We quantified some mental and qualitative concepts about the soil-landscape relationships by nume... more We quantified some mental and qualitative concepts about the soil-landscape relationships by numerical analysis of landforms in soil identification using diversity indices and conditional probability with a given sample size in Darab and Khosuyeh plains (a rural district) in the south of Iran in Fars province. The geomorphology map was prepared based on the Zinck method and used as a basic design for soil sampling. Finally, 200 soil profiles (0-150 cm) were excavated and described. Diversity indices and conditional probability were calculated based on soil taxonomic and geomorphological hierarchies. The results showed that diversity indices increase from landscape to landform level. The lowest and highest diversity indices were obtained at each geomorphic level for the soil order and soil family. The geomorphic diversity based on the soil taxonomy hierarchy showed that soil orders, including Entisols and Inceptisols, are observed in various landscapes and landforms. In contrast, som...
The effects of geological conditionwere assessed on density of Soil Organic Carbon (SOC) and Nitr... more The effects of geological conditionwere assessed on density of Soil Organic Carbon (SOC) and Nitrogen (N)in a sequence of hillslope landscape, derived from different lithology i.e. loess deposit, reworked loess, marl with mixed siltstone and shale, reddish brown clay deposits and older loess in the semiarid area of northern Iran. However, other factors can influence SOC and N density such as land use, topography and climate with geology, pasture land use have been selected with a homogeneous climate to study their influence on density SOC and N of different lithology. Total of 108 soil samples were selected from two layers of 0-20 cm (surface) and 20-40 cm (subsurface). Results showed higher amount of SOC and N density, Cation Exchange Capacity (CEC) and silt were in surface layer of loess deposit that is related to vegetation density and root growth in this material than other conditions. On the contrary, the amounts of mentioned parameters were the lowest in marl. However, there w...
There are various methods and models for land evaluation. These methods are classified according ... more There are various methods and models for land evaluation. These methods are classified according to the number of used resources. The best resources capabilities or potentials and land use can be found by analyzing one of the main resources, in the lands close relations to ecologic resources exist. Soil has a great potential for introducing the studied region specifications. Hence, it challenges the mistaken belief stating: “soil is only valid for agricultural applications and it is weak in measuring developing and planning domain.” The present study is done with the aim to achieve the best land use according to a single-factor (soil) model for tourism planning. A soil map was obtained using a combination of conventional and digital soil mapping methods. In conventional process soils were mapped using aerial photo interpretation and physiographic methods and in digital process an elevation model and satellite images were used. Based on the field works and laboratory analyses, the so...
In this work, crop (vineyards) development measured by means of the Normalized Difference Vegetat... more In this work, crop (vineyards) development measured by means of the Normalized Difference Vegetation Index (NDVI) from detailed remote sensing data area related to statistical significant differences of soil properties in different soil units (clusters). A detailed map of top soil properties of a 50-ha sample catchment was generated by means of digital soil mapping techniques, based on spatial interpolation methods and cluster analysis. The digital soil map was based on 40 soil samples distributed throughout the catchment. The NDVI was compute from bands 6 (red edge) and 5 (red) of the WorldView-2 satellite, with 2-m pixel resolution. The differences of the NDVI in each soil mapping unit were compared using ANOVA, which proved a significant difference of the NDVI in the soil map units (a = 0.001). Different multiple range tests (LSD, Tukey and Duncan) were carried out to compare NDVI means in each soil map unit. The tests showed contradictory results, showing significant differences...
Although, traditional soil surveys provide information to serve a wide range of applications, fro... more Although, traditional soil surveys provide information to serve a wide range of applications, from detailed to reconnaissance scales, in researches such as soil erosion at detailed scale precise information about top soil is needed. In these cases, a detailed map of top soil properties units can be more efficient than traditional soil maps. Thus, the objective of this research was to map major soil units on the basis of interpolation methods and cluster analysis of the most important soil properties determining soil erosion. Ten primary maps were created based on 40 soil samples using kriging and local polynomial interpolation. These included: bulk density, coarse and fine particle content (5 fractions), organic carbon and water retention capacity (3 components). After individual spatial interpolation of these properties, cluster analysis (Isodata algorithm) was applied using GIS. The result produced an unsupervised map of the top soil properties with 18 cartographic units. For soil...
ABSTRACT Land use change may escalate the process of splash erosion as the primary mechanism caus... more ABSTRACT Land use change may escalate the process of splash erosion as the primary mechanism causing water erosion. The objective of this study was to investigate the impacts of different management practices and land uses on splash erosion in a semiarid region in Iran. The major land uses in the area were pasture, degraded pasture, dry land farming, and irrigated farming. For the purposes of this study, soil properties including organic matter, CaCO3, surface shear strength (SSS), particle size distribution, mean weight diameter (MWD), and the topographic attributes were measured. Soil splash erosion was measured at 80 different locations under the following four conditions comprising different values of slope (S:%) and rainfall intensity (RI:mm·h− 1): 5-50, 5-80, 15-50, and 15-80, respectively, using the multiple splash sets (MSS) especially designed and tailored for the purposes of this study. A completely randomized design was used in which soil texture and the land use systems were independently analyzed. The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression (MLR) analysis. It was found that the splash erosion in the study region was mainly influenced by landuse and soil management practices rather than by intrinsic soil properties like tested textures. The average splash erosion values among landuse types are: degraded pasture > cultivated farming > pasture; this is claimed to be associated with the lower organic matter content and shear strength due to overgrazing and untimely grazing. The FLR models outperformed the MLR ones (p > 0.01). MWD and SSS attributes were the most effective variables in estimating soil splash, indicating the structural susceptibility of the soils to management practices. Based on the results obtained, MWD and SSS may be regarded as important indices of splash erosion.
