Original research articles

Effects of soil water-holding capacity and soil N-NO3 and K on the nutrient content, vigour and yield of cv. Tempranillo vine and the composition of its must and wine

Abstract

The aim of this study was to assess the relationships between the soil water-holding capacity (SWHC), nitrate nitrogen (N-NO3) and extractable potassium (K) content of soil; the vine nutrient content, vigour and yield; and the quality of the musts and wines in DOCa Rioja vineyards over a five-year period (2010–2014). The SWHC, N-NO3 and extractable K content of the soil were analysed in twelve cv. Tempranillo (Vitis vinifera L.) plots. Vine yield, nutritional parameters and the K and polyphenol compounds in the musts and wines were determined. In general, both the SWHC and the soil N-NO3 content were positively correlated with the N and K content of the petiole, the shoot weight and the bunch weight, but negatively correlated with the polyphenol and anthocyanin content of the musts and wines. The K extractable content was also correlated with the K content of the petiole and with the K in the musts and wines. The SWHC, the N-NO3 and the extractable K are soil parameters which have been linked to the growth and ripening of the vine plant and to the composition of the must and wine. For DOCa Rioja agroclimatic conditions, assessment of these soil parameters would enable selecting soils that support obtaining wines of better quality.

Introduction

The key variables for vine cultivation and quality grape (Vitis vinifera L.) production are climate, soil, and plant material. Together with topography and agronomic practices, these variables constitute a unique ecosystem known as the terroir (Seguin, 1986; van Leeuwen et al., 2004; van Leeuwen and Seguin, 2006; van Leeuwen et al., 2018).

Climate has the greatest effect on most vine parameters, followed by soil and cultivar, and its characteristics strongly influence berry growth and composition (Tomasi et al., 2015). With regard to climatic parameters, sunshine hours and temperature do not have a decisive impact on the quality of the vintage, in contrast to rainfall and its distribution throughout the vines' growing cycle, especially in summer. Therefore, the effects of climate and soil on vine development and grape composition can largely be explained by their influence on vine water status (van Leeuwen et al., 2004). In regions with a Mediterranean climate, rainfall varies greatly from year to year, and the availability of water for the vineyard can thus vary substantially between vintages (Catarino et al., 2018; Grainger et al., 2021; Lima et al., 2021). In addition, the impact of soil characteristics on the vine plant can also be shaped by this inter- and intra-annual climatic variability.

The soil in which vines grow influences both the quality and the style of a wine (Reynolds et al., 2010). Soil characteristics may directly influence the water availability for plants, which will in turn determine the physiological performance of the grapevines (Brillante et al., 2015; Brillante et al., 2016). Variations in whole plant physiology are associated with grape flavonoid composition (Yu et al., 2020) and thus affect wine quality. Ideally, vineyards should be established in areas where soil temperature (relative to air temperature), soil water-holding capacity (relative to rainfall and potential evapotranspiration), and soil nitrogen (N) availability are optimum for the type of wine to be produced (van Leeuwen et al., 2018).

The effect of the soil on vine behaviour is well known to be mediated through the varying water content levels and the consequent effects on vine water status (Seguin, 1970, 1986; van Leeuwen and Seguin, 1994). The available water in the root zone is one of the factors affecting plant growth, yield, bunch rot and berry composition (Echeverría et al., 2017). In addition, mild water deficits can reduce berry size (Smart, 1974; Intrigliolo and Castel, 2010; Intrigliolo et al., 2016) and increase the anthocyanin and tannin content in the berry skin of red grape varieties (Matthews and Anderson, 1988; van Leeuwen and Seguin, 1994; Koundouras et al., 1999; García-Esparza et al., 2018; Pérez-Álvarez et al., 2021; Lizama et al., 2021). Consequently, determining the moisture retention capacity of the complete soil profile can help to identify how much water is available for the cultivation of the vine in each type of soil, therefore aiding in assessing the effect of the vineyard development.

Moreover, the availability of mineral soil nutrients is vital for vine development due to the impact of the soil nutrients on growth, ripening, vigour and yield (Keller, 2005). However, Seguin (1986) found no relationship between soil minerals and wine quality, except for N. Both N deficiency and N excess have negative impacts on grapevine development and grape composition (Verdenal et al., 2021), but otherwise, little evidence indicates that soil minerals are major drivers of terroir expression (van Leeuwen et al., 2018). Previous research has shown that N fertilization increases vigour and yield (Kliewer and Cook, 1971; Spayd et al., 1994; Verdenal et al., 2021), extends the vegetative growth period (competing with and delaying grape ripening), increases grape sensitivity to fungal diseases (Thomidis et al., 2016) and can also decrease the polyphenol and anthocyanin content (Hilbert et al., 2003; Delgado et al., 2004; Schreiner et al., 2018). Without the addition of N fertilizer, the vine N status depends on the organic matter content in the soil, its mineralization rate and the carbon/nitrogen (C/N) ratio. Since nitrate N (N-NO3) is the main form of N that grapevines can assimilate, its content in the soil at the point of maximum growth of the vine (flowering) may be related to the nutritional level of N in the vine and its vigour and to the polyphenol and anthocyanin levels in the grape (Pérez-Álvarez et al., 2013). Consequently, it is necessary to look more deeply into determining the N-NO3 levels of soils in which vines are grown to better understand the effect of N-NO3 on vine development and grape quality.

With respect to potassium (K), vine roots absorb this nutrient very easily as a monovalent cation that has high mobility in the plant at the intracellular level and at longer distances between organs through the phloem and the xylem (Arrobas et al., 2014). This element is essential for shoot growth, resistance to water stress and diseases, and the transport and translocation of the assimilated substances (Mpelasoka et al., 2003; Wang et al., 2013). Potassium indirectly benefits the synthesis of phenolic compounds during ripening, which is related to the presence of carbohydrates in the berries (Mohammed et al., 1993).

Vine K deficiency leads to the production of grapes with high acidity and lower anthocyanin content (Griesser et al., 2017). However, an excess of K may lead to a magnesium (Mg) deficiency in the vine, producing musts with higher pH and considerable reduction in total acidity, accompanied by a loss of colour in red wines (Ramos and Romero, 2017). These musts and wines are strongly susceptible to biological spoilage (Mpelasoka et al., 2003), and wines from grapevines with an excess of K need the addition of acid to prevent a lack freshness due to high pH (Rühl et al., 1992). In summary, the availability of K in the soil could be related to the vigour and yield of grapevines and to the quality of the musts and the wines.

The current study was based on the hypothesis that the edaphic parameters, including moisture reserves and levels of K and N, strongly influence the physiological processes of the vines by directly affecting the balance between their vegetative vigour and the production of grapes (van Leeuwen and Seguin, 1994).

