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Drought is one of the meteorological disasters to which maize is most vulnerable during its seedling stage in Northeast China. The absence of phenological data impedes the precise evaluation of the likelihood of drought during this phase. In response to these issues, this study develops a phenology model and reconstructing the data. Furthermore, it effectively assessed drought risk at the site scale by utilizing drought indicators. Using reconstructed phenological data from 217 sites from 1981 to 2015, we analysed the duration and trends of each phenological period and assessed the spatial and temporal distribution of drought frequency at each growth stage. The study demonstrated that the average date ranges for the sowing, emergence, three‐leaf, and seven‐leaf stages annually were 115–138 days, 130–151 days, 135–160 days, and 150–180 days, respectively. Additionally, there was a significant trend towards earlier dates in all phenological stages. Our research reveals notable fluctuations in drought frequency during various growth stages of early maize in Northeast China. Particularly, the period from the three‐leaf to the seven‐leaf stages emerges as the most drought‐prone, while the initial emergence to three‐leaf stage also shows considerable vulnerability. On average, the frequency of drought events during the critical three‐ to seven‐leaf stage stands at 35%. This average is surpassed in regions like Heilongjiang, northwest Jilin, northern Inner Mongolia, and southwest Liaoning, indicating a heightened risk in these areas. The early maize growth stage drought types are mainly light and moderate drought, with the three‐leaf to seven‐leaf stage, and Heilongjiang and Inner Mongolia, as the key stages and regions of concern, respectively. Identifying the principal types of drought and their occurrence in distinct regions and growth stages is pivotal for averting and reducing disasters. [ABSTRACT FROM AUTHOR]

Understanding how spring phenology of temperate trees responds to global change is essential for assessing the vegetation dynamics in the future. Besides chilling and forcing temperatures, the influences of other environmental and biotic factors on leaf unfolding date (LUD) are still unclear. Based on long-term records (1960–2015) of LUD for 6 typical temperate tree species at 4242 stations in Europe, we first used a classic process-based model (the Unified model) to describe the relationship between chilling and forcing requirements of LUD. Furthermore, we used 3 explainable machine learning (ML) algorithms (RF, EBM, and GAMI-Net) to quantify the influences of 45 biotic and environmental factors on the LUD. The root-mean-square error (RMSE) of the ML-based models averaged from all species (7.03 to 7.33 days) was lower than the Unified model (8.73 days). The ML algorithms detected 2 biotic (elevation and previous leaf senescence date) and 3 temperature-related variables (chilling accumulation, freezing days in March and annual temperature range) with high importance for most species. The trees adapted to a higher elevation or with later leaf senescence date in the previous year exhibited later LUD. The increase in chilling accumulation, freezing days in March and the annual temperature range could reduce the forcing requirement of LUD. Water-related and light-related variables were only important for one or two species. The increase in relative humidity in February could advance LUD, while soil moisture in March had the opposite effect. The increase in March day length and downward surface shortwave radiation exerted an advancing influence on the LUD of light-sensitive species. Our results suggest that incorporating multiple biotic and environmental factors into ML algorithms not only could effectively improve the accuracy of phenological prediction but also enhance our understanding of how spring phenology responds to each factor.

The ongoing climate change‐induced shifts in flowering phenology have emerged as a consequential force impacting biodiversity and ecosystems. Despite the globally recognized significance of flowering phenology as a key reproductive attribute, studies in subtropical regions have been relatively fewer, particularly in comparison to temperate and cold regions. Additionally, the nuanced response of deciduous and evergreen plants to climate change remains insufficiently explored. In addressing this gap, we built a phenological model and a generalized linear mixed effect model to assess the differential responses of key flowering phenological traits, that is, first flowering date (FFD), peak flowering date (PFD), end of flowering date (EFD), and flowering duration (FD), to climate factors (temperature and precipitation) between deciduous and evergreen plants. We observed distinct responses in flowering phenological traits to climate change between deciduous and evergreen plants. Specifically, the advancement of FFD, PFD, and EFD in deciduous in response to temperature rise exceeded that in evergreen plants. FD in evergreen plants exhibited a stronger extension to temperature increase compared to deciduous. Conversely, the phenological change of evergreen plants in response to decreasing precipitation was greater than that of deciduous ones. Since temperature is a decisive climatic factor in affecting phenological changes, climate change‐induced advances in flowering phenology of deciduous plants are still larger than evergreen plants. Projections from our phenological model under future climate scenarios (SSP 1‐2.6 and SSP 5‐8.5) indicate a continuous enlargement of difference in flowering phenology between deciduous and evergreen plants, with this trend escalating into the future (2100>2070>2050>2030). The larger extension in FD of evergreens to climate change suggests a potential increase in their proportion within subtropical forest communities relative to deciduous plants. These insights contribute significantly to our understanding of the intricate dynamics of climate‐induced changes in subtropical plant ecosystems. [ABSTRACT FROM AUTHOR]

