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IMPORTANCE OF CANOPY POROSITY INTO VINEYARD AND THE
RELATIONSHIP WITH THE GRAPE MATURITY. DIGITAL ESTIMATION
METHOD
IMPORTANCE DE LA POROSITE DE LE COUVERT VEGETAL DANS LE VIGNOBLE ET LA
RELATION AVEC LA MATURITE DU RAISIN. METHODE D'ESTIMATION
Mario de la FUENTE1*, Rubén LINARES, Pilar BAEZA and José Ramón LISSARRAGUE
1 Departamento de Producción Vegetal: Fitotecnia. Escuela Técnica Superior de Ingenieros Agrónomos. Universidad Politécnica de Madrid.
C/ Senda del Rey s/n, 28040, Madrid, Spain.
*Corresponding author: de la Fuente, M. phone: +34 915491137, Fax: +34 915491137, Email: mario.delafuente@upm.es
SUMMARY
In warm and dry climates, the use of porous systems should be required in order to allow a better leaf distribution inside the plant, causing
more space in the clusters area and enhancing determined physiological processes so in the leaf (photosynthesis, ventilation, transpiration) as
in berry (growth and maturation). Plant geometry indexes, yield and must composition have been studied in three different systems: sprawl
with 12 shoots/m (S1); sprawl system with 18 shoots/m (S2) and vertical positioned system or VSP with 12 shoots/m (VSP1). Total leaf area
increases as the crop load does, whoever surface area depends on to two factors: crop load and the training system (VSP vs. sprawl), which
can provide differences in leaf exposure efficiencies. The main objective of this study was to validate digital photography measurements
used to compare porosity differences among treatments and, as they affect plant microclimate and, therefore, yield and berry quality. Also,
all previous studied indexes (LAI, SA, SFEr) tended to overestimate the relationship between exposed leaf surface and porosity of each
treatment, but the use of digital method proved to be an effective tool in order to assess canopy porosity. Results showed that not positioned
and free systems (sprawl) scored between 25-50% more porosity in the clusters area than the fixed vertical system (VSP), which resulted in a
better plant microclimate for test conditions, mainly by improving the exposure of internal clusters and internal canopy ventilation. On the
other hand, higher crop load treatment (S2) showed a real increase in yield (16%) without any relevant change into must composition, even
improving total anthocyanin content into berry during ripening.
RESUME
Dans les climats chauds et secs, l'utilisation de systèmes poreuses devrait être nécessaire afin de permettre une meilleure distribution de la
feuille à l'intérieur de la vigne, causant plus d'espace dans la zone des grappes et l'amélioration des processus physiologiques déterminés ainsi
dans la feuille (photosynthèse, ventilation, transpiration) comme en raisin (croissance et maturation). Index de la géométrie des plantes,
rendement et doit la composition ont été étudiés dans les trois systèmes différents : système non positionnée avec 12 pampre (S1) ; système
non positionnée avec 18 pampre (S2) et le système de positionnement vertical ou VSP avec 12 pampre (VSP1). La surface foliaire totale
augmente avec la charge, la surface foliarire qui repose sur deux facteurs : charge et système de formation (VSP contre système non
positionnée), qui peut fournir des différences dans la feuille d'efficacités de l'exposition. L'objectif principal de cette étude était de valider les
mesures de photographie numérique utilisés pour comparer les différences de porosité entre les traitements et, car ils modifient le
microclimat des plantes et, par conséquent, rendement et qualité des baies. Aussi, tous les index étudiés précédentes (LAI, SA, SFEr) avaient
tendance à surestimer la relation entre la surface foliaire exposée et la porosité de chaque traitement, mais l'utilisation de la méthode
numérique s'est avérée pour être un outil efficace pour évaluer la porosité de la couvert végétal. Les résultats ont montré que les systèmes
non positionnés et libres (système non positionnée) a marqué entre 25-50 porosité plus dans le domaine des grappes que le système vertical
fixe (VSP), qui a donné lieu à un microclimat de meilleur plante pour les conditions de l'essai, principalement par l'amélioration de
l'exposition des grappes internes et ventilation interne canopée. En revanche, plus charge de traitement (S2) ont montré qu'une réelle
augmentation du rendement (16%) sans changement pertinent dans doit composition des moûts, même teneur en anthocyanes total
amélioration en berry au cours du maturite.