The literature contains virtually no studies that have investigated the link between the soil moisture reserves and the available N-NO3 and K with respect to the nutrient content, growth and yield of the vine, while also analysing the composition of the musts and the wines. To better understand how soil and possible interactions with climate affect vine nutrient content, vigour and yield, as well as the composition of musts and wine, the available water reserves, soil N-NO3 availability and soil extractable K were studied in various cv. Tempranillo vineyards in the Najerilla Valley (DOCa, Rioja, Spain), over five seasons.

Materials and methods

1. Study area

The study was conducted in the Uruñuela vine-growing area, in the Rioja Alta subzone of the Denominación de Origen Calificada (DOCa) Rioja in northern Spain. This area encompasses fluvial (terrace) and torrential (alluvial fan and glacis) quaternary deposits that cover Neogene sedimentary materials. The primary landforms are slopes and platforms caused by intense water modelling, with altitudes ranging from 440 to 583 m above sea level.

In general, the climate in the area is Mediterranean according to the UNESCO aridity index, with a slight influence of the Atlantic or Oceanic climate. Annual average temperature, rainfall and evapotranspiration data were registered by an agro-climatic station of the Government of La Rioja (www.larioja.org/siar) located in the same mesoclimate area as the experimental plots (42°27′43″N, 2°42′46″W; altitude: 465 m above sea level).

The climatic parameters studied included annual mean temperature (°C), annual mean rainfall (mm), annual solar radiation (MJ m2), annual potential evapotranspiration (ETo) calculated using the Penman-Monteith formula, and precipitation intervals (mm) during various phases of the vine cycle. The climatic parameters were recorded for five seasons (2010–2014) and are summarized in Table 1. Table 1 also shows the climate series from 2004 to 2018.

Table 1. Climatic data (annual mean temperature (ºC) and precipitation (mm), solar radiation (MJ m-2) and reference evapotranspiration (mm) of years 2010 to 2014 in the study area and variation (%) respect to average value of the historical series 2004-2018.


Climatic data*

2010

2011

2012

2013

2014

Tª m

(º C)

12.5

11.6

(-7.2)

12.8

(2.4)

12.5

(0.0)

12.0

(-4.0)

13.1

(4.8)

Annual mean P (mm)

476

383.6

(-19.4)

345.4

(-27.4)

455.8

(-4.2)

677.6

(42.4)

559.2

(17.5)

P Jan-May (mm)

227.9

152.2

(-33.2)

172.8

(-24.2)

179.0

(-21.5)

380.8

(67.1)

221.4

(-2.9)

P Apr-Oct (mm)

240.1

209.4

(-12.8)

176.8

(-26.4)

300.4

(20.1)

314.0

(30.8)

235.0

(-2.1)

P Sept-Oct (mm)

67.1

73.4

(9.4)

33.4

(-50.2)

122.2

(82.1)

68.8

(2.5)

86.8

(29.4)

Solar Rad. (MJ.m-2)

5216

5089

(-2.4)

5262

(0.9)

5284

(1.3)

4909

(-5.9)

5177

(0.7)

ETo

(mm)

977

945

(-3.2)

977

(0.0)

998

(2.1)

880

(-9.9)

958

(-1.9)

* Complete climatic series (2004-2018); Tª m: mean temperature; P: precipitation; Jan: January; Apr: April; Oct: October; Sept: September; Rad: Radiation; ETo: crop reference Evapotranspiration.

Previous soil mapping of this area characterized 24 cartographic units of soils over 1,000 ha, revealing moderate to high variability. The soils were formed from silt-sand parent materials with gravel, pebbles and stones. The main pedogenesis processes were a translocation of carbonates and clay illuviation (Martínez-Vidaurre, 2017; Martínez-Vidaurre et al., 2003).

2. Experimental design and description of vineyards plots

This study was conducted over five seasons (2010–2014) in twelve vineyards of Tempranillo grafted on Richter-110 rootstock. The geographic coordinates of each plot selected are shown in Table 2. All the vineyards were located over platforms with field slopes lower than 2 % and the twelve vineyards were less than 1 km apart, so that the climatic conditions are considered homogeneous between the vineyards for a given vintage.

Table 2. Geographic coordinates (ETRS89 projection system) and average altitude (m) of each vineyard plot.


Plot

Longitude

Latitude

Altitude (m)

1

2º42´56”

42º27´5”

476

2

2º42´54”

42º27´10”

475

3

2º41´6”

42º27´49”

508

4

2º41´22”

42º27´36”

517

5

2º41´34”

42º27´30”

510

6

2º42´28”

42º27´40”

472

7

2º40´59”

42º27´21”

552

8

2º41´15”

42º27´18”

558

9

2º40´29”

42º27´21”

565

10

2º40´22”

42º27´28”

566

11

2º42´9”

42º27´55”

467

12

2º40´30”

42º27´36”

564

The plant material was between 20 and 35 years old, so grapevines were in full production. Planting density was 2,900 to 3,100 grapevines per hectare with vines at approximately 1.20 × 2.70 m (vine and row spacing) in an east-west row orientation. Vine training systems were bilateral cordon and goblet/bush vine. Within each vineyard, three adjacent rows of 50 vines were selected (plots) for taking samples and measurements.

Chemical weed control was achieved beneath the vines. Soil tillage to eliminate competitive sward was carried out in the inter-row every four to six weeks during the growing season (February to August) using a cultivator. The vines were not irrigated or fertilised.

3. Soil description and soil analysis

In each vineyard plot, a mini-bagger excavator was used to make two pits, each measuring 2 m long, 1 m wide and 1 to 2 m deep (May 2010). These pits were used to determine the effective soil depth, the depth reached by vine roots (rooting depth), the soil horizons, and the percentage of coarse elements (> 2 mm) by volume in each horizon. Horizons were sampled, soil samples were air dried, and the ground soil was sieved to 2 mm. The soil samples were analysed to determine pH and electrical conductivity in water with a soil/solution ratio of 1:2.5. Organic matter content was determined by dichromate oxidation (Nelson and Sommers, 1982), soil texture by laser diffraction particle size (Diffractometer LSTM 13 320, Beckman Coulter) and carbonate total by infrared (EQUILAB CO-202). In addition, soil extractable K content was determined by the Mehlich 3 method (Zhang et al., 2009).

Each year, four soil sub-samples were collected by auger when vines were at the flowering phenological stage (12 to 21 June) in each vineyard. For each sub-sample, soil was collected randomly in the inter-row at three depths (0–15, 15–30 and 30–45 cm) and bulked to give a composite sample.

The soil samples were air dried, ground and passed through 2 mm sieves. Next, N-NO3 was extracted from soil samples with 2 mol l−1 KCl and measured by colorimetry at 550 and 660 nm, using a SEAL AutoAnalyzer 3HR (Seal Analytical, Hamburg, Germany), based on segmented flow. To express N as N-NO3 and in kilograms per hectare, the percentage of coarse fragments (> 2 mm) and the soil bulk density, recorded using the core method, were also determined in each sample.