Experimental seasonal forecasts using the INM-CM5 climate model have been applied as the input data for the temperature–time phenological model of birch pollination. A test method has been developed within the framework of the joint model for the seasonal forecasting of the time of the beginning of birch pollen dispersion in the European territory of Russia. Verification of this method on seasonal retrospective forecasts of the INM-CM5 model (in 1991–2019) has shown an adequate reproduction of the days of the beginning of birch pollen season simulated for the same period based on ERA5 reanalysis. The mean systematic errors are ±2 days, and the spatial correlation coefficients exceed +0.84. The forecasts of the date of pollen dispersion beginning in 2022 simulated from the experimental operational seasonal forecasts using the INM-CM5 model with a monthly lead time and with a zero lead time are also evaluated. It is shown that the errors in forecasting the onset of pollen dispersion are ±5–10 days, with smaller errors of forecasts with a 1-month lead time. The results allow us to conclude that the seasonal forecast of the surface temperature based on the INM-CM5 model can be used as input information for the temperature–time phenological model for the operational forecast of the start time of birch pollen dispersion in the European territory of Russia. [ABSTRACT FROM AUTHOR]

Vegetation green-up onset date (VGD) is a key indicator of ecosystem structure and processes. As the largest and highest alpine ecoregion, the Tibetan plateau (TP) has experienced markable climate warming during the past decades and showed substantial changes in VGD. However, the existing process-based phenology models still cannot simulate interannual variations in satellite-derived VGD. In this study, we developed a data-driven VGD model for the TP based on the Long short-term memory neural network (called VGD-LSTM). VGD-LSTM considers the complicated nonlinear relationship between VGD and multiple climatic and environmental drivers, including the time series of temperature (daytime, daily minimum, and daily mean) and precipitation, as well as nonsequential variables (elevation and geolocation). Compared with the benchmark process-based VGD model for the TP (i.e., the hierarchical model), VGD-LSTM greatly improved the simulation of interannual VGD variations. We calculated the correlation coefficients (R) between satellite-derived VGDs and VGD simulations during 2000–2018; the percentages of pixels with R values above 0.5 increased from 15% for the hierarchical model to 41% for VGD-LSTM. The advanced trend in the satellite-derived VGD on the entire TP during 2000–2018 (−0.37 day/year) was captured well by VGD-LSTM (−0.33 day/year) but was underestimated by the hierarchical model (−0.08 day/year). According to VGD-LSTM simulations, VGDs on the TP are projected to advance by 8–10 days by 2100 relative to 2015–2020 under high shared socioeconomic pathway scenarios. This study suggests the potential of artificial intelligence in phenology modeling for which the physiological processes are difficult to be fully represented.

Temperature is the basic factor that differentiates vegetation around the world. All field experiments require the indication of the range of temperatures occurring in a given growing season. Temperature is an important factor determining fruit plant production, both in the growing season and in the winter dormant period. Various air temperature indicators were developed in a way that allowed the best possible description of adaptations of species, cultivars, and regions of adaptations to cultivation. They are based on experimentally obtained data and calculated optimal temperatures of growth and development of plants in particular development stages. In horticulture, the description of dependencies of the growth and development of plants on weather began to be accompanied with the development of simulation models. The aim of this manuscript was a new review of fruit plant temperature indices to predict abiotic and biotic hazards in fruit production for various selected types of fruit crops in a seasonal temperate climate. This is especially important due to the growing risk of climate change, which significantly alters local growing conditions. Therefore, it is very important to evaluate and present a set of specific indicators for producers, which we have reviewed from the current literature and presented as follows. Climatic conditions characteristic of a given region should be of key importance for the selection of species for commercial cultivation and planning of protection measures. [ABSTRACT FROM AUTHOR]

