Self-Efficacy, Principals' Support, Stages Of Concern In Integrating E-Learning In The Jordanian Discovery Schools

This study examines the effect of self-efficacy, perceptions of principals’ support and stages of concerns of teachers in Jordan Discovery schools on the integration of elearning into their teaching. The moderator variables identified were gender and teaching experiences. A total of 350 teachers were randomly stratified from all secondary Discovery schools in the four districts (strata) of the capital, Amman. The Concerns Based Approach Model and Rogers’ Diffusion of Innovation theory were used in this study. Data was gathered quantitatively by the use of 4 self-reporting instruments to measure (1) teachers’ self-efficacy, (2) perception of principals’ support, (3) teachers’ stages of concern and (4) teachers’ efforts to integrate e-learning into the teaching and learning process

Predicted soil organic carbon stocks and changes in Jordan between 2000 and 2030 made using the GEFSOC modelling system

Estimates of soil organic carbon (SOC) stocks and changes under different land use systems can help determine vulnerability to land degradation. Such information is important for countries in and areas with high susceptibility to desertification. SOC stocks, and predicted changes between 2000 and 2030, were determined at the national scale for Jordan using The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. For the purpose of this study, Jordan was divided into three natural regions (The Jordan Valley, the Uplands and the Badia) and three developmental regions (North, Middle and South). Based on this division, Jordan was divided into five zones (based on the dominant land use): the Jordan Valley, the North Uplands, the Middle Uplands, the South Uplands and the Badia. This information was merged using GIS, along with a map of rainfall isohyets, to produce a map with 498 polygons. Each of these was given a unique ID, a land management unit identifier and was characterized in terms of its dominant soil type. Historical land use data, current land use and future land use change scenarios were also assembled, forming major inputs of the modelling system. The GEFSOC Modelling System was then run to produce C stocks in Jordan for the years 1990, 2000 and 2030. The results were compared with conventional methods of estimating carbon stocks, such as the mapping based SOTER method. The results of these comparisons showed that the model runs are acceptable, taking into consideration the limited availability of long-term experimental soil data that can be used to validate them. The main findings of this research show that between 2000 and 2030, SOC may increase in heavily used areas under irrigation and will likely decrease in grazed rangelands that cover most of Jordan giving an overall decrease in total SOC over time if the land is indeed used under the estimated forms of land use. (C) 2007 Elsevier B.V. All rights reserved

Preparation of consistent soil data sets for modelling purposes: Secondary SOTER data for four case study areas

The common GIS-based approach to regional analyses of soil organic carbon (SOC) stocks and changes is to define geographic layers for which unique sets of driving variables are derived, which include land use, climate, and soils. These GIS layers, with their associated attribute data, can then be fed into a range of empirical and dynamic models. Common methodologies for collating and formatting regional data sets on land use, climate, and soils were adopted for the project Assessment of Soil Organic Carbon Stocks and Changes at National Scale (GEFSOC). This permitted the development of a uniform protocol for handling the various input for the dynamic GEFSOC Modelling System. Consistent soil data sets for Amazon-Brazil, the Indo-Gangetic Plains (IGP) of India, Jordan and Kenya, the case study areas considered in the GEFSOC project, were prepared using methodologies developed for the World Soils and Terrain Database (SOTER). The approach involved three main stages: (1) compiling new soil geographic and attribute data in SOTER format; (2) using expert estimates and common sense to fill selected gaps in the measured or primary data; (3) using a scheme of taxonomy-based pedotransfer rules and expert-rules to derive soil parameter estimates for similar soil units with missing soil analytical data. The most appropriate approach varied from country to country, depending largely on the overall accessibility and quality of the primary soil data available in the case study areas. The secondary SOTER data sets discussed here are appropriate for a wide range of environmental applications at national scale. These include agro-ecological zoning, land evaluation, modelling of soil C stocks and changes, and studies of soil vulnerability to pollution. Estimates of national-scale stocks of SOC, calculated using SOTER methods, are presented as a first example of database application. Independent estimates of SOC stocks are needed to evaluate the outcome of the GEFSOC Modelling System for current conditions of land use and climate. (C) 2007 Elsevier B.V. All rights reserved

An increased understanding of soil organic carbon stocks and changes in non-temperate areas: national and global implications

National and sub-national scale estimates of soil organic carbon (SOC) stocks and changes can provide information land degradation risk, C sequestration possibilities and the potential sustainability of proposed land management plans. Under a GEF co-financed project, `The GEFSOC Modelling System¿ was used to determine SOC stocks and projected stock change rates for four case study areas; The Brazilian Amazon, The Indo-Gangetic Plains of India, Kenya and Jordan. Each case study represented soil and vegetation types, climates and land management systems that are under represented globally, in terms of an understanding of land use and land management systems and the effects these systems have on SOC stocks. The stocks and stock change rates produced were based on detailed geo-referenced datasets of soils, climate, land use and management information. These datasets are unique as they bring together national and regional scale data on the main variables determining SOC, for four contrasting non-temperate eco-regions. They are also unique, as they include information on land management practices used in subsistence agriculture in tropical and arid areas. Implications of a greater understanding of SOC stocks and stock change rates in non-temperate areas are considered. Relevance to national land use plans are explored for each of the four case studies, in terms of sustainability, land degradation and greenhouse gas mitigation potential. Ways in which such information will aid the case study countries in fulfilling obligations under the United Nations Conventions on Climate Change, Biodiversity and Land Degradation are also considered. The need for more detailed land management data to improve SOC stock estimates in non-temperate areas is discusse

Dispersive liquid-liquid microextraction combined with dispersive solid-phase extraction for gas chromatography with mass spectrometry determination of polycyclic aromatic hydrocarbons in aqueous matrices

This study describes a dispersive liquid–liquid microextraction combined with dispersive solid-phase extraction method based on phenyl-functionalized magnetic sorbent for the preconcentration of polycyclic aromatic hydrocarbons from environmental water, sugarcane juice, and tea samples prior to gas chromatography with mass spectrometry analysis. Several important parameters affecting the extraction efficiency were investigated thoroughly, including the mass of sorbent, type and volume of extraction solvent, extraction time, type of desorption solvent, desorption time, type and amount of salt-induced demulsifier, and sample volume. Under the optimized extraction and gas chromatography-mass spectrometric conditions, the method revealed good linearity (10–100000 ng/L) with coefficient of determination (R2) of ≥0.9951, low limits of detection (3–16 ng/L), high enrichment factors (61–239), and satisfactory analyte recoveries (86.3–109.1%) with the relative standard deviations < 10% (n = 5). The entire sample preparation procedure was simple, rapid and can be accomplished within 10 min. This method was applied (after pretreatment) to 30 selected samples, and the presence of studied analytes was quantified in 17 samples