot. 27:505–542.

Lampridi, M. G., Kateris, D., Vasileiadis, G., Marinoudi, V., Pearson, S., Sørensen, C. G., et al. (2019). A case-based economic assessment of robotics employment in precision arable farming. Agronomy.

Martínez Sánchez, M. A.; Díaz Herrera, M.; Lima Fernández,A. I. (2014). Un análisis bibliométrico de la producción académica española en la categoría de Trabajo Social del Journal Citation Report - A bibliometric analysis of Spanish production of Social Work category according to the Journal Citation Report. Cuadernos de Trabajo Social, Vol. 27-2, p. 429-438.

Mendes, J. A. J.; Careta, C. B.; Zuin, V. G.; Gerolamo, M. C. (2021). In search of maturity models in agritechs. IOP Conf. Series: Earth and Environmental Science 839 022083. IOP Publishing.

Miranda J, Ponce P, Molina A, Wright P (2019) Sensing, smart and sustainable technologies for Agri-Food 4.0. Comput Ind 108:21–36.

Moral-Muñoz, José A.; Herrera-Viedma, Enrique; Santisteban-Espejo, Antonio; Cobo, Manuel J. (2020). “Software tools for conducting bibliometric analysis in science: An up-to-date review”. El profesional de la información, v. 29, n. 1, e290103.

Pham, X.; Stack, M. 2018. How data analytics is transforming agriculture. Business Horizons, v. 61, n. 1, p. 125-133.

Ratnatunga, J., e Romano, C. (1997). A "Citation Classics" Analysis of Articles in Contemporary Small Enterprise Research. Journal of Business Venturing 12. 197-212.

Rincon-Patino, J.; Ramirez-Gonzalez, G.; Corrales, J. 2018. Exploring machine learning: A bibliometric general approach using Citespace. F1000 Research, 7. 1240.

Schulz,P.; Prior, J.; Kahn, L. e Hinch, G. (2021). Exploring the role of smartphone apps for livestock farmers: data management, extension, and informed decision making. The Journal of Agricultural Education and Extension. https://doi.org/10.1080/1389224X.2021.1910524

Spanaki, K.; Sivarajah, U.; Fakhimi, M.; Despoudi, S.; Irani, Z. Disruptive technologies in agricultural operations: a systematic review of AI‑driven AgriTech research. Annals of Operations Research.

Tilney, M.; Leclerc, R.; Demarest, E. (2015). AgTech Investing Report: YEAR IN REVIEW 2014. AGFUNDER.

Van-Eck, Nees-Jan; Waltman, Ludo (2010). “Software survey: VOSviewer, a computer program for bibliometric mapping”. Scientometrics, v. 84, n. 2, pp. 523-538.

Waltman, Ludo; Van-Eck, Nees-Jan; Noyons, Ed C. M. (2010). “A unified approach to mapping and clustering of bibliometric networks”. Journal of Informetrics, v. 4, n. 4, pp. 629-635.

Wezel, A., Casagrande, M., Celette, F., Vian, J., Ferrer, A., & Peigné, J. (2014). Agroecological practices for sustainable agriculture A review. Agronomy for Sustainable Development, 34(1), 1–20.

Yoon, B. K., Tae, H., Joshua A. Jackman, Supratik Guha, Cherie R. Kagan, Andrew J. Margenot, Diane L. Rowland, Paul S. Weiss, e Nam-Joon Cho. (2021). Entrepreneurial Talent Building for 21st Century Agricultural Innovation. ACS Nano, 15, 10748−10758

Zhai Z, Martínez J F, Beltran V and Martínez N L. 2020. Decision support systems for agriculture 4.0: Survey and challenges Comp. Elect. Agri. 170 105256.

Zupic, I. e Cater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, Vol. 18(3) 429-472.

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Published

2022-04-29

How to Cite

Bueno, L. O., Mendes, J. A. J. ., Oliveira, A. Y. ., & Gerolamo, M. C. . (2022). Agriculture startups (AgTechs): a bibliometric study using SciMAT and VOSviewer software: Startups agrícolas (AgTechs): un estudio bibliométrico utilizando el software SciMAT y VOSviewer. International Journal of Professional Business Review, 7(2), e0312. https://doi.org/10.26668/businessreview/2022.v7i2.312