The water-holding capacity (SWHC) for each identified horizon within the root zone was calculated according to the equations of Saxton and Rawls (2006) which use electrical conductivity, organic matter content, particle size distribution and the percentage by volume of coarse elements. The total SWHC for each plot was calculated by the sum of SWHC in each horizon described at rooting depth.

Table 3 presents the soil classification (USDA, 2006) and the main soil physical-chemical characteristics of each vineyard plot.

Table 3. Soil classification and main soil physicochemical characteristics of each vineyard plot.


Plot

Soil

classification

(USDA, 2006)

pH (H2O)

Electric

conductivity

(dS/m)

Organic

Matter

(%)

Clay

(%)

Silt

(%)

Sand

(%)

CaCO3

(%)

Rooting

depth

(cm)

Soil water-holding capacity

(SWHC)

(mm)

Ap horizon (0-15 cm)

Control section

1

Fluventic Haploxerept

8.20

0.13

1.05

24.1

28.7

47.2

1.3

110

146.5

2

Fluventic Haploxerept

8.15

0.15

1.00

20.6

38.8

40.6

0.5

110

128.5

3

Typic Calcixerept

8.40

0.20

1.30

17.8

56.9

25.3

28.7

90

87.7

4

Calcic Palexeralf

8.35

0.12

0.73

17.1

56

26.9

1.5

72.5

78.1

5

Calcic Palexeralf

8.60

0.11

0.48

16.3

56.8

26.9

1.2

82

73.6

6

Calcic Haploxeralf

8.55

0.13

0.69

18.7

49.5

31.8

6.5

78

62.2

7

Petrocalcic Palexeroll

8.50

0.14

1.11

19.6

45.5

34.9

23.8

63.5

63.1

8

Petrocalcic Palexeroll

8.40

0.15

1.87

22.9

40

37.1

14.7

62.5

60.2

9

Petrocalcic Palexeralf

8.50

0.11

0.65

21.9

51.3

26.8

3.4

73

59.4

10

Petrocalcic Palexeralf

7.95

0.19

0.73

14.1

58.6

27.3

1.0

75

58.6

11

Calcic Haploxeralf

7.20

0.43

0.61

17.1

52.4

30.5

0.5

85

57.3

12

Typic Calcixerept

8.35

0.14

0.97

18.5

52.5

29

3.5

71

56.3

4. Grapevine nutrient content

In each plot at the veraison stage (23 August 2010, 24 August 2011, 16 August 2012, 28 August 2013 and 19 August 2014), 60 leaves were sampled on leaves opposite to the second cluster (Romero et al., 2010). In each leaf, petioles were separated, washed with tap water and rinsed with distilled water. Plant material samples were dried at 60 °C in a forced-air oven for 72 h, and ground through a 0.5 mm sieve with an ultra centrifugal mill (Retsch ZM1).

Total N content in leaf petioles was determined with a CNS elemental analyser (TruSpec CN, LECO). Contents were expressed on a dry weight basis as grams per 100 g of sample material. Potassium content was determined in petiole tissue by microwave hydrogen peroxide digestion and inductively coupled plasma–optical emission spectroscopy (ICP-3300 DV, Perkin Elmer).

5. Grapevine agronomic performance

All the vineyard plots were hand harvested when berries reached the optimum technical maturity for Tempranillo in Rioja, which occurred when berries presented approximately 13 % v/v probable alcoholic strength. The harvest was normally carried out between 22 September and 6 October for all vintages. At harvest, in each plot, the number of clusters per vine and yield (total harvest weight ha1) were recorded to calculate the average bunch weight per vine (kg vine1).

Finally, at postharvest (end of November or beginning of December), 20 vines were randomly chosen in each plot, and wood pruning weight (g) and shoot number were determined to calculate the average shoot weight (g).

6. Grape sampling and analytical parameters of must

Just before the start of the harvest, random samples of 600 berries were collected. Six clusters were collected from 20 grapevines distributed randomly throughout each experimental plot, and five berries (two on opposite sides from the top of the cluster, two on opposite sides from the middle, and one at the tip of the cluster) were picked from each cluster. In the laboratory, 200 berries were separated, counted and weighed to obtain the average berry weight (g). These 200 berries were then crushed using a masticator (IUL Instruments GmbH, Königswinter, Germany) to obtain must. Malic acid and K contents of the must were determined according to Organization International of the Vine and Wine (OIV) methods (OIV, 2014).

An additional 200 berries were treated with HCl 1 % heated at 40 °C for 30 min. In this extract, the anthocyanin content in berry skins was determined by the method of Ribéreau-Gayon and Stonestreet (1965) and the total polyphenol index (TPI) in the berry skin extract was determined by spectroscopy, with ultraviolet absorption measured at 280 nm (Ribéreau-Gayon et al., 1972). Finally, colour intensity was determined after measuring absorption at 420, 520 and 620 nm in a UV-V spectrophotometer, according to the European Community Official Methods (European Commission, 1990).

7. Vinification

Microvinification was conducted identically for all plots. At harvest, for every plot, 30 to 45 kg of grapes were taken for vinification. First, the hand-harvested grapes were weighed for each plot and then crushed, after which stems were removed. Later, an aliquot of 3.5 kg of paste from each plot was placed in a glass vessel adapted for winemaking. Alcoholic and malolactic fermentations were performed by selected yeast strains of Saccharomyces cerevisiae and a selected bacteria strain of Oenococcus oeni, respectively. The wines were made following the protocol established by Sampaio et al. (2007).

8. Wine analysis

The oenological parameters K and colour intensity were measured according to OIV methods (OIV, 2014), anthocyanin content was determined by the method of Ribéreau-Gayon and Stonestreet (1965) and the TPI was determined by measuring the absorbance at 280 nm after conventional dilution of samples.

9. Statistical analysis

Correlations between soil, vine, must and wine properties data were calculated. Plot effects on mean values (2010–2014) of measured variables were tested using ANOVA (univariate linear model), and comparisons among treatment means were made using the LSD multiple range test calculated at P < 0.05. The percentages of variance attributable to plot and year factors and their interaction were also determined. Principal component analysis (PCA) was conducted considering soil, vine, must and wine properties. All statistical analysis were done using SPSS for Windows (Chicago, IL, USA).

Results

Table 3 shows the main physical-chemical characteristics of the soils described in the twelve selected plots. Their Ap horizons were characterised as having a basic pH, low organic matter content and a silt-loam texture. The range of SWHC, estimated from the control section of each profile, ranged from 56 to 146 mm.

Table 4. Mean values in each plot of soil, grapevine, must and wine properties studied in period 2010-2014.


Plot

SWHC

(mm)

soil extractable

K

Ap horizon

(ppm)

soil N-NO3-

0-45 cm

(kg/ha)

petiole

% N

petiole

% K

shoot

weight

(g)

bunch

weight

(g)

pH

Malic

acid

(g/l)

K

(mg/l)

TPI

Anthoc.

(mg/g)

pH

colour

intensity

K

(mg/l)

TPI

Anthoc.

