Average Growing Season Temperature Research Articles

Meta-Analysis of the Effects of Straw Incorporation on Maize Yield and Water Use Efficiency in China under Different Production Conditions

Maize plays a crucial role in China’s grain production, with a cultivation area reaching 44.22 million hectares and an annual yield of 289 million tons in 2023. However, the challenge remains on how to further increase maize yield and water use efficiency (WUE) without adding to the environmental burden. To systematically evaluate the impact of straw incorporation under varying production conditions on maize yield and WUE, this study collected experimental data from multiple locations across China. A meta-analysis was conducted to compare the effects of straw incorporation versus no incorporation, and the main influencing factors were identified using correlation analysis and a random forest model. The results indicate that straw incorporation significantly enhances both maize yield and WUE, with the most pronounced improvements observed under conditions of an average growing season temperature of 19–23 °C, soil pH of 6.5–7.5, low initial soil organic matter content, and deep plowing for straw incorporation. Additionally, moderate nitrogen application rates and straw incorporation amounts (9000–15,000 kg·ha−2) also significantly boost maize yield and WUE. Field management practices and meteorological conditions are identified as the primary factors affecting maize yield and WUE under straw incorporation conditions. Therefore, straw incorporation stands out as an effective agricultural practice for achieving high maize yields and efficient resource utilization. The findings of this study provide valuable insights for global food security and the sustainable development of agriculture.

Challenges to Viticulture in Montenegro under Climate Change

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture.

Assessing the influence of climate controls on grapevine biophysical responses: a review of Ontario viticulture in a changing climate

Climate change presents unique challenges for grape growers across the world. In Ontario, three distinct viticultural regions are experiencing climatic shifts towards warmer growing seasons. According to historical records collected from Environment and Climate Change Canada, Lake Erie North Shore has transitioned from an intermediate-to-warm growing season classification, the Niagara Peninsula from the lower to upper limits of the intermediate zone, and Prince Edward County from cool to intermediate, when analyzing their average growing season temperatures. Terroir is directly related to vine water status, an indicator of grapevine stress. Biophysical responses controlled by air temperature and precipitation include fluctuations in vapour pressure deficits, evapotranspiration, and water-use-efficiency rates, as well as soil water content levels. By conducting an extensive literature review, the development of a conceptual model addresses how variations in climatic controls, under the scope of climate change, may influence grapevine water status, biophysical responses, and associated production outcomes for Ontario vineyards. Cool-to-intermediate air temperatures, when paired with increased precipitation will lead to no or low vine stress, increasing photosynthesis and transpiration rates, and variable plant water-use-efficiency levels, producing higher yields and lower quality grapes, if no management strategies are applied. Oppositely, higher air temperature as a product of climate change, when paired with variable precipitation may produce mild-to-severe stress, reducing yield, and increasing grape quality. With the appropriate management strategies, both traditional and new, growers may be able to accommodate for the influence of climate change on their vineyards.

Effect of Tree Size Heterogeneity on the Overall Growth Trend of Trees in Coniferous Forests of the Tibetan Plateau

Tree growth is under the combined influence of abiotic and biotic factors. Trees with different sizes may respond differently to these factors, implying that tree size heterogeneity may also modulate the overall growth trend. To test this hypothesis, we focused on the radial growth trends of natural subalpine forests on the Tibetan Plateau. We first extended the iterative growth model (IGM) to the tree ring scale (IGMR) to test the applicability of the generalized metabolic growth theory to tree growth. As predicted by the IGMR, the radial growth of trees at the aggregate scale is constrained by a unimodal pattern. Using the IGMR, we reconstructed the historical best growth trajectory (HBGT) of trees within the same community based on the tree with the largest radius and/or longest age in the community. From the average difference between the HBGT and the current radial growth rate of trees with different sizes, we constructed an indicator that can measure the overall variation in tree radial growth. Based on this indicator, we found a negative effect of tree size heterogeneity on the overall variability of tree growth across elevations. Further analysis also revealed that the radial growth rate of trees on the Tibetan Plateau has increased significantly compared to the past, where the growing season average temperature and annual minimum temperature were negatively and positively correlated with tree growth below and above the treeline, respectively. Our study not only confirmed that the overall variability of tree growth depends on tree size heterogeneity but also proposed an indicator that reveals net changes in the tree radial growth rate relative to the past. These theoretical advances are highly beneficial for understanding changes in the extent of subalpine forests.

