The study was conducted between 2023 and 2025 in the P.D.O. Campo de Borja, situated in the northwest of the province of Zaragoza (Aragón, Spain), in a transition zone between the Iberian System mountains (Moncayo) and the Ebro Valley. The geoclimatic environment of Campo de Borja is characterised by a marked continental climate with Atlantic winter and Mediterranean summer influence (Ministerio de Agricultura Pesca y Alimentación, 2024) . This results in thermal contrasts and low rainfall, with a mean annual temperature of 15.3 °C and a mean annual rainfall ranging between 350 and 450 mm. The cold and dry north-westerly wind is a predominant climatic factor. Vineyards extend between 350 and 900 meters in altitude, predominantly on brown-calcareous soils and terrace soils with high gravel content and good drainage (Ministerio de Agricultura Pesca y Alimentación, 2024) . A total of 80 plots were selected across different municipalities of the P.D.O. Campo de Borja. The detailed characteristics of each studied plot are provided in the Table S1. The selection was performed in collaboration with participating wineries to ensure a representative sample of the area's viticultural heritage. All plots belong to the Grenache variety and include plots with well-documented planting ages and others of unknown age. A multimodal data collection approach was implemented in all selected plots.

Historical aerial photographs constitute a valuable documentary resource for researching geographical evolution, as they faithfully reflect the state of a territory at different moments in time. The Instituto Geográfico Nacional indicates that this resource is fundamental for analysing changes in agricultural land use; consequently, aerial images were obtained from this source to reconstruct the history of the study plots. The time series covered a range from the American flight B series (1956–1957) to the current flights of the National Aerial Orthophotography Plan (PNOA) of 2024, provided by the Instituto Geográfico Nacional (Instituto Geográfico Nacional, 2025) . The specific flights used as a basis for this study are detailed in Table 1. As can be seen, the availability of graphic information tends to decrease as the analysis goes further back towards the mid-20th century. The historical record starts with the American flight B series and the Interministerial flight, between which there is a time gap of approximately 20 years. Subsequently, the overlap detected between the Interministerial and National flights suggests a greater availability of images during that specific decade. In these cases, the IGN categorises images from various years by general flight names, such as National or Interministerial, with internal time ranges that can vary between 5 and 13 years. To help determine age ranges with greater precision, we have chosen to identify the exact year of acquisition indicated by the IGN within those periods. Regarding more recent stages, the study has relied on the annual PNOA, which offers photography every 2 to 3 years, covering an annual record from 2004 to the present. Finally, the most current PNOA has been used, which provides a mosaic composed of the most recent aerial photographs processed by the IGN. This resource aims to provide the visual representation closest to the current reality of the vineyard for the end of the study in 2025. It is worth noting that the number of available images varies significantly between different locations in Spain. Therefore, this work only details those that specifically cover the study areas.

For image processing and analysis, Geographic Information Systems (QGIS, 3.10.14) software was used. In this software, the historical images were compared with current cartography (PNOA) to ensure correct spatial alignment and minimise the distortions inherent to old aerial photographs. Systematic temporal mosaics were generated for each plot, facilitating temporal review and change identification. This diachronic analysis was used to identify the first visible appearance of the vineyard plantation, establishing a minimum real age for the plot (Figure 1).

The most recent aerial photographs were also used to identify the spatial situation of the vines, which allowed, on the one side to determine if they were trained as goblet (vines seen as individual points) or trellised (seen as rows) and, in the case of goblet vines, to determine their spacing and their planting pattern as marco real (square pattern) or tresbolillo (triangular pattern) (Figure 2). The potential of aerial imagery for determining the training system was assessed on all the plots we visited (14), and the planting patterns were checked on two plots using a tape measure.

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To refine the age of old vines that aerial images cannot detect, a morphological study of the vine trunk was conducted in a subsample of 14 plots (selected from the 80 total plots). This selection was restricted to vineyards classified as old according to P.D.O. records, with their existence verified through visual analysis of the historical photographs series available for the area, thereby ensuring the vines reached a verifiable minimum age. This analysis focused on correlating the physical dimensions of the vine with its potential age. In each of the 14 plots, a targeted sampling was made, and between 6 and 8 vines representative of the overall vineyard average were selected, vines that could be clearly identified to be as old as the vineyard and where the arms appeared to be undamaged or reconstructed to measure. The following morphological variables were recorded in situ during winter, after pruning (Figure 3):

Total length of the vine (L _t): measured in centimetres (cm) from soil level to the last pruning cut in clearly defined arms in each vine.

Number of visible pruning cuts (N _cuts): count of visible old wood cuts on the vine, assuming one cut per year.

Total distance between pruning cuts (D _cuts): accumulated longitudinal distance between the visible pruning cuts counted, measured in centimetres (cm).

