Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions.

Las metodologías educativas requieren actualización constante, dado que la educación se concibe como un constructo dinámico en permanente transformación. En este sentido, los procesos formativos deben ser cíclicos y adaptarse a la actualización docente y a las necesidades de aprendizaje de los estudiantes. En este marco, el enfoque STEM ha demostrado su utilidad en diversos estudios a nivel global, especialmente en áreas como matemáticas, ciencias y otras disciplinas. El presente artículo analiza la incidencia del enfoque STEM en el aprendizaje de las matemáticas mediante una metodología cualitativa de tipo documental-bibliográfica. Para ello, se establecieron criterios de inclusión y exclusión que permitieron seleccionar 13 documentos relevantes, los cuales fueron sistematizados a través de una matriz de análisis. Los hallazgos evidencian la creciente pertinencia del enfoque STEM en la educación matemática, particularmente cuando se articula con estrategias como el Aprendizaje Basado en Problemas (ABP), dinámicas didácticas, construcción activa del conocimiento y estudios de caso. Estas metodologías favorecen el desarrollo de habilidades y competencias en los estudiantes, permitiéndoles aplicar los conocimientos matemáticos en la resolución de problemas de la vida cotidiana.

Artisanal farmers in developing regions capture limited commercial value due to information asymmetries, market fragmentation, and informal intermediation, where commercialization, rather than production, is the main bottleneck for rural income. This study formalizes artisanal agricultural commercialization as a buyer-recommendation problem in a sparse bilateral market. We propose a context-aware hybrid system that combines content-based and collaborative filtering with economic and geographic variables. Because no transactional dataset exists for the target region (Cesar, Colombia), a proof-of-concept evaluation (Phase A) uses the Brazilian Olist e-commerce dataset as a structural proxy, benchmarking 13 models under a temporal leave-one-out protocol. A Reciprocal Rank Fusion ensemble achieved the best ranking performance (NDCG@10 = 0.4031), an item-based neighborhood model had the lowest rating-prediction error, and content-based filtering proved to be the most informative component under extreme sparsity. Differences were confirmed using Friedman, Nemenyi, bootstrap, and effect size tests. These offline indicators show technical feasibility but do not demonstrate gains in producer income or field adoption rates. Real-world validation, contextual adaptation, and fairness assessment in rural Cesar are proposed for future work (Phase B).