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R2) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R2 = 0.86, MAE = 0.62 Mg/ha), GBM (R2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

Future climatic conditions may threaten adaptation capacities for vineyards along Lake Neuchâtel, Switzerland

In Switzerland, as elsewhere in the world, climate change is challenging viticulture. Knowledge of the potential impacts is essential for preparing adaptation measures. Two aspects directly impacted by increasing temperatures are the choice of grapevine varieties and the location of vineyards. To help address these impacts, we analysed future trends in two bioclimatic indices, average growing season temperature (GST) and Huglin’s heliothermal index (HI), in the Swiss canton of Neuchâtel. We conducted our analysis based on regional climate change scenarios referring to the emission pathways RCP4.5 and RCP8.5. Under the assumption of RCP8.5, trends in GST and HI indicate that the climate in this region will become too hot for most grapevine varieties currently cultivated, especially Pinot noir. Moreover, adaptation problems under RCP8.5 are expected to originate from an increase in climate extremes in both temperature and precipitation. Results based on RCP4.5 indicate a broader scope for adaptation, as the climate will remain suitable for a larger number of grapevine varieties within the current altitudinal limits of the Neuchâtel vineyards. In theory, an altitudinal shift of Pinot noir would also be possible under this emission pathway. In practice, however, the possibility of establishing vineyards above 600 m would be limited by the presence of protected forests and rocky areas. Our results highlight that vineyards in this region will need important adaptation measures if anthropic greenhouse gas emissions do not decrease rapidly and considerably, limiting the global temperature increase to < 1.5 °C.

Climate Projections for Pinot Noir Ripening Potential in the Fort Ross-Seaview, Los Carneros, Petaluma Gap, and Russian River Valley American Viticultural Areas

An unbiased MACA CMIP5 ensemble that optimized calculation of the growing season average temperature (GST) viticulture climate classification index throughout Northern California’s Fort Ross-Seaview (FRS), Los Carneros (LC), Petaluma Gap (PG), and Russian River Valley (RRV) American Viticultural Areas (AVAs) was applied to compute the GST index and Pinot noir specific applications of the grapevine sugar ripeness (GSR) model on a mean decadal basis from the 1950s to the 2090s using RCP4.5 and RCP8.5 projections of minimum and maximum daily temperature. From the 1950s to the 2090s, a 2.1/3.6, 2.4/4.2, 2.3/4.0, 2.3/4.0, and 2.3/4.0 °C increase in the GST index and a rate advance of 1.3/1.9, 1.1/1.8, 1.3/2.0, 1.2/1.9, and 1.2/1.9 days a decade was computed for FRS, LC, PG, RRV, and across all four AVAs while using the RCP4.5/RCP8.5 climate projections, respectively. The GST index and GSR model calculations were highly correlated across both climate projections and their fitted models were used to update the Pinot noir specific upper bound for the GST index throughout each AVA using a published optimal harvest window for the northern hemisphere. At a 220 g/L target sugar concentration, the updated upper bound was 17.6, 17.5, 17.6, 17.5, and 17.6 °C for FRS, LC, PG, RRV, and across all four AVAs. For a 240 g/L sugar concentration, it was 17.9, 17.8, 17.9, 17.8, and 17.9 °C. The results from this study together with comparable results recently reported for the Willamette Valley AVA of Oregon using a different downscaled CMIP5 model archive suggest spatial invariance, albeit sugar concentration dependent, for the updated Pinot noir specific upper bound for the GST climate index.

