Arid Regions Of Iran Research Articles

Preliminary assessment of genetic diversity and population connectivity of the Mugger Crocodile in Iran

The Mugger Crocodile (Crocodylus palustris) is a threatened reptile inhabiting the Indian Sub-continent and Western Asia. Despite its “Vulnerable” conservation status, data about population genetic structure and connectivity are unavailable. This study makes a preliminary assessment of the genetic diversity, population structure and habitat connectivity of C. palustris in Iran. Ten tissue samples collected along the Sarbaz-Bahukalat basins were analysed and a set of 12 microsatellites was genotyped. Genetic diversity indices were estimated and population substructuring was assessed through Bayesian clustering analysis. Potential connectivity was verified through Remote Sensing water indexes, further implemented in a circuit analysis. Low genetic diversity was observed (mean observed heterozygosity = 0.35; mean expected heterozygosity = 0.43) and no population structure was found (K = 1). Water index and circuit analysis suggested possible connection among sites. This study highlights the potential vulnerability of crocodile populations and the importance of habitat connectivity for their persistence in the arid regions of Iran.

The extrapolation of soil great groups using multinomial logistic regression at regional scale in arid regions of Iran

Soil information is essential for sustainable management of ecosystems. In many parts of Iran, soil information is either not available or difficult to obtain. Therefore, when no detailed map or soil information is accessible in a region of interest, one way to obtain information is to extrapolate information from other parts having soil information. This study was conducted to determine whether - machine-learning extrapolation method extracted from the reference region, i.e. Zarand can estimate soil classes in the interest region, i.e. Bam and reduce the costs of soil mapping. To identify similarities between reference and interest regions, homology of soil forming factors was determined using Gower's similarity index. Then, the multinomial logistic regression was extracted from the reference region and applied into the interest region to estimate soil classes. Moreover, soil classes were predicted in the interest region using direct soil observations; finally, the accuracy of the soil maps obtained from both methods was assessed. Based on Gower index, the study regions, namely Bam and Zarand, were to some extent similar in term of soil forming factors. The results showed that although the soil map derived from the extrapolation process indicated appropriate spatial coverage of soil classes in the interest area, the resultant predictive map of calibrated-in process was slightly more accurate, i.e. higher κ, lower Brier scores. Acceptable levels of predictive accuracy (60%) were achieved using extrapolated model while costs simultaneously significantly lowered. This study put forward the view that the extrapolation method was quite useful in predicting soil classes within areas where soil mapping by calibrated-in method might be too costly or time consuming or where soil observations may not be sufficient. Nevertheless, this research encouraged us to use extrapolated method to fill the gaps in the present soil map of Iran and to apply it as the base map to increase and improve the efficiency of digital soil mapping.

The influence of iranian islamic architecture on the jewish house (west sābāt house) of bam citadel

Iranian Islamic architecture beyond the evident typology is formed through a conceptual framework, which has a great deal of importance to show suitable and compatible-with-climate designs. Specifically in the hot and arid regions of Iran, vernacular architecture has frequently intimated the formation of cities and the architectural combinations. The ancient city of Bam by itself is a field, the origin of which can be considered as one of the first foci for the organization of civilization in the hot and arid environments of Iran throughout history, which shows traces of architecture that organizes spaces in such a way that they function as a medium to emphasize the relevance of human lifestyle. This article describes the history of Jewish House briefly, wherein the mansion was one of the loftiest traditional houses in Bam Citadel, and later classifies the effects of natural and cultural criteria in its climate- compatible architectural design. In fact, we can get a complete resource about the norms of Iranian Islamic architecture on the traditional Jewish House and the qualities and functions that encouraged this family tendency for utilizing Iranian Islamic architecture in their residence.

