Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector

Vitor Joao Pereira Domingues Martinho

ISBN 10: 3031546075 / ISBN 13: 9783031546075

Editore: Springer Nature Switzerland, Springer International Publishing Feb 2024, 2024

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 148 pp. Englisch.

Codice articolo 9783031546075

Dati bibliografici

• Titolo: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
• Editore: Springer Nature Switzerland, Springer International Publishing Feb 2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031546075
• ISBN 13: 9783031546075
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 9 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 9 millimetri di profondità
• Peso: 236 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Vítor João Pereira Domingues Martinho is Coordinator Professor with Habilitation at the Polytechnic Institute of Viseu, Portugal. He received his Ph.D. in Economics from the University of Coimbra, Portugal. With a profound academic background, he has also served as Invited Professor at the University of Trás-os-Montes and Alto Douro (Portugal) and currently holds the position of Invited Professor at the Polytechnic Institute of Coimbra (Portugal). From 2006 to 2012, Professor Martinho served as President of the Scientific Council, President of the Directive Council, and President of the Agricultural School belonging to the Polytechnic Institute of Viseu, Portugal. Additionally, he was President of the Direction of the Association of Forest Producers of Viseu, Portugal. Martinho has published several technical and scientific papers and serves as Referee for various scientific and technical journals. He actively participates in the evaluation of national and international projects.