As spring frost proves to be an increasing risk throughout Slovenia and Europe, a better assessment of frost risk is needed. The statistical approach presented in this article consists of the conditional probability that the last spring frost occurs before budburst or flowering. The analysis was conducted using two separate phenological models and phenological data of various grapevine (Vitis vinifera L.), apple (Malus domestica), and sweet cherry (Prunus avium L.) varieties in locations across Slovenia. The increase in risk of spring frost for grapevine ranged from 1 to 1980, from 0.06 to 12 for apple, and from 1 to 180 for sweet cherry. Overall, the varieties most prone to frost proved to be Refošk (Teran) and Merlot grapevine varieties as well as the Germersdorf sweet cherry variety. We have identified the location in the hilly region with moderate climate where the Bobovec apple variety is grown as the least exposed to frost. Although counterintuitive, the GDD generally proved somewhat more efficient than the two-phase phenological model BRIN, although not in all cases. For the purpose of the study, the phenological models were calibrated, and the model parameters can serve as invaluable information for further research of this topic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim: Phenological models have become a vital tool for predicting future phenological responses to global climate change. Recently, machine learning (ML) has been used successfully to develop phenological models based on ground observations. However, fitting an observation series that has been observed for only a few years (or even a decade) can easily lead to overfitting, and it is still a great challenge to predict future phenology accurately based on a short observation series. Here, based on historical ground phenological observations, we construct an ML‐based binary classification phenological model that can be applied to temperate trees. Innovation: We thoroughly describe the construction process of a species‐specific ML‐based binary classification phenological model that is suitable for phenological predictions in both spring and autumn. Through experiments, we evaluate 18 commonly used ML classification algorithms and the effects of two parameters on the prediction performance of the model. Finally, we compare the performance between the binary classification model and six widely used ecophysiological models for accuracy of spring phenological prediction. Main conclusions: The median root mean square error (RMSE) of the binary classification model for the first flowering date (93 observation series) was only 2.99 days, which proves that it is a good method of phenological prediction for temperate trees. This model can effectively overcome the insufficient sample size of ground observations for specific species and provide new insights of phenological predictions for tropical and subtropical trees. The comparison of different ML algorithms showed that the median root mean square errors of the six algorithms were <3.5 days, and lower than those of six ecophysiological models (>3.7 days) in prediction of spring phenology. In addition, this model can help us to infer plant physiological mechanisms and drivers of phenological change and provide more accurate predictions of plant phenological responses to climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Temperature is the basic factor that differentiates vegetation around the world. All field experiments require the indication of the range of temperatures occurring in a given growing season. Temperature is an important factor determining fruit plant production, both in the growing season and in the winter dormant period. Various air temperature indicators were developed in a way that allowed the best possible description of adaptations of species, cultivars, and regions of adaptations to cultivation. They are based on experimentally obtained data and calculated optimal temperatures of growth and development of plants in particular development stages. In horticulture, the description of dependencies of the growth and development of plants on weather began to be accompanied with the development of simulation models. The aim of this manuscript was a new review of fruit plant temperature indices to predict abiotic and biotic hazards in fruit production for various selected types of fruit crops in a seasonal temperate climate. This is especially important due to the growing risk of climate change, which significantly alters local growing conditions. Therefore, it is very important to evaluate and present a set of specific indicators for producers, which we have reviewed from the current literature and presented as follows. Climatic conditions characteristic of a given region should be of key importance for the selection of species for commercial cultivation and planning of protection measures.

As spring frost proves to be an increasing risk throughout Slovenia and Europe, a better assessment of frost risk is needed. The statistical approach presented in this article consists of the conditional probability that the last spring frost occurs before budburst or flowering. The analysis was conducted using two separate phenological models and phenological data of various grapevine (Vitis vinifera L.), apple (Malus domestica), and sweet cherry (Prunus avium L.) varieties in locations across Slovenia. The increase in risk of spring frost for grapevine ranged from 1 to 1980, from 0.06 to 12 for apple, and from 1 to 180 for sweet cherry. Overall, the varieties most prone to frost proved to be Refošk (Teran) and Merlot grapevine varieties as well as the Germersdorf sweet cherry variety. We have identified the location in the hilly region with moderate climate where the Bobovec apple variety is grown as the least exposed to frost. Although counterintuitive, the GDD generally proved somewhat more efficient than the two-phase phenological model BRIN, although not in all cases. For the purpose of the study, the phenological models were calibrated, and the model parameters can serve as invaluable information for further research of this topic.

The study aimed to develop temperature-based models to predict the budburst and flowering dates in grapevine. The models were developed using phenological data for 20 wine cultivars grown in the region of Sremski Karlovci (Serbia) and temperature observations over the 1986-2007 period. The input variable for the budburst model was the mean daily temperature averaged over the period from 1 March to the event onset, while the input variable for the flowering model was the maximum daily temperature averaged over the period from 15 April to the event onset. The models proved to be capable of predicting the onset of budburst and flowering in grapevine with high accuracy. For 20 cultivars studied, the mean absolute differences between the observed and predicted budburst and flowering dates were on average 4 and 3 days, respectively.

