A Forecasting Method for Fertilizers Consumption in Brazil

A Forecasting Method for Fertilizers Consumption in Brazil

Eduardo Ogasawara (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), Daniel de Oliveira (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), Fabio Paschoal Junior (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), Rafael Castaneda (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), Myrna Amorim (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), Renato Mauro (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), Jorge Soares (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil), João Quadros (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil) and Eduardo Bezerra (CEFET-Federal Center of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil)
DOI: 10.4018/jaeis.2013040103
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Abstract

Tracking information about fertilizers consumption in the world is very important since they are used to produce agriculture commodities. Brazil consumes a large amount of fertilizers due to its large-scale agriculture fields. Most of these fertilizers are currently imported. The analysis of consumption of major fertilizers, such as Nitrogen-Phosphorus-Potassium (NPK), Sulfur, Phosphate Rock, Potash, and Nitrogen become critical for long-term government decisions. In this paper we present a method for fertilizers consumption forecasting based on both Autoregressive Integrated Moving Average (ARIMA) and logistic function models. Our method was used to forecast fertilizers consumption in Brazil for the next 20 years considering different economic growth for the entire country.
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In this section we present basic time series notation and concepts that are described in section 2.1. Section 2.2 presents forecasting methods commonly used. Finally, section 2.3 presents related work for fertilizers forecasting.

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