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EN
Barley is a plant in Europe that occupies an important place in the structure of cultivated plants. The main use of barley is for brewing beer. Even in Kosovo, the main part of barley production is used in the beer industry. The beer industry in Kosovo produces beer prepared from barley; it is liked by the consumer, not only in Kosovo but also in Albania. Our brewing industry mainly uses locally grown barley. Our farmers have planted the traditional cultivars of barley but, recently and in the future, new cultivars have been introduced and introduced, preferring those that give high yields but also with good chemical and technological indicators. Our study aimed to determine the influence of climatic (temperature and humidity) and soil factors on the yield and quality of barley production in both areas of its cultivation. The presence of mineral elements in the soil are necessary for barley such as: Calcium, Iron, Magnesium, Phosphorus, Potassium, Zinc and climatic factors for the production of beer barley.
2
Content available Using of wind erosion equation in GIS
EN
The vulnerability of soil by wind erosion using the wind erosion equation (WEQ) in geographic information systems (GIS) was demonstrated on the cadastral area of Přísnotice, southeast of the Czech Republic. Raster layers were created for input factors of WEQ and potential soil loss by wind was calculated using the Raster Calculator. Subsequently, map outputs showing the potential vulnerability of soil to wind erosion were made. In the first variant, where the protective effect of the barriers was not taken into account, the wind erosion intensity reached 47.6 t.ha-1.year-1. In the second variant, where the protective effect of the barrier was assumed, the loss of soil was lower, the maximum value was 4.7 t.ha-1.year-1. The soil loss limit was exceeded, even in a situation where a relatively high protective barrier effect has been proposed. The advantage of determining of wind erosion vulnerability using ArcGIS is that it is possible to identify particular parts of soil blocks from the map outputs, which are the most vulnerable. Similarly, it is possible to design a windbreak network and simulate its protective effect using ArcGIS.
EN
Climatic factors are the most important of all the factors that influence the hydrograph shape of a river; this is because they are dynamic in space and time. The presence of hydrographs with a similar shape in long-term observation series of water runoff indicates that from time to time in the catchment of a river the same conditions of water runoff formation repeat. This is due to the cyclicity of climatic and – as a consequence – hydrological processes. The classification of hydrographs based on the similarity of their shapes on the Desna River (observation period 18852010) was carried out using the geometrical similarity criterion (ρ) and the average Euclidean distance between values of hydrograph pairs (η). 41 classes of hydrographs, which included from 1 to 6 hydrographs, were obtained. Among them were determined 12 classes with low probability, 9 classes with medium probability and 20 classes with high probability. An analysis of the conditions of hydrograph formation for each of the identified classes was performed for the following factors: the sums of daily air temperatures and precipitation during the period of positive air temperature (from July) and the period of negative air temperatures, and also during the period of temperature transition through 0ºC up to the peak discharges, index of depth of soil freezing, index of snow water equivalent, index of soil moisture content, and index of snowmelt. In general, hydrographs which are included in one class have similar climatic conditions to those of water runoff formation. However, in some cases, a combination of different values and dynamic factors can lead to the formation of similar hydrograph shapes.
4
Content available remote Wybór rozkładu oddziaływań klimatycznych z wykorzystaniem metody Bayes’a
PL
Wybór typu zmiennej losowej na podstawie wyników badań polega najczęściej na aproksymacji parametrów przyjętego rozkładu. Jednak dostępne wyniki badań lub obserwacji często nie pozwalają na dostatecznie precyzyjne dopasowanie jednego z powszechnie stosowanych typów rozkładu zmiennej losowej. W artykule przedstawiono metodę opartą na wnioskowaniu Bayesa, która umożliwia oszacowanie miary dopasowania i optymalnego wyboru jednego z typowych rozkładów zmiennej losowej oraz wyznaczenie dystrybuanty liniowej kombinacji testowanych rozkładów. Zastosowanie przedstawionej metody zilustrowano na przykładzie oceny i wyboru dystrybuanty rozkładu reprezentującego obciążenie śniegiem gruntu oraz wyznaczenia kombinacji testowanych rozkładów w celu określenia wartości charakterystycznej obciążenia śniegiem dla przyjętego okresu powrotu.
EN
Single distribution functions are usually selected based on a best-fit approach theorem but often available random data cannot be accurately described by any of the commonly used types of the random variables. The paper presents a method based on Bayesian approach which solves problems of selecting the single distribution function and combining of probabilities contending different probability functions. The method is illustrated on the selection of single distribution function and application of Bayesian method in combining these functions to determine the characteristic value of snow load for an assumed return period.
EN
Comparison of climatically controlled phases of higher rainfall and flood frequency with the anthropogenic phases of effective soil erosion documents various overlappings and superpositions. The wetter phase with high frequency of extreme events leads to the transformation of relief, well expressed in the ecotonal zones and in the high mountains. Increased human activity controls an accelerated soil erosion, overbank deposition and tendency to breading. The most distinct changes, leading to the passing of thresholds and transformation of slopes and valley floors are connected with the coincidence of wetter climate and high anthropogenic pressure. Those phases were recognized in Central Europe during the middle Neolithic period, the early phase of the Roman period, the 10th-11th century and the Little Ice Age.
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