Soil science
F. Maghami Moghim; A.R. Karimi; M. Bagheri Bodaghabadi; H. Emami
Abstract
Introduction The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the ...
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Introduction The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the natural resources. However, farmers and stakeholders need to be aware that through their management practices, they affect soil quality and, with the short-term goal of production and greater profitability, lead to soil degradation. They can both use the land economically and improve and maintain soil quality by balancing production inputs and refining their management approaches. There are different management systems of productivity in agricultural lands in Neyshabour plain in northeastern Iran. In addition to the water and soil limitations in the study area, the prevalence of the smallholder system and the unwillingness of farmers to integrate smallholder, has further increased the destruction of soils in the study area. The objective of this study was to assess the changes in soil quality index in surface soil and profile (0-100 cm) and calculate the correlation between soil quality index and alfalfa and rapeseed yield in rangeland and agricultural areas managed by smallholders, total owners, and Binalood Company in the study area.Materials and Methods A total of 21 soil profiles were described in the total owner, smallholder and Binalood company management system and sampled from the alfalfa and rapeseed lands. Questionnaires were prepared with the help of farmers and experts in the study area based on Analytic Hierarchical analysis (AHP) method. The physical and chemical characteristics of the soil samples were determined. The important soil characteristics affecting plant growth were determined by interviewing farmers and experts study area. Soil quality index in the minimum data set (MDS) was calculated by two methods of principal component analysis (PCA) and expert opinion (EO), by additive and weighted methods in surface soil and profile. To achieve a single value for each soil properties in the soil profile, two methods of weighted mean and weighted factor were used. To evaluate the accuracy of the assessment, the correlation between soil quality index and alfalfa and rapeseed yield was investigated of the various management system.Result and DiscussionThe results showed that the highest additive and weighted soil quality index at both surface and soil profile in both PCA and EO methods were in rangeland. It was due to lack of cultivation and maintaining organic matter comparing to agricultural land. The total owner management system due to its economic power and the use of appropriate and scientific methods comparing to smallholder management system, showed the highest additive and weighted soil quality index. In all management system, the EO-calculated weight index by weighted factor method had the highest value due to assigning the suitable weight for soil characteristics. The correlation analyses soil quality indices with canola and alfalfa indicated that the EO soil quality calculated by weighted factor for the soil profile were more correlated than surface soil in total owner system and the Binalood company. Weight coefficient method due to the application of different weights to each layer based on their importance, showed a higher soil quality index in both EO and PCA sets than the weighted average method. The reason for better EO performance probably is that the PCA is a reducing the dimensions, meanwhile, the minimum data selection in the EO method is based on regional experts which are familiar with cause-and-effect relationship of the soil properties. Due to the relatively good correlation of the yield of the studied products, with the soil quality index, an appropriate management needs to maintain and improve soil quality, especially in the smallholder system, as well as meeting the nutritional needs of these products.Conclusion Soil quality assessment in this study indicated that calculation of the soil quality index only considering the surface soil properties may not provide complete information for the farmers and land managers. Then inclusion of both surface and profile soil properties with farmers' knowledge and study area experts are essential for sustainable soil management. On the other hand, the differences in the management system also affected the soil quality index. Although the smallholder management system due to low input, especially chemical fertilizers, water and agricultural implements, had a high potential concerning environmental issues, but in terms of production, total owner and Binalood company management systems because of their high economic strength had the higher soil quality index. The farmers and stakeholders of the total owner management systems should be considered despite the proper management, however due to high inputs of fertilizer and water, especially in the Binalood company, the production may not be sustainable. Therefore, for further studies, calculating the water consumption in the desired management systems is recommended.
M. Bagheri-Bodaghabadi
Abstract
Introduction: In land suitability evaluation using parametric method, Khiddir or square root method (LQSI) and/or Storie method (LSI) are employed to calculate land index (LI), then suitability classes could be determined based on the LI. However, the obtained LI should be corrected according to the ...
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Introduction: In land suitability evaluation using parametric method, Khiddir or square root method (LQSI) and/or Storie method (LSI) are employed to calculate land index (LI), then suitability classes could be determined based on the LI. However, the obtained LI should be corrected according to the minimum rating (Rmin) and then the suitability classes should be determined. The existing functions to correct the LI should be mathematically continuous at all points in order to prevent from losing some LIs and their consequent suitability classes. In the functions represented by Sys, there is a continuity for S1 (suitable), S2 (moderately suitable) and S3 (marginal suitable) classes, but for N (unsuitable) the presented functions are not continuous. Therefore, presented functions for N1 and N2 classes can be very misleading since they are not able to distinguish between N1 and N2 classes and have problem to calculate them. Materials and Methods: In this study, the existing functions in the literature were mathematically evaluated for each land suitability classes. Properties and criteria for determining land suitability classes are shown in Table1. In parametric approach, land index (uncorrected land index) is calculated using Kiddir and Storrie methods as shown in equations 1and 2, respectively. The relationships between uncorrected land indices and corrected land indices are presented in Table 2. (1) (2) According to continuity rules, the necessary corrections were made for N1 and N2 classes. Then numerical simulation was employed to assess the obtained results from the both existing and purposed functions and compared them with one another. For this purpose, one million random values were created for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range and the other seven characteristics were random numbers between Rmin and 100. For example, in the S3 class, a minimum random number is in the range of 40 to 60 and seven other characteristics were between the Rmin and 100. Finally, a total of two million random simulations were created. Results and Discussion: Based on the minimum, maximum and mean obtained values the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. The results showed that for N1 and N2 classes the correction functions should be respectively 12.5 + 0.314LQSI and 0.5LQSI for the Khiddir method and 12.5+ 0.313LSI and 0.5LSI for the Storie method to maintain the both the continuity of the correction functions for all classes and the corrected land index to be in the defined range for each class. The two million times simulation results also confirmed the accuracy of the obtained functions Therefore, it is suggested to use the proposed functions in determining N1 and N2 classes instead of Sys’s functions. Conclusion: The use of the usual land index, which is conventionally calculated by the Khiddir or Storie method, called uncorrected land index (UCLI), can be largely misleading without being corrected and converted to the corrected land index (CLI), causing the wrong land suitability classes. Therefore, it is very important to use the relationships that have been developed for this purpose to correct the usual land index. The findings of this study showed that the current functions, although at the order level can distinguish between unsuitable order (N) from the S3 class, but separation between classes N1 and N2 are very difficult to calculate. For this reason, new relationships for N1 and N2 classes were calculated and presented. Therefore, it is suggested that N1 and N2 classes can be used instead of the relationships presented.
