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

Format:

Book

Author/Creator:

Martinho, Vítor João Pereira Domingues.

Series:

SpringerBriefs in Applied Sciences and Technology, 2191-5318

Language:

English

Subjects (All):

Machine learning.

Production management.

Agriculture--Economic aspects.

Agriculture.

Power resources.

Environmental economics.

Machine Learning.

Production.

Agricultural Economics.

Resource and Environmental Economics.

Local Subjects:

Machine Learning.

Production.

Agricultural Economics.

Resource and Environmental Economics.

Physical Description:

1 online resource (138 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9783031546082

3031546083

OCLC:

1423282367