(mg/l)

soil

grapevine

must

wine

1

146.5

348

41.74b

0.54d

1.24d

125.14c

314.29bc

3.42ab

2.88bc

1738de

79.28a

1.37ab

3.74b

7.14a

1484c

45.40a

526.66ab

2

128.5

424

65.11c

0.62e

2.49e

166.52d

422.80d

3.45b

3.45c

1949e

76.75a

1.21a

3.79b

7.14ab

1724d

48.57a

523.53a

3

87.7

179

32.74ab

0.48c

0.73bc

112.90bc

320.68c

3.43ab

2.27ab

1584bcd

82.74a

1.46abc

3.70b

8.51ab

1333bc

51.75ab

610.32abc

4

78.1

120

28.69ab

0.55d

0.60bc

103.95b

231.80a

3.39ab

2.24ab

1523bcd

94.66bc

2.04fg

3.63b

13.98def

1256b

64.59bcd

876.61ef

5

73.6

170

21.70a

0.47bc

0.70bc

66.54a

200.77a

3.39ab

1.85a

1488bc

96.79bc

1.88ef

3.64b

16.63fg

1280bc

70.86d

883.16f

6

62.2

205

34.88ab

0.47bc

0.61bc

97.91b

264.81abc

3.40ab

2.05ab

1551bcd

82.15a

1.59bcd

3.63b

10.76bcd

1279bc

56.85abcd

698.65bcde

7

63.1

113

17.51a

0.38a

0.55b

60.02a

254.73abc

3.30ab

1.56a

1384ab

105.45c

1.84def

3.42a

18.60g

1029a

69.94cd

726.64cdef

8

60.2

399

28.70ab

0.44b

0.75bc

65.52a

212.55a

3.47b

1.87a

1548bcd

94.60bc

1.93fg

3.72b

13.52def

1252b

64.22bcd

777.67cdef

9

59.4

155

20.02a

0.44bc

0.24a

73.02a

236.13a

3.25a

1.75a

1180a

96.48bc

1.66cde

3.30a

14.90ef

886a

57.53abcd

674.22abcd

10

58.6

157

20.39a

0.48c

0.72bc

116.02bc

259.99abc

3.44b

2.18ab

1581bcd

104.00c

2.19g

3.63b

14.21def

1254b

65.42bcd

851.44ef

11

57.3

416

26.67ab

0.47bc

0.73bc

116.08bc

272.05abc

3.45b

2.12ab

1653cd

86.66ab

1.58bcd

3.70b

9.31abc

1352bc

55.15abc

689.51abcde

12

56.3

244

23.89ab

0.45bc

0.86c

69.20a

243.57ab

3.39ab

2.36ab

1664cd

100.26c

1.96fg

3.70b

12.73cde

1372bc

65.13bcd

830.86def

Different letters indicate significant differences among plots by LSD test (p < 0.05).
SWHC: soil water-holding capacity; TPI: total polyphenol index.

Table 4 shows the mean values of soil, grapevine, must and wine properties for each plot in the period of 2010–2014. The analysed parameters of grapevine must and wine did not change significantly based on the SWHC. Compared with plots with a SWHC < 87.7 mm, plots with a SWHC > 128 mm tended to present higher percentages of N and K (% N and % K, respectively) in petiole, higher shoot and bunch weights; higher K content in must and wine; and lower TPI and anthocyanin content in must and wine. This tendency was only a trend since some plots that had an SWHC < 87.7 mm did not present significant differences with respect to plots with SWHC ≥ 128 mm. Similarly, with respect to soil K, plots with higher soil K tended to have higher petiole % K, must and wine K, and must and wine pH. In addition, the significant differences of soil N-NO3 among plots were related to significant changes in the analysed properties. Thus plots 1 and 2, which had a higher soil N-NO3 than plots 5, 7, 9 and 10, had higher % N and % K in the petiole, lower TPI and anthocyanin content in the must, and lower wine colour index than plots 5, 7, 9 and 10.

Figure 1. Linear correlations between petiole N (%) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between petiole K (%) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. c) Linear correlations between shoot weight (g) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. d) Linear correlations between bunch weight (kg) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. Symbols *, ** and *** indicate that the correlations are significant at p < 0.05, p < 0.01 and p < 0.001, respectively.

Figure 1 presents the correlations between the SWHC of the soil profile and the nutritional parameters, vigour and yield of the vine studied with regard to the percentage of N and K of the petioles at veraison, the mean shoot weight and the mean bunch weight in the five years studied (2010–2014). The SWHC of soils was positively correlated with % N of the petioles in 2010, 2011, 2013 and 2014. Similarly, % K in the petioles was positively correlated with the soil SWHC in all years studied. The parameters of vine vigour, shoot weight and bunch weight were also positively correlated with the SWHC in the years 2010, 2011, 2012 and 2013 and in 2011, 2012 and 2014, respectively.

Figure 2. a) Linear correlations between musts acid malic (mg l-1) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between K in musts (mg l-1) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. c) Linear correlations between total polyphenols index (TPI) in musts and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. d) Linear correlations between anthocyanins in musts (mg l-1) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. Symbols * ** and *** indicate that the correlations are significant at p < 0.05, p < 0.01 and p < 0.001, respectively.

In Figure 2, the correlations between the SWHC and the parameters analysed in the musts (malic acid, K, polyphenols and anthocyanins) are shown for each of the five vintages studied. The SWHC was positively correlated with the malic acid content in the five years analysed and with the K content in the musts in 2010 and 2013 seasons. In the case of TPI of the musts, the correlation with the SWHC was negative for the years 2010, 2011, 2012 and 2014. Similarly, in 2011, 2012 and 2014, the correlation between the SWHC in the soil and the anthocyanin content of the musts was negative.

Figure 3. a) Linear correlations between colour intensity of wines and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between K in wine (mg l-1) and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. c) Linear correlations between total polyphenols index (TPI) in wines and soil water-holding capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. d) Linear correlations between anthocyanin in wines (mg l-1) and available water capacity (mm) for each year 2010, 2011, 2012, 2013 and 2014. Symbols *, ** and *** indicate that the correlations are significant at p < 0.05, p < 0.01 and p < 0.001, respectively.

Regarding the effects of the SWHC on the wine properties, Figure 3 shows the correlations of the SWHC with the wine parameters, including the colour intensity, K, TPI and anthocyanins over the years of the study (2010–2014). The wine colour intensity was negatively correlated with the SWHC in 2010, 2011 and 2012. Similarly, the TPI of wines was negatively correlated with the SWHC in 2010, 2011, 2012 and 2014, as was the anthocyanins content for the years 2010, 2011 and 2012. The content of K in the wines was positively correlated with the SWHC in the 2010, 2011 and 2013 seasons.

Figure 4. a) Linear correlations between petiole N (%) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between petiole K (%) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. c) Linear correlations between shoot weight (g) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. d) Linear correlations between bunch weight (kg) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. Symbols *, ** and *** indicate that the correlations are significant at p < 0.05, p < 0.01 and p < 0.001, respectively.

