The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

Simulation of the water balance in cropping systems is an essential tool, not only to monitor water status and determine drought but also to find ways in which soil water and irrigation water can be used more efficiently. However, besides the requirement that models are physically correct, the spatial representativeness of input data and, in particular, accurate precipitation data remain a challenge. In recent years, satellite-based soil moisture products have become an important data source for soil wetness information at various spatial-temporal scales. Four different study areas in the Czech Republic and Austria were selected representing Central European soil and climatic conditions. The performance of soil water content outputs from two different crop-water balance models and the Metop Advanced SCATterometer (ASCAT) soil moisture product was tested with field measurements from 2007 to 2011. The model output for soil water content shows that the crop model Decision Support System for Agrotechnology Transfer performs well during dry periods (<30% plant available soil moisture (ASM), whereas the soil water-balance model SoilClim presents the best results in humid months (>60% ASM). Moreover, the model performance is best in the early growing season and decreases later in the season due to biases in simulated crop-related above-ground biomass compared with the relatively stable grass canopy of the measurement sites. The Metop ASCAT soil moisture product, which presents a spatial average of soil surface moisture, shows the best performance under medium soil wetness conditions (30–50% ASM), which is related to low variation in precipitation frequency and under conditions of low-surface biomass (early vegetation season).

The Dark Energy Survey is undertaking an observational programme imaging 1/4 of the southern hemisphere sky with unprecedented photometric accuracy. In the process of observing millions of faint stars and galaxies to constrain the parameters of the dark energy equation of state, the Dark Energy Survey will obtain pre-discovery images of the regions surrounding an estimated 100 gamma-ray bursts over 5 yr. Once gamma-ray bursts are detected by, e.g., the Swift satellite, the DES data will be extremely useful for follow-up observations by the transient astronomy community. We describe a recently-commissioned suite of software that listens continuously for automated notices of gamma-ray burst activity, collates information from archival DES data, and disseminates relevant data products back to the community in near-real-time. Of particular importance are the opportunities that non-public DES data provide for relative photometry of the optical counterparts of gamma-ray bursts, as well as for identifying key characteristics (e.g., photometric redshifts) of potential gamma-ray burst host galaxies. We provide the functional details of the DESAlert software, and its data products, and we show sample results from the application of DESAlert to numerous previously detected gamma-ray bursts, including the possible identification of several heretofore unknown gamma-ray burst hosts.

Climate change is expected to affect optimum agricultural management practices for autumn-sown wheat, especially those related to sowing date and nitrogen (N) fertilization. To assess the direction and quantity of these changes for an important production region in eastern Austria, the agricultural production systems simulator was parameterized, evaluated and subsequently used to predict yield production and grain protein content under current and future conditions. Besides a baseline climate (BL, 1981–2010), climate change scenarios for the period 2035–65 were derived from three Global Circulation Models (GCMs), namely CGMR, IPCM4 and MPEH5, with two emission scenarios, A1B and B1. Crop management scenarios included a combination of three sowing dates (20 September, 20 October, 20 November) with four N fertilizer application rates (60, 120, 160, 200 kg/ha). Each management scenario was run for 100 years of stochastically generated daily weather data. The model satisfactorily simulated productivity as well as water and N use of autumn- and spring-sown wheat crops grown under different N supply levels in the 2010/11 and 2011/12 experimental seasons. Simulated wheat yields under climate change scenarios varied substantially among the three GCMs. While wheat yields for the CGMR model increased slightly above the BL scenario, under IPCM4 projections they were reduced by 29 and 32% with low or high emissions, respectively. Wheat protein appears to increase with highest increments in the climate scenarios causing the largest reductions in grain yield (IPCM4 and MPEH-A1B). Under future climatic conditions, maximum wheat yields were predicted for early sowing (September 20) with 160 kg N/ha applied at earlier dates than the current practice.

