Spatial variability assessment of some soil nutrient elements using geostatistical methods (Case study: Chadegan, Isfahan province)

Document Type : Research Paper

Authors

1 Soil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran

2 Soil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center. Agricultural Research, Education and Extension organization (AREEO), Isfahan, Iran.

Abstract

Evaluating the spatial variability of soil properties is an important prerequisite for precision agriculture. This research was conducted on 84 soil samples from different areas of Chadegan city (Isfahan province). With the aim of evaluating the spatial variability of some soil nutrient elements, including organic carbon, soil-available phosphorus, potassium, zinc, copper, manganese, and iron. The spatial correlation of each variable with a specific semi-variable and the best fitting model for each variable were selected. Interpolation was done using normal Kriging and Inverse Distance Weighting with powers of 1 to 3 methods. The accuracy of the distribution maps of these variables was evaluated by the Mean bias error (MBE) the standard root mean square error (NRMSE), and the coefficient of determination (R2). The results showed that all studied properties had moderate spatial dependence, which shows the effect of management factors such as fertilization, plowing, irrigation, etc. on these variables. The exponential model was the most accurate to predict organic carbon, phosphorus, potassium and zink variables while iron, copper, and manganese were best fitted with an spherical model. For phosphorus, iron, and copper variables, the Inverse Distance Weighting with the power of 1 (IDW-1) and for organic carbon, potassium, zinc, and manganese normal kriging methods were recognized as the best interpolation methods. According to the spatial distribution maps, the studied area is sufficient in terms of potassium, copper and manganese nutrients, and in other cases, the use of fertilizers and organic materials is necessary to increase soil fertility.

Keywords

Main Subjects

Spatial variability assessment of some soil nutrient elements using geostatistical methods (Case study: Chadegan, Isfahan province)

EXTENDED ABSTRACT

Introduction

The existence of spatial variability in soil properties entails site-specific management for balanced plant nutrition towards achieving sustainable crop production which is the epitome of precision agriculture. In this respect, the application of geostatistical techniques is becoming standard for input-based crop production. spatial variability mapping is an efficient geospatial tool and technique for the diagnosis of nutrient-related limitations and their management. The aim of this study was to evaluate the spatial variability and frequency distribution of some soil nutrient elements, including organic carbon, soil-available phosphorus, potassium, zinc, copper, manganese, and iron.

Methods

The study was conducted on 84 soil samples, in agricultural lands of Chadegan (Isfahan province). The spatial correlation of each variable with a specific semi-variable and the best fitting model for each variable were selected using GS+ version 9 software. Interpolation was done using normal Kriging and Inverse Distance Weighting with powers of 1 to 3 methods. The accuracy of the distribution maps of these variables was evaluated by the Mean bias error (MBE) the standard root mean square error (NRMSE), and the coefficient of determination (R2).

Results and Discussion

The results of the geostatistical analysis showed that all studied properties including organic carbon, soil-available phosphorus, potassium, zinc, copper, manganese and iron had moderate spatial dependence. The moderate spatial dependence could result from variation in environmental factors such as flood water, irrigation, fertilizers addition, high water table level or agriculture practices, and different agricultural managements. The exponential model was the most accurate to predict organic carbon, phosphorus, potassium and zink variables while iron, copper, and manganese were best fitted with an spherical model. Also, among the studied characteristics, organic carbon, phosphorus, and potassium had the smallest effective range respectively. For phosphorus, iron, and copper variables, the Inverse Distance Weighting with the power of 1 (IDW-1) and for organic carbon, potassium, zinc, and manganese normal kriging methods were recognized as the best interpolation methods. Spatial distribution maps according to the most accurate techniques showed that the greatest soil nutrient elements were measured in the west and the north parts. This means that the soil fertility condition was better in the southern and western parts of the study area, and the amount of nutrients in the soil decreased towards the central and eastern areas. In addition to management factors such as fertilization and irrigation, this is due to the climate and more rainfall in these areas, as well as the higher quality of irrigation water in these areas. In general, according to the obtained results, it was found that geostatistical modeling can show continuous changes in soil parameters with acceptable accuracy. Therefore , these results could help to adopt methods for crop improvement including proper crop choice and site-specific fertilizer recommendations to optimize the management of inputs and sustainable production.

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Iranian Journal of Soil and Water Research
Volume 55, Issue 6
September 2024
Pages 1001-1016

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