IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios

Los choques de precios han sido por largo tiempo uno de los principales problemas que los agricultores se enfrentan en países en desarrollo. Este problema crea un riesgo a su inversión y estilo de vida cuando se encuentran con precios bajos al momento de la cosecha, llevándolos a situación de pobrez...

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Autores:: Niño Chaparro, Gustavo Enrique
Niño Chaparro, Alejandro
Chaparro Pesca, Jorge Alberto

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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network_name_str	REDICUC - Repositorio CUC
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dc.title.spa.fl_str_mv	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
dc.title.translated.eng.fl_str_mv	AI in Colombian Food Markets: Using Machine Learning to Address Price Crisis
title	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
spellingShingle	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios Machine learning crops price crises policy targeting. Aprendizaje automático crisis precios alimentos políticas focalización.
title_short	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
title_full	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
title_fullStr	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
title_full_unstemmed	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
title_sort	IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios
dc.creator.fl_str_mv	Niño Chaparro, Gustavo Enrique Niño Chaparro, Alejandro Chaparro Pesca, Jorge Alberto
dc.contributor.author.spa.fl_str_mv	Niño Chaparro, Gustavo Enrique Niño Chaparro, Alejandro Chaparro Pesca, Jorge Alberto
dc.subject.eng.fl_str_mv	Machine learning crops price crises policy targeting.
topic	Machine learning crops price crises policy targeting. Aprendizaje automático crisis precios alimentos políticas focalización.
dc.subject.spa.fl_str_mv	Aprendizaje automático crisis precios alimentos políticas focalización.
description	Los choques de precios han sido por largo tiempo uno de los principales problemas que los agricultores se enfrentan en países en desarrollo. Este problema crea un riesgo a su inversión y estilo de vida cuando se encuentran con precios bajos al momento de la cosecha, llevándolos a situación de pobreza. Gobiernos regionales generalmente responden a las crisis de manera ineficiente, repartiendo ayudas indiscriminadamente. A pesar de que gobiernos locales pueden aplicar muchas herramientas para prevenir los cambios drásticos en los precios agrícolas, esas herramientas tienden a ser muy costosas y difíciles de implementar. El principal objetivo de esta investigación es mostrar la posibilidad de usar una herramienta de machine learning que sea costo efectivo que predice las municipalidades más propensas a ser afectadas por un shock de precios, permitiendo a los gobiernos locales dirigir eficazmente la asistencia donde más se necesita. Dos modelos son usados en este articulo, random forest y arboles de decisión. Los hallazgos sugieren que usando estructuras simples de árbol de decisión y Random Forest, se logra predecir hasta un 79% de los municipios afectados por el choque. Este articulo muestra que esta estructura simple de machine learning puede equipar a los gobiernos con datos confiables para ser usados en crisis de precios a un costo bajo de focalización.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-01-01 00:00:00 2024-11-18T16:28:46Z
dc.date.available.none.fl_str_mv	2024-01-01 00:00:00 2024-11-18T16:28:46Z
dc.date.issued.none.fl_str_mv	2024-01-01
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.references.eng.fl_str_mv	Babenko, B., Hersh, J., Newhouse D., Ramakrishnan, A., & Swartz., T. (2017). Poverty mapping using convolutional neural networks trained on high and medium resolution satellite images, with an application in mexico. arXiv preprint arXiv:1711.06323. https://doi.org/10.48550/arXiv.1711.06323 Burke, M., Bergquist, L., & Miguel, E. (2019). Sell low and buy high: arbitrage and local price e ects in kenyan markets. The Quarterly Journal of Economics, 134(2), 785-842. https://doi.org/10.1093/qje/qjy034 Caruana, J. (2005). Monetary policy, financial stability and asset prices. Technical report (No. 0507). Banco de España. https://ideas.repec.org/p/bde/opaper/0507.html Caruanas, J., Food price volatility and its implications for food