The aims of this study were: 1) to map the different soil parameters using three geostatistical a... more The aims of this study were: 1) to map the different soil parameters using three geostatistical approaches, including; ordinary kriging (OK), cokriging (CK), and regression kriging (RK), 2) to compare the accuracy of maps created by the mentioned methods, and 3) to evaluate the efficiency of using ancillary data such as satellite images, elevation, precipitation, and slope to improve the accuracy of estimations. In the rangelands of the Poushtkouh area of central Iran, 112 soil samples were collected. The maps of different soil parameters were created using the mentioned methods. To assess the accuracy of these maps, cross-validation analyses were conducted. The cross-validation results were assessed by the root mean square error (RMSE) and normal QQ-plot together with sum and average error to suggest the best estimation approach for mapping each soil parameter. The results have shown that, in most cases, taking the ancillary data into account in estimations has increased the accura...
The purity of soil map units and their quality for various uses like land suitability evaluation ... more The purity of soil map units and their quality for various uses like land suitability evaluation are always questioned. The main
objective of this study was to compare the precision of qualitative land suitability classification based on geostatistical and conventional
soil mapping methods for main irrigated crops in the Shahrekord Plain, Central Iran. A regular grid sampling method consisting 104
sample points was designed and soil samples were collected. Ordinary kriged maps were achieved for all studied soil attributes after
physico-chemical analyses. Afterward, to combine kriged maps and ecological requirements of the studied crops, a script was designed in
ILWIS 3.4 software and consequently, kriged qualitative land suitability maps were generated. Conventional qualitative land suitability
was also mapped based on the representative pedon analysis in each soil map unit. Finally, comparison of the conventional and kriged
maps was carried out using the statistical method, error matrix. The results showed that the overall accuracies of wheat, sugar beet,
potato and alfalfa maps were 39.8%, 24.3%, 18.7% and 18.6% at subclass category, respectively, whereas these values increased to
80.9%, 82.3%, 23.7% and 82.3% at class level, respectively. Hence, it can be stated that thanks to the relative facility of conventional
soil survey compared with geostatistical methods, this method can be expressed as a preferable way for handling a usual land suitability
evaluation design; but using soil map units as land suitability delineations may lead to unsatisfactory results in estimation of quantity
and type of existing limitations.
Soil texture is a key variable that reflect a number
of soil properties such as soil permeability... more Soil texture is a key variable that reflect a number of soil properties such as soil permeability, water holding capacity, nutrient storage and availability, and soil erosion. The main objective of this study was to produce the kriged maps of soils of the Shahrekord region, central Iran. One hundred four soil samples were collected on a 375-m2 sampling grid from the depths of 0–30, 30–60, and 60– 100 centimeter, and their particle sizes were determined using hydrometer method. The results showed a moderately spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, increasing clay and therewith observation of heavier soil textures is evident from surface to subsurface layers of the soils in the studied area due to rainfall and/or irrigation agriculture. These findings indicated that study of the soil texture variation with depth can be used as a clue for site-specific management and precision agriculture. Moreover, we suggest further analysis by using other data layers like topographical parameters, land use, parent material, soil erosion, and any other information which might influence the spatial distribution of soil texture.
Soil texture is an important physical soil property
that may contribute to variations in many soi... more Soil texture is an important physical soil property that may contribute to variations in many soil functions as well as nutrient storage and availability, water retention, and soil erosion. Although several methods for determining the texture classes of soil particles have been proposed, differences among hydrometer reading times have presented challenges in determining the precise soil texture classes. Therefore, this study was conducted to evaluate the effects of hydrometer reading time on the spatial variability of soil textures in the Rafsanjan area, southeast Iran. To accomplish this, 77 soil samples were collected on a 500-m square sampling grid from depths of 0–40, 40–80, and 80–120 cm, and their particle sizes were determined through analysis for 40 s, 2 h, 6.5 h, and 8 h using the Bouyoucos hydrometer method. The results showed a strong spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, the differences among hydrometer reading times did not have a significant impact on determination of coarse soil texture classes, although they did influence determination of the finer classes. Although the 8 h reading time provided the most accurate response with respect to mechanical analysis of a soil, after 6.5 h the hydrometer could also largely (more than 80.0 %, on average) achieve this goal. Additionally, the 2 h hydrometer reading time could also be useful for the initial assessment or general overview of the soil texture in a certain region; however, it is not recommended for precision agriculture or site-specific management.
