Spatially Discrete GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.)32-37Views:70
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 located 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.
Conceptional Model of Regional Agricultural Water Management System199-209Views:87
Our study focuses on the water management improvement of the Hajdúsági-löszhát (loess ridge). The Hajdúsági-löszhát (loess ridge) is an intensive agricultural area. At the same time, the problem of increasing water demand is still not solved, so towards of safety production irrigation should be improved. To realise this should be known not even agricultural water demands but industrial and urban ones as well, thus a complex water management system is required to be worked out.
In the first part of the research, the water demand in the area is mapped, then a conceptional model of the Hajdúsági-löszhát’s (loess ridge’s) water management system is created. After collecting data the water management scenario is summarized in a real time model splitted into five periods.
During the research, the instruments of spatial informatics (GIS) are used to get acquainted with the variation of the hydrological parameters in space and time. To understand and simulate the different decision making processes and to choose the right decision alternative, a decision support system is created with the use of spatial informatics data.
In addition, considering the potentially right decision alternative, irrigation will be started in practice, an effect and after-effect inquiry will be made, and the results will be analysed, evaluated and summarized. Finally, a suggestion to the most adequate irrigation technology will be made.
Assay of runoff conditions using a Digital Elevation Model124-129Views:82
We can get information about water conditions of plane areas by analyzing their relief. By using the Digital Elevation Model, we can get proper information about runoff conditions in the terrain surface, which is the basis for ponding analysis. In our study, a Digital Elevation Model of the sample plot in Hajdúsági-löszhát (loess ridge) was created that makes possible the determination of runoff conditions of the surface. Convergence and divergence of runoff direction showed the rate of ponding and the location of possible inland-water spots. By using this model, concrete location of those areas that tend to be overmoistured could be determined. Knowledge of this phenomenon can be an effective instrument for farmers in optimizing crop growing, and additionally in performing water management interventions in proper time and space.
Spatially Continuous GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.)56-61Views:96
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 sampling 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.
Optimization of Density of Sugar Beet (Beta vulgaris L.) Production Quotas by Pointwise Geostatistic Methods46-50Views:78
The regional distribution of the Hungarian sugar beet production quotas was developed by the conventional concurrency relationships. In our research we analyzed 320 sectors of 9 factories with geostatistic methods in a GIS environment. The applied researches of spatial mean, spatial deviation, deviational ellipse have been introduced by us in this speciality. We used two different methods in our optimization inquiries, where the spatial segment of the standard deviational ellipse was based on a more robust preliminary data processing solution, and this is why it is a less parametricable method. The inquiry of the spatial buffer zones in production sectors ensures an obvious optimization possibility. We considered the supply route distances in both cases as a modeling boundary condition. Our results show that we introduced an effective decision making method to the occurent replanning of the production sectors with the pointwise density inquiries and the geometric analogy that was fitted to it.