This systematic review analyzes the evolution of Machine Learning and Big Data applications in agriculture from 2021 to 2025, with particular emphasis on how recent technological advances facilitate the transition from precision agriculture to Intelligent Agricultural Ecosystems. A comprehensive literature search was conducted across Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, SpringerLink, and MDPI, following the PRISMA 2020 guidelines. After duplicate removal and a two-stage screening process (title/abstract screening followed by full-text assessment), eligible peer-reviewed studies were systematically extracted using a structured coding matrix encompassing six analytical domains: crops, soil, weather and water, land use, animal systems, and farmer decision-making. The findings reveal a substantial increase in ML-driven agricultural analytics. Although Random Forest and Convolutional Neural Networks remain widely adopted, recent studies demonstrate a marked shift toward advanced Deep Learning architectures, integrated cloud–edge–device infrastructures, Federated Learning frameworks for privacy-preserving collaboration, Explainable AI techniques to enhance transparency, and governance-oriented mechanisms to ensure interoperability. Notwithstanding these advances, several persistent challenges remain, including limited generalizability across diverse agroclimatic contexts, the high costs associated with high-quality data annotation, the integration of heterogeneous and multimodal datasets, and infrastructural constraints related to connectivity. These developments are synthesized within the IAE conceptual framework, underscoring governance- and lifecycle-aware orchestration MLOps as a critical differentiator that transcends purely technology-centric approaches.

Food insecurity is a critical issue in the Americas, with severe impacts in the Caribbean, Mesoamerica, and South America, particularly affecting older adults in Indigenous and rural contexts where it intersects with poverty, gender, and ethnicity. This study aims to provide an in-depth understanding of the current research about food insecurity among older Indigenous adults in Latin America and the Caribbean. A comprehensive literature review was conducted, utilizing specific search queries and the population, intervention, comparison, and outcome (PICO) strategy across multiple databases to identify the pertinent studies. The findings indicate an increase in academic output on this topic since 2018, with significant emphasis on the interplay between climate change and food insecurity. The review highlights the importance of developing targeted food programs, reforming policies, and fostering collaboration between academia and local communities to implement practical interventions. Despite the growing body of literature, a notable research gap persists in rural areas of Latin America and the Caribbean. This study underscores the necessity of balancing the geographic distribution of research and emphasizes the preservation of cultural practices and the adaptation of public policies to support traditional food practices. It advocates for culturally sensitive interventions and interdisciplinary collaboration to formulate comprehensive strategies. The originality and value of this study lie in its focused analysis of older Indigenous adults, contributing crucial insights to the international literature on food security.

Food security remains a critical challenge for older adults in rural Indigenous communities, particularly among the Mapuche people. This study presents an analysis of public policies that address the food security of Mapuche older adults in rural Chile. Using an interpretative qualitative approach, we explore the alignment between government programs and the lived experiences of this population. Findings indicate that existing policies lack cultural pertinence, focusing primarily on market-driven agricultural production rather than self-sufficiency and traditional food systems. Participants highlight the loss of community farming practices, environmental degradation, and the imposition of external production models as key factors exacerbating food insecurity. In addition, health and education policies do not integrate Indigenous knowledge and food habits, which further limits their effectiveness. This study highlights the need for culturally inclusive public policies that support local food sovereignty, sustainable agricultural practices, and the empowerment of Indigenous communities.

One of the factors that has steadily eroded the legitimacy of public institutions is the recurrence of corruption and irregularities in state management, especially in procurement processes, budget allocation, and public appointments. These phenomena have intensified public mistrust and highlight the urgent need for tools to strengthen transparency in the digital sphere. In this context, e-government platforms have become key mechanisms for promoting openness and accountability in public administration. However, ensuring transparency in electronic procedures remains challenging due to the lack of standardized auditing mechanisms and system interoperability. The objective of this study is to design, implement, and technically evaluate a software architecture for an electronic platform that operationalizes a maturity-based diagnostic model to assess transparency in electronic procedures within public organizations. The proposed platform enables real-time monitoring, centralized data management, automated reporting, and evidence-based transparency assessment. The architecture follows a structured design process aligned with ISO 25010 quality attributes, incorporating microservices, database replication, and load balancing to enhance system performance and security. Performance tests identified bottlenecks in authentication, query processing, and concurrent user load, leading to optimizations such as query indexing, caching, and message queue implementation. The results indicate that a well-structured software architecture enhances government transparency by ensuring auditable, traceable, and secure public data access. The platform provides a self-diagnostic tool for institutions to assess their transparency maturity based on open government principles.