Assessment of the spatial variability of anthocyaninsin grapes using a fluorescence sensor: relationshipswith vine vigour and yield

J. Baluja • M. P. Diago • P. Goovaerts • J. Tardaguila

� Springer Science+Business Media, LLC 2012

Abstract The use of new, rapid and non-invasive sensors in the field allows the

collection of many observations which are necessary to assess the spatial variability of

berry composition. The aim of this work was to study the spatial variability in anthocyanin

content in grapes and to quantify its relationship with the vigour and yield in a commercial

vineyard. The study was conducted in a Tempranillo (Vitis vinifera L.) vineyard (Navarra,

Spain). A new, hand-held, non-destructive fluorescence-based proximal sensor was used

for monitoring the anthocyanin content in grapes at veraison and harvest. Yield, vine

vigour, spectral (normalized difference vegetation index and plant cell density) and

chlorophyll (SPAD and simple chlorophyll fluorescence ratio) parameters were measured.

Yield variability within the vineyard was the largest of all the parameters. Fluorescence-

based anthocyanin indices were less variable at harvest than at veraison. The vigour

parameters (main shoot length, total shoot length and shoot pruning weight) were posi-

tively correlated to yield; the chlorophyll and the spectral indices were negatively corre-

lated with berry anthocyanin production. The correlations between vigour, yield and

anthocyanin content in grapes varied substantially in time and space across the vineyard.

Keywords Precision viticulture � Geostatistical analysis � Geographically weighted

regression � Grapevine

AbbreviationsANTH_RG Anthocyanin fluorescence index

FERARI Fluorescence excitation ratio anthocyanin relative index

MSL Main shoot length

NDVI Normalized difference vegetation index

PCD Plant cell density

J. Baluja � M. P. Diago � J. Tardaguila (&)Instituto de Ciencias de la Vid y del Vino, University of La Rioja, CSIC, Gobierno de La Rioja,26006 Logrono, Spaine-mail: javier.tardaguila@unirioja.es

P. GoovaertsBioMedware, Inc., 3526W Liberty, Suite 100, Ann Arbor, MI 48103, USA

123

Precision AgricDOI 10.1007/s11119-012-9261-x

SFR Simple chlorophyll fluorescence ratio

SPW Shoot pruning weight

TSL Total shoot length

Introduction

The last few years have seen the development of precision viticulture for the study and

management of spatial variability in vineyards (Proffit et al. 2006; Arno et al. 2009;

Bramley 2010; Hall et al. 2011). The success of the approach resides in the possibility of

designing different strategies for managing the vineyard based on vegetative growth

(Tardaguila et al. 2011), vineyard yield (Bramley and Hamilton 2004), and even the

composition of grapes (Bramley 2005; Tisseyre et al. 2008; Trought and Bramley 2011).

The spatial variation of sugar, titratable acidity and pH in winegrapes was studied in

France by Tisseyre et al. (2008). The spatial variability of grape composition has also been

evaluated prior to harvest in Australia, and showed greater variability in phenols and

anthocyanins than in sugars and pH (Bramley 2005). Recently, a study of the spatial and

temporal variation of sugars, pH and acidity in a Sauvignon blanc (Vitis vinifera L.)

vineyard was conducted in New Zealand (Trought and Bramley 2011) to quantify the

changes during the ripening period and thereby determine when to harvest based on the

acidity and sugar accumulation.

It would be worth exploring the spatial variation of other key compounds in grapes and

quality (anthocyanins, phenols, etc.) during the ripening period as these parameters are not

always related to technological variables (total soluble solids and acidity). The anthocyanin

content in grapes is responsible for the final colour of wine and is a very important factor that

helps define its quality (Iland et al. 2004; Kennedy 2010). This parameter has traditionally been

measured using destructive chemical methods (Saint-Cricq de Gaulejac et al. 1998; Iland et al.

