Relationships between soil depth and terrain attributes in a semi arid hilly region in western Iran

Soil depth generally varies in mountainous regions in rather complex ways. Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time, effort and consequently relatively large budget to perform. This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran. For this, one hundred sampling points were selected using randomly stratified methodology, and considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope; and soil depth was actually measured. Eleven primary and secondary topographic attributes were derived from the digital elevation model (DEM) at the study area. The result of multiple linear regression indicated that slope, wetness index, catchment area and sediment transport index, which were included in the model, could explain about 76 % of total variability in soil depth at the selected site. This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale

Prediction of Soil Solum Depth Using Topographic Attributes in Some Hilly Land of Koohrang in Central Zagros

Introduction: Soil depth is defined as the depth from the surface to more-or-less consolidated material and can be considered as the most crucial soil indicator, affecting desertification and degradation in disturbed ecosystems. Soil depth varies as a function of many different factors, including slope, land use, curvature, parent material, weathering rate, climate, vegetation cover, upslope contributing area, and lithology. Topography, one of the major soil forming factors, controls various soil properties. Thus, quantitative information on the topographic attributes has been applied in the form of digital terrain models (DTMs). The prediction of soil depth by topographic attributes depends mainly on: i) the spatial scale of topographic variation in the area, ii) the nature of the processes that are responsible for spatial variation in soil depth, and iii) the degree to which terrain-soil relationships have been disturbed by human activities. This study was conducted to explore the relationships of soil depth with topographic attributes in a hilly region of western Iran. Materials and Methods: The study area is located at Koohrang district between 32°20′ to 32°30′ N latitudes and 50°14′ to 50°24′ E longitudes, in Charmahal and Bakhtiari province, western Iran. The field sites with an area of 30,000 ha are located on the hillslopes at about 20% transversal slope. The soils at the site are classified as Typic Calcixerepts, Typic Xerorthents and Calcic Haploxerepts for the representative excavated profiles in summit, shoulder and backslope, respectively. The soils located at footslope and toeslope were classified as Chromic Calcixererts. Measurements were made in twenty representative hillslopes of the studied area. At the selected site, one hundred points were selected using randomly stratified methodology, considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope during sampling. Overall, 100 profiles were dug and described; and the solum thickness was measured for each profile. DEM data were created by using a 1:2,5000 topographic map. Topographical indices were generated from the DEM using TAS software. Terrain attributes in two categories, primary and secondary (compound) attributes; primary attributes are included elevation, slope, aspect, catchment area, dispersal area, plan curvature, profile curvature, tangential curvature, shaded relief. Secondary or compound attributes such as soil water content or the potential for sheet erosion, stream power index, wetness index, and sediment transport index. Correlation coefficients to define relationships between soil depth and terrain attributes, and analysis of variance by Duncan test were done using the SPSS software. The statistical software SPSS was used for developing multiple linear regression models. Terrain attributes were selected as the independent variables and soil depth was employed as dependent variable in the model. Thirty sampling sites were used to validate the developed soil-landscape model. In testing soil-landscape model, we calculated two indices from the observed and predicted values included mean error (ME) and root mean square error (RMSE). Results and Discussion: The soil depth in the studied profiles varied from 30 cm to 150 cm with an average of 108.6 cm. Relatively high variability (CV = 76%) was obtained for soil depth in the study area. The linear correlation analysis of the 12 topographic attributes and one soil property (soil depth), showed that there was a significant correlation among 36 of the 77 attribute pairs. Soil depth showed high positive significant correlations with catchment area, plan curvature, and wetness index, and showed high negative correlation with sediment transport index, sediment power index and slope. Low positive significant correlations of soil depth were identified with tangential curvature, and profile curvature. Moreover, soil depth was negatively correlated with elevation. The rest of the topographic attributes including aspect, shaded relief, and dispersal area were not significantly correlated with soil depth. Many of these relationships are similar to those found in other landscapes. The results of analysis of variance showed that there are significant differences for soil depth among the selected slope positions in the studied area. The highest values of soil depth were observed in the downslope positions including footslope and toeslope. The lowest soil depth was observed in shoulder position with the highest rate of soil erosion. Conclusions: It seems that the high variability for soil depth depends on topography of the field, and the landscape position, causing differential accumulation of water at different positions on the landscape; and moreover the soil erosion and deposition processes, resulting in high variability in the soil depth. We found relatively high correlation coefficients of soil depth with two groups of topographic attributes (erosional processes and water accumulation). Empirical model (MLR) using selected terrain attributes explains 76% of the variation of soil depth in the studied area. The terrain attributes that best predicted soil depth variability in the selected site were mainly the attributes that had significant relationships with soil depth. The dominant attributes in the MLR model included slope, wetness index, catchment area and sediment transport index

The Effect of Seaweed Extract and LED on the Growth and flowering of Two Lisianthus Cultivars

Lisianthus is a slow-growing flowering plant whose seed germination and growth is a challenge in the tropics. Due to the marketability of this type of cut flower, this study was performed in two experiments to investigate the effect of seaweed extract (SWE) and LED on the growth of seedlings of two cultivars of lisianthus (namely Ariana and Mariachi) and its effect on flowering as a factorial experiment in a completely randomized design. The first experiment consisted of LED treatments with blue, red, blue-red, and white light at concentrations of 125, 250, 500, 750 and 1000 ml/L of seaweed extract and control treatment (no treatment) with 8 replications. According to the results of the first experiment, the greatest leaf width and stem diameter were observed in the presence of blue light and the greatest leaf diameter was observed in the presence of red-blue combined light. In the second experiment, conducted 60 days after the second experiment, the greatest number of flower buds per plant and stem length were observed under the white light condition. Also, in Arena cultivar, concentrations of 500 and 750 ml/L of seaweed extract led to a greater number of flower buds per plant than the control treatment. Increases in morphological traits of leaf width and diameter, leaf length, and internode length were observed upon exposure to blue light and red-blue combined light in both of the experiments. Enhancements in most of the morphological traits in both experiments was observed in the presence of blue light and red-blue LED combined light. According to the results of this study, it seems that production of Arena cultivar in the presence of blue light and a concentration of 500 ml /L of seaweed extract is more feasible than the other cultivar and light and seaweed amendments

Relationships between grain protein, Zn, Cu, Fe and Mn contents in wheat and soil and topographic attributes

The knowledge on the relationships of protein and micronutrient concentration in wheat grain with edaphic characteristics could provide valuable information for site specific fertilization of crops for producing grains denser in micronutrients such as iron (Fe) and zinc (Zn) in rainfed agriculture. In this study, we used soil properties and topographic parameters in the artificial neural network (ANN) methodology as power tool for improving models for predicting wheat grain micronutrient and protein contents in the hilly regions of western Iran. Soil and grain samples were collected from 1 m2 plots using stratified random method, whereas the slope positions were considered as the basis of soil sampling, at 100 selected points. The mean grain Zn, Fe, Cu (copper) and Mn (manganese) concentrations were 37.02, 65.86, 14.79 and 44.93 mg-1 kg-1, respectively, and mean grain protein was 13.76%. Application of the ANN models for predicting of Zn, Fe, Cu, Mn and protein contents in grains improved prediction 96.77, 95.45, 124.13, 125 and 109.75 %, respectively, over the multiple linear regression (MLR) models. The topographic parameters wetness index, plan curvature and shaded relief, and the selected soil properties total nitrogen (TN), soil organic matter, available phosphorus, and DTPA-extractable micronutrients were identified as the most important parameters for explaining the variability in wheat grain quality at the study area