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The Pennsylvania State University
The Graduate School
College of Agricultural Sciences
Department of Plant Science
EVALUATING THE IMPACTS OF VITICULTURAL AND ENVIRONMENTAL FACTORS ON
ROTUNDONE IN NOIRET GRAPES
A Thesis in
Horticulture
by
Andrew D. Harner, Jr.
Ó 2019 Andrew D. Harner, Jr.
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science,
May 2019
ii
The thesis of Andrew Harner was reviewed and approved* by the following:
Michela Centinari
Assistant Professor of Viticulture
Thesis Advisor
Ryan J. Elias
Associate Professor of Food Science
Richard Marini
Professor of Horticulture
Justine Vanden Heuvel
Associate Professor
Horticulture Section, School of Integrative Plant Science
Cornell University
Erin L. Connolly
Professor
Head of the Department of Plant Science
*Signatures are on file in the Graduate School.
iii
Abstract
The grape-derived sesquiterpenoid rotundone is responsible for the ‘black pepper’ aroma
of several wine grape varieties, including the interspecific hybrid Noiret. Numerous studies have
evaluated the effects of major climatic variables, including both temperature and solar radiation,
on the accumulation of rotundone in wine grapes. However, only a few studies have assessed the
effects of common viticultural management practices, and no studies have assessed both climatic
and viticultural influence together in a single study to fully investigate which variables have the
strongest influence on rotundone concentrations. Over the 2016 and 2017 seasons, we evaluated
the influence of 21 different viticultural, meso- and microclimatic variables on the concentrations
of rotundone in Noiret wine grapes at 7 Pennsylvania and New York vineyards with distinct
environmental conditions. Vineyard-scale post-veraison temperatures and solar radiation had
robust and positive correlations with rotundone concentrations measured at harvest. At the level
of the fruiting zone, rotundone concentrations correlated negatively with post-veraison
temperatures below 15 °C, above 30 °C, and pre-veraison fruit sun exposure. Rotundone
concentrations were also strongly correlated to several grapevine tissue nutrients, including
calcium, potassium, and magnesium. A four-variable model was constructed using multiple
linear regression analysis of the vineyard-scale data. This model can be used by growers to
identify vineyards with potential for producing ‘peppery’ wines, as well as assist growers with
manipulating rotundone concentrations via implementation of canopy management practices to
achieve wines with the desired levels of rotundone and related ‘pepperiness.’
iv
TABLE OF CONTENTS
LIST OF FIGURES……………………………………………………………………...v
LIST OF TABLES………………………………………………………………………vi
ACKNOWLEDGEMENTS……………………………………………………………vii
Chapter 1: Climatic and agronomic influence on major grape-derived aroma compounds..........................................................................................................................1
1.1 Grape-derived chemical compounds drive wine aroma...........................................1 1.2 Climatic influence on specific wine grape aroma-active compounds......................4 1.3 Agronomic influence on wine grape aroma compounds..........................................7 1.4 Biological, chemical, and sensorial characteristics of rotundone..........................11 1.5 Climatic and agronomic influence on rotundone concentrations..........................15
Chapter 2: Weather conditions during fruit ripening, vine nutrient status, and vine size collectively influence rotundone concentrations in cool-climate Noiret wine grapes................................................................................................................................20 2.1 Introduction................................................................................................................20 2.2 Methods and Materials..............................................................................................23 2.3 Results.........................................................................................................................37 2.4 Discussion...................................................................................................................54 2.5 Conclusion..................................................................................................................69
Chapter 3: Conclusion.....................................................................................................71 References.........................................................................................................................74 Appendices........................................................................................................................83
Appendix A: Fruit maturity and production metrics........................................83
Appendix B: Nutrient concentrations and water status....................................84
Appendix C: Supplemental details on mesoclimate model selection...............85
Appendix D: Supplemental details on microclimate model selection..............90
v
LIST OF FIGURES
Figure 1: Map of vineyard study sites............................................................................24
Figure 2: Noiret grape rotundone concentrations for 2016 and 2017.........................44
Figure 3: Partial validation plot for the selected model...............................................50
vi
LIST OF TABLES
Table 1: Vineyard characteristics for all study sites.....................................................25
Table 2: All measurements taken during the study period..........................................36
Table 3: Mesoclimatic data for the study period..........................................................38
Table 4: Microclimatic degree-hour data for the post-veraison period......................40
Table 5: Microclimatic solar exposure data for the study period...............................41
Table 6: Correlations between rotundone and measured variables...........................46
Table 7: Examples of SAS RSQUARE option output..................................................48
Table 8: Regression equation for the chosen mesoclimatic data model......................48
Table 9: Regression equation for the chosen microclimatic data model....................53
vii
ACKNOWLEDGMENTS
The success of this project was directly due to the support of all those involved, as this
thesis would not have come to fruition otherwise. I would like to thank my advisor, Michela
Centinari, who offered me the opportunity to pursue viticultural research and has been a
consistent source of support and guidance throughout the entirety of this project, from its
inception and all the way through and past the writing process. Many thanks is extended to my
committee: to Rich Marini, who was instrumental in teaching me multivariate regression and the
necessary statistics for this project; to Ryan Elias, who assisted with all chemical analyses and
rotundone extractions; and to Justine Vanden Heuvel, who helped coordinate and manage all of
the experimental sites in the Finger Lakes. Thanks to Don Smith, and the many members of the
Centinari and Elias labs, past and present, who have provided critical advice, support, and
assistance with data collection and various analyses.
This project would not have been possible without the collaboration of Bryan Hed and
Steve Lerch, who I am indebted to for their aid with data collection and management of
experimental sites. Much appreciation is also extended to Tracey Siebert, Markus Herderich, and
Sheridan Barter of the Australian Wine Research Institute, as we would not have been able to
identify, extract, or analyze rotundone without their thorough help and collaboration. The
support of the AWRI was essential to the success of this project.
I would like to deeply thank my family for their enduring support throughout my time as
a graduate student, and for always being encouraging of my academic pursuits from the very
beginning. And, lastly, my sincere gratitude and love are extended to Clara Miller, who has
provided endless support throughout this project and with my research overall, and who
continues to be a strong source of encouragement in all that I do.
1
Chapter 1: Climatic and agronomic influence on major grape-derived aroma compounds
1.1 Grape-derived chemical compounds drive wine aroma
Perceived wine aroma and flavor is chemically complex, and both are dependent on the
presence of specific aroma-active chemical compounds and their interactions within the wine
matrix. The majority of compounds responsible for wine aroma are grape-derived secondary
metabolites (González-Barreiro et al., 2013). To date, the most studied classes of secondary
metabolites in wine grapes and those most critical for wine aroma are the pyrazines, mono- and
sesquiterpenes, C13-norisoprenoids, volatile thiols, and phenylpropanoids (Robinson et al., 2014).
During the fermentation process some of these compounds are extracted directly from grape
tissue—namely the skins or pulp—in a free, volatile form, while others are glycosylated and
structurally bound to a sugar moiety, and thus rendered inactive unless enzymatically or
chemically hydrolyzed (Robinson et al., 2014). It is this combination of both free and bound
aroma compounds that contribute to and influence perceived wine aroma.
Current knowledge about aroma-active chemicals in wine focused on investigations of
specific varietal aromas, with much research focusing on monoterpene and sesquiterpene
compounds. Monoterpenes, one example of the variety of compounds that comprise the terpene
class, drive the aromas of so-called aromatic white varieties, including Riesling, Müller-Thurgau,
Gewürztraminer, and many of the Muscats (Mateo and Jiménez, 2000). For example, the
monoterpenes linalool and citronellol can impart floral and citrus notes to many varietal wines,
an example being Gewürztraminer (Robinson et al., 2014; Waterhouse et al., 2016). Recent
research has also focused on another monoterpene, 1,8-cineole, or eucalyptol, as it contributes
aromas of ‘eucalypt,’ ‘mint,’ and ‘camphor’ to wines. Concentrations of this monoterpene were
heavily influenced by the inclusion of material other than grapes (MOG) into the must of Shiraz
2
wines, namely leaves of Eucalyptus species, in addition to grapevine leaves and stem tissue from
vines that were near Eucalyptus trees (Capone et al., 2012; Black et al., 2015). A variety of
terpenes, such as the monoterpene cis-rose oxide and the sesquiterpene rotundone, have been
labelled “aroma impact compounds” (Guth, 1997; Wood et al., 2008) due to their low perception
thresholds and ability to impart strong, specific aromas to specific wine varieties. Cis-rose oxide
has been associated with important ‘lychee’ aromas of Gewürztraminer wines (Ong and Acree,
1999), while rotundone imparts potent ‘black pepper’ aromas to Shiraz, Duras, Noiret, and many
other red- and white-fruited varieties (see, for example, Herderich et al., 2015; Homich et al.,
2017).
Another group of compounds within the terpenoids, C13-isoprenoids are abundant in
aromatic varieties and important contributors to the aromas of Semillon, Sauvignon blanc,
Merlot, Shiraz, and Cabernet Sauvignon (Robinson et al., 2014). In terms of wine aroma
perception, the most important C13-norisoprenoids studied have been b-damascenone, 1,1,6-
trimethyl-1,2-dihydronapthalene (TDN), and b-ionone (Waterhouse et al., 2016). b-damascenone
imparts aromas of ‘cooked apple’ and ‘floral’ notes to wines of many varieties, while TDN
contributes a ‘kerosene’ or ‘petrol’ note, which is considered a fault mainly in aged Riesling
wines (Waterhouse et al., 2016). Various carotenoids present in grapes are chemical precursors
of C13-norisoprenoids, and as such research has focused on the manipulation of fruit sunlight
exposure to modulate C13-norisoprenoid concentrations in fruit and wines, with most studies
focusing on TDN given its importance to Riesling wines (Kwasniewski et al., 2010; Waterhouse
et al., 2016).
Pyrazines, another major grape-derived class of aroma-active compounds, have also been
well studied (Robinson et al., 2014). This is namely due to the role that 3-isobutyl-2-
3
methoxypyraize (IBMP) plays in wine aroma and flavor, as IBMP has been associated with
‘green bell pepper’ and related herbaceous notes in Cabernet Franc, Sauvignon blanc, and
Cabernet Sauvignon (Ryona et al., 2008; Robinson et al., 2014). Although low concentrations of
IBMP might impart varietal character and vegetative aromas (Allen et al., 1991), high
concentrations are associated with excessively herbaceous notes that are typically undesirable
(Robinson et al., 2014). Previous work indicated that IBMP concentrations in key wine grapes
varieties like Cabernet Franc and Merlot might be manipulated through management practices
that alter the fruit zone microclimate (Scheiner et al., 2010). Although IBMP is the most
explored methoxypyrazine to date, 3-isopropyl-2-methoxypyrazine (IPMP) is also important in
wine as its presence can be an indication of ladybug taint, caused by the extraction of IPMP from
cluster-borne Multicolored Asian Ladybeetles (Harmonia axyridis) (Robinson et al., 2014). This
compound contributes to aromas of ‘bell pepper,’ ‘asparagus,’ and ‘peanut’ in white wines, and
when the proportion of extracted beetles increases, a subsequent decrease in fruit and floral
intensity of wines has been observed (Pickering et al., 2004; Robinson et al., 2014).
A few other classes of compounds contribute important positive or negative aromas to
wines, including many sulfur-containing volatile compounds. The sulfur-containing aroma
compounds that contribute the most to specific varietal aromas are polyfunctional thiols, named
due to their chemical structure (Waterhouse et al., 2016). The polyfunctional thiols are especially
relevant due to their low perception threshold as impact compounds (Waterhouse et al., 2016)
and their ability to impart specific, fruity notes. Specific and major examples include 4-
mercapto-4-methylpentan-2-one (4MMP), 3-mercaptohexan-1-ol (3MH), and 3-mercaptohexyl
acetate (3MHA), and these compounds have been associated with ‘guava,’ ‘passionfruit,’ and
‘grapefruit’ aromas (Robinson et al., 2014; Waterhouse et al., 2016). These compounds are
4
especially prevalent in Sauvignon Blanc wines, and have also been identified within a variety of
red and white wines of various styles, including Riesling, Semillon, Petit Manseng, Gros
Manseng, and Grenache wines (Waterhouse et al., 2016).
Although a variety of other compounds exist in grapes and wine that contribute to wine
aromatics, the aforementioned chemical groups represent the most widely studied within the
scope of manipulating their concentrations in the fruit and or wine. Given that many of these
compounds are either extracted directly from the grapes or serve as precursors for other aroma-
active compounds, it has been reasoned that specific viticultural practices, such as canopy leaf
removal, can affect their concentrations (González-Barreiro et al., 2013). Moreover, research into
the seasonal accumulation patterns of major aroma compounds is also important in that it could
assist with choosing the right timing of the application of specific viticultural practices (Scheiner
et al., 2010; Zhang et al., 2016). Much of this research has focused exclusively on Vitis vinifera
varieties, however, and further investigation into the aromatic compounds present in Vitis
interspecific hybrid grapes increasingly planted in the Northeastern and Midwest U.S. is
warranted. This would further expand the knowledge of viticultural influence on aroma
compounds, and allow for the application of such knowledge to a wider range of winegrowing
regions.
1.2 Climatic influence on specific wine grape aroma-active compounds
Climate encompasses all the environmental conditions related to temperature, solar
radiation, precipitation, and humidity within a given location, which synergistically influence
both the primary and secondary physiological processes of grapevines (Iland et al., 2011).
Differences between climatic factors among the winegrowing regions of the world drive
regional-driven varietal aroma trends (Robinson et al., 2014). Many studies indicated that the
5
mesoclimate, and more particularly temperature and heat accumulation, can influence the
concentrations of important aroma-active compounds in grapes and wines. Sauvignon blanc
grapes and wines produced in cool-climate regions, for example in New Zealand, tend to have
higher concentrations of IBMP and other methoxypyrazines than grapes and wines produced in
warmer climates (Lacey et al., 1991). Recent research has demonstrated the importance of intra-
season weather variation as well, as pre-veraison temperatures are more influential than total
seasonal heat accumulation in regard to IBMP concentrations in Cabernet Franc grapes and
wines (Scheiner et al., 2012). Rotundone accumulation is also sensitive to regional climate, as
grapes produced from cool climates tend to have higher concentrations of rotundone than those
produced in warmer climates. Furthermore, regional climate strongly influenced rotundone in
Grüner Veltliner wines produced from different Austrian winegrowing areas (Herderich et al.,
2015; Nauer et al., 2018).
The effects of solar radiation on grape-derived aroma compounds is well documented
with specific classes of compounds, though it is important to note that most studies have
evaluated these effects at the fruiting zone scale, and not assessed broad mesoclimatic
differences in radiation across vineyards and winegrowing regions. Most research has evaluated
different levels of fruit solar exposure, either via leaf removal (Lee et al., 2007; Kwasniewski et
al., 2010; Scheiner et al., 2010; Feng et al., 2014; Homich et al., 2017) or artificial shading
(Bureau et al., 2000; Skinkis et al., 2010; Zhang et al., 2015a). Important trends have emerged
from these and other studies; for example, increased fruit sun exposure favorably increased the
concentrations of a variety of C13-norisoprenoids and monoterpenes in various varieties, while
methoxypyrazine concentrations can be inhibited by increased fruit exposure (Robinson et al.,
2014).
6
Less work has evaluated the effects of sun exposure on concentrations of sesquiterpenes,
polyfunctional thiols, and other sulfur-including volatile compounds in grapes and wines, though
the sesquiterpene rotundone might reach higher concentrations in Shiraz grapes from shadier
clusters and vines (Zhang et al., 2015a). Most research has addressed the effects of solar
radiation intensity, though radiation quality may be more important when considering C13-
norisoprenoids, given that they are products of carotenoid degradation (Robinson et al., 2014).
Further investigation into radiation intensity and quality, in addition to the effects of radiation
when considered independent from the effects of radiation-induced warming, is necessary to
better understand the relationships between sunlight and important classes of grape-derived
aroma compounds.
The total amount and patterns of seasonal precipitation are strictly correlated to grapevine
water status which can directly or indirectly after various aromatic compounds (Robinson et al.,
2014; Black et al., 2015). The relationship between concentrations of IBMP in Cabernet
Sauvignon wines and water deficit is not clearly defined. It has been hypothesized that water
deficit might increase various ‘fruity’ compounds, including esters, lowering the perceived
‘vegetal’ and ‘bell pepper’ aromas in the wine (Chapman et al., 2005). However, increasing
levels of water deficit might also negatively affect ester concentrations (Talaverano et al., 2017).
Decreased vine water status also had a variable effect on monoterpene and C13-isoprenoid
concentration: whereas water deficit increased concentrations of b-damascenone in Merlot
wines, no effect on b-ionone was observed (Ou et al., 2010). Similarly, concentrations of the
monoterpenes nerol, geraniol, nerolidol, and citronellol were higher in water stressed vines,
while concentrations of linalool were unchanged (Ou et al., 2010). The sesquiterpene rotundone
is sensitive to vine water status, as Duras grapes grown under higher water deficits had higher
7
concentrations this aroma compound (Geffroy et al., 2014). Despite these associations, it is still
unclear whether these relationships are mainly due to direct influence of water deficit on the
biosynthetic processes responsible for the production of aroma compounds, or rather to indirect
effects on vine canopy size and fruiting zone exposure (Robinson et al., 2014).
Climatic parameters interact with other environmental factors, including soil
characteristics, and cultural practices to definite “terroir,” or more specifically, the wine sensory
attributes of a specific variety within a given geographic context (Van Leeuwen and Seguin,
2006). Similar attempts to classify the distinctiveness of an individual winegrowing region has
led to the development of the concept of ‘regional typicity.’ Riesling wines displayed region-
specific aromatic and flavor profiles across both Germany and Canada (Fischer et al., 1999;
Douglas et al., 2001). Similarly, Malbec wines from both Mendoza and California exhibited
region-specific sensory and chemical traits (King et al., 2014). Previous work has also attempted
to define intra-region typicity of Riesling wines from various locations within the Finger Lakes
of central New York (Nelson, 2011). Identifying regional identities for a region or country’s
commercially important wine grape varieties has economic and marketing implications, but also
emphasizes the underlying role that climate has on wine grape and wine aromatic and flavor,
coupled with variety-specific genotypic traits. More efforts to understand and define regionality
where it exists may shed further light on the various climatic, geological, and environmental
factors that influence and drive wine grape aromatics and flavor.
