نشریه آب و خاک
سال بیست و چهارم شماره 2 (پیاپی 10، خرداد - تیر 1389)

AbstractRainwater harvesting (RWH) is one of the most effective water resources management techniques for confrontation with water shortage condition. RWH which is basically known as a traditional method, has been developed rapidly and being used widely in dryland countries throughout the world. RWH is defined as collecting and storing rainwater from nearby catchment and delivering it to planting area during critical stages of plant requirement. RWH can be divided into two main categories which is known as micro and macro catchment methods. Many factors such as rainfall amount and distribution, ground topography, soil type, economical and social aspects are important for selecting a specific RWH category. In the present paper, design and performance of a real size macro catchment RWH project located in a semi arid region (N-E of Iran), is described. The project is comprised from a 5000 m2 plastic covered catchment, 500 m3 ground reservoir and an experimental cultivation area located next to runoff catchment which is planted with dryland wheat. Planting area is consisted of 8 scaled plots with dimension of 6m by 85 m. Following a randomized completely blocks design, four plots were considered for supplementary irrigation and the rest four replications were accounted as control (without irrigation). In an effort to conserve more water, drip irrigation system was used with 4 lit/hr/m discharge capacity. The results of two years study indicated that, compared to the conventional dry land farming undertaken in the control plots, wheat grain yield was increased by 70% and 87% respectively. During the study period, 35% and 70% of wheat water requirement was supplied from RWH system during plant growth period respectively. The result showed that it would be possible to have productive wheat cultivation in the regions with 250 mm annual precipitation (in average), if one can employ an effective RWH system. Keywords: Rainwater harvesting, Supplementary irrigation, Rainfed wheat, Mashhad regionAbstractRainwater harvesting (RWH) is one of the most effective water resources management techniques for confrontation with water shortage condition. RWH which is basically known as a traditional method, has been developed rapidly and being used widely in dryland countries throughout the world. RWH is defined as collecting and storing rainwater from nearby catchment and delivering it to planting area during critical stages of plant requirement. RWH can be divided into two main categories which is known as micro and macro catchment methods. Many factors such as rainfall amount and distribution, ground topography, soil type, economical and social aspects are important for selecting a specific RWH category. In the present paper, design and performance of a real size macro catchment RWH project located in a semi arid region (N-E of Iran), is described. The project is comprised from a 5000 m2 plastic covered catchment, 500 m3 ground reservoir and an experimental cultivation area located next to runoff catchment which is planted with dryland wheat. Planting area is consisted of 8 scaled plots with dimension of 6m by 85 m. Following a randomized completely blocks design, four plots were considered for supplementary irrigation and the rest four replications were accounted as control (without irrigation). In an effort to conserve more water, drip irrigation system was used with 4 lit/hr/m discharge capacity. The results of two years study indicated that, compared to the conventional dry land farming undertaken in the control plots, wheat grain yield was increased by 70% and 87% respectively. During the study period, 35% and 70% of wheat water requirement was supplied from RWH system during plant growth period respectively. The result showed that it would be possible to have productive wheat cultivation in the regions with 250 mm annual precipitation (in average), if one can employ an effective RWH system.

AbstractUranium is a natural heavy metal widely dispersed across the earth crust. In many cases due to anthropogenic disturbance, it has been found beyond its natural abundance which resulted in soil-water pollution. The objective of this study was to investigate the capability of Iranian natural zeolite clinoptilolite for removal of uranium (VI) from aqueous solutions. In order to optimize removal efficiency of uranium, bath techniques were used as a function of various parameters, i.e., initial uranium concentration, pH solution, shaking time and dose of zeolite. The sorption of uranium was investigated by using Langmuir and Freundlich model. Maximum uranium uptake by zeolite achieved at 200 mg U L-1, pH 5 and 20 h contact time. The results of the experiment also revealed that Langmuir model was able to predict uranium adsorption mechanisms by zeolite more accurate than Freundlich model. Considering low cost and great natural resource of zeolite in Iran, it is suitable for environmental applications.

AbstractIn this research, four meteorological set of data including maximum temperature, minimum temperature, precipitation and radiation from ECHO-G, under A1scenario have been used for climate change detection over south khorasan. ECHO-G is a General circulation model that currently is used in Hamburg university and Korea meteorological research institute. In this research climate change assessment has been studied for the period of 2010-2039. Analysis of downscaled meteorological parameters by Lars-WG model over six meteorological stations of South khorasan have been performed. The results showed that annual mean of precipitation will increase by 4 percent. Annual mean temperature are projected to increase by 0.3 °C, with maximum temperature increase of 1°C in winters. Our results revealed that the number of dry days in northern stations including Boshruyeh, Ferdous and Ghayen will increase in Comparison to the their normal values but it will decrease in the southern stations of Birjand, Khor and Nehbandan.

