Isotopes are defined as species of atoms of a chemical element located in the same position in the periodic table. Thus, isotopes share an atomic number, i.e., they have the same number of protons but have different atomic mass and physical properties, as the number of neutrons in their nuclei is different. They can be classified into two major groups: stable (maintaining a constant concentration on Earth over time) and radioactive (disintegrating at predictable rates to form other isotopes).

For most light elements, such as hydrogen, carbon, nitrogen and oxygen, one of the isotopes is greatly prevalent (>98 % of the atoms belong to that stable isotope form), the others being present only in trace amounts. The concentration of isotopes in natural compounds varies due to their slightly different mass-dependent behaviour in natural processes. As isotope discrimination is a function of the parameters characterising the process, the study of the relative content of other stable isotope forms can be very informative for many disciplines, ranging from human nutrition (Davies, 2020) to paleobiology (Fisher, 2018) and planetary sciences (Joy et al., 2020). In the particular case of plant and environmental sciences, the stable isotopes most frequently considered are those included in the four light elements mentioned above (Marshall et al., 2007), although there are also interesting applications for the study of the stable isotopes of other elements such as B, Ba, Ca, S or Sr (Bullen and Chadwick, 2016; Dawson et al., 2002; Sun et al., 2018). The relevance of these forms is due to their abundance on the Earth's surface and their involvement in relevant biological processes (Adams and Grierson, 2001). The stable isotopes of heavier elements, such as B, S, Sr and Mg, are also used in plant research, though less often.

Viticulture has already considered stable isotopes a valuable source of information, the main applications of which have been reviewed by Santesteban et al. (2015). The most frequently analysed variations in isotope composition are those in carbon, as they have been shown to be a reliable estimator of plant water status along the season (Gaudillere et al., 2002; Herrero-Langreo et al., 2013; Santesteban et al., 2016; Van Leeuwen et al., 2009). There is also a relatively high amount of research dealing with hydrogen and/or oxygen isotopes, which mainly provide information on the water sources and evaporation processes (Ingraham and Caldwell, 1999; Martin and Martin, 2003; West et al., 2007).

Studying nitrogen-stable isotopes in plant and environmental sciences is a useful tool to better understand N cycling processes and provide insights into historical N availability and ecosystem dynamics (Craine et al., 2015). In viticulture, although ¹⁵N measurement has been used quite profusely when artificially enriched sources of N are added to study the dynamics of N absorption and translocation (Baldi et al., 2017; Hajrasuliha et al., 1998; Morinaga et al., 2003; Schreiber et al., 2002; Verdenal et al., 2021; Vos et al., 2004; Walker et al., 2022; Zapata et al., 2004), or even in paleobotanical research (Joka et al., 2024), quite surprisingly, it has not been until the last years that research in viticulture has incorporated the measurement of the natural abundance of nitrogen isotope forms (Santesteban et al., 2014; Stamatiadis et al., 2007). Grapevine leaf, cane and must samples show lower δ¹⁵N values than those of the corresponding soil (Durante et al., 2016; Paolini et al., 2016), as the bulk δ¹⁵N of plant tissue depends not only on inorganic primary nitrogen sources but also on isotope fractionation during uptake and assimilation. More recently, works conducted in Switzerland have investigated the impact of soil management and water availability on δ¹⁵N in solid wine residues, observing decreased δ¹⁵N values associated with water stress and the competitive effect of a cover crop (Spangenberg and Zufferey, 2018; Spangenberg and Zufferey, 2023), and the same team has lately investigated variations in δ¹⁵N grapevine leaves as affected by early water stress and leaf age (Spangenberg et al., 2020; Spangenberg et al., 2021). Altogether, the works presented above constitute only a small set of information but show the potential interest of measuring the variations in nitrogen isotope forms that occur naturally in grapevine.

Similarly, it is remarkable that while within-field variations in carbon isotope ratio (δ¹³C) have been quite frequently reported (Herrero-Langreo et al., 2013; Santesteban et al., 2017; Van Leeuwen et al., 2018), there is only one work that, to our knowledge, has reported variations of δ¹⁵N at a within-field scale (Stamatiadis et al., 2007). Within-field variability is a feature that it increasingly considered in vineyard management due to the development of precision agriculture (Santesteban et al., 2019). Many studies consider the implications of variations between parts of a vineyard on agronomic performance (Bramley et al., 2019; Ledderhof et al., 2017; Urretavizcaya et al., 2017; Verdugo-Vásquez et al., 2018), those variations being mainly related to variations in soil composition and depth associated with changes in topography (Bramley et al., 2011; Santesteban et al., 2013; Scarlett et al., 2014). However, there is much less information on how changes in soil characteristics may affect nutrition and how these variations should be considered to implement variable rate fertiliser application strategies (Gatti et al., 2018; Gatti et al., 2019), and the study of δ¹⁵N could be relevant in this regard.

