More than 2 billion cups of coffee (cafe arábica) are consumed worldwide each day. The livelihood of 120 million people depends on the coffee supply chain.
Coffee futures are the second most popular commodity traded worth over $100 billion annually.
But COFFEE is in TROUBLE!
Coffee rust leads to losses of more than $500 million worldwide. Coffee rust is the main disease that causes plant leaves to turn yellow.
This project will explore the following questions:
- Does coffee rust affect production and futures prices?
- Can visualizations be used to help draw conclusions about potential relationships between these variables?
There is no known research on this topic to date.
The project focuses on rust, production and futures data from Brazil, Colombia and Papua New Guinea from 1989-2013.
Data was collected from 10 different sources and the acquisition process is described here.
These three countries produce 48% of the world’s coffee. The coffee maps shows the regions in each country cafe arábica is grown.
- Is there a link between coffee rust and the amount of coffee produced?
- Is there a link between coffee rust and futures prices?
Variables include 337 observations with the following features:
- Rust percent
- Production amount
- Futures prices
Data visualizations help to accept or not accept the following hypotheses:
- More rain = more coffee rust
- Higher temperatures = more coffee rust
- More coffee rust = less production
- More coffee rust = lower futures prices
- More coffee production = lower futures prices
ANALYSIS AND RESULTS
During the exploratory data analysis part of the project, summary statistics were calculated for each variable.
Rain average was 183.55 cm per month with a range of 0.2-407.7 inches. Temperature average was 25.12 C with a range of 23.36-27.16 C. Rust average was 16.41% with a range of 0.33-50%. Production average was 1731.40 (1000-60 kg bags of beans) per month with a range of 80.33-3832.67. Futures average was $93.19 USD with a range from $21.98-$175.18 USD.
The next visualization was a simple line plot that represents the relationship between coffee rust and production and coffee rust and futures. From the line plot, it’s difficult to tell if rust is correlated to production amount.
From the line plot of rust versus futures, it appears there may be a positive correlation between the variables when the rust is less than 50%. The ranges in data values for futures is much larger (80-175) than the ranges for rust (0-50). Logarithmic scales were not used for either visualization to prevent misleading the reader.
The next step in the analysis was making a correlation matrix. A correlation matrix visualization shows the amount each variable is correlated to another variable.
Positive correlation (red) values means variables change in the same direction. Negative correlation values (blue) means variables change in the opposite direction. The intensity scale on the right shows how much the variables are correlated. A higher, darker red value of 0.8 is the highest positive correlation while the darkest blue value of -0.8 is the highest negative correlation.
The following conclusions can be drawn from the correlation matrix.
- Rain and temperature are not correlated to each other.
- Rain is negatively correlated (about -0.6) with production, meaning that if the rain increases, production decreases.
- Temperature is negatively correlated (about -0.3) with futures, meaning that if temperature increases, futures decrease.
- The production is positively correlated (about 0.4) with futures. If production increases, futures increase.
Finally, a linear regression plot of rust versus futures shows the amount that futures decrease as rust increases.
Linear Regression of Rust versus Futures
- Futures and Rust are positively correlated to one another using a polynomial regression. If rust increases so do futures prices.
- More rain does not affect coffee rust
- Higher temperatures = less coffee rust
- More coffee rust = more production
- More coffee rust = lower futures prices
- More coffee production = higher future prices
- More rust = lower futures prices (Accept original hypothesis)
- Do not accept other original hypotheses
- Alves, Marcelo de Carvalho. Cafeicultura De Precisão Na Proteção de Plantas: Monitoramento de Pragas E Doenças. Expocafe 2012 slide 28. https://pt.slideshare.net/cafeicultura/expocafe-marcelo. Accessed 17 October 2017.
- Avelino, J., H. Zelaya, A. Merlo, A. Pineda, M. Ordoñez and S. Savary. The intensity of a coffee rust epidemic is dependent on production situations. Ecological Modeling, 197 (2006): 431-447.
- Avelino, J., Cristancho, M., Georgiou, S. et al. The coffee rust crisis in Colombia and Central America (2008-2013): impacts, plausible causes and proposed solutions. Food Sec. (2015) 7: 303. https://doi.org/10.1007/s12571-015-0446-9.
- Bock, K.R., 1962b. Dispersal of uredospores of Hemieia vastatrix under field conditions. Trans. Br. Mycol. Soc. 45, 63-74.
- Cintra, M., C.A.A. Meira, M.C. Monard, H.A. Camargo. And L.H.A. Rodrigues. The Use of Fuzzy Decision Trees for Coffee Rust Warning in Brazilian Crops. 2011 11th International Conference on Intelligent Systems Design and Applications. DOI:10.1109/ISDA.2011.6121847. Accessed 17 October 2017.
- Color-Hex Color Palettes. http://www.color-hex.com/color-palette/50402. Accessed 21 November 2017.
- Commodity Futures Price Quotes for Coffee. http://data.tradingcharts.com/futures/quotes/kc.htmlAccessed 11 September 2017.
- Custudio, Adriano Augusto de Paiva et al . Comparison and validation of diagrammatic scales for brown eye spots in coffee tree leaves. Ciênc. agrotec., Lavras , v. 35, n. 6, p. 1067-1076, Dec. 2011 . Available from . Accessed 3 October 2017. http://dx.doi.org/10.1590/S1413-70542011000600005.
