vol. 10 núm. 2 (2025): julio – diciembre

This study aims to interpret the daily practices and dynamics of agricultural management in companies located in the Municipality of Colón, Zulia State, Venezuela, in response to the incorporation of Artificial Intelligence as a disruptive technology. Methodologically, the research was conducted using a qualitative approach, specifically ethnomethodology. The key participants included five individuals: managers, agricultural producers, agricultural technicians or advisors, and heads of technological or innovation departments in agricultural companies. They were interviewed using a semi-structured questionnaire, allowing participants the option to elaborate on their answers. Data analysis was carried out through Grounded Theory, employing open, axial, and selective coding. This process enabled an interpretation of agricultural management as a meaning-making construction that goes beyond a traditional academic definition, encompassing subjective experiences that blend leadership, operational functionality, economic sustainability, and environmental adaptability. In this sense, it represents a notion built from the rural context, where classical models, emerging practices, and hybrid work organization experiences converge. Within this context, Artificial Intelligence is not presented as an autonomous agent of change but rather as a relational device. Accordingly, the present study offers an interpretative framework to understand that technological disruption does not occur in neutral territories, but rather in landscapes deeply woven with knowledge, memories, resistances, and collective aspirations.

The soil–plant–atmosphere continuum constitutes a dynamic and interdependent system frequently disrupted by conventional agricultural practices. The present study corresponds to the preparation or reflexive diagnosis phase of action research aimed at improving agroecological techniques applied to soil management in production units undergoing agroecological transition, considering climate change scenarios. The geographical scope encompasses the municipalities of Libertador, Campo Elías, Sucre, Andrés Bello, Alberto Adriani, Tulio Febres Cordero, Pinto Salinas, and Zea (Andean Region), as well as the municipalities of Colón and Catatumbo in the Southern Lake Maracaibo region (Zulia Region). The methodology included a participatory diagnosis through mixed data collection techniques that enabled the description, recording, systematization, and interpretation of information, allowing for the evaluation of sustainability and biodiversity indices in production units. Sustainability indicators related to biodiversity were analyzed: cultivated diversity, plant species richness in plots, weed and pest management strategies, presence of forest patches, and tillage systems. Shannon diversity index (H') and Pielou evenness index (J') were applied for quantitative analysis, yielding J' values between 0.23 and 0.93 in production units ranging from 0.8 to 300 hectares, respectively. The results revealed lower acceptability of indicators in units exceeding 50 hectares and satisfactory values in smaller-scale units. The findings were shared with the research team and producers from both regions, promoting social appropriation of knowledge and strengthening decision-making processes grounded in local agroecological evidence.

La inteligencia artificial (IA) ha dejado de ser una aspiración futurista para convertirse en un instrumento tangible que redefine la producción agropecuaria. El crecimiento demográfico y el aumento de la demanda alimentaria, asociados a los retos de sostenibilidad ambiental y cambio climático, presionan al sector a evolucionar hacia sistemas más eficientes y resilientes. Diversos estudios internacionales reportan que el aprendizaje automático y otras técnicas de IA mejoran la productividad agrícola, reducen costos y permiten gestionar grandes volúmenes de datos, aunque enfrentan desafíos relacionados con la calidad y disponibilidad de datos, la infraestructura tecnológica y la capacitación de los productores. Este artículo presenta una revisión de literatura sobre las principales tendencias, aplicaciones, beneficios y retos de la IA en el sector agropecuario a nivel global. Se analiza la adopción de distintas tecnologías, desde algoritmos de aprendizaje automático y redes neuronales profundas hasta herramientas de Internet de las cosas (IoT) y robótica, en áreas como la detección temprana de plagas y enfermedades, la gestión del riego y los nutrientes, el mapeo agrícola, la ganadería y la acuicultura. Además, se contrasta la producción científica mundial con la generada en Colombia, evidenciando avances importantes, pero también una limitada producción nacional. Se sintetizan datos sobre los artículos revisados (año, país, tecnología, sector y resultados) y se incluyen gráficas que ilustran la distribución temporal, geográfica y sectorial de las investigaciones. Finalmente, se discuten los hallazgos a la luz de la agenda de desarrollo agropecuario colombiano y se proponen líneas futuras de investigación.

This article aims to conduct a theoretical review of ethnography as a qualitative strategy for descriptive-analytical studies on social practices and rural development. Methodologically, it is grounded in the qualitative paradigm, employing a bibliographic design and a descriptive documentary research approach. This involved using scientific articles indexed in various electronic repositories and institutional documents, which provided a broad overview of the topic. The review addressed the origins and foundations of ethnography, the characteristics of the method, ethnographic typologies, and research design. The results indicate that the cyclical nature of ethnographic design and the constant interaction in the data collection and analysis processes make it difficult to establish predetermined steps and phases in its design and implementation. For this reason, developing a field design for verification ensures its relevance and requires a methodological approach that aligns with our scientific understanding. Therefore, it is recommended that researchers not approach the field with pre-established hypotheses or stipulate a rigidly structured or linear model beforehand. However, this does not mean that researchers should enter the field without any methodological framework or objective. If they do, the research will be empty and meaningless.

Biological collections are veritable treasures of information that provide an invaluable historical perspective on biodiversity. These collections play an important role in various fields, both in research from taxonomy and ecology to the conservation of biodiversity. Thanks to biological collections, it is possible to find a source of historical record where covers the distribution and composition of different species over the years, in addition, by preserving these specimens, researchers can compare biological diversity at different times, which makes it possible to detect changes and/or loss of genetic diversity. To this end, a research was carried out with a descriptive non-experimental field design in 120 hectares of the María Lucia Metropolitan Park of the University Corporation of Meta. The biological collection of lepidoptera, being an important component in ecosystems, provides valuable information on biodiversity, climate effects and anthropogenic impacts. On the other hand, these biological collections play a crucial role in terms of education and scientific dissemination, so that in academic institutions they are not simply stores of specimens, living repositories that document the rich diversity of a specific region. The collections foster a greater understanding and appreciation of nature, in a world where biodiversity is threatened, these riches of information not only store the past but also guide the future of environmental conservation.

This article analyzes the limitations of cash-based accounting and the fair value measurement required by IAS 41 in tropical livestock enterprises and proposes an alternative managerial accounting framework based on the accrual principle. The methodology relies on an analytical and comparative review of international financial reporting standards, a systematization of recent literature on biological asset valuation, and a conceptual analysis of the tropical bovine production cycle, integrating criteria for cost classification, allocation, and capitalization. The results show that fair value measurement can generate high accounting volatility when reliable active markets are absent, while cash accounting distorts profitability by misaligning costs and revenues in prolonged biological cycles. Evidence further indicates that stage-based costing, cost capitalization, and the inclusion of opportunity costs produce more stable and management-relevant metrics. The discussion argues that structural characteristics of tropical systems—long production cycles, productive heterogeneity, market informality, and environmental risks—undermine the strict methodological applicability of IAS 41; therefore, incorporating total economic cost and the Risk-Adjusted Cost of Production (RACP) improves the assessment of financial and environmental sustainability. In conclusion, the study recommends adopting accrual-based systems, strengthening cost traceability, and using alternative valuation methods when fair value measurement is not reliable.