Predicción de lógicas de costeo en pequeños productores agrícolas mediante árboles de decisión en ecosistemas cafeteros
Palabras clave:
costeo agrícola, pequeños productores, ecosistema cafetero, árboles de decisión, contabilidad agrícola
Resumen
El objetivo principal del estudio consiste en analizar las lógicas de costeo en pequeños productores agrícolas del ecosistema cafetero de Caicedonia, Valle del Cauca (Colombia), con el propósito de explorar cómo distintos factores técnicos, económicos y ecológicos influyen en la formación del costo de producción por cultivo. A través del uso de árboles de decisión, se modelaron relaciones entre variables como altitud, densidad de siembra, mano de obra e insumos, considerando los sistemas agrícolas de café, naranja y plátano. La investigación adopta un enfoque cuantitativo apoyado en técnicas de aprendizaje automático, empleadas para identificar patrones de costeo que varían según las condiciones del entorno y las decisiones de manejo. Los resultados muestran que las estructuras de costo difieren entre cultivos y responden a combinaciones específicas de factores agroecológicos y económicos. En conjunto, el trabajo concluye con la propuesta de un enfoque metodológico que vincula la contabilidad agrícola con la modelación de datos, ofreciendo una herramienta útil para comprender la diversidad productiva y mejorar la planificación económica en contextos rurales de montaña.
Citas
Adelesi, O. O., Kim, Y.-U., Webber, H., Zander, P., Schuler, J., Hosseini-Yekani, S.-A., MacCarthy, D. S., Abdulai, A. L., van der Wiel, K., Traore, P. C. S., & Adiku, S. G. K. (2023). Accounting for weather variability in farm management resource allocation in northern Ghana: An integrated modeling approach. Sustainability, 15(9), 7386. https://doi.org/10.3390/su15097386
Adnan, N., & Akber, N. S. (2026). Sustainability strategies and capital costs: A study of non-financial disclosure in the GCC agri-food industry. Sustainable Futures, 11, 101590. https://doi.org/10.1016/j.sftr.2025.101590
Amankwah, A., Ambel, A., Gourlay, S., Kilic, T., Markhof, Y., & Wollburg, P. (2024). Fertilizer price shocks in smallholder agriculture. World Bank.
Atapattu, A. J., Perera, L. K., Nuwarapaksha, T. D., Udumann, S. S., & Dissanayaka, N. S. (2024). Challenges in achieving Artificial Intelligence in agriculture. En S. S. Chouhan, A. Saxena, U. P. Singh & S. Jain (Eds.), Artificial Intelligence Techniques in Smart Agriculture (pp. 15-38). Springer. https://doi.org/10.1007/978-981-97-5878-4_2
Awiti, H. A., et al. (2022). Smallholder farmers climate-smart crop diversification and variable cost structure in western Kenya. Frontiers in Sustainable Food Systems, 6, 842987. https://doi.org/10.3389/fsufs.2022.842987
Barakat, S., Elkhouly, H. I., Sofey, A., & Harraz, N. (2025). A hybrid machine learning model for predicting agricultural production costs: Integrating economic sensitivity analysis and environmental factors in Egypt. Journal of Environmental Management, 390, 126371. https://doi.org/10.1016/j.jenvman.2025.126371
Bellon, M. R., Benard, N., Vizcaino, M., Merrigan, K., & Wharton, C. (2024). True cost accounting using life cycle assessment methods and data: A case study comparing palm, rapeseed, and coconut oils for sustainability and nutrition. Sustainability, 16(23), 10366. https://doi.org/10.3390/su162310366
Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758
Bernal, C. S. R., & Álvarez, J. F. (2021). El impacto de los ingresos generados por la agricultura familiar en la superación de la pobreza de los campesinos colombianos. Papel Político, 26, 1-23. https://doi.org/10.11144/Javeriana.papo26.iiga
Bhatia, M. S., Chaudhuri, A., Kayikci, Y., & Treiblmaier, H. (2024). Implementation of blockchain-enabled supply chain finance solutions in the agricultural commodity supply chain: A transaction cost economics perspective. Production Planning & Control, 35(12), 1353-1367. https://doi.org/10.1080/09537287.2023.2180685
