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Evaluating the potential climatic suitability for premium wine production is crucial for adaptation planning in Europe. While new wine regions may emerge out of the traditional boundaries, most of the present-day renowned winemaking regions may be threatened by climate change. Here, we analyse the future evolution of the geography of wine production over Europe, through the definition of a novel climatic suitability indicator, which is calculated over the projected grapevine phenological phases to account for their possible contractions under global warming. Our approach consists in coupling six different de-biased downscaled climate projections under two different scenarios of global warming with four phenological models for different grapevine varieties. The resulting suitability indicator is based on fuzzy logic and is calculated over three main components measuring (i) the timing of the fruit physiological maturity, (ii) the risk of water stress and (iii) the risk of pests and diseases. The results demonstrate that the level of global warming largely determines the distribution of future wine regions. For a global temperature increase limited to 2°C above the pre-industrial level, the suitable areas over the traditional regions are reduced by about 4%/°C rise, while for higher levels of global warming, the rate of this loss increases up to 17%/°C. This is compensated by a gradual emergence of new wine regions out of the traditional boundaries. Moreover, we show that reallocating better-suited grapevine varieties to warmer conditions may be a viable adaptation measure to cope with the projected suitability loss over the traditional regions. However, the effectiveness of this strategy appears to decrease as the level of global warming increases. Overall, these findings suggest the existence of a safe limit below 2°C of global warming for the European winemaking sector, while adaptation might become far more challenging beyond this threshold., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Temperature and photoperiod are two major environmental cues shaping the leaf senescence of temperate tree species. However, how the control of leaf senescence is split between photoperiod and temperature is unknown for many ecologically important species. Here, we conducted a growth chamber experiment to test the effects of temperature (6, 9, 18 and 21°C) and photoperiod (8 and 16 h daylength) on leaf senescence of two temperate tree species (Quercus mongolica Fisch. and Larix principis-rupprechtii Mayr.) distributed in montane forest of China. The results showed that low temperature (LT) alone could induce leaf senescence of both species under long daylength (LD) conditions, but the leaf senescence of L. principis-rupprechtii was more sensitive to the decrease in temperature than that of Q. mongolica under the LD condition. Short daylength (SD) alone could only induce the leaf senescence of L. principis-rupprechtii, suggesting that the photoperiod sensitivity varies between species. SD could accelerate the LT-induced senescence, but the effect of SD reduced with the decrease in temperature. Based on these findings, we developed a new autumn phenology model by incorporating interspecific differences in the photoperiod sensitivity of leaf senescence. Compared with the three existing process-based autumn phenology models, the new model was more robust in simulating the experimental data. When employing these models to available long-term phenological data, our new model also performed best in reproducing the observed leaf senescence date of two closely related species (Quercus robur L. and Larix decidua Mill.). These results enhance our understanding of how LT and SD control leaf senescence. The prediction of the climate change impacts on forest carbon uptake could be improved by incorporating this new autumn phenological model into the terrestrial biosphere models., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.

