IDEAS home Printed from https://ideas.repec.org/a/ags/revi24/340984.html
   My bibliography  Save this article

Applying analytic hierarchy process (AHP) to identify decision-making in soybean supply chains: a case of Mato Grosso production

Author

Listed:
  • Toloi, Rodrigo Carlo
  • Reis, João Gilberto Mendes dos
  • Toloi, Marley Nunes Vituri
  • Vendrametto, Oduvaldo
  • Cabral, José António Sarsfield Pereira

Abstract

This paper aims to identify and analyze the factors that influence the decision of Mato Grosso’s farmers to produce soybean using the Analytic Hierarchy Process (AHP). We found evidence that decisionmaking of soybean production is related to rural production aspects such as climate, financing, cost of inputs, and soil quality rather than marketing and logistics. The novelty of this paper is the empirical analysis of the decision-making in agricultural production using AHP. The decision model was created and tested considering 21 farmers and 19 experts linked to the soybean production. Three different scenarios were considered: farmers’ view, experts’ view, and combined view. Our findings indicate that farmers and experts agree with rural aspects are predominant in the decision to plant soybean. Moreover, logistics have been used as an important flag of soybean competitiveness on international trade by soybean stakeholders in Brazil. However, our results show that logistics impact in the soybean decision-making process is low. Due to data limitation access, this study focuses only on Mato Grosso. However, this study has an exploratory character and presents empirical results that may help to understand soybean production over the country.

Suggested Citation

  • Toloi, Rodrigo Carlo & Reis, João Gilberto Mendes dos & Toloi, Marley Nunes Vituri & Vendrametto, Oduvaldo & Cabral, José António Sarsfield Pereira, 2022. "Applying analytic hierarchy process (AHP) to identify decision-making in soybean supply chains: a case of Mato Grosso production," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 60(2), January.
  • Handle: RePEc:ags:revi24:340984
    DOI: 10.22004/ag.econ.340984
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/340984/files/Rodrigo%20Carlo%20Toloi.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.340984?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ricardo Silveira Martins & Daniele Rebechi & Celso A. Prati & Honório Conte, 2005. "Decisões estratégicas na logística do agronegócio: compensação de custos transporte-armazenagem para a soja no estado do Paraná," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 9(1), pages 53-78.
    2. Fountas, S. & Wulfsohn, D. & Blackmore, B.S. & Jacobsen, H.L. & Pedersen, S.M., 2006. "A model of decision-making and information flows for information-intensive agriculture," Agricultural Systems, Elsevier, vol. 87(2), pages 192-210, February.
    3. Puchalsky, Weslly & Ribeiro, Gabriel Trierweiler & da Veiga, Claudimar Pereira & Freire, Roberto Zanetti & Santos Coelho, Leandro dos, 2018. "Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand," International Journal of Production Economics, Elsevier, vol. 203(C), pages 174-189.
    4. Goldsmith, Peter D. & Hirsch, Rodolfo, 2006. "The Brazilian Soybean Complex," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 21(2), pages 1-8.
    5. Damien Arvor & Mariana M. Gonçalves & Sébastien Moine & Maxime Vitter, 2010. "The evolution of the soybean industry in Mato Grosso [L’évolution du secteur du soja au Mato Grosso]," Post-Print hal-01107560, HAL.
    6. Rose, David C. & Sutherland, William J. & Parker, Caroline & Lobley, Matt & Winter, Michael & Morris, Carol & Twining, Susan & Ffoulkes, Charles & Amano, Tatsuya & Dicks, Lynn V., 2016. "Decision support tools for agriculture: Towards effective design and delivery," Agricultural Systems, Elsevier, vol. 149(C), pages 165-174.
    7. Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

