Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R² was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

In the medieval period, beavers were widespread throughout Poland. In the 13th century, the number of animals began to decline. After the Second World War, it was assumed that Eurasian beavers were no longer present in Poland. In 1974, the programme “Active protection of European beavers in Poland” was launched. Reintroduction was a very important element of the active protection of beavers. According to an inventory carried out in 1977, there were estimated to be over 1000 individuals. Long-term reintroduction efforts by hunters and scientists have caused beavers to be present throughout Poland. In recent years, the beaver population has increased significantly. This situation has created conflicts between farmers, foresters and beavers. In Poland, the beaver is considered a partly protected species. This manuscript discusses the dynamics of the beaver population in Poland and needed actions for future sustainable management.

The forest cover in Poland reaches almost 30% of the country’s area. Polish forests are characterized by high biodiversity. Unfortunately, in recent years, the forests of Central Europe have been affected by climate change problems, in particular meteorological drought. In Poland, even those stands which consist of species that were widely recognized as drought tolerant and easily adaptable to environmental changes are beginning to die. The article presents engineering methods applicable to forest environment protection, largely developed at the University of Life Sciences in Poznań and implemented by the State Forests—National Forest Holding in Poland, to minimize the effects of drought. Among the issues raised are ways to protect forests against fires, modern technologies for fire road surface construction and small-scale water retention in forests. A comprehensive solution to problems related to progressive drought is a must. Scientists and foresters are observing the dying of large areas of stands and, at the same time, intensification of wildlife migration due to the search for new habitats as a consequence of the drought. Therefore, the issue of building animal crossings during the current dynamic expansion of the road network in Poland has also been presented in the paper. Another subject pointed to in the text is forest tourism. Forests provide opportunities for recreation and rest to society. However, the increasing tourist pressure in some regions may cause adverse environmental effects. Finally, the paper shows some examples of supporting forest environment protection using remote sensing techniques. Generally, the aim of the paper is to present experiences and comprehensive solutions implemented in Poland.

The Spearman rank correlation coefficient is a non-parametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of the relationship between two variables. It is a measure of a monotonic relationship that is used when the distribution of the data makes Pearson's correlation coefficient undesirable or misleading. The Spearman coefficient is not a measure of the linear relationship between two variables. It assesses how well an arbitrary monotonic function can describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. Unlike Pearson's product-moment (linear) correlation coefficient, it does not require the assumption that the relationship between variables is linear, nor does it require that the variables be measured on interval scales; it can be applied to variables measured at the ordinal level. The purpose of this study is to compare the values of Pearson's product-moment correlation coefficient (treating the data in a quantitative way) and Spearman's rank correlation coefficient (treating the same data in a somewhat "qualitative" way) and their statistical significance for six Pinus sylvestris L. traits (original – for Pearson's coefficient and ranked – for Spearman's coefficient) estimated from all observations, object means (for trees) and medians. The results show that the linear and rank correlation coefficients are consistent (as to direction and strength). In cases of divergence in the direction of correlation, the correlation coefficients were not statistically significant, which does not imply consistency in decision-making. Estimation of correlation coefficients based on medians is robust to outlier observations and factors that linear correlation is then very similar to rank correlation.