Análisis comparativo de técnicas de pronóstico para variables de producción y precios de café colombiano

Ximena Cifuentes Wchima; Luis Miguel  MEJIA GIRALDO

doi:10.22490/21456453.10108

Vol. 17 No. 2 (2026)

Published 2026-09-26

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Comparative analysis of forecast techniques for production variables and prices of colombian coffee

DOI: https://doi.org/10.22490/21456453.10108

Ximena Cifuentes Wchima La Gran Colombia University

Luis Miguel Mejia Giraldo La Gran Colombia University

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Abstract
References

This research work revolves around the stochastic modeling of simple and multiple regression of variables associated with the coffee business; finding highly significant simple nonlinear models (p value = 0.0000) of variables such as domestic consumption in thousands of bags, initial gross stocks, the price paid to producers and the price of coffee on the New York stock exchange. In turn, it can be seen that highly significant multiple regression models are consolidated between the variables price paid to producers, price of coffee on the New York Stock Exchange and the difference between both prices (p value = 0.0000). These models demonstrate its high goodness of fit and potential for the predictive projection of these variables from the stochastic context without forgetting the importance of the reasoning of the market dynamics of its fundamental analysis.

keywords: Coffea, Modeling, Regression

Angelsen, A. & Kaimowitz, D. (Eds.), (2001). Agricultural Technologies and Tropical Deforestation. CABi. DOI: https://doi.org/10.1079/9780851994512.0000

Ayalew, B. & Sekar, I. (2016). Trends and regional disparity of maize production. India. J. Development and Agricultural Economics, 8(9), 193-199. https://www.researchgate.net/publication/308741013_Trends_and_regional_disparity_of_maize_production_in_India DOI: https://doi.org/10.5897/JDAE2015.0702

Ayyanathan, N. & Kannammal, A. (2014). Share price time series forecasting for effective supply chain information exchange. International Journal of Logistics Systems and Management, 18(1), 139-158. https://doi.org/10.1504/IJLSM.2014.062125 DOI: https://doi.org/10.1504/IJLSM.2014.062125

Azadi, H., Ho, P. & Hasfiati, L. (2011). Agricultural land conversion drivers: a comparison between less developed, developing and developed countries. Land Degradation & Development, 22(6), 596-604. https://www.researchgate.net/publication/227698151_Agricultural_land_conversion_drivers_A_comparison_between_less_developed_developing_and_developed_countries DOI: https://doi.org/10.1002/ldr.1037

Bacci L.A., Mello L.G., Incerti T., Paiva A.P. & Balestrassi P.P. (2019). Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated factor scores. International Journal of Production Economics, 212, 186-211. https://www.sciencedirect.com/science/article/pii/S0925527319300878 DOI: https://doi.org/10.1016/j.ijpe.2019.03.001

Chand, R. & Raju, S.S. (2009). Instability in Indian agriculture during different phases of technology and policy. Indian Journal of Agricultural Economics, 64(2), 188-207.

Consuegra M. & Garcia-Verdugo J. (2017). Measuring the functional efficiency ofagricultural futures markets. Australian Agricultural and Resource Economics Society, 61(2):232-46. DOI: https://doi.org/10.1111/1467-8489.12196

Cuaresma, C.J., Hlouskova, J. & Obersteiner, M. (2018). Fundamentals, speculation or macroeconomic conditions? Modeling and forecasting Arabica coffee prices. European Review of Agricultural Economics, 45, 583-615. DOI: https://doi.org/10.1093/erae/jby010

Deb, U. & Soumitra, P. (2015). Groundnut production performance in Bangladesh: a district level analysis. Economic Affairs, 60(3), 391-400. DOI: https://doi.org/10.5958/0976-4666.2015.00056.X

El-Rasoul, A.A.E.Y., Shehab, S.M.H. & Maghraby, H.E.S. (2020). Trends and Decomposition Growth Analysis of the Most Important Cereal Crops in Egypt. MPRA Paper No. 100231. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://mpra.ub.uni-muenchen.de/100231/8/MPRA_paper_100231.pdf

Food and Agriculture Organization of the United Nations [FAO]. (2015). Climate Change and Food Security: Risks and Responses. http://www.fao.org/3/i5188e/i5188e.pdf.

Food and Agriculture Organization of the United Nations [FAO]. (2021). The Impact of Disasters and Crises on Agriculture and Food Security. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://openknowledge.fao.org/server/api/core/bitstreams/30c0d98d-1c21-48ef-b5d9-8d988e6fa6f2/content

Federación de Cafeteros de Colombia [FCC]. 2021. https://federaciondecafeteros.org/wp/listado-noticias/cafe-crece-214-en-primer-trimestre-y-jalona-pib-agropecuario-de-33/#:~:text=Bogot%C3%A1%2C%20mayo%2018%20de%202021,agropecuario%20de%203%2C3%25

Fousekis, P. (2017). Price co-movement and the hedger’s value-at-risk in the futures markets for coffee. Agricultural Economics Review, 18, 35-47.