Nitrogen has two stable isotopes in nature, ¹⁴N and ¹⁵N, mostly found as the lightest isotopic form, ¹⁴N (99.634 %), whereas the heaviest form, ¹⁵N, represents 0.366 % of the total (Hoefs, 2009). Variations in nitrogen isotope compositions are measured as the relative deviation of the sample heavy-to-light isotope ratio ¹⁵N/¹⁴N from the international reference, that is, atmospheric N₂ gas, i.e., the nitrogen isotope ratio (δ¹⁵N), calculated as detailed in Eq. 1, and expressed either as its per mille (‰) value or as mUr (1 mUr = 1 ‰)

δ 15 N ‰ = N s a m p l e 15 / N s a m p l e 14 N s t a n d a r d 15 / N s t a n d a r d 14 - 1   × 1000

[Eq. 1]

Plant uptake of nitrogen through the roots is known not to induce significant isotope discrimination during the absorption process, particularly when the external nutrient concentration is low (Billy et al., 2010; Santesteban et al., 2015). On the contrary, there are substantial differences in the nitrogen isotope ratio (δ¹⁵N) among the sources plants may take nitrogen from. In this regard, organic matter usually shows much higher δ¹⁵N values than inorganic fertilisers (Bateman and Kelly, 2007). For example, ammonium nitrate fertilisers show a range of δ¹⁵N between –1.4 and +2.6 ‰, while in manure and compost, δ¹⁵N ranges from 3.5 to 16.2 ‰, the average values being +0.2 and +8.1 ‰, respectively. The typical range for δ¹⁵N values in plant tissues is around –10 ‰ to +10 ‰ (Craine et al., 2015), and the source of N is the main factor determining the δ¹⁵N values observed in plant tissues (Kendall et al., 2007). Some environmental factors, such as water availability and temperature, influence N mineralisation, NH₃ volatilisation, and denitrification processes and may, therefore, change the δ¹⁵N of the source N in soil solutions (Högberg, 1997). In this regard, denitrification is known to induce ¹⁵N enrichment of the residual nitrate (enrichment factor between −15 and 30 ‰), volatilisation and nitrification also cause isotopic depletion (average enrichment factors −20 ‰ and −25 ‰, respectively), whereas ammonification usually causes only a small fractionation (−1 ‰) (Billy et al., 2010; Kendall et al., 2007). Additionally, ¹⁵N to ¹⁴N fractionation occurs during uptake, translocation and assimilation can also affect δ¹⁵N (Kalcsits et al., 2014), the latter contributing to a greater extent to the changes observed (Craine et al., 2015; Evans, 2001). as the enzymatic reactions involved selectively generally favour lighter isotopes (¹⁴N) over heavier isotopes (¹⁵N). Furthermore, for a certain plant organ, the relative contributions of newly absorbed N and remobilised N from different plant reserve organs can modify its δ¹⁵N (Kolb and Evans, 2002; Robinson et al., 2000; Spangenberg et al., 2021).

Taking all the previous into account, there is a need for generating knowledge that permits understanding sources of δ¹⁵N variation in grapevines. In this work, we present the results of several independent experiments in an attempt to highlight the potential interest of using this measure in viticulture.

In all experiments, agronomic evaluation was conducted following standard procedures. In short, as agronomic features, yield and its components were determined by counting and weighing all clusters produced in ten vines per replicate or sampling point. Berry composition was determined using two berry samples per replicate or sampling point. Samples were carried to the lab at low temperature (4–6 °C) for analysis, weighed to determine mean berry weight (BW), and a 100-berry subsample homogenised with an LMU 9018 American blender (Man, México) for 10 s at full speed. Part of this homogenate (100 g approx.) was filtered with a gauze tissue and used to measure total soluble solids (TSS) and pH. Yeast assimilable nitrogen (YAN) was determined using Fourier-transform infrared spectroscopy (FTIR), and total anthocyanins and phenolics were measured following the Cromoenos® method using 200-berry subsamples. This method consists of a fast extraction of phenolics following a procedure and reagents provided by the Bioenos company (www.bioenos.com) and has been shown to predict wine colour and composition similarly or even better than other classical procedures (Kontoudakis et al., 2010).

In terms of the sampling used to determine nitrogen isotope composition, the same structure in the experiments designed to determine the influence of the source of nitrogen and the amount of inorganic nitrogen added was used (experiments 1.1. and 1.2. in methodology). At veraison, a 25-petiole sample was taken at each replicate to determine the N content and δ¹⁵N and, at harvest, two 50-berry samples per replicate were taken, one being used to determine δ¹⁵N in whole berries and the other to determine δ¹⁵N in seeds. In all cases, samples were oven-dried at 75 °C and ground to a fine powder prior to δ¹⁵N analysis. In the analyses performed to evaluate within-field variability (experiment described in point 1.3), 50 berry samples were taken at harvest from each SP. In the case of the vineyard in Leza, samples were oven-dried, ground to a fine powder, and then analysed, while those from Traibuenas vineyard were analysed using filtered and oven-dried must samples.

