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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector

This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical i...

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Bibliographische Detailangaben
1. Verfasser: Martinho, Vitor Joao Pereira Domingues (VerfasserIn)
Körperschaft: SpringerLink (Online service)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Ausgabe:1st ed. 2024.
Schriftenreihe:SpringerBriefs in Applied Sciences and Technology,
Schlagworte:
Machine learning.
Production management.
Agriculture > Economic aspects.
Power resources.
Environmental economics.
Machine Learning.
Production.
Agricultural Economics.
Resource and Environmental Economics.
Online-Zugang:https://doi.org/10.1007/978-3-031-54608-2
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Inhaltsangabe:
  • Chapter 1. Predictive machine learning approaches to agricultural output
  • Chapter 2. Applying artificial intelligence to predict crops output
  • Chapter 3. Predictive machine learning models for livestock output
  • Chapter 4. Predicting the total costs of production factors on farms in the European Union
  • Chapter 5. The most important predictors of fertiliser costs
  • Chapter 6. Important indicators for predicting crop protection costs
  • Chapter 7. The most adjusted predictive models for energy costs
  • Chapter 8. Machine learning methodologies, wages paid and the most relevant predictors
  • Chapter 9. Predictors of interest paid in the European Union’s agricultural sector
  • Chapter 10. Predictive artificial intelligence approaches of labour use in the farming sector.

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