1.3 Agronomic influence on wine grape aroma compounds
Wine grape growers implement a variety of viticultural practices within a given season to
produce quality grapes and wines in accordance with consumer preferences (Wolf, 2008). These
practices might require crop load (yield to vegetative growth ratio) adjustment or manipulation
8
of the fruiting zone to improve microclimate and reduce pest and pathogen pressure. Whereas
some practices can directly manipulate vine vegetative tissue (e.g., leaf removal, shoot thinning,
canopy hedging, and dormant pruning), others directly affect the vine’s reproductive tissue, the
fruit (e.g., cluster thinning and shoot thinning). The former practices can directly adjust
grapevine canopy density, vine size, and crop load via the removal of vegetative biomass and the
thinning of canopy shoots and leaf layers (Wolf, 2008). Cluster thinning can be used to adjust
yield and crop load through direct removal of fruit. This has the effect of reducing vine yield and
crop load and has the possibility of inducing a compensatory effect such that individual clusters
and berries may reach higher sugar concentrations and greater mass (Dami et al., 2006).
Canopy management practices can alter the accumulation and concentrations of grape-
derived aroma compounds and precursor molecules, giving growers a chance to tailor berry and
wine aroma characteristics to match those that are preferred by consumers. Of these practices,
fruiting zone leaf removal, a management strategy used in many winegrowing regions, has been
the most widely studied and reviewed. Leaf removal is likely the most useful practice available
to growers to manage concentrations of both favorable and unfavorable aroma compounds in the
field. Removing leaves in the fruiting zone enhances sunlight and air penetration and thus
reduces disease pressure and might enhance fruit ripening (Smart and Robinson, 1991; Hed et
al., 2014). By manipulating the microclimate, it is possible to also induce changes on berry
secondary metabolites and many grape-derived aroma and flavor compounds (Alem et al., 2018).
Numerous studies have focused on the intensity of leaf removal and how varying levels
of leaf cover and sun exposure affects grape aroma compounds and important precursors
(Reynolds et al., 1996; Lee et al., 2007; Skinkis et al., 2010; Geffroy et al., 2014; Feng et al.,
2014; Homich et al., 2017), while others have explored the timing of leaf removal (Kwasniewski
9
et al., 2010; Scheiner et al., 2010; Zhuang et al., 2014; Komm and Moyer, 2015; Verzera et al.,
2015; Homich et al., 2017; Hickey et al., 2018). Both types of research will be briefly reviewed
here, in addition to other canopy management practices.
Overall, increased fruit sun exposure due to fruiting zone leaf removal favorably
enhanced the concentration of both free, aroma-active and glycosidically-bound monoterpenes in
several varieties, such as Riesling, Gewürztraminer, Golden Muscat, and Traminette (Reynolds
et al., 1991; Macauley and Morris, 1993; Reynolds et al., 1996; Skinkis et al., 2010).
Furthermore, leaf removal was more effective than shoot hedging in increasing free monoterpene
concentrations (Reynolds et al., 1996). Since leaf removal enhances fruit sun exposure,
overexposure of clusters to sunlight under particularly warmer growing conditions may,
however, reduce final free monoterpene concentrations; this suggests that leaf removal as a
management method for increasing desirable aroma compounds may be better suited to cool
climates (Skinkis et al., 2010). Because of the negative relationship between fruit sun exposure,
temperature, and IBMP concentration (Ryona et al., 2008), fruiting zone leaf removal has also
been used to favorably reduce IBMP concentrations in Cabernet franc and Merlot grapes at
harvest in cool climates (Scheiner et al., 2010). Leaf removal also increased the concentrations of
various C13-norisoprenoids, and namely b-damascenone to the greatest extent, in Nero d’Avola
wines (Verzera et al., 2016). Limited research has focused on the effects of leaf removal on
sesquiterpenes and results are still inconclusive.
The effects of leaf removal on aroma compounds may considerably change depending on
phenological stage of the berries at the time of application. Timing of leaf removal application
can be important for IBMP concentrations in Cabernet franc grapes; early season leaf removal,
applied 10 to 40 days after full bloom, had the most pronounced effect when compared to later
10
treatment timings (Scheiner et al., 2010). Leaf removal timing is also important for major C13-
norisoprenoids, as removing leaves at pre-veraison (~33 days post-fruit-set) was more effective
at increasing TDN concentrations in Riesling juices and wines as compared to earlier (fruit-set
stage) or later (post-veraison) leaf removal applications (Kwasniewski et al., 2010). Given that
excessive concentrations of TDN are typically considered undesirable, tradeoffs may exist
between implementing leaf removal to enhance berry maturation, reduce disease pressure, and to
adjust the concentrations of important aroma compounds to desired levels. Taken altogether, it is
necessary to further study the timing of leaf removal as it relates to major aroma chemical classes
and specific impact compounds, so that wine grape producers can better implement leaf removal
strategies to achieve production goals related to grape quality.
Although less studied than fruiting zone removal, cluster thinning could also modify the
concentration of aroma-active compounds. For example, cluster thinning increased total bound
monoterpenes in Shiraz grapes, but without altering other specific aroma compounds of interest,
such as C13-norisoprenoids (Bureau et al., 2000). Similarly, a study conducted on Chardonnay
Musqué confirmed positive effects of cluster thinning in increasing both free and bound
monoterpene concentrations in grapes (Roberts et al., 2007). Conversely, cluster thinning did not
impact rotundone accumulation in Duras wines, suggesting that responses to cluster removal
might not be uniform across all terpenoids (Geffroy et al., 2014). It is important to note that it is
not the reduction in yield per se which influences development of aroma-active compounds, but
rather the manipulation of the vine crop load. Crop load adjustment via varying degrees of
dormant pruning severity, for example, indicated a strong negative relationship between the
number of viable buds kept (i.e., both the potential vine size and yield) and IBMP in Cabernet
Sauvignon wine grapes (Chapman et al., 2004).
11
The effects of other canopy management practices, such as hedging and shoot thinning,
on the development of aroma compounds have been little explored. A reduction of vegetative
growth and fruit shading obtained through hedging positively influenced free monoterpene
concentrations in grapes at harvest (Reynolds et al., 1996). Similarly, mild shoot thinning
increased berry concentrations of both free and bound monoterpenes, when compared to no
thinning (Reynolds et al., 1994), likely due to an increase of fruit sunlight exposure. While
fruiting zone leaf removal mainly affects canopy microclimate, other management practices such
as shoot thinning influence multiple vine parameters simultaneously (i.e., fruit shading, crop
load, and vegetative growth). Therefore, it is challenging to experimentally decouple the effects
of crop load versus microclimate manipulation on aroma development when applying these
practices.
1.4 Biological, chemical, and sensorial characteristics of rotundone
Rotundone (C15H22O), a sesquiterpenoid ketone of the guaiene family responsible for the
aroma of ‘black pepper’ in wines, has been of the utmost interest to both scientists and the wine
industry since its isolation from Shiraz wine in 2008 (Wood et al., 2008; Mattivi et al., 2011).
Since then it has been identified in several Vitis vinifera varieties, including both red-fruited
(e.g., Shiraz, Mourvèdre, Duras, Gamay, Schioppettino, Pinot Noir, and others) and white-fruited
varieties (e.g., Grüner Veltliner) (Wood et al., 2008; Mattivi et al., 2011; Geffroy et al., 2014;
Logan et al., 2015; Geffroy et al., 2016a). Recently it was extracted from the red-fruited
interspecific Vitis hybrids Noiret and Koshu, in addition to the white-fruited hybrid variety
Muscat Bailey A (Goto-Yamamoto et al., 2015; Takase et al., 2015; Homich et al., 2017). The
presence of rotundone in a variety of both grapes and wines of Vitis vinifera and interspecific
12
Vitis varieties suggests that it may be present in other wine grape varieties and species of
economic importance, in addition to a variety of other sesquiterpene-producing plant species.
Prior to its extraction from grapes and wines, rotundone was originally extracted from
tubers of the purple nutsedge plant (Cyperus rotundus L.) in 1967, from which it derives its name
(Kapadia et al., 1967). It has also been extracted from myriad other non-Vitis plants, including
another Cyperus species, a variety of culinary herbs, such as thyme (Thymus vulgaris L.), basil
(Ocimum basilicum L.), and rosemary (Rosmarinus officinalis L.), and from a variety of plant
species used for incense production, including various Aquilaria spp. (Lam.) and Boswellia
sacra (Fleuck) (Kapadia et al., 1967; Ishihara et al., 1991; Ishihara et al., 1993; Pandey et al.,
2002; Wood et al., 2008; Naef et al., 2011; Niebler et al., 2016). Moreover, rotundone has
recently been identified within and extracted from the peel and juice of grapefruit (Citrus x
paradisi Macfad.), in addition to apples (Malus domestica Bork. nom. illeg. cv. Sun-Fuji) and
commercial mango (Mangifera indica L. cv. Alphonso) puree (Nakanishi et al., 2017a;
Nakanishi et al., 2017b). Lastly, rotundone has been extracted directly from oak wood used for
aging of alcoholic spirits, and is thereby present within many oak-aged spirits in addition to un-
aged tequila, suggesting that it is also present within the blue agave (Agave tequilana F.A.C.
Weber) plant (Genthner, 2014).
Despites its extraction from myriad plant species and plant-based products, analysis of
the physiological processes involved in rotundone synthesis and accumulation has almost
entirely focused on grape-derived rotundone. Rotundone is primarily, if not exclusively,
produced within the berry exocarp during the accumulation period, though non-fruit tissue (i.e.,
rachises, shoots, and leaves) remain sources with high concentrations of potentially extractable
rotundone (Caputi et al., 2011; Takase et al., 2016b; Zhang et al., 2016).
13
In several grape varieties, such as Shiraz, Duras, and Vespolina, accumulation pattern for
rotundone begins immediately following veraison, and increases slowly for about 3 to 4 weeks
after veraison before accumulating rapidly at the end of the ripening period, at about 6 to 8
weeks following veraison (Caputi et al., 2011; Herderich et al., 2012; Geffroy et al., 2014;
Logan, 2015; Zhang et al., 2016). Indeed, in Duras, peak rotundone concentrations are reached
around 44 days following veraison and concentrations thereafter decrease, suggesting that there
is a possible point at which rotundone ceases accumulation or is instead degraded, depending on
ripening period length and environmental conditions (Geffroy et al., 2014).
To understand mechanisms of rotundone synthesis, recent work focused on
transcriptional analysis of the specific phytochrome enzyme responsible for rotundone
production (Takase et al., 2016b) and two specific polymorphisms of a terpene synthase gene
that are related to the production of the precursor, a-guaiene (Drew et al., 2016). Transcription
patterns of the phytochrome responsible for rotundone formation in Shiraz and Merlot berries,
VvSTO2, support the reported trend of post-veraison accumulation and late-season degradation,
as VvSTO2 transcription and rotundone concentrations reached a peak at about 14 weeks in
Shiraz before decreasing afterwards (Takase et al., 2016b). Additionally, VvSTO2 transcription
was higher in a high-rotundone variety when compared to a low-rotundone variety (i.e., Shiraz
versus Merlot), suggesting a possible genetic influence on rotundone synthesis, aside from
regulation via a-guaiene availability (Takase et al., 2016a; Takase et al., 2016b). Aerial
oxidation of a-guaiene to rotundone – via (2R) and (2S)-hydroperoxyguaiene and (2R)- and (2S)-
rotundol intermediaries – can occur, but it is most likely that rotundone is enzymatically oxidized
within the berry exocarp (Huang et al., 2014; Huang et al., 2015; Takase et al., 2016b). Berry-
derived concentrations of a-guaiene exhibit a temporal accumulation pattern in the berry exocarp
14
similar to rotundone (Takase et al., 2016b); it also accumulates to higher concentrations in cooler
conditions (Takase et al., 2016a). Recent elucidation of VvGuaS, an allele of the sesquiterpene
synthase-encoding gene VvTPS24, as the enzymatic source of a-guaiene in Shiraz also highlights
the possibility of a genetic predisposition for high a-guaiene synthesis, and subsequently high
rotundone accumulation, though further research is necessary (Drew et al., 2016)
As an aroma-active sesquiterpene, rotundone is potent at low concentrations. Reported
sensory thresholds for rotundone perception are approximately 16 ng/L in red wine, while in
water it is 8 ng/L, with about 20% of panelists being anosmic (Wood et al., 2008). It is a very
hydrophobic compound with a mostly apolar structure (Log Kow = 4.98); this most likely hinders
its extraction from grape exocarps in the must during red wine fermentation, and leads to only
about 5.0-7.0% of berry rotundone concentrations at harvest being present in the finished wine
(Caputi et al., 2011). It has been suggested that increased ethanol concentrations in wines might
help rotundone extraction (Caputi et al., 2011; Geffroy et al., 2016a), but efforts to increase
Duras wine rotundone concentrations via use of macerating enzymes, thermovinification, and
extended fermentation time all failed to significantly increase rotundone concentrations when
compared to a standard vinification protocol (Geffroy et al., 2017). The inclusion of non-grape
materials into the must, however, can increase rotundone concentrations in wines, as inclusion of
leaves and stem tissue yielded rotundone concentrations in Shiraz wines 6 times greater than that
of the control (Capone et al., 2012).
Rotundone concentrations in Shiraz, Gamay, and Noiret red wines are strongly and
positively associated with perceived pepperiness (Wood et al., 2008; Geffroy et al., 2016a;
Homich et al., 2017). In addition to the noted specific anosmia of rotundone concentrations – up
to concentrations of 4000 ng/L (Wood et al., 2008) – consumer acceptance of peppery notes (i.e.,
15
high rotundone concentrations) in red wines is mixed. Geffroy et al. (2016a) found that those
who preferred Gamay wines with stronger ‘peppery’ notes were professionals and managers that
were more likely to spend more money for a bottle of wine. According to Geffroy et al. (2018),
the panelists’ responses to rotundone was variable and split along 3 main groups: whereas young
panelists with little wine knowledge and tasting experience preferred an unspiked control to
Duras wines with moderate to high concentrations of rotundone, older panelists with strong wine
knowledge and higher tasting experience preferred a moderate amount of rotundone (<46 ng/L)
and rejected wines with concentrations above 30g ng/L. A third cluster of panelists preferred
wines with rotundone concentrations exceeding 94 ng/L, and this cluster was comprised mainly
of managers who both frequently consume wine and appreciate ‘peppery’ notes in wine (Geffroy
et al., 2018). Although these studies are few and relegated to analysis of only Gamay and Duras
wines, they provide critical insights into consumer acceptance and perception of rotundone and
related ‘peppery’ notes in wines, and how variable these response are based on consumer
characteristics.
1.5 Climatic and agronomic influence on rotundone concentrations
Vineyard mesoclimate influences final rotundone concentration in wine grapes; cool-
climate regions typically produce grapes and wines with greater rotundone concentrations than
warm climate regions (Geffroy et al., 2014; Herderich et al., 2015; Geffroy et al., 2016a). In
addition to mesoclimatic influence, viticultural practices such as fruiting zone leaf removal may
alter rotundone concentration and subsequent ‘black pepper’ aroma in wine by modifying cluster
microclimate conditions (Geffroy et al., 2014; Homich et al., 2017). However, the relationship
between fruiting zone microclimate and rotundone development within the grape berries is still
unclear. Leaf removal at veraison lowered rotundone concentration in Duras wines as compared
16
to an undefoliated control (Geffroy et al., 2014), whereas increasing cluster sunlight exposure
from pea-size berry stage to harvest increased rotundone concentration in Noiret grapes and wine
as compared to vines with highly shaded clusters (Homich et al., 2017). Furthermore, Shiraz
berries from cluster portions naturally shaded by the canopy had higher rotundone concentrations
when compared to more sun exposed cluster portions (Zhang et al., 2015a).
Differences in the timing of leaf removal, as well as in climatic conditions (e.g., heat
accumulation, growing season length) and fruit maturity at harvest amongst experimental sites
may explain contradictory results regarding leaf removal. Shiraz fruit exposed to air zone
temperatures above 25 °C had lower rotundone concentrations as compared to fruit exposed to
cooler temperature (Zhang et al., 2015b). Therefore, it is possible that in hot or warm climates,
leaf removal may expose grape berries for long periods of time to excessively high temperatures
that may inhibit rotundone synthesis or accumulation. Conversely, in cooler climates leaf
removal may increase berries temperature to ranges (e.g., < 25 °C) that may facilitate, or perhaps
not affect, rotundone synthesis and accumulation. This would suggest that there are temperature-
based thresholds at which rotundone accumulation is either facilitated or inhibited.
Rotundone concentration can greatly vary within a single vineyard due to soil
characteristics and topography (Scarlett et al., 2014). These spatial variation trends were stable
across seasons, indicating that soil characteristics and topography might have measurable and
consistent, however indirect, effects on patterns of rotundone accumulation (Bramley et al.,
2017). It is unlikely that differences in grapevine vegetative vigor is driving spatial distribution
of rotundone accumulation, but these studies suggest that future work should include soil- and
topography-related characteristics in analyses of rotundone.
17
Despite the existing research focused on probing the relationships between rotundone and
various environmental factors, and to a lesser extent, viticultural variables, few studies have
sought to evaluate these associations in tandem (Geffroy et al., 2014; Zhang et al., 2015a).
Attempting to do so, and in turn creating a hierarchy of variables by degree of influence, would
be beneficial in further understanding the relationships that drive rotundone accumulation and
concentration in berries at harvest.
Advanced statistical methods are critical to assess the effects of interrelated factors on a
given variable. Indeed, multiple linear regression was applied to analysis of bitter pit incidence in
Honeycrisp apples in order to determine which nutrients and tree characteristics best predict the
disease (Baugher et al., 2017), while partial least squares regression was used to determine which
viticultural and environmental factors drive IBMP in Cabernet franc grapes (Scheiner et al.,
2012). Multiple linear regression was also used to assess the various factors that directly and
indirectly affect grape berry mass (Triolo et al., 2018). Further, applications of comparative
metabolomic and transcriptomic approaches to analysis of berry-derived metabolites typically
utilize multivariate statistics, including principal components analysis and various types of
discriminant analysis (Anesi et al., 2015).
Models have been developed for rotundone concentrations in Shiraz grapes and wines:
the percentage of post-veraison degree-hours above 25 °C (i.e., DH25) was used to construct a
predictive model for wine rotundone concentrations, while the Gompertz function was used to
construct a rotundone accumulation model that could predict berry rotundone concentrations and
illustrate accumulation patterns using calendar days since fruit set or cumulative DH above 25 °C
(Zhang et al., 2015b). However, due to the complexity of the reported relationships between
concentrations of aroma compounds and both climatic and viticultural variables, it is therefore
18
important to include both types of variables to validate the significance of previously reported
associations, as well as identify those that may have been overlooked.