This study was carried out in rangelands of Poushtkouh area, central Iran. The aims of the study ... more This study was carried out in rangelands of Poushtkouh area, central Iran. The aims of the study were to create the map of different soil parameters using Ordinary Kriging (OK), Cokriging (CK), and Regression Kriging (RK), compare the accuracy of maps created by mentioned methods and evaluate the efficiency of using ancillary data such as satellite images, elevation, precipitation, and slope to improve the accuracy of maps. Totally 112 soil samples were collected across the study area. Results showed that ancillary data are significantly correlated to the target variables and can increase accuracy of prediction. To compare accuracy of map of each soil parameters created by different approaches, cross validation and root mean square eroor (RMSE) were used, demonstrating that for mapping AM and Gyps the CK model performed the best, The OK model performed second best, and the RK model had the worst performance. To estimate Clay, Gravel, Sand, and lime the most accurate model is RK, the accuracy of CK is lower than RK and OK has the lowest accuracy. Based on the RMSE values, the most accurate EC map has been achieved from the OK method. Although, the CK method has created an EC map with a comparable accuracy. Nevertheless, the RK has represented the minimum accuracy among the other methods for predicting the EC map.
Land use change may escalate the process of splash erosion as the primary mechanism causing water... more Land use change may escalate the process of splash erosion as the primary mechanism causing water erosion. The objective of this study was to investigate the impacts of different management practices and land uses on splash erosion in a semiarid region in Iran. The major land uses in the area were pasture, degraded pasture, dry land farming, and irrigated farming. For the purposes of this study, soil properties including organic matter, CaCO3, surface shear strength (SSS), particle size distribution, meanweight diameter (MWD), and the topographic attributeswere measured. Soil splash erosionwas measured at 80 different locations under the following four conditions comprising different values of slope (S:%) and rainfall intensity (RI:mm·h−1): 5–50, 5–80, 15–50, and 15–80, respectively, using the multiple splash sets (MSS) especially designed and tailored for the purposes of this study. A completely randomized design was used in which soil texture and the land use systems were independently analyzed. The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression (MLR) analysis. It was found that the splash erosion in the study region was mainly influenced by landuse and soil management practices rather than by intrinsic soil properties like tested textures. The average splash erosion values among landuse types are: degraded pasture N cultivated farming N pasture; this is claimed to be associated with the lower organic matter content and shear strength due to overgrazing and untimely grazing. The FLRmodels outperformed theMLR ones (p N 0.01).MWD and SSS attributeswere themost effective variables in estimating soil splash, indicating the structural susceptibility of the soils to management practices. Based on the results obtained, MWD and SSS may be regarded as important indices of splash erosion.
Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since ... more Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.
Land evaluation is a critical step in land-use planning. Although many methods have been develope... more Land evaluation is a critical step in land-use planning. Although many methods have been developed since the formulation of the FAO framework for land evaluation, several of the more traditional approaches still remain in widespread use but have not been adequately evaluated. Contrary to more recent land evaluation systems, which need considerable data, these systems only require basic soil and landscape information to provide a general view of land suitability for major types of land use. As the FAO initially presented its qualitative framework for land-use planning, based on two previous methods developed in Iran and Brazil, in this study we assessed the reliability and accuracy of a traditional land evaluation method used in Iran, called land classification for irrigation (LCI), comparing its results with several qualitative and quantitative methods and actual yield values. The results showed that, although simpler than more recently developed methods, LCI provided reliable land suitability classes and also showed good relationships both with other methods analysed and with actual yields. Comparisons between qualitative and quantitative methods produced similar results for common crops (a barley–alfalfa–wheat–fallow rotation). However, these methods performed differently for opportunist crops (such as alfalfa) that are more dependent on income and market conditions than on land characteristics. In this work, we also suggest that using the FAO method to indicate LCI subclasses could help users or managers to recognize limitations for land-use planning.
We quantified some mental and qualitative concepts about the soil-landscape relationships by nume... more We quantified some mental and qualitative concepts about the soil-landscape relationships by numerical analysis of landforms in soil identification using diversity indices and conditional probability with a given sample size in Darab and Khosuyeh plains (a rural district) in the south of Iran in Fars province. The geomorphology map was prepared based on the Zinck method and used as a basic design for soil sampling. Finally, 200 soil profiles (0-150 cm) were excavated and described. Diversity indices and conditional probability were calculated based on soil taxonomic and geomorphological hierarchies. The results showed that diversity indices increase from landscape to landform level. The lowest and highest diversity indices were obtained at each geomorphic level for the soil order and soil family. The geomorphic diversity based on the soil taxonomy hierarchy showed that soil orders, including Entisols and Inceptisols, are observed in various landscapes and landforms. In contrast, som...