Autumn phenology variation in temperate grassland is a direct indicator of land degradation, and a phenology‐based model has been used to simulate human‐induced land degradation. It is important to reveal how environmental factors influence autumn phenology of herbaceous plants. In this study, we examined the effects of temperature, photoperiod, and soil moisture on the leaf withering date (LWD) of four herbaceous species (Xanthium sibiricum, Plantago asiatica, Iris lactea, and Taraxacum mongolicum) at 15 sites in the Inner Mongolian steppe from 1981 to 2012. Then, we developed two process‐based models coupling the effects of the three factors based on an existing cold degree–days (CDD)‐based model for predicting the LWD of the four species. The new models showed better accuracy than CDD model during validation. The model structure and parameters suggested that soil moisture likely influenced LWD nonlinearly and presented a multiplicative interaction with temperature and photoperiod. In addition, photoperiod had divergent effects on the accumulation of cold temperature for different species and sites. For X. sibiricum, the photoperiod only determined the start of cold temperature accumulation. Considering the carryover effect of spring phenology did not improve model performance. Our study also showed that local adaptation of plants was commonly ignored when using same parameters in model calibration with observation data from multiple sites; however, model calibrations using site‐specific data were less stable, and the parameters could not be extrapolated across space. It will be necessary to establish a generalized regional model driven by both climatic and genotypic factors in future research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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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; r2 = 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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Phenological modeling is not only important for the projection of future changes of certain phenophases but also crucial for systematically studying the spatiotemporal patterns of plant phenology. Based on ground phenological observations, we used two existing tem-perature-based models and 12 modified models with consideration of precipitation or soil moisture to simulate the bud-burst date (BBD) of four common herbaceous plants—Xanthium sibiricum, Plantago asiatica, Iris lactea and Taraxacum mongolicum—in temperate grasslands in Inner Mongolia. The results showed that (1) increase in temperature promoted the BBD of all species. However, effects of precipitation and soil moisture on BBD varied among species. (2) The modified models predicted the BBD of herbaceous plants with R2 ranging from 0.17 to 0.41 and RMSE ranging from 9.03 to 11.97 days, better than classical thermal models. (3) The spatiotemporal pattern of BBD during 1980–2015 showed that species with later BBD, e.g. X. sibiricum (mean: day of year 135.30) exhibited an evidently larger spatial difference in BBD (standard deviation: 13.88 days) than the other species. Our findings suggest that influences of temperature and water conditions need to be considered simultaneously in predicting the phenological response of herbaceous plants to climate change. [ABSTRACT FROM AUTHOR]

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 (r2 = 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.

Ecosystem models provide an effective approach to quantify the terrestrial carbon cycle, but the lack of accurate phenological information prevents them from better simulations of the physical processes. Compared with spring phenology (i.e. the start of the growing season, SOS), the vegetation phenology in autumn (the end of the growing season, EOS) is not well-simulated and it is challenging to incorporate vegetation phenology into ecosystem models. The simulation of EOS based on temperature and photoperiod was widely accepted, such as Delpierre et al (2009 Agric. For. Meteorol. 149 938–48)’s model (DM), yet its accuracy has not been fully discussed at a regional scale. Here, we developed a regional autumn phenological model (DMS) with inputs of temperature, photoperiod, and water availability for China’s terrestrial ecosystems. The new DMS model significantly improved the representation of EOS in terms of the lower root mean square error (RMSE), higher model efficiency, and a higher percentage of significant correlation with the referenced EOS. We observed widespread delaying trends of EOS with an average rate of 0.1 d yr ^−1 for vegetated areas over 2001–2018. We further incorporated the improved EOS into the boreal ecosystem productivity simulator (BEPS) and found that the phenology-modified BEPS model had better performances in predicting annual gross primary productivity (GPP) with ∼28% lower RMSE than the original model when testing against GPP measurements from flux tower sites. From 2001 to 2017, the interannual GPP simulated by the modified BEPS model showed an increasing trend with a rate of 6.0 g C m ^−2 yr ^−2 . In conclusion, our study proves that water availability is of great significance for modeling autumn phenology, and the incorporation of phenological dates into an ecosystem model is helpful for productivity simulation.