The overarching goal of this thesis and the study discussed in Chapter 2 is to develop a
predictive model that assesses the degrees of influence that both viticultural and climatic factors
have on rotundone production in Noiret wine grapes. Given that both regional and microclimatic
factors might affect rotundone concentrations, this thesis aims to define these relationships at
both the vineyard (i.e., mesoclimate) and the fruiting zone level (i.e., microclimate). Specifically,
this thesis addresses two objectives that are central to the study described in Chapter 2: (i) to
identify the key climatic and viticultural variables that influence rotundone concentration in
Noiret grapes ; and (ii) to investigate the role of berry sunlight exposure and temperature on
rotundone accumulation in Noiret grapes at harvest. Until recently, rotundone-based research
exclusively focused on cold-tender Vitis vinifera varieties (e.g., Shiraz, Duras, etc.) that cannot
be reliably grown in many of the winegrowing regions of the northeastern United States. The
recent extraction of rotundone from Noiret (Vitis spp.), a variety released by Cornell University
in 2006 (Reisch et al., 2006), offers an opportunity to explore the dynamics of rotundone
production in another Vitis species and confirm if previously observed relationships with
varieties of Vitis vinifera parentage grown in warmer climates also exist with one of Vitis hybrid
parentage.
Findings from this work can also provide a basis for developing management
recommendations for Noiret wine grape growers throughout the Northeast and Midwest U.S., so
that growers can produce grapes with rotundone concentrations that match consumer
preferences. Moreover, results from this study could be useful for other economically relevant
varieties grown in the northeastern and midwestern United States where rotundone has been
19
identified (i.e., Grüner Veltliner). Taken altogether, this study seeks to advance understanding of
rotundone accumulation, as well as the dissemination of information related to rotundone
concentration dynamics in Noiret wine grapes that can be used to assist the growth of the wine
industries throughout the cool-climate wine regions of the United States.
20
Chapter 2: Weather conditions during fruit ripening, vine nutrient status, and vine size
collectively influence rotundone concentrations in cool-climate Noiret wine grapes
2.1 Introduction
Many chemical classes of plant secondary metabolites produced within grape berries are
aroma-active (Waterhouse et al., 2016). Amongst these aroma molecules are a few that are
classified as impact compounds for their ability to impart a specific aroma to a wine or drive its
varietal character. Aroma impact compounds and their interactions are an essential component of
wine quality as they can contribute to pleasant or unpleasant sensory wine perception. A well-
documented example is 3-isobutyl-2-methoxypyrazine (IBMP), a secondary metabolite present
in wines made from several wine grape varieties, including Sauvignon blanc and Cabernet Franc
(Scheiner et al., 2012; Robinson et al., 2014). IBMP contributes an aroma of ‘green bell pepper’
which, when present in high concentration, is generally considered an undesirable wine attribute
(Scheiner et al., 2012; Robinson et al., 2014).
In 2008, the sesquiterpene rotundone (C12H22O) was identified as the impact compound
responsible for the key ‘black pepper’ aroma of Shiraz wines (Wood et al., 2008). Since its first
extraction from Australian Shiraz grapes and wine, it has been identified and extracted from
other red-fruited Vitis vinifera varieties across many wine-producing regions (i.e., Duras and
Gamay in France, Mourvèdre and Durif in Australia, and Vespolina from Italy; Wood et al.,
2008; Caputi et al., 2011; Geffroy et al., 2014) and to a lesser extent in white-fruited V. vinifera
varieties (Riesling and Grüner Veltliner in Austria, Italy, and Slovakia; Caputi et al., 2011;
Herderich et al., 2012). Most recently, rotundone was extracted from grapes and wine of a red-
fruited Vitis interspecific hybrid variety, Noiret (Homich et al., 2017).
21
Rotundone is a strong volatile impact compound, with a detection threshold of 16 ng/L in
red wine (Wood et al., 2008). It slowly accumulates mainly within the berry exocarp beginning
at veraison, and within a few weeks accumulates at a quicker rate before reaching a
concentration plateau late in fruit ripening phase (Geffroy et al., 2014; Zhang et al., 2015b;
Zhang et al., 2016). Wine consumers have positive perceptions of rotundone in most cases
(Geffroy et al., 2018). According to Geffroy et al. (2016a), ‘peppery’ wines are mainly
appreciated by wine connoisseurs, who are typically more willing to pay higher prices for a
bottle of wine when compared to consumers with less wine tasting experience and who drink
wine at a lesser rate. Although two sensory studies have reported varying degrees of anosmia at
20% (Wood et al., 2008) and 31% (Geffroy et al., 2018) of panelists, the presence of rotundone
in high concentrations is nonetheless economically important for growers of varieties that
include rotundone, like Shiraz or Duras.
The primary determinant of rotundone concentration in finished wine is the concentration
present in the grapes at harvest (Geffroy et al., 2014; Homich et al., 2017). Therefore, several
studies have focused on identifying factors responsible for rotundone accumulation in the fruit to
predict how seasonal or vineyard environmental conditions as well as grower practices might
influence the ‘peppery’ intensity of the resulting wine (for examples, see: Geffroy et al., 2014;
Zhang et al., 2015a; Zhang et al., 2015b; Bramley et al., 2017; Homich et al., 2017) . Similar to
other aroma impact compounds (e.g., IMBP, monoterpenes, and C13-norisoprenoids), rotundone
accumulation in grapes depends upon climatic factors. Rotundone accumulation in Vitis vinifera
and Vitis hybrid varieties was positively associated with cool temperatures (i.e., cool vintages or
cool sites) (Herderich et al., 2015; Homich et al., 2017). Additional research revealed that the
relationship between rotundone concentration and temperature is spatially structured within an
22
individual grape cluster. Cooler portions of the cluster tend to accumulate higher concentrations
of rotundone than those exposed to higher temperatures (Zhang et al., 2015a).
The relationships between rotundone and other weather parameters, such as precipitation
and solar radiation, were also investigated. Grapes grown in shade, whether due to vineyard row
orientation, position of a cluster within the canopy or of a berry within an individual cluster, had
higher concentration of rotundone when compared to grapes grown with higher solar exposure
(Herderich et al., 2015). The increased rotundone concentrations were attributed to the direct
effect of solar radiation, to the increased temperatures of berries with high sun exposure, or
perhaps their combination. Moreover, within the same grape variety, rotundone concentration at
harvest was higher during a wetter season, as compared to a drier season (Geffroy et al., 2014).
Less clear is the influence of cultural practices on rotundone accumulation. The timing
and severity of fruiting zone leaf removal, a popular canopy management strategy, has been the
most studied, because it influences fruit sun exposure and temperature. However, the
manipulation of the fruiting zone microclimate has yielded contrasting effects with regards to
rotundone accumulation in the fruit, likely because of the different weather conditions of the
experimental sites and phenological timing of leaf removal. Additionally, most studies have
explored relationships between rotundone and a given weather or plant variable (e.g., ambient
temperature, solar radiation, vine water status) without incorporating multiple variables into a
single, cohesive analysis. However, combinations of environmental and viticultural factors might
operate in tandem to determine rotundone concentration in the fruit and ‘peppery’ intensity of the
wine. Understanding the relative importance of these variables on rotundone concentration
within a given system may help clarify which vineyard sites or viticultural management methods
are more conducive to producing wines with a desired level of pepperiness.
23
This study sought to address this knowledge gap and incorporate a multitude of
environmental, viticultural, and physiological data into a single study to assess which variables
had the greatest influence on rotundone concentrations within Noiret wine grapes. Given that
rotundone was only recently extracted from Noiret, this study also aims to further our
understanding of these relationships within the context of cool-climate Vitis hybrid production.
The objectives of this study were twofold: (1) to identify the key climatic and viticultural
variables that influence rotundone concentration in Noiret grapes using 7 vineyards with varying
weather conditions; and (2) to investigate the relationships between fruit sunlight exposure, berry
temperature, and rotundone accumulation in Noiret grapes at harvest.
2.2 Methods & Materials
2.2.1 Experimental Design
The study was conducted in 2016 and 2017 at seven Noiret (Vitis hybrid cross of
NY65.0467.08 and Steuben) vineyards located in Pennsylvania (n = 3) and New York State (n =
4), U.S. (Figure 1). The three Pennsylvania vineyards included three commercial vineyards
located in State College (Site 1), Falls (Site 2), and North East (Site 3; Table 1). In New York
there were two commercial vineyards in Portland (Site 4) and Branchport (Site 5), and two
research vineyards at the Cornell University AgriTech in Geneva (Site 6; Site 7). Sites 5, 6 and 7
were in the Finger Lakes American Viticultural area (AVA).
The vines were trained to two different training systems (Table 1). At site 1 all the vines
were trained to a bilateral high wire cordon (HWC) at a height of 1.8 m, whereas at sites 2, 3,
and 4 all the vines were trained to a bilateral cordon vertical shoot positioned (VSP) system at a
height of 0.9 to 1.1 m. Both training systems were used at sites 5 and 6 in separate vineyard
blocks, with a section of the vineyard trained to HWC and another to VSP. Further information
24
regarding vineyard age, vine and row spacing, rootstock, soil series classification, and basic soil
texture characteristics is summarized in Table 1. Disease, pest, and canopy management
practices (e.g., shoot thinning, shoot training, hedging) were performed by the grower cooperator
in accordance with standard commercial practices for hybrid Vitis cultivars in the eastern U.S.
(Wolf, 2008).
Figure 1. Map of vineyards chosen for the study. A dark red circle was imposed at the geographical coordinates of each study site, with the two circles representing sites 5 and 6 overlapping due to close geographical proximity.
25
Table 1. Location and vineyard information for the Noiret sites used in the multivariate analysis. Site Treatment Location Rootstock Spacing
(m/row x m/vine)
Traininga system
Vineyard ageb
Soil seriesc
1 C State College, PA 101-14 Mgt 1.83 x 2.44 HWC 10 Hublersburg silt loam 1 LR State College, PA 101-14 Mgt 1.83 x 2.44 HWC 10 Hublersburg silt loam 2 C Falls, PA Own-rooted 1.83 x 2.44 VSP 15 Lordstown channery silt loam 2 LR Falls, PA Own-rooted 1.83 x 2.44 VSP 15 Lordstown channery silt loam 3 C North East, PA Own-rooted 1.83 x 2.44 VSP 7 Chenango gravelly silt loam 3 LR North East, PA Own-rooted 1.83 x 2.44 VSP 7 Chenango gravelly silt loam 4 C Portland, NY Own-rooted 1.83 x 2.44 VSP 16 Chenango gravelly loam 4 LR Portland, NY Own-rooted 1.83 x 2.44 VSP 16 Chenango gravelly loam 5 C Branchport, NY 101-14 Mgt 1.83 x 2.44 HWC 7 Valois gravelly silt loam 5 LR Branchport, NY 101-14 Mgt 1.83 x 2.44 HWC 7 Valois gravelly silt loam 5 C Branchport, NY 101-14 Mgt 1.83 x 2.44 VSP 14 Langford-Erie channery silt loam 5 LR Branchport, NY 101-14 Mgt 1.83 x 2.44 VSP 14 Langford-Erie channery silt loam 6 C Geneva-RS, NYd Own-rooted 2.70 x 3.60 HWC 9 Honeoye loam 6 LR Geneva-RS, NY Own-rooted 2.70 x 3.60 HWC 9 Honeoye loam 6 C Geneva-RS, NY Own-rooted 2.70 x 3.60 VSP 9 Honeoye loam 6 LR Geneva-RS, NY Own-rooted 2.70 x 3.60 VSP 9 Honeoye loam 7 C Geneva-CN, NYe 101-14 Mgt 2.70 x 3.60 HWC 10 Honeoye loam 7 LR Geneva-CN, NY 101-14 Mgt 2.70 x 3.60 HWC 10 Honeoye loam
aHWC: High-wire cordon; VSP: Vertical shoot-positioned system. bVineyard age determined as number of years from planting to the beginning of the study (2016). cData sourced from the USDA National Resources Conservation Service (NRCS) Web Soil Survey, https://websoilsurvey.sc.egov.usda.gov. dVineyard located at Cornell University AgriTech Research South (RS) farm. eVineyard located at Cornell University AgriTech Crittenden (CN) farm.
26
At each vineyard, two panels (i.e., two sections of two-post spaces) of 3-4 contiguous
vines (1.83-2.70 m long row each) were selected for data collection. The two experimental units
were randomly assigned to either a control (C; fruiting zone non-defoliated) or fruiting zone leaf
removal treatment (LR). Noiret vines typically exhibit high vegetative growth with highly
shaded clusters (Vanden Heuvel et al., 2013). Fruiting zone leaf removal was used to maximize
the range of temperatures and cluster sun exposure across sites to better assess relationships
between rotundone concentration and these micrometeorological factors. Our goal was not to
assess differences between C and LR treatments, as the treatments were not replicated at any site.
Fruiting zone defoliation was imposed pre-veraison at Eichhorn-Lorenz (E-L) phenological stage
31, defined as “berry pea-size stage” (Coombe, 1995). Leaves were removed from each shoot
within the fruiting zone, from the basal node to that above the distal cluster. Leaves were
removed multiple times during both seasons, as a previous study suggested that pre-veraison leaf
removal coupled with maintained fruiting zone sun exposure may increase rotundone
concentration in Noiret fruit, as compared to highly shaded fruit harvested from vigorous vines
(Homich et al., 2017). Fruiting zone defoliation was implemented on the same experimental
vines during the 2016 and 2017 seasons.
2.2.2 Site-specific weather conditions
Vineyard air temperature, rainfall, and photosynthetically active radiation (PAR) were
recorded at 15-minute intervals with HOBOÒ weather sensors and dataloggers (Onset Computer
Corporation, Bourne, MA) at sites 1, 2, 3, and 4, starting on June 23 and ending on October 31 in
2016, and starting on May 1 and ending on October 31 in 2017. At the other sites, the same data
were obtained from Network for Environment and Weather Applications (NEWA) weather
27
stations (http://newa.cornell.edu). A NEWA weather station was located at site 5 and within 0.71
and 1.57 km from site 6 and 7, respectively.
As HOBOÒ weather stations only measured solar radiation from 400 to 700 nm (PAR),
regression between PAR and solar radiation was calculated. Briefly, a wide-spectrum (measuring
a total wavelength range of 300-1100 nm) silicon pyranometer was added to the HOBOÒ
weather station at site 1 to record solar radiation and PAR concurrently. The 30-minute average
PAR was linearly related to solar radiation (y = 0.5099x – 0.0302; r2= 0.96; n = 477) and used to
convert PAR values to solar radiation (µmol/m2/s to W/m2) for the four HOBOÒ weather
stations. Concurrently, NEWA-sourced solar radiation data was converted from Langley units to
W/m2 to have comparable values across all sites.
Several mesoclimatic (i.e., site specific) parameters were calculated for each site (Table
2). Seasonal growing degree days (GDD) were calculated from May 1 to harvest using 10 °C as a
baseline (GDD = [(maximum temperature + minimum temperature)/2] – 10). Additionally,
cumulative GDD were calculated from the onset of veraison to harvest (GDDv) for each site.
To assess seasonal solar radiation, described here as ‘vineyard solar-hours,’ total solar-hours
(SH800), expressed as the total number of hours that exceed 800 W/m2 (average hourly solar
radiation > 800 W/m2) were calculated for each site from May 1 to harvest for 2016 and 2017.
Rationale for using 800 W/m2 as a threshold is based on the reasoning that the value indicates
full-sun ambient vineyard conditions. Total solar-hours were also calculated from veraison to
harvest (SH800v).
2.2.3 Fruiting zone weather conditions
At each site wireless temperature data loggers (iButton Fob, Model DS9093Fl,
Embedded Data Systems, Lawrenceburg, KY) were used to record air temperature at 20-minute
28
intervals in the fruiting zone of C and LR vines throughout the 2016 and 2017 seasons. Two
sensors were placed within each experimental unit at the trellis wire closest to the fruiting zone,
and averaged data from both sensors in each experimental unit was calculated. Post-veraison
berry flesh temperature was measured at site 1 from September 16 to October 5, 2017, from E-L
36 “berry with intermediate Brix values” to E-L 38 “berries harvest-ripe” on two randomly
chosen clusters from each experimental unit. For each cluster, five 12.7 mm hypodermic
thermocouple probes (Model HYP1-30-1/2-T-G-60-SMP-M, Omega Engineering, Stamford,
CT) were inserted into a berry at different locations within a cluster (top-east, top-west, mid-
west, bottom-east, bottom-west). All 20 thermocouples were connected to a data logger unit
(CR6, Campbell Scientific, Logan, UT) and berry flesh temperature was continuously measured
and logged at 20-minute intervals throughout the measurement period. Linear regression was
used to fit berry flesh temperature data to the air temperature data for both the LR and C
treatments (LR: y = 1.2034x – 2.4302, r2 = 0.98, n = 96; CON: y = 0.6802x + 2.3627, r2 = 0.98, n
= 96). The regression equations were used to estimate berry temperature for all the other sites for
both seasons.
Post-veraison vineyard thermal time (sensu Zhang et al., 2015b) was calculated as degree
hours (DH) index based on estimated berry temperatures. The percentage of post-veraison
average hourly temperatures that fell within 10-15 °C, 15.1-20 °C, 20.1-25 °C, 25.1-30 °C, 30.1-
35 °C, 35.1-40 °C, and >40.00 °C were calculated (DH10, DH15, DH20, DH25, DH30, DH35, and
DH40, respectively). Each DH index was calculated as:
DHx = [(number of hours between T1 – T2 from veraison to harvest / number of hours
between veraison and harvest) *100];
29
where x is the base temperature of the DH range, T1 is the lower threshold temperature, and T2
was the upper threshold temperature. Degree hours were calculated to confirm a previously
reported negative relationship between rotundone concentration in grapes and fruiting zone air
temperatures between 25 and 30 °C, in addition to exploring the nature of this relationship across
other DH ranges (Zhang et al., 2015a). Due to a large period of missing data, it was not possible
to calculate DH ranges for sites 1, 2, 3, 4, and 5 in 2016. Values were calculated for sites 6 and 7
in 2016, and all sites in 2017.