Erratum to: Investigation of the atmospheric mesoscale circulation patterns and their simulation with WRF-CHEM model of the dust storm occurrence over the southern coast of the Caspian Sea

For the very first time, the mesoscale circulation patterns and synoptic-dynamic structure of the atmospheric systems that led to the dust emission to the south coast region of Caspian Sea (SCRCS) were identified and classified using the region synoptic stations’ observations of 2005–2013. Satellite measurements and images, NCEP/NCAR reanalysis data, and Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-CHEM) model products were also used in this study. Results showed that in 49 % of cases, cyclonic circulations over the Middle East deserts were the main transporter of dust particles into the atmosphere where then transferred to the SCRCS by southerly winds over the Alborz mountains in the lower troposphere and by westerly waves in the middle and upper troposphere. During the warm seasons, the surface heating lead to the development of mesoscale thermal low pressures over the hot deserts on the eastern regions of the Caspian Sea, like Turkmenistan and Qura Qum. Those heat lows were responsible for the 38 % of the occurred events. Turbulence and instabilities in the lower troposphere were identified as the second important dust emitter to the atmosphere where those dust particles transported to the SCRCS with the strong northeasterly wind. The third pattern by 13 % of cases was belonging to the mesoscale thermal low pressure that was developed over the arid regions of Iran like Dasht-e-Kavir. Because of the nature of the turbulence in the lower troposphere and heat lows, the ascent of dust particles by these two mechanisms was limited to a shallow layer in the troposphere. The results of simulation with the WRF-CHEM model, analysis of moderate resolution imaging spectroradiometer (MODIS) images, and spatial zoning of atmospheric optical depth (AOD) confirmed the results of the synoptic study.

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

In arid regions, knowledge of the variation in soil texture is crucial for land management because it affects soil physical, chemical, biological and most importantly hydrological properties. The availability of information on soil texture is scarce even though it is required to support land‐use management and sustainable development. Because it is costly to obtain information about the individual particle‐size fractions (PSFs), we used digital soil mapping methods (DSM) with environmental covariates that are less costly to obtain. Specifically, we explored the use of a digital elevation model and remote sensing data as environmental covariates to predict the vertical (i.e. 0–0.15, 0.15–0.3, 0.3–0.6 and 0.6–1 m) and lateral variation in PSFs over a 150‐km2 area in central Iran. We used a combination of equal‐area spline depth functions and three data‐mining techniques: multiple linear regression (MLR), artificial neural networks (ANN) and the neuro‐fuzzy inference system (ANFIS). In addition, we explored the effect of the reduction in dimension of feature space with ant colony optimization (ACO) and correlation‐based feature selection (CFS) on the accuracy of prediction of spatial models for each PSF. The results showed that the prediction of clay at 0–0.15‐m depth with ACO indicated the importance of including Landsat ETM+, the digital numbers of band 7 of Landsat images (B7) and clay index, whereas at 0.60–1‐m depth the wetness index and multi‐resolution valley bottom flatness index (MRVBF) were important. Model evaluation by leave‐one‐out cross‐validation with 191 soil observations indicated that the predictions by the ACO‐based ANFIS model (RMSE = 4.51% and R2 = 0.74 for clay at 0–0.15‐m) were more accurate than those by MLR and ANN. Spatial prediction was also better for the topsoil (0–0.15‐m) than at depth (RMSE = 7.1% for clay at 0.6–1 m); therefore, we conclude that the environmental covariates tested cannot resolve subsurface variation as accurately. Nevertheless, we recommend prediction by the ACO‐based ANFIS model and splines of lateral and vertical distribution of PSFs in other arid regions of Iran with the same agro‐ecological conditions.HighlightsDigital soil mapping of particle size‐fractions (PSF) by adaptive neuro‐fuzzy inference and ant colony optimization. Use of ant colony optimization (ACO) to assist in feature selection of environmental covariates. Neuro‐fuzzy inference system (ANFIS) superior to multiple linear regression (MLR) and artificial neural networks (ANN). PSF prediction by ACO‐based ANFIS model and splines is optimal.

