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Exploring Data Preprocessing and Machine Learning Methods for Forecasting Worldwide Fertilizers Consumption

Fertilizer consumption is relevant in the agribusiness industry, governments, and research entities worldwide. The prediction of fertilizer consumption is a critical input in the food and organics production chain. Therefore, the increase in its production could be planned adequately without compromising the environment. The fertilizer consumption analysis through time is a big challenge because the available data is scarce, and satisfactory Machine Learning models are difficult to be obtained. Although some initiatives applying machine learning and statistical methods are commonly used to predict fertilizer consumption, a thorough evaluation of data analytics approaches to improve predictions under different step-ahead horizons is needed. We explored ways to optimize the temporal data model construction, considering different approaches through pair combinations between data preprocessing and Machine Learning methods. We evaluated these approaches for the NPK fertilizer real data in the top ten countries that demand it. The obtained results showed that using the proposed analytic tools may be a way to get reliable predictions to plan future demands.