Nominalphrasen in literarischen Texten : Strukturtypen und Funktionen beim Figurenentwurf in Werken des 20. und 21. Jahrhunderts

Nominalphrasen und ihre Teile tragen wesentlich dazu bei, Wissen über literarische Figuren einzuführen und eingeführtes figurenbezogenes Wissen an relevanten Stellen zu aktualisieren. Das vorliegende Buch bewegt sich an der Schnittstelle von Grammatik und Textlinguistik: Anhand von ausgewählten Werken des 20. und 21. Jahrhunderts wird systematisch und detailliert dargestellt, welche Strukturtypen von Nominalphrasen eingesetzt werden, um bei der Figureneinführung bzw. beim Weiterreden über literarische Figuren bestimmte Dimensionen der Figurencharakterisierung anzusprechen. In einer Fallstudie wird darüber hinaus nach der Dynamik des Wissensaufbaus im Textstrom gefragt

О перспективе извлечения йода из продукта утилизации окислителя ракетного топлива

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time

A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models

The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat, SDATA-20-01059

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033).Two scientific publications have been published based on some of these data here

Integraal waterbeheer : kritische zone en onzekerheden : integraal hoofdrapport

In het kader van het Nationaal Modellen- en Datacentrum (NMDC) is in 2011 het NMDC innovatieproject 'Integraal waterbeheer - van kritische zone tot kritische onzekerheden' gestart (www.nmdc.eu). Dit project heeft tot doel om de modellen voor bodem, water, vegetatie en klimaat(verandering) door samenwerking beter op elkaar aan te laten sluiten, daarbij beter geschikt te maken om effecten van klimaatverandering te berekenen en om de verschillende typen onzekerheden bij dit soort studies in beeld te brengen. Het project is uitgevoerd door Alterra, Deltares, KNMI, PBL en TNO. In twee cases (Baakse Beek en Walcheren) hebben zij hun state-of-the-art modellen voor meteo, gewasgroei, vegetatie-ontwikkeling, hydrologie en geologie ingezet en aan elkaar gekoppeld. Dit rapport behandelt integraal de resultaten van het innovatieproject. De resultaten van de case voor de Baakse Beek zijn specifiek opgenomen in een NMDC deelrapport (Van Ek et al., 2012). Voor de case Walcheren wordt verwezen naar een artikel in voorbereiding (Kroes, J. et al., 2013). De resultaten bieden nieuwe inzichten in de vocht- en zouthuishouding van de bodem, potenties voor grondwaterafhankelijke natuur en groei van landbouwgewassen in het huidige klimaat en projecties voor klimaatverandering rond 2050. In het project zijn verschillende methoden toegepast om inzicht te krijgen in verschillende onzekerheden, hetgeen voor dergelijke integrale (model)studies praktische aanknopingspunten biedt voor de analyse van onzekerheden en effectieve samenwerking tussen de instituten

Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 +/- 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges

Wheat yield sensitivity to climate change across a European transect for a large ensemble of crop models

The sensitivity of a 26 member ensemble of process based wheat simulation models to perturbations in baseline temperature and precipitation was examined to construct impact response surfaces (IRS) of simulated yields

Climate change impact and adaptation for wheat protein

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production