domain: phys chim

text:
Mohamed Errami

Gisèle El Dib

M Cazaunau

E Roth

R Salghi

Abdelwahid Mellouki

A Chakir
email: abdel.chakir@univ-reims.fr

Atmospheric degradation of Pyridine: UV Absorption Spectrum and reaction with OH radicals and O 3

Keywords: kinetics, UV absorption cross-sections, rate coefficient, tropospheric lifetimes, OH radicals, Ozone, nitrogen containing aromatic compounds, atmospheric simulation chamber

Atmospheric degradation of pyridine: UV absorption spectrum and reaction with OH radicals and O3

Introduction:

Pyridine (C 5 H 5 N), a heterocyclic nitrogen-containing organic compound, is used as intermediate or solvent in agrochemical and pharmaceutical industries such as the synthesis of vitamins, sulphamides, disinfecting products, dyes and explosives, rubber and paint, in the drilling of fuel and gases and in food industries [START_REF] Worthing | The Pesticide Manual[END_REF][START_REF] Atkinson | [END_REF]. Because of its relatively high vapour pressure ( 18 torr at 20°C) a substantial quantity may escape into the atmosphere during its use. Pyridine may also be released into the atmosphere as primary pollutant from numerous sources such as biomass burning, automobile exhaust, coal tars and tobacco smoke [3,4,5].

Due to the localised and sometimes irregular nature of the sources, the concentration of pyridine is expected to be highly variable with time and location. The presence of pyridine ion in the clean air of Mauna Loa Observatory, Hawaii, suggests a long atmospheric lifetime of its parent neutral species [6].

Similarly to other volatile organic compounds, once in the atmosphere, pyridine may be removed by sunlight induced photolysis and/or chemical reactions with atmospheric oxidants such as OH and NO 3 radicals, Cl atoms and ozone [START_REF] Atkinson | [END_REF]. The atmospheric persistence of pyridine can be estimated using the kinetic parameters of these reactions. However, so far, the atmospheric degradation of pyridine has been subject to only a limited number of studies. The rate coefficient of the reaction of OH with pyridine has been measured only by two groups [START_REF] Atkinson | [END_REF]7] while the reaction of pyridine with ozone has been investigated only once [START_REF] Atkinson | [END_REF]. In order to better define the atmospheric fate of pyridine, its reactions with the atmospheric oxidants should be well characterized. For this purpose, we have conducted studies on its reactions with OH radicals and ozone. In addition, we have determined its UV-VIS absorption cross sections in order to evaluate the photolysis as atmospheric loss process. The obtained data are compared with the previous determinations and the atmospheric lifetime of pyridine is estimated using the measured parameters.

Experimental:

UV absorption spectrum device: The UV absorption spectrum of pyridine was measured at GSMA-Reims using a D 2 Lamp-Monochromator System. Experiments were carried out between 200 and 350 nm at temperature 353±1 K, in order to avoid the loss of pyridine to the walls, and under static conditions. The experimental set up and procedure used in this work have been presented in details in previous studies [8,9], therefore, only a brief description is given here. calibrated spectrophotometer (HR 640) equipped with a 2400 line mm -1 grating and a dispersion of 0.26 nm mm -1 . At the exit slit of the monochromator, the beam passes through the absorption pyrex cell of 50 cm of length and 2 cm of diameter equipped with quartz optical windows. The signal is then measured by a Hamamatsu R 955 photomultiplier tube.

Vapours of pyridine were introduced into the absorption cell under vacuum. The pressure of pyridine inside the cell was varied from 0.14 to 9.8 Torr and was measured by (0 -10) Torr MKS Baratron capacitance manometers at the beginning and the end of each measurement, in order to check for the stability of the concentration. The temperature was measured by platinum resistance sensors positioned at the two extremities of the cell. The absorption crosssection (σ λ ) values at a given wavelength λ were determined using the Lambert-Beer's law (1):

(1)

Where I n ( ) is the spectrophotometer background noise, I 0 ( ) is the intensity of the issued radiation under vacuum in the absence of pyridine and I( ) is the transmitted intensity measured at each wavelength in the presence of pyridine. P is the pressure (in Pascal), T the temperature (in kelvin), l the optical path, R the gas constant and N A the Avogadro's number.

The concentrations introduced in the absorption cell and the path lengths were chosen in such way to obtain optical density values between 0.1 and 2, conditions for which the cross-section is determined with good accuracy.

Simulation Chamber:

The kinetic studies of the reaction of pyridine with OH and O 3 were conducted in P ~ (760 ± 5) Torr of air at T = 298 ± 2 K using a simulation chamber at ICARE-CNRS (Orléans). The experimental set up has been described in details earlier [10,11] therefore, it will only be presented briefly here.

The simulation chamber is made of Teflon with a volume of 7.3 m 3 and a surface/volume ratio of 3 m -1 . A calibrated gas cylinder equipped with pressure sensors was used to introduce gas reactants into the chamber. Vapours of liquid reactants were introduced into the chamber by placing a known volume in a bubbler which was then flushed by purified air. In order to ensure rapid mixing (2 minutes) of reactants, two fans made of Teflon were used. The pressure in the chamber was continuously kept slightly above the atmospheric pressure in order to avoid contamination from outside air. Air purification system (Donaldson Ultrafilter, Deutschland GmbH) equipped with an adsorption dryer was used to generate purified air with residual concentrations of ozone and hydrocarbons below than 1 ppb. Temperature and relative humidity data were continuously measured by a combined sensor. Calibrated mass flow controllers (200 Lmin -1 , Tylan General, 2900 Series and 50 L min -1 Bronkhorst Hi-TEC) were used to fill and flush the chamber. The facility is equipped with different type of lamps.

In this work, 2 to 4 lamps (UV-A T-40 L, Viber Lourmat) providing UV radiation with a maximum emission at 254 nm were used to generate OH radicals from the photolysis of H 2 O 2 . The rate coefficient for the reaction of pyridine with OH was determined relative to that for acetone. The reaction of pyridine with O 3 was measured under pseudo first order conditions where the concentration of O 3 was in large excess compared to that of pyridine

([O 3 ] 0 >>[pyridine] 0 )
. Ozone was generated by discharge in O 2 and its concentration was measured by FTIR . Pyridine and acetone were monitored by using the in-situ FTIR technique and PTR-TOF-MS (Ionicon analytik). The chamber is equipped with a White mirrors system with an optical path of 148 m. This multiple reflection system is coupled to a Fourier Transform InfraRed (FTIR) spectrometer (Nicolet 5700 Magna) used for the detection in situ of organic molecules. Infrared spectra were recorded every 5 minutes by co adding 40 to 130 interferograms with a resolution of 1 cm -1 over the spectral range 600-4000 cm -1 . 'The dilution rate was determined by adding SF 6 to the air mixture, so experiments were carried out several times and rate dilution values of (0.95 to 1.3) ×10 -5 s -1 were obtained'.

Chemicals:

The used chemicals were obtained from Sigma Aldrich: Acetone (99.9%) and C 5 H 5 N (99.9 %) were further purified by repeated freeze pump thaw cycles before use. H 2 O 2 (50% in water) was concentrated by bubbling air through the solution to remove water for several days prior to use. SF 6 (Alphagaz 1) and Helium (Alphagaz 1) were provided by Air

Liquide

Results and Discussion: UV absorption cross-sections of pyridine:

The absorption cross-section values obtained are presented in Table 1 and shown in Figure 1. The measurements were performed in a series of 50 to 100 nm adjacent regions with a 10 nm overlap. The reported values represent the average of 3 to 8 independent measurements at several pressures. The recorded spectra have been found to be reproducible (the variation did not exceed 20% over the studied spectral range). As seen in Figure 1, the obtained spectrum exhibits a broad absorption band in the spectral region 220 -310 nm with a maximum of 4.36×10 -18 cm 2 molecule -1 located at 249 nm.

Pyridine is isoelectronic with benzene, so the observed absorption band is most probably due to the -* electronic transition. However, due to the presence of nitrogen atom in the aromatic ring the n-* transition is expected to occur and might be overlapping with -* band.

Several studies were found in the literature dealing with the spectroscopic analysis of the UV absorption spectrum of gaseous pyridine [12,13,14]. These studies showed that the maximum of absorbance which corresponds to -* transitions is located close to 250 nm. For the transition n-* , the maximum of absorption is located near to 290 nm. However, to our knowledge, no UV absorption cross-sections studies, in the gas phase, exist in the literature for pyridine.

Uncertainties on the obtained cross-sections values were estimated to be lower than 20% resulting mainly from the uncertainty on the concentration and the purity degree of pyridine.

In order to minimize the uncertainty due to wall loss, the cell was heated up to 353 K and the pressure of pyridine introduced into the cell was largely lower than its vapour pressure. The used sample of pyridine was purified by repeated freeze-pump-thaw cycles before use. Other sources of errors may include the instability of the signal; this error was reduced by averaging several measurements.

Reaction with OH radicals:

The rate coefficient for the reaction of OH with pyridine was measured relative to that of acetone:

Pyridine + OH products k pyridine Acetone + OH products k acetone
where k pyridine and k acetone are the rate coefficients of the reaction of OH with pyridine and acetone, respectively.

In addition to these reactions, both reactants, pyridine and acetone, may be lost by secondary processes such as dilution, the loss to the wall of the chamber and the photolysis under UV radiation. In order to evaluate these losses, several tests were performed prior to To assess losses of reactants by dilution, SF 6 was added to the air mixture to quantify the dilution rate. The global secondary loss rates obtained for acetone was k P (acetone) = 1.1×10 -5 s -1 shows that, the loss of acetone is mainly due to dilution. For pyridine, the global secondary loss rates was k P (pyridine) = 2.16 × 10 -5 s -1 obtained under radiation is almost the same as that obtained in the absence of radiation showing that the photolysis of this compound is negligible compared to other processes (dilution and wall loss).

The pyridine and acetone concentration-time profiles were described using the following equation: The rate coefficient for the reaction of pyridine with OH was derived from the slope of the plot shown in Figure 2 according to equation (2). A good linearity was observed with a correlation coefficient greater than 95 % and an intercept close to zero. A value of k pyridine = (5.4 ± 0.8) ×10 -13 cm 3 molecule -1 s -1 was obtained using k acetone = (1.84 ± 0.25)× 10 -13 cm 3 molecule -1 s -1 at 298 K [15].