2004) which are time-consuming and labour intensive. These drawbacks do not facilitate the

analysis of a high number of samples within a short time period, which is required for a thorough

study of the spatial variability in anthocyanin and phenol contents in berries under precision

viticulture production. Moreover, since these wet chemistry procedures are destructive (involve

berry sampling), the changes in spatial standards cannot be studied throughout maturation as

berry samplings may have a direct effect on the final grape production per plant.

Recently, a method known as chlorophyll fluorescence screening, based on the com-

parison of the chlorophyll fluorescence emission after being excited at two different

wavelengths, has been developed, which enables the phenolic content of grapes to be

determined (Agati et al. 2007; Cerovic et al. 2008). This method could be useful in

precision viticulture, given that the anthocyanin content in berries can be determined

almost instantly at any time during the growing season. This would make it possible for

grape-growers to manage the phenolic maturation of the crop and finally harvest the grapes

based on accurate information. In this regard, a new, commercially available fluorescence

sensor has been evaluated as an efficient tool for determining the phenolic content in

different grapevine varieties in the laboratory and in the vineyard (Cerovic et al. 2008; Ben

Ghozlen et al. 2010a, b). This portable sensor has also been manually used for mapping

grape quality properties in preliminary studies (Cerovic et al. 2009; Baluja et al. 2011) and

determining the anthocyanin content in grapes upon arrival at the winery (Le Moigne et al.

2010). Recently, this fluorescence sensor, mounted in a commercial harvester, was used to

measure on-the-go the anthocyanin content in grapes in Australia (Bramley et al. 2011a).

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So far, most studies in precision viticulture have addressed relationships between vigour

and yield parameters with winegrape composition to indirectly evaluate grape quality

attributes (Bramley and Hamilton 2004; Lamb et al. 2004; Cortell et al. 2005; Hall et al.

2011). It would be challenging to make a large number of spatial grape composition

measurements as would be necessary for any geostatistical analysis used in precision

viticulture. However, the spatial variability of grape yield in a vineyard has been widely

studied in Australia (Bramley and Lamb 2003; Bramley 2005) thanks to the sensors

mounted on a harvest machine. Cerovic et al. (2009) observed a poor relationship between

yield and anthocyanin content in grapes, emphasizing the importance of acquiring infor-

mation for both parameters at harvest. Remote sensing multispectral imaging has been used

to predict grape composition, but a low correlation between vine vigour (measured as

NDVI) and anthocyanin content in grapes was observed (Lamb et al. 2004). Recently, Hall

et al. (2011) studied the within-season temporal variation in the correlations between

vineyard canopy and winegrape composition and yield. In these studies, the correlations

were established for the whole plot, on the supposition that the correlations obtained could

be extrapolated to the whole vineyard, but the significance of these spatially-variable

correlations has not been investigated. Panten et al. (2010) indicated that the coefficient of

determination between two variables in a whole plot cannot be reliably extrapolated for

certain viticulture parameters. The use of new spatial statistical methods such as geo-

graphically weighted regression (GWR) (Fotheringham et al. 2002) could allow assessment

of the significance of spatially variable relationships between yield and vigour with grape

composition within the vineyard.

Selective harvest, based on the results provided by assessment of the spatial variability,

has begun to be adopted by grape-growers as a feasible strategy to improve wine quality

(Bramley 2010; Bramley et al. 2011b). Recently, scientists in Australia (Bramley et al.

2011c) demonstrated that selective harvest is a feasible and profitable strategy for the

industry even for large wine production volumes. Site-specific zoned management for

selective harvest could make it possible to produce a range in wine qualities and opti-

mizing the cultural practices could potentially increase revenues for the wine industry.

Clearly there are a number of questions remaining before selective harvest can be

routinely used in world viticulture, particularly in Europe (different climates and grapevine

varieties) where precision viticulture is in its infancy. The objective of this work was to

study the spatial structure and variability in the anthocyanin content in a Tempranillo

(V. vinifera L.) vineyard at veraison and harvest using a new fluorescence sensor, and to

explore its relationship with vine vigour and yield.

