%0 Journal Article
	%A M. Rüstü Karaman and  Tekin Susam and  Fatih Er and  Servet Yaprak and  Osman Karkacıer
	%D 2009
	%J International Journal of Geological and Environmental Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 32, 2009
	%T Simulation of Organic Matter Variability on a Sugarbeet Field Using the Computer Based Geostatistical Methods
	%U https://publications.waset.org/pdf/12640
	%V 32
	%X Computer based geostatistical methods can offer effective data analysis possibilities for agricultural areas by using
vectorial data and their objective informations. These methods will help to detect the spatial changes on different locations of the large
agricultural lands, which will lead to effective fertilization for optimal yield with reduced environmental pollution. In this study, topsoil (0-20 cm) and subsoil (20-40 cm) samples were taken from a
sugar beet field by 20 x 20 m grids. Plant samples were also collected
from the same plots. Some physical and chemical analyses for these
samples were made by routine methods. According to derived variation coefficients, topsoil organic matter (OM) distribution was more than subsoil OM distribution. The highest C.V. value of
17.79% was found for topsoil OM. The data were analyzed
comparatively according to kriging methods which are also used
widely in geostatistic. Several interpolation methods (Ordinary,Simple and Universal) and semivariogram models (Spherical,
Exponential and Gaussian) were tested in order to choose the suitable
methods. Average standard deviations of values estimated by simple
kriging interpolation method were less than average standard
deviations (topsoil OM ± 0.48, N ± 0.37, subsoil OM ± 0.18) of measured values. The most suitable interpolation method was simple
kriging method and exponantial semivariogram model for topsoil,
whereas the best optimal interpolation method was simple kriging
method and spherical semivariogram model for subsoil. The results
also showed that these computer based geostatistical methods should
be tested and calibrated for different experimental conditions and semivariogram models.
	%P 254 - 257