shokrollah asghari; Mahmood Shahabi
Abstract
Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and ...
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Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and planning land development and reclamation schemes. The soil hydraulic and mechanical parameters are very important factors that affect water and chemical transport in soil pores. In the salt-affected soils, saturated hydraulic conductivity (Ks) is very low due to the high contents of sodium and weak aggregate stability. Penetration resistance (PR) indicates soil mechanical strength to penetration of a cone or flat penetrometer; it is important in seedling, root growth and tillage operations. Generally, PR values exceed 2.5 MPa, while root elongation is significantly restricted. The analysis of spatial variability of Ks and PR is essential to implement a site-specific soil management especially in the salt-affected lands. The objective of this study was to evaluate the influence of two different bare and agricultural land uses on the spatial variability of Ks and PR in the salt-affected soils around Lake Urmia.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ to 45° 36ʹ 38ʺ E and 38° 6ʹ 37ʺ to 38° 7ʹ 42ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), mean weight diameter of aggregates (MWD), sodium adsorption ratio (SAR) and electrical conductivity (EC), were measured in the collected soil samples. The EC and SAR were measured in 1:2.5 (soil: distilled water) extract. Ks was measured using constant or falling head method. Bulk density (BD) and field water content (FWC) were measured in the undisturbed soil samples taken by steal cylinders with 5 cm diameter and height. Total porosity calculated from BD and particle density (PD). PR was directly measured at the field using a cone penetrometer. The best fit semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum coefficient of determination (R2). Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze the spatial variability of Ks and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating Ks and PR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria.The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
Results and Discussion: According to coefficient of variation (CV) from the study area, the most variable soil indicator was Ks (CV=155.6%), whereas the least variable was PD (CV= 3.05%) both in bare land use. The Lognormal distribution was found for Ks data in the studied region. The Pearson correlation coefficients (r values) indicated that there are significant correlations between Ks and OC (r=0.36), sand (r=0.60), SAR (r=-0.35), EC (r=-0.22), BD (r=-0.52), TP (r= 0.31), silt (r=-0.60), and clay (r=-0.43). Also, significant correlations were obtained between PR and FWC (r=-0.32), BD (r=0.21), and TP (r=-0.21). The spatial dependency classes of soil variables were determined according to the ratio of nugget variance to sill expressed in percentages: If the ratio was >25% and <75%, the variable was considered moderately spatially dependent; if the ratio was >75%, variable was considered weakly spatially dependent; and if the ratio was <25%, the variable was considered strongly spatially dependent. The strong spatial dependences with the effective ranges of 2443m were found for Ks. The PR and PD variables had the least (335 m) and the highest (2844 m) effective range, respectively. The range of influence indicates the limit distance at which a sample point has influence over another points, that is, the maximum distance for correlation between two sampling point. The models of fitted semivariograms were spherical for Ks and exponential for PR. According to RMSE and CCC criteria, there was not found significant difference between Ks estimates by OK and IDW interpolation methods. The high CCC and low RMSE values for OK compared with IDW indicated the more precision and accuracy of OK in estimating PR in the studied area. Generally, the spatial maps showed that from agricultural to bare land use by nearing to Lake Urmia, the BD and PR increased and consequently TP and Ks decreased.
Conclusion: The results showed that Ks negatively related to the SAR, EC, BD, silt and clay and positively related to the OC, sand, MWD and TP in the study area. Also, PR negatively related to the FWC and TP and positively related to the BD and silt. The spatial dependency was found strong for Ks. The PR revealed the smallest effective range (335 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 335 m instead of 100 m in order to save time and minimize cost.
shokrollah asghari; Mahmood Shahabi
Abstract
Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In ...
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Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In the salt-affected soils, aggregate stability is weak due to the high contents of sodium. The analysis of spatial variability of mean weight diameter of aggregates (MWD) and sodium adsorption ratio (SAR) is necessary to implement a site-specific soil management especially in the salt-affected soils. The main object of this study was evaluating the effects of different land uses (bare and agriculture) on the spatial variability of MWD and SAR in the salt-affected soils around Urmia Lake.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ E and 38° 6ʹ 37ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), CaCO3, pHe, MWD, SAR and electrical conductivity (EC), were measured in the collected soil samples. Thewet sieving method was used to determine MWD of wet aggregates. The sieves were: 2, 1, 0.5, 0.25 and 0.106mm. The EC and SAR were measured in 1:2.5 (soil: distilled water) extra. The SAR was calculated from concentrations of Na+ and Ca+ + Mg+. The best fit semivariogram model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2). Ordinary kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of MWD and SAR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating MWD and SAR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
Results and Discussion: According to the results of coefficient of variation (CV) from the study area, the most variable (CV=113.05%) soil indicator was SAR (bare land use), whereas the least variable (CV= 3.52%) was pHe (agricultural land use). The Pearson correlation coefficients (r value) indicated that there are significant (P
I. Esfandiarpour Borujeni
Abstract
Soil salinity and sodicity are considered as the important factors limiting the plants growth. This study was conducted to assess the influence of the sample size on the accuracy of estimation of soil salinity and sodicity status, made by ordinary kriging, inverse distance weighting and spline estimators ...
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Soil salinity and sodicity are considered as the important factors limiting the plants growth. This study was conducted to assess the influence of the sample size on the accuracy of estimation of soil salinity and sodicity status, made by ordinary kriging, inverse distance weighting and spline estimators in Eslamieh area, Rafsanjan city. First, electrical conductivity (EC) and sodium adsorption ratio (SAR) were measured for 100 observation points, collected from three depths using a regular grid sampling pattern with an interval of 500 meter. These properties were mapped using aforesaid estimators. Then, random omission of 20, 40 and 60 samples from the total primary dataset (100 samples), was done and in each new situation, EC and SAR were mapped again. At the end of all 10 stages used to omit the samples, the index of standardized root mean square error (RMSE%) was measured for each estimator. Finally, the obtained contents of RMSE% were statistically compared using Friedman and Wilcoxon tests. The results showed using relatively high number of samples (all 100 observation points), three analyzed estimators have no significant difference (95% confidence level). In the cases of lower sample sizes, Friedman test revealed a significant difference among the estimators; whereas using Wilcoxon test, as a supplementary procedure, no significant difference was observed between the results obtained from ordinary kriging and inverse distance weighting. Hence, thanks to the relatively good precision, ease of processing and lower required sampling points, the inverse distance weighting estimator is recommended for future studies in the studied area.
