Abstract: Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
Abstract: In this paper spatial variability of some chemical and
physical soil properties were investigated in mountain rangelands of
Nesho, Mazandaran province, Iran. 110 soil samples from 0-30 cm
depth were taken with systematic method on grid 30×30 m2 in
regions with different vegetation cover and transported to laboratory.
Then soil chemical and physical parameters including Acidity (pH),
Electrical conductivity, Caco3, Bulk density, Particle density, total
phosphorus, total Nitrogen, available potassium, Organic matter,
Saturation moisture, Soil texture (percentage of sand, silt and clay),
Sodium, Calcium, magnesium were measured in laboratory. Data
normalization was performed then was done statistical analysis for
description of soil properties and geostatistical analysis for indication
spatial correlation between these properties and were perpetrated
maps of spatial distribution of soil properties using Kriging method.
Results indicated that in the study area Saturation moisture and
percentage of Sand had highest and lowest spatial correlation
respectively.
Abstract: The objective of this research is to study principal
component analysis for classification of 67 soil samples collected from
different agricultural areas in the western part of Thailand. Six soil
properties were measured on the soil samples and are used as original
variables. Principal component analysis is applied to reduce the
number of original variables. A model based on the first two
principal components accounts for 72.24% of total variance. Score
plots of first two principal components were used to map with
agricultural areas divided into horticulture, field crops and wetland.
The results showed some relationships between soil properties and
agricultural areas. PCA was shown to be a useful tool for agricultural
areas classification based on soil properties.