Increased seasonal variation in weather is a growing reality and concern in the context of climate change. Should we be reconsidering how we measure a season? This article highlights findings from an extensive study, considering climate change impacts on viticulture over four growing seasons.
The last few seasons, the agricultural sector has faced extreme seasonal events, not always quantifiable using traditional climate indices (heat summations over the growing season). As climate exerts a dominant influence on wine production, driving baseline suitability, largely controlling crop production and quality and ultimately driving economic sustainability of wine producing enterprises. Small changes in climate have the potential to bring about significant changes in the management of existing vineyards, as well as changes in the varieties planted in wine regions worldwide. Driving potential shifts in suitable zones for viticulture with some regions becoming too warm all together, while others become more viable. The selection and application of climatic indices need to be reviewed in the context of climate change, especially the classification classes that are internationally used to describe the indices. South Africa is warm to intermediate, with many areas classified as too hot. There are, however, many environmental factors moderating the daily temperatures, as well as irrigation supporting viable growth and production in hot areas, making these areas cooler and viable for high quality table wines. Factors such as aspect, altitude, site exposure and distance from the sea or large water bodies generate various mesoclimates and unique environments for viticulture.
Due to the complexity of factors influencing the environment and the sensitivity of the grapevine to change, the site and seasonal variability should be explained using higher resolution information. Bioclimatic indices are effective in highlighting general site and seasonal differences, and for global comparisons. The classification classes set for the climatic indices are also too large, making it difficult to see smaller changes. Finer scale analysis, using hourly data as frequency analysis (how many hours at a specific temperature range) and for diurnal analysis (the tempo of daily change) of sites and seasons is important, within the climatic indices describing a site or season. Hourly information highlights the diurnal trend of warming, speed of warming and diurnal cooling for temperature, relative humidity and wind.
Figure 1 describes the study sites ranging from a cooler site, to intermediate sites and a warmer site. Environmental conditions quantified using traditional bioclimatic indices, additional variables and hourly resolution data. The hourly data analysis did not consider pre-set cut-offs in the weather data, as seen in other climatic studies, but rather analysed the entire temperature, wind and relative humidity profiles, allowing the data to “express itself” (classes used can be seen in Table 1).
FIGURE 1. Western Cape map with districts highlighted and main study areas A and B. Zoomed in sections A and B show the main study areas with the selected sites (encircled areas).
Within all this research, what should a viticulturist be looking at, at field level from season to season?
Using the traditional indices, it was clear that over the four growing seasons there was a general trend of warming. The Geoviticulture MCC System provides more detail on the site and seasonal behaviour as more elements, other than only daily maximum and mean temperatures are considered, a weighting of cool night index and dryness index are integrated into the index. This improved the quantification of the site and seasonal variability of the study sites (Table 2). The extremity of sites identified in the classification with the dryness index variable added, showed the cooler site (Elgin) to have the lowest dryness index compared to Vredendal with the highest. The extreme sites (Elgin and Vredendal) did not highlight seasonal variation, however, the intermediate sites in the coastal region had more pronounced seasonal variability driven either by temperature or dryness. The additional variables affect biological systems, emphasising the importance of considering more variables when quantifying the impact of climate shifts (at regional, local and plant levels) even though the index is still limited, as it is a seasonal summary.
Additional climatic analyses at higher temporal resolution (hourly) improves the understanding of daily kinetics of change at the vineyard level to explain grapevine responses spatially, and over vintages. Hourly threshold classes provides more detail for each site as a seasonal summation in hours observed at specific temperature classes (level 1 – 9). Temperature differences driven by site and season variability are shown in more detail with frequency analysis (Figure 2). This frequency data can improve in-season grapevine management, as more hours at high temperatures in the critical growing periods with lower relative humidity at a site would allow more precise management to also optimally accommodate water constraints.
The analysis showed more information of hours at warmer temperature classes, with the cooler sites showing more hours at lower temperature classes, as expected. Overall, the Somerset West site had more than double the observed hours at 10 – 15°C compared to the other sites. This could be due to more windy conditions. Within the study, Vredendal which is the warmest site, reached the highest maximum values compared to the other sites in the study, but the duration at higher temperatures was shorter than that of the other sites. Understating the diurnal cycle of a day in terms of temperature, wind and relative humidity is important to match the correct grapevine cultivar and to optimise the physiological functioning of the grapevine.