Enhanced point quadrat analysis (EPQA; Meyers and Vanden Heuvel, 2008) was
performed three times per season per site to assess canopy density and fruiting zone sunlight
penetration. Each year, EPQA was measured when leaves were removed for the first time (E-L
31, “berry pea-size stage”), again at 50% veraison (E-L 35, “veraison”), and at post-veraison
between E-L 36 “berries with intermediate Brix values” and E-L 37 “berries not quite ripe”
stages. Point Quadrat Analysis (PQA) was performed by inserting a thin metal rod into the
grapevine fruiting zone at 20 cm intervals perpendicular to the vine row for a total of 36 insertion
points per experimental unit (Smart and Robinson, 1991). PQA analysis was coupled with PAR
measured within 2 hours of solar noon on the same day, given full-sun conditions, using a LI-
250A quantum ceptometer (LI-COR Bioscience, Lincoln, NE). Four PAR measurements were
taken per vine within each experimental unit: ambient PAR was measured first within the row
with the ceptometer raised and facing skyward, followed by three within-canopy measurements
at different orientations (directly upwards, or 0° relative to the vertical canopy, 45° towards the
clusters, and lastly 45° away from the clusters) to capture the full variability of within-canopy
PAR. Within-canopy values were divided by ambient values to calculate the ratio of PAR
penetrating the canopy.
30
Characteristics related to canopy density and fruit sunlight exposure were then analyzed
using Canopy Exposure Mapping Tools (v. 1.7, freeware from J.M. Meyers, Cornell University,
Ithaca, NY; Meyers and Vanden Heuvel, 2008). The software was used to calculate leaf layer
number (LLN), occlusion layer number (OLN), percent interior clusters (PIC), percent interior
leaves (PIL), cluster exposure layer (CEL), leaf exposure layer (LEL), and leaf and cluster flux
availability (LEFA and CEFA, respectively).
2.2.4 Vine vegetative growth and yield components
At each commercial vineyard, harvest date was determined by the grower cooperator. At
harvest, all the clusters were weighed and counted; average cluster weight was calculated as the
total yield divided by the total number of clusters. Twenty clusters were randomly collected from
each experimental unit at harvest, stored at -20 °C, and later used for berry weight, chemical
composition, carbon isotope composition, and rotundone quantification analyses. Pruning weight
was measured during the dormant season, between February and March 2017 and 2018. All yield
and pruning weights were measured using a hanging scale with a 0.01 kg accuracy (Pelouze
7710, Rubbermaid, Inc., Huntersville, NC). Crop load was calculated as yield divided by pruning
weight (Ravaz index). In 2016, an early commercial harvest at site 5 resulted in loss of yield and
related data for the experimental VSP-trained vines; additionally, pruning data were lost for 2017
at site 2 due to mixing of pruned canes between C and LR vines.
2.2.5 Vine nutrient and water status
To assess grapevine nutrient status, 30 leaf petioles were randomly collected from each
experimental unit at veraison in 2016 and 2017. Petioles were sampled from the youngest,
mature, healthy leaves of primary bearing shoots, from both sides of the canopy (Wolf, 2008).
Samples were dried at 60 °C for 48 hours and submitted to The Pennsylvania State University
31
Agricultural Analytical Services Laboratory for macronutrient (N, P, K, Mg, Ca, S) and
micronutrient (Mn, Fe, Cu, B, Zn) analyses by acid digestion and ICP elemental analysis (Huang
et al., 1985).
A 200-berry sample was randomly taken from the frozen clusters collected at harvest for
each experimental unit to assess vine water status via carbon isotope composition (δ13C) analysis
(Gaudillère et al., 2002). Plant tissue δ13C is reported across a gradient of negative values, with
more negative values correlating strongly with higher water availability and pre-dawn water
potentials and less negative values with lower water availability and an increasing likelihood of
drought stress (Bchir et al., 2016). Among the plant tissues analyzed, δ13C of berries at harvest
showed the highest correlation with grapevine water status (Bchir et al., 2016). The 200-berry
sample was split into two subsamples, oven-dried for six days at 60 °C, frozen with N2 gas,
ground into a powder, and submitted to the Cornell University Stable Isotope Laboratory for EA-
IRMS analysis. The results were expressed as ‰ δ13C, or the difference in carbon isotope
composition of the grape sample relative to that of the Pee Dee Belemnite internal standard.
Carbon isotope composition was calculated as:
δ13C = [(Rg – Rpdb) / Rpdb] x 1000;
where Rg = 13C/12C ratio of the grape sample and Rpdb = 13C/12C ratio of the Pee Dee Belemnite
standard.
2.2.6 Fruit chemistry and rotundone analysis
In both years and for each experimental unit, fruit chemical composition (total soluble
solids [TSS], pH, and titratable acidity [TA]) was measured on a 100-berry sample selected from
the frozen clusters collected at harvest. Frozen berry samples were thawed in a water bath at 60
°C and crushed for juice chemistry analysis. Total soluble solids were measured using a hand-
32
held refractometer (Master, Atago USA, Inc., Bellevue, WA) and pH using a benchtop pH-meter
(Orion Star A111, Thermo Fisher Scientific, Waltham, MA). Titratable acidity was assessed
using an autotitrator (G20, Mettler Toledo, Columbus, OH) on a 10 mL juice sample titrated to
an endpoint pH of 8.2 with a 0.1 M NaOH solution. Average berry weight was calculated using a
200-berry sample taken from the frozen harvested clusters.
Berry processing for rotundone extraction and analysis followed the protocol used by
Homich et al. (2017). A berry sample of 125 g was taken from each frozen grape sample per
experimental unit; the berries were deseeded, flash-frozen using N2 gas, and ground into a fine
powder using a kitchen blender (Sunbeam Products, Inc., Boca Raton, FL). Each sample was
then transferred to a glass jar and the headspace purged with argon gas for 30 seconds before
sealing the jar with the cap. Jars were stored at -80 °C until rotundone extraction. Rotundone was
extracted from 25 g of frozen grape powder spiked with 100 µL of d5-rotundone (516 µL/L in
ethanol) internal standard. Fifty mL of acetone were added to the spiked grape powder and the
solution was orbitally shaken at 225 rpm for one hour. Extracts were vacuum-filtered using a
0.10 µm glass fiber filter paper (Pall Corp., Port Washington, NY) and placed into a N blow-
down evaporator (RapidVap Vertex+ Dry Evaporator, Labconco Corp., Kansas City, MO) to
allow for evaporation of the sample solvent at 40 °C until an aqueous residue of ~20 mL
remained. The residue was diluted to 85 mL with model wine (12% ethanol, 5 g/L tartaric acid,
pH 3.2), split into two Teflon fluorinated ethylene propylene centrifuge tubes (Nalgene Nunc
International Corp., Rochester, NY) and centrifuged for 12 minutes at 4000 rpm (Homich et al.,
2017).
Solid phase extraction (SPE) was performed on the supernatant according to a modified
version of Siebert et al. (2008) protocol using a TELOS 12-position manifold (Kinesis Inc.,
33
Vernon Hills, IL) and 6 mL SPE cartridges (Phenomenex Strata styrene-divinylbenzene [SDB-L]
500 mg/6 mL tubes, Phenomenex, Torrance, CA). All SPE tubes were first conditioned with one
cartridge volume (about ~5-6 mL) of n-pentane/ethyl acetate (4:1) solution, one cartridge volume
of methanol, and a final cartridge volume of model wine. Following conditioning, the aqueous
berry sample extracts were loaded onto the SPE tubes and washed with a cartridge full of
ultrapure water and a final wash of n-pentane (2 mL, discarded). Elution was performed using
two 5 mL aliquots of n-pentane/ethyl acetate solution (9:1) per SPE tube and the eluted extracts
were collected in two 10 mL glass culture tubes per sample. A N blowdown evaporator was used
to evaporate the sample solvent until dryness. The dry residues were reconstituted in 0.5 mL of
pure ethanol (0.25 mL per sample tube), followed by an addition of 6.5 mL of ultrapure water
(3.25 mL per sample tube). The reconstituted extracts were transferred to 10 mL GC vials with
magnetic screwcaps and frozen at -80 °C until rotundone analysis.
To achieve high resolution separation between rotundone present in the grape sample and
the added, deuterated internal standard, and avoid peak interferences within chromatographic
analysis, rotundone analysis was conducted via solid phase microextraction multidimensional
gas chromatography-mass spectrometry (SPME-MDGC-MS) by the Australian Wine Research
Institute (AWRI, Glen Osmond, SA) according to the protocol outlined in Geffroy et al. (2014).
2.2.7 Data analysis and multivariate model construction
Data analysis was performed using SAS statistical software (v. 9.4, SAS Institute, Cary,
NC). Relationships between all measured variables (Table 2) were first evaluated visually using
SAS’s PROC GPLOT; PROC CORR was then used to assess linear correlations between
rotundone concentration and the 21 variables presented in Tables 3, 4, and 5. Variables that were
correlated with rotundone (Pearson’s coefficients > 0.5, p < 0.05) were again plotted using
34
PROC GPLOT to assess linearity. PROC REG was used to develop a series of multiple linear
regression models and subsequently identify a subset of variables that could be used for a
predictive model.
Multiple linear regression models were developed with combined data for 2016 and
2017, as the number of observations per year (n =16 for 2016, n =18 for 2017) were insufficient
for regression analysis by year. Models were first constructed using three selection options,
including FORWARD selection (a=0.1), BACKWARD elimination (a=0.1), and STEPWISE
selection (a=0.1). The resulting models were compared, considering the coefficient of
determination (r2), the adjusted r2, Mallow’s conceptual predictive criterion (Cp), and mean
square error (MSE).
The RSQUARE option in PROC REG was used to request all possible regressions, and
all possible combinations of variables were evaluated using r2, adjusted r2, Cp, MSE, Bayesian
information criterion (BIC), and Akaike information criterion (AIC). A set of candidate models
were selected to evaluate model diagnostics with the R, INFLUENCE, VIF, and COLLINOINT
options in PROC REG. Influence statistics generated by the INFLUENCE option (Hat Diagonal
statistic, CovRatio Statistic, DFFITS, DFBETAS, and PRESS) were used to test for influential
observations within the data set. Variance inflation indices were requested using the VIF option.
The COLLINOINT option generated collinearity diagnostics, including eigenvalues, condition
indices, and eigenvalue-associated variance values for each variable. Using this method, a
parsimonious final predictive model (i.e., a predictive model with high explanation and the
fewest necessary variables) was selected for predicting rotundone concentration in Noiret grapes.
The same statistical approach was used to identify the fruiting zone weather variables
listed in Table 2 that had the greatest influence on rotundone concentrations at harvest. Results
35
from this regression analysis are not intended for predictive use, but for determining which
micrometeorological conditions (e.g., continuous fruiting zone berry temperature or fruiting zone
sun exposure measured three times) had the strongest influence on rotundone concentrations.
36
Table 2. Vine and climate measurements recorded during 2016 and 2017 to predict rotundone concentration in the fruit at harvest.
Vine Metrics Climate Production Metrics Nutrient and Water Statusa Mesoclimateb Microclimatec
Yield Nitrogen Temperature Air temperature Cluster number Phosphorous GDD Berry temperature Cluster weight Potassium GDDv CEFA
Berry weight Magnesium Rainfall LEFA
Pruning weight Calcium Rainfallv DH10
Crop load Berry δ13C Solar radiation DH15
Juice soluble solids SH800 DH20
Juice pH SH800v DH25
Juice titratable acidity DH30 DH35 DH40
aBerry δ13C = Ratio of 13C:12C measured in grape berries at harvest. bGDDv = Veraison-to-harvest growing degree days; Rainfallv = Veraison-to-harvest rainfall; SH800 = Seasonal solar-hours above 800 W/m2; SH800v = Veraison-to-harvest solar-hours above 800 W/m2. cCEFA = Cluster exposure flux availability; LEFA = Leaf exposure flux availability; DH10 = Percentage of veraison-to-harvest degree-hours between 10.1 and 15 °C; DH15 = Percentage of veraison-to-harvest degree-hours between 15.1 and 20 °C; DH20 = Percentage of veraison-to-harvest degree-hours between 20.1 and 25 °C; DH25 = Percentage of veraison-to-harvest degree-hours between 25.1 and 30 °C; DH30 = Percentage of veraison-to-harvest degree-hours between 30.1 and 35 °C; DH35 = Percentage of veraison-to-harvest degree-hours between 35.1 and 40 °C; DH40 = Percentage of veraison-to-harvest degree-hours greater than 40 °C.
37
2.3 Results
2.3.1 Site-specific weather conditions
Overall, the 2016 growing season was warmer and drier than the 2017 season (Table 3).
Seasonal heat accumulated from May 1 to the day of grape harvest was higher in 2016 for all the
seven sites evaluated; GDD averaged 1512 ± 41 in 2016 and 1386 ± 64 in 2017. There was less
variation in heat accumulation between sites in 2016 than in 2017: in 2016, GDD varied from
1438 (site 5) to 1551 (site 3), while in 2017 GDD ranged from 1292 (site 1) to 1468 (site 4). In
2016, the sites with the lowest SH800 were sites 6 and 7 (259), while that with the highest SH800
was site 3 (1043; Table 3). In 2017, sites with the lowest and highest SH800 were site 5 (712) and
site 4 (1054), respectively. Most sites exhibited low variation in SH800 between the two years
except for sites 5, 6, and 7, located in the Finger Lakes AVA: site 5 had 37% higher SH800 in
2017 when compared to 2016, while SH800 was almost four times higher at sites 6 and 7 in 2017
compared with 2016.
Although 2017 was overall a cooler season, GDD from veraison to harvest were higher in
2017 as compared to 2016 in six out of the seven sites (Table 3). Inter-site variation in GDDv
was lower in 2016 compared to 2017. GDDv ranged from 249 (site 5) to 360 (site 1) in 2016 and
from 253 (site 1) to 457 (site 4) in 2017. Similarly, the veraison-to-harvest period was sunnier in
2017 than in 2016, except for sites 1 and 2, where site 1 was cloudier in 2017 and site 2 was
similar across both years (Table 3). In 2016 there was greater inter-site variation in SH800v as
well; SH800v ranged from 0 (sites 6 and 7) to 244 (site 1) in 2016, and 120 (site 5) to 284 (site 4)
in 2017.
Cumulative rainfall was higher in 2017 for all sites except for sites 3 and 4 (Table 3).
Seasonal rainfall ranged from 237 (site 6) to 476 mm (site 2) in 2016; in the following year it
38
ranged from 302 (site 3) to 636 mm (sites 6 and 7). The sites located in the Finger Lakes AVA
region (sites 5, 6, and 7) had the lowest rainfall in 2016, but the highest in 2017.
2.3.2 Fruiting zone weather conditions
The DH range with the highest proportion of hours between veraison and harvest was
within the 15.1 to 20 °C range (DH15) for all experimental units except for one LR unit at site 6
(HWC) in 2016, the C experimental unit at site 7 in 2016, and the LR unit at site 4 in 2017. The
DH range with the highest proportion of hours for the first unit was DH10, while DH10 and DH15
had the same percentage of hours for the second unit , and DH20 for the third unit, respectively
(Table 4). All C experimental units were cooler than the LR counterparts at each site, as they had
Table 3. Weather data measured for all the experimental sites in 2016 and 2017. Year Site GDD GDDva Rainfall
(mm) Rainfallvb
(mm) SH800
c SH800vd
2016 1 1526 360 319 86 868 244 2 1474 312 475 120 1026 213 3 1551 278 382 110 1043 169 4 1547 307 395 162 1011 189 5 1438 249 287 73 520 68 6 1523 293 237 96 259 0 7 1528 291 238 97 259 0
2017 1 1292 253 495 35 873 176 2 1417 341 512 81 949 213 3 1401 375 301 16 974 234 4 1468 457 384 197 1054 284 5 1305 271 515 84 712 120 6 1405 320 635 79 995 191 7 1412 327 635 79 996 216
aGDDv = Veraison-to-harvest GDD. bRainfallv = Veraison-to-harvest rainfall. cSH800 = Seasonal solar-hours above 800 W/m2. dSH800v = Veraison-to-harvest solar-hours above 800 W/m2.
39
higher DH10 and DH15 values and lower DH25, DH30, DH35, and DH40 for all the units that have
data (Table 4).
Overall, within-treatment variation tended to be lower at higher temperatures (Table 4).
At low temperatures (DH10) within-treatment variation across sites was greater for C than LR
units (19.41% to 31.50% for C, vs. 14.63% to 24.68% for LR). Similarly, within-treatment
variation at the higher temperature range (DH30) tended to be greater for C than LR units (0 to
4.97% for C, vs. 5.83 to 9.32% for LR).
Across all sites and years, except for the C unit at site 2 in 2017, fruit was exposed for
fewer hours to temperatures above 30 °C as compared to temperatures within the 25.1 - 30 °C
range (DH40 < DH35< DH30 < DH25). DH35 was below 1% for all the C experimental units. DH35
values for the LR experimental units ranged from 3.22% (site 6 VSP) to 3.65% (site 7) for 2016
and 1.37% (site 4) to 4.57% (site 1) for 2017. DH40 was negligible or below 1% across all sites,
indicating that post-veraison berry temperatures rarely exceeded 40 °C.
As expected, the percentage of ambient photon flux intercepted by both clusters (CEFA)
and leaves (LEFA) was greater for the LR units as compared to the C for all sampling dates and
both years (Table 5). During the ripening period, CEFA ranged from 0% (site 1) to 30% (site 3)
for the C, and from 34% (site 5 HWC) to 83% (site 2) for the LR in 2016. In 2017, CEFAr
varied from 0% (site 5 HWC) to 23% (site 4) for the C and from 23% (site 5 HWC) to 82% (site
4) for the LR units (Table 5).
