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Modelos predictivos para la seguridad alimentaria en América Latina: Una revisión de alcance.

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Title: Modelos predictivos para la seguridad alimentaria en América Latina: Una revisión de alcance.
Alternate Title: Predictive models for food security in Latin America: A scoping review.
Authors: Trejos-Suárez, Juanita1 juanita.trejos@udes.edu.co, Cuadrado Pardo, Lina Valeria2 linvalecupa@gmail.com, Esteban Tabares, Josuepth2, Garzón, Sandra3, Bryon, Alejandro2, Alarcón, Zaida2
Source: Archivos Latinoamericanos de Nutrición. abr-jun2025, Vol. 75 Issue 2, p129-142. 14p.
Subjects: ARTIFICIAL neural networks, FOOD security, NATURAL resources management, FOOD safety, PREDICTION models
Abstract (English): Introduction: Food security in Latin America faces significant challenges due to factors such as climate change, social inequality, and economic instability, highlighting the need for advanced tools for analysis and management. This article reviews the current state of the literature on predictive models applied to food security in Latin America, with an emphasis on the Colombian context during the period 2014-2024. Objective: To describe the methodological approaches, algorithms used, and their practical applications in this field. Materials and Methods: A scoping review following PRISMA-ScR guidelines was conducted, which included 65 relevant studies. Results: The findings highlight the predominance of climatic, agricultural, and technological variables, while socioeconomic and health/nutritional categories were underrepresented. The most used algorithms were Random Forest and Artificial Neural Networks, both at 16.9%. The main areas of focus were the sustainable management of natural resources (26.2%), the prediction of agricultural yield (21.54%), and the impacts of climate change and food quality and safety (13.85% each). Conclusions: The integration of broader data categories and developing more robust models are essential to strengthening food security in the region, contributing to sustainable development goals and more effective public policies. [ABSTRACT FROM AUTHOR]
Abstract (Spanish): La seguridad alimentaria en América Latina enfrenta desafíos significativos debido a factores como el cambio climático, la desigualdad social y la inestabilidad económica, lo que resalta la necesidad de herramientas avanzadas para su análisis y gestión. Este artículo revisa el estado actual de la literatura sobre modelos predictivos aplicados a la seguridad alimentaria en Latinoamérica, con énfasis en el contexto colombiano durante el período 2014-2024. Objetivo: Describir los enfoques metodológicos, los algoritmos utilizados y sus aplicaciones prácticas en este ámbito. Materiales y métodos: Se realizó una revisión de alcance siguiendo los lineamientos PRISMA-ScR, que incluyó 65 estudios relevantes. Resultados: Los hallazgos destacan el predominio de variables climáticas, agrícolas y tecnológicas, mientras que las categorías socioeconómicas y sanitarias/ nutricionales estuvieron subrepresentadas. Los algoritmos más utilizados fueron Bosques Aleatorios y Redes Neuronales Artificiales, ambos con un 16,9%. Las principales áreas de enfoque fueron la gestión sostenible de recursos naturales (26,2%), la predicción del rendimiento agrícola (21,54%) y los impactos del cambio climático y la calidad y seguridad de los alimentos (13,85% cada una). Conclusiones: La integración de categorías de datos más amplias y el desarrollo de modelos más robustos son fundamentales para fortalecer la seguridad alimentaria en la región, contribuyendo a los objetivos de desarrollo sostenible y a políticas públicas más efectivas. [ABSTRACT FROM AUTHOR]
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Database: MedicLatina
Description
Abstract:Introduction: Food security in Latin America faces significant challenges due to factors such as climate change, social inequality, and economic instability, highlighting the need for advanced tools for analysis and management. This article reviews the current state of the literature on predictive models applied to food security in Latin America, with an emphasis on the Colombian context during the period 2014-2024. Objective: To describe the methodological approaches, algorithms used, and their practical applications in this field. Materials and Methods: A scoping review following PRISMA-ScR guidelines was conducted, which included 65 relevant studies. Results: The findings highlight the predominance of climatic, agricultural, and technological variables, while socioeconomic and health/nutritional categories were underrepresented. The most used algorithms were Random Forest and Artificial Neural Networks, both at 16.9%. The main areas of focus were the sustainable management of natural resources (26.2%), the prediction of agricultural yield (21.54%), and the impacts of climate change and food quality and safety (13.85% each). Conclusions: The integration of broader data categories and developing more robust models are essential to strengthening food security in the region, contributing to sustainable development goals and more effective public policies. [ABSTRACT FROM AUTHOR]
ISSN:00040622
DOI:10.37527/2025.75.2.006