Coat colour variation is determined by many genes, one of which is the melanocortin receptor type 1 (MC1R) gene. In this study, we examined the whole coding sequence of this gene in four species belonging to the Canidae family (dog, red fox, arctic fox and Chinese raccoon dog). Although the comparative analysis of the obtained nucleotide sequences revealed a high conservation, which varied between 97.9 and 99.1%, we altogether identified 22 SNPs (10 in dogs, six in farmed red foxes, two in wild red foxes, three in arctic foxes and one in Chinese raccoon dog). Among them, seven appeared to be novel: one silent in the dog, three missense and one silent in the red fox, one in the 3'-flanking region in the arctic fox and one silent in the Chinese raccoon dog. In dogs and red foxes, the SNPs segregated as 10 and four haplotypes, respectively. Taking into consideration the published reports and results of this study, the highest number of missense polymorphisms was until now found in the dog (9) and red fox (7).

(1) Wood is a widely available raw material on the market, which satisfies the industrial demand and which is used both as a source of biomass for the wood materials industry in a broad sense and for energy-supplying purposes. These areas prove the functional values and the possibilities of the directional use of low-quality wood products. One of the factors influencing the overall balance of the wood biomass is the form and quality of the wood material that cannot be further processed mechanically. This study was conducted to determine the influence of this material by presenting the dependence between the level of wood biomass resources and the conditions of wood acquisition and processing in Poland. (2) The basic directions of biomass acquisition were verified in correlation with the level of its acquisition from forest areas and with the form of by-products generated by sawmills. The research was based on the data from reference publications and analysis of the processing of raw wood in sawmills. The research was conducted on raw hardwood and softwood from coniferous and deciduous forests in Poland. (3) The research confirmed the influence of the processing method on the form and share of by-products. It also showed that the form of the wood biomass obtained was influenced by the region of Poland. (4) The research showed that the regionalisation and wood processing directions were correlated with the structure of the wood biomass acquired.

Currently, woodchips and logging residues form the greatest share of biomass fuels used to generate heat in combined heat and power plants. They are supplied from various regions of the EU. The calorific values of the wood species used as biomass may vary significantly depending on the moisture and composition of the fuel, harvest seasonality, location, and other factors. This article presents the main resources of forest biomass and its characteristic features, as well as the calorific value of woodchips depending on the moisture content. Our research is based on the source data of forest resources from the State Forests National Forest Holding (PGLLP) in Poland. The research conducted by the main forestry enterprise in Poland covered a period of four years. The data on the harvesting of woodchips and logging residues converted into the calorific values of biomass were based on our research and a review of reference publications. Standard methods were used in the research, which included an analysis of the species and assortment structure of the forest biomass of energetic significance that was available for use. The research showed that the moisture content of the woodchips and lump wood was about 30%. The average annual energy value of the wood in the total area of forest resources was 0.07 GJ/ha, whereas the highest value was 0.14 GJ/ha. Between 2018 and 2021, the average energy resources of forest biomass in Poland increased from 351.8 TJ to 498.4 TJ.

The aim of the study was to test the applicability of forecasting in the analysis of the variability of prices and supply of wood in Poland. It relies on the autoregressive integrated model (ARIMA) that takes into account the level of cyclic, seasonal, and irregular fluctuations and the long-term trend as tools for the assessment of the predictions of the prices of selected medium-sized wood assortments. Elements of the time series were determined taking into account the cyclical character of the quarterly distribution. The data included quarterly information about the supply (amount) and prices (value) of wood sold by state forests in the years 2018–2022. The analysis was conducted for the most popular assortments: logging slash (M2, M2ZE), firewood S4, and medium-sized wood S2AP. In the period studied (years 2018–2022), the average rate of price variation was widely scattered. The average rate of price variation for the M2ZE assortment amounted to 7%. The average rate for M2 assortment was 1%, while the medium-sized S2AP assortment displayed the greatest variation of 99%. This means that between 2018 and the present, the price increased by nearly 100%. No major fluctuations were observed for the S4 assortment and its average rate of variation amounted to 0%. The analysis found seasonal variation was observed only for S4 firewood, the price of which went up each year in October, November, and December. For this reason, the forecast was made with the seasonal autoregressive integrated moving average (SARIMA) version of the model. It is difficult to forecast the price of wood due to variations in the market and the impact of global factors related to fluctuations in supply.

This paper presents the application of prediction in the analysis of market price volatility in Polish conditions of wood processing by-products in the form of biomass. The ARIMA model, which takes into account cyclical, seasonal, irregular fluctuations of historical data on the basis of which the forecast and long-term trends of selected wood products were made, was used in predicting prices. Comparisons were made between the ARIMA prediction method and the multiplicative Winters–Holt model. During the period studied (2017–2022), the changes in the market price of biomass were characterized by a wide spread of values. On average, the price of these products increased from 2017 to the end of 2022 by 125%. The price prediction analysis showed seasonal fluctuations in the case of wood chips. The uncertainty in price prediction is due to changes in supply resulting from the influence of global factors. The Diebold–Mariano test of matching accuracy confirms that the price prediction of the analyzed by-product sorts using the ARIMA and WH models is possible. The conclusion reached by comparing these two methods is that each can be used under certain market conditions of certain assortments. In the case of a stable wood product, the choice of the ARIMA model should be resolved, while in the case of price volatile products, WH will be a better choice. The difference between the predicted and actual price with ARIMA ranged from 2.4% to 11.6% and for WH from 3.7% to 29.8%.