Key Words: sprawl, training system, porosity, canopy, grape composition.
Mots –Clés: système non positionnée, systèmes de conduit, couvert végétal, porosité, composition des raisins.
INTRODUCTION
The plant geometry and training system should be joined with a proper sunlight and temperature microclimate in
the clusters area and, also in the rest of the plant (Spayd, et al. 2002). In warm and dry climates is required the
use of porous systems that allow a better leaf distribution inside the plant, cause more space in the clusters area
and enhance several physiological processes so in the leaf (photosynthesis, aeration, transpiration) as in berry
(growth and maturation). Several authors have showed the relevance of 3D spatial measures to describe the
architecture of leaf plant (Schultz 1995; Mabrouk, et al. 1997; Gladstone and Dokoozlian, 2003), for adequate
canopy management and also, can estimate degree shading in clusters area (key factor in the berry ripening
development). Digital image analysis are common used in vineyards to estimate crop coefficients or radiative
balance models (Pieri, 2010). Porosity canopy measurement provides information on leaf surface distribution
along the shoot and its spatial situation into plant air system or canopy volume (Gladstone and Dokoozlian,
2003).
At harvest, the value of incident photosynthetic active radiation (PAR) inside the canopy is usually low (about
10% of total ambient radiation). The degree of canopy density changes that percentage and there is a positive
correlation among PAR, leaf area and density into clusters area (Dokoozlian and Kliewer, 1995). Sunlight, air
ventilation within canopy, temperature cluster and microclime is affected by exposure and radiation percentage
received during growth and maturation period. If the lighting inside clusters area decreases during berry state
development, berries will produce less solutes accumulation and also, polyphenols and anthocyanins too
(Dokoozlian and Kliewer, 1995). On the other hand, too much cluster lighting can cause excessive higher
temperatures into cluster areas and produce degradation for these compounds (Spayd, et al. 2002).
Likewise, there are many factors which having important effects into plant microclimate and are related with
training system, so that is the reason for comparing different training systems in this study. Sprawl is a porous
training system with alternating spur-pruned uniform distribution along horizontal cordon that caused spacing
clusters zone. VSP on the other hand, is a vertical, rigid positioning system that shoots and leaf area caused a
linear clusters zone, usually closely spaced. Results show porosity differences among three treatments and will
be compared and related with other typical canopies measures, such as leaf area index (L.A.I.), surface area
(S.A.) or point quadrat method which are more complicated to take than a picture.
MATERIAL AND METHODS
This field experiment was conduced over two consecutive seasons (2006 and 2007) into an experimental trial in
Toledo (Spain), under a fine clay-sandy soil (Palexeralf, Soil Survey Staff, 2003) with a 50 cm depth clay
superficial horizon (50-55% of clay). The weather conditions were Mediterranean semiarid (Papadakis, 1966).
The cultivar was Syrah grafted on 110R and spaced 1.2 m inside the NO-SW orientated rows and 2.7 m between
rows. Irrigation system drippers (3·l h-1) were spaced 1.2 m along the planting line and the amount applied was
equal for all treatments. Climatic conditions of 2006 and 2007 were significantly different being the 2006 a
campaign extremely warm while 2007 did not. Differences can be observed mainly in growing degree day
accumulated (2000 vs. 2525 GDD), rainfall (168 vs. 246 mm) and in evapotranspiration reference (1211.1 vs.
