Assessing the Importance of Sampling Distance on the Spatial Structure of Available Potassium in the Agricultural Soils of Shahrekord Plain

Document Type : Research Article

Authors

M. Barati Zanyani ¹

M.H. Salehi ¹

A. Hossienpur ¹

A. Jafari ²

¹ Department of Soil Sciences and Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

² Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

https://doi.org/10.22067/jsw.2026.95838.1504

Abstract

Introduction
Nowadays, the application of variable-rate fertilization is primarily based on soil analysis maps. These maps play a crucial role in decision-making for fertilizer management and the optimization of plant nutrient use. However, the sampling distance is often determined without considering the spatial variability of soil properties, which can lead to reduced map accuracy and inappropriate fertilization recommendations. Previous studies have shown that the density and spacing of soil samples have a significant impact on the accuracy of geostatistical models and the quality of maps produced using methods such as kriging. Moreover, soil management practices, including varying fertilizer application patterns, crop rotation, and historical land use, can strongly influence the spatial patterns of soil nutrients. Therefore, investigating the effect of sampling distance on the spatial distribution of major soil nutrients, particularly available potassium, can help optimize soil sampling and provide more precise recommendations for variable-rate fertilization. The present study aimed to examine the effect of sampling distance on the spatial structure of available potassium in the agricultural soils of the Shahrekord Plain.
 
Material and Methods
For this purpose, 120 soil samples were collected using a regular grid sampling design with a spacing of 1335 meters from the surface layer of agricultural soils in the Shahrekord plain, covering an area of 21400 ha, and the available potassium content was determined in the laboratory. Descriptive statistics of the data, including the minimum, maximum, mean, median, coefficient of variation, and skewness of the variable, were calculated, and the normality of the data distribution was assessed using histograms and the Kolmogorov–Smirnov test. The experimental variogram of available potassium was constructed, and various theoretical models were fitted to it. Subsequently, the variogram parameters and the spatial dependency class of the variable were determined. Model validation was performed by adjusting the initial parameters so that the final optimized model had a mean error (ME) close to zero and a minimum mean squared error (MSE). Ordinary kriging in ArcGIS 10.3 was then used to produce maps of available potassium.
 
Results and Discussion
The available potassium in the soils ranged from 120 to 812 mg kg⁻¹, with a coefficient of variation of 38.2%. The data were not normally distributed; however, the log-transformed data followed a normal distribution. Geostatistical analysis indicated that the exponential model best fitted the data. The nugget effect was 0.108, and the range of this variable was estimated at 53300 meters, indicating that the selected sampling distance is much smaller than the spatial variability range; therefore, the sampling distance appears to be sufficient. However, the exponential variogram is nearly flat at short distances, showing little change in semivariance, which reflects the influence of strong soil management practices, such as varying fertilizer application patterns, crop rotation, and historical land use, on data dispersion. The spatial dependence of the studied variable was 50.2%, which falls into the moderate spatial dependence class. The mean error (ME) of the estimated available potassium was –0.0008, and the mean squared error (MSE) was 0.011, indicating the unbiasedness and high accuracy of the estimation. The maps produced using the kriging method exhibited acceptable accuracy. This was achieved despite the increased variability at short distances and the disruption of regular spatial patterns caused by intensive soil management practices.
 
Conclusion
According to the findings, using an appropriate sampling distance is essential to accurately represent the spatial variability of major soil nutrients, particularly potassium. The findings of this study indicated that the current sampling distance is not sufficient to accurately represent the spatial variability of available potassium in the soils under investigation. Therefore, for future studies, it is recommended to investigate the effect of sampling distance at distances shorter than 1,335 meters to evaluate the model sensitivity and the accuracy of kriging maps at larger scales. This can help optimize soil sampling and provide more precise recommendations for variable-rate fertilizer application. Furthermore, it is recommended that future studies also consider the role of soil management practices in the spatial modeling of potassium, as these factors can strongly influence the spatial distribution of soil nutrients.
 
Acknowledgements
The authors would like to thank the Soil Science Department of the University of Shahrekord for providing equipment and facilities.

Keywords

Available potassium

Distance

Kriging

Sampling

Variogram

Subjects

• Soil science

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

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