security and policy. Springer, 2016. https://link.springer.com/book/10.1007/978-3-319-28201-5 Christian, P., & Dillon, B. (2018). Growing and learning when consumption is seasonal: long-term evidence from Tanzania. Demography, 55(3), 1091-1118. https://doi.org/10.1007/s13524-018-0669-4 Crockett, A. (1996). The theory and practice of financial stability. De Economist, 144(4), 531-568. https://doi.org/10.1007/BF01371939 Dan, E.A., Oladejo, B.F., & Ekong, V. E. (2023). A Model for Predicting Food Insecurity in Nigeria using Deep Learning. Egyptian Computer Science Journal, 47(1). http://ecsjournal.org/Archive/Volume47/Issue1/1.pdf DANE. Boletin tecnico: Encuesta nacional agropecuaria. 2018. https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena D’Souza, A., & Jolliffe, D. (2013). Conflict, food price shocks, and food insecurity: The experience of afghan households. Food Policy, 42(6621):32-47. https://doi.org/10.1016/j.foodpol.2013.06.007 Edwards, W., von Winterfeldt, D., & Moody, D. L. (1988). Simplicity in decision analysis: An example and a discussion. In D. E. Bell, H. Raiffa, & A. Tversky (Eds.), Decision making: Descriptive, normative, and prescriptive interactions (pp. 443–464). Cambridge University Press. https://doi.org/10.1017/CBO9780511598951.022 Haile, M.G., Kalkuhl, M., & Braun., J.(2016). Worldwide acreage and yield response to international price change and volatility: A dinamic panel data analysis for wheat, rice, corn and soybeans. American Journal of Agricultural Economics, vol 98, no 1, p.172-190. https://doi.org/10.1093/ajae/aav013 Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B, & Ermon., S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. DOI: 10.1126/science.aaf7894 Jordan, M.I, &Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. DOI: 10.1126/science.aaa8415 Letta, M., Montalbano, P., & Pierre, G. (2022). Weather shocks, traders’ expectations, and food prices. American Journal of Agricultural Economics, vol 104 no 3, p. 1100-1119. https://doi.org/10.1111/ajae.12258 Magrini, M.B, Martini, G., Magne, M.A, Duru, M., Couix, N., Hazard, L. & Plumecocq, G. (2019). Agroecological transition from farms to territorialised agri-food systems: Issues and drivers. In Bergez, JE., Audouin, E., Therond, O. (eds) Agroecological transitions: FromTheory to practice in local participatory design, (pp. 69-98). Springer, Cham. https://doi.org/10.1007/978-3-030-01953-2_5 Nakelse, T., Dalton, T.J, Hendricks, N.P, & Hodjo, M. (2018). Are smallholders farmers better or worse o from an increase in the international price cereals?. Food Policy, 79, 213-223. https://doi.org/10.1016/j.foodpol.2018.07.006 Neudert, R., Hecker, L.P, Randrianarison, H., Kobbe, S. (2021). Are smallholders disadvantaged by “adouble sell low, buy high” dynamics on rural markets in madagascar?. Development Southern Africa, 38(2), 208-229. https://doi.org/10.1080/0376835X.2020.1818550 Pal, M. (2005). Random forest classifier for remote sensing classification. International journal of remote sensing, 26(1), 217-222. https://doi.org/10.1080/01431160412331269698 Ranganathan, G., Bestak, R., Palanisamy, R., & Rocha, Á. (2022). Pervasive Computing and Social Networking. Springer Singapore. https://link.springer.com/book/10.1007/978-981-16-5640-8 Ruiz-Real, J.L, Uribe-Toril, J.A, Torres, J.A, & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98-117. https://doi.org/10.3846/jbem.2020.13641 Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39. Doi: 10.17849/insm-47-01-31-39.1 Sahn, D.E. (1989). Seasonal variability in third world agriculture: The consequences for food security. The Johns Hopkins University Press. https://worldveg.tind.io/record/11363/ Wagener, A., & Zenker, J. (2021). Decoupled but not neutral: The Effects of Counter-Cyclical Cash Transfers on Investment and Incomes in Rural Thailand. American Journal of Agricultural Economics, 103(5), 1637-1660. https://doi.org/10.1111/ajae.12172 Yasir, M., Afzal, S., Latif, K., Chaunhary, G.M., Malik, N. Y., Shahzad, F., & Song, O. Y. (2020). Mujtaba, G. An efficient deep learning based model to predict interest rate using twitter sentiment. Sustainability, 12(4), 1660. https://doi.org/10.3390/su12041660 Zhou, Y., & Baylis, K. (2019, June). Predict Food Security with Machine Learning: Application in Eastern Africa. In 2019 Annual Meeting, July 21-23, Atlanta, Georgia (No. 291056). Agricultural and Applied Economics Association.
