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- W2904661501 abstract ": Implementing a successful soil management program requires knowledge of the amount of spatial features and their impact. Soil properties are one of the most important spatial variables affecting yield variability at farm scale. Therefore, in this research, the correlation between 10 soil variables was evaluated in terms of physical and chemical properties using principale component analysis (PCA) in croplands of Namin- City. For this purpose, 20 profiles (0 to 100 cm) were diged and discripted in a regular grid network of 500 meters in an area of 470 ha of agricultural lands at the Namin area of Ardabil city. Soil characteristics including soil texture, acidity, electrical conductivity, calcium carbonate (lime), organic matter, saturated and initial moisture content and coarse fragments were measured. After testing the normality of data by Kolmogorov-Smirnov test, based on the results and after Varimax rotation, four PCs in the first layer (0-30 cm), three PCs in second layer (30-60 cm) and third layer (60-90 cm), which had eigenvalues 1 or more accounted for 80.7, 77.3, 73.3 percent of variability in measured soil properties. Acording to the results of GS+, spherical and gaussian models were distinguished as the best variography models. The range of influence for most properties was 5110 meters. The spatial dependency percentage of most parameteres were medium and strong. The results of this research can be used to improve programs for sustainable management of soil in this area,and also to preparation of soil maps related to soil properties." @default.
- W2904661501 created "2018-12-22" @default.
- W2904661501 creator A5045778271 @default.
- W2904661501 date "2017-01-01" @default.
- W2904661501 modified "2023-09-27" @default.
- W2904661501 title "Prediction of Vertical Calcium Carbonate Distribution Using Geostatistics and Principal Companent Analysis at Namin Region" @default.
- W2904661501 hasPublicationYear "2017" @default.
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