1064.6 mm; Eto) index too. Trial was designed with three treatments placed into four blocks at random and each
experimental plot consists of 20 control plants separated by rows and vines edge. Three treatments studied, in
order to assess the impact of training system and crop load, were: i) VSP1, Espaldera or vertical positioned
system (VSP) with 12 shoots/m crop load, ii) S1, Sprawl with 12 shoots/m crop load and iii) S2, Sprawl with 18
shoots/m crop load. (50% crop load more than VSP1 and S1).Vines were spur pruned and trained to a bilateral
cordon at a height of 1.40 m to the floor. The sprawl system had a single couple vegetation wires from 0.4 m to
the basal wire and they opened 0.6 m between wires. VSP system had a couple wires from 0.3 m to the basal
wire and a higher wire at 1.5 m to basal wire.
The measures of the total leaf area index (LAI; m2 leaf·area m-2 soil) were taken in accordance with a
modification of the method described by Carbonneau (1976) according to Sánchez de Miguel et al., (2010). Five
shoots were measured in two vines by treatment and block. Surface area (SA; m2 external foliar·m-2 surface soil)
was calculated based on inner geometric parameters of each system. Five measures were taken at two different
heights, in two vines by treatment and block. In VSP treatment, the area was likened to a parallelepiped and
measures were taken from vegetation lateral wall (total vegetation height, basal vegetation zone and fruiting
zone) and the width of vegetation row. For both sprawl system treatments were calculated by estimated
perimeter with flexible tape and vectorial graphic design program (Cad 2008®) to calculate the circular section of
plant wall along the row.
On the other hand, surface exposed real (SFEr) was calculated such as Carbonneau (1995) described as result of
multiply radiation intercepted percentage by vegetation cover and total leaf area developed (LAI) by the plant. It
took data from radiation throughout ripening in different day hours (8, 12 and 16 s.t.) for this variable calculated.
Spatial aerial parts distribution of the plant were measured by Point Quadrat (PQ) method described by Smart
(1985) in the same vines that previous vegetation measures did. Real porosity percentages were calculated
through processing tool photography program (Adobe Photoshop CS3®). Photographs were taken by night and
with the only flash lighting from the digital camera (to well discriminate gaps from leaves). Pictures from
vegetal wall were taken between two consecutive vines for each block (the same vines used for the geometric
measures) and with a distance same as the width of between plant lines (2.7 m).
A reproductive yield study was done during harvest (30/08/2006 and 05/09/2007) in ten previously selected
plants for each treatment and block. Cluster number, average cluster weight, average berry weight, berry number
per cluster and yield (kg m-1) were calculated individually for each harvested plant. A digital field scale was used
for experimental data measures. Also, at harvest a 100-berry sample per single plot was collected to follow 100-
berries weight (g), SST (ºBrix), pH and phenol maturity according to Glories (2001) method, so final values
corresponded to harvest date of each year.
RESULTS AND DISCUSSION
Canopy measures
In both years (Table I), total leaf area of greater crop load treatment (S2) was higher than the other two
treatments with lesser load (VSP1 and S1) between 27-33% over all vegetative cycle, as was likely, emphasizing
differences before stopping vegetative prior to ripening. There were not significant differences between
treatments in relation to growth of secondary shoots. These differences show that total leaf area is directly
related to the level of crop load left in the plant, and did not cause any increase in secondary leaf area between
low load treatments (VSP1 and S1) compared to the higher load treatment (S2). Surface area exposed (Table 1)
at maturity showed that sprawl treatments obtained higher values than VSP (10-30% compares to S treatments in
2006 and 2007 respectively in 2007) when the crop load effect made them open up the top vegetation centre.
