Decision-making in agribusiness based on Artificial Intelligence




Decision Making, Agribusiness, Artificial Intelligence


Purpose: Artificial Intelligence (AI) tools have become popular in the most diverse contexts of use. This research sought to investigate how AI tools were applied in agribusiness by assisting producers in decision-making.

Design / methodology / approach: To this end, online and semi-structured interviews were conducted with managers and rural producers who use this type of technology on their properties.

Findings: AI was found to be present in machinery, software, and other applications applied for crop monitoring, soil quality verification, and management in general. Users are quite optimistic with the results, especially in decision support during planting period. These differences are perceived before and after technologies utilization. However, interviewers still believe that the human presence is fundamental in the farming.

Research limitations / implications: As limitations, it is highlighted the schedule for conducting the interviews, as well as the fact that they were performed online. Despite this, it was possible to verify the importance of the use of technology for the agribusiness sector, serving as support for the management of rural properties.

Originality / value: In the Information Systems studies field to relate the use of AI and decision-making in a sector such as agribusiness is something recent and innovative.


Download data is not yet available.

Author Biographies

Marina Valim Bandeira, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS

PhD student in Administration at the Universidade do Rio Grande do Sul (UFRGS). She develops research in the area of Information Systems and Technology, having, among her research interests, the behavioral influences and impacts of the use of mobile technologies on individuals.

Léia Michele Ferreira de Souza Móta, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS

PhD student in Agribusiness at the Universidade Federal do Rio Grande do Sul. She has experience in Information Technology, working mainly on the following topics: Systems Analysis, Database, Big Data, Internet of Things, Agile Methodologies.

Ariel Behr, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS

Doctor in Administration with emphasis on Information Systems and Decision Support from Universidade Federal do Rio Grande do Sul (UFRGS). Adjunct Professor at Universidade Federal do Rio Grande do Sul (UFRGS), at the Department of Accounting and Actuarial Sciences (DCCA), at the Postgraduate Program in Administration (PPGA/EA/UFRGS) and at the Postgraduate Program in Controllership and Accounting ( PPGCont). He has experience in the field of Accounting Sciences, Information Systems and Online Education.


Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Information Economics and Policy, 47, 1-6.

Araújo, M. J. (2005). Fundamentos de agronegócios. Editora Atlas SA.Awasthi, Y. (2020). Press “A” for Artificial Intelligence in Agriculture: A Review. JOIV: International Journal on Informatics Visualization, 4(3), 112-116.

Baird, A., & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1).

Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1-6.

Bardin, L. (2011). Analise de Conteúdo. São Paulo: Ed. Revista e Ampliada.Barnard, C. I. (1971). As funções do executivo. São Paulo: Atlas.

Borth, M. R., Iacia, J. C., Pistori, H., & Ruviaro, C. F. (2014). A visão computacional no agronegócio: Aplicações e direcionamentos. 7º Encontro Científico de Administração, Economia e Contabilidade (ECAECO).

Breitenbach, R. (2014). Gestão rural no contexto do agronegócio: desafios e limitações. Desafio Online, 2(2), 141-159.Bruun, E. P., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2).

Centro de Estudos Avançados em Economia Aplicada – CEPEA. Índices Exportação do Agronegócio – 3º Trimestre de 2019. Piracicaba: CEPEA, ESALQ/USP, 2020. Disponível em:

Cook, P., & O’Neill, F. (2020). Artificial Intelligence in Agribusiness is Growing in Emerging Markets.

Daft, Richard L. Organizações: teorias e projetos. Tradução de Cid Knipel Moreira e revisão técnica de Reinaldo O. Silva. Thomson Pioneira, 2002.

Ferreira, K. F. O., Guimarães, L. de O., Salume, P. K., & Doyle, M. L. de F. C. P. (2022). Analysis of the entrepreneurial process from effectuation and causation logic: a case study in two companies from Minas Gerais. Revista De Administração Da UFSM, 15(1), 83–104.

