Vitor Joao Pereira Domingues Martinho
Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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- More about Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
The book presents machine learning approaches to identify important predictors of crucial variables in the agricultural sector, focusing on the European Union and the Farm Accountancy Data Network (FADN). It uses IBM SPSS Modeler procedures and models to support policy and management planning.
Format: Paperback / softback
Length: 135 pages
Publication date: 22 February 2024
Publisher: Springer International Publishing AG
Machine learning approaches are presented in this book to identify the most important predictors of crucial variables for managing production units and designing agriculture policies. The focus is on the agricultural sector in the European Union, utilizing statistical information from the Farm Accountancy Data Network (FADN). Statistical databases provide a wealth of information for various indicators, and the primary task is to identify the most important predictors. The book offers methods for identifying the most relevant variables that support the development of adjusted farming policies and management plans. These subjects are of significant interest to students, public institutions, and farmers. To achieve these objectives, the book employs IBM SPSS Modeler procedures and the corresponding models suggested by the software. The book is read by students in production engineering, economics, and agricultural studies, as well as public bodies and managers in the farming sector.
Dimension: 235 x 155 (mm)
ISBN-13: 9783031546075
Edition number: 1st ed. 2024
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