The overall error in the rate coefficient value originates from the uncertainty on the rate coefficient of the reference compound ( The obtained k pyridine value is in good agreement with those of (4.9±0.4)×10 -13 and (5.31±0.60)×10 -13 cm 3 molecule -1 s -1 reported by Atkinson et al, [START_REF] Atkinson | [END_REF] and Yeung et al, [7], respectively. In their work, Atkinson et al, [START_REF] Atkinson | [END_REF] studied the reaction of pyridine with OH at 296±2 K and atmospheric pressure in a 5800-L, Teflon-coated environmental chamber, with irradiation being provided by a 24-kW xenon arc. The OH radicals were generated either by the photolysis of methyl nitrite at wavelength >290 nm or by the dark N 2 H 4 -O 3 reaction. The reactants and reference compounds were monitored by the FTIR absorption spectroscopy. The rate coefficient of the reaction of pyridine with OH, determined by Yeung et al, [7], was measured using a turbulent flow technique combined with high pressure chemical ionization mass spectrometry at 100 Torr pressure and 298 K. OH radicals were generated using the reaction of H with NO 2 . Moreover, our experimental value is 30% higher than that of k pyridine = 3.7×10 -13 cm 3 molecule -1 s -1 obtained by using AOPWIN, 2000 v.1.92 [START_REF] Epi | Atmospheric Oxidation Program for Microsoft Windows[END_REF] based on SAR calculations [START_REF] Kwok | [END_REF].

The reaction of pyridine with OH is expected to proceed mainly by addition on the meta position for which the electronic density is higher than ortho-and para-positions, as reported in previous theoretical studies [7,18]. In their study, Vivekananda et al, [18], used standard ab-initio and density functional theory (B3LYP)6-31+G(d,p) basis set to study the pyridinehydroxyl adduct. The obtained results showed that OH addition to the two carbon atoms in the meta-position amounted to 81-98% of total reactivity and the nitrogen atom was calculated to be virtually unreactive toward OH attack. Theoretical calculations on the addition of OH to pyridine have been also performed by Yeung et al, [7] 2003 using the B3LYP/6-31G(d,p) and TST MPW1K/6-31+G(d,p) levels. The meta position was found to be much more favorable than the other positions for OH addition to pyridine because of the meta-directing nature of the relatively electronegative substituent. However, according to the study carried out by Barckholtz et al, [19], the H-atom abstraction channel seems to become kinetically and thermodynamically preferred over the addition channel at temperature lower than 298 K in particular for attack ortho-and para-sites.

As expected, the reactivity of pyridine with OH radicals is slower (2.5 times) than that for benzene (k benzene =1.2 × 10 -12 cm 3 molecule -1 s -1 recommanded by IUPAC [20]. This is due to the inclusion of a nitrogen atom into the aromatic ring that lowers the reactivity toward OH radicals [7] due to the electron-withdrawing effect of the nitrogen atom.

The comparison with pyrrole (C 4 H 5 N) shows that the reactivity of OH with pyridine is almost 200 times slower than that of pyrrole [21] suggesting that the radical addition to five-membered rings is kinetically preferred and thermodynamically more exoergic than radical addition to six-membered rings [19].

Reaction with O 3 :

The rate coefficient for the reaction of pyridine with O 3 was determined under large The initial concentrations were 4430 ppb for ozone and in the range 70-200 ppb for pyridine. The loss rates of pyridine in the absence and presence of ozone were determined from the plot showed in Figure 3. In the presence of O 3 , the monitoring starts 10 minutes after the introduction of ozone into the chamber. obs k values of (1.85 ± 0.4)×10 -5 s -1 and (2.2 ± 0.4) ×10 -5 s -1 were found in the absence and the presence of ozone, respectively. A value of (3.28 ± 1.70) × 10 -20 cm 3 molecule -1 s -1 was obtained for 3 O k according to relation (5).

The reaction of pyridine with O 3 has been the subject of only one study carried out by Atkinson et al. [START_REF] Atkinson | [END_REF] by using a 175-L FEP Teflon reaction chamber at 296 K and 735 Torr of air. The rate coefficient was measured by monitoring the increased rates of O 3 decay in the presence of known concentrations of pyridine in large excess compared to that of ozone. An upper limit of 1.1×10 -20 cm 3 molecule -1 s -1 was obtained.

Atmospheric implications:

The data obtained in this work were used to estimate the tropospheric lifetime, X , of pyridine due to its removal by chemical reaction with atmospheric photo-oxidants (X= OH, Cl, NO 3 or O 3 ) and the tropospheric lifetime due to photolysis, photolysis , according the equations ( 6) and ( 7): ] X [ k 1 X X (6) where [X] represents the average concentration of the atmospheric photo-oxidant and k x the rate coefficient for the reaction of pyridine with the photo-oxidant. Average global tropospheric concentrations of 1×10 6 molecule cm -3 [2] 1×10 3 molecule cm -3 [22] and 7×10 11 molecule cm -3 [23] for OH, Cl and O 3 were used, respectively. P photolysis J 1 (7) where J p is the averaged photo-dissociation rate constant. J p values were calculated for different zenith angles varying from 20° to 50° at 40 o N latitude corresponding to the morning of a cloudless day using the following relationship:

J p = ( ) ( ) I( ) d (8)
where ( ) is the absorption cross-section at wavelength >290 nm (cm 2 molecule -1 ) determined in this work, ( ) is the primary quantum yield of photodissociation at wavelength , and I( ) is the actinic flux of the solar radiation at wavelength (photons cm -2 s -1 ). The actinic flux values at sea level were taken from reference [24]. A quantum yield of unity was used since the overall quantum yield for pyridine is not known. Therefore, the calculated J p values represent an upper limit of the photodissociation rate constants, and the deduced lifetimes constitute lower limits. Values of tropospheric lifetimes are summarized in Table 2. Moreover, given the relatively low Henry's constant of pyridine (1.1×10 -5 atm m 3 mol -1 [25], it can be can deduced that the photochemical process are the main routes of atmospheric removal of this compound since it is degraded in less than 2 days (Table 2). It should be noted, that photolysis studies of pyridine under natural solar radiation are required in order to confirm our results. In addition, information regarding the reaction products resulting from atmospheric degradation of this species is sorely needed in order to evaluate its atmospheric impact.

  kinetic studies. Measurements were carried out in the absence of radiation for the acetone/pyridine/H 2 O 2 /air mixture to check for dark reactions and wall losses. The concentrations of acetone and pyridine were monitored by PTR-TOF-MS and GC/MS during 1 hour. Additional tests were performed in the absence of H 2 O 2 and under radiation in order to under light intensities and radiation time similar to those employed during our kinetic experiments (3 to 4 hours).

  concentrations of the reactants before irradiation and at time t, respectively and p k and p k' are the decay rates resulting from secondary losses for pyridine and acetone, respectively.

  and the error on the determination of the slope slope slope shown in Figure2derived from the least squares analysis (4 %). The error on the rate coefficient was therefore calculated according to equation (

3 Ok

 3 excess of O 3 compared to that of pyridine. The concentration of pyridine was monitored by PTR-TOF-MS and that of O 3 by UV absorption. The rate coefficient of the reaction of pyridine with O 3 was determined by monitoring the consumption of pyridine according to the following equations: and p k is the loss rate of pyridine in the absence of O 3 .

Figure 1 :

 1 Figure 1: UV absorption spectrum of pyridine obtained in this work over the spectral range 200-350 nm at 353±1 K.

Figure 2 :

 2 Figure 2: Relative rate plot for the reaction of pyridine with OH in the presence of acetone.

Table 1 :

 1 Absorption cross-section σ (10 -19 cm 2 molecule -1 ) of pyridine over the wavelength range (λ) 200-350 nm at 353±1 K.

	λ	σ	λ	σ	λ	σ	λ	σ
	200	32.49	240	29.25	280	3.04	319	0.02
	201	32.91	241	30.98	281	2.44	320	0.02
	202	24.40	242	33.91	282	2.52	321	0.02
	203	17.10	243	36.04	283	3.00	322	0.01
	204	10.50	244	37.75	284	1.68	323	0.02
	205	7.00	245	38.95	285	1.23	324	0.01
	206	5.49	246	39.47	286	1.04	325	0.02
	207	4.02	247	40.98	287	0.97	326	0.02
	208	3.15	248	43.19	288	0.70	327	0.01
	209	3.03	249	43.60	289	0.53	328	0.01
	210	2.42	250	43.53	290	0.44	329	0.02
	211	3.13	251	42.62	291	0.33	330	0.02
	212	2.83	252	41.96	292	0.28	331	0.01
	213	2.90	253	41.90	293	0.22	332	0.02
	214	3.30	254	42.65	294	0.16	333	0.01
	215	3.14	255	41.13	295	0.12	334	0.01
	216	3.44	256	39.58	296	0.11	335	0.02
	217	3.68	257	35.91	297	0.08	336	0.01
	218	4.09	258	34.13	298	0.07	337	0.01
	219	4.59	259	33.12	299	0.06	338	0.02
	220	5.28	260	31.35	300	0.04	339	0.01
	221	5.71	261	29.53	301	0.03	340	0.02
	222	6.39	262	28.04	302	0.03	341	0.01
	223	7.15	263	26.15	303	0.02	342	0.01
	224	7.50	264	24.36	304	0.03	343	0.01
	225	8.25	265	21.72	305	0.02	344	0.02
	226	9.06	266	19.30	306	0.02	345	0.01
	227	9.91	267	17.05	307	0.02	346	0.02
	228	10.04	268	15.35	308	0.02	347	0.01
	229	12.28	269	13.88	309	0.02	348	0.01
	230	12.85	270	12.07	310	0.02	349	0.01
	231	14.39	271	11.86	311	0.02
	232	16.06	272	9.59	312	0.02
	233	17.42	273	8.25	313	0.02
	234	19.11	274	7.36	314	0.02
	235	20.05	275	7.59	315	0.02
	236	21.60	276	5.75	316	0.02
	237	24.37	277	5.07	317	0.02
	238	25.91	278	4.60	318	0.02
	239	28.11	279	3.84	319	0.02

Table 2

 2 Photodissociation rate constants J p (upper limits) as a function of solar zenith angle and the estimated tropospheric lifetimes of pyridine with respect to photolysis and its reactions with atmospheric photooxidants. value calculated using the photolysis rate determined at =30° b value calculated by using the rate constant found in this work c value calculated by using the rate constant reported by reference[26] (Zhao et al, 2007)

	Photodissociation rate constants J p (s -1 )	photolysis	OH	NO3	Cl	O3
	=20°	=30°	=40°	=50°
	8.15 10 -6 7.58 10 -6	6.71 10 -6	5.55 10 -6	> 1.5 d

a

Acknowledgements

The authors would like to acknowledge the programs VOLUBILIS (MA/10/226), PHC-Maghreb (32618SA) the Labex Voltaire (ANR-10-LABX-100-01), and the PIVOTS Project for support.