The effects of geological conditionwere assessed on density of Soil Organic Carbon (SOC) and Nitr... more The effects of geological conditionwere assessed on density of Soil Organic Carbon (SOC) and Nitrogen (N)in a sequence of hillslope landscape, derived from different lithology i.e. loess deposit, reworked loess, marl with mixed siltstone and shale, reddish brown clay deposits and older loess in the semiarid area of northern Iran. However, other factors can influence SOC and N density such as land use, topography and climate with geology, pasture land use have been selected with a homogeneous climate to study their influence on density SOC and N of different lithology. Total of 108 soil samples were selected from two layers of 0-20 cm (surface) and 20-40 cm (subsurface). Results showed higher amount of SOC and N density, Cation Exchange Capacity (CEC) and silt were in surface layer of loess deposit that is related to vegetation density and root growth in this material than other conditions. On the contrary, the amounts of mentioned parameters were the lowest in marl. However, there w...
There are various methods and models for land evaluation. These methods are classified according ... more There are various methods and models for land evaluation. These methods are classified according to the number of used resources. The best resources capabilities or potentials and land use can be found by analyzing one of the main resources, in the lands close relations to ecologic resources exist. Soil has a great potential for introducing the studied region specifications. Hence, it challenges the mistaken belief stating: “soil is only valid for agricultural applications and it is weak in measuring developing and planning domain.” The present study is done with the aim to achieve the best land use according to a single-factor (soil) model for tourism planning. A soil map was obtained using a combination of conventional and digital soil mapping methods. In conventional process soils were mapped using aerial photo interpretation and physiographic methods and in digital process an elevation model and satellite images were used. Based on the field works and laboratory analyses, the so...
In this work, crop (vineyards) development measured by means of the Normalized Difference Vegetat... more In this work, crop (vineyards) development measured by means of the Normalized Difference Vegetation Index (NDVI) from detailed remote sensing data area related to statistical significant differences of soil properties in different soil units (clusters). A detailed map of top soil properties of a 50-ha sample catchment was generated by means of digital soil mapping techniques, based on spatial interpolation methods and cluster analysis. The digital soil map was based on 40 soil samples distributed throughout the catchment. The NDVI was compute from bands 6 (red edge) and 5 (red) of the WorldView-2 satellite, with 2-m pixel resolution. The differences of the NDVI in each soil mapping unit were compared using ANOVA, which proved a significant difference of the NDVI in the soil map units (a = 0.001). Different multiple range tests (LSD, Tukey and Duncan) were carried out to compare NDVI means in each soil map unit. The tests showed contradictory results, showing significant differences...
Although, traditional soil surveys provide information to serve a wide range of applications, fro... more Although, traditional soil surveys provide information to serve a wide range of applications, from detailed to reconnaissance scales, in researches such as soil erosion at detailed scale precise information about top soil is needed. In these cases, a detailed map of top soil properties units can be more efficient than traditional soil maps. Thus, the objective of this research was to map major soil units on the basis of interpolation methods and cluster analysis of the most important soil properties determining soil erosion. Ten primary maps were created based on 40 soil samples using kriging and local polynomial interpolation. These included: bulk density, coarse and fine particle content (5 fractions), organic carbon and water retention capacity (3 components). After individual spatial interpolation of these properties, cluster analysis (Isodata algorithm) was applied using GIS. The result produced an unsupervised map of the top soil properties with 18 cartographic units. For soil...
ABSTRACT Land use change may escalate the process of splash erosion as the primary mechanism caus... more ABSTRACT Land use change may escalate the process of splash erosion as the primary mechanism causing water erosion. The objective of this study was to investigate the impacts of different management practices and land uses on splash erosion in a semiarid region in Iran. The major land uses in the area were pasture, degraded pasture, dry land farming, and irrigated farming. For the purposes of this study, soil properties including organic matter, CaCO3, surface shear strength (SSS), particle size distribution, mean weight diameter (MWD), and the topographic attributes were measured. Soil splash erosion was measured at 80 different locations under the following four conditions comprising different values of slope (S:%) and rainfall intensity (RI:mm·h− 1): 5-50, 5-80, 15-50, and 15-80, respectively, using the multiple splash sets (MSS) especially designed and tailored for the purposes of this study. A completely randomized design was used in which soil texture and the land use systems were independently analyzed. The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression (MLR) analysis. It was found that the splash erosion in the study region was mainly influenced by landuse and soil management practices rather than by intrinsic soil properties like tested textures. The average splash erosion values among landuse types are: degraded pasture > cultivated farming > pasture; this is claimed to be associated with the lower organic matter content and shear strength due to overgrazing and untimely grazing. The FLR models outperformed the MLR ones (p > 0.01). MWD and SSS attributes were the most effective variables in estimating soil splash, indicating the structural susceptibility of the soils to management practices. Based on the results obtained, MWD and SSS may be regarded as important indices of splash erosion.