The correlations between the soil N-NO3 (kg ha1) present in the 0–45 cm layer of the soil of each plot and the % N and % K in the petioles and the shoot and bunch weight in the years 2010–2014 are shown in Figure 4. The content of N in the petioles was positively correlated with the soil N-NO3 in the 2010, 2011, 2013 and 2014 seasons, and also with the K petiole content in 2010, 2011, 2012 and 2013. Moreover, the shoot weight was positively correlated with the N-NO3 in the soil in 2010, 2011, 2012 and 2013. The bunch weight was positively correlated with the soil N-NO3 in the 2010, 2011, 2012 and 2014 seasons.

Figure 5. a) Linear correlations between malic acid (mg l-1) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between K in must (mg l-1) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. c) Linear correlations between total polyphenol index (TPI) in musts and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. d) Linear correlations between anthocyanins in musts (mg l-1) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. Symbols *, **, and *** indicate that the correlations are significant at p < 0.05, p < 0.01 and p < 0.001, respectively.

Regarding the effect of the N-NO3 available in the soil on the characteristics of the musts, Figure 5 shows the correlations of the N-NO3 available at a depth of 0 to 45 cm in the soil with the TPI and the content of malic acid, K and anthocyanins of the must in the years 2010 to 2014. The malic acid content was positively correlated with the N-NO3 in the soils in 2010, 2011, 2012 and 2013. In the same way, the correlation was positive between the must K content and N-NO3 in the soil in 2010, 2011 and 2013. In turn, the values of TPI of the musts were negatively correlated with the content of N-NO3 in all the years under study, with similar behaviour for the anthocyanin content in the musts in 2011, 2012, 2013 and 2014.     

Figure 6. a) Linear correlations between colour intensity of wines and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between K in wines (mg l-1) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. c) Linear correlations between total polyphenols index (TPI) in wines and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. d) Linear correlations between anthocyanins in wines (mg l-1) and soil N-NO3- (kg ha-1) content in 0-45 cm soil depth for each year 2010, 2011, 2012, 2013 and 2014. Symbols *, **, and *** indicate that the correlations are significant at p < 0.05, p<0.01 and p < 0.001, respectively.

Figure 6 shows the correlations between the soil N-NO3 available at a depth of 0–45 cm in the soil and the following wine parameters: colour intensity, K, TPI and anthocyanins over the period 2010–2014. The colour intensity showed a negative correlation with the soil N-NO3 in 2010, 2011, 2012 and 2013. For K, a positive correlation with the soil N-NO3 was observed for the years 2010, 2011, 2012 and 2013. In contrast, the wine TPI values were negatively correlated with the soil N-NO3 in the years 2010, 2011 and 2012, as was the anthocyanin content in the wines in 2011 and 2012. These results agree with the negative correlations observed between the N-NO3 available in the soil and the total polyphenols and anthocyanins in the musts.

Figure 7. a) Linear correlations between petioles K (%) and extractable K in soil Ap horizon for each year 2010, 2011, 2012, 2013 and 2014. b) Linear correlations between K in musts (mg l-1) and extractable K in soil Ap horizon for each year 2010, 2011, 2012, 2013 and 2014 c) Linear correlations between K in wine (mg l-1) and extractable K in soil Ap horizon for each year 2010, 2011, 2012, 2013 and 2014. Symbols *, **, *** indicate that the correlations are significant at p < 0.05, p < 0.01, and p < 0.001 respectively.

Figure 7 shows the correlations of the soil extractable K from the Ap horizon (0–15 cm) of the soil and the petiole % K at veraison and the K content in the musts and in the wines. The soil extractable K in the superficial horizon was positively correlated with the levels of K in the petioles in the vintages of 2010, 2011, 2012 and 2013 and with the K in the musts in 2010, 2012, 2013 and 2014. This correlation was maintained with the K in the wines, such that positive correlations were found between the K content in the wines and the soil extractable K in the years 2010, 2011, 2012 and 2013.

Table 5. Correlation coefficients of soil holding-water capacity, soil N-NO3- and soil K with pH of must and wine.


SWHC

soil

N-NO3-

soil

K

SWHC

soil

N-NO3-

soil K

SWHC

soil

N-NO3-

soil

K

SWHC

soil

N-NO3-

soil

K

SWHC

soil

N-NO3-

soilK

SWHC

soil

N-NO3-

soil

K

2010

n=12

2011

n=12

2012

n=12

2013

n=12

2014

n=12

2010-2014

n=60

must pH

0.54

0.51

0.47

0.05

0.26

0.54

0.17

0.27

0.59*

0.42

0.46

0.48

-0.04

0.25*

0.12

0.12

0.51***

0.27*

wine pH

0.60

0.73*

0.78**

0.44

0.60*

0.63*

0.07

0.25

0.45

0.60*

0.62*

0.68*

0.45

0.04

0.48

0.34**

0.53***

0.47***

Symbols *, **, *** indicate that the correlations are significant at p < 0.05, p < 0.01, and p < 0.001 respectively. SWHC, soil water-holding capacity.

Table 5 shows correlation coefficients of SWHC, soil N-NO3 and soil extractable K with pH of must and wine for each year and for the period 2010–2014. For each year individually, must pH was only correlated with soil N-NO3 in 2014 and with soil K in 2012. Regarding wine, pH was correlated with soil N-NO3 and with soil extractable K in 2010, 2011 and 2013, and with SWHC in 2013. For the overall period of 2010–2014, must pH was correlated with soil N-NO3 and soil extractable K, and wine pH was correlated with SWHC, soil N-NO3 and soil extractable K.

Table 6. Percentage of variance attributable to plot and year and its interaction


plot

year

plot x year

% variance

petiole % N

80.39

1.32

18.29

petiole % K

90.89

5.46

3.65

shoot weight

83.80

6.27

9.93

bunch weight

54.86

16.28

28.86

must pH

18.21

66.03

15.76

Malic acid

40.13

49.54

10.34

must K

53.54

36.49

9.97

must TPI

59.53

15.24

25.23

must anthocyanins

69.99

2.82

27.20

wine pH

51.55

37.32

11.14

wine K

65.58

12.80

21.62

wine color intensity

66.78

16.86

16.36

wine TPI

40.06

43.28

16.67

wine anthocyanins

49.97

27.06

22.97

TPI: total polyphenol index.

Table 6 shows the variance percentage attributable to plot and year and their interaction for each parameter studied. The percentage variance of the petiole % N, petiole % K and shoot weight fundamentally depended on plot factor, while the plot factor contributed more to the variance than the year factor for bunch weight; K, TPI and anthocyanins of must; and pH, K, colour index and anthocyanins of wines. For must pH and malic acid and for wine TPI, the variance due to the year factor was higher than that due to the plot factor. As for the interaction between plot and year factors, the highest variance percentage values were for the bunch weight, TPI and must anthocyanins.