N Verdugo-Vásquez

C Acevedo-Opazo

H Valdés-Gómez

M Araya-Alman

I García De Cortázar

B Tisseyre

SPATIAL VARIABILITY OF PHENOLOGY IN TWO IRRIGATED GRAPEVINE 1 CULTIVAR GROWING UNDER SEMI-ARID CONDITIONS 2

Keywords: Vitis vinifera, berry maturity, within field variability, temporal variability, 33 management zones, climate change

Knowledge and monitoring of the grapevine phenology during the season are important 13 requirements for characterization of productive regions, climate change studies and planning 14 of various production activities at the vine field scale. This work aims at studying the spatial 15 variability of grapevine phenology at the within field scale. It was conducted on two fields, 16 one of cv Cabernet Sauvignon of 1.56 ha and the other of cv Chardonnay of 1.66 ha, both 17 located in Maule Valley, Chile. Within each vine field, a regular sampling grid was designed, 18 to carry out weekly measurements of phenology and maturation. The main results show that 19 there is a significant spatial variability in the phenological development and maturation at 20 the within field scale for both fields. This variability is spatially organised and temporaly 21 stable from the beginning of the season (post-budburst) to harvest and over the years. A 22 cluster analysis allowed us to define two clearly contrasted zones in terms of phenology and 23 maturation in both fields, explained by the microclimate. The magnitude of difference 24 between zones varied from 4 to 9 days depending on phenological stages and from 5 to 43 25 days for maturation. These differences are similar and comparable to that observed at larger 26 scales or under scenarios of climate change. These results highlight the necessity to better 27 take into account this variability to improve sampling and to base decisions of production 28 activities (spraying, harvest, pruning, etc.) application on more relevant information. Further 29 investigations should determine the environmental factors that determine the observed spatial 30 variability.

INTRODUCTION 4

Grapevine phenology is the study of the stages of growth as budburst, flowering and veraison, 5 which are repeated every season and depend mainly on climatic and hormonal factors 6 [START_REF] Mullins | Biology of the Grapevine[END_REF][START_REF] Jones | Climate Influences on Grapevine Phenology , Grape 29 Composition , and Wine Production and Quality for Bordeaux , France[END_REF]. Knowledge and monitoring of the different 7 phenological stages of the grapevine during the season are important requirements for 8 planning of various production activities at the vine field scale [START_REF] Mullins | Biology of the Grapevine[END_REF]. [START_REF] Quénol | Analyse du climat aux échelles locales dans le contexte du changement 10 climatique[END_REF] example, for the management of powdery mildew in the vine there are control methodologies 10 based on the monitoring of the pathogen and plant phenology so as to make phytosanitary 11 applications at the most susceptible phenological stages (flowering and fruit set), thereby 12 reducing the number of phytosanitary treatments [START_REF] Campbell | Riesgo de oídio (Erysiphe necator) de la 26 vid en relación con el desarrollo de los racimos[END_REF]Bramley et al. 2011). 13 Likewise, for irrigation management it has been observed that the application of regulated 14 deficit irrigation on specific phenological periods of post-setting and post-veraison optimizes 15 the vegetative growth, the yield and the final quality of the berries [START_REF] Ojeda | Influence of pre-24 and postveraison water deficit on synthesis and concentration of skin phenolic 25 compounds during berry growth of Vitis vinifera cv. Shiraz[END_REF]16 together with its fruit ripening makes it possible to optimize the harvesting process, often 1 supporting a differentiated management of vine fields (Trought and Bramley 2011). 2

The importance of monitoring the phenology of the vine as a decision support information 3 has motivated numerous investigations at different spatial scales, for example, at meso scale 4 (vineyards, more than 200 ha surface) models that predict phenological events have been 5

developed and can be used to plane farming operations at this scale (Ortega-Farías et al. Eccel 2010, 2011). There have also been studies on the spatial variability of the phenology 11 of grapevines at the macro scale, in order to determine optimum pedo-climatic zones for the 12 production of quality grapes [START_REF] Tesic | Environmental effects on 28 cv Cabernet Sauvignon ( Vitis vinifera L .) grown in Hawke ' s Bay[END_REF]) and to establish spatial patterns of evolution 13 of berry maturity in different areas of Australia [START_REF] Petrie | Advancement of grapevine maturity in Australia 3 between 1993 and 2006: putative causes, magnitude of trends and viticultural 4 consequences[END_REF]. Generally speaking, the findings reported in these researches have large spatial scale 22 applicabilities (macro and meso scales), which is at odds with the needs of growers for whom 23 the basic unit of management is the vine field (surface less than 5 ha, micro scale) which is 24 characterized by a same variety, training system and management practices. [START_REF] Campbell | Riesgo de oídio (Erysiphe necator) de la 26 vid en relación con el desarrollo de los racimos[END_REF] In recent years, several authors have observed that in agriculture and especially in viticulture, 26 there is significant spatial variability at the micro scale (within field level) in the production 27 variables such as plant water status (Acevedo-Opazo et al. 2010; Acevedo-Opazo et al. 2013), 28 vegetative expression [START_REF] King | Within-vineyard variability in vine 1 vegetative growth , yield , and fruit and wine composition of Cabernet Sauvignon in 2 Hawke 's Bay, New Zealand[END_REF], yield [START_REF] Bramley | Understanding variability in winegrape 11 production systems 1 . Within vineyard variation in yield over several vintages[END_REF]Tardaguila et 29 al. 2011) and the quality components of the berries [START_REF] Bramley | Understanding variability in winegrape 11 production systems 1 . Within vineyard variation in yield over several vintages[END_REF]Baluja et al. 2013). This observed variability was mainly attributed to differences in soil (Tardaguila 1 et al. 2011). 2

In this light, one wonders whether the vine field is a unit of homogeneous management in 3 relation to the evolution of its phenological cycle during the season. If this is not the case, 4

then the methods traditionally used by wine producers to characterize the phenological stage 5 of their vine fields would not be appropriate to represent the spatial variability of the fields. 6 Indeed, in practice, wine industry does not carry out more than two phenological observations 7 per field. These two observations are assumed to be representative of the whole vine field 8 and sometimes to other neighbouring fields of the same variety. Thus, traditional methods 9 could result in inadequate decisions of interventions . 10 To our knowledge, studies on spatial variability on climate and phenology have focused at 11 macro or meso scale (surface > 200 ha) [START_REF] Bonnefoy | Temporal and spatial analyses of temperature in a French wine-producing area: 2 The Loire Valley[END_REF][START_REF] Irimia | Spatial 25 Distribution of Grapes Sugar Content and its Correlations with Climate Characteristics 26 and Climate Suitability in the Huși (Romania) Wine Growing Region[END_REF]Quénol 12 2013; Quénol et al. 2014), without considering the micro scale (spatial scale < 5 ha). The aim 13 of this study is therefore to investigate the spatial variability of grapevine phenology at the 14 within field scale, answering the following questions: i) is there a spatial variability in the 15 phenological development of the vine at the field scale? ii) is the spatial variability observed 16 stable over time? and iii) is it worth to define management zones in relation to the phenology 17 of the fields? [START_REF] Sadras | Predicting the time course of grape ripening[END_REF]

19

MATERIALS AND METHODS 20

Experimental Fields 21

The study was conducted in two fields, one of cv. Cabernet Sauvignon of 1.56 ha (field 1) 22 and the other of cv. Chardonnay of 1.66 ha (field 2), both located in the Panguilemo 23 Experimental Station of the University of Talca (Maule Valley), Chile (Fig. 1). The 24 characteristics of both fields are summarized in Table 1. The region is characterized by 25

Mediterranean climatic conditions, while the soil belongs to the Talca and San Rafael series 26 (Ultic Haploxeralfs) (Soil Survey Staff 1999). Both vineyards were managed according to 27 the conventional agricultural practices used in the commercial vineyards of central Chile in 28 terms of canopy management, fertilization, pest and disease control, pruning and irrigation, 29 over all the seasons of the study period. Within each vine field a regular sampling grid was 30 designed, one with 18 measurement sites (25x25 m) for field 1 (Fig. 2 a) and one with 19 measurement sites (25x25 m) for field 2 (Fig. 2 b). Each site of the grid was represented by 1 four consecutive plants. The borders of the fields and sampling sites within each field were 2 geo-referenced with a differential global positioning system receiver (DGPS) (Trimble, 3

Pathfinder ProXRS, Sunnyvale, California, USA) and stored as Eastern and Northern 4 coordinates (Datum WGS84, UTM projection, Zone 19S) to perform the mapping and spatial 5

analysis. 6

An automatic weather station (Adcon Telemetric, A730, Klosterneuburg, Austria) installed 7 under reference conditions, at 300 m from the vineyards, provided data such as air 8 temperature, relative humidity, solar radiation, precipitation, wind speed and direction of the 9 wind at 15-minute intervals. The sensors were installed at 2.5 m above the soil surface, except 10 for the temperature and relative humidity sensors which were located 1.5 m above the soil 11 surface. This information was used to estimate reference evapotranspiration (ET0), using the 12 Penman-Monteith equation (Allen et al. 2006) and the sum of growing degree-days (GDD), 13 from 1 st May to harvest and for each phenological period considered (i.e. flowering or 14 veraison). Sum of GDD was calculated as the sum of the daily difference between the average 15 air temperature minus 10°C (Winkler 1974). 16 Additionally, considering that the main variable that affects phenological development of the 17 The phenological observations were made from budburst to veraison, every 7 days in 30 30 shoots chosen systematically among the 4 vines of a site of the grid. The same shoots were always measured date after date over a season. On each site of the grid, mean value of 1 phenology measured over the 30 shoots was calculated. Phenology was estimated using the 2

Eichhorn and Lorenz phenological scale as modified by Coombe (Coombe 1995) (Table 2). 3

