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Spring frost can be a limiting factor in sweet cherry (Prunus avium L.) production. Rising temperatures in spring force the development of buds, whereby their vulnerability to freezing temperatures continuously increases. With the beginning of blossom, flowers can resist only light frosts without any significant damage. In this study, we investigated the risk of spring frost damages during cherry blossom for historical and future climate conditions at two different sites in NE (Berlin) and SW Germany (Geisenheim). Two phenological models, developed on the basis of phenological observations at the experimental sweet cherry orchard in Berlin-Dahlem and validated for endodormancy release and for warmer climate conditions (already published), were used to calculate the beginning of cherry blossom in Geisenheim, 1951-2015 (external model validation). Afterwards, on the basis of a statistical regionalisation model WETTREG (RCP 8.5), the frequency of frost during cherry blossom was calculated at both sites for historical (1971-2000) and future climate conditions (2011-2100). From these data, we derived the final flower damage, defined as the percentage of frozen flowers due to single or multiple frost events during blossom. The results showed that rising temperatures in this century can premature the beginning of cherry blossom up to 17 days at both sites, independent of the used phenological model. The frequency and strength of frost was characterised by a high temporal and local variability. For both sites, no significant increase in frost frequency and frost damage during blossom was found. In Geisenheim, frost damages significantly decreased from the middle of the twenty-first century. This study additionally emphasises the importance of reliable phenological models which not only work for current but also for changed climate conditions and at different sites. The date of endodormancy release should always be a known parameter in chilling/forcing models.

Six phenological models, two simple forcing (F)-models and one sequential chilling/forcing (CF)-model, each with and without day length (DL)-term in the forcing approach were optimised (2001–2010) and validated (2011–2015) on very accurate blossoming data of an experimental sweet cherry orchard at Berlin-Dahlem (cultivar ‘Summit’). In parallel, in 3 seasons (2011/2012–2013/2014) climate chamber experiments were performed in order to determine the end of dormancy for ‘Summit’, which is usually an unknown or uncertain parameter in phenological modelling. Additionally, in the season 2013/2014 an in situ climate change experiment on three trees in the sweet cherry orchard were arranged, which was used to validate the phenological models for distinctly warmer climate conditions at the experimental site. On the basis of our climate chamber experiments we quantified the chilling requirement of ‘Summit’ trees. Thus, we were able to identify a CF-model for the beginning of sweet cherry blossom which is mostly physiologically based and works well for current and for future climate conditions at the experimental site. This paper also shows how phenological models can fail under warmer climates, if either the model is too simple or the model parameters are wrong. Additional, we confirmed that phenological models with DL-term in the forcing approach clearly surpassed the conventional phenological models without this parameter. The reason for this behaviour is extensively discussed.

Spring frost is a significant production hazard in nearly all temperate fruit-growing regions. Sweet cherries are among the first fruit varieties starting their development in spring and therefore highly susceptible to late frost. Temperatures at which injuries are likely to occur are widely published, but their origin and determination methods are not well documented. In this study, a standardized method was used to investigate critical frost temperatures for the sweet cherry cultivar 'Summit' under controlled conditions. Twigs were sampled at four development stages ("side green," "green tip," "open cluster," "full bloom") and subjected to three frost temperatures (-2.5, -5.0, -10.0 °C). The main advantage of this method, compared to other approaches, was that the exposition period and the time interval required to reach the target temperature were always constant (2 h). Furthermore, then, the twigs were placed in a climate chamber until full bloom, before the examination of the flowers and not further developed buds started. For the first two sampling stages (side green, green tip), the number of buds found in open cluster, "first white," and full bloom at the evaluation date decreased with the strength of the frost treatment. The flower organs showed different levels of cold hardiness and became more vulnerable in more advanced development stages. In this paper, we developed four empirical functions which allow calculating possible frost damages on sweet cherry buds or flowers at the investigated development stages. These equations can help farmers to estimate possible frost damages on cherry buds due to frost events. However, it is necessary to validate the critical temperatures obtained in laboratory with some field observations.