Using historical weather data and a novel season temperature index to classify winegrape growing zones in Australia

• Australian wine regions can be classified into different climatic categories; • Drier growing seasons were observed for many wine regions from 1911 to 2017; • Warmer growing season/night-time temperature were observed for most wine regions; • The climatic changes in different wine regions differed over historical time periods; • Many wine regions became less suitable to grow temperate grape varieties. Viticultural production and wine quality are profoundly affected by climate. Characterising winegrape-growing regions based on climatic variables provides scientific guidance for the wine industry in vineyard site and winegrape cultivar selection. This research used a modified multicriteria climatic classification system based on three representative indices, Huglin index, cool night index and growing season relative soil moisture to classify 63 southeastern Australian winegrape-growing regions into different climatic categories, within different historical time periods from 1911 to 2017. The pattern of changes in climatic classification over these time-periods were identified for each region and illustrated in the principal component analysis (PCA) plot. A second classification system based on growing season average temperature index was further used to identify the temperature variations among regions. Based on this, a novel grapevine suitability index was generated to indicate the suitability of growing key commercial winegrape varieties in a specific winegrape-growing region. Clear changes in regional climate across the three historical time periods were observed in majority of the studied regions with clear differences between regions. Warmer growing season, warmer night-time temperature and drier growing season were the most frequently observed trends. GST analysis showed that median temperature increased for majority of winegrape-growing regions with more extreme hot climatic conditions appearing in recent years. Some winegrape-growing regions have become less suitable for temperate winegrape varieties and may need to move to more heat tolerant varieties, while some cold regions became more suitable for winegrape production. Climatic variations were observed among non-official geographic indications (GIs), which provided the scientific basis for the possible separation of these regions. This study will assist the wine industry to better understand the climatic variations between viticulture regions, and provide guidance for the selection of the most suitable winegrape varieties within each region and the selection of regions to grow particular varieties.

Growing season average temperature range is the optimal choice for Q10 incubation experiments of SOM decomposition

Temperature sensitivity (Q10) of soil organic matter (SOM) decomposition is an essential parameter that reflects the feedback relationship between climate warming and atmospheric CO2 concentration and plays a key role in accurately estimating changes in soil carbon pools and their feedback to climate change. Setting the incubation temperature range (ITR) scenario is essential to accurately estimate the temperature sensitivity of SOM decomposition; however, this has been widely ignored. To address this issue, we conducted a systematic incubation experiment using 54 soils covering the most typical ecosystems in China and nine ITR scenarios. The results showed that ITR scenarios had a significant effect on Q10 in different ecosystems and soil types. Combining the results of fitting mean annual temperature (MAT) to the Q10 of different ITR and the interpretation rate of factors (including climate, soil, and microbial community composition) to the Q10 of different ITR, the results indicate that the growing season average temperature range (GSA) scenario had better performance than the other ITR scenarios and should be the optimum ITR scenario. Furthermore, the variation of main influence factors of Q10 across different ecosystems should be accounted for to accurately predict the feedback between soil C cycle and climate change. Overall, our findings highlight the importance of the ITR for the estimates of Q10 using widespread experimental data, providing a reference for subsequent experiments accurately measure Q10, as well as compare and compile global data to better predict the feedback between the global carbon cycle and climate change.

Predicting wine prices based on the weather: Bordeaux vineyards in a changing climate

Each grapevine cultivar needs a certain amount of cumulated heat over its growing season for its grapes to ripen properly. In the 20th century’s Bordeaux vineyard, the average growing season temperature was not always sufficient, thus higher than usual summer temperatures were on average linked with higher grape and wine quality. However, over the last 60+ years, global warming gradually increased the vineyard’s temperatures up to the point where additional growing season heat is not required anymore, and can even become detrimental to wine quality: hence the positive effect of higher-than-usual summer temperatures has progressively vanished. In this context, it is unknown whether any weather variable is still a good predictor of a vintage’s quality. Here we provide a predictive model of wine prices, based only on weather data. We establish that it predicts a vintage’s long-term quality more accurately than a world-class expert rating this same vintage in the year following its production. We first design a corpus of features suited to the grapevine lifecycle to extract from them the most powerful drivers of wine quality. We then build a predictive model that leverages Local Least Squares kernel regression (LLS) to factor in the time-varying nature of climate impact on the grapevine. Hence, it is able to outperform previous models and even provides a better predictive ranking of successive vintages than the grades given by world-famous wine critic Robert Parker. This predictive power demonstrates that weather is still a very efficient predictor of wine quality in Bordeaux. The two main features on which this model is built—following grapevine’s phenological calendar and using an LLS architecture to let the input-output relationship vary over time—could help model other agricultural systems amidst climate change and adaptation of production processes.