However, the best indicator of the relationship between vegetation amount and surface porosity into the canopy
is the ratio called surface real exposed (SFEr; Carbonneau, 1995). Results measured at 8, 12 and 16 s.t. reflect
(Table 1) a greater exposure (during all day) of treatment S2 in relation to the other treatments, reaching much
higher values compared to VSP (increase between 36.4% and 68.4%, P<0.001). S1 obtained intermediate values,
so that it was clear, a combined effect between crop load and training system caused an increase of canopy plant
volume, decreasing crowed vegetation cover and increasing leaf exposure (14-29%) of open systems versus rigid
vertical positioning system. The division of the canopy in more vegetation planes can increase yield and crop
quality (Bordelon et al. 2008), and also, the quality of wines.
These differences are very interesting in warm climates, where one of the main goals is not cause leaf and
clusters overexposure in order to prevent premature senescence and berry overripening process respectively (de
la Fuente, 2009). Increasing load and with a not positioned free exposure, the plant shows a higher overhead
opening and exposed a higher number of leaves to solar radiation but during less time, because flow radiation
unit per leaf is smaller, so senescence process is not caused easily. Also, total leaf area is more efficient because
is working with a larger number of inner leaves than rigid vertical systems, where the number of leaves layers is
usually minor, increasing the leaf exposure to solar radiation, but lowering the undesirable effect of premature
senescence in basal leaves due to excessive heat.
Porosity
Aerial parts plant positional study by PQ showed lower vegetation density (Table II) in S treatments, with
around 2 and 3 extra-layers more than VSP1. Several authors (Gladstone and Dokoozlian, 2003; Kliewer et al.,
2000 and Vanden Heuvel et al., 2004) obtained values of the leaf layer number in previous trials and considered
at appropriate values of LLN in cluster area between 1.5 to 2 for trellis and 3-4 for other open systems with high
density. Data from trial treatments are within the optimal definition intervals (2-4 LLN) for each training systems
calculated by previous researchers.
Likewise, while there were a higher number of clusters in S treatments comparing with VSP system (differences
among 1.0-1.7 and 0.8-1.6 in 2006 and 2007 respectively, P<0.05), there were a higher percentage of clusters not
subjected to direct radiation (24.9-19.0% more for S2 and S1 in 2007, P<0.01). Several autors obtained
differences in porosity values between 30-10% compairing divided and not divided training systems (Kliewer et
al., 2000; Gladstone and Dokoozlian, 2003; Bordelon et al., 2008).
The results of porosity by digital photography analysis (Table II) show how VSP treatment obtained lower
values of porosity along all the vegetation cover. In the first 40 cm (clusters zone), S1 presented higher porosity
thanVSP (48 and 19% for 2006 and 2007 respectively) with the same crop load. Also, load increase does not
affect to system porosity, because S2 shows higher values in this area during 2006 (+38.6%, P<0.001) or similar
(in 2007) compared to VSP1 treatment. But, that is a key question: Is there a reliable and fast method to calculate
the porosity of a system? However, today it is still difficult to obtain an estimated method to calculate the real
porosity value of a training system. The PQ method defines the number of layers of leaves and the percentage of
non-contacts (gaps) so directs porosity measurements, and like other often used parameters (LAI and SA), are
indirect methods (less precise) and, moreover, certain measures may be overestimated and some system
discontinuities are not consider.
Therefore, porosity variations are priority to assess a correct leaf area distribution in the plant. Results of digital
photography shown that open systems get better porosity into cluster area between 25 and 50%.that improves the
exposure of inner clusters and would enhance thermal effects such as lowering internal temperature due to
increase ventilation into the canopy. Also, in training systems studies is not only important how many leaves
layers have the canopy, but also the total volume occupied by them. With the PQ method is possible to estimate
correctly the LLN, but not the percentage of gaps into the canopy, so this method should be only applied to
compare values of porosity with the same training system, while the use of digital photography allow to study
different training systems or vine areas or volumes and really estimate the canopy porosity inside the plant.