- Cunha, R.L; Mendes, A.N.G.; Chalfoun, S.M. Controle químico da ferrugem do cafeeiro (Coffea arabica L.) e seus efeitos na produção e preservação de enfolhamento. Ciência e Agrotecnologia, v. 28, n.5, p.990-996, 2004.
- De Carvalho Alves, M., da Silva, F.M., Sanches, L. et al. Geospatial analysis of ecological vulnerability of coffee agroecosystems in Brazil. Appl Geomat (2013) 5: 87. https://doi.org/10.1007/s12518-013-0101-0.
- Economics of coffee. Wikipedia: https://en.wikipedia.org/wiki/Economics_of_coffee. Accessed 3 October 2017.
- Escobar, Kimberly. “Coffee Rust is killing Latin American Plants.” http://www.dbknews.com/2016/10/28/umd-research-grant-coffee-rust-fungus/. Accessed 18 October 2017.
- Galli, F.; Carvalho, P.C.T. Doenças do cafeeiro. In: GALLI, F. (Coord.). Manual de fitopatologia. São Paulo: Editora Ceres, 1980. 587p. V.2. p.128-140.
- Hemileia Vastatrix. Wikipedia article. Accessed 22 October 2017. https://en.wikipedia.org/wiki/Hemileia_vastatrix
- Japiassú, L. B, A. W.R. Garcia, A.E. Miguel, M.S. A. Mendonça, C.H.S. Carvalho, R.A. Ferreira. Influencia da Carga Pendente, Do Espaçamento e de Fatores Climáticos no Desenvolvimento da Ferrugem do Cafeeiro. Estação de Avisos Fitossanitários Do Mapa/Fundação Procafé. http://www.sapc.embrapa.br/arquivos/consorcio/spcb_anais/simposio4/p292.pdf. Accessed 10 October 2017.
- Jaramillo J., Muchugu E., Vega F.E., Davis A., Borgemeister C., Chabi-Olaye A. (2011). Some Like It Hot: The Influence and Implications of Climate Change on Coffee Berry Borer (Hypothenemus hampei) and Coffee Production in East Africa. PLoS ONE 6(9): e24528. https://doi.org/10.1371/journal.pone.0024528.
- Kim, K. and W. Lee. Stock market prediction using artificial neural networks with optimal feature transformation. Neural Computing and Applications (2004), 13: 225-260. doi: 10.1007/s00521-004-0428-x.
- Lamouroux, N., F. Pellegrin, D. Nandris and F. Kohler. The Coffea arabica Fungal Pathosystem in New Caledonia: Interactions at Two Different Spatial Scales. J. Phytopathology 143, 403-413 (1995).
- Luaces, O., L. H.A. Rodrigues, C. A.A. Meira, A. Bahamonde. Using nondeterministic learners to alert on coffee rust disease. Expert Systems with Applications 38(2011): 14276-14283.
- Magrach A, Ghazoul J (2015). Climate and Pest-Driven Geographic Shifts in Global Coffee Production: Implications for Forest Cover, Biodiversity and Carbon Storage. PLoS ONE 10(7): e0133071. doi:10.1371/journal.pone.0133071.
- Montage, D. Algorithmic Trading of Futures via Machine Learning. Stanford University CS229 Course material. http://cs229.stanford.edu/proj2014/David%20Montague,%20Algorithmic%20Trading%20of%20Futures%20via%20Machine%20Learning.pdf. Accessed 11 September 2017.
- Paulo, E.M., Montes, S.M.N.M., & Fischer, I.H.. (2013). Progresso temporal da ferrugem alaranjada em cultivares de cafeeiro no Oeste de São Paulo. Arquivos do Instituto Biológico, 80(1), 59-64. https://dx.doi.org/10.1590/S1808-16572013000100009.
- Quinones, P., Javier, A. Effects of Daylength and Soil Humidity on the Flowering of Coffee Coffea Arabica L. in Colombia. Rev.Fac.Nal.Agr.Medellín, Medellín , v. 64, n. 1, p. 5745-5754, June 2011 . Available from . Accessed 3 Oct. 2017.
- Perez-Ariza, CB, Nicholson, AE & Flores, MJ 2012, Prediction of coffee rust disease using Bayesian networks. in A Cano, M Gomez-Olmedo & TD Nielsen (eds), Proceedings of the Sixth European Workshop on Probabilistic Graphical Models. University of Granada, Granada Spain, pp. 259 – 266, European Workshop on Probabilistic Graphical Models (PGM), Granada Spain, 1 January.
- Seven Things You Must Know about Coffee Futures. https://tradingsim.com/blog/7-things-you-must-know-about-coffee-futures/. Accessed 12 September 2017.
- Zambolim, L.; Vale, F.X.R.; Pereira, A.A.; Chaves, G.M. Café (Coffea arábica L.): Controle de doenças causadas por fungos, bactérias e vírus. In: VALE, F.X.R.; ZAMBOLIM, L. (Ed.). Controle de doen- ças de plantas. Viçosa: Suprema Gráfica e Editora, 1997. pp.83-180.