It considers measurements of leaf number, length of shoots (cm) and berry diameter (mm). 4

The phenological scale assigns a number to each phenological measurement (expressed in 5 units of Phenological Scale, PS, Table 2), allowing us to represents phenology as a numeric 6 variable. For data analysis it was used only the three major phenological stages (post-7 budburst, flowering and veraison). Veraison for cv Cabernet Sauvignon was considered by 8 berries colour change, while for cv Chardonnay it was considered by berry softness and 9 colour change simultaneously. 10

b) Maturation (expressed as total soluble solids, TSS) 11

From veraison to harvest, measurements of total soluble solids (TSS) were performed as 12 indicator of grapevine maturity. TSS was chosen as the most typical indicator used to define 13 harvest time. This measurement was done by a thermo-compensating refractometer (BRIX30 14 model, Leica, USA), by randomly selecting eight clusters from each site of the grid. For each 15 cluster two berries were sampled at the top, the middle and the bottom of the cluster. 16

Therefore, TSS value of each site of the grid and each date corresponded to a sample of 48 17 berries. Three sampling dates were considered, called Pre-Ha 1 (25 days before harvest), Pre-18 Ha 2 (12 days before harvest) and Ha (Days of the harvest). For both fields, harvest (Ha) 19 was defined at 22°Brix. This last value corresponding to the optimal maturity at harvest in 20 our conditions. 21

22

Analysis method 23

For main phenological stages and maturity dates, basic statistics such as standard deviation, 24 maximum/minimum values and coefficient of variation (CV) were calculated. These 25 statistics were expressed in units of Phenological Scale (PS) and TSS (ºBrix) respectively for 26 phenology and maturation. 27

A variographic analysis was performed to study the spatial structure of the phenological data 28 and the maturity at each date for both experimental fields. Omnidirectional semivariograms 29 were calculated according to the methodology proposed by Webster and Oliver (2001). The three basic parameters of the semivariogram were obtained: nugget (C0), sill (C0+C1) and 1 range (a), which define the degree and scale of spatial variation among the observations. The 2 computation of a semi variogram is usually not recommended with a number of points as low 3 (Webster and Oliver 1992). However, for this experiment, the quality of data collected 4 (manual measurements averaged over a large number of individuals) and the significant 5 spatial organisation of the fields allowed us to consider such an analysis. From 6 semivariogram parameters the degree of spatial dependence (DESP) also called the 7

Cambardella index, was derived by using the ratio between the nugget and the total 8 semivariance of the semivariogram (sill), expressed as a percentage (Cambardella et al. 9

1994), Eq. 1. 10

𝐷𝐸𝑆𝑃(%) = [ C 0 C 0 + C 1 ] * 100 (1) 11
The Cambardella index allowed us to compare the relative size of the nugget effect for each 12 date of the experiment. Thus, the values of 𝐷𝐸𝑆𝑃 ≤ 25% indicate a strong spatial 13 dependence; 25% ≤ 𝐷𝐸𝑆𝑃 ≤ 75% indicate a moderate spatial dependence, and the values 14 of 𝐷𝐸𝑆𝑃 > 75% correspond to a weak spatial dependence (Wu et al. 2008). In addition to 15 DESP, the mean correlation distance (MCD) [START_REF] Han | Spatial variability of soil 21 properties on two center-pivot irrigated fields[END_REF] was estimated (Eq. 2). 16

MCD(𝑚) = 3 8 * [ C 1 C 0 + C 1 ] * a (2) 17
The MCD provided an estimate of the distance at which the data have a high spatial 18 dependence [START_REF] Han | Spatial variability of soil 21 properties on two center-pivot irrigated fields[END_REF]. 19

Mapping spatial distribution 20

Maps with both phenological and maturation variables were done in order to visualize the 21 phenology and maturation of the fields. To this end, the method of interpolation Block-22 Kriging as proposed by Baluja et al. (2013) was used. The scale of the maps was defined 23 using an equidistant range, with two intervals. 24

Analysis of the Temporal Stability of the Spatial Variability (TSSV) of the grapevine 25 phenology and maturation 26

To quantify the TSSV of both phenology and maturation, the Kendall coefficient of 27 concordance (W) was calculated according to the methodology described by Tisseyre et al. 28 (2008). W ranges from 0, in the case of no temporal stability (total desagreement in site order 29

Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5 between dates), to 1 in the case of temporal stability [START_REF] Saporta | Probabilité, Analyse des données et Statistique. Analyse des données et 21 statistiques[END_REF]). This analysis was carried 1 out in two ways: 2 -to quantify the TSSV of phenology and maturity over the seasons, it was performed 3 separately on the main phenological stages (post-budburst, flowering and veraison) and on 4 the maturation (Pre-Ha 1, Pre-Ha 2 and Ha), 5

-to quantify the intra-season TSSV, it was performed on the main phenological stages and 6 maturation dates measured within each season. 7

Zoning of the vine fields based on grapevine phenology and maturation 8

For each field, the zoning of both phenology and maturation was conducted with a cluster 9 analysis using the centroid squared euclidean distance [START_REF] Flores | Variabilidad Espacial del Rendimiento de Uva y Calidad del Mosto en 4 Cuarteles de Vid cv . Cabernet Sauvignon y Chardonnay en Respuesta a la Variabilidad 5 de Algunas Propiedades del Suelo[END_REF]. For phenology zones 10 most relevant phenological stages of the vineyard management (post-budburst, flowering and 11 veraison) were considered while three main dates (Pre-Ha 1, Pre-Ha 2 and Ha) were 12 considered for maturation. For both fields and each dates, two clustering operation were 13 therefore performed, one related to phenology and the other one to maturation. The clustering 14 was conducted to provide two classes for each field under consideration. Considering the 15 high spatial organisation of our data, this clustering method was expected to highlight within 16 field zones. In the rest of the document, zones will refer to the classes resulting from the 17 For maturation the three dates (Pre-Ha 1, Pre-Ha 2 and Ha) were transformed into days 29 through the implementation of a maturity index (MI). The MI quantifies the increase in TSS 30 (°Brix) for each degree-day accumulation (Eq. 5):

𝑀𝐼 = 𝑆𝑆 𝐻 -𝑆𝑆 𝑀 ∑ 𝐺𝐷𝐷 𝑛 𝑗=1
(5) 1

Where: MI = maturity index (°Brix d °C-1 ); SSH = total soluble solids measured at harvest 2 (°Brix); SSM = total soluble solids measured at post-veraison (15 days after veraison, TSS 3 >15°Brix); GDD = growing degree day (°C d -1 ); j corresponds to the measurement day of 4 SSM and n to the day of SSH. 5

This approach assumes that after a period of rapid increase in TSS (at veraison), TSS 6 increases linearly over time (Sadras and Petrie 2012). The MI was estimated for both fields, 7 obtaining a specific value for each cultivar. SSM and SSH were estimated by averaging values 8 measured over the whole field for each field. MI was used to estimate the number of days 9 explaining observed difference in maturity between the whole field and the within field 10 zones. (Eq. 6).

11 12 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝑑𝑎𝑦𝑠 = 𝑆𝑆 𝐹𝑖-𝑆𝑆 𝑍𝑖 MI * 𝐺𝐷𝐷 𝐴 (6) 13
Where: SSFi = average total soluble solids of all sampling sites of the fields at date i (°Brix); 14

Climate characterization 29

The climatic characterization for all the study seasons is presented in Table 3. The mean air 1 temperature during the period between 1 st May and the harvest ranged between 15 and 15.7 2 °C. The third season (2011-12) presents the highest temperature, showing this season was 3 warmer (with mean temperature values above 20 °C) during the period from flowering to 4 harvest. These temperatures lead to higher sum of growing degree-day values, which 5 fluctuated between 1455 and 1640 °C d -1 for field 1 (cv Cabernet Sauvignon) and between 6 1385 and 1523 °C d -1 for field 2 (cv Chardonnay). These values are well above the 1150 and 7 1300 °C d -1 considered adequate to properly mature respectively, cv Chardonnay and cv 8

Cabernet Sauvignon [START_REF] Gladstones | Viticulture and environment[END_REF]). Regarding the precipitation, these were mainly 9 similar during the last three seasons and 23% lower during the first season (2009-2010). As 10 a result of the low rainfall and evapotranspiration, a climate water deficit (P-ET0) was 11 observed, which fluctuated between 464 and 553 mm for the different seasons; the first 12 season showed the lowest water deficit. Finally, the climatic conditions of the seasons studied 13 can be considered similar, except the 2012-2013 season which presents higher sGDD value. 14

Non-spatialized analysis of the grapevine phenology and maturation 15

Main statistics of the phenology and the maturity are presented in Table 4 andTable 5 16 respectively. For field 1, the phenological stages of post-budburst, flowering and veraison 17 presented a range of variation between 1 and 6 units on PS for the 4 seasons. Meanwhile, for 18 field 2, these ranges varied between 2 and 5 units of PS (Table 4). For both fields the stage 19 of veraison presented the lowest dispersion during seasons (smallest range). For the 20 maturation period, ranges of variation varied from 3.6 to 6.0 °Brix for field 1 whereas for 21 field 2 they varied from 2.7 to 4.9 °Brix (Table 5). With regards to the variability between 22 seasons, the range values remained similar over the seasons, suggesting that there is a 23 temporal stability in the dispersion of the phenological stages and maturation. 24

The coefficients of variation (CV) for both fields show that the phenological stage with the 25 greatest variation during all the seasons was post-budburst, with values that ranged between 26 8.3 to 12.8% and 6.4 to 15.6% for field 1 and field 2 respectively. The veraison presented the 27 lowest variability. In both fields and over all seasons of the experiment, CV value decreases 28 from post-budbreak to veraison. This result may be due to the proposed scale (Table 2 This first analysis highlight variability in the phenological development and maturation at 7 the within field scale. Next sections aim at studying whether this variability is organised 8 spatialy and if the observed variability is significant enough to justify site specific 9 managements. 10

