Determination of econometric factors impacting coffee production in Minas Gerais

Authors

DOI:

https://doi.org/10.33448/rsd-v11i6.29264

Keywords:

Path analysis; Correlation; Coffee; Econometric components.

Abstract

The study of the relationships between the econometric components of coffee production, estimated by correlations, for example, is of great relevance. These metrics provide useful information for the decision process in the production chain of this commodity. However, the quantification and interpretation of the correlation’s magnitude do not imply direct and indirect effects applicable to the agribusiness reality. In this context, trail analysis presents as a viable alternative. The objective of this work was, through trail analysis, to determine the direct and indirect effects of econometric components on coffee production. The data used are from coffee producing municipalities in Minas Gerais, in the period from 2008 to 2013, in which coffee production was observed as the basic (dependent) variable and as independent (explanatories) variables the harvested area of the grain, the average age of the workers in the field, the average remuneration of workers in the activity, the price paid for the product and the number of producing properties per municipality. There was a strong variation with year and municipality effects, with the rest of the explanation referring more to the primary variables (planted area and number of properties in the municipality), trail coefficients 0.38 and 0.05, respectively. The other variables interfere indirectly, through the modification of both. Path analysis proved to be useful in elucidating part of the coffee production chain variability and can be used as an aid in making business decisions in the sector.

Author Biographies

Luciano Ribeiro Galvão, Universidade de São Paulo

PhD student in Statistics and Experimentation at USP - University of São Paulo Master in Statistics at the Department of Statistics at the Federal University of Lavras Graduating in Mathematics Agronomist Engineer Leading the Interdisciplinary Group on Agrifood Systems (GISA) at the State University of Mato Grosso - UNEMAT ( 09/2020 - Current) (dgp.cnpq.br/dgp/espelhogrupo/7090664098716506) linked to CNPq. Lead advisor of the Center for Computational Statistics (NECOMP) of the University of the State of Minas Gerais - UEMG (04/2019 - 05/2020) (dgp.cnpq.br/dgp/espelhogrupo/7761194786013965) linked to CNPq. Github: https://github.com/G4LV40 Wordpress: https://infoanalisys.wordpress.com/ Medium: https://medium.com/@lucianogalvao111 Linkedin: https://www.linkedin.com/in/luciano -galv%C3%A3o-0b504b15a/

Júlio Sílvio de Sousa Bueno Filho, Universidade Federal de Lavras

Graduated in Agronomy from the Federal University of Viçosa (1989), he obtained a master's degree (1992) and a doctorate (1997) in Agronomy (Genetics and Plant Breeding) at ESALQ-USP, Piracicaba. Full Professor at the Federal University of Lavras, MG. He has experience in the field of Applied Statistics, with an emphasis on design of experiments, modeling of planned experiments and Bayesian analysis of mixed models and categorized data. He has worked mainly on the following topics: Quantitative Genetics (mixed linear and generalized mixed models, incomplete block designs, Bayesian analysis), statistical modeling of complex experiments applied to: agricultural sciences, educational evaluation, food science, tournament designs, etc.

Andrezza Kellen Alves Pamplona, Instituto Federal do Triângulo Mineiro

Graduated in Mathematics at the Federal University of Uberlândia (2011), Master and Doctor in Statistics and Agricultural Experiments at the Federal University of Lavras (2014). I am currently a teacher of EBTT at the Instituto Federal del Triângulo Mineiro - Uberaba campus.

Caio Peixoto Chain, Universidade Federal Rural do Rio de Janeiro

graduation in Economic Sciences from the Federal Rural University of Rio de Janeiro (2010), a master's and doctorate in Business Administration from the Federal University of Lavras (2014) in the line of research: Business management, economy and markets. He works in the areas of Public Administration, Regional Economics, Agroindustrial Management and Finance.

References

Abic. (2021). Associação Brasileira da Indústria de Café. Indicadores da Indústria de Café. https://estatisticas.abic.com.br/estatisticas/indicadores-da-industria/indicadores-da-industria-de-cafe-2021.

Brasil. (2015a). Ministério do Trabalho e Emprego. Cadastro Geral de Empregados e Desempregados: CAGED estatístico 2012. http://bi.mte.gov.br/bgcaged/rais.php.

Brasil. (2015b). Ministério do Trabalho e Emprego. Relação Anual de Informações Sociais: RAIS vínculos 2012. http://bi.mte.gov.br/bgcaged/rais.php.

De Carvalho, S. P. (1994). Métodos alternativos de estimação de coeficientes de trilha e índices de seleção, sob multicolínearidade. (Tese de Doutorado). Universidade Federal de Viçosa. Viçosa.

Carvalho, C. G. P., Arias, C. A. A., De Toledo, J. F. F., De Oliveira, M. F., & Vello, N. A. (2002). Correlações e análise de trilha em linhagens de soja semeadas em diferentes épocas. Pesquisa Agropecuária Brasileira. 37(3), 311-320.

Carvalho, F. I. F, Lorencetti, C., & Benin, G. (2004). Estimativas e implicações da correlação no melhoramento vegetal. Pelotas: UFPel.

Conab. (2021). Companhia Nacional de Abastecimento. Acompanhamento da Safra Brasileira de Café. 8(1) – Safra 2021 – Primeiro levantamento. Brasília, 1-71.

Conab. (2022). Companhia Nacional de Abastecimento. Acompanhamento da Safra Brasileira de Café. 9(1) – Safra 2022 – Primeiro Levantamento. Brasília, 1-60.

Cruz, C. D. et al. (2004). Análise de trilha. In: Cruz, C. D., Regazzi, A. J., & Carneiro, P. C. S. (Ed.). Modelos biométricos aplicados ao melhoramento genético. Viçosa, 180 193.

De Oliveira, R. M., de Oliveira, R. A., Neves, J. C. L., de Oliveira, E. M., Botelho, M. E., & de Oliveira, J. T. (2021). Relação entre índice de colheita do milho e eficiências de fósforo por meio de análise de trilha. Research, Society and Development, 10(2), e13510212358-e13510212358.

Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford Press.

Li, C. C. (1956). The concept of path coefficient and its impact on population genetics. Biometrics, 12(2), 190-210.

Li, C. C. (1975). Path Analysis: a primer. Pacific Grove: The Boxwood Press.

Lira, S. A. Análise de correlação: análise teórica e de construçãodos coeficientes com aplicações. (Dissertação de Mestrado). Universidade Federal do Paraná. Curitiba.

Martinez, H. E. P., Augusto, H. S., Cruz, C. D., Pedrosa, A. W., & Sampaio, N. F. (2008). Crescimento vegetativo de cultivares de café (Coffea arabica L.) e sua correlação com a produção em espaçamentos adensadoshttps://doi.org/10.4025/actasciagron.v29i4.404

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis. J. Wiley & Sons.

R Core Team. (2022). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2015. http:// www.R-project.org/.

Severino, L. S., Sakiyama, N. S., Pereira, A. A., Miranda, G. V., Zambolim, L., & Barros, U. V. (2008). Associações da produtividade com outras características agronômicas de café (Coffea arábica

De Souza, L. A. (2021). Correlação genética e análise de trilha em genótipos de algodoeiro de fibra colorida. 21 p. Trabalho de Conclusão de Curso (Graduação em Agronomia) - Universidade Federal de Uberlândia. Uberlândia.

Souza, T. V., Ribeiro, C. M., Scalon, J. D., & Guedes, F. L. (2014). Relações entre componentes de rendimento e características morfológicas de milho. Magistra, 26(4), 495-506.

Teixeira, A. L., Gonçalves, F. M. A., Rezende, J. C. D., Carvalho, S. P. D., Pereira, A. A., Moraes, B. F. X. D., & Teixeira, L. G. V. (2012). Seleção precoce para produção de grãos em café arábica pela avaliação de caracteres morfológicos. Pesquisa agropecuária brasileira, 47, 1110-1117.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research. 20(7), 557-585.

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Published

01/05/2022

How to Cite

GALVÃO, L. R. .; BUENO FILHO, J. S. de S. .; PAMPLONA, A. K. A. .; CHAIN, C. P. . Determination of econometric factors impacting coffee production in Minas Gerais. Research, Society and Development, [S. l.], v. 11, n. 6, p. e39611629264, 2022. DOI: 10.33448/rsd-v11i6.29264. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/29264. Acesso em: 22 nov. 2024.

Issue

Vol. 11 No. 6

Section

Agrarian and Biological Sciences

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Copyright (c) 2022 Luciano Ribeiro Galvão; Júlio Sílvio de Sousa Bueno Filho; Andrezza Kellen Alves Pamplona; Caio Peixoto Chain

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