Materials and methods

Experimental layout

The study was conducted during season 2010 in a Tempranillo (V. vinifera L.) 2.2 ha

vineyard located in Ayegui (lat. 42�3805300 long. 2�202500; 518 m), Navarra, Spain.

Grapevines were planted in 2004 on a sandy-clay soil at 2.4 9 1.6 m (inter- and intra-row)

and trained to a vertically shoot-positioned, spur-pruned cordon with a bud load of 10

nodes per linear meter/vine. A regular grid of 67 experimental blocks (30 blocks/ha) with

20-m intervals was defined. Each experimental block comprised three adjacent vines which

were geo-referenced using a Leica Zeno 10 GPS (Heerbrug, St. Gallen, Switzerland), with

real time kinematic correction, working at \30 cm precision.

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Vigour and yield measurements

The chlorophyll content in leaves and vigour parameters were determined at veraison (12

August 2010). The chlorophyll content in three fully expanded main leaves per vine was

assessed using the SPAD chlorophyll meter (Minolta, Plainfield, Illinois, USA). The

vigour parameters, such as main shoot length (MSL) and total shoot length (TSL) per

vine were also measured, in two representative shoots per vine. At harvest (5 October

2010), three vines per block were manually harvested and yield per vine was weighted in

the field using a hanging scale with 10 g readout (Kern & Sohn GmbH, Balingen-

Frommern, Germany). In December, all vines were pruned to two-buds per spur and the

pruning weight and number of shoots per vine were appraised, from which the shoot

pruning weight (SPW) was calculated (weight per shoot). For all vigour and yield

parameters, experimental data were collected on the three adjacent vines per experi-

mental block.

Aerial multispectral images

Remotely sensed imagery (airborne digital multispectral imagery with 50 cm ground

spatial resolution) of the vineyard was obtained at veraison (12 August 2010). The spectral

indices, normal difference vegetation index (NDVI), as defined by Rouse et al. (1974) and

plant cell density index (PCD), as described by Dobrowski et al. (2003) were computed.

These two indices provide a proxy measure of vine vigour (Johnson 2003; Hall et al. 2011).

Fluorescence measurements

A new, hand-held, non-destructive fluorescence-based proximal sensor, called Multiplex�

(Force-A, Orsay, France), was used for monitoring the anthocyanin content in berries. The

Multiplex� sensor used for this study was described in detail recently by Ben Ghozlen

et al. (2010a). It has four LED-matrix light sources: 373 nm (Ultraviolet, UV), 470 nm

(Blue, B), 516 nm (Green, G) and 635 nm (Red, R) and three synchronised detectors for

fluorescence recording: yellow (YF), red (RF) and far-red (FRF). The fluorescence ratios

linked to phenol and chlorophyll contents in grapes were based on FRF_UV (FRF excited

by UV light), FRF_R (FRF excited by red light), FRF_G (FRF excited by green light) and

RF_R (RF excited by red light).

The fluorescence indices used in this work were defined as:

ANTH RG ¼ logðFRF R= FRF GÞ

FERARI ¼ logð5000 = FRF RÞ

SFR ¼ FRF R= RF R

The FERARI (fluorescence excitation ratio anthocyanin relative index) and ANTH_RG

(anthocyanin fluorescence index) indices have been successfully correlated with the

anthocyanin content in grapes (Cerovic et al. 2008; Ben Ghozlen et al. 2010b) while SFR

(simple chlorophyll fluorescence ratio) was related to chlorophyll content (Gitelson et al.

1999).

At veraison and harvest, for each vine of any experimental block, three representative

bunches were measured in triplicate with the Multiplex� device in three adjacent vines,

from the east side of the canopy. A total of 1 809 measurements (27 measurements per

block 9 67 blocks) were taken at veraison and harvest in the vineyard.