Yield components, berry sampling and juice analysis
During 2006 and 2007 reflected a main crop load effect was showed (Table III), where higher load treatment
(S2) had an increment of 16% in yield than the others treatments. On the other hand, S2 showed an average
bunch weight lower (from 17.0 to 22.5%) and reduced the number of berries (from 12 to 21%) per cluster, but it
was equilibrated by a higher cluster number per vine (from 32 to 35%) and during 2007 with the same average
berry weight (only in 2006 was lower, between 4.4 to 9.2%), caused by a higher crop load. Therefore, with an
increment of load will get berry size decrease but berries number increase, which has direct effect in total yield
and as same time, an increase in skin/flesh ratio during harvest.
Leaves and cluster microclimate are the key factor (Vanden Heuvel et al. 2004) for determinating the acidity
contents, pH and K must and last, wine composition. Differences obtained during 2007 for acidity and pH values
are not quantitatively significant (8-7%) and are probably due to a greater exposure to radiation clusters, which
increases final pH (Bergqvist et al. 2001 and Spayd et al. 2002). Data from total and extractable anthocyanin
(Table III) content reflect that there is an effect of increasing shading clusters area in the final berry synthesis of
anthocyanins, which is very useful in winemaking process (Haselgrove et al. 2000). It should also be
remembered that cv. Syrah is very sensitive to changes in thermal effects during total anthocyanins synthesis
(Spayd et al. 2002). This effect causes differences in berry anthocyanins content, which are heavier in extremely
hot conditions (2007), getting around 20% in open and not positioned free systems.
Finally, crop load does not change must composition seriously, but it increase total plant yield (Junquera et al.,
2009) becouse gives more clusters but less exposed to sunlight and with the training system, prevent the
degradation of anthocyanins at the end of ripening. The effect of the load is less important than using open
training systems, which increase phenolic and anthocyanic berry content modifying light and thermal
microclimate through spatial distribution of vegetation and shading effects in the plant.
CONCLUSIONS
Double effect due to not positioned open system (sprawl) and crop load increment gave to the plant a higher leaf
exposure and a lower vegetation density, which in hot or arid climates means a great microclimate of plant
improvement. Digital photography is a simple, fast and effective tool to evaluate possible differences refers to
porosity and leaf area exposure between training systems. It appears that porosity increase in sprawl treatments
between 25-50% in cluster area compares to VSP and caused a better plant microclimate.
Finally, free and non-positioned systems can help to improve plant microclimate, influence positively on
anthocyanic berry composition but do not change must composition and then allowing a yield increase if there is
enough water availability in plant-soil system.
REFERENCES
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Sauvignon and Grenache in the Central San Joaquin Valley of California. Am.J.Enol. Vitic. 52(1).
Bordelon B. P., Skinkis P. A., Howard P.H. 2008. Impact of training system on vine perfomance and fruit composition of Traminette. Am. J.
enol. vitic 59(1), 39-46.
Carbonneau A. 1995. La surface foliare exposée potentielle. Le progrès agricole et viticole 112(9), 204-212.
de la Fuente M. 2009. Caracterización geométrica, ecofisiológica y evaluación agronómica de sistemas continuos de vegetación libre
(SPRAWL) vs. espaldera para atenuar la sobreexposición de hojas de racimos en cv. syrah (Vitis vinifera L.) en viñedos de zonas cálidas.
301 p. Tesis doctoral. E.T.S.I.A. Producción Vegetal: Fitotecnia. Madrid. Universidad Politécnica de Madrid.
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Gladstone E. A., Dokoozlian N. 2003. Influence of leaf area density and trellis/training system on the light microclimate within grapevine
canopies. Vitis 42(3), 123-131.
Glories Y. 2001. Caracterisation du potentiel phènolique : adaptation de la vinification. Le Progrès Agricole et Viticole. 118 (15 -16) : 347 –
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Haselgrove L., Bottinf D., Heeswijck R., Rod P.B., Dry P., Ford C., Illand P.G. 2000. Canopy microclimate and berry composition: the effect
of bunch exposure on the phenolic composition od Vitis vinifera L. cv Shiraz grape berries. Aus. J. Grape and Wine Research 6: 141-149.
