year 14, Issue 3 (Autumn 2024)
E.E.R. 2024, 14(3): 43-65 |
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Marefati H, Shahab Arkhazloo H, Asghari S, soltani toolarood A A. The effect of gully erosion on integrated soil quality indices in South of Ardabil. E.E.R. 2024; 14 (3) :43-65
URL: http://magazine.hormozgan.ac.ir/article-1-847-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-847-en.html
Department of soil science and engineering faculty of agriculture and natural resources, University of Mohaghegh Ardabili., Ardabili, Iran. , hose_shohab@yahoo.com
Abstract: (1684 Views)
1- Introduction
Soil erosion is one of the most important environmental hazards. Investigating the impact of soil erosion on soil quality is possible through the evaluation of soil quality indices in eroded and non-eroded points. Studying the severity of soil erosion in order to protect the soil and investigate and evaluate its risks is of particular importance. There is a significant relationship between increasing soil erosion and decreasing soil quality, therefore soil quality and erosion are closely related phenomena. The main prerequisite for dealing with all types of erosion, including gully erosion, is to predict the risk of gully formation and its effects in different places. The purpose of this study in the south of Ardabil city is to calculate the soil quality indicators and investigate the effect of soil erosion on the quality indicators, compare the correlation between the most important characteristics affecting the soil quality (MDS) and the total characteristics affecting the soil quality (TDS) and provide an index distribution map. The quality of the soil is determined with the help of geostatistical methods.
2- Methodology
The studied area is a part of Mullah Ahmed watershed near Amin Lo village, 10 km south of Ardabil city. A part of this area was selected with an area of about 50 hectares, which had active Gullies. After identifying and investigating the study area, 48 samples were taken from the depth of 0 to 30 cm of the soil surface, including 24 non-eroded and 24 eroded points. Sampling from each point was done based on the change of topographical conditions and variety of soil characteristics, especially soil color, and in the form of paired points. After laboratory analysis, the total of the measured features was considered as the total data set (TDS) and the principal component analysis (PCA) method was used to select the minimum data set (MDS) (Doran and Parkin, 1994). The contribution of each feature (COM) was calculated by factor analysis (FA) in TDS and MDS sets (Shukla et al; 2004). Then, soil quality indices were calculated using IQI and NQI relationships (Doran and Parkin, 1994). After calculating the soil quality indices at the sampling points in the region, continuous maps of the distribution of soil quality indices were obtained using two methods, Kriging and IDW, with the help of GS Plus 5.1 and Arc GIS 10.8 software.
3- Results
According to the results of PCA test, Sand, Clay, pH and Ec variables in eroded points and BD, Sand, Clay and n variables in non-eroded points were selected as MDS set. Pearson's correlation for soil quality indices in two sets of TDS and MDS in eroded and non-eroded points was significant at 5% level. According to the significant results, the use of the MDS set can optimally calculate the soil quality indices in non-eroded points instead of the TDS set. According to the distribution maps of soil quality and the findings of Qi (2009), the studied area is in grade III to II in terms of soil quality. In eroded points, in IQI and NQI indices, Kriging method was the best in TDS set and IDW method was the best in MDS set. While in the non-eroded points, except for the NQI index in the TDS set, in the rest of the soil quality indices, the Kriging method showed a better and more accurate performance.
4- Discussion & Conclusions
Due to the significant correlation in non-eroded points, the use of MDS can acceptably calculate the soil quality indices (IQI and NQI) in non-eroded points, and in these points the IQI and NQI indices calculated using TDS are much superior. It is not compared to MDS. In general, in the studied area, based on the coefficient of explanation (R^2) and the mean square error, the kriging method was more effective in showing the distribution of soil quality indices compared to the IDW method. The results of all these researches show that the optimal and appropriate method for estimation and estimating data can be different depending on the variable. The limited size of the studied area, sample intervals, the number of samples, the heterogeneity of the area in terms of variables and the existence of a trend can also influence the selection of estimation methods. According to the results obtained from the studied area, soil erosion has a significant effect on some soil quality characteristics. According to this research, conducting similar researches in other parts of Iran under different soil and climate conditions, along with a wide range of erosion effects on soil quality, general modeling of soil quality is important. The result of this can help to develop and create a national index of soil quality, and the existence of this index can provide the possibility of permanent monitoring of the soil condition at the level of agricultural and natural areas of the country and predict the possibility of soil destruction and erosion.