• Autumn phenology-climate relationships were analyzed for 15 herbaceous species. • Effects of precipitation and temperature/light on autumn phenology are opposite. • Simulation results show that drought promotes leaf senescence in most species. • The responses of autumn phenology to climate change are location-specific. Autumn phenology is important in determining the growing season length and controlling carbon and energy exchanges in terrestrial ecosystems. However, our knowledge on the interaction processes of vegetation autumn phenology and climate changes remains limited, especially for herbaceous plants. In this study, we comprehensively analyzed the responses of autumn phenology of grassland vegetation to climate changes by using ground-observed brown-down date records of 15 grass species and daily temperature, precipitation, and day length data at six stations. Aside from conducting correlation analysis, we also simulated the brown-down date with a newly developed model by incorporating the effect of drought stress (CDDP) into the traditional chilling-degree-days (CDD) model and compared it with the CDD model. Another revised CDD model included the effect of day length (CDDD) and null model (multiyear average, NM). The statistical results showed a predominant significant negative correlation between the brown-down date and previous temperature/day length in 27.3%/40.9% of site species but a predominant significant positive correlation between the brown-down date and previous precipitation in 54.6% of site species. The opposite effects of previous precipitation and previous temperature/day length on the brown-down date were induced by local thermal–moisture conditions. With regard to the modeling results, the CDDP model was selected as the optimal model for 73% of site species with insufficient water supply in preseason, while the CDD model was selected as the optimal model for 18% of site species with a relatively wet but cold preseason. The CDDD model was selected as the optimal model for only two cases. The average estimation error based on the CDDP model (7.4 days) was lower by 2.0/1.5/1.7 days than that based on the CDD/CDDD/NM model. Overall, this study comprehensively demonstrated the important role of water availability in controlling the autumn phenology process of herbaceous plants. [ABSTRACT FROM AUTHOR]

• Phenological model based on development rate functions versus demographic model. • Age distribution of the individuals in the overwintering stage. • Introduction of temperature- and age-dependent fecundity rate functions. • Introduction of temperature-dependent mortality rate functions. • Application to the codling moth. Pest phenological models describe the cumulative flux of the individuals into each stage of the life cycle of a stage-structured population. Phenological models are widely used tools in pest control decision making. Despite the fact that these models do not provide information on population abundance, they share some advantages with respect to the more sophisticated and complex physiologically-based demographic models. The main advantage is that they do not require data collection to define the initial conditions of model simulation, reducing the effort for field sampling and the high uncertainty affecting sample estimates. Phenological models are often built considering the developmental rate function only. To the aim of adding more realism to phenological models, in this paper we explore the consequences of taking three additional elements into account: the age distribution of individuals which exit from the overwintering phase, the age- and temperature-dependent profile of the fecundity rate function and the consideration of a temperature-dependent mortality rate function. Numerical simulations are performed to investigate the effects of these elements with respect to phenological models considering development rate functions only. To further test the implications of different models formulation, we compare results obtained from different phenological models to the case study of the codling moth (Cydia pomonella) a primary pest of the apple orchard. The results obtained from model comparison are discussed in view of their potential application in pest control decision support. [ABSTRACT FROM AUTHOR]

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 (R2) ranging from 0.37 to 0.67, but fail to simulate the changes in FD accurately (R2 ranging from 0.09 to 0.18). The growing degree day model could improve the R2 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. [ABSTRACT FROM AUTHOR]

Tardive frosts, i.e. frost events occurring after grapevine budburst, are a significant risk for viticultural practices, which have recently caused substantial yield losses over different winegrowing regions of France, e.g. in 2016 and 2017. So far, it is unclear whether the frequency of late frosts events is destined to increase or decrease under future climatic conditions. Here, we assess the risk of tardive frosts for the French vineyards throughout the 21st century by analyzing temperature projections from eight climate models and their statistical regional downscaling. Our approach consists in comparing the statistical occurrences of the last frost (day of the year) and the characteristic budburst date for nine grapevine varieties as simulated by three different phenological models. Climate models qualitatively agree in projecting a gradual increase in temperature all over the France, which generally produces both an earlier characteristic last frost day and an earlier characteristic budburst date. However, the latter notably depends on the specific phenological model, implying a large uncertainty in assessing the risk exposure. Overall, we identified Alsace, Burgundy and Champagne as the most vulnerable regions, where the probability of tardive frost is projected to significantly increase throughout the 21st century for two out of three phenological models. The third phenological model produces opposite results, but the comparison between simulated budburst dates and observed records over the last 60 years suggests its lower reliability. Nevertheless, for a more trustworthy risk assessment, the validity of the budburst models should be accurately tested also for warmer climate conditions, in order to narrow down the associated large uncertainty. [ABSTRACT FROM AUTHOR]