Kliewer W. M., Wolpert J. A., Benz M. 2000. Trellis and vine spacing effects on growth, canopy microclimate, yield and fruit composition
of Cabernet-Sauvignon. V International symposium on grapevine physiology, Jerusalem - Israel, ISHS.21-31.
Junquera, P., Jiménez L., Sánchez de Miguel P., Lissarrague J.R. 2009. Effect of shoot number on vine balance and berry composition of
Tempranillo grapevines. 16th International GiESCO Symposium, 2009. University of California, Davis (CA, USA). 419-424.
Mabrouk H., Sinoquet H., Carbonneau A. 1997. Canopy structure and radiation regime in grapevine II: Modelling radiation interception and
distribution inside the canopy. Vitis 36(3), 125-132.
Pieri P. 2010. Modelling radiative balance in a row-crop canopy: Cross-row distribution of net radiation at the soil surface and energy
available to clusters in a vineyard. Ecological Modelling, 221, 802–811.
Sánchez de Miguel P., Junquera P., de la Fuente M., Jiménez L., Linares R., Baeza P., Lissarrague J.R. 2011. Estimation of vineyard leaf
area by linear regression. Spanish Journal of Agricultural Research 9(1): 202 - 212
Schultz H. R. 1995. Grape canopy structure, light microclimate and photosynthesis I. A two dimensional model of the spatial distribution of
surface area densities and leaf ages in two canopy systems. Vitis 34(4), 211-215.
Smart, R. E. 1985. Principles of grapevine canopy microclimate manipulation with implications for yield and quality. A review. Am.J.Enol.
Vitic. 36(3), 230-239.
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Merlot berries. American Journal of Enology and Viticulture 53(3), 171-182.
Vanden Heuvel J. E., Proctor J. T. A., Sullivan J.A., Fisher K.H. 2004. Influence of training/trellising system and rootstock selection on
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ACKNOWLEDGEMENTS
The authors gratefully acknowledge the effort of Osborne Distribuidora S.A. company for technical and financial support for the
implementation of this project (MEC, IDI: P030260221). Also, D. Juan Dominguez Torre (REDISEÑA S.L.) for their invaluable and
uninterested support by assistance in digital image analysis
Table I
Vegetative development (LAI, m2·m-2), surface area (SA, m2·m-2) and surface real exposed (SFEr) for three
treatmets at harvest.
Développement végétatif (LAI, m2·m-2), surface habitable (SA, m2·m-2) et real surface exposée (SFEr) pour trois
treatmets à la récolte.
Table II
Point Quadrat and digital porosity estimation method for three treatmets during maturation period.
Point Quadrat et méthode d'estimation de porosité numérique pour trois treatmets au cours de la période de
maturation.
Table III
Yield partitioning and must composition in 2006 and 2007 growing seasons for three treatments at harvest.
Composantes du rendement et moût en 2006 et 2007 pour les trois traitements à la récolte.
…
1 EEM: standard average error for n= 40 and 8 samples per yield and must composition respectively.
2 Sig: significant differences; ns, *, ** and *** means to there is no significant differences, P<0,05, P<0,01 and P<0,001
respectively. The values with the same letter are equal (T. Duncan). P-values were determined by analysis of variance.
Main Lateral Main Lateral 8 s.t. 12 s.t. 16 s.t. 8 s.t. 12 s.t. 16 s.t.
