Proyecto eMadrid: metodologías educativas, ludificación y calidad

Esta comunicación presenta un conjunto de trabajos de investigación sobre metodologías docentes, ludificación y calidad realizados en el seno del proyecto eMadrid, de la Comunidad Autónoma de Madrid. En primer lugar se resumen los trabajos realizados en los dos primeros años del proyecto. Posteriormente se presentan las líneas de trabajo previstas para los dos años restantesEstos trabajos se han financiado parcialmente por el proyecto eMadrid (S2013/ICE-2715) de la Comunidad de Madrid, los proyectos FLEXOR (TIN2014-52129-R), RESET (TIN2014-53199-C3-1-R) e iProg (TIN2015-66731-C2-1-R) del Ministerio de Economía y Competitividad, y el proyecto “Adaptación de la metodología PhyMEL a la formación clínica mediante el uso de simuladores” financiado por la empresa Medical Simulato

Assessing variations of extreme indices inducing weather-hazards on critical infrastructures over Europe?the INTACT framework

Extreme weather events are projected to be more frequent and severe across the globe because of global warming. This poses challenging problems for critical infrastructures, which could be dramatically affected (or disrupted), and may require adaptation plans to the changing climate conditions. The INTACT FP7-European project evaluated the resilience and vulnerability of critical infrastructures to extreme weather events in a climate change scenario. To identify changes in the hazard induced by climate change, appropriate extreme weather indicators (EWIs), as proxies of the main atmospheric features triggering events with high impact on the infrastructures, were defined for a number of case studies and different approaches were analyzed to obtain local climate projections. We considered the influence of weighting and bias correction schemes on the delta approach followed to obtain the resulting projections, considering data from the Euro-CORDEX ensemble of regional future climate scenarios over Europe. The aim is to provide practitioners, decision-makers, and administrators with appropriate methods to obtain actionable and plausible results on local/regional future climate scenarios. Our results show a small sensitivity to the weighting approach and a large sensitivity to bias correcting the future projections.This work has been carried out within the activities of INTACT project, receiving funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° FP7-SEC-2013-1-606799. The information and views set out in this paper are those of the authors and do not necessarily reflect the opinion of the European Union. We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5

Rendimiento de grano y calidad del forraje de amaranto (Amaranthus spp.) cultivado a diferentes densidades en el noreste de México

Se evaluaron cuatro genotipos de Amaranthus hypochondriacus (655, 653, 153-5-3, y Criollo Tlaxcala) y uno de Amaranthus cruentus (genotipo 33) bajo cuatro densidades de población (DP): 31250; 41666; 62500 y 125000 plantas/ha, durante los ciclos agrícolas primavera-verano (PV) 2000, otoño-invierno (OI) 2001 y OI 2002, en la estación experimental de la Facultad de Agronomía de la Universidad Autónoma de Nuevo León, México. En cada ciclo agrícola se utilizó un diseño en parcelas divididas sobre bloques completos al azar con dos repeticiones. Se evaluaron las características agronómicas de rendimiento de grano (RG), rendimiento de materia seca (MS), altura de planta (AP), diámetro del tallo (DT) y longitud de panícula (LP). Únicamente en OI 2001 se evaluó contenido de proteína bruta (PC), cenizas (C), fibra detergente ácida (FDA) y fibra detergente neutra (FDN) en tallo y hoja. Tanto la interacción triple A x B x C (genotipos x densidades x años), como la doble A x C (genotipos x años) resultaron estadísticamente significativas (p<0,05) para todas las variables de rendimiento. El mayor RG en PV 2000 se registró en el genotipo 655 con 2221 kg/ha, mientras que en OI 2001 y 2002 el genotipo con mayor RG fue 33 con 1274 y 1926 kg/ha, respectivamente. El mayor rendimiento de grano se obtuvo con la densidad de población de 125 mil plantas/ha para todos los genotipos, en todos los ambientes de prueba. En cuanto a PC el genotipo 33 fue el que presentó los mayores valores para tallo y hoja con 95 y 248 g/kg, respectivamente. Para FDA los mayores valores fueron de 594 g/kg en el genotipo 655, y de 252 g/kg para el genotipo 653. Con respecto a FDN el genotipo 655 fue el de mayor contenido tanto en tallo como en hoja con 731 g/kg y 474 g/kg, respectivamente. Sobre la base de una mayor concentración de PC en la hoja y su mayor RG, el genotipo 33 es el que se recomienda para siembra extensiva en el ciclo OI, y el genotipo 655 para el ciclo de PV

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

Regionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0

The world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way

Extinction risk of Mesoamerican crop wild relatives

Ensuring food security is one of the world's most critical issues as agricultural systems are already being impacted by global change. Crop wild relatives (CWR)—wild plants related to crops—possess genetic variability that can help adapt agriculture to a changing environment and sustainably increase crop yields to meet the food security challenge. Here we report the results of an extinction risk assessment of 224 wild relatives of some of the world's most important crops (i.e. chilli pepper, maize, common bean, avocado, cotton, potato, squash, vanilla and husk tomato) in Mesoamerica—an area of global significance as a centre of crop origin, domestication and of high CWR diversity. We show that 35% of the selected CWR taxa are threatened with extinction according to The International Union for Conservation of Nature (IUCN) Red List demonstrates that these valuable genetic resources are under high anthropogenic threat. The dominant threat processes are land use change for agriculture and farming, invasive and other problematic species (e.g. pests, genetically modified organisms) and use of biological resources, including overcollection and logging. The most significant drivers of extinction relate to smallholder agriculture—given its high incidence and ongoing shifts from traditional agriculture to modern practices (e.g. use of herbicides)—smallholder ranching and housing and urban development and introduced genetic material. There is an urgent need to increase knowledge and research around different aspects of CWR. Policies that support in situ and ex situ conservation of CWR and promote sustainable agriculture are pivotal to secure these resources for the benefit of current and future generations

Interacción genotipo x ambiente y análisis de estabilidad en genotipos de amaranto (Amaranthus spp.)

Artículo de Investigació

SELECCIÓN DE GERMOPLASMA AUTÓCTONO DE AMARANTO (Amaranthus cruentus L.)

Artículo de Investigació