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Wet chemistry analysis

In order to establish the correlations between the anthocyanins-related fluorescence indices

(ANTH_RG and FERARI) and the anthocyanin content in berries, 13 additional validation

blocks of three adjacent vines each, were randomly chosen and sampled at harvest. In each

block three samples of 50 berries were randomly taken and weighed. These samples were

then frozen and stored at -18�C until wet chemistry analysis. Berries were allowed to

partially thaw prior to homogenization, and temperature was kept under 5�C at all times.

Each sample of 50 berries was homogenized using an Ultra Turrax grind mixer (Ika,

Staufen, Germany) at high speed (14 000 rpm for 1 min). The anthocyanin concentration

in grapes was determined as described by Iland et al. (2004).

Statistical, geostatistical analysis and mapping

Prior to geostatistical analysis, the fluorescence-based data were pre-processed and box and

whisker plots were prepared to detect the presence of potential outliers. Out of a total of

1 809 measurements, only 2.5 and 3.0% of data were removed as outliers for veraison and

harvest, respectively.

For all viticultural and fluorescence-based parameters, variograms were computed using

Vesper software (Minasny et al. 2005). Spatial interpolation was based on the omni-

directional variograms which provided the best model fit based on the sum of squared

errors, root mean square error and Akaike information criterion (Webster and Oliver 2007).

Mean correlation distance (MCD) (Han et al. 1996) was computed for the spherical

models as a way to study the distance at which high spatial dependence occurred. Fur-

thermore, Cambardella classification (Cambardella et al. 1994) was applied to compare the

different degrees of spatial dependence for each variable.

All parameters were mapped by kriging with an external drift (Goovaerts 2000), using

NDVI as secondary information because of its correlation with these variables by means of

Spacestat version 2.6 software (Biomedware, Ann Arbor, USA), to avoid the problems

derived of non-stationary in some of the variograms. Cross validation indicated that

incorporation of NDVI improved the prediction accuracy over ordinary kriging.

Pearson correlations were assessed using PASW Statistics 18 (IBM, New York, USA),

as well as to study the gross-variation of the data, in terms of coefficient of variation (CV)

and spread. Spread was computed as the ratio between the range (maximum–minimum)

and median values, and was expressed in percentage (Bramley 2005).

GWR was used to explore the local correlation between yield, SPW and fluorescence-

based indices determined at harvest using the GWR 3.0 software (Fotheringham et al.

2002). The fixed Gaussian kernel technique was employed in the GWR procedure

(Fotheringham et al. 2002).

Results

To validate the effectiveness of the ANTH_RG and FERARI indices as a tool to determine

the anthocyanin content in berries, the regression curves between these fluorescence

indices and the anthocyanin concentration in berries measured by wet chemistry proce-

dures were assessed as shown in Fig. 1. At harvest, both ANTH_RG and FERARI were

significantly correlated to the anthocyanin concentration, although with opposite signs:

negative for ANTH_RG and positive for FERARI.

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The summary statistics reported in Table 1 indicate that most of the vigour, yield and

fluorescence-based variables determined in the vineyard exhibited considerable variation.

On the basis of the CV and the spread, yield variation was by far the largest and up to three

or four-fold more than vigour parameters, such as MSL and SPW (Table 1). The variation

in leaf chlorophyll content, expressed as SPAD values, and that of the chlorophyll content

in the berries, determined by the SFR index, were very similar. For the fluorescence-based

anthocyanin indices, their variation was reduced at harvest as compared to veraison,

especially for FERARI, whose spread and CV values decreased by approximately 80%

(Table 1). However, neither the CV nor the spread offered information on whether the

variation was spatially structured or not.