Soil erosion is one of the most important environmental hazards. Investigating the impact of soil erosion on soil quality is possible through the evaluation of soil quality indices in eroded and non-eroded points. Studying the severity of soil erosion in order to protect the soil and investigate and evaluate its risks is of particular importance. There is a significant relationship between increasing soil erosion and decreasing soil quality, therefore soil quality and erosion are closely related phenomena. The main prerequisite for dealing with all types of erosion, including gully erosion, is to predict the risk of gully formation and its effects in different places. The purpose of this study in the south of Ardabil city is to calculate the soil quality indicators and investigate the effect of soil erosion on the quality indicators, compare the correlation between the most important characteristics affecting the soil quality (MDS) and the total characteristics affecting the soil quality (TDS) and provide an index distribution map. The quality of the soil is determined with the help of geostatistical methods.
2- Methodology
The studied area is a part of Mullah Ahmed watershed near Amin Lo village, 10 km south of Ardabil city. A part of this area was selected with an area of about 50 hectares, which had active Gullies. After identifying and investigating the study area, 48 samples were taken from the depth of 0 to 30 cm of the soil surface, including 24 non-eroded and 24 eroded points. Sampling from each point was done based on the change of topographical conditions and variety of soil characteristics, especially soil color, and in the form of paired points. After laboratory analysis, the total of the measured features was considered as the total data set (TDS) and the principal component analysis (PCA) method was used to select the minimum data set (MDS) (Doran and Parkin, 1994). The contribution of each feature (COM) was calculated by factor analysis (FA) in TDS and MDS sets (Shukla et al; 2004). Then, soil quality indices were calculated using IQI and NQI relationships (Doran and Parkin, 1994). After calculating the soil quality indices at the sampling points in the region, continuous maps of the distribution of soil quality indices were obtained using two methods, Kriging and IDW, with the help of GS Plus 5.1 and Arc GIS 10.8 software.
3- Results
According to the results of PCA test, Sand, Clay, pH and Ec variables in eroded points and BD, Sand, Clay and n variables in non-eroded points were selected as MDS set. Pearson's correlation for soil quality indices in two sets of TDS and MDS in eroded and non-eroded points was significant at 5% level. According to the significant results, the use of the MDS set can optimally calculate the soil quality indices in non-eroded points instead of the TDS set. According to the distribution maps of soil quality and the findings of Qi (2009), the studied area is in grade III to II in terms of soil quality. In eroded points, in IQI and NQI indices, Kriging method was the best in TDS set and IDW method was the best in MDS set. While in the non-eroded points, except for the NQI index in the TDS set, in the rest of the soil quality indices, the Kriging method showed a better and more accurate performance.
4- Discussion & Conclusions
Due to the significant correlation in non-eroded points, the use of MDS can acceptably calculate the soil quality indices (IQI and NQI) in non-eroded points, and in these points the IQI and NQI indices calculated using TDS are much superior. It is not compared to MDS. In general, in the studied area, based on the coefficient of explanation (R^2) and the mean square error, the kriging method was more effective in showing the distribution of soil quality indices compared to the IDW method. The results of all these researches show that the optimal and appropriate method for estimation and estimating data can be different depending on the variable. The limited size of the studied area, sample intervals, the number of samples, the heterogeneity of the area in terms of variables and the existence of a trend can also influence the selection of estimation methods. According to the results obtained from the studied area, soil erosion has a significant effect on some soil quality characteristics. According to this research, conducting similar researches in other parts of Iran under different soil and climate conditions, along with a wide range of erosion effects on soil quality, general modeling of soil quality is important. The result of this can help to develop and create a national index of soil quality, and the existence of this index can provide the possibility of permanent monitoring of the soil condition at the level of agricultural and natural areas of the country and predict the possibility of soil destruction and erosion.
Keywords: Cumulative quality index, Geostatistics, Iran, Nemouro quality index, Soil erosion, Soil quality.
Received: 2024/04/22 | Published: 2024/10/1
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