Shifts in vegetation phenology induced by climate change are substantially modifying various ecosystem processes, and those changes can in turn affect weather and climate systems. Realistic modeling of spring vegetation green-up is critical to improving process-based ecosystem models of the Tibetan Plateau (TP) and for better understanding of the coupling between TP terrestrial biophysical processes and the Asian monsoon system. However, no model is available for simulating the vegetation green-up date (VGD) across the entire TP. In this study, we first assessed the ability of several existing state-of-the-art phenological models to estimate VGD across the TP. We then modified the existing models by adding environmental constraints identified by partial least-squares analyses. The modified models simulated VGDs with lower estimation errors than other models (Mean absolute error: 8.2 days vs. 8.7–12.9 days; P < 0.01, t -test). Moreover, although our model captured the inter-annual variations in VGD better than any previous model, the correlation coefficient between predicted and remotely sensed VGDs was still low, especially in the western TP. This study revealed the necessity of considering multiple factors in VGD models and highlighted the challenge of developing models that will better represent phenology in future ecosystem models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim: The current work aims to assess the performance of two phenological models - a linear model (Grapevine Flowering Véraison model, GFV) and a curvilinear model (Wang and Engel model, WE) - to warmer temperature conditions for the grapevine variety Pinot noir in Burgundy. Methods and results: Simulations using historical data from the 1973-2005 period were similar between models and consistent with observations. To mimic potential climate warming for 2050 and 2100, 3 °C and 5 °C were added to each daily average temperature of the 1973-2005 dataset. The results showed that the two models simulated similarly the véraison stage of Pinot noir in Burgundy for temperature increases up to 5 °C. However, the simulation by the GFV model was 4.7 days earlier than that by the WE model when 10 °C was added. This difference may reflect the inhibitory effect of high temperatures on plant development incorporated in the equations of the WE model. Finally, both models were tested for three other sites in Europe (Carcassonne, Cagliari and Seville) with quite contrasting climatic conditions. Results obtained showed that both models differed significantly when they were applied at latitudes below 40°N. Conclusion: In cool-climate grape growing regions and for early grapevine varieties, increased temperatures (up to +5 °C) may advance the date of véraison as predicted by heat summation models but produce little difference in predictions between the simpler GFV model and the WE model. Significance and impact of the study: Both models (GFV and WE) satisfactorily reproduce the observed véraison dates for Pinot noir in Burgundy. For the range of temperature increases expected in the future for cool temperate areas, a model that uses a curvilinear response to temperature does not improve significantly the phenological predictions compared with a simple model based on a linear response.

A comprehensive analysis of all the possible impacts of future climate change is crucial for strategic plans of adaptation for viticulture. Assessments of future climate are generally based on the ensemble mean of state-of-the-art climate model projections, which prefigures a gradual warming over Europe for the 21st century. However, a few models project single or multiple O(10) year temperature drops over the North Atlantic due to a collapsing subpolar gyre (SPG) oceanic convection. The occurrence of these decadal-scale “cold waves” may have strong repercussions over the continent, yet their actual impact is ruled out in a multi-model ensemble mean analysis. Here, we investigate these potential implications for viticulture over Europe by coupling dynamical downscaled EUR-CORDEX temperature projections for the representative concentration pathways (RCP)4.5 scenario from seven different climate models—including CSIRO-Mk3-6-0 exhibiting a SPG convection collapse—with three different phenological models simulating the main developmental stages of the grapevine. The 21st century temperature increase projected by all the models leads to an anticipation of all the developmental stages of the grapevine, shifting the optimal region for a given grapevine variety northward, and making climatic conditions suitable for high-quality wine production in some European regions that are currently not. However, in the CSIRO-Mk3-6-0 model, this long-term warming trend is suddenly interrupted by decadal-scale cold waves, abruptly pushing the suitability pattern back to conditions that are very similar to the present. These findings are crucial for winemakers in the evaluation of proper strategies to face climate change, and, overall, provide additional information for long-term plans of adaptation, which, so far, are mainly oriented towards the possibility of continuous warming conditions.

ABSTRACT An impact of global warming on the first-flowering date ( FFD) of cherry, peach and pear in north-eastern Asia are investigated using the Korean national scenario. For the study, we used the dynamically downscaled daily temperature with 12.5 km horizontal resolution derived from five regional climate models ( RCMs) under the same lateral forcing from HadGEM2-AO on the basis of Historical (1981-2010) and four Representative Concentration Pathway ( RCP) (2021-2100) scenarios. According to our analysis of the Historical simulations, the five RCMs and phenological model have good capability in simulating temperature and FFD with uncertainties of about 2.4-3.4 °C and 8.4-13.9 days, respectively, while well reflecting the topographical effect. On the basis of the highest emission scenario ( RCP8.5), the temperature increments of 4.8 °C induce an acceleration in blooming rate, thereby advancing the FFDs of cherry, peach and pear by the end of the 21st century by about 14.5, 15.8 and 14.5 days, respectively, compared with the current reference simulation (Historical). We found high availability of the Korean national scenarios with moderate reliability. We believe that the next generation of the Korean national downscaling project considering diverse lateral forcing will offer better performance and more useful projections to end users. [ABSTRACT FROM AUTHOR]

