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گواهی نمایه سازی مقاله Identify effective auxiliary variables in cokriging method to estimate spatial variability of infiltration rate

عنوان مقاله: Identify effective auxiliary variables in cokriging method to estimate spatial variability of infiltration rate
شناسه (COI) مقاله: JR_AAJ-3-6_002
منتشر شده در ماهنامه پیشرفت های کشاورزی در سال ۱۳۹۳
مشخصات نویسندگان مقاله:

a Yekzaban - Department of Soil Science, Faculty of Agriculture, University of Guilan, Rasht, IRAN.
m Shabanpour - Department of Soil Science, Faculty of Agriculture, University of Guilan, Rasht, IRAN.
n Davatgar - Department of Soil and Water Research, Rice Research Institute of Iran (IRRI), Rasht, IRAN.

خلاصه مقاله:
Measurements of infiltration rate (IR) in the field are costly, time consuming, and relatively cumbersome. IR are sensitive to some soil properties, thus the cokriging with auxiliary variables can sometimes improve estimates for less density sampled primary variable. The objectives of this study were to determine the spatial relationships between IR and some soil properties affecting IR and to identify possibility of using cokriging method. Infiltration rate test were conducted using double ring infiltrometers until steady state.75 field measured IR were obtained at a nearly regular grid spacing of 10 m. The correlation coefficient between IR and OM and silt were comparatively good. Semivariogram and cross-semivariogram of these variables with moderate to strong spatial dependence were fitted into the spherical model. range spatial dependence above mentioned soil properties were generally greater than 24 m. The cross validation analysis showed that both kriging and cokriging provided reasonable estimates for IR. Differences among krigin and cokriging with using OM as auxiliary variable were relatively small. However using silt content as auxiliary data for the estimation of IR in cokriging method was consistently more effective than kriging on IR alone, and could reduce prediction error by 15% as compared kriging method.

کلمات کلیدی:
Cross-semivariogam Correlation Geostatistics Interpolation

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