Hemant, S. (2015). Growth and instability of wheat production in Rajasthan. Indian Journal of Economics and Development, 11(4), 945-950. https://www.researchgate.net/publication/283478760_Growth_and_instability_of_wheat_production_in_Rajasthan DOI: https://doi.org/10.5958/2322-0430.2015.00105.5

Ikuemonisan, E.S., Mafimisebi, T.E., Ajibefun, I. & Adenegan, K. (2020). Cassava production in Nigeria: trends, instability and decomposition analysis (1970-2018). Heliyon, 6(10), e05089. https://www.sciencedirect.com/science/article/pii/S2405844020319320 DOI: https://doi.org/10.1016/j.heliyon.2020.e05089

Laurance, W.F., Sayer, J. & Cassman, K.G. (2014). Agricultural expansion and its impacts on tropical nature. Trends in Ecology & Evolution, 29(2), 107-116. https://www.researchgate.net/publication/259586244_Agricultural_expansion_and_its_impacts_on_tropical_nature DOI: https://doi.org/10.1016/j.tree.2013.12.001

Mahir, M.E. & Abdelaziz, H.H. (2011). Analysis of agricultural production instability in the Gezira scheme. Journal of the Saudi Society of Agricultural Sciences, 10(2), 53-58. https://www.researchgate.net/publication/271882478_Analysis_of_agricultural_production_instability_in_the_Gezira_scheme DOI: https://doi.org/10.1016/j.jssas.2011.03.008

Milas C., Otero J. & Panagiotidis, T. (2004). Forecasting the spot spices of various coffee types using linear and non-linear error correction models. International Journal of Finance and Economics, 9, 227-88. DOI: https://doi.org/10.1002/ijfe.245

Ministerio de Agricultura de Colombia [MAC]. (2021). https://www.minagricultura.gov.co/noticias/Paginas/El-sector-agropecuario-creci%C3%B3-3,8-en-el-PIB-del-segundo-trimestre-de-2021.aspx

Narain, D. (1977). Growth of productivity in Indian agriculture. Indian Society of Agricultural Economics, 32(01), 1-44.

Naveena K. (2017). Statistical modelling for forecasting of price and export of Indian coffee Doctoral dissertation. India: Institute of Agricultural Sciences, Banaras Hindu University; 2015. [8] Naveena K, Singh S, Singh A. Hybrid time series modelling for forecasting the price of washed coffee (Arabica Plantation Coffee) in India. International Journal of Agriculture Sciences, 9(10), 4004-7.

Naveena K., Singh Subedar, Rathod Santosha, Singh Abhishek. (2017). Hybrid ARIMA-ANN modelling for forecasting the price of Robusta coffee in India. International Journal of Current Microbiology and Applied Sciences, 6(7), 1721-6. DOI: https://doi.org/10.20546/ijcmas.2017.607.207

Novanda R.R., Sumartono E., Asriani P.S., Yuliarti E., Sukiyono K., Priyono B.S., Irnad, Reswita, Suryanty, M. & Octalia, V. (2018). A comparison of various forecasting techniques for coffee prices. Journal of Physics.: Conf. Ser. 1114:012119. https://doi.org/10.1088/1742-6596/1114/1/012119 DOI: https://doi.org/10.1088/1742-6596/1114/1/012119

Organización Internacional del Café [OIC]. (2020). Coffee Market Report. http://www.ico.org

Pretty, J. & Bharucha, Z.P. (2014). Sustainable intensification in agricultural systems. Annals of Botany, 114(8), 1571-1596. https://academic.oup.com/aob/article-abstract/114/8/1571/210078?redirectedFrom=fulltext DOI: https://doi.org/10.1093/aob/mcu205

Rao, C.H. (1975). Technological Change and Distribution of Gains in Indian Agriculture. Institute of Economic Growth. The Macmillan Co.

Sadiq, M.S., Singh, I.P. & Ahmad, M.M. (2020). A paradigm shift from area-led to productivity-led production of maize in Nigeria. Journal of Agriculture and Environmental Sciences, 5(2). https://doi.org/10.20372/jaes.v5i2.451

Sen, S.R. (1967). Growth and Instability in Indian Agriculture. Address to the 20th Annual Conference of the Indian Society of Agricultural Statistics, 1-31.

Swain, M. (2014). Sources of growth and instability in agricultural production in western Odisha, India. Asian Journal of Agricultural Development, 11(2), 51-70. https://ajad.searca.org/article?p=467 DOI: https://doi.org/10.37801/ajad2014.11.2.4

Yashavanth B.S., Singh K.N., Paul A. & Paul R.K. (2017). Forecasting prices of coffee seeds using Vector Autoregressive Time Series Model. Indian Journal of Agricultural Science, 87(6), 754-8. https://epubs.icar.org.in/index.php/IJAgS/article/view/70960 DOI: https://doi.org/10.56093/ijas.v87i6.70960

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Cifuentes Wchima, X., & Mejia Giraldo, L. M. . (2026). Comparative analysis of forecast techniques for production variables and prices of colombian coffee. Revista De Investigación Agraria Y Ambiental, 17(2), 139-158. https://doi.org/10.22490/21456453.10108

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