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 = 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Potato ( Solanum tuberosum ) is one of the main food crops in northern Europe, considered to be the fourth most important crop on a global scale after rice, wheat and maize. Climate change leading to longer growing seasons may call for adjustments in timing of planting and harvesting, and in this modeling study we assess potential effects of a warmer climate on potato crop phenology and temperature stress. A phenological potato model was parameterized with three planting dates to assess management impact on the timing of emergence and maturation of both early and late potato. Estimates on phenological development and occurrence of temperature stress were analyzed by comparing two developmental thresholds (0 °C and +2 °C) and three temperature response functions. The potato model was driven by observed gridded climate data and two sets of bias corrected climate model data, representing RCP4.5 and RCP8.5 for the period 1991–2100. The future simulations indicated that a warmer climate and earlier planting may move the timing of harvest up to 1 month earlier, however, potato emergence early in the year will be associated with an increased risk of frost damage in most parts of northern Europe. The areas of west Europe most prone to frost damage today may experience a risk of frost damage in response to climate change. The simulation of early potato development was sensitive to the setting of the developmental threshold, while late potato development was sensitive to the optimum temperature setting. While a linear temperature response function is essentially sufficient for current climate conditions in northern Europe, optimum and upper thresholds should be considered in climate change impact assessments. The potato model runs with temperature data corrected according to quantile-mapping indicated in general a slightly higher risk of temperature stress than the corresponding runs with temperature data corrected by linear scaling. [ABSTRACT FROM AUTHOR]

• Phenological models were calibrated and validated using satellite-derived growing season. • The three spring phenological models well captured the change along aridity gradients. • The heatsum index and alpine meadow prognostic phenological models performed well. • The simulated SOS responded to precipitation and temperature changes differently. Modeling vegetation phenology is crucial to assessing how climate change impacts carbon cycles in terrestrial ecosystems. The process-based biogeochemical model Biome-BGCMuSo is widely used for simulating carbon and water storages and fluxes of grassland ecosystems. However, the lack of accurate phenological information, such as the start of the growing season (SOS), impedes better simulations of the biogeochemical processes in the Tibetan Plateau (TP). Here, based on the snow-free satellite-derived SOS and the end of the growing season (EOS) in the TP during 1982–2018, we calibrated and validated three phenological models for SOS (i.e., the Biome-BGC phenological (BBGC) model, the heatsum growing season index (HSGSI) model, and the alpine meadow prognostic phenological (AMPP) model) and five phenological models for EOS (i.e., BBGC, HSGSI, AMPP, the low temperature and photoperiod multiplicative model induced by photoperiod (TPMP) and temperature (TPMT)) using particle swarm optimization (PSO) algorithm. For SOS, all three phenological models with calibrated parameters performed similarly and all captured the change in SOS well along gradients of aridity. The performance of BBGC, HSGSI, and AMPP models were largely improved with the calibration. The AMPP model simulated SOS with the lowest estimation errors with the mean absolute error (MAE) of 18.67 days and the Kling-Gupta efficiency (KGE) of 0.47 in validation. For EOS, the calibrated HSGSI and AMPP models, with mean MAEs of 9.85 and 9.29 days, respectively, captured the change in EOS well along the gradients of aridity and performed better than other models. The calibration significantly improved the simulation performance of all five models. Therefore, the phenological models can be calibrated and validated at a large scale with snow-free satellite-derived phenological data. Our study recommends that calibration and validation for the phenological model play a vital role in accurately simulating SOS and EOS in the regional carbon cycle simulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Based on phenological and economic output models established and meteorological data from 1972 to 2013, changes in the phenology, frost risk, and economic output of the Longjing-43 tea tree in the Yuezhou Longjing tea production area of China were evaluated. As the local climate has changed, the beginning dates of tea bud and leaf plucking of this cultivar in all five counties studied has advanced significantly by −1.28 to −0.88 days/decade, with no significant change in the risk of frost. The main tea-producing stages in the tea production cycle include the plucking periods for superfine, grade 1, and grade 2 buds and leaves. Among the five bud and leaf grades, the economic output of the plucking periods for superfine and grade 1 decreased significantly, that for grade 2 showed no significant change, and those for grades 3 and 4 increased significantly. The economic output of large-area tea plantations employing an average of 45 workers per hectare and producing superfine to grade 2 buds and leaves were significantly reduced by 6,745-8,829 yuan/decade/ha, depending on the county. Those tea farmers who planted tea trees on their own small land holdings and produced superfine to grade 4 tea buds and leaves themselves experienced no significant decline in economic output. [ABSTRACT FROM AUTHOR]

