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Predictive Machine Learning Approaches to Agricultural Output

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The agricultural sector needs to increase agricultural production to guarantee food security worldwide, however, to achieve these objectives agriculture must improve the sustainability of its activities and processes, specifically improving the efficiency of the sector. In these frameworks, adjusted agricultural planning and management is crucial, where the availability of information plays a determinant role, as well as the consideration of new technologies and methodologies. In the context of the new approaches of analysis, digital methodologies may bring relevant added value, namely those associated with predictive machine learning technologies. From this perspective, this study intends to identify the most adjusted models to predict the European Union farming output, taking into account machine learning approaches and statistical information from the Farm Accountancy Data Network. The results obtained highlight the most important farming variables that must be taken into account to predict the total output in the European Union farms.

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IBM SPSS modeler Farm accountancy data network European Union farms

Citation

Martinho, V.J.P.D. (2024). Predictive Machine Learning Approaches to Agricultural Output. In: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-54608-2_1

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