To quantify the existence of spatial variation and characterize the structure the different

studied parameters within the vineyard, variograms were constructed. The spherical model

(continuous line in Fig. 2) provided a reasonable fit for the experimental variograms

(dotted line) of vigour, yield and chlorophyll content variables (SPAD and SFR). The range

of these variograms, which is the distance at which the sill is achieved and indicates the

distance beyond which no spatial correlation exists, was slightly bigger than 100 m for all

these variables. Furthermore, for all the vigour, yield and chlorophyll parameters, the

nugget effect, which is the discontinuity at the origin of the variogram (unexplained

variance), was very small with respect to the sill value. Concerning the anthocyanin-related

fluorescence indices, their variograms better-fit the spherical models at veraison than at

Fig. 1 Regressions between ANTH_RG and FERARI indices, determined by a fluorescence sensor, andanthocyanin concentration in grapes, determined by wet chemistry analysis in a Tempranillo (Vitis viniferaL.) vineyard located in Navarra (Spain) at harvest. Both regressions showed p value \ 0.01 (**)

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harvest (Fig. 3). Moreover, their range values were smaller at veraison, whereas at both

stages, relatively large nugget effects were observed for ANTH_RG and FERARI

variograms.

Table 1 Descriptive statistics and mean correlation distance (MCD) for vigour parameters (total shootlength—TSL, main shoot length—MSL, shoot pruning weight—SPW), spectral indices (NDVI and PCD),yield, leaf chlorophyll content (SPAD) and fluorescence-based indices of chlorophyll content in grapes(SFR) and fluorescencebased indices of anthocyanins (ANTH_RG and FERARI) in the Tempranillo (Vitisvinifera L.) vineyard located in Ayegui (Navarra, Spain)

Variable Min Max Spread (%) CV (%) Skewness Kurtosis MCD

MSL (cm) 61.1 144.9 78 22.3 -0.23 -1.09 38

TSL (cm) 74.9 272.2 87 24.2 -0.04 0.29 38

SPW (kg) 0.01 0.07 157 45.1 0.01 -1.38 44

SPAD 22.4 36.4 64 14.8 0.32 -0.71 48

Yield (kg) 0.19 4.09 260 67.9 0.23 -1.09 47

SFRver 0.81 1.31 62 13.8 0.25 -0.17 45

ANTH_RGver 0.10 0.15 56 13.6 -0.14 -0.44 16

FERARIver 0.34 0.83 159 38.3 0.65 -0.25 18

ANTH_RGharv 0.30 0.39 50 10.6 0.79 0.38 27

FERARIharv 1.62 1.99 28 5.9 0.21 0.10 26

‘‘ver’’ and and ‘‘harv’’ subscripts denotes that fluorescence indices determined at veraison and harvestrespectively

Min: minimum; Max: maximum; Spread ¼ Maximum�MinimumMedian

; CV: variation coefficient

MCD ¼ 38

að C1

ðC0þC1ÞÞ where a range of the semi-variogram, C1 sill of the semi-variogram, C0 Nugget effect of

the semi-variogram

Fig. 2 Omni-directional experimental variograms (dotted line) and the spherical models fitted (solid line)for vigour parameters, yield, leaf chlorophyll content (SPAD) and fluorescence-based index of chlorophyllcontent in grapes (SFR) in a Tempranillo (Vitis vinifera L.) vineyard located in Navarra (Spain)

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All the vigour, yield and chlorophyll parameters, as well as the spectral NDVI and PCD

indices showed marked spatial structures (Fig. 4). Both NDVI and PCD maps exhibited

maximum and minimum values at the west and east sides of the vineyard, respectively.

Moreover, these two spectral indices showed a spatial pattern that was similar to the ones

displayed by agronomic and chlorophyll-related variables, especially to the MSL and SPW

maps. The patterns of vineyard yield and those of the spectral and vigour indices were

alike. In these maps (Fig. 4), maximum values were observed in the north-west part of the

vineyard while the minimum occurred in the south-east part.

Figure 5 shows the kriged maps for the anthocyanin-related fluorescence indices at

veraison and harvest. At veraison, the spatial pattern of ANTH_RG and FERARI were

alike, and trended toward larger values from west to east in the vineyard. Also at veraison,

a FERARI local minimum area was found in the south-east part of the vineyard. At harvest,

ANTH_RG and FERARI exhibited a negative spatial relationship, and no local minimum

area was observed for FERARI.

