The aim of this study was to investigate the efficiency of ISSR, SSR, and SAMPL marker systems in detecting genetic polymorphism among 30 winter rye inbred lines and to compare the results of cluster analysis performed on data from these marker systems using different statistical methods and coefficients. Each marker system was able to discriminate among the materials analyzed with the lowest value of average genetic similarity (GS) obtained with ISSR markers (0.2888) and the highest with SAMPLs (0.5381). EST-derived SSRs turned out to be less efficient in detecting genetic diversity than those from genomic libraries (average GS values 0.3814 and 0.3221, respectively). The average GS value for combined SSR data was 0.3569. The lack of correlations between similarity and cophenetic matrices obtained with various methods systems suggests that different marker systems should be used simultaneously for a genetic diversity study to exploit as many sources of polymorphisms as possible.
The development of energy production from renewable sources includes the production of energy from photovoltaic installations by prosumers. In Europe, RES development is driven by political goals and requires subsidies during the deployment period, at least as long as the cost of renewable electricity does not reaches grid parity. The study attempts to determine the importance of factors in the development of energy production by prosumers from PV installations in Polish regions. In 2019, the ‘Moj Prad’ program was introduced, applying subsidies to investment costs and the settlement of energy production in the net-metering system. Almost 900 thousand prosumer PV installations were built by the end of 2021, with a total capacity of 5.9 GW. Solar energy share grew from 0.1 to 2.1%. Spatial econometrics models were use in research to determine factors of prosumer PV systems development in Poland (at NUTS-2). Spatial regimes were found in the studied regions, as indicated by a positive autocorrelation (0.75). Considering the pseudo-R-square co-efficient, we can conclude that the spatial error, i.e., factors not included in the GNS model, constitutes approximately 10%. The economic variables included in the Mansky model, i.e., level of salaries and GDP, explain 90% of the variability of installed PV capacity (Nagelkerke pseudo-R-squared value is 0.906). The level of development of prosumer photovoltaic installations (in W per capita) in regions depends primarily on economic factors represented by the level of salaries in a given region. With the increase in salaries by one unit, we also have an increase in installed power capacity in watts per person by 3.52. Surprisingly, the region’s overall wealth did not matter, as the relative number of installations in regions with lower GDP was higher than in others. One can explain that the individual income of households is more important for increasing the number of prosumer installations than the income of the regional economy. The increase in the number of installations in one region contributed to the subsequent increase in their number in neighboring regions.
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