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001 978-3-031-54608-2
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020 _a9783031546082
_9978-3-031-54608-2
024 7 _a10.1007/978-3-031-54608-2
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
100 1 _aMartinho, Vitor Joao Pereira Domingues.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_971481
245 1 0 _aMachine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
_h[electronic resource] /
_cby Vitor Joao Pereira Domingues Martinho.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXI, 135 p. 27 illus., 26 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
505 0 _aChapter 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 _aThis 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.
650 0 _aMachine learning.
_955059
650 0 _aProduction management.
_967141
650 0 _aAgriculture
_xEconomic aspects.
650 0 _aPower resources.
_962154
650 0 _aEnvironmental economics.
_957520
650 1 4 _aMachine Learning.
_955059
650 2 4 _aProduction.
_967142
650 2 4 _aAgricultural Economics.
650 2 4 _aResource and Environmental Economics.
_967450
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031546075
776 0 8 _iPrinted edition:
_z9783031546099
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
_965846
856 4 0 _uhttps://doi.org/10.1007/978-3-031-54608-2
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c57781
_d57781