Figure 8. Principal components analysis. Graphical representation of the loadings of the soil, vine, must and wine properties projected on the dimension defined by the two first components loadings. SWHC (soil water-holding capacity).

TPI: total polyphenol index.

A PCA graphical representation in two dimensions of the studied properties, projected on the plane defined by component 1 and component 2, is shown in Figure 8. In the PCA, the first and second principal components explained 45.869 and 22.728 % of the total variance, respectively (Table 7). The first principal component was strong and positively correlated with SWHC, soil N-NO3, soil K, petiole % N and % K, shoot weight, bunch weight, must malic acid, wine pH and wine K. Component 1 was also negatively and strongly correlated with the TPI and anthocyanins of must and the colour index, TPI and anthocyanins of wine. Principal component 2 was positively correlated with K, TPI and anthocyanins of must and the pH, K, TPI and anthocyanins of wines. Component 2 was also negatively correlated with bunch weight and malic acid (Table 7).

Table 7. Variance explained by each component and component loadings of a two-factor model of the soil, grapevine, must and wine properties studied.


Component

1

2

% variance explained

45.869

22.728

soil water-holding capacity

0.820

-0.085

soil extractable K in Ap horizon

0.692

0.090

soil N-NO3- (0-45 cm)

0.72

0.287

petiole % N

0.755

0.18

petiole % K

0.837

0.281

shoot weight

0.793

-0.094

bunch weight

0.682

-0.325

must pH

0.280

0.819

must Malic Acid

0.550

-0.347

must K

0.654

0.647

must TPI

-0.705

0.428

must anthocyanins

-0.686

0.343

wine pH

0.519

0.764

wine K

0.73

0.575

wine colour intensity

-0.775

0.318

wine TPI

-0.553

0.688

wine anthocyanins

-0.528

0.735

TPI: total polyphenol index.

Discussion

The soils of plots in this study present properties similar to the 123 vineyard soils distributed throughout the Rioja characterized by Peregrina et al. (2010). The SWHC values of the soils of the plots fall within the range for SWHC for vine growing soils (30–200 mm) reported by van Leeuwen (2018).

The variability of SWHC, soil N-NO3 and soil extractable K in the plots suggests that the average values of vine, must and wine cannot be grouped by type of plot (Table 4). Therefore, in order to study the effect of soil, it is necessary to analyse the relationship between each soil parameter and the grapevine, must and wine properties.

Under our conditions, correlations existed between the SWHC and the percentage of leaf N in vines (Figure 1). The absorption of nutrients in ionic form from the soil by the vine roots and the conversion of nutrients into organic compounds consume considerable amounts of energy (Keller, 2010). In the case of the N-NO3, the roots absorb this ionic active form through proton/nitrate cotransport, with an ATP pump (Crawford and Glass, 1998). Hence, the higher the water status of the vine, the greater its capacity to produce the energy necessary for the absorption of N-NO3, which would explain the correlations found in this study. Rühl et al. (1992) studied the fertilisation of N, P and K in vines of the Riesling, Chardonnay and Cabernet-Sauvignon varieties, and they similarly established that a water deficit could reduce the mineral absorption due to less root activity and radicular development. Furthermore, in a study on a Cabernet-Sauvignon vineyard in New Zealand, King et al. (2014) found a greater N-NO3 content in petioles of the vines with greater vigour which developed in the zones of the vineyard with greater soil water-holding capacity.

The positive correlations found between SWHC and vine foliar K content could be related to K being a nutrient that plays a fundamental role in the regulation of water in the vine (Keller, 2010) and its assimilation being reduced under limited moisture conditions (Dundon et al., 1984; Esteban et al., 1999). A similar relationship was found by King et al. (2014), who showed that vines from zones with more moisture reserves in the soil showed greater foliar K content. Foliar K was greater as the availability of moisture increased with irrigation in vineyards of Tempranillo in La Rioja (Spain) (Zaballa and García-Escudero, 1997). This positive relationship was also observed for Cabernet-Sauvignon and Merlot varieties in vineyards in Israel (Klein et al., 2000).

In our study, the positive correlations found between SWHC and K in the must (Figure 2) were likely due to greater K levels in the vegetative tissues causing an increase in K levels in the berries because they represent an important sink for K (Mpelasoka et al., 2003). In the years for which this correlation was not significant, values followed a similar trend as in the years in which they were significant. This finding seems to indicate that the effect of SWHC on the K content of the must is consistent in all the cycles.

This increase of K in the must related to greater SWHC was sufficiently important for it to be transferred to the wine after microvinification, and in this way, provided positive correlations between SWHC and the K content in the wines (Figure 3).

Greater availability of moisture was previously found to lead to increased growth and vegetative development of grapevines (Conradie, 2001; Keller, 2010). Under our conditions, this response was reflected in the positive correlations observed between SWHC and the shoot weight (Figure 1). A similar result, with increased shoot length of vines planted in soils with higher SWHC, was found by Tramontini et al. (2013) in Bordeaux vineyards. In our trial, 2013 was the year with the highest amount of precipitation, which led to good vegetative development and a greater accumulation of reserves in the living tissues of the plants. This could possibly be one reason why the SWHC did not correlate significantly with the shoot weight in 2014, as the vigour differences of the vines grown in soils with different SWHC were reduced.

Another aspect of the vine expression that may be affected by water availability is the development and size of the berry. The water content of the soil from the first stages of growth of the berry until pre-veraison has a strong influence on berry size and thus the weight of the final clusters (Esteban et al., 1999; Ollat et al., 2002). This response would explain the positive correlations found between SWHC and bunch weight (Figure 1). Similar results, with a positive correlation between the SWHC and bunch weight, were reported by Echeverría et al. (2017) in Uruguay, Tramontini et al. (2013) in Bordeaux and Costantini et al. (1996) in the region of Siena (Italy). In La Rioja, berry size was previously observed to be positively correlated with water available in the soil (Ramos and Martínez de Toda, 2019). The lack of correlation between SWHC and bunch weight in 2013 could be due to the high precipitation recorded, which was 60 % higher in the period from January to May and 20 % higher in the period from April to October than average precipitation for this periods (Table 1) This level of precipitation would explain that the soils maintained sufficient levels of moisture prior to the growth period of the berries, hence mitigating the influence of the different SWHC levels among soils.

The content of malic acid in the musts was very sensitive to the vegetative development of the vines. Greater vegetative development resulted in a greater malic acid content. To some extent, this outcome is linked to the presence of malic acid in grapes being very closely controlled by the temperature (Hale, 1977). High vigour may favour bunch shading, which would reduce the temperature and the breakdown of malic acid. Similar results, with higher levels of malic acid in musts from vines cultivated in soils with higher water-holding capacity, were found by van Leeuwen et al. (2004) in Bordeaux and by Costantini et al. (1996) in the region of Siena (Italy). This response of the malic acid to the increase in vigour resulting in a lower temperature at bunch level could explain the higher malic acid content in all the vineyards studied in 2013 compared with the other years studied.