The aims of this study were: 1) to map the different soil parameters using three geostatistical a... more The aims of this study were: 1) to map the different soil parameters using three geostatistical approaches, including; ordinary kriging (OK), cokriging (CK), and regression kriging (RK), 2) to compare the accuracy of maps created by the mentioned methods, and 3) to evaluate the efficiency of using ancillary data such as satellite images, elevation, precipitation, and slope to improve the accuracy of estimations. In the rangelands of the Poushtkouh area of central Iran, 112 soil samples were collected. The maps of different soil parameters were created using the mentioned methods. To assess the accuracy of these maps, cross-validation analyses were conducted. The cross-validation results were assessed by the root mean square error (RMSE) and normal QQ-plot together with sum and average error to suggest the best estimation approach for mapping each soil parameter. The results have shown that, in most cases, taking the ancillary data into account in estimations has increased the accura...
The purity of soil map units and their quality for various uses like land suitability evaluation ... more The purity of soil map units and their quality for various uses like land suitability evaluation are always questioned. The main
objective of this study was to compare the precision of qualitative land suitability classification based on geostatistical and conventional
soil mapping methods for main irrigated crops in the Shahrekord Plain, Central Iran. A regular grid sampling method consisting 104
sample points was designed and soil samples were collected. Ordinary kriged maps were achieved for all studied soil attributes after
physico-chemical analyses. Afterward, to combine kriged maps and ecological requirements of the studied crops, a script was designed in
ILWIS 3.4 software and consequently, kriged qualitative land suitability maps were generated. Conventional qualitative land suitability
was also mapped based on the representative pedon analysis in each soil map unit. Finally, comparison of the conventional and kriged
maps was carried out using the statistical method, error matrix. The results showed that the overall accuracies of wheat, sugar beet,
potato and alfalfa maps were 39.8%, 24.3%, 18.7% and 18.6% at subclass category, respectively, whereas these values increased to
80.9%, 82.3%, 23.7% and 82.3% at class level, respectively. Hence, it can be stated that thanks to the relative facility of conventional
soil survey compared with geostatistical methods, this method can be expressed as a preferable way for handling a usual land suitability
evaluation design; but using soil map units as land suitability delineations may lead to unsatisfactory results in estimation of quantity
and type of existing limitations.
Soil texture is a key variable that reflect a number
of soil properties such as soil permeability... more Soil texture is a key variable that reflect a number of soil properties such as soil permeability, water holding capacity, nutrient storage and availability, and soil erosion. The main objective of this study was to produce the kriged maps of soils of the Shahrekord region, central Iran. One hundred four soil samples were collected on a 375-m2 sampling grid from the depths of 0–30, 30–60, and 60– 100 centimeter, and their particle sizes were determined using hydrometer method. The results showed a moderately spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, increasing clay and therewith observation of heavier soil textures is evident from surface to subsurface layers of the soils in the studied area due to rainfall and/or irrigation agriculture. These findings indicated that study of the soil texture variation with depth can be used as a clue for site-specific management and precision agriculture. Moreover, we suggest further analysis by using other data layers like topographical parameters, land use, parent material, soil erosion, and any other information which might influence the spatial distribution of soil texture.
Soil texture is an important physical soil property
that may contribute to variations in many soi... more Soil texture is an important physical soil property that may contribute to variations in many soil functions as well as nutrient storage and availability, water retention, and soil erosion. Although several methods for determining the texture classes of soil particles have been proposed, differences among hydrometer reading times have presented challenges in determining the precise soil texture classes. Therefore, this study was conducted to evaluate the effects of hydrometer reading time on the spatial variability of soil textures in the Rafsanjan area, southeast Iran. To accomplish this, 77 soil samples were collected on a 500-m square sampling grid from depths of 0–40, 40–80, and 80–120 cm, and their particle sizes were determined through analysis for 40 s, 2 h, 6.5 h, and 8 h using the Bouyoucos hydrometer method. The results showed a strong spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, the differences among hydrometer reading times did not have a significant impact on determination of coarse soil texture classes, although they did influence determination of the finer classes. Although the 8 h reading time provided the most accurate response with respect to mechanical analysis of a soil, after 6.5 h the hydrometer could also largely (more than 80.0 %, on average) achieve this goal. Additionally, the 2 h hydrometer reading time could also be useful for the initial assessment or general overview of the soil texture in a certain region; however, it is not recommended for precision agriculture or site-specific management.
This study was carried out in rangelands of Poushtkouh area, central Iran. The aims of the study ... more This study was carried out in rangelands of Poushtkouh area, central Iran. The aims of the study were to create the map of different soil parameters using Ordinary Kriging (OK), Cokriging (CK), and Regression Kriging (RK), compare the accuracy of maps created by mentioned methods and evaluate the efficiency of using ancillary data such as satellite images, elevation, precipitation, and slope to improve the accuracy of maps. Totally 112 soil samples were collected across the study area. Results showed that ancillary data are significantly correlated to the target variables and can increase accuracy of prediction. To compare accuracy of map of each soil parameters created by different approaches, cross validation and root mean square eroor (RMSE) were used, demonstrating that for mapping AM and Gyps the CK model performed the best, The OK model performed second best, and the RK model had the worst performance. To estimate Clay, Gravel, Sand, and lime the most accurate model is RK, the accuracy of CK is lower than RK and OK has the lowest accuracy. Based on the RMSE values, the most accurate EC map has been achieved from the OK method. Although, the CK method has created an EC map with a comparable accuracy. Nevertheless, the RK has represented the minimum accuracy among the other methods for predicting the EC map.