40
Table 4. Fruiting zone temperature (DH) metrics for 2016 and 2017. Year Site Treatmenta DH10
b (%)
DH15 (%)
DH20 (%)
DH25
(%) DH30
(%) DH35
(%) DH40
(%)
2016 1 C NAc NA NA NA NA NA NA 1 LR NA NA NA NA NA NA NA 2 C NA NA NA NA NA NA NA 2 LR NA NA NA NA NA NA NA 3 C NA NA NA NA NA NA NA 3 LR NA NA NA NA NA NA NA 4 C NA NA NA NA NA NA NA 4 LR NA NA NA NA NA NA NA 5 C NA NA NA NA NA NA NA 5 LR NA NA NA NA NA NA NA 5 C NA NA NA NA NA NA NA 5 LR NA NA NA NA NA NA NA 6 C 29.47 31.04 18.85 10.93 4.97 0.62 0.00 6 LR 24.68 23.95 18.95 11.56 7.60 3.33 0.31 6 C 28.85 30.62 20.00 9.68 3.22 0.10 0.00 6 LR 22.70 24.58 17.60 12.50 6.66 3.22 0.20 7 C 31.50 31.50 20.22 9.24 2.84 0.00 0.00 7 LR 24.08 25.40 17.98 10.97 6.91 3.65 0.30
2017 1 C 21.01 38.13 18.30 11.86 0.67 0.00 0.00 1 LR 15.93 29.66 15.93 15.93 7.96 4.57 0.16 2 C 27.78 39.24 17.74 5.82 0.53 0.53 0.00 2 LR 20.87 28.58 18.27 10.39 9.32 2.24 0.53 3 C 19.41 43.49 24.89 10.56 0.00 0.00 0.00 3 LR 14.63 32.92 21.54 16.26 9.14 1.52 0.00 4 C 20.93 44.13 20.85 9.13 0.08 0.00 0.00 4 LR 14.87 21.12 21.50 14.71 8.64 1.37 0.00 5 C 30.04 33.11 16.11 8.44 0.54 0.00 0.00 5 LR 23.35 25.54 14.91 8.99 7.23 1.86 0.00 5 C 29.27 34.10 16.11 7.45 0.43 0.00 0.00 5 LR 23.68 26.09 15.57 9.10 6.03 1.53 0.00 6 C 27.22 36.38 15.46 7.96 0.64 0.00 0.00 6 LR 20.74 28.61 14.35 9.72 5.83 1.85 0.00 6 C 26.11 37.31 16.75 7.87 0.46 0.00 0.00 6 LR 19.07 29.62 16.48 9.25 6.01 1.48 0.00 7 C 27.06 36.01 15.36 9.14 1.09 0.00 0.00 7 LR 19.29 29.07 16.36 10.05 6.48 1.55 0.00
aC: Control; LR: fruiting zone leaf removal. bPost-veraison degree-hours calculated within temperature ranges of 10-15 °C, 15.1-20 °C, 20.1-25 °C, 25.1-30 °C, 30.1-35 °C, 35.1-40 °C, and >40 °C, respectively. cData unavailable due to temperature sensor error.
41
Table 5. Fruiting zone solar exposure metrics (LEFA; CEFA) for 2016 and 2017. Year Site Treatmenta LEFApb
(%) LEFAv
(%) LEFAr
(%) CEFAp
(%) CEFAv
(%) CEFAr
(%)
2016 1 C 33 32 15 3 18 0 1 LR 44 53 55 38 54 54 2 C 34 41 27 10 24 4 2 LR 62 62 62 73 83 73 3 C 41 32 35 14 30 23 3 LR 58 74 57 79 80 64 4 C 32 37 43 12 21 23 4 LR 58 58 48 58 48 48 5 C 21 19 21 4 8 10 5 LR 53 49 43 52 43 44 5 C 33 25 28 4 3 4
5 LR 48 46 47 55 34 39
6 C 27 20 26 8 10 14 6 LR 46 41 44 37 37 45 6 C 27 27 33 23 13 21 6 LR 56 39 45 55 39 41 7 C 35 34 33 26 9 19 7 LR 61 62 51 51 59 42
2017 1 C 23 15 12 3 2 2 1 LR 47 67 56 50 67 57 2 C 38 18 23 12 1 2 2 LR 73 48 53 63 57 60 3 C 28 39 29 15 15 16 3 LR 51 73 65 66 58 67 4 C 28 38 31 23 15 17 4 LR 69 62 60 82 73 74 5 C 20 15 26 0 0 21 5 LR 42 31 47 46 23 45 5 C 29 25 30 12 11 6 5 LR 38 42 45 45 31 36 6 C 24 14 17 5 3 8 6 LR 40 40 44 46 38 37 6 C 24 18 30 13 3 8 6 LR 40 53 48 37 42 56 7 C 24 14 24 9 0 5 7 LR 35 38 53 39 42 57
aC: Control; LR: fruiting zone leaf removal. bLeaf (LEFA) and cluster (CEFA) exposure flux availability, measured at berry pea-size stage (p), veraison (v), and during grape ripening (r).
42
2.3.3 Viticultural and physiological data
Mean values for yield parameters, pruning weight, crop load, and basic juice chemistry
are reported in Appendix A. As expected, there was large variation in production parameters
which was, at least in part, explained by the different management practices (i.e., shoot and
cluster thinning) used by the grower cooperators. Yield, for example, varied between 1.54 (site 2
LR) and 6.72 (site 5 C) kg/m of cordon in 2016 and from 1.88 (site 2 C) to 6.49 (site 5 LR) kg/m
in 2017. Basic juice chemistry (TSS, pH, TA) values were within the range of those reported for
Noiret in previous studies conducted in the northeast U.S. (Vanden Heuvel et al., 2013; Homich
et al., 2017).
Concentrations of the major macronutrients were at deficiency levels for some of the
sites, although visual symptoms of leaf nutrient deficiency were not observed except for Mg
(Appendix B). For example, concentration of leaf petiole N was at deficiency level (<0.80%) for
a few experimental units in 2016 (site 3 C; site 6) and for more sites in 2017 (site 2; site 4; site 6
LR HWC and VSP units; and site 7 LR). Phosphorus concentration for site 7 (2016, 2017) and
for the C unit at site 3 (2016) was in the deficiency range (<0.14%), while K concentration was
low for (<1.20%) sites 2 and 6 in 2017.
Conversely, there were two sites in 2016 (site 2; site 6 C) and more in 2017 (site 2; site 4;
site 6) that exceeded the recommended late-season P leaf petiole concentration (0.14-0.30%).
Potassium exceeded recommended concentrations (1.2-2.0%) at site 1 in both years, while at site
5 three out of the four experimental units had excessive concentration of K in 2016 (HWC C and
VSP) and 2017 (HWC C and VSP). Likely because of excessive K uptake, Mg concentration at
site 1 in both 2016 and 2017 was at a deficiency level (<0.35%), and visual Mg deficiency
symptoms were observed in both seasons.
43
Berry carbon isotope ratio, a proxy for vine water status, exhibited a moderate degree of
variation between both years (Appendix B). On average, δ13C ranged from -24.8, i.e., lower
water status (site 7 LR), to -29.4, i.e., higher water status (site 2 C), in 2016 and from -27.1 (Site
5 C) to -29.7 (site 6 C) in 2017. Large inter-annual variability was observed at sites 6 and 7,
while δ13C values at sites 1 and 2 remained nearly consistent across both years.
Berry rotundone concentration at harvest exhibited both inter-site and inter-annual
variation (Figure 2). Values ranged from 108.9 ng/kg (site 5 LR) to 830.2 ng/kg (site 1 LR) in
2016 and from 246.6 ng/kg (site 3 LR) to 1176.1 ng/kg (site 4 C) in 2017. The LR unit at site 2
was omitted from the analysis due to issues with sample analysis. Sites 5, 6, and 7 displayed
moderate to high variation between years in rotundone concentration for both treatments—on
average a 18.4% reduction for site 5, a 129% increase for site 6, a 148.2% increase for site 7—
rotundone concentration at site 4 was on average more than four times higher in 2017 when
compared to the previous vintage (Figure 2). Conversely, site 1 experienced the highest decrease
in rotundone concentration from the first to the second year, with 2017 concentration being less
than half that of 2016.
All C units except for site 1 had higher rotundone concentrations, between 0.69% and
64.4%, than the respective LR units in 2017; interestingly, site 1 had higher rotundone
concentrations in the LR unit for both years. Trends were less consistent in 2016: in addition to
site 1, rotundone was higher for LR units at site 5 HWC (5.2%) and site 7 (18.6%) as compared
to the C.
44
Figure 2. Berry rotundone concentrations at harvest 2016 and 2017 for each site and treatment.
45
2.3.4 Multiple linear regression analysis and model selection of a rotundone mesoclimatic
model
Scatter plots indicated that rotundone concentration was linearly related to K, Mg, Ca,
average berry weight, average cluster weight, GDD, GDDv, and rainfall, and both linearly and
quadratically related to SH800 and SH800v (data not shown). Pearson correlation coefficients (r)
were used to assess the strength of linear correlations for both 2016 and 2017 data, as well as for
the data pooled across the two years (Table 6).
The production variables most strongly correlated with rotundone in 2016 and for 2016-
2017 were berry weight and TSS, whereas rotundone was correlated with pH in 2017, but not in
2016. For the remaining production variables, except for cluster weight, the relationships in 2016
and 2017 were inconsistent and altogether were poorly correlated with rotundone concentration.
Interestingly, the relationship between δ13C and rotundone concentration was negative in 2016
and positive in 2017. Amongst leaf petiole nutrients, both Mg and Ca showed the highest,
negative correlations with rotundone.
Overall, weather parameters were better correlated with rotundone when measured from
veraison to harvest instead of for the whole growing season (Table 5). Specifically, SH800v2
exhibited the highest positive correlation with rotundone when data from the two years were
combined (r = 0.71, p <0.001), followed by GDDv. Both linear and quadratic terms were
included in the regression analysis for the variables SH800 and SH800v, as the quadratic terms
were better correlated to rotundone concentration.
46
Table 6. Pearson correlation coefficient representing the linear relationships between rotundone, production, vine water and nutrient status, mesoclimate, and microclimate parameters measured in 2016 and 2017. Rotundone Rotundone Variable 2016 2017 2016
& 2017 Variable 2016 2017 2016
& 2017 Production Site weathera
TSS -0.69 -0.21 -0.48 GDD -0.37 0.50 -0.11 pH -0.03 0.42 0.12 Rainfall 0.46 -0.52 0.09 TA 0.24 -0.46 0.11 SH800
0.38 0.35 0.42 Berry wt 0.46 0.72 0.56 GDDv 0.53 0.75 0.70 Cluster wt 0.46 0.21 0.22 Rainfallv -0.15 0.72 0.33 Cluster no. -0.40 0.12 -0.01 SH800v 0.64 0.60 0.62 Yield -0.19 0.17 0.07 Pruning wt 0.23 -0.22 0.02
Crop load -0.20 0.12 0.04 Vine water and nutrient status Fruiting zone weatherb δ13C -0.70 0.39 -0.33 DH10 0.13 -0.14 -0.30 N 0.56 -0.16 0.11 DH15 0.29 0.27 0.40 P 0.28 0.07 0.23 DH20 -0.21 0.58 0.35 K 0.47 0.20 0.28 DH25
-0.03 0.00 -0.05 Mg -0.56 -0.41 -0.50 DH30
-0.02 -0.23 -0.28 Ca -0.84 -0.10 -0.44 DH35
-0.10 -0.25 -0.30 DH40
-0.19 -0.15 -0.27 LEFAp -0.09 0.12 -0.07 LEFAv 0.04 0.12 0.04 LEFAr -0.17 -0.05 -0.08 CEFAp -0.24 0.07 -0.07 CEFAv 0.00 0.03 -0.01 CEFAr -0.21 -0.08 -0.11 aSH800 = Linear relationship for percent of solar-hours above 800 W/m2; GDDv = Veraison-to-harvest GDD; Rainfallv = Veraison-to-harvest rainfall; SH800v = Percent of veraison-to-harvest solar-hours above 800 W/m2. bDHx = Percent of degree-hours between 10.1-15 °C (DH10), 15.1-20 °C (DH15), 20.1-25 °C (DH20), 25.1-30 °C, 30.1-35 °C (DH30), 35.1-40 °C (DH35), and >40 °C (DH40); LEFA and CEFA = Leaf and cluster exposure flux availability, measured at berry pea-size stage (p), veraison (v), and during grape ripening (r).
47
Several candidate regression models were evaluated using different selection options, but
they did not provide the optimal model with any given predictor variables, as they were prone to
overfitting the data (Freund and Littell, 2006). Therefore, the RSQUARE option was used with
PROC REG to better fit the data and aid in model selection. The best three models out of all
models generated for one-, two-, three-, four-, five-, and six-variable models with the RSQUARE
option are shown in Table 7, including values for various statistical parameters used for model
selection. Analysis of r2, adjusted r2, Cp, AIC, BIC, and MSE values suggested that a six-variable
model may be overfitted (Freund and Littell, 2006) and that a lower-variable model may be
better-suited for predictive purposes (Table 7).
As more variables were added to the models, less and less variation was explained by
each additional variable; this is reflected in the 0.064 increase in adjusted r2 when a fourth
variable is added to the model, for example, when compared to the 0.017 increase when a fifth
variable is added (Table 7). A slight decrease in mean square error (MSE) between the best
fourth- and fifth-variable model indicated that each new variable added again explained a
diminishing proportion of variation, and that models with fewer variables may be better suited
for predictive purposes. Therefore, the first four-variable model was determined to be the best
candidate model for predictive purposes.
48
Table 8. Best regression model equation to be used for rotundone prediction. Year Model r2 Adj. r2 2016 & 2017 Rot. = -0.53 + 0.568 * P – 0.336 * Ca + 0.018 * crop load + 0.003 *GDDv 0.853 0.828
Table 7. The best multi-variable models evaluated during model selection for rotundone prediction. No. of Variables
Model variables r2 Adj. r2 Cpa AICb BICc MSEd
1 SH800v2 0.567 0.551 29.0 -95.6 -95.9 0.034 1 GDDv 0.512 0.494 35.8 -92.2 -92.8 0.038 1 SH800v 0.433 0.412 45.7 -87.8 -88.9 0.045 2 GDDv, Ca 0.703 0.680 14.0 -104.6 -104.0 0.024 2 SH800, SH800v 0.651 0.624 20.5 -99.9 -100.1 0.028 2 GDDv, TSS 0.641 0.613 21.7 -99.1 -99.4 0.029 3 GDDv, Ca, crop load 0.789 0.764 5.23 -112.6 -109.8 0.018 3 GDDv, Ca, pH 0.764 0.736 8.38 -109.3 -107.4 0.020 3 GDDv, Ca, pruning wt 0.761 0.732 8.82 -108.9 -107.1 0.020 4 GDDv, Ca, crop load, P 0.853 0.828 -0.68 -121.0 -113.9 0.013 4 GDDv, Ca, pH, pruning wt 0.842 0.815 0.71 -118.9 -112.6 0.014 4 GDDv, Ca, pruning wt, rainfall 0.839 0.812 1.05 -118.4 -112.3 0.014 5 GDDv, Ca, P, pruning wt, pH 0.873 0.845 -1.17 -123.3 -112.7 0.011 5 GDDv, Ca, P, pruning wt, TA 0.873 0.845 -1.16 -123.2 -112.6 0.011 5 GDDv, Ca, P, pruning wt, yield 0.872 0.845 -1.10 -123.1 -112.6 0.011 6 GDDv, Ca, P, pruning wt, rainfall, cluster no. 0.897 0.870 -2.24 -127.5 -110.9 0.010 6 GDDv, Ca, P, pruning wt, rainfall, yield 0.896 0.868 -2.11 -127.2 -110.8 0.010 6 GDDv, Ca, P, pruning wt, pH, cluster no. 0.894 0.865 -1.80 -126.5 -110.6 0.010 aCp = Mallow’s Cp statistic. bAIC = Akaike information criterion. cBIC = Bayesian information criterion. dMSE = Mean square error.
49
The best three- (GDDv, Ca, and crop load), four- (GDDv, Ca, crop load, and P), and five-
(GDDv, Ca, P, pruning weight, and pH) variable models were chosen as candidate models, and
diagnostic analyses were performed for each model. The analyses suggested an absence of
multicollinearity for the three and four-variable models chosen, while there was a low likelihood
of multicollinearity for the five-variable model (VIF value, tolerance threshold, and F-values are
reported in the Appendix C).
The diagnostic analysis indicated that either the three- (GDDv, Ca, and crop load) or the
four-variable model (GDDv, Ca, crop load, and P) were strong candidates for use as a predictive
model. When considering the diagnostic statistics, F-values, adjusted r2 values, and other model-
selection statistical criteria, and the variables included within the models, the 4-variable model
emerged as the strongest candidate for use as a predictive model due to its increased predictive
power and the low added complexity resulting from the inclusion of an additional variable. The
four-variable model including the variables GDDv, Ca, crop load, and P was thus chosen as the
best predictive model generated through SAS analysis (Tables 7 and 8).
2.3.5 Model validation of predictive rotundone mesoclimatic model
The same four-variable model (GDDv, Ca, crop load, and P) was selected by FORWARD
selection and the RSQUARE option as the optimal fit for the first validation data set (n = 20).
The model equation of the four-variable model was then used to generate predicted rotundone
concentrations for the second validation data subset (n = 16). Predicted rotundone concentrations
values of the validation data plotted against the actual observed values of the original data set
yielded a strong linear relationship (Figure 3). This supported the predictive power and accuracy
of the model. Despite the lack of an external data set for model validation, these two methods
50
allowed for the validation of the four-variable model and supported its use as a predictive model
for determining rotundone concentrations.
Figure 3. Relationship between observed and predicted rotundone concentrations (ng/kg) that were generated using SAS’ PROC SCORE.
51
2.3.6 Multiple linear regression analysis and model selection of a rotundone microclimatic
model
Rotundone concentration was poorly correlated with either sunlight or temperature
indices in the fruiting zone (Table 6). Scatter plots indicated weak negative linear relationships
between rotundone concentration and all DH indices except for DH15 and DH20, with rotundone
exhibiting positive relationships with DH15 in both years while exhibiting a negative relationship
with DH20 in 2016 and a positive relationship in 2017. There was not a clear visual linear trend
between rotundone concentration and CEFA or LEFA for any of the three sampling dates.
Pearson correlation coefficients supported these visual interpretations, as the r values for all DH
indices were low and relationships were insignificant except for DH15, and DH10 when
considered a = 0.10 (DH10: p = 0.14; DH15: p = 0.04; DH20: p = 0.08; DH25: p = 0.80; DH30: p =
0.17; DH35: p = 0.15).
Relationships between rotundone concentration and DH indices varied between vines
with highly shaded clusters (C) and vines exposed to fruiting zone leaf removal (LR). The
indices DH10, DH15, and DH30 were strongly correlated with rotundone concentration for C vines
(r = -0.61, p = 0.03; r = 0.83, p = < 0.000; r = -0.60, p = 0.03), whereas the highest correlating
DH index was DH10 for LR vines (r = -0.57, p = 0.05). Rotundone concentrations of C vines
were negatively correlated with all DH indices, except for DH15, DH20, and DH40, which was not
calculated as berry temperature was never above 40 °C for the C units. However, rotundone
concentration was positively correlated with DH20, DH25, and DH30, but negatively with the
remaining DH indices (DH10, DH15. DH35, DH40) for the LR vines.