Carrero, A. C., Roque, D. I., & Cortés Cortés, J. A. (2025). Impacto del impuesto de renta en el costo promedio ponderado del capital en el sector agropecuario colombiano: 2018-2022. Revista de Métodos Cuantitativos para la Economía y la Empresa, 40. https://doi.org/10.46661/rev.metodoscuant.econ.empresa.10916
Casilimas, L., Corrales, D. C., Solarte Montoya, M., Rahn, E., Robin, M. H., Aubertot, J. N., & Corrales, J. C. (2021). HMP-Coffee: A hierarchical multicriteria model to estimate the profitability for small coffee farming in Colombia. Applied Sciences, 11(15), 6880. https://doi.org/10.3390/app11156880
Caulfield, M. E., Graham, M., Gibbons, J., McNicol, L., Williams, P., Chadwick, D., ... & Arndt, C. (2025). Marginal abatement cost curves of climate-smart agricultural practices to mitigate greenhouse gas emissions from smallholder dairy farms in Kenya. Journal of Cleaner Production, 501, 145281. https://doi.org/10.1016/j.jclepro.2025.145281
Chu, L. K. (2021). Financial access of Latin America and Caribbean firms: What are the roles of institutional, financial, and economic development? Journal of Emerging Market Finance, 20(3), 227-263. https://doi.org/10.1177/09726527211015317
Diaz, R. T., Osorio, D. P., Hernández, E. M., Pallares, M. M., Canales, F. A., Paternina, A. C., & Echeverría-González, A. (2022). Socioeconomic determinants that influence the agricultural practices of small farm families in northern Colombia. Journal of the Saudi Society of Agricultural Sciences, 21(7), 440-451. https://doi.org/10.1016/j.jssas.2021.12.001
Drogo, F., Ignaciuk, A., Neves, B., & Ilicic, J. (2025). True cost accounting for cocoa, coffee, maize, livestock, palm oil, rice, soybeans and wheat. FAO. https://doi.org/10.4060/cd6549en
Fleming, A., Ogilvy, S., O’Grady, A. P., Green, I., Stitzlein, C., & Horner, C. (2024). Designing natural capital accounting for agriculture: Perceptions of farm accountants. Sustainability Accounting, Management and Policy Journal, 15(7), 85–105. https://doi.org/10.1108/SAMPJ-04-2024-0356
Gao, J., Tang, X., Lin, S., & Bian, H. (2021). The influence of land use change on key ecosystem services and their relationships in a mountain region from past to future (1995–2050). Forests, 12(5), 616. https://doi.org/10.3390/f12050616
Gemmill-Herren, B., Baker, L. E., & Daniels, P. A. (2021). True cost accounting for food: Balancing the scale. Taylor & Francis.
Gómez, D., & Pérez, E. M. B. (2024). Sostenibilidad ambiental: Diálogos entre la economía ecológica, el territorio y la territorialidad en el desarrollo resiliente. Intropica: Revista del Instituto de Investigaciones Tropicales, 19(1), 6. https://doi.org/10.21676/23897864.5615
Gupta, G., & Kumar Pal, S. (2025). Applications of AI in precision agriculture. Discovery Agriculture, 3, 61. https://doi.org/10.1007/s44279-025-00220-9
Hernández-Ochoa, I. M., et al. (2022). Model-based design of crop diversification through new arrangements of crops to increase resource use efficiency. Agronomy for Sustainable Development, 42, 71. https://doi.org/10.1007/s13593-022-00805-4
Hilber, I., Bahena-Juárez, F., Chiaia-Hernández, A. C., et al. (2024). Pesticides in soil, groundwater and food in Latin America as part of one health. Environmental Science and Pollution Research, 31, 14333–14345. https://doi.org/10.1007/s11356-024-32036-3
Ilyas, Q. M., Ahmad, M., & Mehmood, A. (2023). Automated estimation of crop yield using artificial intelligence and remote sensing technologies. Bioengineering, 10(2), 125. https://doi.org/10.3390/bioengineering10020125
Kryszak, Ł., Świerczyńska, K., & Staniszewski, J. (2023). Measuring total factor productivity in agriculture: A bibliometric review. International Journal of Emerging Markets, 18(1), 148-172. https://doi.org/10.1108/IJOEM-04-2020-0428
Mellaku, M. T., & Sebsibe, A. S. (2022). Potential of mathematical model-based decision making to promote sustainable performance of agriculture in developing countries: A review article. Heliyon, 8(2). https://doi.org/10.1016/j.heliyon.2022.e08968
OECD. (2022). Insights into the measurement of agricultural total factor productivity and the environment. OECD Publishing.
