Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning

Svetlana Kresova and Sebastian Hess
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Svetlana Kresova: Department of Agricultural Markets, University of Hohenheim, 70599 Stuttgart, Germany

Agriculture, 2022, vol. 12, issue 7, 1-18

Abstract: In this study, official data from Russia’s regions for the period from 2015 to 2019 were analysed on the basis of 12 predictor variables in order to explain the regional raw milk price. Model training and hyperparameter optimisation were performed with a spatiotemporal cross-validation technique using the machine learning (ML) algorithm. The findings of the study showed that the RF algorithm had a good predictive performance Variable importance revealed that drinking milk production, income, livestock numbers and population density are the four most important determinants to explain the variation in regional raw milk prices in Russia.

Keywords: milk price; Russia; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:7:p:1006-:d:860509

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