The present work is the first of two reviews on applied modeling in the field of aerobiology. The aerobiological predictive models for pollen and fungal spores, usually defined as predictive statistical models, will, amongst other objectives, forecast airborne particles' concentration or dynamical behavior of the particles. These models can be classified into Observation Based Models (OBM), Phenological Based Models (PHM), or OTher Models (OTM). The aim of this review is to show, analyze and discuss the different predictive models used in pollen and spore aerobiological studies. The analysis was performed on published electronic scientific articles from 1998 to 2016 related to the type of model, the taxa and the modelled parameters. From a total of 503 studies, 55.5% used OBM (44.8% on pollen and 10.7% on fungal spores), 38.5% PHM (all on pollen) and 6% OTM (5.4% on pollen and 0.6% on fungal spores). OBM have been used with high frequency to forecast concentration. The most frequent model of OBM was linear regression (18.5% out of 503) on pollen and artificial neural networks (4.6%) on fungal spores. In the PHM, the principal use was to characterize the main pollen season (flowering season) based on the model of growth degree days. Finally, OTM have been used to estimate concentrations at unmonitored areas. Olea (14,5%) on pollen and Alternaria (4,8%) on fungal spores were the taxa most frequently modelled. Daily concentration was the most modelled parameter by OBM (25.2%) and season start day by PHM (35.6%). The PHM approaches include greater model diversity and use fewer independent variables than OBM. In addition, PHM show to be easier to apply than OBM; however, the wide range of criteria to define the parameters to use in PHM (e.g.: pollination start day) makes that each model is used with a lesser frequency than other models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The small tomato borer, Neoleucinodes elegantalis (Guenée, 1854) is a multivoltine pest of tomato and other cultivated solanaceous plants. The knowledge on how N. elegantalis respond to temperature may help in the development of pest management strategies, and in the understanding of the effects of climate change on its voltinism. In this context, this study aimed to select models to describe the temperature-dependent development rate of N. elegantalis and apply the best models to evaluate the impacts of climate change on pest voltinism. Voltinism was estimated with the best fit non-linear model and the degree-day approach using future climate change scenarios representing intermediary and high greenhouse gas emission rates. Two out of the six models assessed showed a good fit to the observed data and accurately estimated the thermal thresholds of N. elegantalis. The degree-day and the non-linear model estimated more generations in the warmer regions and fewer generations in the colder areas, but differences of up to 41% between models were recorded mainly in the warmer regions. In general, both models predicted an increase in the voltinism of N. elegantalis in most of the study area, and this increase was more pronounced in the scenarios with high emission of greenhouse gases. The mathematical model (74.8%) and the location (9.8%) were the factors that mostly contributed to the observed variation in pest voltinism. Our findings highlight the impact of climate change on the voltinism of N. elegantalis and indicate that an increase in its population growth is expected in most regions of the study area.

Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere-atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980-2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod-induced shifts in spring leaf-out across latitudes (up to 1.7 days per latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf-out and advancing late leaf-out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf-out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect., (© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

The calibration of a reliable phenological model for olive grown in areas characterized by great environmental heterogeneity, like Italy, where many varieties exist, is challenging and often suffers from a lack of observations, especially on budbreak. In this study, we used a database encompassing many phenological events from different olive varieties, years, and sites scattered all over Italy to identify the phases in which site-enlarged developmental rates can be well regressed against air temperature (Developmental Rate function, DR ) by testing both linear and nonlinear functions. A K-fold cross-validation (KfCV) was carried out to evaluate the ability of DR functions to predict phenological development. The cross-validation showed that the phases ranging from budbreak (BBCH 01 and 07) to flowering (BBCH 61 and 65) and from the beginning of flowering (BBCH 51) to flowering can be simulated with high accuracy ( r 2 = 0.93-0.96; RMSE = 3.9-6.6 days) with no appreciable difference among linear and nonlinear functions. Thus, the resulting DRs represent a simple yet reliable tool for regional phenological simulations for these phases in Italy, paving the way for a reverse modeling approach aimed at reconstructing the budbreak dates. By contrast, and despite a large number of phases explored, no appreciable results were obtained on other phases, suggesting possible interplays of different drivers that need to be further investigated., Competing Interests: The authors declare no conflict of interest.

Background: A likely increasing demand for varieties mixtures, landraces and genetic diversity in cropping systems will underpin calls for models able to generalize phenological development at the species level, at the same time as providing the expected range of phenological variability. In the present article, we aimed to obtain a generalized phenological model of durum wheat (Triticum durum, Desf.)., Results: Using a large phenological dataset embracing field data collected under different sowing dates, varieties and locations over the Italian peninsula, we searched for the phenophases enabling the best linear approximations between developmental rates and air temperature, aiming to minimize the residual variability from drivers other than temperature, as genetic and environmental diversity. The developmental rates of the resulting phases were then examined with respect to the mean daylength to determine possible additional relations with photoperiod. If a correlation with daylength was also present, the developmental rate is calibrated by multiple linear regression, or otherwise by simple linear regression of temperature. The resulting calibration, tested on an independent data subset, confirms that the model is able to generalize wheat development over the Italian peninsula with high accuracy (mean absolute error =3-8 days; r 2  = 0.75-0.98), regardless of the wheat variety., Conclusion: The generalized phenological model is potentially suitable for many agro-ecological and large-scale applications. It is hoped that the model will aid in situations where phenological observations to parameterize a model are still lacking, as is probably the case for landraces and underutilized crop varieties. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)

