Effect of wheat (

Our purpose was to quantify the effect of rhizosphere processes on the fractions of Copper (Cu) in 10 soils amended with sewage sludge under greenhouse conditions by using a rhizobox. For amended soils, 1% (w/w) of sewage sludge was added to soil samples and then amended soils were incubated at field capacity, for 1 month. After incubation, soils were put in rhizobox and seeds of wheat were planted. Plants were harvested after 8 weeks and rhizosphere and bulk soils were separated. Fractions of Cu in the rhizosphere and bulk soils were determined. The results showed that Cu extracted using several extractants in rhizospheric soils were significantly (P<0.01) lower than in bulk soils. In the rhizosphere of amended soils the average of residual Cu, Cu associated with iron-manganese oxides, Cu associated with organic matter, Cu associated with carbonates and exchangeable Cu were 18.8, 2.10, 1.00, 0.37 and 0.24 mg kg−1 respectively, whereas above fractions in the bulk soils were 18.1, 2.43, 0.80, 0.42 and 0.30 mg kg−1 respectively. This study illustrated that Cu-fractions in the wheat rhizosphere were different compare to bulk soils in sewage sludge amended soils

Zinc fractionation in the rhizosphere of wheat (

Rhizosphere is a microbiosphere and has quite different chemical, physical and biological properties from bulk soils. A greenhouse experiment was performed to compare fractionation of Zinc (Zn) between rhizosphere and bulk soils amended with sewage sludge (1% w/w of sewage sludge to soil). Fractions of Zn were determined in two subsamples (rhizosphere and bulk soils). The results indicated concentration of Zn-fractions (except carbonates-associated) in the rhizosphere soils were significantly (p<0.05) different from concentrations of Zn-fractions in the bulk soils. Also, results revealed that significant correlation (p<0.05) between Zn associated with iron-manganese and yield and uptake indices in the rhizosphere and bulk soils were found. The results of this research illustrated that rhizosphere is a small zone but important environmental zone in soils with quite different properties

Assessment of Tree and Multiple Linear Regressions in Estimation of Cation Exchange Capacity

Introduction: Estimation of cation exchange capacity (CEC) with reliable soil properties can save time and cost. Pedotransfer function (PTF) is a common method in estimating certain soil properties (e.g. CEC) that has been wieldy used for many years. One of the common techniques that have been used to develop PTFs is multiple linear regressions. In this method, all easily obtained soil properties are linearly related to certain soil properties. In addition to multiple linear regressions method, more complex techniques such as artificial neural networks and regression tree have been used to develop PTFs. The regression tree method is a well-known method for analyzing the environmental science which determines optimal separation point of independent variables.The purposes of this study were to evaluate and compare tree and multiple linear regressions in estimating cation exchange capacity with reliable soil properties. Materials and Methods: For this work, 106 soil samples of Unsaturated Soil hydraulic database (UNSODA), which contain a wide range of soil texture classes, were used. The examples were divided into 2 sets including 81 and 25 soil samples for developing and validating multiple linear regression and tree regression, respectively. For estimating CEC with tree and multiple regressions, soil texture properties, organic matter, pH and bulk density were used. To develop multiple linear regressions and create the tree structure, at first, correlation between cation exchange capacity with other soil properties were evaluated; then, soil properties that had significant correlation were chosen to introduce software. As well, the suggested linear function and tree structure were compared with 2 famous pedotranser functions including Bell and Van-kolen and Breeuwsma et al., which have been used for estimating CEC.For investigating the performance of multiple linear regression and tree regression to estimate CEC 1:1 lines, determination coefficient (R2), mean error (ME), root mean square error) RMSE), and geometric mean error (GMER) were used. Statistica 8.0 software that was developed by ESRI was used to develop multiple linear regressions and generate tree structure. Results and Discussion: The results showed for developing multiple linear regression model to estimate CEC among all inputs parameters (sand, silt, clay, organic matter, pH and bulk density) only just two parameters including organic (with r=0.70) and clay percentage (with r=0.59) had a significant coefficient, so organic and clay percentage appeared, and suggested multiple linear regression models based on this two parameters, with coefficient of 3.183 and 0.274, respectively, were developed. Also, only organic matter and clay percentage from inputs parameter in tree were shown. In tree structure most nods were divided into 2 Childs nods based on organic matter and only in the left side of tree structure in the second level clay percentage was appeared. Regression tree in two data sets (validation and development) based on R2, RMSE, ME and GMER had a high quality for CEC estimation than regression methods. Proposed linear regression model had high performance than Bell and Van-kolen and Breeuwsma et al. to estimate CEC. Conclusions: The main aim of this study was to investigate the efficiency of multiple linear regression model and regression tree to predict cation exchange capacity (CEC) based on relationships between CEC and easily measurable soil properties. For this work, 106 soil samples of UNSODA data set were used. Results showed that just clay percentage and organic matter that had higher correlation with CEC appeared in suggested linear regression and tree structure. Based on 1:1 lines, R2 ,RMSE, ME and GMER, tree regression model had higher performance than all linear regression models (suggested function , Bell and Van-kolen and Breeuwsma et. al.) to estimate cation exchange capacity. As well, suggested function had more efficiency than Bell and Van-kolen and Breeuwsma to predict CEC

Thinness, overweight and obesity in a national sample of Iranian children and adolescents: CASPIAN Study.

BACKGROUND: This study was conducted to assess the national prevalence of different grades of nutritional status (underweight, normal weight, overweight and obesity) among Iranian school-students and to compare the prevalence of overweight and obesity using three different sets of criteria. METHODS: This cross-sectional national survey was conducted on a representative sample of 21 111 school students including 10 253 boys (48.6%) and 10 858 girls (51.4%) aged 6-18 years, selected by multistage random cluster sampling from urban (84.6%) and rural (15.4%) areas of 23 provinces in Iran The percentage of subjects in the corresponding body mass index (BMI) categories of the Centers of Disease Control and Prevention (CDC), the International Obesity Task Force (IOTF) and the obtained national percentiles were assessed and compared. RESULTS: There was no gender differences in BMI, but was higher in boys living in urban than in rural areas (18.4 +/- 3.88 vs. 17.86 +/- 3.66 kg/m(2) respectively, P < 0.05). The prevalence of underweight was 13.9% (8.1% of boys and 5.7% of girls) according to the CDC percentiles, and 5% (2.6% of boys and 2.4% of girls) according to the obtained percentiles. According to the CDC, IOTF and national cut-offs, the prevalence of overweight was 8.82%, 11.3% and 10.1% respectively; and the prevalence of obesity was 4.5%, 2.9% and 4.79% respectively. The prevalence of overweight was highest (10.98%) in the 12-year-old group and that of obesity (7.81%) in the 6-year-old group. The kappa correlation coefficient was 0.71 between the CDC and IOTF criteria, 0.64 between IOTF and national cut-offs, and 0.77 between CDC and national cut-offs. CONCLUSIONS: The findings of this study warrant the necessity of paying special attention to monitoring of the time trends in child obesity based on uniform definitions, as well as to design programmes to prevent and control associated factors

ExoFiT: ExoMars-Like Field Trials – a Mission Simulation.

International audienceHere, we focus on the ExoMars-Like Field Trial (ExoFiT), a series of realistic mission simulations based on the ESA ExoMars rover mission slated to be launched in 2020. The mission will carry a suite of instruments to carry out its mission objectives focused on geological and exobiological researc