Coordinated evaporative demand and precipitation maximize rainfed maize and soybean crop yields in the USA

AbstractTo understand how climate change affects crop yields, we need to identify the climatic indices that best predict yields. Grain yields are most often predicted using precipitation and temperature in statistical models, assuming linear dependences. However, soil water availability is more influential for plant growth than precipitation and temperature, and there is ecophysiological evidence of intermediate yield maximizing conditions. Using rainfed maize and soybean yields for 1970–2010 across the USA, we tested whether the aridity index, that is, the ratio of precipitation and potential evapotranspiration seasonal totals and a proxy of soil water availability, better predicts yield than growing season precipitation total, average temperature and their interaction. We also tested for non‐monotonic responses allowing for intermediate yield‐maximizing conditions. The aridity index alone explained 77% and 72% of maize and soybean yield variability, compared with 78% and 73% explained by temperature, precipitation and their interaction. Yield responses were non‐monotonic, with yields maximized at intermediate precipitation and temperature as well as at intermediate aridity index of 0.79 for maize and 0.98 for soybean. The yield maximizing precipitation also increased with growing season average temperature, faster in maize than soybean. The intermediate yield maximizing conditions show that rainfed maize and soybean yields could both increase and decrease depending on whether climatic conditions come closer to or deviate from the yield maximizing conditions in the future. In most counties, during 1970–2010, the precipitation and aridity index were lower and temperature higher compared with those maximizing yields, suggesting that climate change will reduce yields.

Exploring the uncertainty in projected wheat phenology, growth and yield under climate change in China

Exploring and quantifying the uncertainties in climate impact assessment with multiple climate-crop models is crucial to reducing the total uncertainty and guiding adaptation strategies for crop production. Here, we carried out a climate-crop ensemble simulation to measure the uncertainty in estimated climate impacts on China's wheat productivity by the 2050s. The ensemble included the simulations conducted with the three-DSSAT wheat model ensemble. As for the future climate, five Global Climate projections (GCMs) under two Representative Concentration Pathways (RCP4.5 and 8.5) and two CO2 concentrations were selected. Our results indicate that the median of simulated yield change was between 4.5% ∼ 5.5%, and -7.7% ∼ -5.6% respectively under elevated and current CO2 concentrations by 2050s compared to 1981–2010. The median of simulated phenology change was nearly -12 ∼ -10 d In percentage terms, higher uncertainty in national yield change was observed compared to phenology change. The total relative contributions of climate projections, crop models, and RCP scenarios have been more than 70% of the total uncertainty of national phenology and yield change. Crop models have accounted for the largest uncertainty of irrigated yield, while crop models and climate projections almost contributed a similar share of the total uncertainty of rainfed yield. These findings highlight the distribution of uncertainty and sources of uncertainty both at the national and grid scales, which would provide a more comprehensive understanding of uncertainties in future yield prediction. Our results also showed that larger uncertainty has been observed in warmer regions (growing season average temperature > 20 °C) than in cooler regions, while the wet regions (growing season rainfall > 400 mm) would suffer smaller uncertainty than dry regions. These findings emphasize the relationships between uncertainty and climate factors, which offers insights for improving crop models and designing adaptation strategies.

