Jürgen Junk

A High-Resolution Cumulative Degree Day-Based Model to Simulate Phenological Development of Grapevine

Common cumulative degree day models used to forecast grape growth stages often are only of local validity, restricted to a limited number of phenological stages, or do not take into consideration that the forcing effect of temperature is limited at higher temperatures. A new model was developed to simulate all 26 phenological stages (according to the BBCH scheme; Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) of Vitis vinifera L. Müller-Thurgau between budburst and harvest. Sixty time series of grape phenology from four European countries were used to set up and validate the model. Three cumulative degree day models (starting with budburst: BBCH 09) with one, two, or three optimized temperature threshold values were compared. The incorporation of an upper threshold temperature, above which a further increase of the temperature will not accelerate plant development, and of a heat threshold, above which a further increase of the temperature leads to a development deceleration, significantly improved the accuracy of the model compared to previous cumulative degree day approaches. The threshold triplet 5ºC, 20ºC, and 22ºC for lower (base), upper, and heat threshold temperature, respectively, allowed the most precise forecast. In 70.5 or 95.8% of the cases, phenological stages were correctly predicted in 3 or 7 days (assuming daily mean temperatures of 20ºC), respectively, around the predicted cumulative degree day. The model can be used for a range of applications in viticultural research and practical viticulture and could further be parameterized for other varieties.

Reconstruction of daily solar UV irradiation by an artificial neural network (ANN)

Long-term records of solar UV radiation reaching the Earths surface are scarce. Radiative transfer calculations and statistical models are two options to re-construct decadal changes in solar UV radiation from long-term records of measured atmospheric parameters that contain information on the effect of clouds, atmospheric aerosols and ground albedo on UV radiation. Based on earlier studies, where the long-term variation of daily solar UV irradiation was derived from measured global and diffuse irradiation as well as atmospheric ozone by a non-linear regression method(1), we have chosen another approach for the re-construction of time series of solar UV radiation. An Artificial Neural Network (ANN) has been trained with measurements of solar UV irradiation taken at the Observatories Potsdam and Lindenberg in Germany as well as measured parameters with long-term records such as global and diffuse radiation, sunshine duration, horizontal visibility and column ozone. This study is focused on the re-construction of daily broad-band UV-B (280-315 nm), UV-A (315-400 nm) and erythemal UV irradiation (ER). Due to fast changes in cloudiness at mid-latitude sites, solar UV irradiance shows an appreciable short-term variability. One of the main advantages of the statistical method is that it uses doses of highly variable input parameters calculated from individual spot measurements that are taken at short time steps, and thus do contain the short-term variability of solar irradiance. Our study has been supported by the European SCOUT-O3 project funding. The ANN model results have been evaluated within the European action COST726(2).

Screening and Scenarios of Traffic Emissions at Trier, Germany

Scope and BackgroundIn the course of the European Council Directive on permissible air pollutant limit values, valid starting from 2005 there is an urgent call for action, particularly for fine dust (PM10). Current investigations (Junk & Helbig 2003, Reuter & Baumüller 2003) show that the limit values in certain places in congested areas are exceeded. Only if it is possible to locate these Hot Spots purposeful measures to reduce the ambient air pollution can be conducted. For an efficient identification of these Hot Spots numerical computer models or establishing special measurements networks are too expensive. Using the statistical model STREET 5.0 (KTT 2003) a cost-effective screening of the air pollution situation caused by the traffic can be done.MethodsSTREET is based on the 3-dimensional micro-scale non-hydrostatic flow- and dispersion model MISCAM (Eichhorn 1989). The results of over 100.000 different calculations with MISCAM are stored in a Database and used to calculate the emissions with STREET. In collaboration with the city council of Trier more than 150 streets were investigated, mapped, and calculated. A special urban climate measuring network supplies the necessary meteorological input data about the wind field and precipitation events in the valley of the Moselle. Information about road width and road orientation as well as building density was derived from aerial photographs. Traffic censuses and mobile air pollutants measurements supplied the remaining input data. We calculated the mean annual air pollutant concentrations for NO2, CO, SO2, O3, benzene as well as PM10-.ResultsA comparison of the model results with the values obtained from the stations of the central emission measuring network of Rhineland-Palatinate (ZIMEN, annual report 2002) shows very good agreements. The model was not only used to calculate the annual air pollutant but also for urban planning and management. The absolute level of the air pollutant is mainly dependent on the amount of traffic in the street canyons. Therefore four different case-scenarios with varying quantity of traffic were calculated and interpreted for each street. The results of the calculation show that on the basis of the mean values for both NO2 and benzene, it is not to be expected that the limits will be exceeded significantly.PerspectivesFurthermore the model can be used to find the maximum tolerable numbers of cars for a street without exceeding the air pollutant thresholds.

Future Changes in Human-Biometeorological Index Classes in Three Regions of Luxembourg, Western-Central Europe

Projected climate change will cause increasing air temperatures affecting human thermal comfort. In the highly populated areas of Western-Central Europe a large population will be exposed to these changes. In particular Luxembourg—with its dense population and the large cross-border commuter flows—is vulnerable to changing thermal stress. Based on climate change projections we assessed the impact of climate change on human thermal comfort over the next century using two common human-biometeorological indices, the Physiological Equivalent Temperature and the Universal Thermal Climate Index. To account for uncertainties, we used a multimodel ensemble of 12 transient simulations (1971–2098) with a spatial resolution of 25 km. In addition, the regional differences were analysed by a single regional climate model run with a spatial resolution of 1.3 km. For the future, trends in air temperature, vapour pressure, and both human-biometeorological indices could be determined. Cold stress levels will decrease significantly in the near future up to 2050, while the increase in heat stress turns statistically significant in the far future up to 2100. This results in a temporarily reduced overall thermal stress level but further increasing air temperatures will shift the thermal comfort towards heat stress.

Size-Segregated Atmospheric Particle Mass Concentration in Urban Areas in Luxembourg

This study examines the dependencies between emission sources, meteorological conditions, and particle mass concentration on different temporal and spatial scales. Particulate matter (PM)10, PM2.5, and PM1 were measured in an urban area in Southwest Luxembourg during two field campaigns carried out between 2008 and 2010. Data sampling at various suburban and urban sites accounts for different emission sources and human exposure to particle pollution. Long-range transport and regional source characteristics dominate PM1 mass concentrations resulting in high Pearson correlation coefficients (0.77–0.96) and low coefficients of divergence (0.12–0.18) between measurement stations. In comparison, the coarse particle fractions PM10−PM2.5 and PM2.5−PM1 show higher spatial gradients between stations, which are mainly governed by local sources with lower Pearson correlation coefficients (0.39–0.70) and higher coefficients of divergence (0.21–0.61). The PM10−PM2.5 particle fraction is largely influenced by road transport characteristics reproducing weekday and daily cycles. PM10 mass concentration during winter is dominated by the finer particle fractions, due to domestic heating, whereas concentrations during spring and summer are mostly coarse particles, originating from pollen and windblown dust. Particle mass concentrations of different size fractions were sensitive to dispersion. Unstable atmospheric boundary conditions during the day caused a shift of the PM2.5 /PM10 and PM1/PM2.5 ratios to smaller values in comparison to neutral boundary conditions. A comparison between large-scale clean air and pollution episodes showed differences in the daily cycle of PM10−PM2.5 resulting from local emission sources, transport, and removal.

A threshold-based weather model for predicting stripe rust infection in winter wheat

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999 to 2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity >92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤0.1 mm (with the dekad having these conditions for 5 to 20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥0.90 and false alarm ratios were ≤0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications.

First report of wheat leaf rust in the Grand Duchy of Luxembourg and the progress of its appearance over the 2003-2008 period.

Wheat leaf rust caused by Puccinia triticina Eriks. was identified for the first time in 2000 in the Grand Duchy of Luxembourg on the basis of orange-to-brown, round-to-ovoid, erumpent uredinia (1 to 1.5 mm in diameter) scattered on the upper and lower leaf surfaces and producing orange-brown urediniospores that are subgloboid, approximately 20 μm in diameter, and with up to eight germ pore scattered in thick, echinulate walls. In a second phase, wheat was monitored weekly (starting from Zadoks growth stage 30, pseudo stem erection) during the 2003-2008 cropping seasons for wheat leaf rust. Disease severity (percentage of leaf area with symptoms) was recorded in four, replicated field experiments located in three villages (Diekirch District: Reuler; and Grevenmacher District: Burmerange and Christnach), which are representative of the different agroclimatological zones of Luxembourg. A significant difference in severity was observed between the sites (P < 0.01) and the years (P < 0.05). Over the 6-year period, Burmerange and Reuler consistently showed the highest and lowest disease severity, respectively. In 2003 and 2007, Burmerange (a southern site with the highest average spring temperatures of 13.6 and 14.0°C, respectively) showed the highest disease severity with 66 and 57%, respectively, whereas the lowest severity (<1% for both years) was observed in the north at Reuler (site with the lowest average spring temperatures of 12.0 and 12.4°C, respectively). Christnach, located midway between Reuler and Burmerange, showed an intermediate disease severity with 7% (2003) and 22% (2007). The disease appeared at growth stages 77 (late milk) and 87 (hard dough) in the period 2003-2005, but at an earlier stage (45, boots swollen) for 2006-2008 (P < 0.001). In 2005, low severity was recorded due to a severe drought during May, June, and July. A reason for this earlier appearance of leaf rust occurrences in the two districts may be related to an increase in the average spring temperature (average March to May temperature for Luxembourg was 8.3°C for the 1971-2000 period, 9.5°C for the 2003-2005 period, 9.9°C for the 2006-2008 period, 2007 was exceptional with 11.9°C, P < 0.01). In the past, cereal disease management strategies were oriented toward the control of predominant and yield-reducing diseases such as that caused by Septoria tritici Desm. Because the succession of mild winters and warm springs during the last 5 years allowed the early occurrence and the fast development of wheat leaf rust in the Grand Duchy of Luxembourg, it is advisable to take this disease into account in fungicide application schemes.

Winter honey bee colony losses, Varroa destructor control strategies, and the role of weather conditions: Results from a survey among beekeepers

Sets of treatments that were applied against varroa mites in the Luxembourgish beekeeper community were surveyed annually with a questionnaire between the winters 2010/11 and 2014/15. The average temperature and the precipitation sum of the month, when the respective varroa control method was applied were considered as co-variables when evaluating the efficacy of varroa control regimes. Success or failure of control regimes was evaluated based on the percentage of colonies lost per apiary in the winter following the treatment(s). Neither a positive nor a negative effect of formic acid (concentration 60%, w/v) on the colony losses could be found, irrespective of the weather conditions around the time of application. The higher concentration of 85% formic acid was linked with reduced colony losses when applications were done in August. Colony losses were reduced when Thymovar was applied in July or August, but applications in September were associated with increased losses compared with apiaries not treated with Thymovar during the same period. Apilife application in July as well as Apivar applications between July and September were associated with reduced colony losses. The removal of the drone brood and trickled oxalic acid application had beneficial effects when being done in April and December, respectively. Relatively warm (3.0±1.3°C) and wet (507.0±38.6mm/2months) conditions during the winter months December and January and relatively cool (17.2±1.4°C average monthly temperature) and wet (110.8±55.5mm/month) conditions in July were associated with elevated honey bee colony losses.

Making air quality indices comparable--assessment of 10 years of air pollutant levels in western Europe.

To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO2, O3, and PM10. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM10, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality.

Forecasting the breaching of the control threshold for Ceutorhynchus pallidactylus in oilseed rape

The cabbage stem weevil Ceutorhynchus pallidactylus (Mrsh.) (Col.: Curculionidae) is a common pest in oilseed rape Brassica napus L. throughout Europe. The abundance of the cabbage stem weevil was monitored in field surveys by using yellow water traps between 2007 and 2012 at five locations in Luxembourg. We forecast the abundance of the cabbage stem weevil at levels above the control threshold in oilseed rape in springtime. If mean winter temperatures between 5 and 13 February are closer to 4.0 °C than to −2.4 °C, no breaching of the control threshold is to be expected in the subsequent March. The monitoring effort might be saved in those years. Leave‐one‐out cross‐validation revealed that whether the control threshold was exceeded could be correctly predicted based on the observed temperature differences identified in 22 out of 27 cases (81.5%) in the present study. The present approach can be easily transferred to regions where air temperature data are available, although it should not be used without prior local validation.