The calculation of the individual vine age (A _vine) was performed in two steps. First, the mean annual growth rate (G _vine) was determined using the total distance between visible cuts (D _cuts) and the number of recorded cuts (N _cuts):

G v i n e = D c u t s   N c u t s                 (Equation 1)

where G _vine is expressed in cm/year. To establish a chronological reference, a standardised regional growth rate (G _vine_ _ref) was calculated by averaging the individual rates obtained across the 14 sampled vineyards.

Subsequently, the estimated age of the vine (A _vine) was calculated by dividing the total vine length (L _t) by this annual growth rate (G _vine):

A v i n e = L t G v i n e             (Equation 2)

where A _vine is expressed in years.

The genetic identity determination of both wine grape cultivars and rootstocks was initiated with field studies conducted across 20 vineyards (14 old, 6 young). During these visits, a detailed ampelographic characterisation was conducted to assess the diversity of the existing plant material. Leaf tissue samples were collected from vines presenting morphological characteristics distinct from the primary cultivar Grenache. Regarding rootstocks, sampling prioritised leaf tissue. However, in vines where canopy sampling was impossible due to mechanical removal of the rootstock vegetation, root samples were collected. Following collection, all samples were placed in coded cryotubes and transported to the laboratory at the Public University of Navarre (UPNA), where they were stored at –80 °C until molecular analysis.

The plant material, cryopreserved leaf tissue and root samples were processed using distinct preparation protocols. Leaf samples were homogenised via mechanical fragmentation with tungsten beads in a microdismembrator (1 minute at 2000 rpm) (B. Braun Biotech International, Melsungen, Germany). In the case of root samples, the bark was removed with a scalpel due to its high polyphenolic content, which can interfere with the extraction process, and then the remaining material was ground with sandpaper to obtain a fine powder.

Genomic DNA isolation was performed using the DNeasy Plant Pro commercial kit (Qiagen), following the manufacturer’s instructions. The DNA quality and concentration were determined in a FLUOstar Omega (BMG Labtech). The purified DNA was stored at –20 °C for subsequent analysis. For genetic characterisation, microsatellite loci, or Simple Sequence Repeats (SSRs), were amplified. These regions consist of tandem repeat units where the number of repeats is highly polymorphic among grapevine cultivars, enabling precise identification of varieties. Amplification of the SSR regions was performed using three independent multiplex PCRs denoted as A, B and C. The reactions were carried out using fluorescently labelled primers and the Multiplex PCR Master Mix (Qiagen®), following the manufacturer’s instructions. One primer of each pair was fluorescently labelled with VIC, NED, PET or 6-FAM dyes. Amplification conditions were carried out following the protocols described by Ibáñez et al. (2009), with several modifications for A and B multiplex PCRs as detailed in Urrestarazu et al. (2015). Fragment analysis was performed in an ABI PRISM 3730 sequencer (Applied Biosystems, Foster City, CA, USA), using 500-LIZ as internal marker size, and the resulting electropherograms were processed to assign fragment sizes with Peak Scanner Software version 1.0 (Applied Biosystems, Foster City, CA, USA). The Table S2 details the SSR marker profile (VVS2, VVMD5, VVMD7, VVMD25, VVMD27, VVMD28, VVMD32, ZAG62 and VRZAG79) used for varietal identification by comparison with reference profiles from the database Vitis International Variety Catalogue (VIVC) ( Maul & Töpfer, 2015).

The analysis of the 80 selected plots is presented in Table 2, which shows the data obtained from the analysis of historical aerial photography and the vine ages provided by the official D.O.P. Campo de Borja records. Regarding the training systems, which serve as an indicator of planting age, the majority of the surveyed plots (75 %) are planted using a trellis system, while the remaining 25 % consist of goblet vines. Within these goblet-trained vineyards, 70 % follow a marco real pattern, whereas the remaining 30 % are established in a tresbolillo configuration. Furthermore, an analysis of row spacing shows that 28.8 % of vineyards have a row spacing of 2 to 2.5 m, while the remaining 71.2 % range from 3 to 3.5 m.

To estimate plantation age ranges, we established a chronological timeline using only the available aerial flights with spatial resolution: Interministerial (1977), National flight (1985), OLISTAT (1997), SIGPAC (1999), and PNOA (2006, 2009, 2012, 2015, 2018, 2021, and 2024). The level of uncertainty in the age estimation is defined by the time interval between the first photograph showing the vines and the immediately preceding flight. These ranges are detailed in the final column of Table 2. The maximum uncertainty interval applies to vineyards first identified in 1977, which are estimated to be over 48 years old. Conversely, the minimum intervals range from 1 to 4 years for more recent plantations, due to the increased frequency of aerial surveys. However, it is important to note that local factors such as cloud cover or complex terrain relief occasionally reduced image quality, leading to wider uncertainty intervals in certain locations. When comparing these estimates with the official registered ages (from 3 to 82 years), different degrees of deviation were observed. In some plots ( e.g., ID 31 and 35), the registered year matches the age range estimated from aerial photographs. In many other plots, small deviations between 1 and 5 years are present. This range could be considered negligible, as they are a consequence of the gaps between aerial flights. However, larger deviations of up to 35 years exist in specific cases ( e.g., ID 7 and 27). These wide intervals are primarily due to the lack of available aerial surveys prior to 1977, an issue that increases the uncertainty of maximums for older vineyards.