Grain harvesting is one of the most weather-dependent processes in agriculture. Grain moisture contents decide on machinery use and costs and determine the necessary grain harvesting and preservation capacities of farms. The objective of this work is to investigate whether and how recent climate changes in the German state of Brandenburg affect the available field time and the required combine harvester capacity. Weather data, the beginning of harvesting, available hours with defined grain moisture contents and total required harvesting capacity are analyzed for the years 1961–2013 for winter wheat, winter rye, winter barley and spring barley. The trends found differ for the four cereal crops. Compared with 53 years ago, today harvesting starts significantly earlier for two cereal crops (−16 days for spring barley and −11 days for winter wheat). The available harvesting hours show a clear and highly significant increase for winter wheat (up to +9%), a distinct and highly significant decrease for winter barley (up to −20%), as well as a slight and significant decrease for winter rye (up to −3%). Inter-annual variability decreases for winter wheat and increases for winter barley. The unfavorable changes for winter barley do not ultimately affect the total required machinery capacity due to the separate harvesting period and relatively small cropping area for winter barley. In contrast, the primarily favorable trends for winter wheat lead to an increase in the total required combine harvester capacity, since the earlier harvesting period overlaps with the rye harvesting period.

The Pan European Phenology (PEP) project is a European infrastructure to promote and facilitate phenological research, education, and environmental monitoring. The main objective is to maintain and develop a Pan European Phenological database (PEP725) with an open, unrestricted data access for science and education. PEP725 is the successor of the database developed through the COST action 725 “Establishing a European phenological data platform for climatological applications” working as a single access point for European-wide plant phenological data. So far, 32 European meteorological services and project partners from across Europe have joined and supplied data collected by volunteers from 1868 to the present for the PEP725 database. Most of the partners actively provide data on a regular basis. The database presently holds almost 12 million records, about 46 growing stages and 265 plant species (including cultivars), and can be accessed via http://www.pep725.eu/ . Users of the PEP725 database have studied a diversity of topics ranging from climate change impact, plant physiological question, phenological modeling, and remote sensing of vegetation to ecosystem productivity.

Seven different model approaches to calculate the onset of sour cherry blossom for the main growing regions in Rhineland-Palatinate (Germany) were compared. Three of the approaches were pure forcing models (M1, M2, M2DL) and the remaining four models were combined sequential chilling-forcing (CF) models. Model M1 was the commonly used growing degree day (GDD) model in which the starting date of temperature accumulation (t 1), the base temperature (T BF) and the forcing requirement F* were optimized on the basis of observed data. Because of a relatively late optimal starting date (t 1 = 1 March), the model can be applied only to calculate the onset of cherry blossom for present climate conditions. In order to develop forcing models that could possibly be used to estimate possible shifts in the timing of cherry blossom due to climate change, the starting date t 1 of the models was intentionally set to 1 January (M2, M2DL). Unfortunately, model M2 failed in both the optimization and validation period. The introduction of a daylength term (DL) in model M2DL improved model performance. In order to project possible shifts in the timing of plant phenological events, combined CF-models are preferred over pure GDD-models. For this reason four CF-models were developed with (M3DL, M4DL) and without (M3, M4) consideration of daylength in the GDD-approach. The chilling requirement was calculated using chilling hours (M3, M3DL) and chill portions (M4, M4DL). Both models without daylength estimated implausible model parameters and failed model validation. However, models M3DL and M4DL showed meaningful model parameter estimations and the error between modelled and observed data was markedly reduced. Moreover, the models optimized and validated (internal validation) for one sour cherry growing region in Germany, were applied successfully to calculate the beginning of the blossom period in other regions in Europe and even at one station in North America (external validation).

A theoretical study proves that the common Spring-Warming model, which is widely used in phenological studies and frequently described in the literature, has systematic defects that do not allow a reliable projection of phenological stages for the future (e.g., up to 2100). When calculating spring phenological phases (e.g., beginning of blossom or leaf unfolding, etc.), defects occur because either the advance in blossom is included implicitly in the model and cannot be calibrated sufficiently to observations, or the model parameters attain unphysiological values or lie in a range so that a prognosis for the far future cannot be accomplished. Therefore, the introduction of a daylength term is suggested, which improves the Spring-Warming model and eliminates almost all of the discussed shortcomings. The performance of this improved model is demonstrated by calculating the beginning of apple blossom in Germany. For this purpose, we compared the improved model (M1) with three different versions of the original Spring-Warming model (M2–M4). The models were calibrated (optimized) using observed blossoming and temperature data (1962–2009), which have been regionalized on a 0.2° grid. The optimization was done for a representative grid point. The performance of the various model versions in predicting the beginning of apple blossom was compared with observations from independent years, which were not used in the optimization. Also, the beginning of blossom and its possible future changes were calculated with these models, using temperatures from the Regional Climate Model REMO-UBA with GHG emission scenario A1B (2001–2100). The new daylength term improved the performance of model M1 remarkably, and the model calibration automatically led to model parameters with meaningful values. These results, which were confirmed by other fruit tree species and locations, provided strong evidence that the conventional Spring-Warming models in phenology must be extended by photoperiodic sensitivity, at least for species which are photosensitive.