Using ground‐penetrating radar to explore the cemented soil horizon in an arid region in Iran

ABSTRACTPetrosalic horizon (cemented by soluble salts) is identified as a root‐restricting layer in the arid and semi‐arid regions. The knowledge of the variability of cemented layers within the soil profiles provides valuable information to decision‐makers for agricultural and/or engineering activities. This study was conducted to evaluate the potential of ground‐penetrating radar in detecting petrosalic horizon within the soil pedon in an arid area (Kerman Province, southeastern Iran). The measurements were performed using an impulse ground‐penetrating radar system with a centre frequency of 250 MHz along ten parallel transects of 100‐m length and 10‐m distance between two consecutive transects at two sites. The results of the soil stratigraphy indicated the presence of petrosalic horizons at 12‐ to 36‐cm depth. The ground‐penetrating radar images showed patterns corresponding to these soil horizons. The interpreted radargrams were quite consistent with the findings of the soil pedons. It is, therefore, suggested that ground‐penetrating radar could be employed to detect the boundaries between different soil horizons in the arid areas with various degrees of conductivity. This, in turn, helps differentiate soil‐mapping units and improve the reliability and accuracy of digital soil maps. The results confirmed that ground‐penetrating radar could be used as a rapid, efficient and non‐destructive tool providing highresolution and continuous visualisation of the soil horizons to detect the presence and depth of petro‐salic horizons. Such information could be crucial in making decisions in agricultural practices (e.g., creating gardens) and engineering activities (e.g., constructing roads and buildings).

A study on the thermal behavior of traditional residential buildings: Rasoulian house case study

Traditional buildings throughout the globe provide instructive examples of sustainable solutions to building design problems. In recent years, researchers and architects have conducted different studies that represent the principles of Iran’s climate-responsive architecture. Yazd traditional buildings are examples of Iranian architecture that bear characteristics of environmentally friendly buildings. Thermal performance and comfort condition of these buildings in summer have been the matter of interest in recent studies.In this article, summer sections of Yazd traditional homes and their thermal behavior were studied. Yazd is located in hot and arid regions of Iran. EnergyPlus software program was used to investigate the thermal behavior of this house. To validate the developed model, a field study was carried out with lascar electronics data logger. The results reveal that internal temperature of summer sections in all seasons has less fluctuation than the outdoor temperature. Also, weather temperature in underground spaces is closer to the comfort temperature in comparison with spaces near to the ground floor. Therefore, to provide thermal comfort condition in these homes the least amount of non-renewable energy is needed.

Amygdalus Scoparia as a New Feedstock for Biodiesel Production

Biodiesel is an attractive alternative fuel because of its nontoxicity and biodegradability which can be produced from vegeTable oils and animal fats. Finding a proper feedstock has an important role on the biodiesel characteristics and the price. Therefore in this research, amygdalus Scoparia oil was used as a cheap potential feedstock for biodiesel production which is widespread in arid regions in Iran. This native plant contains 58-60% oil which is consist of 62.81% oleic acid and 23.54% linoleic acid. Biodiesel was produced from transesterification of extracted oil in reaction with methanol in the presence of potassium hydroxide as a catalyst. The reaction variables used were methanol/oil molar ratio (4:1-7:1), catalyst concentration (0.25-1.5%), and reaction time (1-7 h). Optimal conditions for methanolysis were 1% KOH concentration, MeOH/oil of molar ratio 6:1 for a period of 7 h. The yield of biodiesel produced under optimal conditions were 97.32. Overall, amygdalus Scoparia kernel is a promising feedstock for biodiesel production and large cultivation will help to reduce the product cost.

Studying spatial and temporal changes of aridity in Iran

Optimum use of water in agriculture, drinking, and industry is necessary in arid and semi-arid regions like Iran, which face serious water tensions. Since studying aridity and its trend of changes has a significant role in water resources management and urban planning, this research has investigated spatial and temporal changes and trend of aridity changes in Iran as an arid country. To study the issue, 51 weather stations of Iran which covered a 30-year statistical period (1981–2010) were selected. Climatic variables, namely precipitation, vapor pressure, local pressure, temperature, maximum temperature, and relative humidity and required data to calculate the potential evapotranspiration were extracted from Iran meteorology organization data. In next step by applying Ai, Im, and Iq aridity indices, zoning of the aridity was done after studying the aridity traits and its change. Then the most compatible aridity index was introduced according to the values of correlation between climatic factors of aridity indices and the magnitude of each main climatic area of Iran. Furthermore, in order to recognize aridity changes in Iran during the selected 30-year period, Mann-Kendal nonparametric test was used. Correlation analysis results showed that 35.4 and 64.6 % of Iran arid areas show more compatibility with Im and Ai, respectively, which means that these indices are more appropriate to study aridity in arid regions of Iran. Also, it was revealed that 5.6 and 94.3 % of Caspian south plain region has more compatibility with Ai and Iq indices, respectively. As a matter of fact, 77.8, 0.75, and 21.4 % of the country area can be studied better by Ai, Iq, and Im, respectively. Also, the results of Mann-Kendal nonparametric test confirmed that aridity is developing in Iran. In other words, 80.4 % of Iran area had increasing trend of aridity during the selected period and in just 19.6 % of the area, aridity trend was not significant or decreasing.