Land use change may escalate the process of splash erosion as the primary mechanism causing water... more Land use change may escalate the process of splash erosion as the primary mechanism causing water erosion. The objective of this study was to investigate the impacts of different management practices and land uses on splash erosion in a semiarid region in Iran. The major land uses in the area were pasture, degraded pasture, dry land farming, and irrigated farming. For the purposes of this study, soil properties including organic matter, CaCO3, surface shear strength (SSS), particle size distribution, meanweight diameter (MWD), and the topographic attributeswere measured. Soil splash erosionwas measured at 80 different locations under the following four conditions comprising different values of slope (S:%) and rainfall intensity (RI:mm·h−1): 5–50, 5–80, 15–50, and 15–80, respectively, using the multiple splash sets (MSS) especially designed and tailored for the purposes of this study. A completely randomized design was used in which soil texture and the land use systems were independently analyzed. The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression (MLR) analysis. It was found that the splash erosion in the study region was mainly influenced by landuse and soil management practices rather than by intrinsic soil properties like tested textures. The average splash erosion values among landuse types are: degraded pasture N cultivated farming N pasture; this is claimed to be associated with the lower organic matter content and shear strength due to overgrazing and untimely grazing. The FLRmodels outperformed theMLR ones (p N 0.01).MWD and SSS attributeswere themost effective variables in estimating soil splash, indicating the structural susceptibility of the soils to management practices. Based on the results obtained, MWD and SSS may be regarded as important indices of splash erosion.
Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since ... more Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.
Land evaluation is a critical step in land-use planning. Although many methods have been develope... more Land evaluation is a critical step in land-use planning. Although many methods have been developed since the formulation of the FAO framework for land evaluation, several of the more traditional approaches still remain in widespread use but have not been adequately evaluated. Contrary to more recent land evaluation systems, which need considerable data, these systems only require basic soil and landscape information to provide a general view of land suitability for major types of land use. As the FAO initially presented its qualitative framework for land-use planning, based on two previous methods developed in Iran and Brazil, in this study we assessed the reliability and accuracy of a traditional land evaluation method used in Iran, called land classification for irrigation (LCI), comparing its results with several qualitative and quantitative methods and actual yield values. The results showed that, although simpler than more recently developed methods, LCI provided reliable land suitability classes and also showed good relationships both with other methods analysed and with actual yields. Comparisons between qualitative and quantitative methods produced similar results for common crops (a barley–alfalfa–wheat–fallow rotation). However, these methods performed differently for opportunist crops (such as alfalfa) that are more dependent on income and market conditions than on land characteristics. In this work, we also suggest that using the FAO method to indicate LCI subclasses could help users or managers to recognize limitations for land-use planning.
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Papers by Mohsen Bagheri
objective of this study was to compare the precision of qualitative land suitability classification based on geostatistical and conventional
soil mapping methods for main irrigated crops in the Shahrekord Plain, Central Iran. A regular grid sampling method consisting 104
sample points was designed and soil samples were collected. Ordinary kriged maps were achieved for all studied soil attributes after
physico-chemical analyses. Afterward, to combine kriged maps and ecological requirements of the studied crops, a script was designed in
ILWIS 3.4 software and consequently, kriged qualitative land suitability maps were generated. Conventional qualitative land suitability
was also mapped based on the representative pedon analysis in each soil map unit. Finally, comparison of the conventional and kriged
maps was carried out using the statistical method, error matrix. The results showed that the overall accuracies of wheat, sugar beet,
potato and alfalfa maps were 39.8%, 24.3%, 18.7% and 18.6% at subclass category, respectively, whereas these values increased to
80.9%, 82.3%, 23.7% and 82.3% at class level, respectively. Hence, it can be stated that thanks to the relative facility of conventional
soil survey compared with geostatistical methods, this method can be expressed as a preferable way for handling a usual land suitability
evaluation design; but using soil map units as land suitability delineations may lead to unsatisfactory results in estimation of quantity
and type of existing limitations.
of soil properties such as soil permeability, water holding
capacity, nutrient storage and availability, and soil erosion.