Recent climate changes have had distinct impacts on plant development in many parts of the world. Higher air temperatures, mainly since the end of the 1980s, have led to advanced timing of phenological phases and consequently to an extension of the general growing season. For this reason it is interesting to know how plants will respond to future climate change. In this study simple phenological models have been developed to estimate the impact of climate change on the natural vegetation in Saxony. The estimations are based on a regional climate scenario for the state of Saxony. The results indicate that changes in the timing of phenophases could continue in the future. Due to distinct temperature changes in winter and in summer, mainly the spring and summer phases will be advanced. Spring phenophases, such as leafing or flowering, show the strongest trends. Depending on the species, the average timing of these phenophases could be advanced by 3-27 days by 2050. Phenophases in autumn show relatively small changes. Thus, the annual growth period of individual trees will be further extended, mainly because of the shift of spring phases. Frequent droughts in summer and in autumn can compensate for the earlier leafing of trees, because in this case leaf colouring and leaf fall would start some weeks earlier. In such cases, the growing period would not be really extended, but shifted to the beginning of the year.

Distinct changes in air temperature since the end of the 1980s have led to clear responses in plant phenology in many parts of the world. In Germany phenological phases of the natural vegetation as well as of fruit trees and field crops have advanced clearly in the last decade of the 20th century. The strongest shift in plant development occurred for the very early spring phases. The late spring phases and summer phases reacted also to the increased temperatures, but they usually show lower trends. Until now the changes in plant development are still moderate, so that no strong impacts on yield formation processes were observed. But further climate changes will probably increase the effect on plants, so that in the future stronger impacts on crop yields are likely.

To investigate the annual and spatial variability in the beginning of growing season across Europe, phenological data of the International Phenological Gardens for the period 1969-1998 were used. The beginning of growing season (BGS) was defined as an average leaf unfolding index of 4 tree species (Betula pubescens, Prunus avium, Sorbus aucuparia and Ribes alpinum). The study shows significant changes in the mean air temperatures from February to April and in the average BGS in Europe since 1989. In the last decade the mean temperature in early spring increased by 0.8°C. As a result the average BGS advanced by 8 d. Between 1989 and 1998, 8 out of 10 years tend towards an earlier onset of spring. The earliest date was observed in 1990. The relationships between air temperature and the beginning of growing season across Europe were investigated by canonical correlation analysis (CCA). The spatial variability of both fields can be described by 3 pairs of CCA patterns. The first pattern, which explains most of the variance, shows a uniform structure with above (below) normal temperatures in whole Europe and consequently an advanced (delayed) beginning of growing season. The other 2 patterns show regional differences in the anomaly fields. Whereas the second CCA pattern has a meridional structure, the third pattern shows a zonal distribution. In all cases the anomalies of the regional air temperature and of the beginning of growing season corre- spond very well. The correlation coefficients between the anomaly fields range between 0.90 and 0.66. For all patterns appropriate examples in the observed data were found.

To investigate the impact of recent climatic changes on the plant development in Europe, this study uses phenological data of the International Phenological Gardens for the period 1969–1998. For this study, the leafing dates of four tree species (Betula pubescens, Prunus avium, Sorbus aucuparia and Ribes alpinum) were combined in an annual leaf unfolding index to define the beginning of growing season. The end of growing season was defined using the average leaf fall of B. pubescens, P. avium, Salix smithiana and R. alpinum. A nearly Europe-wide warming in the early spring (February–April) over the last 30 years (1969–1998) led to an earlier beginning of growing season by 8 days. The observed trends in the onset of spring corresponded well with changes in air temperature and circulation ( North Atlantic Oscillation Index (NAO-index)) across Europe. In late winter and early spring, the positive phase of NAO increased clearly, leading to prevailing westerly winds and thus to higher temperatures in the period February–April. Since the end of the 1980s the changes in circulation, air temperature and the beginning of spring time were striking. The investigation showed that a warming in the early spring (February–April) by 1 ◦ C causes an advance in the beginning of growing season of 7 days. The observed extension of growing season was mainly the result of an earlier onset of spring. An increase of mean annual air temperature by 1 ◦ C led to an extension of 5 days. © 2001 Elsevier Science B.V. All rights reserved.