Effect of the interaction of water and nitrogen on sunflower under drip irrigation in an arid region

Sunflower has become an important crop for oil production in many arid regions across the globe. However, with water as a scarce resource, efficient water and nutrient management systems need to be identified. The goal of this project was to determine the interaction between water and nitrogen (N) for sunflower grown in an arid environment. Experiments were conducted during 2010 and 2011 in an arid region in Iran that included two sunflower hybrids, four levels of surface drip irrigation, ranging from severe deficit irrigation to over irrigation, and three levels of N fertilizer, i.e. 0, 47, and 93kgNha−1. The treatments were arranged in a strip-plot design with complete randomized blocks with three replications. Irrigations were based on daily monitoring of soil moisture in the experimental plots. For both hybrids, irrigation and the interaction of water and N had a significant effect (P<0.01) on total biomass, seed yield, oil yield, and N Use Efficiency (NUE). Maximum sunflower seed and oil production (4031 and 1635kgha−1, respectively) occurred in over irrigation and 93kgNha−1. The lowest soil N uptake was 31kgha−1 for deficit irrigation, while the highest was 99.5kgha−1 for over irrigation. The highest seed NUE was 26.7kgkg−1 and oil NUE was 12.2kgkg−1 for over irrigation with no N input. Increasing the amount of N fertilizer was not a suitable strategy under severe deficit irrigation. Overall, the optimal levels of N depended on the sunflower hybrid type and varied for different levels of irrigation. Adjusting N fertilizer rates to meet crop requirements based on the amount of water applied and the type of hybrid not only prevented a negative environmental impact, but also increased yield and conserved agricultural inputs for sunflower grown in an arid region.

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

ABSTRACTSpatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECav and horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted ECe over the study area accurately (R2 = 0.87, ME = −1.04 and RMSE = 16.36 dSm−1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.

Modeling forage production by using climatic factors and drought indices in humid and arid regions of Iran

AbstractAs the utilization of rangelands in Iran has exceeded 2.2 times its carrying capacity, increasing numbers of livestock, especially in drought years, have dramatic effects on rangelands. In this regard, the prediction of forage production is an important management strategy to mitigate the consequences of drought. In Plour and Saveh Range Experimental Sites, climatic factors such as precipitation, temperature (mean, maximum and minimum), evapotranspiration, standardized precipitation index, reconnaissance drought index, and/or a combination of these factors were used to construct a predictive model of forage production. For each climatic variable and/or index, 33 time periods of 1–4, 6 and 9‐months were specified. We used principal components analysis, stepwise regression and best subset regression, to reduce the number of variables and then the appropriate time periods selected. To select a model, assessment statistics of correlation coefficient, mean of bias error, root mean of square error, mean of absolute relative error and ideal point error were used. Finally models derived from combined climatic factors and drought indices were selected for the prediction of forage production at both study areas. In the arid region of Saveh, production is more effected by precipitation, while in the humid and colder region of Plour, minimum temperature had more effects on plant growth. In Saveh, due to a lack of rainfall in February and minimum temperatures in March, production has direct and indirect relationships with drought and with maximum temperatures, respectively. In the Plour, production has a direct relationship with the maximum temperatures of March–June and drought of February–March and has an indirect relationship with the maximum temperatures of May–June.

Determination of number of check dams by artificial neural networks in arid regions of Iran.