The spring indices, models that represent the onset of spring season biological activity, were developed using a long-term observational record from the mid-to-late twentieth century of three species of lilacs and honeysuckles contributed by volunteer observers across the nation. The USA National Phenology Network (USA-NPN) produces and freely delivers maps of spring index onset dates at fine spatial scale for the USA. These maps are used widely in natural resource planning and management applications. The extent to which the models represent activity in a broad suite of plant species is not well documented. In this study, we used a rich record of observational plant phenology data (37,819 onset records) collected in recent years (1981-2017) to evaluate how well gridded maps of the spring index models predict leaf and flowering onset dates in (a) 19 species of ecologically important, broadly distributed deciduous trees and shrubs, and (b) the lilac and honeysuckle species used to construct the models. The extent to which the spring indices predicted vegetative and reproductive phenology varied by species and with latitude, with stronger relationships revealed for shrubs than trees and with the Bloom Index compared to the Leaf Index, and reduced concordance between the indices at higher latitudes. These results allow us to use the indices as indicators of when to expect activity across widely distributed species and can serve as a yardstick to assess how future changes in the timing of spring will impact a broad array of trees and shrubs across the USA.

Although it is well-known and established that light plays important roles in plant development, up to now, there is no substantial improvements in how to deal with the light factor of spring phenology under natural condition. By monitoring the local meteorologic data and mature dates of two types (male and female) of flower from four pecan cultivars during 9 years, it was found that the complementary pattern of growing degree day and sunshine duration helped to maintain a threshold of driving force related to the maturity of pecan flower during 9 years. A novel photothermal time model based on the linear combination of growing degree day and sunshine duration was then proposed and validated to interpret the variance of mature dates of pecan cultivars. Comparative analysis showed that the new model had made extremely significant improvements to the traditional thermal time model. In addition, this model introduced the conversion coefficient K, which quantified the effect of light on the flowering drive, and revealed the differences of base temperature among cultivars. This was the first time that sunshine duration instead of photoperiod was adopted to develop into a verified model on spring phenological event of tree species. It will help to model the spring phenologies of other tree species more reasonably.

Existing evidence demonstrates that the first flowering date (FFD) of most plant species became earlier in response to temperature increase over the past several decades. However, the studies on changes in flowering duration (FD) were limited. By using the non-parametric Theil-Sen estimator, this study investigated the temporal trends in 127 time series of FFD, end of flowering date (EFD), and FD of 97 woody plants from 1963 to 2014 at three sites (Harbin, Beijing, and Xi'an) in North China. The relationship between flowering phenophases and temperature was analyzed using two phenological models. The results showed that most of FFD and EFD time series exhibited an apparent advancing trend. Among them, trends of 52.0% (40.9%) of FFD (EFD) time series were significant (P < 0.05). FFD and EFD time series (95.3 and 89.8%, respectively) responded negatively and significantly to preseason temperature (P < 0.05). The direction of FD changes varied among sites and species. On average, a shortening trend of FD was observed at Harbin (-0.51 days decade -1 ), with 7.5% of species significantly. However, FD on average extended by 0.42 and 0.93 days decade -1 at Beijing (24.5% significantly) and Xi'an (28.9% significantly), respectively. The regression models could simulate the interannual changes in FFD and EFD with the mean goodness of fit (R 2 ) ranging from 0.37 to 0.67, but fail to simulate the changes in FD accurately (R 2 ranging from 0.09 to 0.18). The growing degree day model could improve the R 2 for simulating FFD and EFD except for FD. Therefore, more phenological models need to be tested, and more drivers of FD need to be further investigated.