AbstractSoil drying and rewetting (DRW) cycles are considered as environmental stress that would affect the physical, chemical and biological properties of soil and nutrient cycles. The study of soil drying and rewetting effects on plant nutrition and soil fertility is very important and crucial specially their environmental effects. In this study, the effects of 1, 2, 3, 4 and 5 drying and rewetting cycles on dissolved organic carbon (DOC) and phosphorus (DOP) and available forms of nitrogen (nitrite, nitrate and ammonium) in two Alfisols in a completely randomized design with three replications were investigated. The results showed that with increasing DRW cycles dissolved organic carbon, organic and inorganic phosphorus and ammonium decreased significantly in soil solution compared to control (samples were kept in wet condition). Reduction in DOC and DOP could be due to the higher mineralization of organic carbon and phosphorus. Dissolved organic nitrogen decreased significantly after three and four drying and rewetting cycles in soil 1 and soil 2 respectively. The highest amount of dissolved organic nitrogen was obtained in five drying and wetting cycles treatment.

AbstractFertilizer Use Efficiency (FUE) is one way of improving the fertilizer management in agriculture. In this study, spinach (Spinacea Oleracea) was used as a case study vegetable to determine its nitrogen use efficiency in Varamin, Iran. An experiment was conducted using split plot design with three replications. Treatments included of five levels of N as 0, 150, 200, 250 and 300 kg ha-1 and five levels of phosphate as 0, 37.5, 50, 62.5 and 75 kg P2O5ha-1. Both fresh weight and nitrate content of leaf were measured. The optimum value of FUE obtained by applying 250 kg Nha-1.

AbstractAmong rhizospheric bacteria, great attention has been paid to the group of plant growth promoting rhizobacteria (PGPR), and their role in increasing the growth and health of plants. Therefore, it is used largely as inoculum all over the world. The rhizospheric bacterium, which has the genus of Flavobacterium, and promotes plants growth, has been studied in recent years. In the present research, 65 samples of rhizospheric soil were taken to isolate Flavobacterium from the rhizosphere of wheat cultivated in different regions of Iran. To isolate, cultivate, and preserve Flavobacterium, different and diverse formulations such as ATCC 647, 65 ATTC M1, and M1 Medium, which were recommended for this bacterium, were used for the development of a new specified culture media (FIM). Sixty-one isolates attributed to Flavobacterium were isolated and purified using specified culture medium (FIM). The genus and species were identified through microscopic, physiological and biochemical tests. The results obtained from our tests indicated that there are 5 species F. multivorum, F.odoratum, F. thalpophilum, F.balastinum and F. indoltheticum in the rhizosphere of wheat. Among the isolated species, F.odoratum showed the most frequency (72 percent), and the F. thalpophilum and F. balastinum and F. indoltheticum possessed the least frequency (1.6 percent).

AbstractEfficient use and management of organic matter are important aspects of sustainable agricultural. Organic matter is relatively low in majority of soils in Iran, and continuous use of N fertilizer would create environmental hazards. Therefore, the combined use of N fertilizer and organic wastes is an effective strategy. The purpose of this study was investigating of residual effects of different sources of organic matters with or without nitrogen on wheat dry matter and soil chemical characteristics. Treatments included two organic matter sources (municipal solid waste compost and cow manure), in four rates (0, 1, 2, and 4%) and three N levels (0, 75, 150 mg kg-1 soil). The experiment was carried out in factorial manner in a completely randomized design with three replications. Treatments applied in rice and the residual effects were studied on growth and chemical composition of wheat and some soil chemical characteristics after wheat harvest. Application of both organic matter sources enhanced wheat growth. With compost, addition of N did not affect crop growth while with cow manure, application of N led to increase wheat growth in all organic matter levels. The effect of N was the most in the higher cow manure levels. Application of N alone did not affect wheat growth. Wheat plants enriched by either of the two organic wastes accumulated more phosphorus (P), potassium (K), iron (Fe), manganese (Mn), chlorine (Cl) and sodium (Na) than control plants. Post harvest soil sampling indicated that addition of organic matter significantly increased study chemical characteristics. Organic matter, total nitrogen, available P, Fe, Zn, Cu and Mn, soil electrical conductivity and sodium adsorption ratio in soil enriched with organic matter were more than those in control. In general, residual effects of cow manure particularly with chemical nitrogen on dry matter and soil chemical characteristics were more than those of compost.