Penagos, Á. J. T., Osorio, J. C., & Vidal-Holguín, C. J. (2021). Sustainability improvement of coffee farms in Valle del Cauca (Colombia) through system dynamics. En Advances in Production Management Systems (pp. 607-617). Springer. https://doi.org/10.1007/978-3-030-85914-5_64
Polwaththa, K. P. G. D. M., Amarasinghe, S. T. C., & Amarasinghe, A. A. Y. D. (2024). Exploring artificial intelligence and machine learning in precision agriculture: A pathway to improved efficiency and economic outcomes in crop production. American Journal of Agricultural Science, Engineering, and Technology, 8(1), 33–47. https://journals.e-palli.com/home/index.php/ajaset/article/view/3843
Potryvaieva, N., & Levchenko, L. (2025). Innovative approaches to cost accounting in agriculture taking into account the principles of the circular economy. Journal of Agricultural Economics and Accounting, 12(1), 18–29. https://doi.org/10.31521/978-617-7149-86-5-94
Rincón-Zapata, C., Restrepo-Ruiz, A. L., Alzate-Cárdenas, M. D. S., Zabala-Salazar, H. E., & Arboleda-Álvarez, O. L. (2021). Desigualdades rurales en Colombia: Aportaciones para el logro de los objetivos de desarrollo sostenible. Revista Lasallista de Investigación, 18(2), 178-200. https://doi.org/10.22507/rli.v18n2a13
Roque, A. F. (2025). From financial neutrality to ecological responsibility: Reformulating agricultural accounting in the era of sustainability and Agriculture 5.0. Journal of Environmental Accounting and Management, 15(2), 70–88. https://doi.org/10.20944/preprints202507.1920.v1
Shen, J. Q., Zhang, Z. Y., & Li, H. B. (2025). Cost-benefit analysis of remanufacturing of used agricultural machinery parts in China. Journal of Cleaner Production, 533, 146944. https://doi.org/10.1016/j.jclepro.2025.146944
Touch, V., Tan, D. K., Cook, B. R., Li Liu, D., Cross, R., Tran, T. A., ... & Cowie, A. (2024). Smallholder farmers’ challenges and opportunities: Implications for agricultural production, environment and food security. Journal of Environmental Management, 370, 122536. https://doi.org/10.1016/j.jenvman.2024.122536
Trentin, C., Ampatzidis, Y., Lacerda, C., & Shiratsuchi, L. (2024). Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review. Smart Agricultural Technology, 9, 100556. https://doi.org/10.1016/j.atech.2024.100556
Vittis, Y., Folberth, C., Bundle, S. C., & Obersteiner, M. (2021). Restoring nature at lower food production costs. Frontiers in Environmental Science, 9, 672663. https://doi.org/10.3389/fenvs.2021.672663
von Braun, J., & McDougall, P. (2023). Full-cost accounting and redefining the cost of food. Agricultural Economics, 54(4), 387–401. https://doi.org/10.1111/agec.12774
Yogeshwaran, R. (2022). Agricultural product price and crop cultivation prediction based on data science technique. Indian Scientific Journal of Research in Engineering and Management, 1(2), 45–58. https://doi.org/10.55041/IJSREM12586
Adnan, N., & Akber, N. S. (2026). Sustainability strategies and capital costs: A study of non-financial disclosure in the GCC agri-food industry. Sustainable Futures, 11, 101590. https://doi.org/10.1016/j.sftr.2025.101590
Amankwah, A., Ambel, A., Gourlay, S., Kilic, T., Markhof, Y., & Wollburg, P. (2024). Fertilizer price shocks in smallholder agriculture. World Bank.
