Assessment of geostatistical models for zoning spatial distribution of some soil properties in Darengan region with different land uses, Fars province

Document Type : Research Paper

Authors

1 Department of Soil Science, Faculty of Agriculture, University of Yasouj, Yasouj, Iran

2 Department of Forestry, Range and Watershed Management, Agricultural and Natural Resources, Yasouj University, Yasouj, Iran

3 Department of Soil and Water Research, Fars Agricultural and Natural Resources Research and Education Center, AREEO, Shiraz, Iran.

Abstract

Determination of the physico-chemical characteristics of soil for sustainable agriculture on large scales is an important factor in achieving a precision agriculture. Different land use and management practices greatly impact soil properties, and knowledge of the variation in soil properties within different land uses, is essential in determining production constraints related to soil characteristics. Laboratory analyses of the soil properties are usually expensive and time consuming. Surmounting these problems is possible using geostatistics. This study was conducted to assess geostatistical methods for the spatial distribution of some soil properties of Darengan region with different land uses in Fars province. 134 surface soil samples at an interval of 1.0 × 1.0 km on a grid design were taken from pasture and agricultural land uses. Physico-chemical characteristics of the soil samples were analyzed. According to the results, the best spatial structure model with the highest accuracy was exponential model for the variables of sand, EC, CCE, pH, and BD, rational quadratic model for silt, and spherical model for clay, OC, and CEC. The spatial structure was weak for CCE, medium for organic carbon, and strong for the other variables. Among the characteristics studied, the variables of silt, clay and cation exchange capacity have the lowest range, and EC has the highest range. Based on the zoning map of the studied properties, the areas with agricultural land use had greater OC, clay, CEC, EC and lower pH. Understanding soil properties with their spatial dependency is of crucial importance for understanding the behavior of soil and hence providing better soil management.

Keywords

Main Subjects

Geostatistical assessment of the spatial distribution of some Soil properties of the soils of Darengan region with different land uses, Fars province

 

EXTENDED ABSTRACT

 

Introduction

Mapping of soil properties is an important operation as it plays an important role in the knowledge about soil properties and how it can be used sustainably. Soil properties vary in different spatial areas due to the
combined effect of biological, physical, and chemical processes over time, and can vary within farmland or at the landscape scale. Different land use and management practices greatly impact soil properties, and knowledge of the variation in soil properties within different land uses is essential in determining production constraints related to soil characteristics. A tool often used to analyze how soil properties are spatially distributed in an area is geostatistics. It is effective for understanding the magnitude and structure of the spatial variability of the physical and chemical properties. Darengan region, located in the southwest of Shiraz city, is an important agricultural region with pasture, agricultural and garden uses, which has developed a lot in recent years, and a large area of pasture has been changed to agricultural land use. Therefore, this study was conducted to assess geostatistical methods for the spatial distribution of some soil properties of the soils of this region.

Material and Methods

The study area, with an area of about 20000 ha, is located 40 km southwest of Shiraz city, in the center of Fars province. The annual rainfall and temperature of the region are about 340 mm and 17.1°C, respectively. A total of 134 soil samples (from pasture and agricultural land uses) were collected from the surface (0–30 cm) at an approximate interval of 1.0 × 1.0 km on a regular grid design. Routine soil analysis includes soil texture, pH, EC, CCE, BD, OC, and CEC were measured in the laboratory. Descriptive statistical analysis was carried out and in geostatistical analysis, the semivariogram was calculated for each soil variable. Different models of two deterministic and geostatistical methods were used to estimate the soil characteristics in unsampled points in the study area. Deterministic methods include global polynomial interpolation, local polynomial interpolation, inverse distance weighting and Radial Basis Function. Geostatistical methods include Kriging, Cokriging and Empirical Bayesian Kriging. In all three mentioned geostatistical methods, three types of simple prediction; ordinary prediction and universal prediction, were used. In this study, 104 models, including 5 deterministic models and 99 geostatistical models, were used to select the most suitable model with the strongest spatial structure. All geostatistical and deterministic studies were carried out in ArcGIS 10.7.1 to achieve the most suitable interpolation model in terms of accuracy and precision.

Results and Discussion

The results revealed that, based on precision criteria, exponential co-kriging was the best method for interpolating sand, EC, CCE, pH, BD, spherical co-kriging for clay, OC., and CEC, and co-kriging rational quadratic for silt. The spatial structure was obtained for CCE, weak, for organic carbon, medium, and for the other variables, strong. Among the characteristics studied, the variables of silt, clay and CEC have the lowest range, and EC has the highest range. Variography analysis indicated that the ranges of influence for sand, silt, clay, EC, CCE, OC, pH, BD, and CEC, were 2733, 2000, 2004, 10553, 2290, 2584, 3448, 2361 m, respectively, and the RSME varied between 0.017 (for BD) and 5.75 (for CCE). For geostatistical analysis of soil variables, the value of the nugget: sill ratio ranges from 0% (sand, clay, BD, and CEC) to 175.5% (CCE), which indicates that internal (e.g., the soil-forming processes) factors were dominant over external (e.g., human activities) factors. However, the soil sand, silt, clay, EC, pH, BD, and CEC had a strong spatial dependency with a nugget: sill ratio of <25% since it was induced by structural factors. Meanwhile, OC had moderate spatial dependency with a nugget: sill ratio of 25–75% since this variable was mostly determined by both internal and external factors and CCE had weak spatial dependency with a nugget: sill ratio of >75%. Based on the zoning map of the studied properties, the areas with agricultural land use had greater organic carbon, clay, CEC, EC and lower pH. The results of this study showed the effectiveness of geostatistics and GIS techniques as powerful tools for spatial management of soil characteristics. In general, it seems that the studied properties were mainly influenced by factors such as topography, parent material and land use. Considering the variability of soil characteristics as well as the different influential ranges of these variables, it is suggested that for reducing costs, the sampling intervals of the soils be based on the influential range.

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Iranian Journal of Soil and Water Research
Volume 55, Issue 1
April 2024
Pages 97-116

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