The main objective of this study was to produce the kriged
maps of soils of the Shahrekord region, central Iran. One
hundred four soil samples were collected on a 375-m2
sampling grid from the depths of 0–30, 30–60, and 60–
100 centimeter, and their particle sizes were determined
using hydrometer method. The results showed a moderately
spatial correlation in the soil particles among sampling soil
layers and across the study area. Moreover, increasing clay
and therewith observation of heavier soil textures is evident
from surface to subsurface layers of the soils in the studied
area due to rainfall and/or irrigation agriculture. These findings
indicated that study of the soil texture variation with
depth can be used as a clue for site-specific management and
precision agriculture. Moreover, we suggest further analysis
by using other data layers like topographical parameters,
land use, parent material, soil erosion, and any other information
which might influence the spatial distribution of soil
texture.
that may contribute to variations in many soil functions as well
as nutrient storage and availability, water retention, and soil
erosion. Although several methods for determining the texture
classes of soil particles have been proposed, differences
among hydrometer reading times have presented challenges
in determining the precise soil texture classes. Therefore, this
study was conducted to evaluate the effects of hydrometer
reading time on the spatial variability of soil textures in the
Rafsanjan area, southeast Iran. To accomplish this, 77 soil
samples were collected on a 500-m square sampling grid from
depths of 0–40, 40–80, and 80–120 cm, and their particle sizes
were determined through analysis for 40 s, 2 h, 6.5 h, and 8 h
using the Bouyoucos hydrometer method. The results showed
a strong spatial correlation in the soil particles among
sampling soil layers and across the study area. Moreover, the
differences among hydrometer reading times did not have a
significant impact on determination of coarse soil texture
classes, although they did influence determination of the finer
classes. Although the 8 h reading time provided the most
accurate response with respect to mechanical analysis of a
soil, after 6.5 h the hydrometer could also largely (more than
80.0 %, on average) achieve this goal. Additionally, the 2 h
hydrometer reading time could also be useful for the initial
assessment or general overview of the soil texture in a certain
region; however, it is not recommended for precision agriculture
or site-specific management.
objective of this study was to investigate the impacts of different management practices and land uses on splash
erosion in a semiarid region in Iran. The major land uses in the area were pasture, degraded pasture, dry land
farming, and irrigated farming. For the purposes of this study, soil properties including organic matter, CaCO3,
surface shear strength (SSS), particle size distribution, meanweight diameter (MWD), and the topographic attributeswere
measured. Soil splash erosionwas measured at 80 different locations under the following four conditions
comprising different values of slope (S:%) and rainfall intensity (RI:mm·h−1): 5–50, 5–80, 15–50, and
15–80, respectively, using the multiple splash sets (MSS) especially designed and tailored for the purposes of
this study. A completely randomized design was used in which soil texture and the land use systems were independently
analyzed. The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression
(MLR) analysis. It was found that the splash erosion in the study region was mainly influenced by landuse
and soil management practices rather than by intrinsic soil properties like tested textures. The average splash
erosion values among landuse types are: degraded pasture N cultivated farming N pasture; this is claimed to be
associated with the lower organic matter content and shear strength due to overgrazing and untimely grazing.
The FLRmodels outperformed theMLR ones (p N 0.01).MWD and SSS attributeswere themost effective variables
in estimating soil splash, indicating the structural susceptibility of the soils to management practices. Based on
the results obtained, MWD and SSS may be regarded as important indices of splash erosion.
to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron
artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal
ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate
the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had
a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB)
classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case
of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and
validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of
classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to
landforms and/or lithology as soil-forming factors, should be used as ANN input data.
since the formulation of the FAO framework for land evaluation, several of the more traditional
approaches still remain in widespread use but have not been adequately evaluated. Contrary to more
recent land evaluation systems, which need considerable data, these systems only require basic soil
and landscape information to provide a general view of land suitability for major types of land use.
As the FAO initially presented its qualitative framework for land-use planning, based on two previous
methods developed in Iran and Brazil, in this study we assessed the reliability and accuracy of a
traditional land evaluation method used in Iran, called land classification for irrigation (LCI),
comparing its results with several qualitative and quantitative methods and actual yield values. The
results showed that, although simpler than more recently developed methods, LCI provided reliable
land suitability classes and also showed good relationships both with other methods analysed and
with actual yields. Comparisons between qualitative and quantitative methods produced similar
results for common crops (a barley–alfalfa–wheat–fallow rotation). However, these methods
performed differently for opportunist crops (such as alfalfa) that are more dependent on income and
market conditions than on land characteristics. In this work, we also suggest that using the FAO
method to indicate LCI subclasses could help users or managers to recognize limitations for land-use
planning.
objective of this study was to compare the precision of qualitative land suitability classification based on geostatistical and conventional
soil mapping methods for main irrigated crops in the Shahrekord Plain, Central Iran. A regular grid sampling method consisting 104
sample points was designed and soil samples were collected. Ordinary kriged maps were achieved for all studied soil attributes after
physico-chemical analyses. Afterward, to combine kriged maps and ecological requirements of the studied crops, a script was designed in
ILWIS 3.4 software and consequently, kriged qualitative land suitability maps were generated. Conventional qualitative land suitability
was also mapped based on the representative pedon analysis in each soil map unit. Finally, comparison of the conventional and kriged
maps was carried out using the statistical method, error matrix. The results showed that the overall accuracies of wheat, sugar beet,
potato and alfalfa maps were 39.8%, 24.3%, 18.7% and 18.6% at subclass category, respectively, whereas these values increased to
80.9%, 82.3%, 23.7% and 82.3% at class level, respectively. Hence, it can be stated that thanks to the relative facility of conventional
soil survey compared with geostatistical methods, this method can be expressed as a preferable way for handling a usual land suitability
evaluation design; but using soil map units as land suitability delineations may lead to unsatisfactory results in estimation of quantity
and type of existing limitations.
of soil properties such as soil permeability, water holding
capacity, nutrient storage and availability, and soil erosion.