Maize (Zea mays) is a major staple food in Africa. However, maize production is severely reduced by damage caused by feeding lepidopteran pests. In East and Southern Africa, Chilo partellus is one of the most damaging cereal stem borers mainly found in the warmer lowland areas. In this study, it was hypothesized that the future distribution and abundance of C. partellus may be affected greatly by the current global warming. The temperature-dependent population growth potential of C. partellus was studied on artificial diet under laboratory conditions at six constant temperatures (15, 18, 20, 25, 28, 30, 32 and 35 °C), relative humidity of 75±5% and a photoperiod of L12:L12 h. Several non-linear models were fitted to the data to model development time, mortality and reproduction of the insect species. Cohort updating algorithm and rate summation approach were stochastically used for simulating age and stage structure populations and generate life-table parameters. For spatial analysis of the pest risk, three generic risk indices (index of establishment, generation number and activity index) were visualized in the geographical information system component of the advanced Insect Life Cycle modeling (ILCYM) software. To predict the future distribution of C. partellus we used the climate change scenario A1B obtained from WorldClim and CCAFS databases. The maps were compared with available data on the current distribution of C. partellus in Kenya. The results show that the development times of the different stages decreased with increasing temperatures ranging from 18 to 35 °C; at the extreme temperatures, 15 and 38 °C, no egg could hatch and no larvae completed development. The study concludes that C. partellus may potentially expands its range into higher altitude areas, highland tropics and moist transitional regions, with the highest maize potential where the species has not been recorded yet. This has serious implication in terms of food security since these areas produce approximately 80% of the total maize in East Africa. [ABSTRACT FROM AUTHOR]

The influence of temperature on the development, mortality, fecundity and life table parameters of two important noctuid African cereal pests, Busseola fusca and Sesamia calamistis was investigated under laboratory conditions. Experiments were carried out with larvae reared on artificial diet under eight constant temperatures (12°C, 15°C, 18°C, 20°C, 25°C, 28°C, 30°C and 35°C) and a 12L: 12D photoperiod. Life table parameters were calculated using Insect Life Cycle Modelling ( ILCYM) software. At 12°C and 35°C insects failed to develop. Mean development time for both species decreased with increasing temperature for all stages. Between 15°C and 30°C, mean larvae development time is divided by four for both species and adult mean longevity is divided by 1.5 and 2.5, for both sexes of S. calamistis and B. fusca, respectively. Fecundity varied according to temperature; the highest was estimated at 22°C and 24°C for B. fusca and S. calamistis, respectively. The lower thermal threshold for B. fusca and S. calamistis was, respectively, 6°C and 9°C, while the upper thermal threshold was 31°C and 32°C, respectively. The highest intrinsic rate of natural increase for B. fusca was obtained at 25°C while for S. calamistis it was obtained at 28°C. The highest net reproduction was obtained at 25°C for both species, but it was higher for S. calamistis than for B. fusca. The shortest population doubling time was observed at 25°C for B. fusca and at 28°C for S. calamistis. The optimum temperature range for development of both species was 25-28°C. The lower lower thermal threshold found for B. fusca than for S. calamistis and the higher upper thermal threshold found for S. calamistis than for B. fusca explain in part the observed distribution of both species in sub-Saharan Africa with S. calamistis occurring in all the agro-ecological zones but being usually more common than B. fusca in savannah lowland and B. fusca reported mainly from mid and high altitude areas. [ABSTRACT FROM AUTHOR]

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.)

• Climate change impact on vine phenology was estimated. • A phenology model was developed and validated for the Sultana grape cultivar. • Early grapevine development will be advanced, maturation stage will be extended. • The model is applicable to other Sultana growing regions with a similar climate. Climate change is expected to pose major challenges to viticulture. The projected increase in temperature in the Mediterranean region due to climate change is likely to influence the timing of grapevine phenological stages. This study developed and validated a phenological model, from budbreak to maturation phenological stages, based on the growing degree days for the Sultana grapevine cultivar on Crete Island, Greece, and used the model to assess potential changes in future phenology timing employing different climate change scenarios. A dataset of unpublished phenological observations from 20 locations spanning four decades was used to validate the phenological model. The root mean squared difference (RMSD) of the calibration-validation procedure was estimated between 5.4 and 11.5 days, depending on the phenological stage. The model outperformed for the flowering and maturity stages. The highest RMSD was found for the shoot development stages. Projections determined an earlier occurrence of the different phenological stages. Near future climate (2020–2060) projections indicate budbreak advancement by 7 to 8 days and maturity by 4 to 5 days on average. For the far future (2060–2100), the respective changes are 11 to 18 and 7 to 9 days earlier. Discussion on the underlying uncertainty sources is provided. [ABSTRACT FROM AUTHOR]