Technological development makes it possible to simplify and accelerate decision-making processes by adequately processing and evaluating large volumes of data. Sub-data obtained from large data sets have a very important practical role in asset valuation, forecasting and valuing delineated or difficult-to-map areas, or in the context of por...tfolio management. Land valuation is a separate segment within asset valuation and it requires a specific methodological approach on behalf of evaluators. In this study, the authors compared the transaction data of arable land and the value of other land use categories. Based on empirical assessments, the authors developed proposals for the fast and cost-effective determination of the value of land use categories other than arable land - mainly meadows and pastures.
The homogeneity of a study area of 20x20 m used for beetroot production in North-West Hungary was analysed with geo-statistical methods on the basis of measured plant and soil parameters. Based on variogram calculations (Equation 1 and 2), the yield surface showed homogeneity in North-South direction. Considering the results, decrease of sampli...ng distance to 17 m can be suggested. The direction of the variability of yield (Figure 1) could be modelled with a direction variogram based on analysis of the variogram surface. In the study, developed methodological processes are presented for the analysis of spatial relationship between measured production and soil parameters. 5 spatial evaluation methods for yield surface were compared (Table 1). On the basis of the analysed methods, it can be stated that different methods (LP, RBF) should be used when the reasons for locally extreme yields are in focus than in case when the yield surface of the whole area is estimated (IDW, GP). Using adequate parameters the kriging method is applicable for both functions. Similarly to the results of an ordinary Pearson correlation analysis, spatial correlation analysis was shown using soil pH and Cu concentration data. The results of cross variogram analysis (Equation 2) and the North-South direction of the variogram surface showed negative correlation (Figure 3). Based on simulation calculations, decrease of 30% in sampling points resulted in increase of 12% in error for the total sample number considering Cu concentration. The method provides a tool to decrease the cost of sampling and sample analyses of spatially correlating features, and to increase the reliability of spatial estimation using a better sampling strategy with the same sample number.
Fulfilment of the increasing quality requirements of sugar beet production can be analysed with sampling of plants and soil at the cultivated area. Analyses of the spatial characteristics of samples require exact geodetic positioning. This is applied in practice using GPS in precision agriculture. The examinations were made in a sample area loc...ated in north-western Hungary with sugar beet test plant. According to the traditional sample taking procedure N=60 samples were taken in regular 20 x 20 m grid, where besides the plant micro and macro elements, the sugar industrial quality parameters (Equations 1-2) and the agro-chemical parameters of soils were analysed. Till now, to gain values of mean, weighted mean and standard variance values, geometric analogues used in geography were adapted, which correspond to the mean centre (Equation 3), the spatially weighted mean centre (Equation 4), the standard distance (Equation 5), and the standard distance circle values. Robust spatial statistical values provide abstractions, which can be visually estimated immediately, and applied to analyse several parameters in parallel or in time series (Figure 1). This interpretation technique considers the spatial position of each point to another individually (distance and direction), and the value of the plant and soil parameters. Mapping the sample area in GIS environment, the coordinates of the spatially weighted mean centre values of the measured plant and soil parameters correlated to the mean centre values showed a northwest direction. Exceptions were the total salt and calcium-carbonate contents, and the molybdenum concentration of the soil samples (Table 1). As a new visual analysis, the spatially weighted mean centre values of the parameters as eigenvectors were projected to the mean centre values as origin. To characterize the production yield, the raw and digested sugar contents of the sample area, the absolute rotation angles of the generated vectors were determined, which indicate numerically the inhomogenity of the area (Figure 2). The generated spatial analogues are applicable to characterise visually and quantitatively the spatial positions of sampling points and the measured parameters in a quick way. However, their disadvantage is that they do not provide information on the tightness and direction of the spatial correlation similarly to the original statistical parameters.