Climate change projections for UK viticulture to 2040: a focus on improving suitability for Pinot Noir

Between 1981–2000 and 1999–2018, growing season average temperatures (GST) in the main UK viticulture regions have warmed ~1.0 °C and are now more reliably >14.0 °C GST. This warming has underpinned the rapid expansion of the UK viticulture sector and its current focus on growing grape varieties for sparkling wine. Near-term (2021–2040) climate change may condition opportunities for further variety and/or wine style changes. Using the latest high-resolution (5 km) ensemble (×12) of downscaled climate change models for the UK (UK Climate Projections; UKCP18) under Representative Concentration Pathway (RCP) 8.5, we calculate near-term trends and variability in bioclimatic indices (BCIs). We simulate the projected repetition of the UK’s highest yielding season—2018—and use an analogue approach to model the 1999–2018 mean growing season temperatures from Pinot Noir producing areas of Champagne (France), Burgundy (France) and Baden (Germany) over the UK during 2021–2040. We also project, across the UK for the 2021–2040 period, BCI values of recent high-quality vintage years from Champagne and Burgundy. GST are projected to increase from a 1999–2018 spatial range of 13.0 (minimum threshold) –15.7 °C to a future (2021–2040) range of 13.0–17.0 °C, and Growing Degree Days (GDD) from 850 (minimum threshold) –1267 to 850–1515. Growing season precipitation (GSP) is projected to decline in some UK viticulture areas but is not modelled as a limiting viticulture factor. High inter-annual weather variability is simulated to remain a feature of the UK viticulture climate and early season frost risk is likely to occur earlier. Large areas of the UK are projected to have >50 % of years within the bioclimatic ranges experienced during the 2018 growing season, indicating potential higher yields in the future. The 1999–2018 mean Champagne, Burgundy and Baden GST and GDD are projected for much of England and some areas in the far south and south-east of Wales during 2021–2040, with significant areas projected to have >25 % of years within the BCI ranges of top Champagne vintages. These results indicate greater potential for Pinot Noir for sparkling wines and shifting suitability to still red wine production. Accounting for changes in variety suitability and wine styles will be essential to maximise opportunities and build resilience within this rapidly expanding wine region.

Temperature-based Climate Projections of Pinot noir Suitability in the Willamette Valley American Viticultural Area

In this study, we consider the complete archive of the 32 Coupled Model Intercomparison Project Phase 5 (CMIP5) daily Localized Constructed Analogs (LOCA) downscaled historic datasets and their observational data that were used for downscaling and bias corrections to develop an ensemble that optimises calculation of the growing season average temperature (GST) viticulture climate classification index throughout the Willamette Valley (WV) American Viticultural Area (AVA). Ensemble directed spatiotemporal calculations, using LOCA CMIP5 historic and RCP4.5 future datasets of minimum and maximum daily temperature, were performed throughout the WV AVA for the GST index and Pinot noir specific applications of the grapevine sugar ripeness (GSR) model at a 220 g/L target sugar concentration. The GST index and GSR model evaluations were calculated on a mean decadal basis from the 1950s to the 2090s for the WV AVA. The GST index and GSR model calculations both revealed a warming trend with time for the WV AVA. A 3.1 °C increase in the GST index is predicted from the 1950s to the 2090s. The GSR model indicated a rate advance of 2.9 days a decade from the 1960s to the 2080s. However, the application of the GST index and the GSR model portray markedly different characterisations about the suitability of Pinot noir throughout the WV AVA with time. A strong invertible relationship between the GST index and GSR model calculations is observed and exploited to update the Pinot noir specific lower and upper bounds (14.8 °C, 17.6 °C) for the GST index throughout the WV AVA. Pinot noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot noir production is currently at or near its peak value in the upper 80s to the lower 90s.