The total polyphenols and anthocyanins were negatively correlated with the SWHC (Figure 2). This decrease in the content of polyphenols and anthocyanins is associated with higher vigour and canopy development caused by the higher SWHC (as previously mentioned). Higher vigour can induce competition between reproductive and vegetative sinks (Koundouras et al. 1999) and interfere with the synthesis of secondary metabolites such as the anthocyanins and polyphenols (Bravdo and Hepner 1987). Similar results, with a negative correlation between SWHC and the polyphenol content of musts, were found in vineyards with cv. Tannat in Uruguay (Echevarría et al., 2017). Moreover, higher polyphenol content occurred in the musts of Cabernet-Sauvignon vines grown in a soil with higher SWHC in the Conca de Barberá zone (Spain) (Ubalde et al. 2010). Regarding the anthocyanins, lower levels were observed in the musts from vines grown in a soil with higher SWHC in the Conca de Barberá with Cabernet-Sauvignon (Ubalde et al., 2010), in Siena (Italy) with cv. Sangiovese (Bucelli et al., 2010), and in Veneto (Italy) with Cabernet-Sauvignon (Tomasi et al., 2005). In our study, the high precipitation recorded in 2013 relative to the other years reduced the influence of the SWHC in the berry development, and consequently, differences in SWHC had less of an effect on the polyphenols and anthocyanins.

The correlations between SWHC and the polyphenol and anthocyanin content of the wines (Figure 3) accorded with the negative correlations observed between the SWHC and the polyphenol and anthocyanin content of the musts (Figure 2). Hence the reduction in these compounds caused by the higher SWHC was significant enough to be transferred to the wines after microvinification. Similar results were obtained by Ubalde et al. (2010) in vineyards in the Conca de Barberá zone.

Regarding the N status of the vines, the positive correlations found between the soil N-NO3 and the leaf petiole N content (Figure 4) confirm that a greater availability of N-NO3 in the soil leads to a greater assimilation of this nutrient by the vines. Flowering is the critical period for the assimilation of N by the vine (Löhnertz, 1991; Perret, 1993). Thus, our results show that the determination of N-NO3 in the 0–45 cm layer of the soil at flowering is related to the N assimilated by the vines. These results confirm previous studies in Germany by Linsenmeier et al. (2008), who observed that higher levels of soil NO3 provided vines with a higher content of foliar N. In the same way, in the DOCa Rioja appellation (Spain) with Tempranillo, Pérez-Álvarez et al. (2013) reported a positive correlation between the N in both the blade and petiole leaf tissues and the soil N-NO3 determined at the vine flowering stage.

Furthermore, the correlations found between N-NO3 in the soils and the K content in the petioles (Figure 4) indicate that an increase in the availability of N-NO3 in the soil increases vine K assimilation. This result confirms a previous study by Zhang et al. (2010), who showed that the assimilation of NO3 stimulates the net assimilation of K in various crops. This process is likely due to K being a cation which mainly accompanies the anion NO3 during its absorption at the root level (Ivashikina and Feyziev, 1998).

This higher nutritional level of K in vines, induced by high availability of N-NO3, leads to a greater K content in berries, as these represent an important sink for this nutrient (Mpelasoka et al., 2003). Our results showed positive correlations between the soil N-NO3 and the K in the musts (Figure 5) and in the wines (Figure 6). Similar results of synergy between soil N in the soil and berry K content were reported by Brunetto et al. (2009) for Cabernet-Sauvignon in Brazil and by Assimakopoulou and Tsougrianis (2012) for cv. Agiorgitiko in Greece.

The great ability of N to stimulate canopy development has been noted by several authors, such as Conradie (2001) and Pérez-Álvarez et al. (2015). The positive correlations found in our study between the soil N-NO3 and the shoot weight (Figure 4) confirm the capacity of soil N to increase vegetative development. This result is consistent with Linsenmeier et al. (2008) in Germany, Balachandra et al. (2009) in New Zealand and Pérez-Álvarez et al. (2013) in La Rioja.

High soil N-NO3 also results in an increase in berry size, which in turn increases bunch weight (Choné et al., 2001; Thomidis et al., 2016), which is confirmed in our results (Figure 4). Similar increases in grape yield induced by greater amounts of soil NO3 were previously described by Linsenmeier et al. (2008) in Germany and Pérez-Álvarez et al. (2013) in La Rioja.

High soil N-NO3 is also related to higher must malic acid (Figure 4). High soil N-NO3 increased vine vigour and vegetative development resulting in shaded microclimatic at the bunch level, a development that is unfavourable for the metabolic breakdown of the malic acid. Similar results showing an increase in malic acid content in musts from vines receiving fertilizer with N were found by other authors such as Keller et al. (1999) for cv. Pinot noir and Hilbert et al. (2003) for cv. Merlot.

Soil N-NO3 also affects other compounds which determine the quality of the musts. In our study, soil N-NO3 was negatively correlated with polyphenols and anthocyanins (Figure 5). The greater availability of N caused greater vine vegetative development, which is a factor that competes with the accumulation of sugar and pigments in grapes (Bravdo and Hepner 1987). This greater vegetative growth in vines could also have interfered with the metabolic pathways of compounds such as the anthocyanins and polyphenols, ultimately reducing their presence in the grapes (Bravdo and Hepner, 1987). This effect is widely described in the literature, and Pérez-Álvarez et al. (2013) previously found a negative correlation between soil N-NO3 and must anthocyanins and polyphenol content for Tempranillo in the La Rioja region. Further, this negative correlation between soil N-NO3 and the polyphenol content was found in a vineyard of Tempranillo in La Rioja with three different methods of soil management: tillage, barley and clover cover crops (Pérez-Álvarez et al., 2015). Moreover, the intake of N by the vines was found to inhibit the synthesis of anthocyanins in Merlot (Hilbert et al., 2003), Cabernet-Sauvignon (Keller and Hrazdina, 1998), and Pinot noir vines (Keller et al., 1999). Similarly, Delgado et al. (2004) showed that the N intake reduced the accumulation of phenolic compounds in the skins of the grapes in Tempranillo. Finally, a low nutrient content of N in vines induced a high content of anthocyanins and polyphenols in the berries of Cabernet-Sauvignon (Choné et al., 2001) and Merlot (Tregoat et al., 2002) in the Bordeaux region (France).

In the 2013 season, a significant correlation was observed between must polyphenols and anthocyanins and the available soil N-NO3 (Figure 5), but no correlation was established between the SWHC and the must polyphenol and anthocyanin content (Figure 2). These results suggest that in the case of low levels of water deficit during wet seasons, the main factor affecting polyphenols and anthocyanins in grape is the availability of soil N-NO3. Similarly, when correlations exist between the SWHC and soil N-NO3 and the polyphenol and anthocyanin compounds in the same year, these two factors may be mostly additive, rather than interactive (Keller, 2005). The greater availability of N-NO3 in the soil allows the vines to assimilate more N, which also promotes vine vegetative growth. In both cases, the greater vine vegetative development leads to a lower content of anthocyanins and polyphenols in the must.

