Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector - Vitor Joao Pereira Domingues Martinho

Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector

Buch | Softcover
XI, 135 Seiten
2024 | 2024
Springer International Publishing (Verlag)
978-3-031-54607-5 (ISBN)
48,14 inkl. MwSt

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.

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.



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.

Erscheinungsdatum
Reihe/Serie SpringerBriefs in Applied Sciences and Technology
Zusatzinfo XI, 135 p. 27 illus., 26 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 237 g
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Agricultural Economics and Policies • Common Agricultural Policy • Econometric Methodologies • European Union Farms • Farm Accountancy Data Network • machine learning approaches
ISBN-10 3-031-54607-5 / 3031546075
ISBN-13 978-3-031-54607-5 / 9783031546075
Zustand Neuware
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
von absurd bis tödlich: Die Tücken der künstlichen Intelligenz

von Katharina Zweig

Buch | Softcover (2023)
Heyne (Verlag)
20,00
dem Menschen überlegen – wie KI uns rettet und bedroht

von Manfred Spitzer

Buch | Hardcover (2023)
Droemer (Verlag)
24,00