The geographical distribution of the timings of phenological phases is a precondition for detecting regional trends of the annual timings of phenological phases and finding their relationships to climate changes. Therefore phenological maps of Europe have been computed showing long-term means, trends and annual timings of extreme years. In this article maps of the beginning, the end and the length of the growing season as means over the years 1961-1998 as well as for the warm year 1990 are presented. Strong dependences on altitude, longitude and latitude were computed both for single phenological phases and the beginning and end of the growing season. The goodness of fit for the regression equation was between 32% for the end and 83% for the beginning of growing season. The results were consistent with those of similar investigations.

This study examines the relationships between atmospheric influences and grain yields as well as yield components: crop density (ears m −2 ), number of kernels per ear and kernel weight of winter rye. The study uses data of a long-term field experiment at Berlin-Dahlem for the period between 1962 and 1996. The crop density and kernel number of winter rye were positively influenced by warm and sunny weather in autumn. An early start of the growing season after winter was also important for the satisfactory development of these components. Moderate temperatures prior to the beginning of shooting prolonged the period of spikelet formation and led to an increased number of spikelets and finally to a high number of kernels per ear. The kernel weight was negatively influenced by high temperatures and drought during the ripening stage. This weather situation reduced the duration of the grain filling period and thus the kernel weight. The yield of winter rye increased continuously from the mid 1980s as a result of a higher crop density and kernel number. Probably this is a positive climate change effect. Particularly the higher temperatures in winter time and an earlier beginning of the growing season seem to be favourable in this context.

This investigation examines the relationships between atmospheric influences and the grain yield of spring cereals (spring barley, oats). Also, three yield components — crop density (ears/panicles m −2 ), number of kernels per ear/panicle and kernel weight — are considered. The study uses data of a long-term field experiment at Berlin–Dahlem for the period between 1962 and 1996. The results show that grain yields as well as the formation and differentiation of yield parameters are clearly influenced by weather fluctuations. Altogether it is possible to explain nearly 60% of the grain yield variability by meteorological variables for both species. The yield components are strongly influenced by weather in different months: the number of kernels per ear/panicle in May during spikelet formation and development, the crop density in June during tiller reduction (declining number of tillers due to intraspecific competition effects), and, finally, the kernel weight in June and July during grain filling. Grain yields of spring barley are mainly influenced by crop density and number of kernels per ear; for the grain yield of oats, the most important component is the number of kernels per panicle, which depends highly on the weather conditions in May.

In order to investigate the relationships between atmospheric influences and crop yields, long-term observations of meteorological and agronomic parameters are necessary. The agrometeorological field experiment at Berlin-Dahlem (Germany), which was established in autumn 1952, is a unique basis for such studies. The original aim of this experiment was to show how the variability of weather affects the growth, development and yield formation of different crops. On eight plots, potatoes, winter rye, field beans, oats, sugar-beet, maize, spring barley, and yellow lupin are grown on a permanent crop rotation. The crop management from year to year is constant so that the annual variability of crop yield is only the result of climatic fluctuations. In this paper, the long-term experiment is described and the observed crop yields are briefly discussed. Following papers will present results of relationships between atmospheric influences and crop yields for spring and winter cereal, root crops and legumes in detail.

A nonlinear, baroclinic, hemispheric, low-order model of the atmosphere with nonzonal orographic and zonal thermal forcings has been constructed. The model is used to investigate the long-term climate variability by running it over 1100 years. The model runs show a chaotic behavior in a realistic parameter range. With and without a seasonal cycle in the thermal forcing, the model generates decadal climate variations which are of the same order as interannual variations. The maximum variability is found in a broad range of periods between 3 and 44 years. Empirical orthogonal function analysis reveals that these fluctuations are predominantly caused by the interaction between the orographically excited standing wave and the mean zonal flow. The computed power spectra of the principal component time series stress the importance of the high-frequency transients in long-term climate variability.

