Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector

2024
ISBN: 978-3-031-54607-5
Verlag: Springer Nature Switzerland

Buch, Englisch, 135 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 236 g

Reihe: SpringerBriefs in Applied Sciences and Technology

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Martinho, Vitor Joao Pereira Domingues

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Produktionsmanagement, Qualitätskontrolle
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor Agrarökonomie, Ernährungswirtschaft
• Wirtschaftswissenschaften Volkswirtschaftslehre Umweltökonomie

Inhaltsverzeichnis

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.

Über Autor(innen)

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.