Use of data science to predict fertilizer consumption in Brazil
Abstract
Given the world population growth, more and more researchers, governments and industries have been expending energy on studies regarding the production of fertilizers. As it is a critical element for food production, predicting how fertilizer consumption will behave in the next years is an important task. Furthermore, it is only through an efficient forecast that it is possible to plan the increase in production properly, also seeking to mitigate environmental problems that may be caused by the increase in production. Given the described elements, this research explores data science approaches to enable the prediction of fertilizer consumption, through the optimization of model construction. The results obtained indicate that the use of the analytical tools presented here may be a way to obtain reliable forecasts to plan future demands.
Keywords:
fertilizers, data analytics, prediction, machine learning, data preprocessing
References
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Styhr Petersen, H. (1977). Forecasting Danish nitrogen fertilizer consumption. Industrial Marketing Management, 6(3):211–222.
Tenkorang, F. and Lowenberg-Deboer, J. (2009). Forecasting long-term global fertilizer demand. Nutrient Cycling in Agroecosystems, 83(3):233–247.
Thornton, C., Hutter, F., Hoos, H., and Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume Part F128815, pages 847–855. UN (2019). United nations. Technical report, https://www.un.org/en/
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Coulibaly, P., Anctil, F., and Bobee, B. (2001). Multivariate reservoir inflow forecasting using temporal neural networks. Journal of Hydrologic Engineering, 6(5):367–376.
Deadman, D. and Ghatak, S. (1979). Forecasting fertilizer consumption and production: Longand short-run models. World Development, 7(11-12):1063–1072.
Dobermann, A. and Cassman, K. (2005). Cereal area and nitrogen use efficiency are drivers of future nitrogen fertilizer consumption. Science in China. Series C, Life sciences / Chinese Academy of Sciences, 48 Spec No:745–758.
FAO (2019). Food and agriculture organization of the united nations. Technical report, http://www.fao.org.
Gilland, B. (1993). Cereals, nitrogen and population: an assessment of the global trends. Endeavour, 17(2):84–88.
Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann, Haryana, India; Burlington, MA, 3 edition.
Howarth, R., Boyer, E., Pabich, W., and Galloway, J. (2002). Nitrogen use in the United States from 1961-2000 and potential future trends. Ambio, 31(2):88–96.
Hyndman, R. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3):1–22.
Hyndman, R. J. and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4):679–688.
Ogasawara, E., De Oliveira, D., Paschoal Jr., F., Castaneda, R., Amorim, M., Mauro, R., Soares, J., Quadros, J., and Bezerra, E. (2013). A forecasting method for fertilizers consumption in Brazil. International Journal of Agricultural and Environmental Information Systems, 4(2):23–36.
Ogasawara, E., Martinez, L., De Oliveira, D., Zimbrao, G., Pappa, G., and Mattoso, M. (2010). ˜Adaptive Normalization: A novel data normalization approach for non-stationary time series. In Proceedings of the International Joint Conference on Neural Networks.
Palmer, D., O’Boyle, N., Glen, R., and Mitchell, J. (2007). Random forest models to predict aqueous solubility. Journal of Chemical Information and Modeling, 47(1):150–158.
Pires, M., Da Cunha, D., De Matos Carlos, S., and Costa, M. (2015). Nitrogen-use efficiency, nitrous oxide emissions, and cereal production in Brazil: Current trends and forecasts. PLoS ONE, 10(8).
Salles, R., Belloze, K., Porto, F., Gonzalez, P., and Ogasawara, E. (2019). Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164:274–291.
Salles, R., Bezerra, E., Soares, J., and Ogasawara, E. (2015). Evaluating Linear Models as a Baseline for Time Series Imputation. In XXX Simposio Brasileiro de Banco de Dados, Petropolis, RJ.
Sapankevych, N. and Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2):24–38.
Sfetsos, A. and Coonick, A. (2000). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy, 68(2):169–178.
Stewart, W. and Roberts, T. (2012). Food security and the role of fertilizer in supporting it. In Procedia Engineering, volume 46, pages 76–82.
Styhr Petersen, H. (1977). Forecasting Danish nitrogen fertilizer consumption. Industrial Marketing Management, 6(3):211–222.
Tenkorang, F. and Lowenberg-Deboer, J. (2009). Forecasting long-term global fertilizer demand. Nutrient Cycling in Agroecosystems, 83(3):233–247.
Thornton, C., Hutter, F., Hoos, H., and Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume Part F128815, pages 847–855. UN (2019). United nations. Technical report, https://www.un.org/en/
Verma, T. and Pearl, J. (1991). Equivalence and synthesis of causal models. UCLA, Computer Science Department.
Zhang, G. and Berardi, V. (2001). Time series forecasting with neural network ensembles: An application for exchange rate prediction. Journal of the Operational Research Society, 52(6):652–664.
Zhang, W. and Zhang, X. (2007). A forecast analysis on fertilizers consumption worldwide.
Environmental Monitoring and Assessment, 133(1-3):427–434.
Zhao, Y., Ye, L., Li, Z., Song, X., Lang, Y., and Su, J. (2016). A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy, 177:793–803.
Published
2020-06-30
How to Cite
ANDRADE, Adalberto; SALLES, Rebecca; CARVALHO, Flavio; DA SILVA, Eduardo Bezerra; SOARES, Jorge; SOUZA, Cristina; GONZALEZ, Pedro Henrique; OGASAWARA, Eduardo.
Use of data science to predict fertilizer consumption in Brazil. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 9-16.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2020.11176.
