| Abstrakt: |
Soil has various tasks or functions, including the ability to produce crops, store carbon, store water, cycle nutrients and purify water. Soil functions have a high correlation with soil quality. Soil quality indices are often regional and to determine it, it is not possible to consistently use a set of idicators and indices in all regions. In this research, Additive Soil Quality Index (SQIa), the Weighted Additive Soil Quality Index (SQIw) and the Nemero Soil Quality Index (NQI) and the effect of effective characteristics on soil quality in surface soil (0-30 cm) and in the yield of 206 wheat field of East Azarbaijan Province (Tabriz Plain) in 2017 was investigated by measuring physical and chemical properties of soils and wheat grain yield. Organic carbon, bulk density, aveilble potassium and aveilble phosphorus and electrical conductivity were selected as The minimum data set (MDS) using principal component analysis (PCA). Then SQIa, SQIw and NQI were determined using TDS and MDS. The results showed that there is a correlation between wheat yield and soil quality indices using MDS (r=0.60-0.63) and using TDS (r=0.56-0.60) also there was a significant correlation (p <0.01) between the use of TDS and MDS in NQI (r=0.81), SQIw(r=0.84) and SQIa (r=0.88) (p<0.01). Therefore, all three investigated indices are suitable for the study area and using MDS is more suitable than the method of using TDS due to relatively higher correlation and number of features and lower cost.Introduction Soil has various tasks or functions, including the ability to produce crops, store carbon, store water, cycle nutrients and purify water. Soil functions have a high correlation with soil quality. Depending on the purpose, soil quality can be assessed. Therefore, Awareness of the physical, chemical and biological quality of soil in agriculture and natural resources is essential for optimal land management and achieving maximum economic productivity. Soil quality indicators are often regional and to determine it, it is not possible to consistently use a set of identifiers and indicators in all regions. In this study, the Nemero Soil Quality Index (NQI), the Weighted Additive Soil Quality Index (SQIw), and the Additive Soil Quality Index (SQIa) were determined using the total data set (TDS) and minimum data set (MDS) in wheat field of East Azarbaijan Province (Tabriz Plain). Methods In this research, Additive Soil Quality Index (SQIa), the Weighted Additive Soil Quality Index (SQIw) and the Nemero Soil Quality Index (NQI) and the effect of effective properties on soil quality in surface soil (0-30 cm) and in the yield of 206 wheat field of East Azarbaijan Province (Tabriz Plain) in 2017 was investigated by measuring physical (sand, silt and clay percentage, bulk density (Bd), saturated hydraulic conductivity (Ks)) and chemical (Organic carbon (OC), acidity (pH), Calcium carbonate equivalent, aveilble potassium (Kave) and aveilble phosphorus (Pave), and electrical conductivity (EC)) properties of soils and wheat grain yield. Organic carbon (OC), bulk density (Bd), aveilble potassium (Kave) and aveilble phosphorus (Pave), and electrical conductivity (EC) were selected as The minimum data set (MDS) using SPSS 24 by principal component analysis method (PCA). For this purpose, components with Eigen values greater than one were selected and in each component, properties with high loading coefficient up to 10% lower than the highest loading coefficient were selected MDS affecting soil quality. Then, Additive Soil Quality Index (SQIa), the Weighted Additive Soil Quality Index (SQIw) and the Nemero Soil Quality Index (NQI) were determined using the total data set (TDS) and the minimum data set (MDS). Results and Discussion The results showed that there was a significant correlation (p <0.01) between the yield of irrigated wheat and Weighted Additive Soil Quality Index (r=0.63), Additive Soil Quality Index (r=0.61) and Nemero Soil Quality Index (r=0.60) using minimum data set (MDS) method; and using total data set (TDS) method this correlation values were 0.59, 0.60 and 0.56, respectively. also, there was a significant correlation (p<0.01) between the use of total data set (TDS) and minimum data set (MDS) in Nemero Soil Quality Index (r=0.81), the Weighted Additive Soil Quality Index (r=0.84) and Additive Soil Quality Index (r=0.88). Therefore, all three investigated indicators are suitable for the study area and using minimum data set (MDS) is more suitable than the method of using total data set (TDS) due to relatively higher correlation and number of features and lower cost. [ABSTRACT FROM AUTHOR] |
