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Authors
Advisor(s)
Abstract(s)
The dynamics of the agricultural sector depend on the performance of the farms and their respective profitability. The cost control in the farms is particularly important, considering the reduced profit margins in agriculture. In fact, in some contexts, the level of farm costs is very similar to the amounts of income, calling, in many cases, for financial support for the farmers, justified by the need to guarantee food security and social and environmental sustainability. In this framework, contributions that support policymakers and farmers to make decisions that promote farm cost reduction are fundamental. Considering this scenario, this study intends to consider machine learning approaches and data from the European databases to identify the most adjusted approaches to predict the total costs in the farms. This study brought relevant outputs for the design of adjusted measures, plans and instruments for the European Union agriculture and respective processes and activities.
Description
Keywords
Artificial intelligence Adjusted models and predictors Agriculture
Citation
Martinho, V.J.P.D. (2024). Predicting the Total Costs of Production Factors on Farms in the European Union. 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_4
Publisher
Springer, Cham