School Engagement and the Achievement Gap

Evidence of the influence of engagement on learning and achievement is well established. There is also indication of a test-score gap between poor students and middle class students as well as among racial and ethnic groups. This gap continues to be a top priority in educational reform. Since the achievement gap continues to widen for many school districts and states, investigating the possible connection between the engagement gap and the achievement gap deserves needed attention. This study sought to determine the differences in school engagement and achievement levels between students from low and high-SES backgrounds, as measured by free and reduced lunch, and between Caucasian and Hispanic students. The study examined the engagement and achievement levels of approximately 1,200 sixth grade middle school students in a suburban Colorado school district. The students\u27 responses were then analyzed using independent sample t-tests to determine differences. The major findings of this statistical analysis were that slight differences exist between Caucasian and Hispanic students as well as low and high-SES students on the 2007 CSAP scores in reading, writing, math, and science scores and first trimester GPA for the 2007-2008 school year. In addition, there were minimal differences between Hispanic and Caucasian students and low and high-SES students in behavioral engagement, but not in cognitive or emotional engagement. This study has taken an in-depth look at engagement levels, and differences in achievement were also explored. This study has confirmed that an achievement gap exists. However, the results of this study have shown that the achievement gap cannot be explained by an engagement gap. Based on the results of this study, stressing the importance of engagement in school is not likely the answer for closing the achievement gap

The interdependencies between food and biofuel production in European agriculture - an application of EUFASOM

In the continuous quest to reduce anthropogenic emissions of carbon dioxide, the production and use of organically grown fuels in Europe has increased in importance in the recent past. However, the production of so-called biofuels is a direct competitor of agricultural food production for land, labor, water resources etc. with both land use options influencing each other depending on the respective boundary conditions defined by political regulations and economic considerations. In this study we will explore the economic and technical potentials of biofuels in Europe as well as the interdependencies between these two land use options for different economic incentives for biofuels using the European Forest and Agriculture Sector Optimization Model (EUFASOM). Key data on biodiesel and ethanol production have been gathered and are used for calibration of the model. The simulations extend until the year 2030, for which results are presented. Results indicate that moderate production targets of biofuels lead to an expansion of mainly the biodiesel production while more ambitious targets call for a focus on bioethanol. This has to do with the different levels of production efficiency depending on the production output. Growth of bioethanol feedstock is spread over entire Europe while the production of biodiesel feedstock occurs mainly in Central Europe.biodiesel, bioethanol, Europe, EUFASOM, modeling

Towards systematic evaluation of crop model outputs for global land-use models

Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include iterative improvement of parameter assumptions and evaluation of implications of GGCM performances for intended use in the IIASA EPIC-GLOBIOM model cluster. Our approach helps targeting future efforts at improving GGCM accuracy and would achieve highest efficiency if combined with traditional field-scale evaluation and sensitivity analysis

Water productivity and footprint of major Brazilian rainfed crops – A spatially explicit analysis of crop management scenarios

Green water is a central resource for global agricultural production. Understanding its role is fundamental to design strategies to increase global food and feed production while avoiding further land conversion, and obtaining more crop per drop. Brazil is a country with high water availability, and a major exporter of agricultural goods and virtual water. We assess here water use and water productivity in Brazil for four major rainfed crops: cotton, maize, soybeans, and wheat. For this, we use the EPIC crop model to perform a spatially explicit assessment of consumptive water use and water productivity under crop management scenarios in Brazil between 1990 and 2013. We investigate four different land-water interactions: (i) water use and productivity for different management scenarios, (ii) the potential of supplemental irrigation for productivity improvement, (iii) changes in green water use throughout the study period, and finally (iv) potential reduction of land and water demand related to agricultural intensification. The results show that, for the studied crops, green water is the main resource for biomass production, and intensification can lead to great improvements in green water productivity. The results also suggest that, despite achieving higher yields, irrigation-based intensification tends to lower overall water productivity, compared to fertilizer-based intensification strategies. This is, however, regionally and crop-specific. Furthermore, due to higher yields and water productivity, producing the same amount of crop output in irrigated or rainfed intensification scenarios would result in the reduction of resource demand, in the order of 34–58 % for cropland, and 29–52 % for water

Digital soil mapping from conventional field soil observations

We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Risnovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A-D: (i) Luvic Phaeozems (Anthric), (ii) Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A-D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R2 ranging from 23% to 47% (P < 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R2 = 14%, P < 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A 'leave-one-out' cross-validation was calculated for the output maps yielding R2 of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P < 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations

Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models

Land provides vital socioeconomic resources to the society; however, at the cost of large environmental degradation (Verburg et al., 2013). At the crossroads of these dimensions, agriculture becomes increasingly interconnected to various natural and human systems across various scales. In order to inform the design of policies to navigate land use towards a more sustainable operating space, comprehensive global assessment models are increasingly being used. They rely partly on the loose coupling of biophysical crop models to global economic models, via one-way exchange of output variables (Rosenzweig et al. 2013). Accuracy of variables exchanged strongly influences the outcomes assessed at various scales, and its improvement is likely to require iterative improvements. Yet there has been little effort to document, evaluate and compare these exchange variables across models (Mueller & Robertson et al. 2014). We here present a novel dataset (the Hypercube) generated by the Environmental Policy Integrated Model (EPIC) crop model and providing the Global Biosphere Management Model (GLOBIOM) with high-resolution information at global scale on the yield, water, and nutrient needs of 16 crops for 15 different combinations of management. We present the dataset and its links to the EPIC and GLOBIOM model, and the rationale for developing a systematic evaluation of the data, before illustrating them with preliminary results

Uncertainties in global land cover data and its implications for climate change mitigation policies assessment

Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how high will be competition for land between food and bioenergy in the future as well as how much land is there available for afforestation projects? These questions can only be answered if reliable maps of land cover exist. We put this research in the framework of GEOSS, examine how modeling tools can be used for benefit assessment and design an assessment framework. We illustrate the importance of good quality global land cover maps by using cropland extend from the currently best global maps of land cover namely GLC-2000, MODIS, GlobCover and CropLikelyhood as input for the EPIC model (to model crop yields) and global economic land use model GLOBIOM. We use all of the 4 maps and create a maximum crop extend and map. Based on a baseline map and the maximum crop extend map e model effects of climate policies (e.g. the potentials of substitution of fossil fuels with biofuels)

Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model

Water erosion on arable land can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in maize and wheat fields between the years 1980 and 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. By using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we obtained the global median water erosion rates of 7 t ha-1 a-1 in maize fields and 5 t ha-1 a-1 in wheat fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide, the varying water erosion estimates from different equations and the complex distribution of cropland in mountainous regions add uncertainty to the simulation results. To reduce the uncertainties in global water erosion estimates, it is necessary to gather more data on global farming techniques to reduce the uncertainty in global land-use maps and to collect more data on soil erosion rates representing the diversity of environmental conditions where crops are grown

Identification of microRNAs expressed in two mosquito vectors, Aedes albopictus and Culex quinquefasciatus

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression in a variety of organisms, including insects, vertebrates, and plants. miRNAs play important roles in cell development and differentiation as well as in the cellular response to stress and infection. To date, there are limited reports of miRNA identification in mosquitoes, insects that act as essential vectors for the transmission of many human pathogens, including flaviviruses. West Nile virus (WNV) and dengue virus, members of the <it>Flaviviridae </it>family, are primarily transmitted by <it>Aedes </it>and <it>Culex </it>mosquitoes. Using high-throughput deep sequencing, we examined the miRNA repertoire in <it>Ae. albopictus </it>cells and <it>Cx. quinquefasciatus </it>mosquitoes.</p> <p>Results</p> <p>We identified a total of 65 miRNAs in the <it>Ae. albopictus </it>C7/10 cell line and 77 miRNAs in <it>Cx. quinquefasciatus </it>mosquitoes, the majority of which are conserved in other insects such as <it>Drosophila melanogaster </it>and <it>Anopheles gambiae</it>. The most highly expressed miRNA in both mosquito species was miR-184, a miRNA conserved from insects to vertebrates. Several previously reported <it>Anopheles </it>miRNAs, including miR-1890 and miR-1891, were also found in <it>Culex </it>and <it>Aedes</it>, and appear to be restricted to mosquitoes. We identified seven novel miRNAs, arising from nine different precursors, in C7/10 cells and <it>Cx. quinquefasciatus </it>mosquitoes, two of which have predicted orthologs in <it>An. gambiae</it>. Several of these novel miRNAs reside within a ~350 nt long cluster present in both <it>Aedes </it>and <it>Culex</it>. miRNA expression was confirmed by primer extension analysis. To determine whether flavivirus infection affects miRNA expression, we infected female <it>Culex </it>mosquitoes with WNV. Two miRNAs, miR-92 and miR-989, showed significant changes in expression levels following WNV infection.</p> <p>Conclusions</p> <p><it>Aedes </it>and <it>Culex </it>mosquitoes are important flavivirus vectors. Recent advances in both mosquito genomics and high-throughput sequencing technologies enabled us to interrogate the miRNA profile in these two species. Here, we provide evidence for over 60 conserved and seven novel mosquito miRNAs, expanding upon our current understanding of insect miRNAs. Undoubtedly, some of the miRNAs identified will have roles not only in mosquito development, but also in mediating viral infection in the mosquito host.</p