The effect of shade on behavior and physiological attributes of grazing cows in a high altitude subtropical zone is not well established. This work aimed to investigate how social and ingestive behaviors, as well as physiological and other attributes of dairy cows such as milk production, change in a subtropical environment during the hot season either with or without free access to shade. Fourteen lactating cows were kept on pasture either with no shade or with free access to shade for 5 days and their behavior was recorded with instantaneous scan sampled every 10 min, from sunrise, 0530 h (Greenwich mean time, GMT−0200 h) to sunset, 2100 h (GMT−0200 h). Behavior traits included (1) time spent in activities such as grazing, ruminating, resting, lying, standing, walking, seeking shade and staying in the proximity to the water trough and (2) number of events such as water ingestion, aggressive interactions, as well as competition for shade and water. Physiological attributes such as heart and respiratory rates, rectal temperature, number of rumen movements, panting score, as well as milk yield, were evaluated. Time spent in behavioral activities, number of behavioral events and physiological attributes varied between groups (with and without access to shade). Cows with no shade showed increased respiratory and heart rates and panting score at 1300 h, higher values for time of permanence near the water trough, number of competition and aggression events for shade. On the other hand, they showed lower values for time spent resting while lying, ruminating while standing, seeking shade. Access to shade did not change time spent lying, standing, walking with the head up, ruminating while lying, resting while standing, as well as milk yield and number of ruminal movements. Significant interactions between access to shade and days of measurements were detected for time spent walking, ruminating, grazing, resting, number of water ingestion events, competition events near the water trough and for shade, as well as for rectal temperature and panting score measured at 1700 h. In the high altitude subtropical region, access to shade minimizes negative heat stress effects on behavior and physiological aspects of dairy cows.

The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.

The present study investigates regional climate change impacts on agricultural crop production in Central and Eastern Europe, including local case studies with different focuses in Austria, the Czech Republic and Slovakia. The area studied experiences a continental European climate and is characterized by strong climatic gradients, which may foster regional differences or trends in the impacts of climate change on agriculture. To study the regional aspects and variabilities of climate change impacts on agriculture, the effect of climate change on selected future agroclimatic conditions, crop yield and variability (including the effect of higher ambient CO2 concentrations) and the most important yield limiting factors, such as water availability, nitrogen balance and the infestation risks posed by selected pests were studied. In general, the results predicted significant agroclimatic changes over the entire area during the 21st century, affecting agricultural crop production through various pathways. Simulated crop yield trends confirmed past regional studies but also revealed that yield-limiting factors may change from region to region. For example, pest pressures, as demonstrated by examining two pests, are likely to increase due to warmer conditions. In general, higher potentials for cereal yield increase are seen for wetter and cooler regions (i.e. uplands) than for the drier and warmer lowlands, where yield potentials will be increasingly limited by decreasing crop water availability and heat under most scenarios. In addition, yield variability will increase during the coming decades, but this may decrease towards the end of the 21st century. The present study contributes to the interpretation of previously conducted climate change impact and adaptation studies for agriculture and may prove useful in proposing future research in this field.

One of the main problems in estimating the effects of climate change on crops is the identification of those factors limiting crop growth in a selected environment. Previous studies have indicated that considering simple trends of either precipitation or temperature for the coming decades is insufficient for estimating the climate impact on yield in the future. One reason for this insufficiency is that changes in weather extremes or seasonal weather patterns may have marked impacts.

The present study focuses on identifying agroclimatic parameters that can identify the effects of climate change and variability on winter wheat yield change in the Pannonian lowland. The impacts of soil type under past and future climates as well as the effect of different CO2 concentrations on yield formation are also considered. The Vojvodina region was chosen for this case study because it is a representative part of the Pannonian lowland.

Projections of the future climate were taken from the HadCM3, ECHAM5 and NCAR-PCM climate models with the SRES-A2 scenario for greenhouse gas (GHG) emissions for the 2040 and 2080 integration periods. To calibrate and validate the Met&Roll weather generator, four-variable weather data series (for six main climatic stations in the Vojvodina region) were analysed. The grain yield of winter wheat was calculated using the SIRIUS wheat model for three different CO2 concentrations (330, 550 and 1050 ppm) dependent on the integration period. To estimate the effects of climatic parameters on crop yield, the correlation coefficient between crop yield and agroclimatic indices was calculated using the AGRICLIM software. The present study shows that for all soil types, the following indices are the most important for winter wheat yields in this region: (i) the number of days with water and temperature stress, (ii) the accumulated precipitation, (iii) the actual evapotranspiration (ETa) and (iv) the water deficit during the growing season. The high positive correlations between yield and the ETa, accumulated precipitation and the ratio between the ETa and reference evapotranspiration (ETr) for the April–June period indicate that water is and will remain a major limiting factor for growing winter wheat in this region. Indices referring to negative impact on yield are (i) the number of days with a water deficit for the April–June period and (ii) the number of days with maximum temperature above 25 °C (summer days) and the number of days with maximum temperature above 30 °C (tropical days) in May and June. These indices can be seen as indicators of extreme weather events such as drought and heat waves.

