Examinando por Materia "TECNOLOGÍA AGRÍCOLA"

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    Análisis de eficiencia técnico productiva del sector agrícola en Colombia : el papel del riego
    (Universidad EAFIT, 2022) Vélez Colorado, Laura; Molina Guzmán, Luis Alfredo
    The agricultural sector and its technical and productive efficiency have been widely studied from different approaches; the present work is based on a linear and correlation analysis between the capital factors (which are broken down into different criteria) and the production per hectare in each Colombian municipality, initially reviewing the theoretical and literature on the subject and giving a general account of the historical development of the agricultural sector in Colombia, from colonial times to the present. Next, an empirical analysis is made between the quantity produced per municipality and the different capital factors (gravity irrigation, machinery, technical assistance, drip irrigation, manual phytosanitary, chemical fertilizer, chemical phytosanitary, agricultural amendment). In order to study the causality of these relationships, an Ordinary Least Squares methodology (since the Cobb Douglas function is not linear in terms of parameters) and stochastic frontiers are used to estimate technical inefficiency. The empirical results show that all capital factors are important for agricultural production since they were statistically significant at least at 5% and irrigation and land suitability are the most relevant; furthermore, the hypothesis that the agricultural sector in Colombia is quite inefficient was tested.
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    Estrategias para el cierre de brechas identificadas en el Sistema Nacional de Innovación Agropecuaria de los departamentos del Meta, Tolima, Bolívar y Chocó
    (Universidad EAFIT, 2023) Tobón Tobón, Reinaldo; Zartha Sossa, Jhon Wilder
    Today, the development of a territory must be approached in an integral manner. In order to achieve this, strategies that enhance the social, political, economic, cultural, and environmental dimensions must be employed. It is in this way that National Systems of Agricultural Innovation (NSAI) worldwide propose strategic actions aimed at linking and establishing relationships among the various actors in the value chain, fostering innovative processes, and utilizing technology in agricultural practices, among others, with the purpose of achieving this comprehensive development. In this regard, this thesis aims to answer the question: What strategies close the gaps identified in the National Agricultural Innovation System of the Meta, Tolima, Chocó, and Bolívar departments based on the investigation of primary and secondary sources? These regions have gaps in each of the subsystems that make up the NAIS. To achieve this goal, a form-type instrument was applied to 34 experts to understand their perception of the NAIS. Additionally, 50 research papers or case studies conducted in different parts of the world, one diagnostic document prepared by the NAIS and the Technological Institute of Pereira to identify gaps, and four documents from the United Nations Food and Agriculture Organization and Rural Development Agencies were reviewed. As a result of this process, 55 strategies were formulated to mitigate the identified gaps in the NAIS of the Meta, Tolima, Bolívar, and Chocó departments.
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    Predicción del rendimiento de cultivos agrícolas en los cinco corregimientos de la ciudad de Medellín, utilizando modelos de Machine Learning
    (Universidad EAFIT, 2024) Gómez Arango, Alba Miriam; Valencia Diaz, Edison; Zuluaga Orrego, Juan Fernando
    In a global context where agriculture and food production play a crucial role in food security, employment, and sustainability, this study focuses on predicting the yield of agricultural crops in the five districts of Medellín. The main objective is to design a prediction model for nine local crops using machine learning techniques. Medellín is distinguished by its diversity of crops, including peri-urban agriculture characterized by productive small plots distributed across various chagra-type crops. These traditional agricultural practices are carried out by an aging population of farmers. Accuracy in yield prediction becomes essential, as a significant portion of the production is dedicated to self-consumption, with a subsistence focus. However, surpluses are also traded, directly impacting the food security of the local community. The results highlight the effectiveness of machine learning models, particularly Boosting models such as PCA Random Forest and PCA XGB Boosting, in predicting the crops under study. These models demonstrate the ability to capture relationships between variables and the heterogeneity present in territorial production. However, opportunities for improvement related to reducing model errors have been identified, which can be addressed through continuous data collection and technical support provided to farmers. This will not only increase data availability but also contribute to refining the model and understanding performance behavior in the analyzed crops, facilitating decision-making in the agricultural sector of the municipality of Medellín. This project represents a valuable tool for professionals in the agricultural sector and institutions responsible for planning and agricultural development. It offers an innovative approach to sector data analysis, leveraging the advantages of data science. Through these techniques, opportunities are opened to establish strategies, plans, and projects that contribute to crop planning, the management of productive areas in the municipality, and the strengthening of local food security.