The effect of number and type of soil physical and hydraulic properties on representing the soil physical quality (case study: Shabestar Plain)

Document Type : Research Paper

Authors

1 Department of Soil Science, Agricultural Faculty, Tabriz University, Tabriz, Iran

2 Department of Soil Science. Agricultural Faculty, University of Tabriz, Iran

3 Soil and Water Research Institute, Agriculture Research Education and Extension Organization (AREEO), Karaj, Iran.

4 Department of Soil Science, Faculty of Agriculture, Tabriz University, Tabriz, Iran

5 Eastern Azerbaijan Agricultural and Natural Resources research Center, Agricultural Research Education and Extension Organization (AREEO), Tabriz, Iran

Abstract

Making management decisions for the quantitative and qualitative improvement of product production effectively begins with selecting the correct and appropriate set of physical and hydraulic characteristics in the form of a soil physical quality index. In order to investigate the physical quality of Shabaster Plain which were under wheat cultivation and to determine the role of the number and type of properties on the quality of the soils, 94 soils from these lands until the year 2022, were selected. To determine the soil physical quality index (SPQI), the minimum data set (MDS) was used by principal component analysis (PCA). 13 physical, chemical, and hydraulic properties (clay, silt, bulk density, aggregate size distribution, electrical conductivity, sodium adsorption ratio, pH, organic carbon, hydraulic conductivity (K_s), conventional plant available water (CPAW), integral energy (EI), dexter index (S_dex), Kirchhoff potential (M_h0)) were consciously entered into four stages in the principal component analysis so that the output is not only the minimum data set but also the best data set. EC appeared as one of the main components in all arrays. The first array was eliminated from the minimum data set. Comparing the mean soil physical quality index between the arrays with Duncan's test showed a significant difference at the 99% probability level (p<0.01) between the fourth array and the second and third arrays. The high sensitivity coefficient of the fourth array (9.78) with the second and third arrays (5.43) showed that the correct addition of the Kirchhoff potential to the data set, led to different results in terms of classifying soil physical quality. As a result, the quality of the soils decreased from 72% of very suitable and suitable soils and 28% of the soils with severe and very severe restrictions in the second and third arrays to 41% of very suitable and suitable soils and 59% of soils with restrictions in the fourth array. This data demonstrates using easily measured properties, to simplify the soil quality assessment system, does not always produce accurate results.

Keywords

Main Subjects

The effect of number and type of soil physical and hydraulic properties on representing the soil physical quality (case study: Shabestar Plain)

EXTENDED ABSTRACT

 

Introduction

Making management decisions for the quantitative and qualitative improvement of product production effectively begins with selecting the correct and appropriate set of physical and hydraulic characteristics in the form of a soil physical quality index.

Materials and Methods

In order to investigate the physical quality of Shabaster Plain which were under wheat cultivation and to determine the role of the number and type of properties on the quality of the soils, 94 soils from these lands until the year 2022, were selected. To determine the soil physical quality index (SPQI), the minimum data set (MDS) was used by principal component analysis (PCA). 13 physical, chemical, and hydraulic properties were consciously entered into four stages in the principal component analysis so that the output be not only the minimum data set but also the best data set. The first array includes 8 soil properties that are easily measured, the second array includes properties of the first array along with conventional plant available water (CPAW) and hydraulic conductivity (), the third array includes properties of the second array along with integral energy (EI) and dexter index (), and the fourth array includes properties of the third array along with Kirchhoff potential ().

Results and Discussion

The first array was discarded due to the oversimplification of the minimum data set, as it could not properly justify the information of variables. Despite spending more time and cost on the third array than the second array, in both arrays, the three components (CPAW, EC, and OC) remained in the minimum data set and no significant difference was observed in the average of physical quality index between the two arrays. While in the fourth array, the inclusion of  as a property that includes the suction corresponding to soil moisture and hydraulic conductivity at the same time, caused the effect of other properties to be revealed correctly. Comparing the mean soil physical quality index between the arrays with Duncan's test showed a significant difference at the 99% probability level (p<0.01) between the fourth array and the second and third arrays. The high sensitivity coefficient of the fourth array (9.78) with the second and third arrays (5.43) showed that the correct addition of the Kirchhoff potential to the data set, led to different results in terms of classifying soil physical quality. As a result, the quality of the soils decreased from 72% of very suitable and suitable soils and 28% of soils with severe and very severe restrictions in the second and third arrays to 41% of very suitable and suitable soils and 59% of soils with restrictions in the fourth array, it got really, high intense. The important point was the presence of EC in all arrays as one of the main components, which was a sign of the importance of this property in the studied region.

Conclusion

This data demonstrates that using traits that can be easily measured to simplify the soil quality assessment system does not always produce accurate results.

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Iranian Journal of Soil and Water Research
Volume 54, Issue 7
October 2023
Pages 981-1003

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