Spatial variability of the grapevine phenology and maturation 11

The semivariogram model which was generally better adjusted to the different phenological 12 stages and maturation dates was the Gaussian model, with r 2 values ranging between 0.67 13 and 0.99 (Table 6 and7). A significant variation is also observed in the range of the 14 semivariogram which fluctuated between 22.8 and 150 m (Table 6 and7). The range is in 15 almost all cases higher than the sampling distance (25 m). This indicates the sampling 16 distance is large enough to highlight the spatial variation. Range of the semi variogram 17 exceeds in almost all cases length of the fields, showing the presence of a non-stationary 18

phenomenon. 19

For all seasons, all phenological stages and almost all maturation dates, a strong spatial 20 dependence (DESP) was observed (Table 6 and7 The identification of a non-random spatial structure, for all the seasons throughout phenology 24 and maturation could be explained by the environmental factors of the fields, such as the soil 25 conditions and topography and their resulting effect on the microclimate of the canopy (Tesic 26 et al. 2001). Thus, more research should be conducted to determine more precisely which 27 factor or set of factors impact the within field microclimate and determine the spatial 28 structure observed in phenology and maturation. 29 Fig. 5 and Fig. 6 show the spatial distribution of the main phenological stages and maturation 30 respectively for field 1. Fig. 7 and Fig. 8 show the spatial distribution of the main phenological stages and maturation for field 2. For both fields and almost all the maps, two 1 well-contrasted zones resulting from the clustering method can be observed. For field 1, 2 more advanced phenological stages and maturation are observed in the North-Western part 3 of the field. This zone is rather flat and is characterised by a deeper soil and less inclination. 4

Meanwhile, for field 2 more advanced phenological stages and maturation occur in the 5 southern part of the field. This latter zone is located in a slightly lower position, characterized 6 by a higher total soil water availability (data not shown). 7 8

Relationship with canopy temperature 9

Regarding results of temperature sensors (Fig. 9) a relationship between phenology and 10 canopy temperature is clearly highlighted. There are important differences in temperature 11 between zones (defined in Fig. 3) throughout the 37 days of measurements (Fig. 9 a) for field 12 1. Sensor located in zone 1 (Fig. 3) showed higher temperature and GDD (Fig. 9 b), which 13 may explain more advanced phenology and maturation stages (Fig. 5 and Fig. 6). The 14 difference in temperature between zones was 0.4 °C per day and 15 GDD (between DOY 15

and 293). 16

Variability observed in both phenology and maturation (Fig. 5 and6) may correspond to 17 zones where microclimatic conditions are different. In our conditions, at within field scale, 18 we can hypothesize that stable factors as differences in soil type, slope, topography and 19 vegetative expression, could explain these differences in microclimatic conditions 20 (temperature) and consequently differences in grapevine phenology and maturation. Then, 21 determination of environmental factors which drive grapevine phenology and climate 22 variability at the within field scale could constitute interesting investigations to map spatial 23 variability of grapevine phenology at this scale. [START_REF] Tardaguila | Variations of soil 25 properties affect the vegetative growth and yield components of "Tempranillo" 26 grapevines[END_REF] 25

Temporal variability of the grapevine phenology and maturation 26

The spatial distribution of all maps (Fig. 5,6, 7 and 8) presents fairly constant and well 27 defined spatial patterns. These latter remain stable within each season and between seasons. 28 This stability is observed for both fields. Baluja et al. (2013) reported a high temporal 29 stability of spatial patterns of the TSS measured at harvest over 3 seasons in a plot of 2.2 ha 30 of the cultivar Tempranillo in Navarra, Spain. In turn, results obtained by [START_REF] Bramley | Understanding variability in winegrape production systems 2 . 4 Within vineyard variation in quality over several vintages[END_REF] and Tisseyre et al. (2008) show that spatial patterns were not as stable over time for the same 1 variable. In this way, the temporal stability is probably a characteristic of the specific 2 conditions of each of the experimental sites, such as the weather and soil conditions and the 3 in the present investigation, the environmental conditions over the seasons were quite similar 5 (Table 3) and that the harvest date was considering when mean of the field reached 22 °Brix 6 for each seasons, which would explain the stability of the observed patterns. 7 8

The Kendall coefficient of concordance W (Table 8 and9) was used to quantify the TSSV 9 of phenological stages and maturation. Results of TSSV between seasons are shown in Table 10 8. Observed W values are high (W > 0.5) and statistically significant for both fields either 11 for phenology or maturation. This result corroborates the temporal stability observed on 12 maps presented previously (Fig. 5, 6, 7 and8). 13

Results of within season TSSV are shown in Table 9. For both fields, observed W values are 14 high and statistically significant either for phenology (W > 0.54) or maturation (W > 0.78). 15 This high TSSV observed within each season suggests that information generated at the 16 beginning of the season (post-budburst) can be used at a later date to characterize the spatial 17 variability of both phenology and maturation. [START_REF] Sadras | Predicting the time course of grape ripening[END_REF] 19

Zoning of the vine fields according to their phenology and maturation 20

The cluster analysis allowed us to define two clearly contrasted zones in terms of phenology 21 (Fig. 10 a andb) and maturation (Fig. 10 c andd). As expected, zones obtained from the 22 cluster analysis were very similar to zones observed on Fig. 5 to 8. Regarding the phenology, 23 zone 1 (cluster 1) represents 78% and 42% of the area of the field 1 and 2, respectively (Fig. 24 10 a and b). For the maturation, zone 1 (cluster 1) represents 89% and 58% of the area of the 25 field 1 and 2, respectively. Zone 1 presents the most advanced phenological stages (highest 26 values of PS) and most advanced maturation (highest values of TSS), for both fields. Zones 27 presented in Fig. 10 (a, b, c andd) integrates all the dates of the seasons. Therefore they 28 highlight possible management zones. High similarity is observed between zones defined 29 for phenological stage and maturity for both fields. The zone 1 of field 1 differ only in 1 site 30 of the grid between phenology and maturation, while for field 2, it differs in 3 sites. This suggests a strong relationship between the phenological development of the grapevine and 1 the maturation. Parker et al. (2014) observed that differences in maturation (expressed in 2 TSS) at harvest were the result of changes observed at the onset of maturation, during 3 veraison. Therefore, variability of phenology, for example at veraison, may constitute a 4 relevant decision support to define harvest zones of different quality early in the season. 5

In order to verify the opportunity to manage specifically zones identified on our experiments, 6 analysis of the delay in term of days has been performed. This analysis is presented in the 7 next section. 8 9

Grapevine phenology and maturation expressed in days 10

From an operational point of view, vinegrowers need to quantify the difference in days 11 among the different phenological stages and maturation of the zones so as to plane their work. 12 Thus, the numeric phenological stage was turned into chronological days through the 13 For field 1 differences between each zone of the fields ranges from 3 to 5 days depending on 23 the phenological stage considered and from 35 to 36 days depending on the maturity dates 24 considered (Fig. 11 a). Regarding zone 2, it presents the highest deviation from the mean of 25 the field. This is because zone 2 represents a small part of the field, therefore mean field is 26 logically, closer to the mean of the zone 1. 27

For field 2 (Fig. 11 b), observed differences in post-budburst (3 to 4 days) increase slightly 28 in subsequent phenological stages, showing the veraison as the phenological stages that 29 presents the highest difference (5 days) with respect to the mean field (value 0). For 30 maturation, differences between 3 and 5 days were observed. In general, differences observed for field 2 are much smaller than those observed for field 1. This suggest that factors that 1 explain observed differences in both fields may be different or present a lower magnitude of 2 variation in field 2. The magnitude of difference (in days) between zones varied from 4 to 7 3 days for the phenological stages of field 1, with flowering presenting the higher difference. 4

Regarding the maturity of field 1, the difference between zones varied from 42 to 43 days. 5

For field 2, difference varied from 6 to 9 days for phenology, with veraison presenting the 6 higher difference between zones. For maturity of field 2, this difference varied from 5 to 8 7 days. The variability of the phenological stages expressed in days (Fig. 11) was higher for 8 veraison in both fields. Indeed it can be related to the subjectivity of measuring this stage 9

(associated with colour change estimated visually), therefore the definition of zones within 10 field could reduce variability to estimate this phenological stage, improving the accuracy of 11 the estimation. With respect to maturation, variability in days was higher for field 1. 12 13

General discussion 14

This work showed that there is a significant spatial variability in the phenological 15 development and maturation within the studied vine fields. This variability was spatially 16 organised and temporally stable from the beginning of the season (post-budburst) to harvest 17 and over the years. This suggests that factors explaining this phenomenon would be related 18 to stable parameters of the environment affecting microclimate conditions. This effect may 19 be emphasized by the perennial specificity of grapevines. It was shown, for one field that the 20 phenology and maturation zones correspond to zones where the microclimate was different. 21

The observed differences in the microclimate of the field 1 can be explained by stable 22 environmental factors (soil characteristics, slope, soil texture, presence of groundwater and 23 compacted strata that limit the growth of the root, etc.), ie, the spatial variability of stable 24 environmental factors produced differences in the microclimate of each zone, which affected 25 the phenology and maturity of the grapevine. This explains the observed high stability of 26 zones between different seasons for field 1. There is no information on factors that determine 27 the spatial variability of both microclimate conditions and phenological development at the 28 within field scale in the litterature. 29 30 Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5

These results generate new questions concerning the modelling of the spatial variability of 1 the grapevine phenology at the within field scale. Two approaches may be considered for 2 further experiments: a) microclimate monitoring from wireless temperature sensors network, 3 such as works carried out at the meso scale level (Quénol et al. 2014). However, it is 4 important to define the number of sensors to be installed, location of those in the field and 5 the maintenance cost of the system which may limit its practical application (Kunz and 6

Tatham 2012; Primicerio et al. 2013). b) Characterisation of environmental factors at a high 7 spatial resolution. This approach corresponds to using high spatial resolution data 8 (topography, soil characteristics, slope) with low operational costs, for example, obtained by 9 unmanned aerial vehicle [START_REF] Matese | Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for 15 Precision Viticulture[END_REF]. Assuming these factors explain the spatial 10 variability of microclimate and the resulting variability in phenology and maturity, an 11 empirical model combining observations and high resolution spatial data could be considered 12 to model the microscale spatial variability of the phenology. The methodology used for the 13 water status in grapevines (Acevedo-Opazo et al. 2010) could be a relevant approach in this 14

case. 15 16

The observed within field differences may have important implications for crop management. 17

The goal of the following section is to identify the opportunity to manage the observed within 18 field variability for each phenological stages as well as for maturity. 19