Quantitative Effects of Climate Change on Vegetation Dynamics in Alpine Grassland of Qinghai-Tibet Plateau in a County

Alpine grassland in the Qinghai-Tibet Plateau is known to be sensitive to climate change. To quantify the impacts of climate change on alpine ecosystems at small scale, Wulan County in Qinghai Province was taken as the research object, and the relationships between vegetation dynamics and climate changes and the direct and indirect effects of climate factors on vegetation dynamics were analyzed using the methods of ordinary least squares, Pearson correlation analysis and path analysis, on the basis of MOD13A3 data and meteorological data from 2001 to 2020. The results showed that the Normalized Difference Vegetation Index (NDVI) in the growing season of the county and 5 vegetation types showed similar fluctuation processes and relationships with climate factors during the study period. The growing season NDVI (GSN) of shrubland and desert steppe respectively were the highest and lowest. The yearly mean values of GSN over the county ranged from 0.151 to 0.264, and increased extremely significantly with years at a rate of 0.0035 yr−1. Spatially, GSN gradually decreased from northeast to southwest, and 97.2% of the county area showed an increasing trend in GSN. With years, growing season evaporation (GSE) decreased extremely significantly at a rate of 29.6 mm yr−1, while growing season average relative humidity (GSARH) showed a significant increasing trend at a rate of 0.16% yr−1. The correlations and effects of GSE, GSARH, and growing season precipitation (GSP) on vegetation dynamics were weakened in turn. GSE was the main direct effect factor, and the latter two were the indirect effect factors through GSE. The total contribution rates of GSE, GSARH and GSP to vegetation dynamics was about 78.0%. However, growing season average temperature (GSAT) had little effect on vegetation dynamics. This study provides information for understanding the characteristics of vegetation dynamics of alpine grassland in the Qinghai-Tibet Plateau at a small scale.

The Influence of the Agroecological Resources of Crimea on thePrimary and Secondary Metabolites of AligoteGrapes

This research focused on examining the interrelationships between the natural conditions for growing grapes, as well as the quantitative and qualitative characteristics of the harvest. These are important criteria for the scientifically grounded selection of a territory for planting a vineyard, selecting varieties and determining the use of the resulting products. The characteristics of six model vineyards of the Aligote cultivar, located in various natural zones and viticultural regions of the Crimea, were analyzed. The values of climatic indicators were calculated, including the growing degree days above 10∘C (∑Т ∘С10), growing degree days above 20∘C (∑Т ∘С20), Huglin index, Winkler index, average growing season temperature, average September temperature, ratio ∑Т ∘С20/∑Т ∘С10,total precipitation during the year, total precipitation during the growing season, total precipitation in September, and Selyaninov hydrothermal coefficient. These were calculated usinggeoinformation and mathematical modeling for the locations of the analyzed vineyards. The content of the primary metabolites (total sugars, titrated acids and calculated indicators based on them) and secondary metabolites (phenolic components, oxidase activity) of grapes from the model vineyards were analyzed. The range of variation in the studied indicators within the analyzed territories was calculated, and the nature and magnitude of the relationships between the indicators were revealed. A cluster analysis of the analyzed vineyards was carried out and clusters were distinguished according to the degree of similarity in climatic parameters, as well as the content of the primary and secondary metabolites of the grapes.
 Keywords: grapes, agroecological factors, primary and secondary grape metabolites, ampeloecological zoning, terroir

Effects of climate change on bioclimatic indices in vineyards along Lake Neuchatel, Switzerland

In this study, we investigated the consequences of climate change on bioclimatic indices in vineyards along the edge of Lake Neuchatel in Switzerland. Like in other vineyards all around the world, the typicity of wines and the phenology of vines have changed, particularly since the 1970s. Trends in the growing season average temperature and in Huglin’s heliothermal index show that the climate in the Neuchatel vineyards changed from very cool or cool to temperate during the last decades. Trends in the cool night index and in the prior to harvest cool night index both indicate that in the near future this wine region will frequently experience temperate instead of cool nights during the weeks leading up to harvest. Our results highlight the need for adaptation strategies, such as an upward elevational shift for Pinot Noir, as climatic conditions will become too warm at its current location in the next decades. They also show that conditions in this region are already favorable for more thermophilic varieties such as Merlot. In the context of global warming, this kind of analysis should be conducted throughout winegrowing regions in order to develop efficient adaptation strategies at the microclimatic scale.

