Inteligencia artificial y gestión adaptativa en el agro argentino: una alianza estratégica para reducir la incertidumbre cognitiva

Karina Cornejo

doi:10.22490/25392786.11818

Vol. 15 No. 2 (2026)

Published 2026-09-21

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Artículo

Artificial Intelligence and Adaptive Management in Argentine Agriculture: A Strategic Alliance for Reducing Cognitive Uncertainty

DOI: https://doi.org/10.22490/25392786.11818

Karina Cornejo National Agricultural Technology Institute

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Abstract
References

Introduction: The incorporation of artificial intelligence systems into the agricultural sector creates new opportunities to improve decision-making processes in contexts characterized by climatic, economic, biological, and territorial uncertainty. However, the interaction between human rationality and algorithmic rationality generates cognitive challenges that influence technological appropriation among farmers. Methodology: The research adopted a qualitative, exploratory, and analytical approach based on institutional sources, specialized academic literature, and international comparative evidence, complemented by a foresight exercise aimed at identifying future technological development scenarios within agricultural systems. Results: Findings indicate that the adoption of predictive artificial intelligence remains partial and uneven, being concentrated primarily in larger-scale farming operations. Additional barriers were identified, including algorithmic opacity, technological distrust, and perceived loss of decision-making control. Conclusions: The study proposes a conceptual model of adaptive hybrid intelligence that combines algorithmic analysis, local expert knowledge, and foresight tools to reduce cognitive uncertainty while preserving farmers' central role in productive decision-making.

keywords: artificial intelligence, agriculture, cognitive uncertainty, agricultural economics, foresight

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012 DOI: https://doi.org/10.1016/j.inffus.2019.12.012

Banco Interamericano de Desarrollo (BID). (2022). AgTech y transformación digital del agro en América Latina. BID. https://publications.iadb.org

Bolsa de Comercio de Rosario (BCR). (2025). Nubes en el horizonte: Incertidumbre climática y volatilidad de los mercados agrícolas. https://www.bcr.com.ar

Eastwood, C., Klerkx, L., & Nettle, R. (2017). Dynamics and distribution of public and private research and extension roles for technological innovation: A case study of precision agriculture. Journal of Rural Studies, 49, 1–12. https://doi.org/10.1016/j.jrurstud.2016.11.008 DOI: https://doi.org/10.1016/j.jrurstud.2016.11.008

FAO. (2023). Artificial intelligence in agriculture: Status, challenges and opportunities. Food and Agriculture Organization of the United Nations. https://www.fao.org

Felici, S., & Gatti, G. (2025). Evaluación de impacto de la adopción de agricultura de precisión en la región pampeana argentina. INTA. (En prensa).

Joint Research Centre. (2024). Digital transformation of agriculture and rural areas in the EU. Publications Office of the European Union. https://joint-research-centre.ec.europa.eu

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185 DOI: https://doi.org/10.2307/1914185

Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0. NJAS – Wageningen Journal of Life Sciences, 90–91, 100315. https://doi.org/10.1016/j.njas.2019.100315 DOI: https://doi.org/10.1016/j.njas.2019.100315

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674 DOI: https://doi.org/10.3390/s18082674

Miles, I., Saritas, O., & Sokolov, A. (2016). Foresight for science, technology and innovation. Springer. https://doi.org/10.1007/978-3-319-32574-3 DOI: https://doi.org/10.1007/978-3-319-32574-3

Ministerio de Economía de la Nación. (2025). Cadena de valor AgTech: Diagnóstico y perspectivas del ecosistema de tecnologías digitales aplicadas al agro argentino. Gobierno de la República Argentina.

Pape, J.-A., & Speelman, S. (2020). Assessing the adoption and impact of decision support systems in agriculture: A systematic review. Computers and Electronics in Agriculture, 178, 105256. https://doi.org/10.1016/j.compag.2020.105256

Ramírez, R., & Wilkinson, A. (2016). Strategic reframing: The Oxford scenario planning approach. Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780198745693.001.0001

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Simon, H. A. (1957). Models of man: Social and rational. Wiley. DOI: https://doi.org/10.2307/2550441

Wachter, S., & Mittelstadt, B. (2019). A right to reasonable inferences: Re-thinking data protection law in the age of big data and AI. Columbia Business Law Review, 2019(2), 494–620. https://doi.org/10.52214/cblr.v2019i2.3424 DOI: https://doi.org/10.31228/osf.io/mu2kf

Wachter, S., Mittelstadt, B., & Russell, C. (2021). Why fairness cannot be automated. Computer Law & Security Review, 41, 105567. https://doi.org/10.1016/j.clsr.2021.105567 DOI: https://doi.org/10.1016/j.clsr.2021.105567

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How to Cite

Cornejo, K. (2026). Artificial Intelligence and Adaptive Management in Argentine Agriculture: A Strategic Alliance for Reducing Cognitive Uncertainty. Revista Estrategia Organizacional, 15(2). https://doi.org/10.22490/25392786.11818

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