For post-budburst, observed magnitude of variation may have practical applications 20 concerning spring freeze event. Susceptibility of the buds to spring frost depends on the 21 phenological stage of buds [START_REF] Friend | Effect of delaying 7 budburst on shoot development and yield of Vitis vinifera L. Chardonnay "Mendoza" 8 after a spring freeze event[END_REF]). On a controled experimentation, Friend et al. 22 (2011) shown effect of frost was different when occuring on two treatments characterized by 23 a difference of 1 PS (corresponding to 4 days in our study). A significant increase in primary 24 shoots death for the most advanced treatment (at budburst) was observed while reduced 25 dammages were observed in delayed treatment. Differenciate effect of frost had a significant 26 impact on yield at harvest. In our study, observed difference of 4 to 8 days (Fig. 8 a andb) 27 corresponding to 2-3 PS (Fig. 3 and5) for post-budburst could lead to significant differences 28 between zones in case a frost event occurs at this period. Practicaly, the delineation of 29 phenology zones is hardly manageable at this stage since spring frost events are hardly 30 predictible. However, when it occurs, differential effect of the frost may be of importance to understand the resulting within field yield variability and to consider site specific 1 management operations later in the season. 2 3

Flowering is considered as a critical stage for disease development such as powdery mildew 4 and botrytis [START_REF] Campbell | Riesgo de oídio (Erysiphe necator) de la 26 vid en relación con el desarrollo de los racimos[END_REF]). Therefore, this stage determines the application date of 5 specific pesticides to control these diseases (Bramley et al. 2011). Thus, a delay in fungicide 6 application, waiting for the slowest zone of the field to reach the flowering stage, could 7 impact negatively on the proper control of the most advanced zones. In our study, the average 8 differences between the zones were 7 days for field 1 and 6 days for field 2. To our 9

knowledge, there is no information in the literature dealing with the effect of application time 10 on diseases control. However our study provides new background information on the spatial 11 variability of flowering. This results opens opportunities to better understand the success or 12 failure of control strategies and the resulting within field variability of diseases. Information 13 on the spatial variability of the incidence and severity of diseases in vineyards is scarce. 14 Therefore, phenology maps may be useful to determine maturity zones at the within field 8 scale. These maturity zones are the basis of a decision support to manage grape quality at 9 harvest by considering, for exemple differential harvest over time and/or space (Bramley 10 2005). Note that in our experiment, differential harvest would be of great interest in the field 11 1. Indeed, for this field, zone 2 never reaches the expected maturity. This is probably due to 12 the specific soil conditions associated to inadequate irrigation management (Acevedo- Opazo 13 et al. 2013). In this case, zone 2 could be harvested separately. -on the sampling strategy aiming at characterising grapevine phenology of a field at this 5 scale. Indeed, regarding the observed within field variability, sampling quality is of 6 paramount importance to guarantee that differences in phenology is related to macro scale 7 factors and not to micro scale (within field) effects, 8

-on management practices adaptation to climate changes. Our work showed that observed 9 within field variability encompasses change in phenology estimated from climate change 10 scenarios. This may demonstrate the robustness of current vine fields to climate change since 11 within field zones may still be well adapted to the expected climate change. 12 13 CONCLUSIONS 14

To our knowledge, this is the first study highlighting within field spatial variability in the 15 phenological development of the grapevine and maturation simultenaously. In our 16 conditions, this variability proves to be spatially organised and temporal stable since 17

CONFLICT OF INTEREST 4

The authors declare that they have no conflict of interest.

6 2002;

 6 García de Cortázar-Atauri et al. 2009; Caffarra and Eccel 2010; Nendel 2010; Parker 7 et al. 2011; Sadras and Petrie 2012). Moreover, phenological process based models have also 8 been used to assess the impact of climate change on the phenological development of 9 grapevines at macro scales (regions) (Marta et al. 2010; Moriondo et al. 2010; Caffarra and 10

14 15

 14 With regard to the spatial variability of climate at the meso scale, in recent years the 16 TERADCLIM (Quénol and Bonnardot 2014) and ADVICLIM (Quénol et al. 2014) projects 17 modeled spatial variability of climate (temperature) in the main vine production areas of the 18 world. Assuming the availability of relevant calibrated model linking climate variables to 19 vine phenology, these projects may constitute relevant piece of information to model the 20 spatial variability of vine phenology. 21

  grapevine is the temperature[START_REF] Chuine | Plant Development 29 Models[END_REF] Quénol et al. 2014), 8 temperature sensors 18 (Dickson, LogTK500, USA) were installed in the field 1 (cv Cabernet Sauvignon) with the 19 aim to characterize the spatial variability of temperature at the within field scale. 20The sensors were located according to a preliminary analysis of the 2009-10 season. Two 21 zones of phenology were identified. Assuming temporal stability of these zones, 8 sensors(4 22 sensors for each zone) were installed (Fig.3) at 1.5 m above the soil surface and on vine 23 rows. Temperature was recorded every 30 minutes in the period of September (before 24 budburst) until October (pre-flowering) during the 2011-2012 season, corresponding to a 25 total of 37 days of measurements.

clustering. 18 19) 5 6 10 11

 18510 Phenological and maturation characterization expressed in days 20 a) Grapevine Phenology 21In order to provide a practical interpretation of the spatial variability in the phenology 22 observed between budbreak and veraison, the observed phenological stage was turned into 23 chronological days for both fields. This transformation was performed through the 24 implementation of a predictive model of phenology following to the methodology proposed 25 by Ortega-Farías et al. (2002). This model uses the monomolecular Mitscherlich equation 26 (Thornley and Johnson 1990), (Eq. 3). It estimates the phenological stage (expressed in units 27 of Phenological Scale, PS) in terms of thermal accumulation from the time of the budburst. 28 Based on observed values and meteorological data, the model was calibrated for each 29 cultivar. Resulting model allowed us to estimate the growing degree-days required to fulfill 30 a certain phenological stage. Fig. 4 represents the simulation of the proposed model for cv Cabernet Sauvignon and cv Chardonnay. On Fig. 4, vertical lines cutting x-axis correspond 1 to the sum of GDD necessary to reach flowering (PS = 23). This approach is similar to that 2 used in studies conducted by Jorquera-Fontena and Orrego-Verdugo (2010) who studied the 3 effect of climate change on the phenological development of the vine. 4 𝑃𝑠 = 𝑃𝑠 𝑓 -(𝑃𝑠 𝑓 -𝑃𝑠 𝑖 )𝑒 -𝑘(𝑠𝐺𝐷𝐷) (3Where: Ps = current phenological stage (PS), Psf = last phenological stage corresponding to 7 PS = 38, Psi = first phenological stage corresponding to PS = 4, k = rate of phenological 8 development and sGDD = sum of Growing degree-days (°C) from the date corresponding to 9 Psi to the date of Ps.Knowing observed GDD of each day from climatic data, Eq. (3) allowed us to transform PS 12 values in a number of days necessary to reach the phenological stage (Ps) under 13 consideration. At the field level, this approach was used to determine the number of days 14 required to achieve a given phenological stage (DatFi) considering the mean of PS 15 observations on all sampling sites. It was also used to determine the number of days required 16 to achieve a given phenological stage for a within field zone (DatZi) by considering the mean 17 of PS observations belonging to the considered zone. For a given phenological stage, this 18 methodology was used to determine the difference, expressed in days between the average 19 of the whole field and the specific zones of the same field (Eq. 4). 20 21 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝑑𝑎𝑦𝑠 = 𝐷𝑎𝑡 𝐹𝑖 -𝐷𝑎𝑡 𝑍𝑖 (4) 22 23 Where: DatFi = estimated date of the phenological stage i for the whole field, DatZi = 24 estimated date of the same phenological stage i for the within field zone Z. Phenological 25 stages i, corresponds to i = post-budburst, flowering and veraison.

SSZi 18 19

 18 = average total soluble solids of the sites belonging to zone z at the same date i (°Brix), 15 i is the date of measurement corresponding to Pre-Ha 1, Pre-Ha 2 and Ha. MI = Maturity 16 Index (°Brix d °C-1 ); GDDA = Daily mean of GDD from post-veraison (15 days after 17 veraison) to harvest (°C d -2 ).Software and Tools 20For the classical statistical analysis, the Statgraphics Plus 5.1 (StatPoint Inc., Virginia, USA) 21 software was used, while for the geostatistical analysis, the GS+ version 9.0 (Gamma Design 22 Software, LLC, 2008) software was used. For the design of the maps the 3DField (version 23 2.9.0.0., Copyrigtht 1998 -2007, Vladimir Galouchko, Russia) software was used. Finally, 24the Matlab Software (The Mathwork Inc., Natick, MA, USA) was used for the calculation 25 related to phenology model.

  ) which 29 is less sensitive to the changes observed at veraison. During maturation, CV values did not 30 show great variations within each season. Regarding the literature dealing with spatial variability at the within field scale, to our 1 knowledge, no reference on CV values for the phenological stage is available. Concerning 2 the maturation, especially for harvest, Baluja et al. (2013) obtained CV values for total 3 soluble solids measured during the harvest ranging between 5.3 and 7.5% for cv Tempranillo 4 in Spain. These values are similar to that observed in the present study. Similar range of 5 variation were also observed by Tisseyre et al. (2008) and Bramley (2005) for TSS at harvest. 6

  ). Indeed values of DESP are lower or 21 equal to 25%. These results agree with those obtained by Baluja et al. (2013) at least for TSS 22 measured at harvest. 23

  choice of harvest date (Tisseyre et al. 2008; Baluja et al. 2013). It is worth mentioning that, 4

  Fig. 11 present results of the difference between within field zones relative to the mean field 20 considered as reference level equal to 0. Positive values refer to advance in phenological 21 stage and maturation, while negative values refer to delay in these parameters. 22