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spelling	Niño Chaparro, Gustavo EnriqueNiño Chaparro, AlejandroChaparro Pesca, Jorge Alberto2024-01-01 00:00:002024-11-18T16:28:46Z2024-01-01 00:00:002024-11-18T16:28:46Z2024-01-010120-3932https://hdl.handle.net/11323/13738https://doi.org/10.17981/econcuc.Org.481810.17981/econcuc.Org.48182382-3860Los choques de precios han sido por largo tiempo uno de los principales problemas que los agricultores se enfrentan en países en desarrollo. Este problema crea un riesgo a su inversión y estilo de vida cuando se encuentran con precios bajos al momento de la cosecha, llevándolos a situación de pobreza. Gobiernos regionales generalmente responden a las crisis de manera ineficiente, repartiendo ayudas indiscriminadamente. A pesar de que gobiernos locales pueden aplicar muchas herramientas para prevenir los cambios drásticos en los precios agrícolas, esas herramientas tienden a ser muy costosas y difíciles de implementar. El principal objetivo de esta investigación es mostrar la posibilidad de usar una herramienta de machine learning que sea costo efectivo que predice las municipalidades más propensas a ser afectadas por un shock de precios, permitiendo a los gobiernos locales dirigir eficazmente la asistencia donde más se necesita. Dos modelos son usados en este articulo, random forest y arboles de decisión. Los hallazgos sugieren que usando estructuras simples de árbol de decisión y Random Forest, se logra predecir hasta un 79% de los municipios afectados por el choque. Este articulo muestra que esta estructura simple de machine learning puede equipar a los gobiernos con datos confiables para ser usados en crisis de precios a un costo bajo de focalización.Price shocks have long been a challenge for farmers in developing countries, posing a substantial threat to their investments and livelihoods when they encounter low prices at the time of harvest, often pushing them towards poverty. Local governments' crisis responses often use inefficient, indiscriminate aid distribution. While local and regional governments can apply many tools to prevent sudden changes in crop prices, those tools tend to be expensive and difficult to implement in local communities. The principal objective of this study is to illustrate the feasibility of a cost-effective machine learning tool that predicts the most likely affected municipalities by a price shock, enabling local governments to effectively target assistance where it is needed. Two models were used in the article, a random forest and a decision tree algorithm. The findings suggest that, despite using a simple structure in both algorithms, the models were able to predict up to 79% of the municipalities affected by prices shocks. Furthermore, this article highlights that this relatively uncomplicated model structure can equip governments with accurate data, which could be employed in price crisis responses at a lower cost, thereby enhancing the efficiency of aid distribution.application/pdfengUniversidad de la CostaGustavo Enrique Niño Chaparro, Alejandro Niño Chaparro, Jorge Alberto Chaparro Pesca - 2024https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/economicascuc/article/view/4818Machine learningcrops price crisespolicy targeting.Aprendizaje automáticocrisis precios alimentospolíticas focalización.IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de PreciosAI in Colombian Food Markets: Using Machine Learning to Address Price CrisisArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Económicas CUCBabenko, B., Hersh, J., Newhouse D., Ramakrishnan, A., & Swartz., T. (2017). Poverty mapping using convolutional neural networks trained on high and medium resolution satellite images, with an application in mexico. arXiv preprint arXiv:1711.06323. https://doi.org/10.48550/arXiv.1711.06323Burke, M., Bergquist, L., & Miguel, E. (2019). Sell low and buy high: arbitrage and local price e ects in kenyan markets. The Quarterly Journal of Economics, 134(2), 785-842. https://doi.org/10.1093/qje/qjy034Caruana, J. (2005). Monetary policy, financial stability and asset prices. Technical report (No. 0507). Banco de España. https://ideas.repec.org/p/bde/opaper/0507.htmlCaruanas, J., Food price volatility and its implications for food security and policy. Springer, 2016. https://link.springer.com/book/10.1007/978-3-319-28201-5Christian, P., & Dillon, B. (2018). Growing and learning when consumption is seasonal: long-term evidence from Tanzania. Demography, 55(3), 1091-1118. https://doi.org/10.1007/s13524-018-0669-4Crockett, A. (1996). The theory and practice of financial stability. De Economist, 144(4), 531-568. https://doi.org/10.1007/BF01371939Dan, E.A., Oladejo, B.F., & Ekong, V. E. (2023). A Model for Predicting Food Insecurity in Nigeria using Deep Learning. Egyptian Computer Science Journal, 47(1). http://ecsjournal.org/Archive/Volume47/Issue1/1.pdfDANE. Boletin tecnico: Encuesta nacional agropecuaria. 