The accuracy of the operational models can be improved by using observational data to shift the model state in a process called data assimilation. Here, a data assimilation approach using the temperature similarity to control the extent of extrapolation of point-like phenological observations is explored. A degree-day model is used to describe the spring phenology of the bird cherry Padus racemosa in the Baltic region in 2014. The model results are compared to phenological observations that are expressed on a continuous scale based on the BBCH code. The air temperature data are derived from a numerical weather prediction (NWP) model. It is assumed that the phenology at two points with a similar temperature pattern should be similar. The root mean squared difference (RMSD) between the time series of hourly temperature data over a selected time interval are used to measure the temperature similarity of any two points. A sigmoidal function is used to scale the RMSD into a weight factor that determines how the modelled and observed phenophases are combined in the data assimilation. The parameter space for determining the weight of observations is explored. It is found that data assimilation improved the accuracy of the phenological model and that the value of the point-like observations can be increased through using a weighting function based on environmental parameters, such as temperature.

Postbloom fruit drop (PFD) causes lesions on the petals of citrus flowers and induces fruit abscission causing severe damage to production when the flowering period coincides with intense rainfall. The aims of this study were to develop a phenological-climatological model for citrus PFD occurrence and, together with weather data series from several locations, to determine and map the agro-climatic favorability of PFD occurrence in the state of São Paulo, Southern Brazil. A phenological flowering model was developed to identify when citrus flowering occurs. The flowering starts after when a temperature below 10 °C in the months of June or July is reached followed by cumulative rainfall within 5 days of at least 20 mm, and then 96 °C days. Between the beginning of flowering and its peak, 147 °C days are required, and between the peak and its end, approximately 229 °C days, being 206 °C days from the peak to the moment when flowers remaining are about 50 % of total. The relationship between PFD incidence and accumulated rainfall during the critical period (between flowering peak and 50 % of flowers remaining) was adjusted by the Gompertz model (R (2) = 0.99, p < 0.05). After its validation, this model was used to estimate PFD incidence for 29 locations in the state, from 1993 to 2013, which allowed to map the PFD climatic favorability for the state through a Geographical Information System using linear models based on latitude, longitude, and altitude. The obtained map showed a trend of PFD incidence increasing from the northwest of the state of São Paulo towards the south and the coastal region, with medium to very high favorability in the center of the state. The results of this study can be used by growers as a guide for disease control planning as well as for defining the regions where the climatic conditions are likely to escape this disease.

Background: The reliance on and extensive use of pyrethroid insecticides have led to pyrethroid resistance in pollen beetle (Meligethes aeneus). Widespread adoption of best practice in pollen beetle management is therefore needed. Decision support systems (DSSs) that identify the risk period(s) for pest migration can help to target monitoring and control efforts, but they must be accurate and labour efficient to gain the support of growers. Weather data and the phenology of pollen beetles in 44 winter oilseed rape crops across England over 4 years were used to compare the performance of two risk management tools: the DSS proPlant expert, which predicts migration risk according to a phenological model and local weather data, and 'rule-based advice', which depends on crop growth stage and a temperature threshold., Results: Both risk management tools were effective in prompting monitoring that would detect breaches of various control thresholds. However, the DSS more accurately predicted migration start and advised significantly fewer days of migration risk, consultation days and monitoring than did rule-based advice., Conclusion: The proPlant expert DSS reliably models pollen beetle phenology. Use of such a DSS can focus monitoring effort to when it is most needed, facilitate the practical use of thresholds and help to prevent unnecessary insecticide applications and the development of insecticide resistance., (© 2015 Rothamsted Research Ltd. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Bioclimatics is an ancient science that was once neglected by many ecologists. However, as climate changes have attracted increasing attention, scientists have reevaluated the relevance of bioclimatology and it has thus become essential for exploring climate changes. Because of the rapidly growing importance of bioclimatic models in climate change studies, we evaluated factors that influence plant bioclimatology, constructed and developed bioclimatic models, and assessed the precautionary effects of the application of the models. The findings obtained by sequentially reviewing the development history and importance of bioclimatic models in climate change studies can be used to enhance the knowledge of bioclimatic models and strengthen their ability to apply them. Consequently, bioclimatic models can be used as a powerful tool and reference in decision-making responses to future climate changes. The objectives of this study were to (1) understand how climatic factors affect plants; (2) describe the sources, construction principles, and development of early plant bioclimatic models (PBMs); and (3) summarize the recent applications of PBMs in climate change research.