The main objective of this study was to produce the kriged
maps of soils of the Shahrekord region, central Iran. One
hundred four soil samples were collected on a 375-m2
sampling grid from the depths of 0–30, 30–60, and 60–
100 centimeter, and their particle sizes were determined
using hydrometer method. The results showed a moderately
spatial correlation in the soil particles among sampling soil
layers and across the study area. Moreover, increasing clay
and therewith observation of heavier soil textures is evident
from surface to subsurface layers of the soils in the studied
area due to rainfall and/or irrigation agriculture. These findings
indicated that study of the soil texture variation with
depth can be used as a clue for site-specific management and
precision agriculture. Moreover, we suggest further analysis
by using other data layers like topographical parameters,
land use, parent material, soil erosion, and any other information
which might influence the spatial distribution of soil
texture.
that may contribute to variations in many soil functions as well
as nutrient storage and availability, water retention, and soil
erosion. Although several methods for determining the texture
classes of soil particles have been proposed, differences
among hydrometer reading times have presented challenges
in determining the precise soil texture classes. Therefore, this
study was conducted to evaluate the effects of hydrometer
reading time on the spatial variability of soil textures in the
Rafsanjan area, southeast Iran. To accomplish this, 77 soil
samples were collected on a 500-m square sampling grid from
depths of 0–40, 40–80, and 80–120 cm, and their particle sizes
were determined through analysis for 40 s, 2 h, 6.5 h, and 8 h
using the Bouyoucos hydrometer method. The results showed
a strong spatial correlation in the soil particles among
sampling soil layers and across the study area. Moreover, the
differences among hydrometer reading times did not have a
significant impact on determination of coarse soil texture
classes, although they did influence determination of the finer
classes. Although the 8 h reading time provided the most
accurate response with respect to mechanical analysis of a
soil, after 6.5 h the hydrometer could also largely (more than
80.0 %, on average) achieve this goal. Additionally, the 2 h
hydrometer reading time could also be useful for the initial
assessment or general overview of the soil texture in a certain
region; however, it is not recommended for precision agriculture
or site-specific management.
objective of this study was to investigate the impacts of different management practices and land uses on splash
erosion in a semiarid region in Iran. The major land uses in the area were pasture, degraded pasture, dry land
farming, and irrigated farming. For the purposes of this study, soil properties including organic matter, CaCO3,
surface shear strength (SSS), particle size distribution, meanweight diameter (MWD), and the topographic attributeswere
measured. Soil splash erosionwas measured at 80 different locations under the following four conditions
comprising different values of slope (S:%) and rainfall intensity (RI:mm·h−1): 5–50, 5–80, 15–50, and
15–80, respectively, using the multiple splash sets (MSS) especially designed and tailored for the purposes of
this study. A completely randomized design was used in which soil texture and the land use systems were independently
analyzed. The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression
(MLR) analysis. It was found that the splash erosion in the study region was mainly influenced by landuse
and soil management practices rather than by intrinsic soil properties like tested textures. The average splash
erosion values among landuse types are: degraded pasture N cultivated farming N pasture; this is claimed to be
associated with the lower organic matter content and shear strength due to overgrazing and untimely grazing.
The FLRmodels outperformed theMLR ones (p N 0.01).MWD and SSS attributeswere themost effective variables
in estimating soil splash, indicating the structural susceptibility of the soils to management practices. Based on
the results obtained, MWD and SSS may be regarded as important indices of splash erosion.
to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron
artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal
ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate
the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had
a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB)
classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case
of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and
validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of
classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to
landforms and/or lithology as soil-forming factors, should be used as ANN input data.
since the formulation of the FAO framework for land evaluation, several of the more traditional
approaches still remain in widespread use but have not been adequately evaluated. Contrary to more
recent land evaluation systems, which need considerable data, these systems only require basic soil
and landscape information to provide a general view of land suitability for major types of land use.
As the FAO initially presented its qualitative framework for land-use planning, based on two previous
methods developed in Iran and Brazil, in this study we assessed the reliability and accuracy of a
traditional land evaluation method used in Iran, called land classification for irrigation (LCI),
comparing its results with several qualitative and quantitative methods and actual yield values. The
results showed that, although simpler than more recently developed methods, LCI provided reliable
land suitability classes and also showed good relationships both with other methods analysed and
with actual yields. Comparisons between qualitative and quantitative methods produced similar
results for common crops (a barley–alfalfa–wheat–fallow rotation). However, these methods
performed differently for opportunist crops (such as alfalfa) that are more dependent on income and
market conditions than on land characteristics. In this work, we also suggest that using the FAO
method to indicate LCI subclasses could help users or managers to recognize limitations for land-use
planning.