    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. Zysk, Elżbieta & Dawidowicz, Agnieszka & Nowak, Magdalena & Figurska, Marta & Źróbek, Sabina & Źróbek, Ryszard & Burandt, Jakub, 2020. "Organizational Aspects Of The Concept Of A Green Cadastre For Rural Areas," Land Use Policy, Elsevier, vol. 91(C).
    2. Jeroen Ooge & Katrien Verbert, 2022. "Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-Study," Agriculture, MDPI, vol. 12(7), pages 1-25, July.
    3. Milazzo, M.F. & Spina, F. & Cavallaro, S. & Bart, J.C.J., 2013. "Sustainable soy biodiesel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 806-852.
    4. Paul Stefan MARKOVITS, 2024. "Assesing Romanian Farmers’ Motivation For Digitalization: A Unified Theory Of Acceptance And Usage Of Technology (Utaut) Based Research Model," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, vol. 9(1), pages 98-112, March.
    5. Carof, Matthieu & Godinot, Olivier, 2018. "A free online tool to calculate three nitrogen-related indicators for farming systems," Agricultural Systems, Elsevier, vol. 162(C), pages 28-33.
    6. Oksana Hrynevych & Miguel Blanco Canto & Mercedes Jiménez García, 2022. "Tendencies of Precision Agriculture in Ukraine: Disruptive Smart Farming Tools as Cooperation Drivers," Agriculture, MDPI, vol. 12(5), pages 1-15, May.
    7. Prost, Lorène, 2021. "Revitalizing agricultural sciences with design sciences," Agricultural Systems, Elsevier, vol. 193(C).
    8. Zina Mitraka & Sofia Siachalou & Georgia Doxani & Petros Patias, 2020. "Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    9. Mauro Zaninelli & Matías Reyes Pace, 2018. "The O3-Farm Project: First Evaluation of a Business Process Management (BPM) Approach through the Development of an Experimental Farm Management System for Milk Traceability," Agriculture, MDPI, vol. 8(9), pages 1-22, September.
    10. Grothkopf, Carina & Schulze, Holger, 2021. "Empirische Analyse der Einflussfaktoren auf die Digitalisierung der Milchviehhaltung," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317061, German Association of Agricultural Economists (GEWISOLA).
    11. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    12. Mössinger, Johannes & Troost, Christian & Berger, Thomas, 2022. "Bridging the gap between models and users: A lightweight mobile interface for optimized farming decisions in interactive modeling sessions," Agricultural Systems, Elsevier, vol. 195(C).
    13. Michele Rosenberg & Stefano Falcone, 2022. "Agricultural Modernization and Land Conflict," Working Papers 1314, Barcelona School of Economics.
    14. Oyakhilomen Oyinbo & Jordan Chamberlin & Miet Maertens, 2020. "Design of Digital Agricultural Extension Tools: Perspectives from Extension Agents in Nigeria," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 798-815, September.
    15. Claudia V. Montanía & Teresa Fernández-Núñez & Miguel A. Márquez, 2021. "The role of the leading exporters in the global soybean trade," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(7), pages 277-285.
    16. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    17. Darren Yates & Christopher Blanchard & Allister Clarke & Sabih-Ur Rehman & Md Zahidul Islam & Russell Ford & Rob Walsh, 2024. "Combined location online weather data: easy-to-use targeted weather analysis for agriculture," Climatic Change, Springer, vol. 177(9), pages 1-19, September.
    18. Édson Luis Bolfe & Lúcio André de Castro Jorge & Ieda Del’Arco Sanches & Ariovaldo Luchiari Júnior & Cinthia Cabral da Costa & Daniel de Castro Victoria & Ricardo Yassushi Inamasu & Célia Regina Grego, 2020. "Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers," Agriculture, MDPI, vol. 10(12), pages 1-16, December.
    19. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    20. Daniel H. Jarvis & Mark P. Wachowiak & Dan F. Walters & John M. Kovacs, 2017. "Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System," Agriculture, MDPI, vol. 7(8), pages 1-22, August.

    More about this item

    Keywords

    Production Economics;

    Statistics

    Access and download statistics

    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:ags:revi24:340984. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/inrapfr.html .

    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.