Rotundone concentration was poorly correlated with the percentage of sunlight reaching
the leaves (LEFA) or the clusters (CEFA) in the fruiting zone (Table 6). The highest correlation
52
between CEFA, LEFA, and rotundone was when EPQA parameters were measured during fruit
ripening. However, relationships were not strong or significant (CEFAr, p = 0.52; LEFAr, p =
0.61). Different trends emerged when the data was split by treatment: C treatment rotundone
concentration was most highly correlated with LEFA at veraison (r = 0.43, p = 0.08) and CEFA
measured before veraison (r = 0.32, p = 0.20); interestingly, for LR vines both LEFA and CEFA
measured during the fruit ripening period correlated the highest with rotundone concentration
(LEFAr: r = 0.37, p = 0.15; CEFAr: r = 0.43, p = 0.09).
A variety of different candidate models were evaluated using PROC REG and the
FORWARD, BACKWARD, and STEPWISE selection options. A three-variable model was
generated using FORWARD selection as a candidate model (DH15, DH30, and CEFAp) with an
r2 of 0.54 and an adjusted r2 of 0.47. Selection using BACKWARDS elimination generated a
three-variable candidate model (DH10, DH30, CEFAp) with an r2 of 0.57 and an adjusted r2 of
0.51; STEPWISE selection generated the same model. Given the discrepancies in model
selection among these three selection options, the RSQUARE option was used to validate these
selections through comparison of r2, adjusted r2, Cp, AIC, BIC, and MSE values. Results
indicated that a three-variable model (DH10, DH30, and CEFAp) was the best candidate model
with an r2 of 0.57 and an adjusted r2 of 0.51. Further diagnostic analyses reaffirmed the strength
of the three-variable model (results from diagnostic analyses are presented in Appendix D).
Further analysis of model residuals supported the three-variable model as the best
candidate model when compared to a two-variable model for explaining the variation in
rotundone concentrations at harvest due to fruiting zone temperature and sunlight conditions.
Therefore, of the fruiting zone-level weather variables, post-veraison DH10, DH30, and CEFA
measured before veraison explained the most variation in rotundone concentrations (Table 9).
53
Table 9. Best model for explaining fruiting zone weather influence on rotundone concentrations. Year Model r2 Adj. r2 2016 & 2017 Rot. = 0.972 – 0.021 * DH10 – 0.114 * DH30 + 1.230 * CEFAp 0.574 0.511
54
2.4 Discussion
Rotundone concentrations are highly correlated to temperatures and solar radiation
during the ripening period
The primary objective of this study was to identify the key climatic and viticultural
variables associated with rotundone concentration in Noiret wine grapes within the northeast
U.S. Multiple linear regression analysis indicated that post-veraison climatic variables at the
vineyard scale were the strongest predictors of rotundone concentration in Noiret wine grapes at
harvest. Specifically, heat accumulated from veraison to harvest (GDDv) and post-veraison
vineyard solar time (SH800v) were most strongly, and positively, correlated with rotundone
concentration. These variables, when compared to season-long climatic variables, are better
predictors of rotundone concentrations and reaffirm the importance of measuring weather
parameters during the ripening period when rotundone is accumulating in the berries (Zhang et
al., 2015b).
Based on previous literature, we expected that in a cool-climate region with a short
growing season, i.e., the northeast U.S., higher temperatures and solar radiation during fruit
ripening would have decreased and not increased rotundone concentrations. Indeed, wine
rotundone concentrations were negatively and exponentially associated with GDDv, DH25, and
vineyard solar exposure for wines made from Australian Shiraz grapes (Zhang et al., 2015b).
However, Zhang et al. (2015b) calculated post-veraison GDD across 15 seasons at the same
vineyard, while we compared 7 sites with variable weather conditions across two years. The
range of GDDv and solar exposure values reported in our study are wider than those reported in
Zhang et al. (2015b). For example, their post-veraison GDD ranged from about 300 to 408 and
post-veraison solar exposure from 14.2 to 22.8 MJ/m2 day across all 15 seasons. At our sites
55
GDDv varied from 249 to 457, while post-veraison average daily solar exposure means had a
wider range too (7.6 to 21.1 MJ/m2). In addition to being cooler, some sites used in this study
also had lower solar exposure during fruit ripening.
A pattern of regional grouping emerged. Sites in the Finger Lakes AVA (i.e., sites 5, 6,
and 7) were consistently grouped as sites with lower post-veraison solar exposure, heat
accumulation, and rotundone concentrations than most of the other sites. Similarly, sites along
the eastern shore of Lake Erie (i.e., sites 3 and 4) generally exhibited high post-veraison solar
exposure and heat accumulation, but had variable rotundone concentrations between years. This
data suggests that though environmental variables may interact in the northeast U.S. to influence
rotundone concentrations, it is unclear if there is a regionality to rotundone concentrations.
Rotundone concentrations at harvest can be affected by the length of the ripening period.
Rotundone can reach peak concentrations around 44 days following veraison and thereafter
decrease slightly, indicating that an optimal time for harvest may exist that maximizes
concentrations (Geffroy et al., 2014). In our study, the sites with the longest ripening periods
(site 1: 47 days in 2016; and site 4: 52 in 2017) also had the highest rotundone concentrations,
while at site 1 a shorter ripening period in 2017 compared to the previous vintage corresponded
with lower rotundone concentration at harvest. This suggests that the length of the ripening
period might have had a positive role in influencing rotundone concentrations in cool climates.
However, other weather parameters still play a major role in determining rotundone
concentration at harvest, as sites with long ripening periods (i.e., 40 to 45 days), like those in the
Finger Lakes AVA, also had low post-veraison heat accumulation and solar exposure when
compared to the other sites.
56
Production variables do not explain variation in rotundone concentrations
While clear climatic trends were observed, there were few strong associations between
production parameters and rotundone concentrations. Berry weight was the viticultural variable
most strongly correlated with rotundone concentrations. Although berry weight was not selected
as a predictor variable for the multiple linear regression model, the positive relationship with
rotundone suggests that conditions that favor heavier berries might also be conducive to
increased rotundone concentrations. Berry weight was, indeed, significantly correlated with
seasonal GDD (r = 0.37, p = 0.020), GDDv (r = 0.55, p < 0.001), and rainfallv (r = 0.69, p = <
0.001). Inter-seasonal differences in rotundone concentrations were not well correlated with
differences in whole skin-to-juice ratio (Geffroy et al., 2014). Since rotundone is mainly
localized within the berry exocarp and not accumulated within the berry mesocarp (Takase et al.,
2016b), it is unlikely that berry weight had a direct effect on rotundone concentrations.
Concentration of soluble sugars in the fruit at harvest was significantly and negatively
correlated with rotundone concentrations. Considered a metric of grape ripeness, TSS is often
responsive to GDD and seasonal temperatures (Keller, 2015). Thus, as expected, TSS was also
significantly and positively correlated with GDD (r = 0.58, p < 0.001). Despite this correlation,
TSS was not correlated with GDDv (r = -0.22, p = 0.194). It is unclear if there is a direct
relationship between rotundone and TSS; seasons that are warmer and apparently conducive to
higher TSS are also more inhibitory to rotundone accumulation, reflecting the trends between
rotundone and season heat that have been reported for Noiret and other grape varieties
(Herderich et al., 2015; Homich et al., 2017).
Beyond berry weight and TSS, rotundone concentration was not significantly correlated
with any other production variables. A few studies have addressed the relationship between
57
rotundone, production parameters, and viticultural management methods with contrasting
conclusions. For example, thinning the crop at mid-veraison by 40% did not significantly impact
rotundone concentrations in Duras wines, when compared to an unthinned control (Geffroy et al.,
2014). Similarly, reducing crop by 50 and 90% did not significantly influence rotundone
concentrations in Shiraz berries, though the phenological timing of crop thinning was not stated
(Logan 2015). The effects of fruiting zone leaf removal are also inconsistent across studies
(Geffroy et al., 2014; Logan, 2015; Homich et al., 2017). Furthermore, vine vegetative growth
was not a major source of direct influence upon rotundone concentrations in Shiraz grapes
(Scarlett et al., 2014). The results from these studies indicate that direct associations between
rotundone and individual production parameters such as yield and crop load are not strong, and
that any influence due to vine vigor may be instead a result of manipulating fruit light
interception and exposure (Geffroy et al., 2015).
Despite the lack of correlation, crop load was included within the final predictive model.
Correlation is performed with single variables and provides information concerning linear
relationships, but multiple linear regression assesses the relationship between rotundone and a set
of variables. The relative importance of any given variable depends on the other variables in the
model. Thus, it is possible that crop load may explain considerable variation in rotundone
concentration not when it is analyzed by itself, but when included in a multiple linear regression
model. The inclusion of crop load nevertheless suggests that there may be a relationship between
crop load and rotundone that is worth further exploring.
58
Vine nutrients are consistently correlated to rotundone concentrations while water status
varies by year
Rotundone concentration was negatively correlated to seasonal vine water status, similar
to previous work (Geffroy et al., 2014; Geffroy et al., 2016b). However, our data implies that this
relationship breaks down when water is highly abundant and non-limiting. When the data are
separated by year, conflicting trends emerge: d13C correlated negatively and significantly (p =
0.004) in 2016, a relatively dry year, and positively and insignificantly in 2017, a relatively wet
year (p = 0.106). Based on our d13C data, it appears that rotundone is more sensitive to vine
water status during seasons with less precipitation, and particularly if vines may be experiencing
water deficit. This was indeed the case in 2016, when d13C reached values that would indicate
weak-to-moderate (-26 to -25‰) and moderate-to-severe (-25 to -24‰) water deficit
(Santesteban et al., 2015). Furthermore, a large part of New York, including the region where
sites 5, 6, and 7 were located (i.e., the Finger Lakes AVA), experienced severe drought
conditions during the 2016 season (Sweet et al., 2017), which suggests that experimental vines
might have experienced water deficit at some point during the season. Despite different annual
trends, the correlation between seasonal precipitation and d13C was moderate, negative, and
significant in both 2016 (r = -0.54, p = 0.028) and 2017 (r = -0.67, p = 0.002), suggesting that
d13C is indeed an effective index for integrated seasonal vine water status.
Our study is the second to evaluate relationships between rotundone concentrations and
grapevine mineral nutrition. A previous study reported inconclusive relationships between
rotundone concentrations and grapevine macro- and micronutrient concentrations when petioles
are sampled at 6 different time points, beginning at bloom and ending at harvest (Geffroy et al.,
2015). Here, rotundone concentrations were variably correlated with several macro-nutrients
59
(e.g., K, Mg, and Ca) measured at veraison. Both P and Ca were identified as significant
predictor variables and were ultimately included within the final rotundone prediction model.
However, it is unclear if these or other nutrients had direct influence on rotundone accumulation
in the berries. Instead, it is possible that seasons with environmental conditions that lead to
greater plant uptake of Ca and Mg, such as decreased precipitation, likely coincided with
decreased rotundone concentrations. Much like how d13C was strongly correlated with rotundone
in 2016, Ca was also correlated with d13C in 2016 (r = 0.77, p = < 0.001), indicating that Ca
concentration was positively correlated with and possibly influenced by decreasing vine water
status. This is a possibility given the role of Ca in plant responses to abiotic stress (Keller, 2015);
or this association could be linked to decreased uptake of K in drier seasons with reduced
precipitation, and a subsequent increase in Ca could simply be due to this decreased uptake of K.
Phosphorus was also positively associated with seasonal rainfall across the two years (r = 0.39, p
= 0.016), indicating that the seasons and sites with higher rainfall altogether had higher
concentrations of tissue P. The sensitivity of both Ca and P to seasonal rainfall and their
inclusion within the final model imply a potential positive relationship between rotundone and
seasonal rainfall, which would agree with previous literature (Zhang et al., 2015b).
The inclusion of Ca and P within the final model suggests a necessity to further
investigate the relationships between grapevine nutrition and rotundone concentrations.
Grapevine nutrient status was analyzed at veraison, as this is the phenological stage when
nutrients are the most stable and most accurately reflect plant nutrient status (Wolf, 2008). Given
the relationships observed between leaf petiole macronutrients and rotundone, it would be
interesting to analyze the nutrient composition of the berries themselves in relation to rotundone
concentration.
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A four-variable predictive model explained about 82% of rotundone concentration at
harvest
A four-variable multiple regression model with GDDv, Ca, crop load, and P, was the best
candidate model for predicting rotundone concentrations. Including more predictor variables that
explain little additional variation typically increase the r2 values, but overfitting the model causes
the regression coefficients to be unstable. This would result in a complex regression model that
may not have accurate predictive properties. Since a major objective of this study was to identify
the variables that have the strongest relationship with rotundone concentrations, the addition of
statistically unnecessary variables is detrimental because it obscures which variables have a
dominant influence on rotundone concentrations.
Another consideration of this study was that the final predictive model would need to be
simple enough for practical application and field experimentation: the model would need to
include variables that are both significant for explaining rotundone concentration, while also
easily measured by both wine grape growers and other researchers. The four variables selected
satisfy these requirements while maintaining a high degree of predictive power. Analysis of
model residuals and partial model validation further supported the strength of the chosen model,
though to increase confidence in the model it would be necessary to further validate it with an
external data set. This would ideally be done using additional weather, nutritional, production,
and Noiret rotundone data.
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Analysis of the fruiting zone temperature yielded weak correlations between rotundone
and temperature classes
A second objective of this study was to analyze the influence of micrometeorological
parameters on rotundone concentrations at the fruiting zone level. Rotundone concentrations
were not strongly correlated with any berry temperature class (DH) measured between veraison
and harvest, despite the strong correlation between rotundone and vineyard GDDv. We expected
rotundone concentration to be inhibited by air temperatures exceeding 25 °C, but rotundone was
not well correlated with DH indexes. Previous work reported positive associations between
rotundone concentrations in Shiraz grapes and DH10, DH15, and DH20, but negative associations
with DH25 (Zhang et al., 2015a; Zhang et al., 2015b). Here rotundone was negatively associated
with DH10 and DH25, and positive with DH15 and DH20, though the strength of these relationships
was much weaker compared to previously reported associations. The absence of strong
relationships between rotundone concentrations at harvest and DH indexes do not give a clear
understanding of the influence of these temperature classes, and therefore do not support
previously reported associations between rotundone and specific DH classes (i.e., DH25) (Zhang
et al., 2015a; Zhang et al., 2015b).
It is important to note that there are differences in terms of the ranges of rotundone
concentrations and percentage of hours in each DH class reported here, when compared to
previous reports. The range of rotundone concentrations reported here exceed the range reported
for Shiraz grapes (Zhang et al., 2015a); similarly, the ranges of total percentages of hours
between 10 and 15 °C (DH10) and 15.1 and 20 °C (DH15) also exceed those of Zhang et al.
(2015a). Despite this, previous claims that DH25 represents a critical temperature index for
rotundone concentrations were not corroborated here, and instead the strongest relationships
62
were seen with cooler temperatures (i.e., DH15) although relationships were nevertheless poor.
Whether or not this is reflective of how rotundone accumulates in Noiret specifically is unclear,
but it does suggest that cooler berry temperatures might be especially important for rotundone
accumulation when all temperature classes are compared.
Rotundone was poorly correlated to cluster exposure at all measurement points
Contrary to our expectation, cluster sunlight exposure, measured as CEFA, correlated
poorly with rotundone across the season, from treatment application to harvest. Previous work
suggested that increased fruit sun exposure may decrease rotundone concentrations, but these
studies did not directly assess fruiting zone solar exposure (Scarlett et al., 2014; Zhang et al.,
2015a; Zhang et al., 2015b). To date, a single study found that increasing radiation flux reaching
the clusters from pre-veraison to harvest increased rotundone in Noiret grapes and wines, in one
of the two study years (Homich et al., 2017). Differences in experimental design between studies
assessing defoliation-induced influence upon rotundone make comparison difficult because
treatments were imposed at different phenological stages. Homich et al. (2017) evaluated the
effects of leaf removal at E-L 31 “berry pea-size stage” (pre-veraison) and one week after E-L
35, “50% veraison.” Conversely, other studies applied leaf removal at mid-veraison (Geffroy et
al., 2014) or imposed shading treatments at veraison (Zhang et al., 2015a). Results from these
studies indicate that there is not a clear consensus on the effects of sunlight cluster exposure on
rotundone at the fruiting zone scale, and that more research is necessary using treatments
implemented at stages that are phenologically consistent across studies.
In our study, the sites in the Finger Lakes AVA had amongst the lowest post-veraison
solar exposure and warmth, matched by consistently low rotundone concentrations. Thus, even if
63
canopy defoliation leads to greater canopy porosity and fruiting zone solar exposure (i.e., higher
CEFA), it is possible that low levels of solar exposure and warmth at the vineyard-scale could
prove to be a stronger influence on rotundone concentrations. In other words, higher CEFA does
not necessarily guarantee higher fruiting zone temperature and solar exposure if these factors are
already reduced at the mesoclimatic level. The absence of a relationship between cluster
exposure and rotundone concentration at harvest might also in part be explained by the
methodology used to assess fruiting zone canopy density and sunlight availability. Fruiting zone
PAR was measured only three times throughout the season and only at solar noon, which may
not accurately measure canopy solar exposure.
It is also unclear how fruiting zone microclimate might affect the precursor to rotundone
formation, a-guaiene. Rotundone was positively correlated with a-guaiene concentrations during
the veraison-to-harvest period; moreover, higher a-guaiene concentrations also occurred at
cooler sites (Takase et al., 2016a). Therefore, it is reasonable to assume that factors that affect
the concentrations of a-guaiene would consequently affect rotundone concentrations.
Understanding how a-guaiene is influenced by viticultural or climatic factors may assist in
understanding why rotundone responds strongly to specific variables and not others and may
help explain what conditions within the fruiting zone would be most conducive to rotundone
formation. Incorporating an analysis of a-guaiene into further research on rotundone could
therefore help unravel the relationship between the two, in addition to the relationship between
these compounds and both viticultural management and climatic conditions.
