03670nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001700167072002300184072001600207100010100223245016600324250001800490264006900508300006400577336002600641337002600667338003600693347002400729490006600753505079100819520109201610650002202702650002702724650003502751650002102786650002902807650002202836650001602858650002802874650004202902710003402944773002602978776003603004776003603040830006603076856004603142978-3-031-54608-2DE-He21320251030111436.0cr nn 008mamaa240221s2024    sz |    s    |||| 0|eng d  a97830315460827 a10.1007/978-3-031-54608-22doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aMartinho, Vitor Joao Pereira Domingues.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aMachine Learning Approaches for Evaluating Statistical Information in the Agricultural Sectorh[electronic resource] /cby Vitor Joao Pereira Domingues Martinho.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXI, 135 p. 27 illus., 26 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Applied Sciences and Technology,x2191-53180 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.  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. 0aMachine learning. 0aProduction management. 0aAgriculturexEconomic aspects. 0aPower resources. 0aEnvironmental economics.14aMachine Learning.24aProduction.24aAgricultural Economics.24aResource and Environmental Economics.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303154607508iPrinted edition:z9783031546099 0aSpringerBriefs in Applied Sciences and Technology,x2191-531840uhttps://doi.org/10.1007/978-3-031-54608-2