Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production

Emerson Rodolfo Abraham,Pedro Luiz De Oliveira Costa Neto,Oduvaldo Vendrametto,João Gilberto Mendes Dos Reis,Marcos De Oliveira Morais,Aguinaldo Eduardo De Souza,Rodrigo Carlo Toloi

doi:10.3390/agriculture10100475

Abstract

Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer.

Highlights

World’s population is projected to reach 9.8 billion in 2050 [1] and food production needs to increase by 60% to meet the demand [2,3]
The results show that the Artificial Neural Networks (ANN) model is the most efficient method to predict soybean harvest area and production
The data contain 95 years of wheat production (1919–2013), and the results revealed that the algorithms most effective in training methods are Bayesian regularization and Almomani [34] adopted artificial neural networks to predict the biofuel production from agricultural wastes and cow manure at high accuracy

Summary

Introduction

World’s population is projected to reach 9.8 billion in 2050 [1] and food production needs to increase by 60% to meet the demand [2,3]. One reason for that is the developing countries—that have been growing much more rapidly than the industrial countries—are creating implications for world food demand mainly in products such as animal-based, fruits, and vegetables [4]. There is a concern revolving around big agriculture growers such as Brazil and the US using their agriculture areas to produce biofuels [6]. It is in the economy that this relationship between food demand and income are finding shelter. Khan et al [8] predicted fruit production using deep neural networks

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