University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 54 7 2023 09 23 The effect of number and type of soil physical and hydraulic properties on representing the soil physical quality (case study: Shabestar Plain) The effect of number and type of soil physical and hydraulic properties on representing the soil physical quality (case study: Shabestar Plain) 981 1003 94381 10.22059/ijswr.2023.361033.669516 FA Roya Toluee Department of Soil Science, Agricultural Faculty, Tabriz University, Tabriz, Iran Davoud Zarehaghi Department of Soil Science. Agricultural Faculty, University of Tabriz, Iran Naser Davatgar Soil and Water Research Institute, Agriculture Research Education and Extension Organization (AREEO), Karaj, Iran. Mohammad Reza Neyshabouri Department of Soil Science, Faculty of Agriculture, Tabriz University, Tabriz, Iran Ahmad Bybordi Eastern Azerbaijan Agricultural and Natural Resources research Center, Agricultural Research Education and Extension Organization (AREEO), Tabriz, Iran Journal Article 2023 06 19 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. 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. https://ijswr.ut.ac.ir/article_94381_c711b066e31e639650b9483576410777.pdf