M. Bagheri-Bodaghabadi
Abstract
The Importance of Correcting Land Indices in Determining Land Suitability Classes Introduction: Land evaluation plays a decisive role in determining land suitability for the intended uses. For this purpose, various approaches have been proposed, among which the parametric approach has a special place. ...
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The Importance of Correcting Land Indices in Determining Land Suitability Classes Introduction: Land evaluation plays a decisive role in determining land suitability for the intended uses. For this purpose, various approaches have been proposed, among which the parametric approach has a special place. In this approach, the land indices (LIs) are calculated using the Khidir method (the square root) and/or the storrie method, and then the land suitability classes are determined based on the LIs. Unfortunately, in many land suitability studies, the land index has been used without being corrected, called uncorrected land index. This has led to many differences in the results of different approaches of land suitability evaluation. The current study shows the importance of employment of the corrected land index and its effect on land suitability classes. Materials and Methods: In this study land suitability classes were determined by the four methods including 1-simple limitation, 2- number and intensity of limitations, 3- Kiddir (square root) and 4- storrie, using the two cases i.e. the corrected land index and the uncorrected land index. Properties and criteria for determining land suitability classes are shown in Table1. Simple limitation method is based on the Liebig’s law or the law of the minimum. Land classes are defined according to the lowest class level of the land characteristics. Number and intensity of limitations method has been described in table 1. In parametric approach land index (uncorrected land index) is calculated using Kiddir and Storrie methods as shown in equations 1and 2, respectively. The relationships between uncorrected land indices and corrected land indices are presented in table 2. (1) (2) Then, a simulation process was done for the eight characteristics involved in calculating the land suitability index. For this purpose, one million random values were created for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range (Rating in Table 1) and the other seven characteristics were random numbers between Rmin and 100. For example, in the S2 class, a minimum random number is in the range of 60 to 85 and seven other characteristics were between this Rmin and 100. Finally, a total of five million random simulations were created. Results and Discussion: Table 3 shows the results of five million simulations for S1 to N2 classes. Based on the minimum, maximum and mean values obtained, it can be seen that the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. Based on the results, it is clear that the mean values of the land indices for the Storrie method are much lower than the Khiddir ones, but the mean values for the corrected land indices, do not differ too much, in the both the Storrie and Khiddir methods. These results are sufficient to conclude the importance of using the corrected land indices and to show the difference between the results obtained from the corrected land indices and the uncorrected land indices. Tables 4 to 8 show the results of one million simulations for each suitability class. The results showed that using the corrected land indices, the results of the four employed methods are much closer, especially for the Storrie and Khiddir methods. All together, the simple limitation method was more consistent with the Khiddir method. On the other hand, the employed methods differed greatly when the uncorrected land indices were used. The analysis of five million simulations has shown that the contradictory results of land evaluation methods in various studies can be quite logical, mathematically, but with a different probability. Totally, the results of the uncorrected land indices may be largely inaccurate and misleading, and may show unrealistic results. Therefore, it is strongly suggested that the corrected land indices be used in determining the suitability classes, and then the results be compared with the observations in the reality. Conclusions: According to the findings of the current study, it can be illustrated that it is very important and necessary using the corrected land index to determine the land suitability class. The study showed, using the corrected land index leads to the closeness of the results of different methods, so that there is no significant difference between Storrie and Khiddir methods. In general, the results of the Khidir method are closer to the simple constraint method than the Storrie ones, although using the uncorrected land index, there was a very significant difference between the Khiddir and Storrie methods, but using the corrected land index the difference was too small and insignificant.
M. Bagheri-Bodaghabadi; M.H. Salehi; J. Mohammadi; N. Toomanian; I. Esfandiarpour Borujeni
Abstract
Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available ...
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Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available or measuring environmental variables. The objective of this investigation is to study the efficiency of digital elevation model and its derivates for soil mapping using Soli-Land Inference Model (SoLIM) and credibility of its results in the Borujen area, Chaharmahal-va-Bakhtiari province. Eighteen terrain attributes including height, slope (angle), aspect, curvature, minimum curvature, maximum curvature, tangent curvature, profile curvature, planform curvature, flow direction, flow accumulation, direct radiation, diffuse duration, diffuse radiation, area solar radiation, power index, sediment index and wetness index, were derived from the DEM. These derivates as well as three dominant soil subgroups and seven soil families of the region were used to construct the input data matrix of the model. Results showed an accuracy of 65% and 40% for interpolation and extrapolation of the soils at subgroup level, respectively. The accuracy decreased to half when soil families were considered for credibility of the model. Because of using crisp limitations in American Soil Taxonomy system, assessing soil survey results can be miss-leading partially, whereas using SoLIM model shows well the reality of the soils in the field.
Keywords: SoLIM, Fuzzy logic, Digital soil mapping, Digital elevation model
