04050nam a22005655i 4500 978-3-031-54608-2 DE-He213 20251030111436.0 cr nn 008mamaa 240221s2024 sz | s |||| 0|eng d 9783031546082 978-3-031-54608-2 10.1007/978-3-031-54608-2 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Martinho, Vitor Joao Pereira Domingues. author. aut http://id.loc.gov/vocabulary/relators/aut 71481 Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector [electronic resource] / by Vitor Joao Pereira Domingues Martinho. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XI, 135 p. 27 illus., 26 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Applied Sciences and Technology, 2191-5318 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. 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. Machine learning. 55059 Production management. 67141 Agriculture Economic aspects. Power resources. 62154 Environmental economics. 57520 Machine Learning. 55059 Production. 67142 Agricultural Economics. Resource and Environmental Economics. 67450 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031546075 Printed edition: 9783031546099 SpringerBriefs in Applied Sciences and Technology, 2191-5318 65846 https://doi.org/10.1007/978-3-031-54608-2 ZDB-2-SMA ZDB-2-SXMS 57781 57781