This study examines the relationships between meteorological variables (average monthly maximum and minimum air temperature, total monthly precipitation) and crop yields of a long-term field experiment in southern England. The aim is to determine whether the long-periodic changes in crop yields are associated with climatic variations. This investigation uses the grain and straw yields (from 1854 to 1967) of the Broadbalk winter wheat experiment at Rothamsted, UK, which is one of the oldest agricultural field experiments in the world. In this study the main techniques used to describe the crop/weather relationships are correlation analysis and multiple regression. The results show that the long-term changes in crop yields at Rothamsted are partially related to climate variations at this site. The main influence on grain yields is precipitation. In all months there are negative correlations between grain yield and rainfall. A weak positive correlation is found between precipitation and straw yield from April to June (stem elongation) whereas in the other months the correlation coefficients remain negative. The maximum air temperature is positively correlated with the straw yield in all months except May, June and July. Generally it is easier to explain the variability of straw yields by weather variations than it is that of grain yields. On the basis of simple multiple regression models it is possible to explain 33% of the grain and 50% of the straw yield variability by precipitation and temperature variations. Long periodic changes in rainfall and temperature are reflected in estimated crop yields. The lowest grain and straw yields are observed in wet/cold as well as in wet/warm years wheareas the highest grain yields are more frequent in dry or cold years and the highest straw yields in dry or warm years.

The aim of this study was to select a phenological model that is able to calculate the beginning of egg laying of Great Tit (Parus major) for both current and future climate conditions. Four models (M1–M4) were optimised on long-term phenological observations from the Ecological Research Centre Schluchtern (Hessen/Germany). Model M1 was a common thermal time model that accumulates growing degree days (GDD) on an optimised starting date t 1. Since egg laying of Great Tit is influenced not only by air temperature but also by photoperiod, model M1 was extended by a daylength term to give M2. The other two models, M3 and M4, correspond to M1 and M2, but t 1 was intentionally set to 1 January, in order to consider already rising temperatures at the beginning of the year. A comparison of the four models led to following results: model M1 had a relatively high root mean square error at verification (RMSEver) of more than 4 days and can be used only to calculate the start of egg laying for current climate conditions because of the relatively late starting date for GDD calculation. The model failed completely if the starting date was set to 1 January (M3). Consideration of a daylength term in models M2 and M4 improved the performance of both models strongly (RMSEver of only 3 days or less), increased the credibility of parameter estimation, and was a precondition to calculate reliable projections in the timing of egg laying in birds for the future. These results confirm that the start of egg laying of Great Tit is influenced not only by air temperature, but also by photoperiod. Although models M2 and M4 both provide comparably good results for current climate conditions, we recommend model M4–with a starting date of temperature accumulation on 1 January–for calculating possible future shifts in the commencement of egg laying. Our regional projections in the start of egg laying, based on five regional climate models (RCMs: REMO-UBA, ECHAM5-CLM, HadCM3-CLM, WETTREG-0, WETTREG-1, GHG emission scenario A1B), indicate that in the near future (2011–2040) no significant change will take place. However, in the mid- (2041–2070) and long-term (2071–2100) range the beginning of egg laying could be advanced significantly by up to 11 days on average of all five RCMs. This result corresponds to the already observed shift in the timing of egg laying by about 1 week, due mainly to an abrupt increase in air temperature at the end of the 1980s by 1.2 K between April and May. The use of five regional climate scenarios additionally allowed to estimate uncertainties among the RCMs.

A conceptual framework for climate change assessments of international market systems that involve long-term investments is proposed. The framework is a hybrid of dynamic and static modeling. Dynamic modeling is used for those system components for which temporally continuous modeling is possible, while fixed time slices are used for other system components where it can be assumed that underlying assumptions are held constant within the time slices but allowed to vary between slices. An important component of the framework is the assessment of the “metauncertainty” arising from the structural uncertainties of a linked sequence of climate, production, trade and decision-making models. The impetus for proposing the framework is the paucity of industry-wide assessments for market systems with multiple production regions and long-term capital investments that are vulnerable to climate variations and change, especially climate extremes. The proposed framework is pragmatic, eschewing the ideal for the tractable. Even so, numerous implementation challenges are expected, which are illustrated using an example industry. The conceptual framework is offered as a starting point for further discussions of strategies and approaches for climate change impact, vulnerability and adaptation assessments for international market systems.