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R(2), root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R(2) (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

Antinociceptive mechanisms of Bunium persicum essential oil in the mouse writhing test: role of opioidergic and histaminergic systems

Bunium persicum (Boiss.) is an economically important medicinal plant growing wildly in arid regions in Iran. The fruit of B. persicum is widely used in traditional Iranian medicine to control colic pain and dysmen - orrhoea. The aim of the current study was to determine antinociceptive mechanisms of B. persicum essential oil using an acetic acid-induced writhing test as a model of visceral pain and to determine the possible involvement of opioidergic, serotoninergic and histaminergic systems on antinociceptive mechanisms of B. persicum in male mice. In experiment 1, B. persicum was intraperitoneally ( i.p.) injected (0.001, 0.01, 0.05, 0.1, 0.5 and 1%; 10 ml/kg) in Tween-80 (0.5%) and a writhing test served as a model of visceral pain. In experiments 2-5, opioidergic receptor antagonist (naloxone, 2 mg/kg), serotonergic receptor antagonist (cyproheptadine, 4 mg/kg), histamine H 1 -recep- tor antagonist (chlorpheniramine, 20 mg/kg) and histamine H 2 -receptor antagonist (cimetidine, 12.5mg/kg) injection was followed by B. persicum (0.01%; 10 ml/kg) and the writhing test response was measured for 30 min. According to the results, essential oil of B. persicum, administered i.p. (0.001, 0.01, 0.05, 0.1, 0.5, and 1%; 10 ml/kg) in Tween-80 (0.5%), elicited antinociceptive effects in a dose-dependent manner. Moreover, the antinociceptive effect of B. persicum was significantly attenuated by pre-treatment with naloxone, chlorpheniramine and cimeti - dine (P < 0.001). These results suggest that B. persicum-induced analgesia may be mediated via opioidergic and histamine H 1 and H 2 receptors.

Interrelationship Analysis of L-Band Backscattering Intensity and Soil Dielectric Constant for Soil Moisture Retrieval Using PALSAR Data

The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.

The Male Reproductive Cycle of the Spotted Toad-Headed Agama,Phrynocephalus maculatus, in Iran

The spotted toad-headed agama,Phrynocephalus maculatus, distributed in the central and southeastern arid regions of Iran, belongs to the Agamidae family. In this research, the male reproductive cycle of this species was studied from 5 April to 5 August, 2013. Totally, 40 adult males were collected at midday from southern deserts of Damghan County, located in Semnan Province of Iran. Testes were removed and processed for morphometric and histological studies. The spermatogenic cycle begins from early April, mating occurs in mid-May, and it ends in August. Maximum reproductive activity occurs in early June and reduces from early July and ends in August. The numbers of seminal vesicles were 33–127 and their diameter varied between 69.0 and 258.3 microns. The diameter of tunica albuginea varied between 3.0 and 8.1 microns. The diameter of germinal layer varied between 10.0 and 110.0 microns. There were significant differences in macroscopic and microscopic testicular characters between months. Also, there were no significant differences in the testicular and hemipenal characters between the left side and the right side of body. Since spermatogenesis occurs from April through August,P. maculatusfollows an associated reproductive cycle typical for temperate species.

Needle-leaved trees impacts on rainfall interception and canopy storage capacity in an arid environment

In semiarid and arid areas, it is important to understand the amount of rainfall reaching the ground in order to develop effective water plan strategies for the selection of the tree species for afforestation goals. We measured the rainfall interception and canopy storage capacity of individual trees of Pinus eldarica and Cupressus arizonica growing in an afforested Park located in an arid region of Iran. Correlations between percent relative interception and gross rainfall, mean daily wind speed, mean daily air temperature, and mean daily relative humidity were examined both for the wet and dry seasons. Canopy storage capacity was estimated by indirect methods: the Pereira, mean, minimum, and Gash and Morton. The cumulative mean values of percent relative interception and gross rainfall for P. eldarica and C. arizonica trees averaged 66 and 53 %, respectively. Combining meteorological parameters with rainfall depth only slightly improved the correlation with the percent of gross rainfall lost to interception loss in the dry season for P. eldarica (from 0.87 to 0.90) and C. arizonica (from 0.90 to 0.92). For P. eldarica, the mean estimate of canopy storage capacity was 1.31 versus 1.00 mm in C. arizonica trees. Because the storms in this region are small, the large canopy water storage of P. eldarica will result in greater interception losses relative to C. arizonica. Therefore, afforestation efforts would be better served planting C. arizonica over P. eldarica, if maximizing water inputs is a main management goal.

Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran

We evaluated the suitability and performance of boosted regression trees (BRT) as a potential technique for soil mapping using a limited point dataset in an arid region of Iran. The model was applied using two approaches: logistic-BRT as an indirect approach and multiclass-BRT as a direct approach to produce soil class maps. In indirect prediction, the occurrence of relevant diagnostic horizons was first mapped and various maps were then combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. To allow combination of the indicator maps for classification into great groups, a decision tree was defined for linking the occurrence of diagnostic horizons to a soil great group. In direct prediction, the dependent variable was the great group itself and; therefore, the probability distribution of the soil great groups was directly predicted. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes and Landsat data as quantitative variables and a geomorphology map as categorical variable. To assess the added value of the geomorphology map, the most laborious auxiliary variable to obtain, the BRT-performance of 2 data situations was compared: (i) using only the DEM and remote sensing covariates and (ii) additionally using the geomorphology map. Results showed that the geomorphology map contributed importantly to the prediction accuracy. When it was removed from the predictors, the prediction accuracy strongly decreased. The maximum reduction in predictive performance of both indirect and direct models occurred in the absence of geomorphology map, particularly for soil classes whose presence has direct relationship with geomorphic surfaces. Generally, poor predictions seemed to be mainly due to low sample size, high variability of ancillary predictors and the inability of the geomorphology map to differentiate the strata in detail. In most predictions, the purity was better for the direct model. The indirect method predicted a high probability of salic horizon in playa landform, gypsic horizon in gypsiferous hills and calcic horizon in alluvial fans. The indirect method could provide an insight into the causes of errors in prediction at the level of diagnostic horizons, which could help in selecting better covariates.

The Parameters of Sustainable Landscape in Persian Garden Design

This paper concentrates of results based on study on sustainable parameters in the creation of Persian garden in arid regions of Iran. Persian garden has had special role to moderate local climates for many centuries and nowadays the recognitions of its sustainable features could be a strategy to reduce the total energy consumption. By the analysis of sustainable characteristics of Persian garden, the main goal is to identify sustainable parameters in landscape design for present and future with similar climates could be obtained.

Development of Sugar Beet Salt Tolerant Triploid Hybrids

Salinity is a major constraint on crop productivity in arid regions of Iran which decreases sugar beet yield. In order to identify and select salt tolerant triploid hybrids, 20 sugar beet three-way hybrids produced by crosses between 10 salinity improved pollinators and two cytoplasmic male sterile monogerm (231 × 261) and multigerm (MSC2 × A1) lines as seed parents. In 2007–2008 growing seasons, the 20 resulting hybrids along with five check varieties were evaluated in field study using a lattice square (5 × 5) design with three replications, and irrigated with 12 dS m−1 irrigation water. The combined analyses showed that the genotypes differed significantly (P < 0.01) for the most studied traits such as root yield; sugar yield; white sugar yield; sugar content; white sugar content; Na+ content; K+ content; extraction coefficient of sugar and molasses sugar indicating the presence of genetic variation and possibility of selection for sugar beet salinity tolerant lines under saline condition. The mean comparison for yield traits showed that monogerm triploid hybrids (261 × 231) × 19669-T and (261 × 231) × 13030 and multigerm triploid hybrids (MSC2 × A1) × JIT13, (MSC2 × A1) × C3.3, (MSC2 × A1) × 13030 and (MSC2 × A1) × 13022 produced 5.52 t/ha root yield and 1.38 t/ha sugar yield more than the mean of the checks under salinity stress across 2 years. Also, the means of five top hybrids showed 1.52 % sugar content, 1.77 % white sugar content and 3.19 % extraction coefficient of sugar more than the mean of the checks. These hybrids had 0.93 meq/100 gr K+ and 0.81 meq/100 gr Na+ less than the mean of the checks. These promising hybrids are currently being tested in Roudasht Agricultural Research Station as an important step towards development of sugar beet salt tolerant triploid hybrids.