The relationship between the official registered age and the data collected from the aerial photography analysis is illustrated in Figure 4. When comparing training systems, a difference in longevity is observed; the mean age of the vineyards planted in goblet is higher than that of plots established with a trellis system. Regarding the spatial pattern, the results indicate that vineyards with a tresbolillo configuration have a higher average longevity than those with a marco real pattern. Finally, in terms of row spacing, the data indicate that in plots with wider rows (3 to 3.5 m), the mean age decreases compared to plots with narrower rows (2 to 2.5 m).

*Marco real and tresbolillo vineyards correspond to the goblet training system.

The morphological study conducted on a sub-sample of 14 vineyards is presented in Table 3. The results, expressed as mean ± standard deviation to reflect in-plot variability, indicate that the total vine length, measured from the base to the final pruning cut, ranges from 48 cm to 120.4 cm (with standard deviations ranging from 3.42 to 13.64 cm). Regarding annual growth rates, the calculated values for each vineyard vary between 1 and 2.09 cm per year (with standard deviations from 0.15 to 0.67 cm/year), resulting in a regional mean growth rate of 1.6 cm. However, to accurately reflect local conditions, the specific growth rate (G _vine) of each plot was used to estimate its morphological age, corresponding to the column “Morphological study”.

Regarding the comparison of the three age data sources. In vineyards 5, 7, and 80, the morphological estimates limited the open age ranges from aerial photography and are higher than the registered age. In plots 50 and 74, the morphology is between the registered age and aerial range. For vineyard 11, all three age sources show a high degree of coincidence. In the remaining cases, the age estimates through morphological data are lower than those provided by either the official registers or the aerial photography analysis.

The results of the plant material identification conducted across the sub-sample of 14 vineyards selected for their high longevity are presented in Table 4. The collected samples were categorised into dominant and sporadic, based on the frequency observed during field prospections.

Regarding the rootstock identifications, Rupestris du Lot was found to be the predominant variety, representing the dominance in 12 of the 14 studied plots. In the remaining two plots, the majority of rootstocks identified were Richter 110 (Plot 11) and Millardet et Grasset 41 B (Plot 80). In terms of sporadic rootstocks, a diverse range was recorded, with Ganzin 1 being the only variety identified in more than one plot (Plots 28 and 61). Other secondary rootstocks identified include Richter 31, Couderc 3306, Paulsen 1103, and Millardet et Grasset 141A.

Concerning the scion identifications, the presence of Grenache was hegemonic, appearing as the dominant variety in 100 % of the analysed plots. Among the secondary cultivars, Muscat à Petits Grains Blancs was the most frequent, appearing sporadically in two plots (Plots 44 and 50). Other traditional varieties identified in specific locations include Gallera Roja, Rojal Tinta, Jarrosuelto, Viura, and Quiebratinajas.

The results of the varietal identifications for the control plots are presented in Table 5. In this subset, the rootstocks identified as the dominant show a clear change compared to the initial 14-vineyard study. Richter 110 arose as the predominant rootstock, appearing in 4 of the 6 control plots, whereas it had only been recorded once as a dominant rootstock in the previous analysis of ‘old vineyards’ (Table 4). Ruggeri 140 and Millardet et Grasset 41B appeared in the remaining control plots as their primary rootstocks. Regarding sporadic presence, only Richter 110 was identified as a secondary variety in one instance.

In contrast to the rootstock variability, the scion identifications demonstrate absolute consistency with the previous findings. Grenache remained the hegemonic variety, representing the dominant scion in 100 % of the control plots. Also, unlike the vineyards selected for their longevity, no sporadic varieties were detected within these control samples.

The dominant rootstocks identified in Tables 4 and 5 have been categorised into three age ranges to evaluate their distribution patterns (Figure 5). The data reveals a notable change in the choice of plant material over time. In the youngest vineyards (< 25 years), the rootstock identified was composed mainly of Richter 110, and secondary of Millardet et Grasset 41 B, and Ruggeri 140. As the age of the vineyards increases, the presence of these varieties decreases significantly. In the intermediate group (35–55 years), each of these modern rootstocks represents only 12.5 % of the total, as the dominant of these vineyards were established on Rupestris du Lot. Finally, in the oldest age group (> 60 years), Rupestris du Lot becomes the unique primary rootstock.

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