The main objective of the present crop simulation study was to determine the impact of climate change on the winter wheat production of a dry area situated in north-east Austria (Marchfeld region) based on the CERES-Wheat crop-growth simulation model associated with global circulation models (GCMs). The effects of some of the feasible regional- and farm-based adaptation measures (management options) on crop yield and water and nitrogen (N) balance under the climate scenarios were simulated. Climate scenarios were defined based on the ECHAM5, HadCM3 and NCAR PCM GCM simulations for future conditions (2021–50) as described in the Special Report on Emission Scenarios A1B (Nakicenovic & Swart 2000). The potential development, yield, water demand and soil N leaching were estimated for winter wheat and all of the defined climates (including rising CO2 levels) and management scenarios (soil cultivation, windbreaks and irrigation).

The results showed that a warming of 2°C in the air temperature would shorten the crop-growing period by up to 20 days and would decrease the potential winter wheat yield on nearly all of the soil types in the region. Particularly, high-yield reductions were projected for light-textured soils such as Parachernozems. A change from ploughing to minimum tillage within the future scenario would lead to an increase of up to 8% of the mean yield of winter wheat. This effect mainly resulted from improved water supply to the crop, associated with higher soil water storage capacity and decrease of unproductive water losses. Hedgerows, which reduce the wind speed, were predicted to have particularly positive effects on medium and moderately fine-textured soils such as Chernozems and Fluvisols. With both management changes, regional mean-yield level can be expected to be +4% in comparison with no management changes in the future conditions. Compared with the baseline period, water demand for the potential yield of winter wheat would require 6–37 mm more water per crop season (area-weighted average). The highest water demand would be on medium-textured soils, which make up the largest amount of area in the study region. Additionally, the effects of snow accumulation near hedgerows would further increase the yield, but would also lead to higher N leaching rates. However, specific management options, such as minimum tillage and hedgerows, could contribute towards reducing the increasing water demand.

Extended abstract of a paper presented at Microscopy and Microanalysis 2009 in Richmond, Virginia, USA, July 26 – July 30, 2009

Single phase GaGdN and GaGdN:Si films were grown on sapphire substrates. The undoped films were highly resistive films but became conductive with the addition of Si. SQUID magnetometry indicated room temperature ferromagnetism in both types of materials. Structural defects had a strong influence on the magnetic ordering of the material, as seen in a drastic reduction of magnetic moment with degrading crystalline quality. Magnetization of the co-doped film increased with Si content, reaching levels higher than that of the undoped material. Gd-doped AlN films grown in a similar fashion also displayed Curie temperatures above room temperature.

GaN and AlN thin films were implanted with gadolinium (Gd)atoms and characterized using deep ultra-violet (UV) photoluminescence(PL). The Gd-implanted samples were annealed at temperatures up to 1178K in a flowing N2 gas to facilitate recovery of implantation-related damage. Using the output at 195 nm from a quadrupled Ti:sapphire laser, narrow PL emission was observed at 318 nm from the Gd- implanted AlN thin films. This emission is characteristic of the lowest energy 4f transition of the trivalent Gd ion. A boarder emission band, also centered at 318 nm, was observed under excitation at 266 nm. No PL emission was observed from the Gd-implanted GaN thin films at either the bandedge or due to a 4f transition. The dependence of the UV emission on AlN sample temperature was systematically studied. The peak PL emission intensity decreased by less that a factor of 3 over the temperature range of 10 K to 300 K. Decay transients of the UV emission were measured indicating that the lifetime of this emission is very fast.

Existing semiconductor electronic and photonic devices utilize the charge on electrons and holes in order to perform their specific functionality such as signal processing or light emission. The relatively new field of semiconductor spintronics seeks, in addition, to exploit the spin of charge carriers in new generations of transistors, lasers and integrated magnetic sensors. The ability to control of spin injection, transport and detection leads to the potential for new classes of ultra-low power, high speed memory, logic and photonic devices. The utility of such devices depends on the availability of materials with practical (>300K) magnetic ordering temperatures. In this paper, we summarize recent progress in dilute magnetic semiconductors such as (Ga,Mn)N, (Ga,Mn)P and (Zn,Mn)O exhibiting room temperature ferromagnetism, the origins of the magnetism and its potential applications in novel devices such as spin-polarized light emitters and spin field effect transistors.