Machine Learning Approaches for Evaluating Statistical Information in the Agricultural 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 i...
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| Κύριος συγγραφέας: | |
|---|---|
| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
|
| Έκδοση: | 1st ed. 2024. |
| Σειρά: | SpringerBriefs in Applied Sciences and Technology,
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| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.1007/978-3-031-54608-2 |
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| 100 | 1 | |a Martinho, Vitor Joao Pereira Domingues. |e author. |0 (orcid)0000-0001-9967-7940 |1 https://orcid.org/0000-0001-9967-7940 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector |h [electronic resource] / |c by Vitor Joao Pereira Domingues Martinho. |
| 250 | |a 1st ed. 2024. | ||
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| 300 | |a 1 online resource (XI, 135 p. 27 illus.). |b 26 illus. in color. | ||
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| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Production management. | |
| 650 | 0 | |a Agriculture |x Economic aspects. | |
| 650 | 0 | |a Power resources. | |
| 650 | 0 | |a Environmental economics. | |
| 650 | 1 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Production. |
| 650 | 2 | 4 | |a Agricultural Economics. |
| 650 | 2 | 4 | |a Resource and Environmental Economics. |
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| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||