2018. https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-enaD’Souza, A., & Jolliffe, D. (2013). Conflict, food price shocks, and food insecurity: The experience of afghan households. Food Policy, 42(6621):32-47. https://doi.org/10.1016/j.foodpol.2013.06.007Edwards, W., von Winterfeldt, D., & Moody, D. L. (1988). Simplicity in decision analysis: An example and a discussion. In D. E. Bell, H. Raiffa, & A. Tversky (Eds.), Decision making: Descriptive, normative, and prescriptive interactions (pp. 443–464). Cambridge University Press. https://doi.org/10.1017/CBO9780511598951.022Haile, M.G., Kalkuhl, M., & Braun., J.(2016). Worldwide acreage and yield response to international price change and volatility: A dinamic panel data analysis for wheat, rice, corn and soybeans. American Journal of Agricultural Economics, vol 98, no 1, p.172-190. https://doi.org/10.1093/ajae/aav013Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B, & Ermon., S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. DOI: 10.1126/science.aaf7894Jordan, M.I, &Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. DOI: 10.1126/science.aaa8415Letta, M., Montalbano, P., & Pierre, G. (2022). Weather shocks, traders’ expectations, and food prices. American Journal of Agricultural Economics, vol 104 no 3, p. 1100-1119. https://doi.org/10.1111/ajae.12258Magrini, M.B, Martini, G., Magne, M.A, Duru, M., Couix, N., Hazard, L. & Plumecocq, G. (2019). Agroecological transition from farms to territorialised agri-food systems: Issues and drivers. In Bergez, JE., Audouin, E., Therond, O. (eds) Agroecological transitions: FromTheory to practice in local participatory design, (pp. 69-98). Springer, Cham. https://doi.org/10.1007/978-3-030-01953-2_5Nakelse, T., Dalton, T.J, Hendricks, N.P, & Hodjo, M. (2018). Are smallholders farmers better or worse o from an increase in the international price cereals?. Food Policy, 79, 213-223. https://doi.org/10.1016/j.foodpol.2018.07.006Neudert, R., Hecker, L.P, Randrianarison, H., Kobbe, S. (2021). Are smallholders disadvantaged by “adouble sell low, buy high” dynamics on rural markets in madagascar?. Development Southern Africa, 38(2), 208-229. https://doi.org/10.1080/0376835X.2020.1818550Pal, M. (2005). Random forest classifier for remote sensing classification. International journal of remote sensing, 26(1), 217-222. https://doi.org/10.1080/01431160412331269698Ranganathan, G., Bestak, R., Palanisamy, R., & Rocha, Á. (2022). Pervasive Computing and Social Networking. Springer Singapore. https://link.springer.com/book/10.1007/978-981-16-5640-8Ruiz-Real, J.L, Uribe-Toril, J.A, Torres, J.A, & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98-117. https://doi.org/10.3846/jbem.2020.13641Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39. Doi: 10.17849/insm-47-01-31-39.1Sahn, D.E. (1989). Seasonal variability in third world agriculture: The consequences for food security. The Johns Hopkins University Press. https://worldveg.tind.io/record/11363/Wagener, A., & Zenker, J. (2021). Decoupled but not neutral: The Effects of Counter-Cyclical Cash Transfers on Investment and Incomes in Rural Thailand. American Journal of Agricultural Economics, 103(5), 1637-1660. https://doi.org/10.1111/ajae.12172Yasir, M., Afzal, S., Latif, K., Chaunhary, G.M., Malik, N. Y., Shahzad, F., & Song, O. Y. (2020). Mujtaba, G. An efficient deep learning based model to predict interest rate using twitter sentiment. Sustainability, 12(4), 1660. https://doi.org/10.3390/su12041660Zhou, Y., & Baylis, K. (2019, June). Predict Food Security with Machine Learning: Application in Eastern Africa. In 2019 Annual Meeting, July 21-23, Atlanta, Georgia (No. 291056). Agricultural and Applied Economics Association.e24818e24818145https://revistascientificas.cuc.edu.co/economicascuc/article/download/4818/5375Núm. 1 , Año 2024OREORE.xmltext/xml2675https://repositorio.cuc.edu.co/bitstreams/b30832bb-379e-429f-bc7a-b6c0c872a9d4/download8e95e70cd7b01b4e15a242226ddda6d4MD5111323/13738oai:repositorio.cuc.edu.co:11323/137382024-11-18 11:28:46.628https://creativecommons.org/licenses/by-nc-nd/4.0Gustavo Enrique Niño Chaparro, Alejandro Niño Chaparro, Jorge Alberto Chaparro Pesca - 2024metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co

IA en Mercados de Alimentos en Colombia: Usando Machine Learning para Enfrentar Crisis de Precios

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