Evaluating the Spatial Variability of Soil Quality in a Corn Field in Ravansar, Kermanshah Province

Background and Objectives
The spatial variability of soil properties in agricultural fields, in addition to pedological and geological factors, is influenced by various management practices. Awareness of these changes is important for sustainable soil and crop management. To evaluate sustainable management in agricultural lands, understanding soil quality is particularly important. In other words, soil quality classification systems play a crucial role in assessing agricultural production on farms. Over the last few decades, the spatial variability of soil properties has been investigated using geographic information systems (GIS) and geostatistical methods to interpolate soil properties. Zoning the main soil physical parameters is essential for identifying areas with physical problems and carrying out corrective measures at minimal cost to increase crop production. Although research has been conducted on zoning soil characteristics or evaluating soil spatial variability in wheat, rapeseed, and rice farms, no comprehensive studies have been carried out on the physical quality of soil in corn fields. Therefore, this study was conducted to investigate the relationship between soil physical quality and the biological yield of corn in a 45-hectare farm.
Materials and Methods
To investigate soil quality, a large corn field with an area of 45 hectares was selected in Rwansar, Kermanshah province, Iran. Using geostatistical techniques and ArcGIS-9.2 software, the spatial variability of five soil parameters affecting crop yield including bulk density (BD), non-capillary porosity (NCP), saturated hydraulic conductivity (Ks), available water capacity (AWC), and organic carbon (OC), was evaluated on a regular grid (100 m × 100 m). The kriging method was employed to investigate the spatial variations of soil properties. The appropriate interpolation model was selected based on the root mean square error (RMSE) and the coefficient of determination (r²). Due to the flatness of the land and the considerable depth of the underground water, the values of the five aforementioned parameters were used to calculate the physical rating index (PRI). The PRI at each sampling point was determined by multiplying the values of these parameters. Subsequently, the relationship between this index and the biological yield of corn was investigated. SPSS-18 software was used for statistical analysis of the data. Data normalization was performed using square root and logarithmic transformations, along with the Kolmogorov-Smirnov test.
Results
The results revealed that major ranges of semivariogram for Ks and AWC varied between 137–145 m and for BD, OC and NCP they were relatively long (161- 205 m). The range of all the investigated soil characteristics was greater than the sampling distance, indicating that the considered distances were sufficient to represent the spatial variations of the characteristics. The best spatial structure model with the highest accuracy was exponential model for Ks, NCP, and OC, and spherical model for BD and biological yield of corn. Examining the zoning maps showed that the values of Ks, OC, BD, NCP, and AWC varied from 0.006 to 3 cm/h, 0.2 to 1.32%, 1.35 to 1.8 g/cm³, 3.5 to 17.3%, and 4.24 to 16.63%, respectively. The results also revealed that the spatial structure of non-capillary porosity and clay was medium, while the others were weak. Because the semivariogram exhibited similar spatial continuity in all directions, all the variables were isotropic and not affected by external factors. According to the bulk density zoning map, approximately 60% of the soils are dense, with a density of more than 1.6 g/cm³. In other words, the density of the subsurface layer limits the development of plant roots in the soil, resulting in reduced crop production. Areas with a Ks value of less than 0.1 cm/h cover approximately 40% of the northern part of the region. Additionally, only about 10% of the farm has good soil available water, while around 45% of the area faces severe to very severe moisture problems. Finally, the correlation between PRI and the biological yield of corn was strong (r = 0.8832**)
Conclusion
By examining the correlation between the PRI and crop yield, useful information can be obtained to identify areas with inappropriate soil physical quality in a corn field. In other words, using geostatistical methods is an important step toward investigating the spatial variability of soil characteristics and improving soil physical quality. Therefore, it can be stated that the PRI is an important parameter for assessing soil quality to enhance physical conditions and increase crop yield. In general, by preparing zoning maps of the PRI index and biological yield with the help of geostatistics, the soil physical quality in any part of the farm can be identified and improved.
Author Contributions
Conceptualization, Aliashraf Amirinejad; methodology, Amir Rezaei and Aliashraf Amirinejad; software, Amir Rezaei; writing-original draft preparation, Amir Rezaei, writing-review and editing Aliashraf Amirinejad; project administration, Aliashraf Amirinejad. All authors have read and agreed to the published version of the manuscript.
Acknowledgements
The authors would like to thank all participants of the present study.
Data Availability Statement
Data is available on reasonable request from the authors.
Conflict of interest
The authors declare no conflict of interest.
Ethical considerations
The authors avoided data fabrication, falsification, plagiarism, and misconduct.