Seasonal and within season variation (vintage effect) is better understood reviewing the hourly profile for each month. This highlights what part of the season is changing in the context of climate change, information that can be more closely linked to the grapevine responses (Figure 3). January showed more noticeable seasonal and site differences, cooler temperatures (10 – 20°C) decreased, and observations at warmer temperatures (20 – 40°C) increased over the four seasons. December cooler over the four seasons, less hours at higher temperatures (20 – 35°C), March warmed, decreased hours at lower temperatures. The winter months, July showed a slight trend of cooling, more hours at 5 – 10°C. Spring, September warmed with more hours between 15 – 30°C. Relative humidity showed the inverse trend, with observations at lower relative humidity increasing. The summer wind speeds were the highest in the first three seasons, with less light breezes at 0 – 2 m/s, but more hours at higher wind speeds of 4 – 10 m/s.
The study confirmed the hypothesis that grapevine will respond to climate change and continue to do so in the expression of phenology, growth and ripening. Grapevine performances are affected by the constant environmental parameters, despite the differences on vineyard and site level. The finer scale analysis of the climate profile, considering and accounting for the amount of hours at specific classes of temperature, wind and relative humidity with no cut-off thresholds, is a novel approach that explains the grapevine responses. Mapping the frequency of hours at specific thresholds, provides additional information for management at field level. Figure 2, 3 and 4 show the mapping application of hours at specific physiological thresholds important for grapevine functioning. The maps highlight the areas with more or less hours at specific thresholds <20°C (Figure 4), between 25 – 35°C (Figure 5) and greater than 30°C (Figure 6), temperature important for grapevine photosynthesis. This information in a spatial format adds additional information to aid decision making over time and space (www.terraclim.co.za).
FIGURE 2. Average seasonal temperature frequency analysis, total observed hours at each threshold level 1 – 9 for four seasons (S1 – S4) over study sites, a) SomersetW, b) Elgin, c) Stellenbosch2, d) Vredendal, e) Stellenbosch1, and f) Stellenbosch3.
FIGURE 3. Temperature (T), relative humidity (RH) and wind speed (U) frequency analyses as total observed hours at each threshold level for four seasons (S1 – S4) for the month of January for Elgin (left), SomersetW (middle) and Vredendal (right). a) T Elgin, b) T SomersetW, c) T Vredendal, d) RH Elgin, e) RH SomersetW, f) RH Vredendal, g) U Elgin, h) U2 SomersetW, and i) U2 Vredendal.
FIGURE 4. Heat map: November 2018 to March 2019: Hours experienced below 20°C, the areas less favourable for optimal photosynthetic activity (green). The greener areas could require more management practices for more optimal grapevine functioning.
FIGURE 5. Heat map: November 2018 to March 2019: Hours experienced between 25 – 30°C. More hours at 25 – 30°C (greener) are areas of optimal photosynthetic areas, the red areas have less hours at 25 – 30°C, hence would require more management inputs to ensure optimal photosynthesis.
FIGURE 6. Heat map: November 2018 to March 2019: Hours experienced >35°C. More hours above 35°C, red areas have more hours at warmer conditions, less favourable for optimal photosynthesis, more orange to red areas would require management practices to help with the heat.
Globally, the varietal spectrum would change substantially in future since the suitability for the cultivation of a given cultivar is largely temperature driven. Frequency analysis would greatly and effectively guide decision making in this regard. Using the climate profile based on hours within specific classes with no set cut-offs, isolating when in each season there are shifts that would influence grapevine. This detail of seasonal profiling can be used to optimise distribution of a grapevine cultivar in a specific environment, based on grapevine physiology thresholds for optimal functioning in the context of climate change.
The Western Cape wine grape terroir climate variability is dictated by latitude, water sources, topography and climate change. Traditional bioclimatic indices use cumulative units potentially masking some specific events/conditions within seasons. With climate change, seasonal variability and extreme events are increasing. The traditional bioclimatic indices do not account for environmental conditions at plant level, information needed for effective adaption strategies. Frequency analysis based on high resolution (hourly) weather data can detect the extremities and duration of conditions at a specific locality. This article explores the possible climatic details overlooked in the use of traditional bioclimatic indices and daily descriptions of sites compared to hourly profile analysis. The study used six sites with different climatic classifications as described by traditional bioclimatic indices for the period 2012 to 2016. The seasonal climate profile was described based on hours within specific classes with no set cut-offs for temperature, relative humidity and wind specifically. This has provided better insights to guide adaptive strategies, as understanding how the climate is driving the grapevine responses. This analysis gives insights into the temperature ranges and the duration of hours at specific thresholds that could impact the grapevines optimal physiological functioning. Understanding the climatic kinetics/dynamics at field level, aids more effective adaptive strategies.
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