64
Regression analysis indicates DH10, DH30, and early-season exposure are critical for
rotundone
The three fruiting zone microclimate parameters that best explained rotundone
concentration were DH10, DH30, and CEFAp (adjusted r2 = 0.51). This analysis suggests that
rotundone concentrations at harvest are sensitive to multiple berry temperatures ranges, and that
sensitivity to DH25 may not be as important for Noiret rotundone concentrations as they are for
Australian Shiraz, though it is important to note that this reported relationship was between
rotundone and DH25 calculated using fruiting zone temperature, not berry surface temperature
(Zhang et al., 2015a; Zhang et al., 2015b).
Though we have discussed larger, site-wide climatic dynamics that may broadly
influence rotundone concentrations (i.e., GDDv), this model suggests that berry temperatures
also may have an impact on rotundone concentrations. The negative relationships between
rotundone and both DH10 and DH30 suggest that rotundone might be limited by post-veraison
periods that have excessively cool (i.e., between 10 and 15 °C) or hot (above 30 °C)
temperatures. Despite these differences compared to previous literature, the selection of these
indices supports the claim that rotundone is inhibited by high berry temperatures, and instead is
positively correlated with cooler temperatures (DH15 and DH20). It is unclear, however, how this
relates to accumulation and berry concentrations of the chemical precursor, a-guaiene, and the
rate of enzymatic conversion to rotundone.
The selection of CEFAp was unexpected, as pre-veraison solar exposure was not strongly
related to rotundone concentrations at harvest. The underlying mechanism of this positive
association is uncertain but may reflect canopy microclimate conditions better suited to a-
guaiene and rotundone accumulation, as suggested by Homich et al. (2017). Rotundone
65
concentrations were higher in Noiret fruit harvested from vines that had higher pre-veraison
CEFA, when compared to non-defoliated vines (Homich et al., 2017). Indeed, in this study at
most sites the timing of pre-veraison CEFA measurements coincided with the highest levels of
solar exposure in each year (data not shown), with daily accumulated averages of solar exposure
tapering off in the post-veraison period. It is unclear if there is an indirect or direct effect of pre-
veraison solar exposure on rotundone concentrations, but the inclusion of CEFAp and not
CEFAv or CEFAr within this model indicates that there is a temporal or phenological component
to the influence of solar exposure that is critical for consideration and warrants further
inspection.
Although these three variables comprise a simple model with low predictive capabilities,
especially when compared to the linear model developed for site-wide rotundone prediction, it
provided further clarification as to which specific micrometeorological factors had the strongest
influence on rotundone at the fruiting zone-scale. This model also highlights the difficulty of
separating the influence of fruit solar exposure and temperature, and the necessity of further
work. Selecting two berry temperature-related variables and a cluster exposure variable suggests
the interactive and most likely synergistic relationship between these micrometeorological
variables.
Noiret berry rotundone concentrations measured here compare favorably to those reported
in other literature
Within this study, rotundone concentrations exhibited high variation both geographically
and inter-annually ranging from 108 ng/kg (site 6 HWC LR) to 1176 ng/kg (site 4 C). Rotundone
concentrations at one of the experimental sites (site 6 HWC) averaged 293 ng/kg in 2016 and
66
445 ng/kg in 2017. These values were an order of magnitude lower than those measured at the
same site in 2014 and 2015, which ranged from about 1280 ng/kg to 3450 ng/kg (Homich et al.,
2017). Within-site, inter-annual variation in rotundone is not unusual, as up to 40-fold
differences in rotundone concentration across vintages were reported for Australian Shiraz wine
grapes (Bramley et al., 2017).
Additionally, the post-veraison periods were longer in both study years (57 and 54 days,
for 2014 and 2015, respectively) when compared to the post-veraison ripening periods in our
study (40 to 45 days across both 2016 and 2017), suggesting that berries at site 6 might have
been harvested before they reached the late-season spike in rotundone concentration (Homich et
al., 2017). This also suggests that sites with low post-veraison warmth and solar exposure (i.e.,
sites 6 and 7) might require even longer ripening periods to accumulate the highest possible
concentrations of rotundone. More research is necessary in order to assess whether accumulation
patterns for rotundone can be delayed or accelerated if environmental conditions are suboptimal,
as has been mentioned elsewhere (Zhang et al., 2015b).
Overall rotundone concentrations presented (from 108 to 1176 ng/kg) here fall within the
middle range of those reported worldwide, including Australian Shiraz wine grapes. Early
research into rotundone showed concentrations ranging from 10 to 620 ng/kg (Wood et al.,
2008); Scarlett et al. (2014) reported values from 73 to 1082 ng/kg in samples from a single
season, with Bramley et al. (2017) reporting lower values overall in following seasons at the
same site. Across various experiments performed by Zhang et al. (2015a), berry values altogether
ranged from 15 ng/kg to 447 ng/kg. Interestingly, investigations into Japanese Shiraz yielded
rotundone concentrations ranging from an average of 1033 ng/kg to 2342 ng/kg, exceeding those
of Australian Shiraz and the Noiret results reported here (Takase et al., 2015). Conversely,
67
analysis of New Zealand Shiraz yielded a lower average range of rotundone across a three-year
study period (50 ng/kg to 162 ng/kg) that fell below that reported here in Noiret (Logan, 2015).
Despite the widespread focus on Shiraz worldwide, rotundone has been extracted from
grapes of other ‘peppery’ varieties in concentrations that both exceed and approximate Noiret
rotundone concentrations reported here. Noiret concentrations were much lower than those
measured in Italian Vespolina grapes in 2009 and 2010, as average values ranged from about
1420 ng/kg to 5440 ng/kg, while they remained more comparable to a range of average
rotundone concentrations from 540 ng/kg to 1910 ng/kg in Austrian Grüner Veltliner grapes
(Caputi et al., 2011). Takase et al. (2015) report low average concentrations for a handful of
other V. vinifera varieties, including Merlot (62 ng/kg), Sauvignon blanc (27 ng/kg), and
Cabernet Sauvignon (21 ng/kg), in addition to low concentrations for two popular varieties in
Japan, Koshu (60 ng/kg) and Muscat Bailey A (16 ng/kg). Noiret rotundone concentrations
reported here are much higher than these values.
It is of importance to note that rotundone has been extracted from two other interspecific
hybrids, Muscat Bailey A, a cross of V. labruscana cv. Bailey and V. vinifera cv. Muscat
Hamburg, and Koshu, a variety that contains V. vinifera and East Asian Vitis species parentage
(Goto-Yamamoto et al., 2015; Yamada and Sato, 2016). Interestingly, the parentage of Noiret
also contains Muscat Hamburg (i.e., ‘Black Muscat’), but it is currently unknown if Muscat
Hamburg produces rotundone or is the source of this trait in Noiret and Muscat Bailey A
(Reisch, 2006). The low concentrations produced by Muscat Bailey A suggest otherwise. Aside
from these studies, most other studies focusing on rotundone have almost exclusively measured
this aroma compound in wine, and not berries, making direct comparisons of results with prior
findings difficult.
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Implications for Noiret growers and manipulation of rotundone concentrations
Consumer acceptance of 'peppery’ wines is variable for Duras wines, with wine
connoisseurs preferring ‘peppery’ wines with high rotundone concentrations while amateur,
infrequent wine drinkers did not prefer wines with moderate or high rotundone concentrations
(Geffroy et al., 2018). It is likely that this mixed consumer approval can be extended to other
‘peppery’ varieties, like Noiret. Consequently, it becomes an economic incentive for growers to
be able to manage their grapevines so that the desired levels of rotundone can be reached in their
final wines. Although research efforts have not definitively parsed apart the effects of leaf
removal and other viticultural practices on the final concentrations of rotundone in grapes and
wine, in neither V. vinifera varieties nor Noiret, the utility of a predictive model cannot be
understated as it could be used to estimate rotundone concentrations from year to year.
The selected four-variable model includes Ca, P, crop load, and GDDv, variables that are
easy for a grower to measure, thus making it possible for a grower to calculate predicted
rotundone concentrations within a given year. Vine nutrient status is routinely analyzed by
growers at veraison; therefore, Ca and P can be easily assessed. Crop load cannot be calculated
until pruning weights are measured during the dormant growing season, but historic crop load
ratios can be substituted if growers want to calculate predicted rotundone concentrations prior to
harvest. Growers might also be able to estimate GDDv close to harvest, as GDD accumulate
slowly during the late-season period.
The broad understanding of climatic relationships with rotundone concentrations can
also assist growers with identifying new sites for Noiret production as well, as historic weather
data could be used to infer whether it has the potential to be a low- or high-producing rotundone
site, and relate this to local consumer preferences of Noiret wines. In theory, management
69
methods could then be adapted to maximize or minimize a site’s rotundone-producing potential,
depending on the goal at hand, much like existing strategies for managing IBMP in grapes and
wine (Scheiner et al., 2012). Although further research is necessary to understand how
viticultural practices affect parameters that are related to rotundone accumulation, our data
suggest that rotundone concentration in Noiret is heavily influenced by a few measurable and
well understood factors.
2.5 Conclusion
The aim of this study was to first evaluate the relationships between rotundone, climatic,
and viticultural variables at the vineyard-scale, and secondly between rotundone and
micrometeorological conditions at the fruiting zone. For Noiret vineyards in Pennsylvania and
New York, rotundone concentration was positively correlated with post-veraison vineyard
weather conditions, namely heat accumulation and solar radiation. Rotundone was also
negatively correlated with petiole Ca and Mg concentrations, and vine water status. We
developed a predictive model tailored to grower application with high predictive power. The
model includes four easily measurable variables: post-veraison GDD, Ca and P petiolar
concentrations at veraison, and crop load. Model validation supported the accuracy and fitness of
this model, but external validation is now necessary to further test the model. At the scale of the
fruiting zone, rotundone concentrations were poorly and negatively related to fruiting zone solar
exposure and estimated berry temperature. Research is necessary to further investigate the effects
of, and interplay between, berry temperatures and solar radiation. This study nevertheless
emphasizes the primary importance of specific climatic variables in determining final rotundone
70
concentrations in Noiret wine grapes at harvest, and the interactions that exist between climate,
viticultural, and physiological variables that also can influence rotundone concentrations.
71
Chapter 3: Conclusion
Vineyard and fruiting zone climatic conditions are two important variables influencing
aroma-active concentrations in wine grapes. Fruiting zone microclimatic adjustment is critical
for grape growers in humid and cool climates, as it reduces disease pressure, enhances grape
ripening, and potentially modifies the concentrations of desirable and undesirable aroma-active
compounds. Understanding how these practices, the climatic factors they affect, and larger
regional climatic trends influence rotundone accumulation is essential to produce quality,
‘peppery’ wine grapes, whether of Noiret or another rotundone-producing variety. This study
focused on better defining how rotundone accumulation in Noiret grapes is affected by climatic
and viticultural factors, as well as how these variables interact to influence rotundone.
We confirmed the importance of post-veraison climatic conditions on rotundone
concentrations, as rotundone concentrations in Noiret were positively correlated with both
vineyard heat accumulation (GDD) and solar radiation. Our results indicate warmer weather
following veraison is important to rotundone accumulation in a cool-climate region or vintage.
The positive relationships between rotundone concentrations and both seasonal and post-veraison
solar radiation conflicts with previous research, yet reaffirms the general importance of site-
specific conditions on rotundone accumulation. However, it is possible that this association may
be indicative of an interspecific hybrid-specific species response to solar radiation, though
further research is necessary in the northeast U.S. to assess whether these trends persist for V.
vinifera varieties as well.
Rotundone was poorly correlated with weather parameters measured at the fruiting zone
level. Despite the strong association between rotundone concentrations and site solar radiation, it
was weakly correlated with fruiting zone solar exposure. The strongest relationship was with pre-
72
veraison fruit solar exposure, indicating that the relationship between rotundone and early season
exposure warrants further investigation (Homich, 2016). This might be complemented by
concurrent analysis of the chemical precursor to rotundone, a-guaiene, to assess whether early
season conditions are modulating rotundone accumulation directly or indirectly via a-guaiene
concentrations. Our results indicate that berry temperatures between 10.1 - 15 °C and greater
than 30 °C are most influential on rotundone accumulation in Noiret wine grapes, when
compared to other temperature ranges (DH15, DH20, DH25, DH35, DH40). Specifically, rotundone
concentrations were negatively correlated with berry temperatures above 30 °C (DH30).These
insights into micrometeorological influence suggest that vineyard management practices that
manipulate the canopy microclimate are likely to influence rotundone concentrations in the cool-
climate regions of the Northeast and Midwest U.S.
Aside from the strong climatic influence, we report here for the first time a relationship
between rotundone concentration and grapevine base cation nutrient concentrations, specifically
Mg and Ca. Strong relationships between Mg, Ca, and rotundone might be explained by their
close associations with several environmental and weather parameters, like seasonal rainfall, and
not due to a direct effect of nutrient concentration on rotundone biosynthesis. Nonetheless, these
relationships warrant further investigation to better understand the cause of the relationship.
Similarly, given that the relationship between seasonal vine water status (d13C) and rotundone
varied between the ‘dry’ (2016) and ‘wet’ (2017) year, it would be worthwhile to analyze
rotundone accumulation in regions with inter-annual variation in water availability, such as in
Pennsylvania and New York. Altogether, our results indicate that associations between
rotundone concentrations and grape and vine physiological measurements are most likely due to
73
environmental and weather conditions, but further research is necessary to confirm this
hypothesis.
A major component of the study presented here was the development of a predictive
model for grape growers to use for predicting rotundone concentrations in Noiret wine grapes.
Using multiple linear regression analysis, we selected a four-variable model for predictive use
that included measurable and intelligible variables: Ca concentration, P concentration, crop load
ratio, and veraison-to-harvest GDD. With these four variables growers can use the predictive
model to estimate the concentration of rotundone that is expected in grapes from a given vine or
vineyard with high precision. This predictive model is tailored for use within the regional climate
of the northeast U.S. and for Noiret grapes and could be used by growers to identify vineyard
sites and vintages that have the potential for yielding ‘peppery,’ quality Noiret wines. Though
further model validation with an external data set is necessary to supplement the partial
validation that was reported here, this model shows promise for applied use. This study
emphasizes the importance of post-veraison climatic variables affecting rotundone
concentrations in cool-climate regions and how these variables interact with vine physiological
parameters and management to yield varying degrees of rotundone accumulation.
74
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Appendices
Appendix A. Fruit maturity and production metrics for all sites for 2016 and 2017 seasons. Year Site Treat-
menta TSS
(°Brix) pH TA
(g/L) Berry
wt (g)
Cluster wt (g)
Cluster (no./ vine)
Yield (kg/m)
Prun. wt (kg/m)
Crop load (kg/kg)
2016 1 C 18.4 3.61 6.36 1.99 205.9 14.5 2.98 0.82 3.64 1 LR 19.2 3.62 6.10 1.99 177.1 17.2 3.05 0.57 5.31 2 C 18.2 3.26 6.99 2.14 145.2 12.7 1.85 0.46 4.04 2 LR 20.2 3.33 6.35 1.95 109.5 14.1 1.54 0.67 2.30 3 C 20.0 3.46 6.30 1.97 181.4 21.3 3.87 0.54 7.13 3 LR 19.2 3.45 6.78 1.87 174.1 24.1 4.19 0.58 7.18 4 C 19.8 3.42 7.17 1.98 128.1 26.3 3.36 0.27 12.51 4 LR 19.4 3.43 7.37 1.93 137.4 26.8 3.68 0.40 9.18 5 C 19.8 3.41 7.92 1.92 152.3 44.2 6.72 0.43 15.56 5 LR 19.2 3.38 7.72 1.96 136.1 42.4 5.77 0.30 19.17 5 C NAb NA NA NA NA NA NA 0.70 NA 5 LR NA NA NA NA NA NA NA 0.70 NA 6 C 21.0 3.5 7.07 1.94 133.2 29.3 3.90 0.67 5.80 6 LR 20.4 3.42 6.39 1.98 101.5 34.5 3.50 0.49 7.19 6 C 19.6 3.47 4.97 1.88 124.0 32.5 4.03 0.56 7.23 6 LR 20.2 3.46 5.25 2.08 140.7 28.7 4.04 0.45 8.95 7 C 20.2 3.56 5.25 1.78 99.5 16.1 1.61 0.32 5.02 7 LR 20.8 3.52 5.69 1.61 104.1 25.2 2.62 0.27 9.83
2017 1 C 18.0 3.4 9.57 1.74 159.1 29.0 4.61 0.95 4.83 1 LR 18.7 3.37 6.86 1.64 127.3 29.0 3.69 0.45 8.23 2 C 16.9 3.24 7.75 1.96 112.2 16.8 1.88 NAc NA 2 LR 17.8 3.19 7.49 1.94 115.1 22.4 2.58 NA NA 3 C 19.2 3.46 7.21 1.68 146.9 38.3 5.62 0.81 6.93 3 LR 18.2 3.37 7.36 1.79 124.6 35.2 4.38 0.66 6.59 4 C 18.1 3.53 6.99 2.44 131.7 39.3 5.18 0.42 12.37 4 LR 18.0 3.62 7.62 2.20 136.4 30.1 4.10 0.45 9.18 5 C 17.1 3.45 8.77 1.79 133.2 30.6 4.08 0.39 10.43 5 LR 17.8 3.3 8.89 1.77 99.1 53.0 5.25 0.29 18.30 5 C 18.2 3.23 8.56 1.89 151.2 37.3 5.64 0.36 15.88 5 LR 18.0 3.39 8.99 1.80 125.4 51.8 6.49 0.26 25.28 6 C 19.1 3.39 8.31 2.01 144.5 26.2 3.79 0.93 4.08 6 LR 19.8 3.23 8.23 1.95 115.4 34.2 3.94 0.60 6.53 6 C 19.0 3.33 8.61 1.85 112.1 34.0 3.81 0.92 4.17 6 LR 20.4 3.41 7.84 1.76 112.3 39.9 4.48 0.61 7.35 7 C 19.6 3.38 9.02 2.00 121.4 32.5 3.95 1.04 3.78 7 LR 19.2 3.62 7.98 1.89 142.9 27.3 3.91 0.99 3.95
aC: Control; LR: fruiting zone leaf removal. bData unavailable due to commercial harvest of experimental fruit. cData unavailable due to commercial dormant pruning of experimental vines.