Carbon and nitrogen isotope ratio determinations were carried out using, for each biological replicate, three 2 mg technical subreplicates, using an Elemental analyser (NC2500, Carlo Erba, Reagents, Rodano, Italy) coupled to an Isotope Mass Spectrometer (Thermoquest Delta Plus, ThermoFinnigan, Bremen, Germany). Must samples were packed in tin capsules for conversion into CO₂ and N₂ in an elemental analyser (Carlo Erba CHNSO 1108) coupled to an isotope ratio mass spectrometer (Finnigan Mat Delta Plus). Both C and N isotope composition is reported in the delta (δ) notation, the standards being, respectively, the Vienna Peedee Belemnite (V-PDB) and the molecular nitrogen in air (Air-N₂).

The statistical analysis to assess the differences among the treatments was carried out using one-way analysis of variance (ANOVA). Upon establishing the statistical significance of the overall ANOVA, when appropriate, Duncan’s post hoc test was conducted at P < 0.05 to identify specific pairwise differences between treatment groups and the assumptions of ANOVA, including normality and homogeneity of variances, were assessed. Linear regression analysis was employed to assess the relationship between variables. Statistical analyses were performed using R statistical software (R Core Team, 2022). Spatial variability of isotope ratios was assessed using kriging, a geostatistical technique that interpolates and predicts values at unsampled locations based on the spatial autocorrelation of the observed data. The kriged maps were generated using QGIS software v.3.16.

The source of N affected the δ¹⁵N content in the three organs considered (Figure 1), with samples from organically fertilised vines showing higher δ¹⁵N values in the four seasons. Differences were observed in petioles and seeds during the four years, whereas in whole berries, they were observed from the second season on. The results obtained agree with those observed for other species (Bateman and Kelly, 2007; Camin et al., 2011; Choi et al., 2002, 2017; Mie et al., 2022) since the main driver of δ¹⁵N is the nitrogen source due to the relatively lower magnitude of isotope discrimination for during absorption and assimilation of N (Durante et al., 2016; Paolini et al., 2016; Santesteban et al., 2015). In our case, δ¹⁵N of the compost used ranged between 7.5 and 9.1 ‰ depending on the year, whereas that of the inorganic fertiliser ranged from –0.8 to –0.2 ‰.

Table

ns: not significant differences (P > 0.05); *, **, ***: significant differences with P-values < 0.05, < 0.01 and < 0.001, respectively.

Nitrogen isotope ratios showed a consistent trend to be lower in petioles, followed by whole berries, the highest values being observed in seeds (Figure 3a). Similarly, when the δ¹⁵N values observed in dormant shoots were compared to those in berries in samples from two cv. Tempranillo fields located on the same farm, there is also a trend toward lower δ¹⁵N in the vegetative organs (Figure 3b) than in the berries. This trend to observe higher δ¹⁵N in fruits agrees with the observations of Pascual et al. (2013), who showed that fruit δ¹⁵N were ≈2 ‰ higher than in leaves. On the contrary, in rice, differences between organs were much smaller, and leaves exhibited higher δ¹⁵N values than grains, stems, and roots (Wang et al., 2022). Within the cane, we observed a slight trend to have higher δ¹⁵N values at the basal and mid sections than at the upper section (Figure 3b), this trend not being coincident with the observations of Spangenberg et al. (2021) for leaves sampled at those positions in the shoot.

The DR values obtained to compare the suitability of petioles, whole berries and seeds (Figure 3c) as the source of information on the source of the nitrogen used by the vines show that seeds were the most informative organ. The higher DR values indicate that seeds can significantly discriminate better between treatments. Seed nitrogen concentration is known to be greater than that in pulp (Bell and Henschke, 2005), and it could be hypothesised to be, as a consequence, more sensitive to changes in the nitrogen source. However, this statement needs to be confirmed under other experimental conditions that can certainly affect this behaviour. In any case, it is necessary to highlight that, provided the differences in δ¹⁵N among organs are a consequence of a complex interplay between uptake, losses, assimilation and translocation of nitrogen, their comparison could be used as an integrated metric for understanding better nitrogen fluxes, assimilation processes, and allocation dynamics within plant systems (Cui et al., 2020; Kalcsits et al., 2014).

Table

The bars indicate standard error, and letters correspond to different groups as calculated with t-tests. Each experiment was evaluated separately, as denoted by differences in letter capitalisation.