VSP1 1.16b 0.63 1.28b 1.32 1.06b 1.11b 0.47c 0.31c 0.41b 1.08b 1.03b 1.38
S1 1.34b 0.68 1.40b 1.33 1.15b 1.26ab 0.83b 0.56b 1.12b 1.01b 0.74b 1.59
S2 2.00a 0.73 2.12a 1.24 1.50ª 1.31ª 1.15ª 0.98ª 1.23ª 1.78a 1.62a 1.65
EEM
1
(n=8) 0.16 0.104 0.076 0.075 0.06 0.07 0.08 0.08 0.08 0.08 0.08 0.08
Sig
2
* ns *** ns ** * *** *** *** *** *** ns
LAI SFEr
2006 2007 2007Treatment SA
2006 2007 2006
Le af
layers
Gap
fraction
(%)
Int ern al
Clusters
(%) Late ral In te rnal
Le ave s
(%)
Le af
layers
Gap
fraction
(%)
Int ern al
Clusters
(%) Late ral In te rna l
Le ave s
(%)
0-40
cm. 40-70
cm. 70-100
cm. 0-40
cm. 40-70
cm. 70-100
cm.
VSP1 2,20 15,00 77,90 2,7
b
26,4
b
2,20 7,50 65,6
b
3,5
b
44,5
b
7,57
b
20,17
b
62,00
b
5,67
b
7,00
b
23,75
b
S1 2,60 10,00 82,40 5,0
a
58,3ª 2,40 7,50 81,0
a
5,8ª 66,1ª 14,63ª 26,25ª 85,00
a
7,00
a
14,83ª 66,87ª
S2 2,50 20,00 82,10 5,9ª 60,0
a
2,60 5,00 87,3ª 6,5
a
68,1ª 12,33
a
16,20
b
64,64
b
4,87
b
17,00
a
78,25ª
EEM
1
(n=8) 0,23 4,5 0,5 0,41 2,01 0,22 1,8 0,56 0,31 1,53 0,88 1,15 6,73 0,17 1,81 6,3
Sig
2
ns ns ns *** *** ns ns ** ** ** *** ** * ** * **
Vegetation Zone PQ 2006
Cluster Zone
Treatment Clus ter Zone Vegetation Zone % Po ro si ty
2006 2007
PQ 2006
Treatment Nº
Clusters·m
-1
Yie ld ( Kg·m
-2
)Cluster
average
wei ght (g)
100 Berries
average
wei gh t (g)
Nº
berries ·cluste r
-1
Nº
Clusters·m
-1
Yie ld ( Kg·m
-2
)Cluster
average
wei ght (g)
100 Berries
average
wei ght (g)
Nº
berries·cl uste r
-1
VSP1 24.68 b 1.73 b 190.42 a 111.34 a 171.19 a 20.96 b 1.61 b 204.36 a 150.54 b 135.63 a
S1 23.82 b 1.71 b 195.10 a 104.57 b 187.34 a 21.04 b 1.61 b 206.55 a 160.09 a 128.29 a
S2 36.20 a 2.05 a 153.24 b 101.05 c 152.06 b 30.66 a 1.93 a 169.32 b 158.7 a 106.92 b
EEM
1
(n=40) 0.604 0.041 6.09 0.081 1.02 0.46 0.10 7.71 1.39 5.05
Sig
2
** ** ** ** ** *** *** ** *** ***
Yie ld pa rtit ioni ng 2006 Yie ld pa rtit ioni ng 2 007
Treatment ºBrix pH IPT An toci an
extractables
(mg·L
-1
)
Total Antocian
content (mg·L
-1
)ºBrix pH IPT Ant oci an
extractables
(mg·L
-1
)
Total Antocian
content (mg·L
-1
)
VSP1 25.1 3.5 46.7 794.33
1470.35 b
25.2 3.06 b 45.8 931.0
1172.5
S1 25.9 3.5 54.7 936.95
1804.34 a
25.4 3.13 a 51.8 976.5
1228.5
S2 25.8 3.5 52.7 983.94
1903.30 a
24.7 3.20 a 47.7 861.0
1197.88
EEM
1
(n=8) 0.76 0.02 2.54 76.94
81.28
0.27 0.02 4.1 102.7
57.1
Sig
2
ns ns ns ns
*
ns ** ns ns
ns
Must Composition 2006 Must Composition 2007