Pearson correlation coefficients were calculated and their significance was tested for

vigour, yield, chlorophyll, spectral and anthocyanin indices at veraison and harvest

(Table 2). All correlations were significant at p \ 0.01. The vigour parameters (MSL, TSL

and SPW) were positively correlated to yield, the chlorophyll indices (SPAD and SFR) and

the spectral vigour indices (NDVI and PCD), but negatively correlated to ANTH_RG at

Fig. 3 Omni-directional experimental variograms (dotted line) and the spherical models fitted (solid line)for fluorescence-based indices of anthocyanins (ANTH_RG and FERARI) in a Tempranillo (Vitis viniferaL.) vineyard located in Navarra (Spain) at veraison and harvest

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veraison and to FERARI at harvest. The correlations between yield and both spectral and

chlorophyll indicators (NDVI, PCD, SFR and SPAD) were positive, as were the vigour

parameters (Table 2). The correlation between yield and the NDVI was 0.81. The chlo-

rophyll content in the leaves and berries, expressed by SPAD and SFR values, was found to

be positively correlated (r = 0.83), and both exhibited similar correlation coefficients in

sign and value with all other parameters. On the whole, the two studied spectral indices

were highly correlated with the agronomic parameters, chlorophyll and anthocyanin

indices. Regarding anthocyanin content, FERARI values correlated better with all other

variables collected at veraison than at harvest, whereas the opposite was found for

ANTH_RG.

The Pearson coefficient (r) is an indicator of the strength of the correlations between

variables but does not provide any spatial information. GWR makes it possible to incor-

porate spatial coordinates in the analysis and thereby generate maps of the coefficient of

determination for SPW and yield against the fluorescence-based anthocyanin indices

(ANTH_RG and FERARI) at harvest (Fig. 6). The GWR coefficients of determination

between the anthocyanin indices and both yield and SPW were spatially variable across the

vineyard.

Fig. 4 Maps obtained for spectral indices (NDVI and PCD), vigour parameters (TSL total shoot length,MSL main shoot length, SPW shoot pruning weight), yield, leaf chlorophyll content (SPAD) andfluorescence-based index of chlorophyll content in grapes (SFR) in a Tempranillo (Vitis vinifera L.)vineyard located in Navarra (Spain). Maps represent equal intervals

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Discussion

The spatial structure of the vigour, yield and composition within the grape vineyard were

analyzed at two key stages of maturation, veraison and harvest, using a new portable

fluorescence sensor that is capable of instantly determining, in the field, the anthocyanin

content of grapes. This new tool, which is fast and non- destructive, could be very useful in

Fig. 5 Kriged maps obtained for fluorescence-based indices of anthocyanins (ANTH_RG and FERARI) ina Tempranillo (Vitis vinifera L.) vineyard located in Navarra (Spain) at veraison and harvest. Maps representequal intervals

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precision viticulture for the segmentation of the vineyard according to berry colour.

Our results confirm the potential of these non-invasive sensors in modern viticulture

and oenology, as observed in recent studies (Cerovic et al. 2009; Ben Ghozlen et al.

2010a, b).

In general, the anthocyanin content in grapes displayed less variability than yield and

other variables related to vigour (Bramley and Lamb 2003). Moreover, the anthocyanin

content in grapes at veraison showed greater variation than at harvest. These outcomes may

be explained by the asynchronous development of grape berries, specially marked at

veraison. The variograms obtained for this group of parameters had high nugget effects,

leading to smooth kriging surfaces (Bramley 2005). These results were supported by the

moderate spatial dependence computed according to the criteria of Cambardella et al.

(1994). The nugget effect could be related to sampling/analytical errors, or with variations

occurring over distances smaller than the shortest sampling distance (Panten et al. 2010).