AbstractSoil temperature is one of the key parameters affecting most hydrologic and agricultural processes. Therefore, its measurement and prediction is very crucial. So far, the statistical regression methods have been used for estimation of soil temperature for specific location encountering with lack or shortage of data. In this work, soil temperature data are estimated at six different depths for three typical climates (Zahedan, Tehran, Ramsar) by a new approach namely Adaptive Neuro-Fuzzy Inference System (ANFIS), and the results are compared with those of estimated by regression methods. In addition, the most important meteorological parameters (maximum temperature, minimum temperature, mean daily temperature, relative humidity, sunshine hour, and wind speed) which influence soil temperature at the study sites are used during the 15-years period (1992-2006) of study. The comparison of soil temperature data predicted by ANFIS and regression methods indicated that the performance of ANFIS model is 4% more accurate than regression methods. It was found that the accuracy of prediction using ANFIS model for arid climates of Zahedan and Tehran was 12% and 4.5% better than Ramsar (humid), respectively. The statistical comparison of the estimations derived by ANFIS model and the observed soil temperature data of drier climates showed that the coefficients of correlation (r) are reduced (up to 10%) for deeper layers. In contrast, for the humid climate of Ramsar, the model accuracy for near surface layers (5 and 10 cm) was up to 18% less than deeper layers (100 cm).

AbstractMine excavation, concentration and transportation of minerals make Southern Esfahan municipality and suburb of Sepahanshahr vulnerable to pollution and endanger their people by heavy metals. Nowadays, spectrophotometers and spectral reflectance of soils in different parts of spectrum are applied for estimating soil characteristics and pollutants. This research was conducted to study the potential of Landsat ETM+ data for estimating and mapping spatial distribution of heavy elements in the Southern Isfahan municipality. During a field survey 100 surface soil samples were collected randomly. Samples were air dried and fine earth was treated by 4 M HNO3 (at 80 °C) and total Pb, Zn and Cd concentration was measured by Atomic Absorption Spectrophotometer and ICP. Statistical analysis reveals negative and significant correlation coefficient between concentration of heavy metals and visible and NIR data and consequently, possible delineation of heavy metals. Spatial distribution of Pb, Zn and Cd concentration mapped using several stepwise multiple regression equations. Results indicate that concentration of Pb, and Zn are above the thresholds and that of Cd is not serious at the moment in proximity of Sepahanshahr.

AbstractThe present study is attempted to present the minimum required meteorological parameters for reference evapotranspiration estimation at Hamedan region of Iran from 1997 to 1998. Employing Pierson test, six meteorological parameters which are used by Penman-Montieth FAO-56 method including maximum and minimum air temperature, maximum and minimum relative humidity, wind speed and daily sunshine were composed and considered as 4 difference scenarios (called 1, 2, 3 and 4). These scenarios were applied to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for reference evapotranspiration estimation of the area using the Matlab software. The results of the scenarios were evaluated using the actual reference evapotranspiration (lysimeter data). The results showed that increasing of number of input layers data could not be based as obtaining the more exact results. Using the scenario 2, which was based on minimum and maximum temperature as well as daily sunshine, showed more reliable results using the ANN and ANFIS methods. The root mean square error (RMSE), mean absolute error (MAE) and R2 of examination step of this scenario were 0.09, 0.07 mm/day and 0.9, respectively. Overall, the statistic performances revealed that ANN and ANFIS had the same results and similar input layer sensitivity. The iteration times of the ANN and ANFIS methods to reach the best results were 26 and 40, respectively. Comparison between ANN (RMSE= 0.09 mm/day) and standard Penman-Montieth method (RMSE= 0.34 mm/day) confirmed that the intelligence approaches such as ANN are more accurate for reference evapotranspiration estimation.

