Abstract
Trade is essential for countries development. In Brazil, coffee has been one of the most important export items and by large is commercialized as a green or roasted bean. The aim of this article is to establish a prediction mode for coffee exports using time series and trend lines. To do so, we collected the exportation volume from the two main export countries in each segment: Brazil and Germany. A five-year forecasting was produced using regression curves provided by Microsoft Excel. Our results indicated that polynomial fits best and this function is consistent with agricultural production that is conditioned to edaphoclimatic factors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ortigoza, S.A.G.: Paisagens do Consumo: São Paulo, Lisboa, Dubai e Seul. Editora UNESP, São Paulo (2010)
Secretaria Municipal de Educação e Esporte de Goiânia. https://sme.goiania.go.gov.br/conexaoescola/ensino_fundamental/historia-do-comercio-escambo/
World Trade Organization: WTO Data. https://data.wto.org/
Maranhão, R.L.A., Vieira Filho, J.E.R.: Inserção Internacional do Agronegócio Brasileiro. IPEA, Brasília (2017)
Food and Agriculture Organization of the United Nations. http://www.fao.org/home/en/
International Coffee Organization. http://www.ico.org/
Food and Agriculture Organization of the United Nations (2021). http://www.fao.org/faostat/en/#data/TP
Lee, Y., Bateman, A.: The competitiveness of fair trade and organic versus conventional coffee based on consumer panel data. Ecol. Econo. 184, 106986 (2021)
Naegele, H.: Where does the Fair Trade money go? How much consumers pay extra for Fair Trade coffee and how this value is split along the value chain. World Dev. 133, 105006 (2020)
Vogt, M.A.B.: Developing stronger association between market value of coffee and functional biodiversity. J. Environ. Manag. 269, 110777 (2020)
Conceição, J.C.P.d., Ellery Junior, R.G.d., Conceição, P.H.Z.: Cadeia agroindustrial do café no brasil: Agregação de valor e exportação. Boletim de Economia e Política Internacional (24), 37–47 (2019)
Mc Fredries, P.: Fórmulas e Funçães no Microsoft Excel. Ciência Moderna, Rio de Janeiro (2005)
Abraham, E.R., et al.: Time series prediction with artificial neural networks: an analysis using Brazilian soybean production. Agriculture 10(10), 475 (2020)
Escolano, N.R., Espin, J.J.L.: Econometría: Series temporales y modelos de ecuaciones simultáneas. Editorial UMH, Alicante (2016)
Pecar, B., Davis, G.: Time series based predictive analytics modelling: using MS excel, Gloucestershire (2016)
Angeloni, G., et al.: What kind of coffee do you drink? An investigation on effects of eight different extraction methods. Food Res. Int. 116, 1327–1335 (2019)
Conselho dos Exportadores de Café do Brasil. https://www.cecafe.com.br/publicacoes/
Mendonça, V.U.V.M.: Caracterização da Atividade de Exportação de Commodities Agrícolas no Brasil, São Paulo (2015)
Kleemann, J., et al.: Quantifying interregional flows of multiple ecosystem services - a case study for Germany. Glob. Environ. Change 61, 102051 (2020)
Badmos, S., Fu, M., Granato, D., Kuhnert, N.: Classification of Brazilian roasted coffees from different geographical origins and farming practices based on chlorogenic acid profiles. Food Res. Int. 134, 109218 (2020)
Läderach, P., et al.: Systematic agronomic farm management for improved coffee quality. Field Crop Res 120(3), 321–329 (2011)
World Intellectual Property Organization: World intellectual property report 2017: intangible capital in global value chains. World Intellectual Property Organization, Geneva (2017)
Hindsley, P., McEvoy, D.M., Morgan, O.A.: Consumer demand for ethical products and the role of cultural worldviews: the case of direct-trade coffee. Ecol. Econ. 177, 106776 (2020)
Acknowledgments
This study was financed in part by the Coordenação de Aperfeioamento de Pessoal de Nível Superior Brasil (CAPES). Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
da Cruz Correia, P.F., dos Reis, J.G.M., Abraham, E.R., da Costa, J.S. (2021). Predicting Exports Using Time Series and Regression Trend Lines: Brazil and Germany Competition in Green and Roasted Coffee Industry. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-030-85902-2_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-85902-2_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85901-5
Online ISBN: 978-3-030-85902-2
eBook Packages: Computer ScienceComputer Science (R0)
