Future area expansion outweighs increasing drought risk for soybean in Europe

The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self-sufficiency targets for plant-based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3% (RCP 4.5) to 8.7% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4% (RCP 4.5) to 37.7% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study

GASCON : Gestion agro-écologique de la santé des cultures et des organismes nuisibles

Le croisement des sciences agronomiques, de l’écologie appliquée à la gestion des agroécosystèmes,et des sciences humaines et sociales, qu’implique la transition agroécologique, pose de nouveaux défis pour répondre aux enjeux agricoles: intégrer des connaissances de différentes disciplines et produites à différentes échelles d’organisation pour agir en situation; développer des cadres d’analyse et démarches intégrant la diversité de situations à gérer par les acteurs et permettant de construire des réponses adaptées à chaque situation; et concevoir et mettre en œuvre des pratiques d’enseignement et d’apprentissage, qui dotent les apprenants de capacités à penser leur action en contexte, en mobilisant des savoirs et savoir-faire multiples en termes de contenus disciplinaires et des savoir-être pour construire des solutions avec une diversité d’acteurs. Dans le champ de la formation, ces défis nécessitent dès lors de revisiter les contenus des enseignements dispensés, les modalités pédagogiques et les dispositifs de formation existants, de manière à appréhender au mieux la complexité des processus à l’œuvre. Pour autant, peu de travaux s’attardent sur les modalités pratiques de ce changement et de ses implications, alors même que de nombreuses initiatives en matière de pédagogie et d’agroécologie se développent ces dernières années. L’objectif de ce séminaire est de promouvoir une information partagée et l’échange d’expériences pour répondre aux enjeux posés par l’agroécologie dans la formation (transversalité, pluridisciplinarité, approche systémique, pédagogies actives). Ces enjeux peuvent se décliner suivant plusieurs entrées : les thématiques enseignées (agriculture, élevage, territoire, alimentation, ...); les pratiques et les dispositifs pédagogiques mis en œuvre pour aborder ces questions (enseignement numérique, dispositifs expérimentaux, projets professionnels, référentiels, ...);les publics d’apprenants: élèves, étudiants, professionnels, ..

Resilient Computing Curriculum

This Deliverable presents the MSc Curriculum in Resilient Computing suggested by ReSIST. It includes the description of the syllabi for all the courses in the two semesters of the first year, those for the common courses in semester 3 in the second year together with an exemplification of possible application tracks with the related courses. This MSc curriculum has been updated and completed taking advantage of a large open discussion inside and outside ReSIST. This MSc Curriculum is on-line on the official ReSIST web site, where all information is available together with all the support material generated by ReSIST and all other relevant freely available support material.European Commission through NoE IST-4-026764-NOE (ReSIST

Effects of climate input data aggregation on modelling regional crop yields

Crop models can be sensitive to climate input data aggregation and this response may differ among models. This should be considered when applying field-scale models for assessment of climate change impacts on larger spatial scales or when coupling models across scales. In order to evaluate these effects systematically, an ensemble of ten crop models was run with climate input data on different spatial aggregations ranging from 1, 10, 25, 50 and 100 km horizontal resolution for the state of North Rhine-Westphalia, Germany. Models were minimally calibrated to typical sowing and harvest dates, and crop yields observed in the region, subsequently simulating potential, water-limited and nitrogen-limited production of winter wheat and silage maize for 1982-2011. Outputs were analysed for 19 variables (yield, evapotranspiration, soil organic carbon, etc.). In this study the sensitivity of the individual models and the model ensemble in response to input data aggregation is assessed for crop yield. Results show that the mean yield of the region calculated from climate time series of 1 km horizontal resolution changes only little when using climate input data of higher aggregation levels for most models. However, yield frequency distributions change with aggregation, resembling observed data better with increasing resolution. With few exceptions, these results apply to the two crops and three production situations (potential, water-, nitrogen-limited) and across models including the model ensemble, regardless of differences among models in simulated yield levels and spatial yield patterns. Results of this study improve the confidence of using crop models at varying scales

Resilient Computing Courseware

This Deliverable describes the courseware in support to teaching Resilient Computing in a Curriculum for an MSc track following the scheme of the Bologna process. The development of the supporting material for such a curriculum has required a rather intensive activity that involved not only the partners in ReSIST but also a much larger worldwide community with the aim of identifying available updated support material that can be used to build a progressive and methodical line of teaching to accompany students and interested persons in a profitable learning process. All this material is on-line on the official ReSIST web site http://www.resistnoe.org/, can be viewed and downloaded for use in a class and constitutes, at our knowledge, the first, almost comprehensive attempt, to build a database of support material related to Dependable and Resilient Computing.European Commission through NoE IST-4-026764-NOE (ReSIST

Modèles et interopérabilité des modèles

Les modèles font partie des outils largement utilisés par les scientifiques pour comprendre, diagnostiquer et prédire les phénomènes. Ils sont d’une grande diversité. La dynamique Open Science donne une nouvelle optique quant à leur construction et en particulier nécessite de développer la dimension de leur interopérabilité. Cette interopérabilité peut s’appuyer vers plus de standardisation des modèles et/ou par des méthodes d’ingénierie informatique

Record: an integrated framework to build, evaluate and simulate

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Record: an integrated platform for agro-ecosystems study

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