Martinho, Vítor2024-11-122024-11-122024Martinho, V.J.P.D. (2024). Predictive Machine Learning Models for Livestock 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_3http://hdl.handle.net/10400.19/8629Agricultural planning always had an important role in the performance of agriculture, but in our days this component of agricultural management seems to have an increased responsibility, because of the challenges imposed by the current contexts, specifically those related to the sustainability of the associated activities and processes. In fact, currently, it is important to reduce the environmental impacts of the farming dynamics and raise production to deal with the increased demand for food worldwide. The livestock activities are particularly complex and call for adjusted plans and management decisions. The new technologies associated with the digital transition may bring relevant added value, namely to predict outputs. This chapter aims to suggest models and predictors to support the farmers and other stakeholders to better design policies and farm plans. Statistical information from the European Union databases was considered. The results found are useful tools to improve the performance of the European Union farms, particularly those specialised in livestock production.engAccuracyArtificial intelligenceCharacteristics of farms in the European Unionbook part10.1007/978-3-031-54608-2_3