  Bramley et al. (2011) found that incidence and severity of the two main diseases (powdery 15 mildew and botrytis) was spatially variable at the within field scale. The same authors 16 observed that diseases development was associated to the topography (slope), they 17 hypothetised that topography explained differences in microclimate more or less favorable 18 to diseases development. Regarding our results, it can also be hypothesized that topography 19 and the resulting microclimate could affect the date of flowering. Considering homogeneous 20 pesticides application over the vineyards, protection associated with the applications may 21 vary according to spatial variability of flowering. Therefore, spatial variability of diseases 22 may be due to either differences in phenology (and resulting pesticides efficiency) or to 23 environmental factors that directly affect diseases development (Valdés-Gómez et al. 2011). 24 These considerations highlight the necessity to take into account the spatial variability of 25 phenology (and especialy flowering) to better understand factors that affects diseases 26 variability. Specific experiments based on phenology maps and differencial application of 27 pesticides should be considered to properly analyse the spatial variability of diseases 28 development at the within field scale. 29 30 Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5 Regarding the period going from fruit setting to veraison, it is of critical importance to 1 manage irrigation. Spatial variability of phenology may be a relevant decision support to 2 consider site specific management zones of regulated deficit irrigation strategies in such a 3 way to optimize the final quality of the grapes (Ojeda et al. 2002; Acevedo-Opazo et al. 2005; 4 Girona et al. 2009; Acevedo-Opazo et al. 2010). For veraison, Parker et al. (2014) observed 5 that differences in veraison remain stable until harvest affecting the final wine quality. It 6 confirms our results of the temporal stability of the zones of phenology and maturity. 7

14 15 18 ( 22 23

 141822 Identifying the spatial variability of the phenology could be a useful decision support to16 suggest management strategies that seek to homogenize the vine field phenology.Authors 17 have reported that a late winter pruning operations delayed by 4-5 days the date of budburst Dunn and Martin 2000). Thus, site specific pruning operation could reduce differences in 19 phenological development. More generaly, our study points out the interest in delineating 20 phenology zones to optimise field sampling and to improve the efficiency of the various 21 agricultural operations and decisions during the season. This work focused on the variability of the phenology at the within field scale. For terroir 24 delineation or climate change monitoring purposes, other studies focused on the phenology 25 and its variability at meso scale (Barbeau et al. 1998; Webb et al. 2007). At the regional scale 26 (denomination of Loire Valley, France), Barbeau et al. (1998) found differences in dates of 27 budburst, flowering and veraison ranging from 6 to 8 days on cv Cabernet Franc. At the same 28 scale Webb et al. (2007) in Australia and Valdés-Gómez et al. (2011) in Chile, reported the 29 results of simulations on the effect of climate change on the phenological development of the 30 grapevine. Under different climate change scenarios, these authors reported advances in budburst date ranging from 4 to 12 days for cv Cabernet Sauvignon. It is interesting to note 1 that phenology variability observed at larger scales or under scenarios of climate change is 2 similar and comparable to those obtained at the within field scale. This observation raises 3 new questions: 4

  advanced and delayed zones remain stable throughout the growing season (from post-18 budburst to harvest). It was possible to identify two well-contrasted zones of phenology and 19 maturity within each vine field. Observed magnitude of variation was similar to the one 20 observed at larger scales or under scenarios of climate change. These results should be 21 considered for the sampling strategy of phenology and maturation at within field scale and 22 for planning production activities. Differences in phenology may explain the spatial 23 variability observed in other variables such as yield and disease development. Further 24 investigations should determine the environmental factors which drive the observed spatial 25 variability in phenology and maturity, and if possible propose predictive models that consider 26 this variability. 27 Climate Change funded by the University of Talca. Also, the authors would like to thank the 1 support of the Chilean project CD-UBB 1203. 2 3

Fig. 3 2 Fig. 4 6 Fig. 5 9 Fig. 6 Fig. 7 18 Fig. 9 sensors for each zone 21 Fig. 10 24 Fig. 11

 3246596718921102411 Fig. 3 Location of temperature sensors in the field 1 (cv Cabernet Sauvignon). Where: Z1 1 represents zone 1 and Z2 represents zone 2 2 Fig. 4 Phenology model simulation for cv Cabernet Sauvignon (Model Cab) and cv 3 Chardonnay (Model Cha). Where: Lines represent the sum of GDD necessary to fulfill the 4 state 23 in PS (Flowering). R 2 : Represents the degree of fit between phenological stage (PS) 5 and growing degree days (GDD) 6 Fig. 5 Maps of spatial distribution of the main phenological stages, field 1, 2009-10, 2010-7 11, 2011-12 and 2012-13 seasons. a) Post-Budburst, b) Flowering and c) Veraison. a), b) and 8 c) expressed in units of phenological scale (PS) 9 Fig. 6 Maps of spatial distribution of the maturation, expressed in total soluble solids (°Brix), 10 field 1, 2009-10, 2010-11, 2011-12 and 2012-13 seasons. a) Pre-Ha 1 (25 days before 11 harvest), b) Pre-Ha 2 (12 days before harvest) and c) Ha (Harvest) 12 Fig. 7 Maps of spatial distribution of the main phenological stages, field 2, 2011-12 and 13 2012-13 seasons. a) Post-Budburst, b) Flowering and c) Veraison. a), b) and c) expressed in 14 units of phenological scale (PS) 15 Fig. 8 Maps of spatial distribution of the maturation, expressed in total soluble solids (°Brix), 16 field 2, 2011-12 and 2012-13 seasons. a) Pre-Ha 1 (25 days before harvest), b) Pre-Ha 2 (12 17 days before harvest) and c) Ha (Harvest) 18 Fig. 9 Air temperature (a) and growing degree-days (b) for each zone (zone 1 and 2) from 19 the field 1 (cv Cabernet Sauvignon). Each point on the figure represents the average of 4 20 sensors for each zone 21 Fig. 10 Mapping of the cluster analysis for field 1 (a and c) and field 2 (b and d). a) and b) 22 cluster analysis of the main phenological stages. c) and d) cluster analysis of the maturation 23 (total soluble solids) 24 Fig. 11 Days difference between the defined zones (cluster analysis) according to the average 25 of the fields, for the main phenological stages and maturation. (a) field 1 and (b) field 2. The 26 dashed line represents the reference level (0). Each point on the figure represents the average 27 of all seasons considered for each cultivar (4 for field 1 and 2 for field 2). Vertical bars 28 represent the standard deviation 29 30 TABLES 31 32 Table 1 Field characteristics of the Cabernet Sauvignon and Chardonnay experimental fields 33 Property Cabernet Sauvignon Chardonnay Experimental period 2009-13 2011-13 Vineyard age a 13-year-old 17-year-old Rootstock Own-rooted Location (WGS84) 35°22.0' S, 71°35.6' W 35°21.9' S, 71°35.8' W Elevation (m above sea level)

5 FIGURESFigure

 5 FIGURES

5 Figure 6 5 Figure 5 Figure 5 Figure 9 5 Figure 10 5 Figure 11

 565559510511 Figure 5 2009-10 2010-11 2011-12 2012-13 a)

  Acevedo-Opazo, C., Tisseyre, B., Taylor, J. A.,Ojeda, H., & Guillaume, S. (2010). A model 12 for the spatial prediction of water status in vines (Vitis vinifera L.) using high resolution 13 ancillary information. Precision Agriculture, 11(4), 358-378. 14 Acevedo-Opazo, C., Ortega-Farías, S., Hidalgo, C., Moreno, Y., & Córdova, F. (2005). 15 Effects of different levels of water application in post-setting and post-veraison on wine 16 quality cv . Cabernet Sauvignon. Agricultura Técnica, 65(4), 397-410. 17 Allen, R. G., Pruitt, W. O., Wright, J. L., Howell, T. A., Ventura, F., Snyder, R., et al. (2006). Tisseyre, B., Mazzoni, C., & Fonta, H. (2008). Whithin-field temporal stability of some 1 parameters in viticulture: Potential Toward a Site Specific Management. Journal 2 International Des Sciences de La Vigne et Du Vin, 42(1), 27-39. 3 Tisseyre, B., Ojeda, H., Carillo, N., Deis, L., & Heywang, M. (2005). Precision Viticulture 4 and Water Status : Mapping the Predawn Water Potential to Define within Vineyard 5 Zones . In Information and Technology for Sustainable Fruit and Vegetable Production 6 FRUTIC (pp. 337-346). 7 Trought, M. C. T., & Bramley, R. G. V. (2011). Vineyard variability in Marlborough, New 8 Zealand: characterising spatial and temporal changes in fruit composition and juice 9 quality in the vineyard. Australian Journal of Grape and Wine Research, 17(1), 79-89. 10 Valdés-Gómez, H., Gary, C., Cartolaro, P., Lolas-Caneo, M., & Calonnec, A. (2011). 11 Powdery mildew development is positively influenced by grapevine vegetative growth 12 induced by different soil management strategies. Crop Protection, 30(9), 1168-1177. 13 Valdés-Gómez, H., Brisson, N., Acevedo-Opazo, C., Gary, C. and Ortega-Farías, S. (2011). 14 Modelling the effects of Niño and Niña events on water balance of grapevine (cv. 15 Cabernet Sauvignon) in Central valley of Chile. Sixth International Symposium on 16 Irrigation of Horticultural Crops. Acta Horticulturae 889:159-166. 17 Webb, L. B., Whetton, P. H., & Barlow, E. W. R. (2007). Modelled impact of future climate 18 change on the phenology of winegrapes in Australia. Australian Journal of Grape and 19 Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5

	5
	6
	7	REFERENCES
	8	Acevedo-Opazo, C., Ortega-Farias, S., & Fuentes, S. (2010). Effects of grapevine (Vitis
	9	vinifera L.) water status on water consumption, vegetative growth and grape quality: An
	10	irrigation scheduling application to achieve regulated deficit irrigation. Agricultural
	11	Water Management, 97(7), 956-964.
	18 20	Acevedo-Opazo, C., Valdés-Gómez, H., Taylor, J. A., Avalo, A., Verdugo-Vásquez, N., Wine Research, 13, 165-175.
	19 21 20 21 22	Araya, M. et. al. (2013). Assessment of an empirical spatial prediction model of vine water status for irrigation management in a grapevine field. Agricultural Water Webster, R., & Oliver, M. (2001). Geostatistics for environmental scientists. Statistics in Management, 124, 58-68. practice., Chichester, England.
	23 22 23 24	Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil A recommendation on standardized surface resistance for hourly calculation of properties. Journal of Soil Science, 43(1), 177-192.
	24 25 25 26	reference ETo by the FAO56 Penman-Monteith method. Agricultural Water Management, 81(1-2), 1-22. Winkler, A., Cook, J., Kliewer, W., & Lider, L. (1974). General Viticulture (2nd Ed.). CA, USA: University of California.
	26 27 27 28 28 29	Baluja, J., Tardaguila, J., Ayestaran, B., & Diago, M. P. (2013). Spatial variability of grape composition in a Tempranillo (Vitis vinifera L.) vineyard over a 3-year survey. Wu, C., Wu, J., Luo, Y., Zhang, L., & DeGloria, S. (2008). Spatial Prediction of Soil Organic Precision Agriculture, 14(1), 40-58. Matter Content Using Cokriging with Remotely Sensed Data. Soil Science Society of America Journal, 73(4), 1202-1208.
	29 30 30	Barbeau, G., Morlat, R., Asselin, C., Jacquet, A., & Pinard, C. (1998). Behaviour of the Cabernet Franc grapevine variety in varios "Terroirs" of the Loire Valley. Influence of LIST OF FIGURES
	31 31 32 32 33 33	the precocity on the composition of the harvested grapes for a normal climatic year (Example of the year 1988). Journal International Des Sciences de La Vigne et Du Vin, 32(2), 69-81. Fig. 1 Location of Maule Valley in Chile (a) and map of the study area (b) Fig. 2 Measurement grids used in the experiments. 18 grid sites for field 1, cv Cabernet
	34	Sauvignon (a) and 19 sites for field 2, cv Chardonnay (b). Where: Si represents the sampling
	35	site number i