Atapattu, A. J., Perera, L. K., Nuwarapaksha, T. D., Udumann, S. S., & Dissanayaka, N. S. (2024). Challenges in achieving Artificial Intelligence in agriculture. En S. S. Chouhan, A. Saxena, U. P. Singh & S. Jain (Eds.), Artificial Intelligence Techniques in Smart Agriculture (pp. 15-38). Springer. https://doi.org/10.1007/978-981-97-5878-4_2
Awiti, H. A., et al. (2022). Smallholder farmers climate-smart crop diversification and variable cost structure in western Kenya. Frontiers in Sustainable Food Systems, 6, 842987. https://doi.org/10.3389/fsufs.2022.842987
Barakat, S., Elkhouly, H. I., Sofey, A., & Harraz, N. (2025). A hybrid machine learning model for predicting agricultural production costs: Integrating economic sensitivity analysis and environmental factors in Egypt. Journal of Environmental Management, 390, 126371. https://doi.org/10.1016/j.jenvman.2025.126371
Bellon, M. R., Benard, N., Vizcaino, M., Merrigan, K., & Wharton, C. (2024). True cost accounting using life cycle assessment methods and data: A case study comparing palm, rapeseed, and coconut oils for sustainability and nutrition. Sustainability, 16(23), 10366. https://doi.org/10.3390/su162310366
Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758
Bernal, C. S. R., & Álvarez, J. F. (2021). El impacto de los ingresos generados por la agricultura familiar en la superación de la pobreza de los campesinos colombianos. Papel Político, 26, 1-23. https://doi.org/10.11144/Javeriana.papo26.iiga
Bhatia, M. S., Chaudhuri, A., Kayikci, Y., & Treiblmaier, H. (2024). Implementation of blockchain-enabled supply chain finance solutions in the agricultural commodity supply chain: A transaction cost economics perspective. Production Planning & Control, 35(12), 1353-1367. https://doi.org/10.1080/09537287.2023.2180685
Carrero, A. C., Roque, D. I., & Cortés Cortés, J. A. (2025). Impacto del impuesto de renta en el costo promedio ponderado del capital en el sector agropecuario colombiano: 2018-2022. Revista de Métodos Cuantitativos para la Economía y la Empresa, 40. https://doi.org/10.46661/rev.metodoscuant.econ.empresa.10916
Casilimas, L., Corrales, D. C., Solarte Montoya, M., Rahn, E., Robin, M. H., Aubertot, J. N., & Corrales, J. C. (2021). HMP-Coffee: A hierarchical multicriteria model to estimate the profitability for small coffee farming in Colombia. Applied Sciences, 11(15), 6880. https://doi.org/10.3390/app11156880
Caulfield, M. E., Graham, M., Gibbons, J., McNicol, L., Williams, P., Chadwick, D., ... & Arndt, C. (2025). Marginal abatement cost curves of climate-smart agricultural practices to mitigate greenhouse gas emissions from smallholder dairy farms in Kenya. Journal of Cleaner Production, 501, 145281. https://doi.org/10.1016/j.jclepro.2025.145281
Chu, L. K. (2021). Financial access of Latin America and Caribbean firms: What are the roles of institutional, financial, and economic development? Journal of Emerging Market Finance, 20(3), 227-263. https://doi.org/10.1177/09726527211015317
Diaz, R. T., Osorio, D. P., Hernández, E. M., Pallares, M. M., Canales, F. A., Paternina, A. C., & Echeverría-González, A. (2022). Socioeconomic determinants that influence the agricultural practices of small farm families in northern Colombia. Journal of the Saudi Society of Agricultural Sciences, 21(7), 440-451. https://doi.org/10.1016/j.jssas.2021.12.001
Drogo, F., Ignaciuk, A., Neves, B., & Ilicic, J. (2025). True cost accounting for cocoa, coffee, maize, livestock, palm oil, rice, soybeans and wheat. FAO. https://doi.org/10.4060/cd6549en
Fleming, A., Ogilvy, S., O’Grady, A. P., Green, I., Stitzlein, C., & Horner, C. (2024). Designing natural capital accounting for agriculture: Perceptions of farm accountants. Sustainability Accounting, Management and Policy Journal, 15(7), 85–105. https://doi.org/10.1108/SAMPJ-04-2024-0356
Gao, J., Tang, X., Lin, S., & Bian, H. (2021). The influence of land use change on key ecosystem services and their relationships in a mountain region from past to future (1995–2050). Forests, 12(5), 616. https://doi.org/10.3390/f12050616
Gemmill-Herren, B., Baker, L. E., & Daniels, P. A. (2021). True cost accounting for food: Balancing the scale. Taylor & Francis.
Gómez, D., & Pérez, E. M. B. (2024). Sostenibilidad ambiental: Diálogos entre la economía ecológica, el territorio y la territorialidad en el desarrollo resiliente. Intropica: Revista del Instituto de Investigaciones Tropicales, 19(1), 6. https://doi.org/10.21676/23897864.5615
Gupta, G., & Kumar Pal, S. (2025). Applications of AI in precision agriculture. Discovery Agriculture, 3, 61. https://doi.org/10.1007/s44279-025-00220-9
Hernández-Ochoa, I. M., et al. (2022). Model-based design of crop diversification through new arrangements of crops to increase resource use efficiency. Agronomy for Sustainable Development, 42, 71. https://doi.org/10.1007/s13593-022-00805-4
Hilber, I., Bahena-Juárez, F., Chiaia-Hernández, A. C., et al. (2024). Pesticides in soil, groundwater and food in Latin America as part of one health. Environmental Science and Pollution Research, 31, 14333–14345. https://doi.org/10.1007/s11356-024-32036-3
Ilyas, Q. M., Ahmad, M., & Mehmood, A. (2023). Automated estimation of crop yield using artificial intelligence and remote sensing technologies. Bioengineering, 10(2), 125. https://doi.org/10.3390/bioengineering10020125
Kryszak, Ł., Świerczyńska, K., & Staniszewski, J. (2023). Measuring total factor productivity in agriculture: A bibliometric review. International Journal of Emerging Markets, 18(1), 148-172. https://doi.org/10.1108/IJOEM-04-2020-0428
Mellaku, M. T., & Sebsibe, A. S. (2022). Potential of mathematical model-based decision making to promote sustainable performance of agriculture in developing countries: A review article. Heliyon, 8(2). https://doi.org/10.1016/j.heliyon.2022.e08968
OECD. (2022). Insights into the measurement of agricultural total factor productivity and the environment. OECD Publishing.
