La inteligencia artificial y el futuro de la seguridad alimentaria en Nigeria
DOI:
https://doi.org/10.63207/ai.v8i16.177Resumen
La inseguridad alimentaria persiste en Nigeria a pesar de su abundante potencial agrícola, debido a la baja productividad, la debilidad de las infraestructuras y las crisis climáticas. A medida que los sistemas agrícolas mundiales adoptan cada vez más la inteligencia artificial (IA) para la previsión, la supervisión de cultivos y la detección de plagas, este estudio examina su relevancia para el sector nigeriano, dominado por pequeños agricultores. Una revisión sistemática cualitativa de 34 estudios, identificados a través de Scopus, Web of Science, PubMed y Google Scholar y seleccionados mediante un enfoque PRISMA, sintetizó las pruebas sobre las aplicaciones de la IA, las dimensiones de la seguridad alimentaria y las barreras para su adopción. Los resultados muestran que, si bien la IA ha mejorado los rendimientos y reducido los costos en países como India, China y Kenia, su adopción en Nigeria sigue siendo mínima debido a la mala conectividad, los bajos niveles de alfabetización digital, los altos costos y la débil coordinación de políticas. La revisión destaca la necesidad de herramientas de IA localizadas, de bajo ancho de banda y fáciles de usar para los agricultores, así como una gobernanza de datos más sólida. Concluye que, con la mejora de la infraestructura rural, el desarrollo de capacidades y el apoyo a la innovación local, la IA puede mejorar significativamente la seguridad alimentaria de Nigeria.
Descargas
Citas
Adelekan, S. A., Omotayo, A. O. (2021). Digital agriculture in Africa: Implications for smallholder farmers. Journal of Agricultural Extension, 25(3), 45–57.
Akbar, A., Anwar, M., Shah, M. A., Alotaibi, F. (2023). Applications of artificial intelligence in smart agriculture: A comprehensive review. Computers and Electronics in Agriculture, 206, 107645. https://doi.org/10.1016/j.compag.2023.107645 (Consulted on 25/09/2025)
Akinola, O., Adegbite, A., Okunlola, A. (2022). Artificial intelligence applications in Nigerian agriculture: Prospects and challenges. Journal of Agricultural Extension, 26(2), 45–56. https://doi.org/10.4314/jae.v26i2.5 (Consulted on 25/09/2025)
Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., Ruggeri, M. (2019). The digitisation of agriculture: A survey of research activities on smart farming. Array, 3–4, 100009. https://doi.org/10.1016/j.array.2019.100009 (Consulted on 25/09/2025)
Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96–107. https://doi.org/10.1016/j.biosystemseng.2019.02.002 (Consulted on 25/09/2025)
Benos, L., Tagarakis, A. C., Dolias, G.; Berruto, R., Bochtis, D., Busato, P. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758 (Consulted on 25/09/2025)
Booth, A., Sutton, A., Papaioannou, D. (2016). Systematic approaches to a successful literature review (2nd ed.). London: SAGE Publications.
Braun, V., Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa (Consulted on 25/09/2025)
Carter, H. (1989). Food and agriculture: Balancing tradition and change. Food Policy, 14(1), 3–14. https://doi.org/10.1016/0306-9192(89)90022-4 (Consulted on 25/09/2025)
Clapp, J., Moseley, W. G., Garrett, J. (2022). Food security and nutrition: Progress and challenges. Global Food Security, 32, 100605. https://doi.org/10.1016/j.gfs.2021.100605 (Consulted on 25/09/2025)
Cooper, H. (2016). Research synthesis and meta-analysis: A step-by-step approach (5th ed.). Thousand Oaks, CA: SAGE Publications.
Elijah, I. (2019). AGRICULTURAL PROGRAMMES AND FOOD SECURITY IN NIGERIA (1960-2016). A HISTORICAL OVERVIEW.
Eme, O. I., Onyishi, T., Uche, O. A. (2014). Challenges of Food Security in Nigeria: Options before Government. Oman Chapter of Arabian Journal of Business and Management Review, 4(1), 15–25. https://doi.org/10.12816/0016564 (Consulted on 25/09/2025)
FAO. (2006). Food security: Policy brief. Food and Agriculture Organization of the United Nations.
FAO. (2012). Nigeria Country programming framework 2013-2017. Food and Agriculture Organization of the United Nations.
FAO. (2021). The state of food and agriculture 2021: Making agrifood systems more resilient to shocks and stresses. Food and Agriculture Organization of the United Nations.
FAO. (2022). The state of food security and nutrition in the world 2022: Repurposing food and agricultural policies to make healthy diets more affordable. Food and Agriculture Organization of the United Nations.