These factors might have contributed differently to the nugget effect at veraison and

harvest. In addition, the high nugget effect could indicate a strong degree of variation

within the vine, or among vines (Johnstone 1999, Trought et al. 2008). Ranges of the

anthocyanin indices showed a smaller distance of spatial autocorrelation at veraison than at

harvest, with MCD values below the threshold of the sampling grid. The calculation of

MCD provided information about the distance over which a high spatial dependence

occurred at the plot level, that is, it was possible to estimate the maximum subzone size,

within the vineyard, likely to receive site-specific or differential management (Han et al.

1996). At harvest, the ranges of the variograms and the MCD values for the anthocyanin

content in grapes were closer to those obtained for yield and vigour.

Regarding the variation of the different parameters, yield showed the highest variation

coefficient (68%) and spread (260%); these values were similar to those observed in

Australia for the same variables in other varieties (Bramley and Lamb 2003; Bramley and

Table 2 Pearson correlation coefficients (all with p value \ 0.01) for vigour parameters (total shootlength—TSL, main shoot length—MSL, shoot pruning weight—SPW), spectral indices (NDVI and PCD),yield, leaf chlorophyll content (SPAD) and fluorescence-based indices of chlorophyll content in grapes(SFR) and fluorescence-based indices of anthocyanins (ANTH_RG and FERARI) in the Tempranillo (Vitisvinifera L.) vineyard located in Ayegui (Navarra, Spain)

Variables TSL (cm) MSL (cm) SPW (kg) SPAD Yield (kg) NDVI PCD SFR

TSL (cm) –

MSL (cm) 0.64 –

SPW (kg) 0.57 0.88 –

SPAD 0.36 0.64 0.74 –

Yield (kg) 0.50 0.82 0.91 0.77 –

NDVI 0.60 0.81 0.79 0.71 0.81 –

PCD 0.64 0.86 0.81 0.61 0.75 0.92 –

SFRver 0.25 0.58 0.75 0.83 0.80 0.60 0.54 –

ANTH_RGver -0.25 -0.40 -0.58 -0.63 -0.48 -0.71 -0.64 -0.61

FERARIver -0.39 -0.70 -0.73 -0.62 -0.77 -0.51 -0.56 -0.67

ANTH_RGharv 0.34 0.64 0.73 0.76 0.69 0.61 0.57 0.66

FERARIharv -0.23 -0.50 -0.61 -0.65 -0.60 -0.51 -0.42 -0.65

‘‘ver’’ and ‘‘harv’’ subscripts denotes that fluorescence indices determined at veraison and harvestrespectively

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Fig. 6 R2 maps obtained by geographically weighted regression (GWR) for yield (above) and shootpruning weight (below) related to fluorescence-based indices of anthocyanins (ANTH_RG and FERARI) ina Tempranillo (Vitis vinifera L.) vineyard located in Navarra (Spain) at harvest. Maps represent equalintervals

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Hamilton 2004). The analysis of the yield variogram indicated that this parameter had a

well-defined spatial structure, with a low proportion of nugget effect. In fact, these results

were confirmed by the high spatial dependence of the yield according to the criteria of

Cambardella et al. (1994). The variogram ranges indicated that the spatial dependence took

place up to a distance slightly less than half the diagonal of the plot (47 m). Application of

Cambardella’s criteria (Cambardella et al. 1994) showed a high spatial dependence of

vigour and chlorophyll content of leaves and fruit; furthermore, the MCD for these

parameters was similar to that of yield. Vigour parameters and yield had smaller nugget

effect values, higher spatial dependence and larger MCD than anthocyanin-based indices.

These results indicate the importance of the sampling grid design in precision viticulture

studies, as well as the complexity of determining the variables related to grape

composition.

In general, the more vigorous vines had greater leaf and fruit chlorophyll contents, and

produced greater grape yields along with more wood pruning weight. These vigorous vines

also produced grapes with lower anthocyanin content. In our study, the capacity of the

NDVI and PCD indices to predict anthocyanin content in grapes turned out to be moderate.