Table 2

 2 Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5 Representation of the phenological scale of Eichhorn and Lorenz as modified by 5Coombe (Coombe 1995) 6

	). Vertical bars

Table 4

 4 Main statistics of grapevine phenology for the two fields studied 10

		Season	Min	Max		Range	SD		CV (%)
		2009-10	Cab	Cab		Cab		Cab		Cab
		Post-Bu	5		7		2		0.65		10.5
		Fl	20		23		3		0.87		3.9
		Ve	34		35		1		0.34		1.0
		2010-11	Cab	Cab		Cab		Cab		Cab
		Post-Bu	5		7		2		0.47		8.3
		Fl	20		26		6		1.47		6.1
		Ve	34		35		1		0.26		0.8
		2011-12	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha
		Post-Bu	5	9	8	11	3	2	0.89	0.64	12.8	6.4
		Fl	21	22	25	25	4	3	1.35	0.69	5.6	3.0
		Ve	33	33	35	35	2	2	0.54	0.52	1.5	1.5
		2012-13	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha
		Post-Bu	7.0	7.0	11	11	4	4	1.05	1.36	12.1	15.6
		Fl	18	20	21	25	3	5	0.77	1.46	3.9	6.5
	11 12 13 14	Ve Min:minumun, Max: Maximun, SD: Standard deviation, CV: Coefficiente of variation, Range: Range of variation. Post-34 33 35 35 1 2 0.28 0.97 0.8 2.9 Bu: Post-Budbreak, Fl: Flowering, Ve: Veraison. Post-Bu, Fl and Ve expressed in PS. Cab: cv Cabernet Sauvignon, field 1. Cha: cv Chardonnay, field 2.

Table 5

 5 Main statistics of maturation, expressed in Total Soluble Solids (TSS), for the 15 two fields studied 16 Max: Maximun, SD: Standard deviation, CV: Coefficiente of variation, Range: Range of variation. Pre-Ha 17 1: 20 days before Harvest, Pre-Ha 2: 12 days before Harvest. Ha: Harvest. Pre-Ha 1, Pre-Ha 2 and Ha expressed in °Brix.

	Season	Min	Max		Range	SD		CV (%)
	2009-10	Cab	Cab		Cab		Cab		Cab
	Pre-Ha 1	17.6	21.2		3.6		0.96		4.8
	Pre-Ha 2	18.6	22.2		3.6		1.16		5.6
	Ha	19.8	24.0		4.2		1.20		5.3
	2010-11	Cab	Cab		Cab		Cab		Cab
	Pre-Ha 1	17.0	21.6		4.6		1.40		6.8
	Pre-Ha 2	17.2	22.5		5.3		1.56		7.4
	Ha	17.2	23.2		6.0		1.61		7.4
	2011-12	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha
	Pre-Ha 1	17.6	13.8	22.7	18.7	5.1	4.9	1.38	1.23	6.5	7.6
	Pre-Ha 2	18.8	17.6	24.6	22.0	5.8	4.4	1.31	1.04	5.9	5.3
	Ha	18.8	18.5	24.6	22.9	5.8	4.4	1.31	1.02	5.9	4.9
	2012-13	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha	Cab	Cha
	Pre-Ha 1	15.6	15.0	20.9	19.0	5.3	4.0	1.38	1.16	7.1	6.7
	Pre-Ha 2	17.2	18.8	22.4	21.5	5.2	2.7	1.37	0.76	6.5	3.8
	Ha	18.3	21.0	23.9	23.9	5.6	2.9	1.50	0.77	6.8	3.4
	Min:minumun, 18
	Cab: cv Cabernet Sauvignon, field 1. Cha: cv Chardonnay, field 2.

Table 6

 6 Parameters of semivariogram adjusted for each phenological stage evaluated 3 in the seasons 4 Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5 Nugget. C0 + C: Sill. a: Range. DESP (%) Degree of spatial dependence. G: Gaussian, Ex: Exponential, Sp: Spherical, 1 L: Linear. Pre-Ha 1: 25 days before Harvest, Pre-Ha 2: 12 days before Harvest. Ha: Harvest. Pre-Ha 1, Pre-Ha 2 and Ha 2 expressed in °Brix. Cab: cv Cabernet Sauvignon, field 1. Cha: cv Chardonnay, field 2. Kendall Coefficient of Concordance (W) for phenological stage and maturation 5 throughout all the seasons (4 for field 1 and 2 for field 2) 6

				Components of Semivariogram
		Season	Model	R 2	C0	C0 + C	a	DESP (%) MCD (m)
		2009-10 Cab
		Post-Bu	G	0.98	0.001	0.472	80.7	0.21	30.2
		Fl	Sp	0.99	0.017	0.901	100.1	1.89	36.8
		Ve	G	0.86	0.0002	0.162	92.3	0.12	34.6
		2010-11 Cab
		Post-Bu	G	0.99	0.0018	0.266	72.6	0.68	27.0
		Fl	G	0.77	0.001	2.85	81.8	0.04	30.7
		Ve	G	0.99	0.0032	0.1053	112.4	3.04	40.9
		2011-12 Cab
		Post-Bu	G	0.99	0.006	0.859	65.82	0.70	24.5
		Fl	G	0.99	0.058	2.644	98.2	2.19	36.0
		Ve	G	0.83	0.0001	0.298	95.44	0.03	35.8
		2012-13 Cab
		Post-Bu	G	0.74	0.060	1.148	47.1	5.23	16.7
		Fl	G	0.97	0.0010	0.686	67.4	0.15	25.2
		Ve	G	0.97	0.0001	0.128	123.3	0.08	46.2
		2011-12 Cha
		Post-Bu	G	0.93	0.034	0.676	115.4	5.03	41.1
		Fl	G	0.95	0.0010	0.551	54.7	0.18	20.5
		Ve	Sp	0.83	0.0010	1.520	97.3	0.07	36.5
		2012-13 Cha
		Post-Bu	G	0.99	0.166	2.964	106.9	5.60	37.8
		Fl	G	0.99	0.513	2.911	97.9	17.62	30.2
	5 6 7 8 9	Ve C0: Nugget. C0 + C: Sill. a: Range. DESP (%) Degree of spatial dependence. G: Gaussian, Ex: Exponential, Sp: Spherical, G 0.99 1.165 9.486 149.1 12.28 49.0 L: Linear. Post-Bu: Post-Budbreak, Fl: Flowering, Ve: Veraison. Post-Bu, Fl and Ve expressed in PS. Cab: cv Cabernet Sauvignon, field 1. Cha: cv Chardonnay, field 2.
	10	Table 7 Parameters of semivariogram adjusted for each maturation date evaluated in
	11	the seasons
				Components of Semivariogram
		Season	Model	R 2	C0	C0 + C	a	DESP (%) MCD (m)
		2009-10 Cab
		Pre-Ha 1	G	0.94	0	1.041	32.9	0.00	12.3
		Pre-Ha 2	G	0.93	0	1.420	41.0	0.00	15.4
		Ha	G	0.91	0.001	1.470	68.4	0.07	25.6
		2010-11 Cab
		Pre-Ha 1	G	0.98	0	2.031	31.6	0.00	11.8
		Pre-Ha 2	G	0.96	0	2.625	30.3	0.00	11.4
		Ha	G	0.76	0.0010	2.702	64.95	0.04	24.3
		2011-12 Cab
		Pre-Ha 1	G	0.90	0.0000	1.846	31.6	0.00	11.8
		Pre-Ha 2	G	0.92	0.0000	1.793	35.9	0.00	13.5
		Ha	G	0.88	0.0010	1.687	58.72	0.06	22.0
		2012-13 Cab
		Pre-Ha 1	G	0.91	0.2023	1.618	37.49	12.5	12.3
		Pre-Ha 2	G	0.93	0.0000	2.161	39.98	0.00	15.0
		Ha	G	0.99	0.0010	2.627	92.49	0.04	34.7
		2011-12 Cha
		Pre-Ha 1	G	0.92	0.1319	1.693	38.2	7.79	13.2
		Pre-Ha 2	G	0.93	0.0000	1.148	22.8	0.00	8.6
		Ha	L	0.95	0.1415	-	-	-	-
		2012-13 Cha

Table 9

 9 Kendall Coefficient of Concordance (W) for phenological stages and maturation 9 within the seasons 10

	Season	W Kendall	Significance (p<0.01)
	Cabernet Sauvignon (Field 1)
	2009-2010
	PS	0.72	**
	Ma	0.83	**
	2010-2011
	PS	0.76	**
	Ma	0.87	**
	2011-2012
	PS	0.85	**
	Ma	0.80	**
	2012-2013
	PS	0.72	**
	Ma	0.88	**
	Chardonnay (Field 2)
	2011-2012
	PS	0.54	**
	Ma	0.80	**
	2012-2013
	PS	0.81	**
	Ma	0.78	**
	Ps: Phenological stage, Ma: Maturation

Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5

Australian Journal of Grape and Wine Research, 1(2), 104-110.

ACKNOWLEDGEMENTS 29

The authors of this paper wish to thank FONDECYT Project 11110137, National CONICYT 30 Doctoral Fellowship 2013 N°21130504 and PIEI Program of Agricultural Adaptation to

Author-produced version of the article published in Precision agriculture, 2015, N°17(2), p. 218-245. The original publication is available at http://link.springer.com Doi: 10.1007/s11119-015-9418-5