Flick, U. (2008). Introdução à pesquisa qualitativa-3. Artmed editora.Gibbs, G. (2009). Análise de dados qualitativos: coleção pesquisa qualitativa. Bookman Editora.

Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12.

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.

Linaza, M.T.; Posada, J.; Bund, J.; Eisert, P.; Quartulli, M.; Döllner, J.; Pagani, A.; Olaizola, I.G.; Barriguinha, A.; Moysiadis, T.; Lucat, L. (2021). Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy.

Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (2020). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics.

Ogunu-Ebiye, U. G., & Obiani, A. (2021). AGRIBUSINESS RISKS MANAGEMENT AND MITIGATION STRATEGIES. College of Education Academic Staff Union Journal, 4(1), 17-29.

Oliveira, D., & Pereira, S. A. (2008). Análise do processo decisório no agronegócio: abordagem na cadeia de valor da soja. Gestão e sociedade, 2(4).

Ostaev, G. Y., Shulus, A. A., Mironova, M. V., & Smolin, Y. V. (2020). Accounting agricultural business from scratch: management accounting, decision making, analysis and monitoring of business processes. Amazonia Investiga, 9(27), 319-332.

Parekh, V., Shah, D., & Shah, M. (2020). Fatigue detection using artificial intelligence framework. Augmented Human Research, 5(1), 1-17.

Prabakaran, G., Vaithiyanathan, D., & Ganesan, M. (2021). FPGA based effective agriculture productivity prediction system using fuzzy support vector machine. Mathematics and Computers in Simulation, 185, 1-16.

Ranganathaswamy, M. K., & Shankar, A. (2021). Decision-Making Model of Agriculture. International Journal of Modern Agriculture, 10(2), 2987-2995.

Santos, W. M. S., de Alencar, J. R., & Maximo, F. A. (2018). Agricultura Digital: softwares e serviços web disponibilizados pela Embrapa para o agronegócio brasileiro. In Embrapa Informática Agropecuária-Artigo em anais de congresso (ALICE), Campinas. Resumos expandidos... Brasília, DF: Embrapa, 2018.

Schmidt, B., Palazzi, A., & Piccinini, C. A. (2020). Entrevistas online: potencialidades e desafios para coleta de dados no contexto da pandemia de COVID-19. Revista Família, Ciclos de Vida e Saúde no Contexto Social, 8(4), 960-966.

Schwab, K., & Davis, N. (2019). Aplicando a quarta revolução industrial. Edipro.Simon, H. A. (1979). Rational decision making in business organizations. The American economic review, 69(4), 493-513.

Subeesh, A., & Mehta, C. R. (2021). Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture, 5, 278-291.

Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture.

Vaio, Assunta Di; Boccia, Flavio; Landriani, Loris; Palladino, Rosa. Artificial Intelligence in the Agri-Food System: Rethinking Sustainable Business Models in the COVID-19 Scenario (2020). Sustainability 2020, 12(12), 4851;

Waleed, M.; Um, T.; Kamal, T.; Khan, A.; Iqbal, A., (2020). Determining the Precise Work Area of Agriculture Machinery Using Internet of Things and Artificial Intelligence. 2020, 10(10), 3365.

Wang, Y., Huang, L., Wu, J., & Xu, H. (2007, January). Wireless sensor networks for intensive irrigated agriculture. In 2007 4th IEEE Consumer Communications and Networking Conference (pp. 197-201). IEEE.

Zhou, Qian; Jiang, Jiandong; Zhao, Zhangfeng; Zhong, Jiang; Pan, Bosong; Jin, Xiao; Sun, Yuanfang (2019). Research on the Internet of Things Platform Design for Agricultural Machinery Operation and Operation Management. International Conference on Computer and Computing Technologies in Agriculture.

Zylbersztajm, D. & Neves, M. F. (2015) Gestão de sistema de agronegócio. São Paulo: Atlas.




How to Cite

Bandeira, M. V., Móta, L. M. F. de S., & Behr, A. (2022). Decision-making in agribusiness based on Artificial Intelligence. Revista De Administração Da UFSM, 15, 841–853.

Most read articles by the same author(s)