جستجوی مقالات مرتبط با کلیدواژه "spatial structure" در نشریات گروه "آب و خاک"

Soil organic matter (SOM) is a key index in evaluation of the soil degradation and soil carbon sequestration. Therefore, determination of the SOM spatial patterns is essential for developing the suitable strategies of soil and ecosystem management. Geostatistical methods such as kriging have been widely employed to investigate the spatial pattern of different soil properties like SOM. The quality of the spatial maps is significantly influenced by the statistical properties of the raw data. Thus, identification and elimination of spatial outlier data, which has rarely been considered in previous works, is an important step in preparation of accurate and suitable maps of soil organic matter. The aim of the present study was to evaluate the effect of spatial outliers on the spatial pattern of soil organic carbon at the Rozeh-Chay watershed, Urmia, west Azarbayjan province. A total of 89 surface soil samples (0-10 cm) were collected based on the stratified random sampling scheme. After the normalization of the raw SOM data, global outliers were eliminated by box plot method. Spatial outliers were identified by the global and local Moran’s I indices. Spatial correlogram of the global Moran’s I showed the highest spatial autocorrelation at distance of 900 m, which was used as a distance band for preparation of spatial clusters map by local Moran’s I index. Cluster map of the local Moran’s I index showed four spatial outliers which were eliminated to assess their effects on the accuracy of kriging map of SOM. With the elimination of the spatial outliers, the MAE and RMSE of the SOM map were decreased from 0.97 and 1.31 to 0.85 and 1.12, respectively. Therefore, the accuracy of the kriging map increased by 13.5 percent. Generally, it can be concluded that the combination of Moran’s I index and kriging method improves the efficiency of organic matter map in the study area.

This study was carried out as a complete block design with three treatments including, full irrigation, partial root-zone drying in two levels at 75% and 55% of full irrigation to evaluate three Geostatistical methods including Kriging, CoKriging and Weighting Moving Average for interpolating the values of soil electrical conductivity, as the best index for soil salinity. Soil electrical conductivity (EC) was measured using Decagon 5TE TDR sensors. Then zone classification maps of the EC were prepared using a linear relation between EC and soil water content values measured by IDRG SMS T2 sensors. Results of the variograms analysis showed that the effective range of variograms was higher than the maximum space between observed points in the all treatments. Comparing the observed and Geostatistical estimated values of EC based on the evaluation criteria including root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) and index of agreement (d) showed that there was a good agreement between observed and estimated EC values. Duncan test at 5% probability level showed that values of the evaluation criteria for the Kriging method were significantly lower than those for the other methods at the all treatments, and CoKriging method ranked as the second method (p≤0.05). Also there was no significant difference between the Cokriging methods by simultaneous consideration of the electricul conductivity with one of the temperature or soil moisture covariates as the model input.