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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.

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]

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]

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]

• Machine learning algorithms are applicable to phenological model research. • GBDT model with better performance than traditional models. • Temporal changes in temperature sensitivity of LUD significantly decreased. • The magnitude of ST decreased with the increase of preseason temperature variance. Phenological models can effectively infer historically missing phenological data, so as to investigate the long-term relationship between plants and climate change. Large numbers of ecophysiological and statistical models have been developed in the past few decades, but these models have been unable to make accurate predictions based on external data. Machine learning (ML) methods have an advantage over traditional statistical methods for natural science studies. However, only a few phenological models have been coupled with ML methods. In this study, using long-term leaf unfolding date (LUD) observations collected in Harbin, China, we adopted three popular ML algorithms for predicting plant LUD and compared the performances of 10 phenological models. We detected the temperature sensitivity (ST) of the LUD at the species level for the periods 1962–1987 and 1988–2016 (before and after the recent, sudden warming) and temporal changes in ST with a 15-year moving window for each period. The results show that the gradient boosting decision tree (GBDT) model performs obviously better than the other models for external validation data, while avoiding model overfitting. Most species showed an increase in ST during the 1988–2016 period, and the temporal changes in ST significantly decreased during both periods. The temporal changes in ST from the phenological data predicted by the GBDT model is significantly higher than that of other models, which indicates that the traditional phenological models may underestimate the response of LUD to climate warming. We found a prevalent decline in the magnitude of ST with increasing preseason temperature variance at the species level. Our research suggests that machine learning algorithms should be more widely used in future phenological model research, and temporal changes in ST should be investigated in order to broaden our understanding of plants' ability to adapt to future climate change. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]