„A teraz uczę w szkole katolickiej” – doświadczenia początkujących nauczycieli (Early Career Teachers [ECT]) w szkołach katolickich w Lismore i wynikające z nich wnioski odnośnie do wsparcia formacji. Badania pilotażowe

This study is a preliminary investigation of early career teachers (ECT) working in Catholic schools in a large regional Australian diocese. The key aim of the study is to better understand the factors influencing early career teachers, who begin their teaching careers in Catholic schools, and to apprehend their early experiences as teachers to cater for their continuous formation needs. Key findings identify the openness of ECTs to faith-based experiences and the challenges faced in teaching in a Catholic school. Recommendations for early career teacher support and formation are provided considering the findings of this studyNiniejsze opracowanie relacjonuje badania pilotażowe nauczycieli na początkowym etapie kariery (Early Career Teachers [ECT]) w szkołach katolickich w diecezji Lismore w Australii. Głównym celem badań było lepsze poznanie czynników, warunkujących decyzję o rozpoczęciu kariery nauczycielskiej w szkole katolickiej oraz zrozumienie pierwszych doświadczeń, będących udziałem nauczycieli w szkołach katolickich, tak aby lepiej zaspokajać ich bieżące potrzeby formacyjne. Wyniki badań wskazują na otwartość ECT na doświadczenia oparte na wierze oraz wyzwania stojące przed nauczaniem w szkole katolickiej. W świetle wyników badań przedstawiono zalecenia dotyczące wsparcia i formacji nauczycieli na wczesnym etapie kariery

Undertaking Educational Research Following the Introduction, Implementation, Evolution, and Hybridization of Constructivist Instructional Models in an Australian PBL High School

The aim of this paper is to provide an overview of the introduction, implementation, evolution, hybridization, and initial research into the constructivist instructional models deployed within a secondary (high) school in Australia. A concomitant aim is to relate some of the consequences of whole school pedagogical change, which have included the implementation of project- and problem-based learning, the flipped classroom, and a derivative hybridized form, referred to here as “flipped PBL.” Moreover, after a decade of using constructivist approaches, we initiated educational research to better understand some of the effects of these changes and to explore the reasons behind the successful implementation of the models. While still in its infancy, the nature of this research and some of the preliminary findings are detailed here

‘And Now I’m Teaching in a Catholic School’ – The Experiences of Early Career Teachers (ECT) in Lismore Catholic Schools and What Can Be Learned to Support Their Formation: A Preliminary Study

This study is a preliminary investigation of early career teachers (ECT) working in Catholic schools in a large regional Australian diocese. The key aim of the study is to better understand the factors influencing early career teachers, who begin their teaching careers in Catholic schools, and to apprehend their early experiences as teachers to cater for their continuous formation needs. Key findings identify the openness of ECTs to faith-based experiences and the challenges faced in teaching in a Catholic school. Recommendations for early career teacher support and formation are provided considering the findings of this stud

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade

Similar estimates of temperature impacts on global wheat yield by three independent methods

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.<br/

Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively

Global wheat production with 1.5 and 2.0&#176;C above pre-industrial warming

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Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

Reducing uncertainty in prediction of wheat performance under climate change

Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles