Changes in time of sowing, flowering and maturity of cereals in Europe under climate change.

The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985-2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site-year-variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1-3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1-3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.

Modeling deoxynivalenol contamination of wheat in northwestern Europe for climate change assessments.

Climate change will affect mycotoxin contamination of feed and food. Mathematical models for predicting mycotoxin concentrations in cereal grains are useful for estimating the impact of climate change on these toxins. The objective of the current study was to construct a descriptive model to estimate climate change impacts on deoxynivalenol (DON) contamination of mature wheat grown in northwestern Europe. Observational data from 717 wheat fields in Norway, Sweden, Finland, and The Netherlands were analyzed, including the DON concentrations in mature wheat, agronomical practices, and local weather. Multiple regression analyses were conducted, and the best set of explanatory variables, mainly including weather factors, was selected. The final model included the following variables: flowering date, length of time between flowering and harvest, wheat resistance to Fusarium infection, and several climatic variables related to relative humidity, temperature, and rainfall during critical stages of wheat cultivation. The model accounted for 50 % of the variance, which was sufficient to make this model useful for estimating the trends of climate change on DON contamination of wheat in northwestern Europe. Application of the model in possible climate change scenarios is illustrated.

Statistical analysis of agronomical factors and weather conditions influencing deoxynivalenol levels in oats in Scandinavia.

The relationship between weather data and agronomical factors and deoxynivalenol (DON) levels in oats was examined with the aim of developing a predictive model. Data were collected from a total of 674 fields during periods of up to 10 years in Finland, Norway and Sweden, and included DON levels in the harvested oats crop, agronomical factors and weather data. The results show that there was a large regional variation in DON levels, with higher levels in one region in Norway compared with other regions in Norway, Finland and Sweden. In this region the median DON level was 1000 ng g⁻¹ and the regulatory limit for human consumption (1750 ng g⁻¹) was exceeded in 28% of the samples. In other regions the median DON levels ranged from 75 to 270 ng g⁻¹, and DON levels exceeded 1750 ng g⁻¹ in 3-8% of the samples. Including more variables than region in a multiple regression model only increased the adjusted coefficient of determination from 0.17 to 0.24, indicating that very little of the variation in DON levels could be explained by weather data or agronomical factors. Thus, it was not possible to predict DON levels based on the variables included in this study. Further studies are needed to solve this problem. Apparently the infection and/or growth of DON producing Fusarium species are promoted in certain regions. One possibility may be to study the species distribution of fungal communities and their changes during the oats cultivation period in more detail.

Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose.

Mycotoxin contamination of cereal grains can be detected and quantified using complex extraction procedures and analytical techniques. Normally, the grain odour, i.e. the presence of non-grain volatile metabolites, is used for quality classification of grain. We have investigated the possibility of using fungal volatile metabolites as indicators of mycotoxins in grain. Ten barley samples with normal odour, and 30 with some kind of off-odour were selected from Swedish granaries. The samples were evaluated with regard to moisture content, fungal contamination, ergosterol content, and levels of ochratoxin A (OA) and deoxynivalenol (DON). Volatile compounds were also analysed using both an electronic nose and gas chromatography combined with mass spectrometry (GC-MS). Samples with normal odour had no detectable ochratoxin A and average DON contents of 16 microg kg(-1) (range 0-80), while samples with off-odour had average OA contents of 76 microg kg(-1) (range 0-934) and DON contents of 69 microg kg(-1) (range 0-857). Data were evaluated by multivariate data analysis using projection methods such as principal component analysis (PCA) and partial least squares (PLS). The results show that it was possible to classify the OA level as below or above the maximum limit of 5 microg kg(-1) cereal grain established by the Swedish National Food Administration, and that the DON level could be estimated using PLS. Samples with OA levels below 5 microg kg(-1) had higher concentration of aldehydes (nonanal, 2-hexenal) and alcohols (1-penten-3-ol, 1-octanol). Samples with OA levels above 5 microg kg(-1) had higher concentrations of ketones (2-hexanone, 3-octanone). The GC-MS system predicted OA concentrations with a higher accuracy than the electronic nose, since the GC-MS misclassified only 3 of 37 samples and the electronic nose 7 of 37 samples. No correlation was found between odour and OA level, as samples with pronounced or strong off-odours had OA levels both below and above 5 microg kg(-1). We were able to predict DON levels in the naturally contaminated barley samples using the volatile compounds detected and quantified by either GC-MS or the electronic nose. Pentane, methylpyrazine, 3-pentanone, 3-octene-2-ol and isooctylacetate showed a positive correlation with DON, while ethylhexanol, pentadecane, toluene, 1-octanol, 1-nonanol, and 1-heptanol showed a negative correlation with DON. The root mean square error of estimation values for prediction of DON based on GC-MS and electronic nose data were 16 and 25 microg kg(-1), respectively.

Volatiles for mycological quality grading of barley grains: determinations using gas chromatography-mass spectrometry and electronic nose.

The possibility of using an electronic nose or gas chromatography combined with mass spectrometry (GC-MS) to quantify ergosterol and colony forming units (CFU) of naturally contaminated barley samples was investigated. Each sample was split into three parts for (i) ergosterol and CFU analysis, (ii) measurements with the electronic nose and (iii) identification of volatiles collected on an adsorbent with a GC-MS system. Forty samples were selected after sensory analysis to obtain 10 samples with normal odour and 30 with some degree of off-odour. The data set of volatile compounds and the data collected from the electronic nose were evaluated by multivariate analyse techniques. SIMCA classification (soft independent modelling of class analogy) was used for objective evaluation of the usefulness of the data from the GC-MS or electronic nose measurements for classification of grain samples as normal or with off-odour. The main volatile compounds of grain with normal odour were 2-hexenal, benzaldehyde and nonanal, while 3-octanone, methylheptanone and trimethylbenzene were the main volatile compounds of grain with off-odours. Using data from the electronic nose three samples of 40 were misclassified, while data analysis of the volatile compounds detected with the GC-MS, led to six misclassified samples. Regression models (partial least-squares, PLS) were built to predict ergosterol- and CFU-levels with data from the GC-MS or electronic nose measurements. PLS models based on both GC-MS and electronic nose data could be used to predict the ergosterol levels with high accuracy and with low root mean square error of prediction (RMSEP). CFU values from naturally infected grain could not be predicted with the same degree of confidence.

Fungal volatiles as indicators of food and feeds spoilage.

Fungal growth leads to spoilage of food and animal feeds and to formation of mycotoxins and potentially allergenic spores. Fungi produce volatile compounds, during both primary and secondary metabolism, which can be used for detection and identification. Fungal volatiles from mainly Aspergillus, Fusarium, and Penicillium have been characterized with gas chromatography, mass spectrometry, and sensory analysis. Common volatiles are 2-methyl-1-propanol, 3-methyl-1-butanol, 1-octen-3-ol, 3-octanone, 3-methylfuran, ethyl acetate, and the malodorous 2-methyl-isoborneol and geosmin. Volatile sesquiterpenes can be used for taxonomic classification and species identification in Penicillium, as well as to indicate mycotoxin formation in Fusarium and Aspergillus. Developments in sensor technology have led to the construction of "electronic noses" (volatile compound mappers). Exposure of different nonspecific sensors to volatile compounds produces characteristic electrical signals. These are collected by a computer and processed by multivariate statistical methods or in an artificial neural network (ANN). Such systems can grade cereal grain with regard to presence of molds as efficiently as sensory panels evaluating grain odor. Volatile compound mapping can also be used to predict levels of ergosterol and fungal colony-forming units in grain. Further developments should make it possible to detect individual fungal species as well as the degree of mycotoxin contamination of food and animal feeds.

Mucosal topography around implants in edentulous upper jaws. Photogrammetric three-dimensional measurements of the effect of replacement of a removable prosthesis with a fixed prosthesis.

A photogrammetric technique was tested to measure the topography of the mucosa around implants, placed in edentulous upper jaws. Photographs were taken of casts from 6 patients, who all had used a removable overdenture for one year. Another series of photographs was taken on new casts after the use of a fixed prosthesis for a second year. The 6 pairs of photographs were measured and compared in an analytical stereo plotter for surface contour and implant positions. The results from the measurements indicated a trend of general recession of the mucosa after one year with fixed prosthesis, both on the buccal as well as on the palatal side. The mean volume of recession was 222.4 mm3, corresponding to an average of 0.4 mm3/mm2 of mucosa. More recession was generally observed on the palatal side, but obvious variations between the patients were present. In conclusion, the photogrammetric technique was considered to be well suited for analysing tissue contours in various dental situations.

Volatile metabolites produced by six fungal species compared with other indicators of fungal growth on cereal grains.

Six fungal species, Penicillium brevicompactum, P. glabrum, P. roqueforti, Aspergillus flavus, A. versicolor, and A. candidus, were inoculated on moistened and autoclaved wheat and oat grains. They were cultivated in glass vessels provided with an inlet and outlet for air. Air was passed through the vessels to collect volatile fungal metabolites on porous polymer adsorbents attached to the outlet. Samples were collected at two fungal growth stages. Adsorbed compounds were thermally desorbed, separated by gas chromatography, and identified by mass spectrometry. Differences in the production of volatile metabolites depended more on the fungal species than on the grain type. The fungal growth stage was not an important factor determining the composition of volatiles produced. 3-Methylfuran was produced in similar amounts regardless of the fungal species and substrate (oat versus wheat). The production of volatile metabolites was compared with the production of ergosterol and CO2 and the number of CFU. The production of volatile metabolites was more strongly correlated with accumulated CO2 production than with actual CO2 production and more strongly correlated with ergosterol contents of the grain than with numbers of CFU.

Volatile metabolites and other indicators of Penicillium aurantiogriseum growth on different substrates.

Penicillium aurantiogriseum Dierckx was cultivated on six agar substrates (barley meal agar, oat meal agar, wheat meal agar, malt extract agar, Czapek agar, and Norkrans agar) and on oat grain for 5 days in cultivation vessels provided with an inlet and an outlet for air. Volatile metabolites produced by the cultures were collected on a porous polymer adsorbent by passing an airstream through the vessel. Volatile metabolites were collected between days 2 and 5 after inoculation. CO2 production was simultaneously measured, and after the cultivation period ergosterol contents and the numbers of CFU of the cultures were determined. Alcohols of low molecular weight and sesquiterpenes were the dominant compounds found. During growth on oat grain the production of 8-carbon alcohols and 3-methyl-1-butanol was higher and the production of terpenes was lower than during growth on agar substrates. The compositions of the volatile metabolites from oat grain were more similar to those from wheat grain, which was used as a substrate in a previous investigation, than to those produced on any of the agar substrates. Regarding the agar substrates, the production of terpenes was most pronounced on the artificial substrates (Czapek agar and Norkrans agar) whereas alcohol production was highest on substrates based on cereals. The production of volatile metabolites was highly correlated with the production of CO2 and moderately correlated with ergosterol contents, whereas no correlation with the numbers of CFU was found. Thus, the volatile metabolites formed and the ergosterol contents of fungal cultures should be good indicators of present and past fungal activity.