Short-term intensive warming shifts predator communities (Parasitiformes: Mesostigmata) in boreal forest soils

Increasing global mean surface temperatures from climate change will coincide with longer and more frequent short-term, extreme warming events. Because of this, habitats like boreal forests are predicted to have new temperature regimes. Boreal forest soils contain a diverse array of microarthropods and nematodes, which include the main soil predators, mesostigmatic mites (Acari: Parasitiformes: Mesostigmata). Although extensive research exists on how climate warming affects oribatid mite and collembolan communities, fewer studies have examined how warming effects Mesostigmata communities. We tested the effect of short-term (three months), intensive warming (+8 °C) on Mesostigmata communities from the boreal forest using experimental mesocosms containing forest-floor material. We collected moss mats and underlying forest floor organic material from a boreal forest and incubated them within individual mesocosms at 12 °C and 20 °C for three months, where 12 °C represented the long-term average growing season temperature and 20 °C corresponds to the potential extreme surface temperature from climate warming for the region. We enumerated all extracted microarthropods and nematodes, and identified all Mesostigmata adults to the species-level. In total, we counted 24,080 nematode individuals, and 19,582 total microarthropod individuals, of which 3349 individuals (1899 adults and 1450 juveniles) were mesostigmatic mites, consisting of 14 species. Mesostigmatic juvenile and adult abundances, along with adult community-level body mass were higher under warming, which lead to a shift in community composition. Changes to Mesostigmata communities were driven by the greater abundances of parthenogenetic species, primarily Veigaia mitis (Berlese), under warming—a response that has been shown in oribatid mite communities, but not mesostigmatic communities, before. Overall, we found that warming shifted mesostigmatic mite communities in the boreal forest, which has wide ranging implications for the soil food web.

Forecast of Changes in Air Temperatures and Heat Indices in the Sevastopol Region in the 21st Century and Their Impacts on Viticulture

Climate is a limiting factor in viticulture, as it defines favorable areas, grape cultivars, and agrotechnical activities. In the Sevastopol region, viticulture is the main and promising agricultural branch. Using the outputs of the regional climate models from the CORDEX project, the projections of agroclimatic conditions in the Sevastopol region for two future periods (2021–2045 and 2046–2070) under two representative concentration pathways (RCP4.5 and RCP8.5) were obtained. The results in our study show the trend of temperature indices rise (average growing season temperature, effective heat sum, Winkler and Huglin indices) and the region’s transition to higher classes, especially during the second future period (2046–2070). However, despite the higher temperature indices, the Sevastopol region will remain suitable for the growing of grapes cultivars with all ripening periods.

Topoclimate and wine quality: results of research on the Gewürztraminer grape variety in South Tyrol, northern Italy

The aim of the study was to identify the ecological indicators that facilitate predictive analysis and to search for patterns in local geographical information to identify risks and opportunities in viticulture. The study focused on environmental factors that significantly affect the ripeness of the Gewürztraminer grape variety cultivated near Tramin, a village in northern Italy. In particular, the reliability of the new Solar Radiation Identity (SRI) topoclimate classification method was tested, along with its predictive capability in terms of the biosynthetic activity of the vine and the quality of its grapes. The SRI index characterises each vineyard in a precise and comparable way and helps to understand the way in which the topoclimate acts as an important abiotic stress factor for vines. A direct relationship between grape must sugar content and the SRI topoclimate index was observed. Our findings indicate an increase in sugar content of approximately 0.8 °KMW for every 10 points of the SRI index. Thus, a novel prediction model of grape ripening based on an SRI curve analysis is proposed. The correct application of the SRI index could be useful for discriminating and predicting geographical charactersitics of a given area strongly connected to ecological diversity and wine quality. It could support decision making in viticulture in terms of, for example, correctly matching vineyard and grape varieties, reducing wine vulnerability and production risk and predicting optimal ripeness and harvesting days. The use of the SRI prediction curve could help in adopting a more sustainable approach to agriculture and in finding new methods for adapting to climate change, such as by improving the match between the cultivars’ phenological status, vineyard location and growing season average temperature.