پيش بيني مكاني برخي خصوصيات بيولوژيك و بيو شيميايي خاك به كمك آناليز سطح زمين و زمين آمار

چكيده انگليسي :

Spatial Prediction of selected Soil Biological and Biochemical Properties using Terrain Analysis and Geostatistical Techniques Samaneh Tajik Samaneh 10941@yahoo com September 14 2011 Department of Soil Science Isfahan University of Technology Isfahan 84156 83111 IranDegree M Sc Language FarsiSupervisor Dr Shamsollah Ayoubi ayoubi@cc iut ac irAbstract Balance in nutrient cycle is a key indicator that shows significant improvement or degradation ofthe soil system Nitrogen is one of the most important elements affecting crop growth and soil fertility Its deficiency is the major limiting factor in soil productivity and plant growth Soil enzyme activitycan be employed as a measure of soil biological potential Soil enzymes are indicators of soil qualityand fertility and they are considerd as the center of microbial activity and nitrogen transformation insoil This study was conducted to predict three soil enzyme activities potential mineralizable nitrogen PMN and soluble organic nitrogen SON using multivariate linear regression and artificial neuralnetworks Assessment of the efficacy of two modeling approaches and determining the most factorsaffecting the variability of the selected soil enzymes PMN and SON were the other objectives of thisstudy The studied site with an area of 2400 ha in the Zargham Abad hilly region located in Isfahanprovince south Semirom was selected and soil samples were taken from 0 10cm depth at 125 samplingpoints The elevation data were used to create 3 3 m digital elevation models DEM using ILWISsoftware Then primary and secondary topographical indices were generated from the DEM usingILWIS software The soil was air dry and ground to pass through a 2 mm sieve to remove gravel rootsand large organic residues for laboratory measurements Urease L glutaminase and L asparaginaseactivity NMP SON and some soil properties including particle size distribution soil organic carbon total nitrogen calcium carbonate equivalent pH and EC were measured Multivariate linear regressionand artificial neural network modeling for prediction soil enzyme activities NMP and usingtopographic attributes and soil properties were conducted In order to identify the most importantterrain attributes and remote sensing data explaining the variability of SOC sensitivity analysis wasdone using the Hill method The spatial distribution of enzyme activity PMN and SON were exploredby variography analysis and kriging technique The results of study sowed that there were significantcorrelation coefficients between topographic attributes with soil enzyme activities PMN and SON Clay silt TN SOC showed positive significant correlations with wetness index and negativerelationships with some topographic attributes slope plan curvature aspect sediment transport indexand relative stream power that influence soil erosion and depositional processes along the selectedhillslope The results also revealed that regression models could explain only 62 53 and 20 of totalvariability of enzyme activity PMN and SON respectively On the other hand ANN models couldexplain 96 to 98 of total variability of soil enzyme activities and PMN and 94 of SON variabilityin the studied site Sensitivity analysis based upon the developed ANN model showed that aspect andshaded relief among the topographic attributes and calcium carbonate equivalent and soil texture among the soil properties were the most important factor that explained the most variability in thestudied target variables Geostatistical analysis showed that the range of spatial dependence variedamong selected soil parameters L glutaminase had the shortest range of spatial dependence 832m and SON showed the longest 1134m All the five studied parameters including Urease L glutaminaseand L asparaginase activity PMN and SON were moderately spatially dependent The spatial pattern

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پيش بيني مكاني برخي خصوصيات بيولوژيك و بيو شيميايي خاك به كمك آناليز سطح زمين و زمين آمار

پتانسيل معدني شدن نيتروژن , نيتروژن آلي محلول , پارامترهاي توپوگرافي , شبكه هاي عصبي مصنوعي