The negative correlation observed in the musts between the polyphenols and anthocyanins and the soil N-NO3 was also noted between the soil N-NO3 and the colour intensity and polyphenol and anthocyanin content in the wines (Figure 6). Therefore, the reduction of polyphenols and anthocyanins in the must caused by the greater soil N-NO3 were of sufficient magnitude to have a similar effect with regard to polyphenols and anthocyanins in the wines. Hence soil N-NO3 affects the colour and anthocyanins of the wines and therefore exerts a major influence on the oenological potential of the vines.

The positive correlations between the soil extractable K and the foliar K, the K in the musts and the K in the wines (Figure 7), confirmed that the soil extractable K directly affects K levels in the vines, since the absorption of K is a passive process (Keller, 2010). This affinity for the assimilation of K may be related to the great mobility in the vines due to the involvement of a large range of physiological processes (Leibar et al., 2017). As has been pointed out previously, the greater K content of the vines has repercussions on the accumulation of K in the berries (Mpelasoka et al., 2003). Similar results with correlations between the soil extractable K and the foliar K and the K in the musts were described by Assimakopoulou and Tsougrianis (2012) in Greece with Agiorgitiko. In Brazil, Tecchio et al. (2006) also observed such correlations for cv. Niágara Rosada, finding a relation between soil K extractable and the K content of the petioles. In this way, the greater soil extractable K increases the K content in the soil solution and consequently the absorption of K by the plant. This K enrichment in the vegetative parts can be transferred to the grape (Mpelasoka et al., 2003), and finally, to the wine. The fact that the relations between the soil extractable K and the K in the wines are maintained shows the influence of the soil K content on wine quality, since K reduces the free acids in the wine and raises wine pH.

This effect on wine pH is confirmed by the correlations between pH of must and wine and the soil extractable K and soil N-NO3 (Table 5). The correlation data also showed that the relationships were greater with wine pH than with must pH, which could be due to the fermentation that modifies the organic acid concentration in the must but not that of K.

Regarding the variance of the studied parameters, component 1 of the PCA could be attributed to the soil fertility factor. The loading of the SWHC, soil N-NO3 and soil extractable K confirm the similar weight of the three soil parameters in the soil fertility. The great positive correlation with component 1 for petiole % N and % K, shoot weight and bunch weight (Table 7) confirms the influence of these soil properties on the nutrient content and development of the vine that was previously discussed. The importance of soil fertility in the grapevine vegetative development is confirmed by the high variance percentage attributable to the soil factor (Table 6). The negative correlation with component 1 for polyphenols and anthocyanins in must and wine confirms what was previously explained in relation to greater SWHC and a higher soil N-NO3 reducing the polyphenol and anthocyanin content in grapes.

In contrast, component 2 could be attributed to the climatic conditions of the year, specifically to the temperature, since malic acid correlates with this component negatively and to a greater extent. This outcome would occur because the higher temperature causes the organic acids in the grape to decrease (Keller, 2010). In this way, for K and pH, a positive correlation with component 2, which coincides with the must pH increasing with temperature, was observed.

The higher percentage of the variance attributable to the year factor in the must pH and malic acid, (Table 6) confirms the great dependence of the acidity components with the climatic conditions.

The positive correlations with component 2 of polyphenols and anthocyanins in must and wine could be due to the fact that a higher temperature in the vintage would cause greater water stress during grape ripening (Ramos and Martínez de Toda, 2019), which would then cause an increase of these compounds in the grape. This effect of the year factor is attributed to a lower variance percentage relative to the soil factor for must TPI and must anthocyanins (Table 7).

The soil N-NO3 loading factor in component 2 shows that soil N-NO3 availability is also affected by vintage climatic conditions, since temperature affects organic N mineralization. These inter-annual variations have been observed in a vineyard from La Rioja (Pérez-Álvarez et al., 2015), and this result indicates that the vintage climatic conditions could affect the grape characteristics through the soil N-NO3 availability.

Conclusion

Based on the soil and climate conditions of the DOCa Rioja appellation, the SWHC may account for variations in vine vigour and yield and must and wine composition between vineyards with different soil types across seasons. Higher SWHC leads to increases in vine vigour and yield and reductions in the polyphenols and anthocyanins in musts and wines. These effects are not significant in years with high levels of precipitation, as was the case in 2013. Moreover, high SWHC promotes the assimilation of N and K, two of the most important nutrients for the vines. Therefore, to study soil N and K fertility and its effect on vines, must and wines, the SWHC also needs to be considered to enable correct assessment of the effects of these two nutrients.

In addition, the soil N-NO3 content during flowering can explain the N levels in the vines despite changes in the soil N-NO3 levels from year to year due to the effect of different climatic conditions on soil N organic mineralization. In this respect, high soil N-NO3 was found to increase vine vigour and yield and to decrease the content of polyphenols and anthocyanins in musts and wines. Therefore, for managing vineyards to obtain the highest possible must and wine quality, it is important to keep the soil N-NO3 levels in the soil under control to support balanced vine growth and production and consistent quality of musts and wines.

The soil extractable K in the Ap horizon close to the surface (0–15 cm) was positively correlated with the K content in the vines, musts and wines.

Finally, these soil parameters were found to be related to the final composition of the wines from these plots, which confirms an effect of the soil. Since the vinification process can reduce the differences observed in musts, the results obtained show the great effects that these soil parameters in this study had on the wine composition. These observations are important for the agronomic management of the vineyard, from the planning of new plantations (in the case of SWHC) to the management of fertilization (N and K).

Acknowledgements

This work is part of a project financed by the Government of La Rioja (Spain). Eva Pilar Pérez-Álvarez also acknowledges the Spanish Ministry of Science, Innovation and Universities (MCIU) for her postdoctoral grant.

The authors thank Zinio Wineries from Uruñuela (La Rioja) for collaboration.

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Authors


José María Martínez-Vidaurre

https://orcid.org/0000-0002-4476-5385

Affiliation : Instituto de Ciencias de la Vid y del Vino-ICVV (CSIC, Gobierno de La Rioja, Universidad de La Rioja), 26007, Logroño

Country : Spain


Eva Pilar Pérez-Álvarez

https://orcid.org/0000-0002-6937-3986

Affiliation : Instituto de Ciencias de la Vid y del Vino-ICVV (CSIC, Gobierno de La Rioja, Universidad de La Rioja), 26007, Logroño

Country : Spain


Enrique García-Escudero

Affiliation : Instituto de Ciencias de la Vid y del Vino-ICVV (CSIC, Gobierno de La Rioja, Universidad de La Rioja), 26007, Logroño

Country : Spain


Fernando Peregrina

fernando.peregrina@unirioja.es

https://orcid.org/0000-0002-7621-7455

Affiliation : Área de Edafología, Departamento Producción Agraria, E. T. S. I. Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 2040, Madrid

Country : Spain

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