Penagos, Á. J. T., Osorio, J. C., & Vidal-Holguín, C. J. (2021). Sustainability improvement of coffee farms in Valle del Cauca (Colombia) through system dynamics. En Advances in Production Management Systems (pp. 607-617). Springer. https://doi.org/10.1007/978-3-030-85914-5_64
Polwaththa, K. P. G. D. M., Amarasinghe, S. T. C., & Amarasinghe, A. A. Y. D. (2024). Exploring artificial intelligence and machine learning in precision agriculture: A pathway to improved efficiency and economic outcomes in crop production. American Journal of Agricultural Science, Engineering, and Technology, 8(1), 33–47. https://journals.e-palli.com/home/index.php/ajaset/article/view/3843
Potryvaieva, N., & Levchenko, L. (2025). Innovative approaches to cost accounting in agriculture taking into account the principles of the circular economy. Journal of Agricultural Economics and Accounting, 12(1), 18–29. https://doi.org/10.31521/978-617-7149-86-5-94
Rincón-Zapata, C., Restrepo-Ruiz, A. L., Alzate-Cárdenas, M. D. S., Zabala-Salazar, H. E., & Arboleda-Álvarez, O. L. (2021). Desigualdades rurales en Colombia: Aportaciones para el logro de los objetivos de desarrollo sostenible. Revista Lasallista de Investigación, 18(2), 178-200. https://doi.org/10.22507/rli.v18n2a13
Roque, A. F. (2025). From financial neutrality to ecological responsibility: Reformulating agricultural accounting in the era of sustainability and Agriculture 5.0. Journal of Environmental Accounting and Management, 15(2), 70–88. https://doi.org/10.20944/preprints202507.1920.v1
Shen, J. Q., Zhang, Z. Y., & Li, H. B. (2025). Cost-benefit analysis of remanufacturing of used agricultural machinery parts in China. Journal of Cleaner Production, 533, 146944. https://doi.org/10.1016/j.jclepro.2025.146944
Touch, V., Tan, D. K., Cook, B. R., Li Liu, D., Cross, R., Tran, T. A., ... & Cowie, A. (2024). Smallholder farmers’ challenges and opportunities: Implications for agricultural production, environment and food security. Journal of Environmental Management, 370, 122536. https://doi.org/10.1016/j.jenvman.2024.122536
Trentin, C., Ampatzidis, Y., Lacerda, C., & Shiratsuchi, L. (2024). Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review. Smart Agricultural Technology, 9, 100556. https://doi.org/10.1016/j.atech.2024.100556
Vittis, Y., Folberth, C., Bundle, S. C., & Obersteiner, M. (2021). Restoring nature at lower food production costs. Frontiers in Environmental Science, 9, 672663. https://doi.org/10.3389/fenvs.2021.672663
von Braun, J., & McDougall, P. (2023). Full-cost accounting and redefining the cost of food. Agricultural Economics, 54(4), 387–401. https://doi.org/10.1111/agec.12774
Yogeshwaran, R. (2022). Agricultural product price and crop cultivation prediction based on data science technique. Indian Scientific Journal of Research in Engineering and Management, 1(2), 45–58. https://doi.org/10.55041/IJSREM12586
Publicado
2026-04-23
Cómo citar
Largo-Avila, E., Aguirre-Méndez, S. L., Moreno-Betancourt, F., & Rubiano-Granada, J. D. (2026). Predicción de lógicas de costeo en pequeños productores agrícolas mediante árboles de decisión en ecosistemas cafeteros. Revista Venezolana De Gerencia, 31(15), e31e1512. Recuperado a partir de https://produccioncientificaluz.org/index.php/rvg/article/view/45479
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