Imagine an insect so insidious it took classifying the climate of wine-growing regions as part of several measures adopted by winegrowers to defeat it. That’s what University of California-Davis professors A. J. Winkler and Maynard Amerine did in the 1940s with the Winkler Index after decades of hybridization and ongoing refinement of American Vitis vinifera roots imported from Europe to help the state of California recover from an epidemic of phylloxera lice.
Today, California and much of the Northern Hemisphere face a new enemy—global warming—and again the Winkler Index will be at the center of adaptation and recovery. On July 22, U.C. Davis announced1 it would update the Index for the first time in more than 75 years to account for earlier harvest dates and better pest and disease management.
Along the way, professors Winkler and Amerine discovered that the hybridized vinifera grape was not suited to humid summers, as the increase in humidity made the roots and vines more susceptible to certain fungus diseases and insect pests like grapevine phylloxera (Daktulosphaira vitifoliae) that flourish under humid conditions. Over time they began to make astute observations of rainfall, fog, humidity, and duration of sunshine, but Amerine and Winkler’s work and subsequent research found that temperature plays the greatest role in the development of wine grapes.
They began to construct the Index to correlate wine quality with climate, focusing on California viticulture. Wine-producing regions of California were broken into five climatic regions using heat summations above 50° F, or growing degree days (GDD), the temperature they observed at which the grapevines of California generally grew. They took the mean monthly temperature less 10 degrees, multiplied by the number of days in the month, and totaled the degree days for the seven months. They came up with five regions, which still form the bedrock of viticultural climatic data. Region I is the coolest, with grapes that grow with a GDD under 1390 like pinot noir, chardonnay and gewürztraminer. Region V is the warmest, with a GDD greater than 2220 for grape varieties like primitivo, nero d’avola, and fiano.
Calculating Growing Degree Days
To predict vine growth stages such as bloom, veraison, and maturity, grape-growers often use weather-based indicators, like GDD. Grapevine is a plant of warm climate: it requires a sufficient amount of heat for its growth. Daily temperature strongly influences its development. Starting March 1st or April 1st of each year in the Northern Hemisphere, as the grapevines awaken from winter hibernation, farmers count GDD, or heat units, to estimate the growth and development of grapes. Research has shown that not only does GDD provide a more accurate physiological estimate than calendar days alone, but also helps farmers facilitate better management of a crop’s growth stage relative to pest and weed life cycles.
To calculate Growing Degree Days, subtract the grapevine’s threshold temperature of 50°F (10°C) from the mean daily air temperature in any 24-hour period (the mean daily temperature adds together the high and low temperature for the day and divides that value by two). However, if the mean temperature is at or below the base temperature for a crop or pest of interest, the GDD value is zero. If the mean temperature is above the base temperature, then the GDD equals the value of the mean temperature minus the base temperature. GDD values are accumulated during a growing season.
With the help of calculated GDDs, vineyard managers can predict when the insects will be active or when they will be in the most vulnerable phase, so the grower is able to suppress them effectively with a minimum impact on the environment. GDDs are also useful for the suitability of a region for the production of a particular grapevine variety, estimation of the heat stress, and comparing one region to another and one season to another.
Climate Change and Historic Grape Harvest Dates
One of the best indicators of the effect of climate on the grape harvest is an ancient record archive from the Dijon town of Burgundy, France, known as the “Burgundian GHD Series.” In 1836, Etienne Noirot, a land surveyor in Dijon, gathered the Burgundian archive of hand-written recordings dating back to 1354, which logged the dates of fruit ripening each year. Since the 1800s, scientists have been using the record of grape harvest dates to track climatic changes.
More recently, scientists were able to reconstruct a 664-year record of temperatures from the Burgundian GHD Series—the longest homogenous series of grape harvest dates ever assembled—that correlated with the final phenological stage of fruit ripening. Despite inherent temperature calibration errors based on the Paris temperature series from 1659 to 2018, scientists claim the logs reveal strong evidence of climate change in the past few decades. The record is divided into two timeframes: from 1354 to 1987 grapes were on average picked from September 28th onward, whereby from 1988 to 2018 harvests began 13 days earlier. The Burgundian series further demonstrates that outstanding hot and dry years in the past were outliers, while they have become the norm since the transition to rapid warming in 1988.
Precision Viticulture and GDD
While updating the Winkler Index will help farmers plan and prepare for the harvest in the midst of climate change, precision viticulture has the potential to improve matters even more. The latest technology can provide a range of environmental datasets, tools, and techniques to collect, analyze, and visualize data for vineyards over both space and time. As technology becomes more mobile, powerful, and accurate, and output easier to integrate, there is the capability to collect a wider variety of data about the vine plant and the vineyard environment.
TerraviewOS, with its easy-to-use, integrated platform can help track the GDD of a vineyard block automatically in either Celsius or Fahrenheit in order to manage pests and to know when to harvest the yield. The TerraviewOS team provides close guidance to help the user calculate GDD and other important data for yield estimations, as well as interpret images for necessary adjustments.
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