Javaid, M., Haleem, A., Singh, R. P., Suman, R., Rab, S. (2023). Applications of artificial intelligence in agriculture: A comprehensive review. Journal of Ambient Intelligence and Humanized Computing, 14 (5), 4315–4333. https://doi.org/10.1007/s12652-021-03491-8 (Consulted on 25/09/2025)
Kamilaris, A.; Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016 (Consulted on 25/09/2025)
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009 (Consulted on 25/09/2025)
Li, D., Ma, J., Xu, X. (2022). Artificial intelligence for irrigation prediction and management: A review. Agricultural Water Management, 261, 107378. https://doi.org/10.1016/j.agwat.2021.107378 (Consulted on 25/09/2025)
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 (Consulted on 25/09/2025)
Liu, Y., Ma, X., Shu, L., Hancke, G. P., Abu-Mahfouz, A. M. (2021). From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Transactions on Industrial Informatics, 17(6), 4322–4334. https://doi.org/10.1109/TII.2020.3003910 (Consulted on 25/09/2025)
Marvin, H. J. P., Kleter, G. A., Bouwmeester, H. (2022). Artificial intelligence in food supply chains: Enhancing resilience and efficiency. Trends in Food Science & Technology, 120, 215–227. https://doi.org/10.1016/j.tifs.2021.12.006 (Consulted on 25/09/2025)
Matemilola, S., Elegbede, I. (2017). The challenges of food security in Nigeria. Open Access Library Journal, 4(12), 1–22. https://doi.org/10.4236/oalib.1104185 (Consulted on 25/09/2025)
Nwajuibe, C. (2012). Nigeria’s food security challenges. In FAO country reports on food security. Rome: Food and Agricultural Organization of the United Nations.
OECD. (2020). Digital opportunities for better agricultural policies. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/18156797 (Consulted on 25/09/2025)
Ogundeji, A. A., Donkor, E., Onakuse, S. (2020). The impact of mechanization on smallholder rice farmers in Nigeria. Agriculture, 10(12), 624. https://doi.org/10.3390/agriculture10120624 (Consulted on 25/09/2025)
Oyedele, O. A., Akinbile, L. A., Oladosu, R. O. (2021). Digital technologies and sustainable soil management in Africa: Opportunities and challenges. Journal of Sustainable Agriculture, 45(7), 1123–1136.
Rahman, M. A., Irfan, M., Alvi, S. H. (2023). Artificial intelligence in livestock farming: Applications, opportunities, and challenges. Artificial Intelligence in Agriculture, 7, 23–36. https://doi.org/10.1016/j.aiia.2023.04.003 (Consulted on 25/09/2025)
Rangrej, P., Sharma, A., Patel, R. (2023). Artificial intelligence applications in agriculture: A case study of India. International Journal of Agricultural Sustainability, 21(2), 158–171. https://doi.org/10.1080/14735903.2022.2109158 (Consulted on 25/09/2025)
Ryan, M., Peschl, M. F., McNamara, J. (2023). Artificial intelligence in agriculture: Impacts on innovation, sustainability, and food security. Agricultural Systems, 206, 103597. https://doi.org/10.1016/j.agsy.2023.103597 (Consulted on 25/09/2025)
Shepherd, K., Turner, J., Maas, B. (2020). Digital agriculture and food systems in Africa. Outlook on Agriculture, 49(1), 10–17. https://doi.org/10.1177/0030727020904941 (Consulted on 25/09/2025)
Smith, D., Jones, R., Brown, L. (2021). Artificial intelligence and animal health management: A review. Frontiers in Veterinary Science, 8, 678456. https://doi.org/10.3389/fvets.2021.678456 (Consulted on 25/09/2025)
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039 (Consulted on 25/09/2025)
Upton, J. B., Cissé, J. D., Barrett, C. B. (2016). Food security as resilience: Reconciling definition and measurement. Agricultural Economics, 47(S1), 135–147. https://doi.org/10.1111/agec.12305 (Consulted on 25/09/2025)
Webb, P., Coates, J., Frongillo, E. A., Rogers, B. L., Swindale, A., Bilinsky, P. (2006). Measuring household food insecurity: Why it’s so important and yet so difficult to do. Journal of Nutrition, 136(5), 1404S–1408S. https://doi.org/10.1093/jn/136.5.1404S (Consulted on 25/09/2025)
World Bank. (2022). Transforming agriculture in Africa through digital technologies. World Bank Group.
WMO. (2023). State of the global climate 2022. World Meteorological Organization.
Xu, X., Li, Y., Zhang, W. (2020). Intelligent greenhouse systems using artificial intelligence and IoT. Computers and Electronics in Agriculture, 173, 105383. https://doi.org/10.1016/j.compag.2020.105383 (Consulted on 25/09/2025)
Lajoie-O’Malley, A., Bronson, K., Van Der Burg, S.; Klerkx, L. (2020). The future(s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183. https://doi.org/10.1016/j.ecoser.2020.101183 (Consulted on 25/09/2025)
Descargas
Publicado
Versiones
- 15-12-2025 (2)
- 15-12-2025 (1)
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Abideen alamu
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
