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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
Title
Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector [electronic resource] / by Vitor Joao Pereira Domingues Martinho.
ISBN
9783031546082
Edition
1st ed. 2024.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Physical Description
1 online resource (XI, 135 p.) 27 illus., 26 illus. in color.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
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 information from the Farm Accountancy Data Network (FADN). Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software. The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector.
Variant and related titles
Springer ENIN.
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Printed edition:
Format
Books / Online
Language
English
Added to Catalog
March 01, 2024
Series
SpringerBriefs in Applied Sciences and Technology,
Contents
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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