| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 4.86 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
The control of the fertiliser costs in the agricultural sector is fundamental for the profitability of the farms and to mitigate environmental impacts. Indeed, the fertiliser costs have, at least, two components, one related to the fertiliser prices and the other associated with the amount of fertiliser applied in the farming processes. The fertiliser application in agricultural activities has a relevant impact on soil health and water quality. The efficiency of the processes linked with the fertiliser application in the farms is crucial to avoid disruptions in the sustainable development required for agriculture worldwide. In these frameworks, it is important to bring more insights about the predictors of the fertiliser costs in the European Union farms. Taking into account these motivations, this chapter considered artificial intelligence approaches and data from the European Union databases to identify the most adjusted models. The findings of this research contribute to the understanding of the most important variables to promote more sustainability in the European Union farming sector.
Description
Keywords
European Union agriculture Machine learning models Farming indicators
Citation
Martinho, V.J.P.D. (2024). The Most Important Predictors of Fertiliser Costs. 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_5
Publisher
Springer, Cham