Similar relationships among these parameters have been observed by other authors (Lamb

et al. 2004; Hall et al. 2011). It is also important to emphasise the strong negative rela-

tionship observed between leaf chlorophyll content (SPAD) and anthocyanin content in

grapes. The negative relationship that exists between vigour and berry composition, in

terms of sugar concentration, anthocyanin content and other polyphenols has also been

observed by several authors (Cortell et al. 2007; Hall et al. 2011). It is important to

consider the direct effect that the soil may have on vine vigour (Tardaguila et al. 2011).

Besides plant vigour, microclimatic conditions affect the fruits/leaves and the composition

of the berries (Smart and Robinson 1991; Reynolds 2010).

Reversal of the correlation between ANTH_RG and all other parameters from veraison

to harvest was noteworthy. This phenomenon may be explained by the fact that the

ANTH_RG index behaved in such a way that, its value increased with the accumulation

anthocyanins in the skin up to 0.6–0.8 mg/g, and decreased for larger anthocyanin con-

centrations (Ben Ghozlen et al. 2010a). This behaviour was not found for the FERARI

index, which steadily increased with anthocyanin accumulation in the berries.

A close relationship was observed between plant vigour, chlorophyll content and yield.

In fact, high correlations were obtained between the vigour indicators (TSL, SPW, NDVI

and PCD) and yield. Weaker relationships between these variables were observed in earlier

studies (Martinez-Casasnovas and Bordes 2005; Hall et al. 2011). On the other hand, a

strong positive relationship was observed between leaf chlorophyll content (SPAD) and

that in grapes (SFR). In our study, this relationship was explored using statistical and

geostatistical techniques. The variograms obtained for SFR and SPAD were analogous,

with similar proportions of the nugget effect on the final sill, ranges and MCD. Similarly,

the CV and spread of both variables were quite similar.

Finally, it is important to note that the correlation between vigour, yield and the

anthocyanin content in the grapes showed high spatial variability (Fig. 6). In other words,

there were areas within the plot where these variables were closely linked (R2 [ 0.8), but

other areas of the vineyard where these variables were practically independent (R2 \ 0.2).

These results further support the importance of field experimentation through complete

block designs in precision viticulture (Bramley 2010; Panten et al. 2010) and point out that

a strong coefficient of determination between two variables in a given whole plot might not

be applicable for other parts of the vineyard. The results provided by the GWR support the

development and use of new sensors in precision agriculture that can supply rapid and

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pertinent information about the entire vineyard. Traditional indirect relationships based on

vegetative growth, yield and vineyard characteristics (Gray et al. 1997; Krstic et al. 2003,

Tardaguila and Martınez de Toda 2008) or on remote sensing indices (Lamb et al. 2004)

could either over or under estimate the local values of the estimated variable, as these

techniques assumed that the overall behaviour could be extrapolated to the entire vineyard.

Conclusions

The fluorescence sensor facilitated mapping changes in key grape compounds throughout

the growing season and helped delineate zones having different anthocyanin content in the

berries, with a goal to distinguish wines having unique quality characteristics.

A marked spatial structure for vigour, yield, chlorophyll and anthocyanin parameters

was detected within the vineyard. The fluorescence-based optical sensor appeared to be

very useful for describing the vineyards spatial variability in anthocyanin content of grapes

in a fast and non-destructive way, confirming that non-invasive sensors are useful tools for

precision viticulture.

Use of the fluorescence sensor in the vineyard and geostatistical methods may be very

helpful in designing selective harvest strategies based on the anthocyanin content in grapes.

Furthermore, site specific cultural practices based on the results of spatial variability

reported could help reduce variability in vine vigour and yield within plots.

Application of GWR revealed considerable spatial heterogeneity in the relationships

between grape colour with yield and vigour. Spatial regression techniques were essential to

obtain knowledge of spatial correlations between variables, especially if vineyard seg-

mentation strategies are planned.

Acknowledgments We would like to thank Bodegas Pago de Larrainzar for their help in the fieldmeasurements and for the grapes supply. This work was jointly funded by Force-A and the University ofLa Rioja. Special gratefulness to Dr. Zoran Cerovic for his comments and suggestions.

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