84
Appendix B. Nutrient concentrations and water status (via δ13C) of experimental units across all sites for 2016 and 2017. Year Site Treatmenta N
(%) P
(%) K
(%) Mg (%)
Ca (%)
δ13C (‰)
2016 1 C 1.00 0.19 2.61 0.26 1.06 -28.6 1 LR 1.07 0.24 3.14 0.21 1.08 -29.1 2 C 0.88 0.54 1.10 0.84 1.25 -29.4 2 LR 0.89 0.52 1.25 0.68 1.30 -28.3 3 C 0.75 0.13 0.93 1.32 1.82 -28.0 3 LR 0.85 0.15 1.07 1.03 1.82 -28.4 4 C 0.92 0.23 1.18 0.83 2.47 -26.2 4 LR 1.03 0.26 2.29 0.6 2.40 -26.3 5 C 1.06 0.29 2.74 0.60 1.63 -27.2 5 LR 0.97 0.24 1.78 0.82 1.76 -28.1 5 C 1.04 0.18 2.81 0.59 1.78 NAb
5 LR 1.08 0.23 3.23 0.58 1.91 NA
6 C 0.76 0.27 1.41 0.83 2.06 -26.2 6 LR 0.64 0.32 1.23 0.93 2.41 -27.2 6 C 0.69 0.53 0.70 1.35 2.45 -26.2 6 LR 0.70 0.37 0.82 1.18 2.28 -27.6 7 C 0.86 0.13 1.92 0.68 2.29 -25.2 7 LR 0.83 0.10 1.74 0.69 2.07 -24.8
2017 1 C 1.17 0.34 2.53 0.27 1.19 -28.5 1 LR 0.95 0.35 3.23 0.31 1.68 -28.8 2 C 0.75 0.59 0.79 0.78 1.33 -29.4 2 LR 0.72 0.62 0.82 0.91 1.72 -29.1 3 C 1.12 0.17 0.86 0.99 1.88 -27.3 3 LR 1.17 0.17 1.53 0.72 2.08 -27.9 4 C 0.78 0.38 2.04 0.39 1.99 -27.7 4 LR 0.76 0.46 2.48 0.30 1.99 -27.9 5 C 1.01 0.24 2.34 0.85 1.95 -27.1 5 LR 1.01 0.32 1.79 1.01 1.96 -28.2 5 C 0.91 0.19 2.11 0.54 1.83 -27.9 5 LR 0.92 0.26 2.62 0.58 2.01 -28.5 6 C 0.81 0.33 0.93 0.63 2.02 -28.7 6 LR 0.68 0.48 0.63 1.05 2.83 -29.2 6 C 0.67 0.52 0.83 0.94 2.14 -29.7 6 LR 0.72 0.49 0.53 1.15 2.51 -29.1 7 C 0.82 0.27 1.33 0.66 1.93 -28.4 7 LR 0.74 0.19 1.01 0.72 2.04 -29.6
aC: Control; LR: fruiting zone leaf removal. bData unavailable due to commercial harvest occurring prior to harvest of experimental vines.
85
Appendix C: Supporting details regarding mesoclimatic model diagnostic analysis
Multicollinearity analyses were also performed on the selected four- and five-variable
candidate models. Variance inflation (VIF) values were compared to a tolerance threshold of 6.8
for the four-variable model. All variable VIF were below the threshold (GDDv = 1.10; Ca =
1.06; crop load = 1.11; and P = 1.10) suggesting an absence of collinearity. The overall model
was statistically significant (p < 0.05), and had an F-value of 34.92. The highest condition index
value was for the variable P (1.45), followed by crop load (1.38). Additionally, P had a
moderately high proportion of variation value corresponding to crop load (proportion of variation
value = 0.53), and crop load with GDDv (proportion of variation value = 0.64), suggesting that
these predictor variables may not be well estimated.
Multicollinearity analysis of the five-variable model also suggests a low likelihood of
collinearity. A tolerance threshold of 7.87 was calculated for the five-variable model, and all VIF
values fell below this threshold (GDDv = 1.51; Ca = 1.17; P = 1.35; pruning weight = 1.18; pH =
1.59), suggesting a low likelihood of multicollinearity. The model was statistically significant (p
< 0.05) as well, and had a F-value of 31.71. The five-variable model yielded low condition
indices for all predictor variables but had the widest range of eigenvalues reported (from 1.59 to
0.35) from all three models, suggesting that this model has the highest likelihood of
multicollinearity (Freund and Littell, 2006). Furthermore, pH had the highest condition index
and very high proportion of variation values corresponding to itself and GDDv (0.75 and 0.68,
respectively), and pruning weight had a moderately high proportion of variation value
corresponding to Ca (0.52). These results altogether indicate that collinearity may be an issue for
the five-variable model.
86
Analysis of model residuals for all three candidate models was performed in order to
detect the presence of outliers, influential observations, and any violations of normality or
homogenous variance. PROC REG was used with the INFLUENCE option to analyze the
models for the presence of outliers and influential observations, using values generated for the
following statistics: RStudent, Hat Diagonal H, CovRatio, DFFITS, DFBETAS, and PRESS.
Values for the RStudent statistic, used to assess possible outliers, were compared to a calculated
t-distribution with 24 degrees of freedom (DF) for the three-variable model, 23 DF for the four-
variable model, 22 DF for the five-variable model (DF = n - m - 2, where n = the number of
observations within the model, and m = the number of independent variables within the model).
Hat Diagonal H values were used to assess the leverage of individual observations via comparing
values to a critical threshold of 0.27, 0.34, and 0.41, for the three-, four- , and five-variable
models, respectively (threshold, or hi, = 2*(m + 1) / n).
The CovRatio is used to assess whether the inclusion of a given observation within a
model provides the model with increased precision and minimized variance, or decreased
precision and greater variance. The calculated bounds used to assess CovRatio values were 1.41
for the three-variable model, 1.51 for the four-variable model, and 1.62 for the five-variable
model [Bound = (1 + 3*(m + 1)/n)]. Values generated for the DFFITS statistic are used to also
assess the influence of observations, with values greater than 0.74, 0.83, and 0.90 for the
respective three models indicating a high incidence of influence caused by a specific observation
(DFFITS threshold = 2 Ö[(m + 1)/n]). A threshold for DFBETAS is used to identify specific
observations of individual variables that may be influential, and the lower bound is the same for
all models: 0.371, calculated using the formula 2/Ön.
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Diagnostic statistics suggest that the three-variable model includes a few outliers and
influential variables. Four observations (9, 15, 20, and 29) were identified as being possible
outliers in the three-variable model via the corresponding residual and RStudent values.
Observations 23, 24, and 30 had respective h-values above the diagnostic threshold of 0.27 (h-
values were 0.37, 0.32, and 0.37, respectively) and were considered to have high leverage.
Observations 9 and 20 had CovRatio values of 0.44 and 0.40, far from 1.41, the calculated
bound. This suggests that these observations may be affecting the precision of the model;
furthermore, observations 9, 15, and 23 had DFFITS values exceeding the 0.74 threshold in
absolute value with values of 1.23, -1.36, and 1.08, indicating that these observations may also
be influential on the model. Analysis of DFBETAS values revealed that crop load as an
independent variable is responsible for the high influence of observations 15 and 23. These
analyses altogether indicate that observations 9 and 23 may be fit for removal from the three-
variable model.
Diagnostic analysis of the four-variable model also suggests the presence of outliers and
influential observations within the model. Residual analysis of RStudent values indicated that
observations 9, 15, 20, 23, and 29 are potential outliers within the data set. Additionally, h-values
for observations 23, 24, and 30 were 0.37, 0.34, and 0.37, and were all near or above the lower
bound of 0.34, indicating that these observations have high leverage. This mirrors the h-value
analysis of the 3-variable model. For the CovRatio analysis, values of observations 9 (0.99), 15
(0.57), 20 (0.51), and 29 (0.45) deviated from the CovRatio bound of 1.51, indicating that
observations 15, 20, and 29 have a large degree of influence on the model, and observation 9 to a
lesser extent. The DFFITS threshold of 0.83 was exceeded by observation values in absolute
value of observation 9 (1.21), while observation 15 appeared near the threshold (-1.37). This
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again provides further evidence for the high influence of observation 9. Values greater than 0.37
for DFBETAS were also used to identify specific variable observations that are influential: these
include observations 20 (-0.61) especially 23 (0.99) for GDDv, and observation 15 for Ca (0.92),
and observation 9 for P (0.86). These analyses again suggest that observation 9 may be fit for
removal from this model.
The five-variable model has the highest likelihood of containing outliers or influential
observations according to the results of diagnostic analyses. Residuals of the five-variable model
for observations 9, 15, 19, 23, 29 were large, and especially large for observation 20, indicating
that these variables may be outliers. A threshold of 0.41 was used to assess h-values, and only
observation 9 had an h-value that exceeded this threshold (h9 = 0.47), suggesting that it has high
leverage within the model. Analysis of CovRatio values using a bound of 1.62 identified the
same influential observations as the 4-variable CovRatio analysis, as well as some additional
observations: observations 15 (0.85), 19 (0.45), 20 (0.04), and 29 (0.86). Observations 9, 15, 19,
20, 23, 29 violated the DIFFITS threshold of 0.90 with respective values of -1.09, -1.13, 1.33, -
2.84, 1.22, and 1.12, increasing the likelihood that these observations had a high degree of
influence. Further analysis of the DFBETAS values for these observations revealed that
observations 9 and 15 for Ca were influential (0.79 and 0.54, respectively), observations 19, 20,
and 23 were highly influential for GDDv (0.71, -2.03, and 0.99 respectively), pH observations 20
(1.84) and 29 (0.95) were highly influential, and observation 20 (2.05) for P was also highly
influential. These results indicate that many observations and specific variables included within
the five-variable model are problematic and impart a high degree of influence upon the model.
Therefore, the five-variable model may be overfitted and less accurate than the other two
candidate models.
89
Comparatively, the three- and four-variable models have a lower likelihood of containing
outliers and influential variables when compared to the five-variable model. A comparison of the
sum of squared residual (residual SS) values with the predicted residual sum of squares (PRESS)
across the models can be used to further analyze models for the presence of outliers or influential
observations. For example, PRESS statistics that are increasingly larger than the residual SS
suggests the presence of influential observations and outliers. Residual SS values for the three-
variable, four-variable, and five-variable models were 0.45, 0.31, and 0.27, while PRESS statistic
values were 0.64, 0.50, and 0.52. Comparison of these values indicate that the differences
between the actual and predicted SS values within each candidate model were the same for the
three- and four-variable models (PRESS – ResidualSS = 0.19), and larger for the five-variable
model (PRESS – ResidualSS = 0.25). This provides further evidence that the five-variable model
has influential observations or outliers included within the model.
Diagnostic analyses indicate that the five-variable model is not the best model for
predictive purposes. Given the added complexity of the five-variable model when compared to
the three- and four-variable models and the limited increase in r2 and adjusted r2 between the
four- and five-variable models (0.85 and 0.82 for the four-variable model, compared to 0.87 and
0.84 resulting from an added variable in the five-variable model), only the three- and four-
variable models were selected for further analysis as candidate models. Partial regression
leverage plots (PRLP) were created alongside residual plots using PROC REG and the
PARTIAL option to graphically assess potential violations of homogenous variance and
normality assumptions. Generated plots indicated that both the three- and four-variable models
did not violate either assumption and reaffirmed the linearity of the relationships of GDDv, Ca,
cropload, and P with rotundone concentrations when measured at harvest. Thus, no
90
transformation of the data was required to improve the fit of the individual variables or satisfy
the assumptions of normality and homogenous variance.
Appendix D: Supporting details regarding microclimatic model diagnostic analysis
Two models were selected for diagnostic analyses to aid with final microclimatic model
selection: a two-variable model (DH30, and CEFAp), and a three-variable model (DH10, DH30,
and CEFAp). Diagnostic analysis was performed using the same methodology previously
mentioned in Appendix C. Collinearity analysis was first performed, and variance inflation (VIF)
values were analyzed for both models. All variable VIF values for the two-variable model
exceeded the calculated tolerance threshold of 1.97 (DH30 = 4.90; CEFAp = 4.90), indicating a
slight likelihood of collinearity, and the model was statistically significant (p < 0.000) with an F-
value of 10.26. Condition index values for the two variables were low, but the proportion of
variation value corresponding to CEFAp and DH30 was high (proportion of variation value =
0.946) while the eigenvalue for CEFAp was very low (0.107). This suggests that collinearity
does exist within the model.
For the three-variable model, two variables exceeded the tolerance threshold of 2.34
(DH30 = 5.04; CEFAp = 6.60), while the third did not (DH10 = 1.96). Again, this indicates a
slight likelihood of collinearity. The three-variable model had a smaller F-value than the 2-
variable model (F-value = 9.02), and was still statistically significant (p < 0.000). Condition
index numbers for the three variables were low as well, but the proportion of variation value
corresponding to CEFAp and DH30 was high (0.86) while the eigenvalue for CEFAp was very
low (0.090), indicating that this variable may be associated with collinearity within the model.
This is supported by the ranges of eigenvalues, as these values ranged from 0.107 to 1.892 for
91
the two-variable model and 0.090 to 2.43 for the three-variable model, indicating that the three-
variable model has a higher likelihood of including collinear variables (Freund and Littell, 2006).
Overall, these values suggest that collinearity most likely exists within the 3-variable model.
Analysis of model residuals was performed for both models to detect the presence of
outliers, influential observations, and any violations of normality and homogenous variance.
Studentized residual statistics (i.e., RStudent values) were compared to a calculated t-distribution
with 20 degrees of freedom (DF) for the two-variable model, and 19 DF for the three-variable
model (DF = n - m - 2, where n = the number of observations within the model, and m = the
number of independent variables within the model). For both models, observations 11, 23, and
24 were identified as possible outliers using the RStudent statistics.
Comparison of Hat Diagonal H values for all observations in each model with a model-
specific critical threshold (threshold, or hi, = 2*(m + 1) / n) revealed that two observations had
high leverage within each model. For the two-variable model, h-values were compared to a
critical threshold of 0.25 and observations 11 and 24 had values higher than this threshold (h11 =
0.37, and h24 = 0.35). Similarly, for the three-variable model observations 11 and 24 also had
higher h-values (h11 = 0.37, and h24 = 0.35) than the calculated critical threshold of 0.33. These
results suggest that these two observations have high leverage in both candidate models, and that
they may be unfit for retention in the final selected model.
The CovRatio was used to further assess observations and see which observations most
strongly affected the precision of the model and the overall model variance. Values for each
model were compared to model-specific CovRatio bounds that were calculated using the
following equation: critical bounds = [1 + 3*(m + 1)/n]. The CovRatio values for the two-
variable model were compared to 1.37, while the values for the three-variable model were
92
compared to 1.5. Observations 14, 17, and 23 had CovRatio values (0.75, 0.75, and 0.52,
respectively) that deviated the most from the critical threshold of 1.37 for the two-variable
model. Comparatively, for the three-variable model, the CovRatio values for observations 11
(0.60), 14 (0.79), 23 (0.45), and 24 (0.68) were the furthest from the threshold of 1.5. Since these
values were less than the threshold, it is thus likely that the inclusion of these observations in the
dataset would decrease model precision. Moreover, this analysis further supports the removal of
observations 11, 23, and 24 in particular, as multiple diagnostic analyses have identified these
observations as being problematic for model accuracy and precision.
Lastly, the two models were analyzed using DFFITS, DFBETAS, and PRESS statistics,
in order to better understand which observations may be influential and whether any outliers
exist. All DFFITS values for observations within the two-variable model were compared to a
threshold of 0.707, while all DFFITS values for observations within the three-variable model
were compared to a threshold of 0.816; if the absolute value of any DFFITS value exceeded
these thresholds, the observation associated with that value is considered an influential
observation. For the two-variable model, DFFITS values for observations 11 (1.68), 23 (1.56),
and 24 (1.62) all exceeded the calculated threshold of 0.707. Moreover, DFFITS values for
observations 11 (2.00), 21 (0.958) , 23 (1.54), and 24 (1.73) all exceeded the threshold of 0.816,
further supporting the possibility of these observations being influential.
Analysis of DFBETAS provided further clarification of the role of influential
observations in both candidate models, and which independent variables influential observations
were associated with. All DFBETAS values for both models were compared to a threshold of
0.408 (threshold = 2/Ön), with observations and independent variables considered influential if
the respective value exceeded this threshold. Observations 11, 23, and 24 were identified as
93
influential observations for both independent variables in the two-variable model: respective
DFBETAS values for DH30 were 1.46, 1.38, and 0.85, while respective DFBETAS values for
CEFAp were 1.58, 1.11, and 1.33. For the three-variable model, results were more variable:
observation 11 had DFBETAS values exceeding the threshold only for DH30 (1.68) and CEFAp
(1.53); observation 17 had DFBETAS values exceeding the threshold only for DH10 (0.580);
observation 21 had DFBETAS values exceeding the threshold only for DH10 (0.73) and CEFAp
(0.58); observation 23 had DFBETAS values exceeding the threshold for DH30 (1.20), DH10
(0.48), and CEFAp (0.65); and observation 24 had DFBETAS values exceeding the threshold
only for DH30 (0.89) and CEFAp (1.20). These results altogether indicate that observations 11,
23, and 24 are influential observations, and that removal of these observations may increase
model accuracy and precision.
The PRESS statistic was also used to evaluate both candidate models and further assess
for the presence of influential observations. For the two-variable model, the residual SS was
821766 and the PRESS was 1321835; additionally, for the three-variable model, the sum of
squared residuals was 690686, while the PRESS was 1271399. Comparison of these values
indicate that the differences between the actual and predicted SS values differ between models.
There is a larger difference between the actual and predicted SS values for the three-variable
model, which suggests that there is a stronger presence of outliers or influential variables within
the model than in the two-variable model. This reaffirms the findings of the previous diagnostic
analyses, and supports the removal of influential observations from the model.
Overall, diagnostic analyses indicate that there are influential observations in both the
two- and three-variable models. Given that these models were constructed with a low number of
observations per variable, however, removing any of the observations from the models is not
94
advisable. Further, despite the added complexity of the three-variable model due to the inclusion
of a third variable (DH10), this inclusion also helps explain a higher percentage of the overall
variation in rotundone concentrations. Indeed, the adjusted r2 value for the three-variable model
is 0.51 (r2 = 0.57), compared to an adjusted r2 value of 0.44 for the two-variable model (r2 =
0.49). The main objective of model construction using microclimatic variables was to identify
which variables best capture the variation in rotundone concentrations, and the inclusion of DH10
as a third regressor variable helps satisfy this objective. Lastly, though this model identifies a
subset of microclimatic variables that explain about 51% of observed variation in rotundone
concentrations, further data collection is necessary in order to validate this model and support
these associations.
Recommended