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

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

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

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

Determinants of Regional Raw Milk Prices in Russia

Drivers of regional milk price differences across Russian regions are difficult to determine due to limited data availability and restrictions on data collection. In this study, official data from Russian regions for the period from 2013 to 2018 was analysed based on 18 predictor variables in order to explain the regional raw milk price. Due to various data-based restrictions, the use of conventional panel regression models was limited and the analysis was therefore performed based on a Random Forest (RF) machine learning algorithm. Model training and hyperparameter optimization was performed on the training data set with time folds cross-validation. The findings of the study showed that the RF algorithm has a good predictive performance in the test data set even with the default RF values. Finally, the RF variable importance showed that income, gross regional product, livestock density, and milk yield are the four most important variables for explaining the variation in regional milk prices

Housing market of Krasnodar region: current conditions and development trends

The paper substantiates socio-economic importance of housing construction for the development of the Krasnodar Region, considers the pace of housing development, identifies the determinants of the housing market development in the region. The study focuses at the analyzing of the housing construction market structure and the determining of the prospects for its development in the Krasnodar Region based on the macroeconomic indicators of the socio-economic development of the region taken from official data sources. As a method for studying the determinants of the development of the housing construction market in the region, the observation and collection of factors that impact the socioeconomic characteristics of the region were used, which made it possible to identify the main groups of growth factors and containments for development of the housing construction market. For analyzing the structure of the housing construction market, measuring the rate of housing development, the state of mortgage lending, housing market price dynamics, estimating the housing stock by apartment space, distributing apartments depending on the type, technology and cost of construction, a combination of comparative and economic-statistical methods of research were used. In determining the rating of developers of the Krasnodar Region, the method of content analysis of electronic resources containing information about the most construction companies, as well as their performance indicators, was applied. The results obtained in the process of studying the structure of the housing construction market in the region can become an objective basis for determining housing development trends in the Krasnodar Region

Willow wood production on radionuclide polluted areas

ABSTRACT: One of the key environmental problems in Belarus is effective use of agricultural lands contaminated by radionuclide due to the Chernobyl disaster. The alternative method to traditional agricultural crops is fast growing willow cultivation. It is possible to use biomass of willow as renewable energy source. The goal of our investigation was the estimation of environmental aspects of willow wood production on polluted areas. The field study experiments (2007-2010) were conducted at Krichev district of Mogilev region in eastern Belarus. This region characterized by high level of Cs-137 contamination as well as high level of heavy metals pollution. In the first stage of experiments, the concentration of cesium-137 in different parts of willow biomass had been measured and transfer factor calculated. The measuring had been done for leaves, roots, and wood. To control cesium-137 accumulation in willow biomass we apply different types (nitrogen N, phosphorus P and potassium K) and dose of fertilizer. The experiments show that potassium mineral fertilizer is the key factor for radionuclide accumulation control. The optimal dose of potassium is 90 kg per hectare. On the base of experimental results the model of cesium-137 accumulation in the wood for a 21 year has been developed. In accordance with calculation to the end of willow cultivation (21 year) concentration of cesium-137 in wood will not be higher than permitted even with the level of cesium-137 contamination in the soil 1480 kBq/m2 (maximum 140 kqB/m2 with permitted level for firewood is 740 Bq/kg.). The concentration of cesium-137 in the roots increases gradually and get maximum in 21 year (3000 kqB/m2). Our results confirm that in the sum about 0.8 million hectares of radionuclide polluted arable lands partly excluded from agricultural practice in Belarus could be used for willow biomass production