AbstractAgriculture is the largest consumer of water resources in Iran. One of the best options for increasing the irrigation efficiency and better application of precipitation in arid and semi-arid areas is employing of superabsorbent polymers to soil. Polymers can absorb rain and irrigation water, decrease deep percolation and increase water use efficiency. In order to investigate the effects of different rates of hydrophilic polymer superab A200 on water holding capacity and the porosity of soils with different salinity and textures, three factorial experiments were conducted using a completely randomized design with three replications. The polymers were applied to soils of different textures (sand, loam and clay) at the rates of 0.0, 0.2, 0.4 and 0.6 % w/w and salinity of the soils was adjusted at the levels of initial soil salinity (blank), 4 and 8 dS/m. The application of 0.6% w/w of polymer at the lowest salinity level increased available water content by 2.20 and 1.20 times greater than those of controls in the sandy and loamy soils, respectively. Thus application of polymers to soils especially in the sandy soils may increase water holding capacity, may decrease salinity and may help to improve irrigation projects in arid and semi-arid areas.

AbstractTime domain reflectometry (TDR) is a widely used method for measuring the dielectric constant (Ka) and bulk electrical conductivity (σa) in soils. The TDR-measured σa and Ka can be used to calculate the soil solution electrical conductivity, (σp). A theoretical model describing a linear relationship between bulk electrical conductivity, σa, and dielectric constant, Ka, in moist soil was already presented. By using this linear relationship, the pore water electrical conductivity, σp, can be estimated in a wide range of soil types without soil-specific calibration. The objective of this study was to evaluate the linear model presented previously for TDR. The previous study was on light texture soils but in this study we used clay, clay loam, loam, silty clay and silty clay loam textures. The results showed that the linear model performed well for light texture soils but not for heavy textures. Such poor result for heavy texture is mainly due to this fact that dielectric constant pore water was lower than 80 which was proposed as default by model. This study showed that for heavy texture soils dielectric constant of pore water is smaller than light textured soils.

Abstract Accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. In fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. Although, during recent decades, some black box models based on artificial neural networks (ANN), have been developed to overcome this problem and the accuracy privilege to common statistical methods (such as auto regression and moving average time series method) have been shown. However these types of models are implicit and complex in proper network design and can not be simply used by other investigators. In this research the genetic programming (GP) model has been developed as an explicit method for river flow prediction and has been used for investigation the effect of daily discharge trend in Absardeh river flow forecasting. The results have been compared with artificial neural network technique. The results indicated that the proposed GP method performed quite well compared to artificial neural network method and is applicable for river flow prediction.

AbstractFor evaluating NaCl salinity effect on some physiological and qualitative attributes of five winter canola cultivars, an experiment was conducted in a RCD base factorial in three replicates in greenhouse of East Azarbaijan Agricultural Research center in 2008. Five salinity levels as 0, 50, 100, 150 and 200 mM NaCl treated as first factor on five rape seed (Fornax, Licord, Elite, SLM046, Okapi) as second factor. Na, K, Ca, Mg and K/Na ratio in leaves during growth season, and final biologic and seed yield, photosynthesis and respiration rates, fatty acids (linoleic, linolenic and oleic acid) proline, and total, a and b chlorophyll were measured. Results showed that increasing salinity decreased K, K/Na, biologic and seed yield, oil percentage, photosynthesis and respiration rates, fatty acids, total, a and b chlorophyll, but increased Na, Ca, Mg and proline content. SLM046 showed significantly lower contents of Na, Ca, Mg and higher contents of K, and K/Na ratio. It seems that SLM046 had higher yield and efficiency but Elite was susceptible to salinity. Compared to other cultivars SLM046 showed higher content of unsaturated fatty acids, total, a and b chlorophyll across all salinity levels.

AbstractBottom intake is one of the most appropriate systems for diverting discharge in steep rivers. Bottom intake with porous media is a new system of diverting discharge which can be replaced by bottom rack intakes. To investigate the hydraulic flow characteristics on diverted discharge of this intake, an experimental model was designed in which the inflow, diverted discharge and remained flow of porous media intake can be measured. In the present research, measurements of the diverted discharge were performed for different rates of flow, grain size distributions as well as surface slopes of intake with clear water. According to the obtained results, by increasing the inflow discharge, the rate of diverted discharge increases gradually and then reaches to a constant value. Any increment of the surface slope above the intake also decrease the relation diverted flow. It is found that the grain size of the porous media has a great influence on the diverted flow. By increasing the grain size diverted flow increases, too. Dimensional analysis and experimental results were used to estimate the discharge coefficient of porous media. Analytical analysis on experimental data shows that the discharge coefficient of porous media intake is about 0.1. Also an empirical-theoretical relation is proposed to evaluate the diverted discharge of this kind of bottom intakes.