Viticulture

The intravarietal diversity of sugar concentration in Merlot depends more on late evaporation than sugar loading This article is published in cooperation with the 2nd edition of TerclimPro (18–19 February 2025), Bordeaux & Cognac, France. Original language of the article: English.

Victoria Lesbats-Sichel, Patrice This, Thierry Lacombe, Loïc Le Cunff, Charles Romieu
Published : 17 February 2025
DOI: https://doi.org/10.20870/IVES-TR.2025.8506

The rise in temperature associated with climate change can lead to excessive sugar concentrations at harvest. To maintain varietal typicity in the future, more resilient genotypes need to be identified within clonal diversity. We studied a compendium of compositional and developmental  traits in a private collection of 55 accessions and 3 ENTAV-INRA® clones of Merlot. Extensive single berry measurements revealed differences in post phloem arrest water losses at the origin of variations in  sugar concentration among the most extreme genotypes.

Material and method

The private Merlot collection, located in the Pomerol appellation, contains 55 accessions and 3 ENTAV-INRA® clones (182, 343, 347) within a 0.25 hectares plot comprising four blocks of 6 plants per genotype. Vines were grafted on ‘3309 Couderc’ rootstock and planted at a density of 5711 vines/ha (145 x 90 cm) in 2015. Traits analysed included vigour (pruning shoot number and total weight per plant) and measured mid-budbreak date. Cluster growth was estimated by periodically checking the volume of three clusters per genotype and per block1. All genotypes were harvested simultaneously. Yield was estimated by counting the number of bunch per vine and berries per bunches, and determining average berry weight on 3 destemmed bunches per genotype and per block. Berry juices were obtained by gently blending the same samples, without crushing the seeds. Sugar and organic acid concentrations were then determined by HPLC2 and vine water status via carbon isotopic analysis (δ13C)3.

Results and discussion

On average, cluster volume of the whole 2019 harvest increased approximately two-fold from the end of the herbaceous plateau (end of July) to mid-August, before progressively decreasing during the next 30 days until harvest. A similar trend was observed in 2020, but the initial growth rate was slower and the timing of the maximal volume more difficult to determine (Figure 1), which could indicate greater asynchrony in berry development than in 2019. These results indicate that the clusters concentrated sugars upon losing water and shrivelling prior to harvest, as expected after exceeding maximal volume, which corresponds to the cessation of phloem unloading and sugar import in berries4. Moreover, individual clusters mostly differed from each other by showing proportional changes in their sugar and tartaric acid concentrations (Figure 2), with the same slope in 2019 and 2020 as well, even if they were generally more concentrated in 2020. The simplest interpretation of these results is that bunches essentially differed in water content5, while the amounts of tartaric acid and sugars, which had sequentially accumulated during the green stage and ripening, were more or less invariant. The respective effects of the genotype, plant position and environmental variables on canopy and berry related traits were then analysed (Table 1), using dedicated statistical models (Best Linear Unbiased Predictions or BLUPs6 7). Genotypes were then compared, according to their genetic aptitude to concentrate sugars, undergo water deficit or develop voluminous clusters or vigorous canopies, among other traits (Figure 3). With a 1 % v/v probable ethanol variation between the top 4 genotypes and their most diluted counterparts, the genetic diversity of the collection outperformed variations among ENTAV-INRA registered clones. Such a strong clonal variability has already been documented for grape composition, wine quality, leaf stilbene content and downy mildiew resistance in Cabernet franc8. Greater dilutions were associated with increased canopy vigour and resilience to water deficit, together with the largest berry numbers and volume. In order to understand which genes contributed to these changes, screening was carried out to determine which mRNA transcripts were differentially expressed in berries from the two extreme genotypes. This confirmed that the berries were harvested after the arrest of phloem unloading, as illustrated by the sudden repression of aquaporins and sugar transport genes9. A very low number (37 out of 15000) of genes of unclear function displayed preferential expression in each genotype. Consequently, neither genotype statistically departed from the same developmental sequence between the end of the green stage and harvest. However, in the less concentrated genotype, this sequence noticeably slowed down during the final period of water loss associated with over-ripening.

Figure 1. Average berry growth during the 2019 and 2020 vintages.

Figure 2. Sugar and tartaric acid concentrations at harvest in 5 individual Merlot clusters for each of the 58 genotypes.

Figure 3. Principal Component Analysis of the genoBLUP values obtained from three years of phenotyping berry- and canopy-related traits.

Table 1. Phenotypic traits explicative variables. The P-value refers to the genotype effect.

Phenotypic Trait

Explicative variables*

P-value

Heritability

Pruning weight

Year, Resistivity, Genotype, X,Y

1.9e-06

0.61

Weight per shoot

Year, Resistivity, Genotype, X,Y

0.006

0.43

Bunches per shoot

Year, Genotype, X,Y, (Block:Year)

0.0064

0.41

Bunch weight

Year, Genotype, X,Y

0.0053

0.44

Sugar concentration

Year, Resistivity, Block, Genotype,X, (geno:year) , (Block:year)

0.021

0.40

Bunch contraction

Resistivity, Block, Genotype,Y

0.046

0.33

Bunch expansion

Genotype, X, Bunch_position

2.21e-5

0.55

Budbreak

Year, Genotype, X

0.0029

0.43

δ13C

Block, Genotype, X, Y

0.0013

0.50

* X: plant position in the row, Y : row number in the block, resistivity : soil resistivity.

Conclusion

Climate change is causing us to reconsider the objectives of clonal selection. Merlot's diversity becomes particularly apparent during the terminal phase of grape development, when the sugars concentrate due to evaporation. It does, however, seem to mobilise differences in the water use efficiency of the whole plant prior to this last phase. Merlot's diversity opens up possibilities for adaptation to moderate climate change.

Notes

  • 1. Lang, A., & Thorpe, M. R. (1989). Xylem, phloem and transpiration flows in a grape: application of a technique for measuring the volume of attached fruits to high resolution using Archimedes' principle. J. Exp. Bot. 40, 1069–1078. https://doi.org/10.1093/jxb/40.10.1069
  • 2. Bigard, A., Romieu, C., Ojeda, H., & Torregrosa, L. J.-M. (2022). The sugarless grape trait characterised by single berry phenotyping. OENO One, 56(3), 89–102. https://doi.org/10.20870/oeno-one.2022.56.3.5495
  • 3. VAN LEEUWEN C., BOIS B., BRILLANTE L., DESTRAC IRVINE A., GOWDY M., MARTIN D., PLANTEVIN M., de RESSÉGUIER L., SANTESTEBAN L. G., ZUFFEREY V., 2023. Carbon isotope discrimination (so-called δ13C) measured on grape use is an accessible tool to monitor vine water status in production conditions. IVES Technical Reviews, 8 September 2023, Published in English, French, Italian, Spanish, Portuguese and German. https://doi.org/10.20870/IVES-TR.2023.7742
  • 4. Savoi, S., Torregrosa, L. & Romieu, C. (2021). Transcripts switched off at the stop of phloem unloading highlight the energy efficiency of sugar import in the ripening V. vinifera fruit., Horticulture Research. 8. 193-207. https://doi.org/10.1038/s41438-021-00628-6
  • 5. Bigard, A., Romieu, C., Ojeda, H., & Torregrosa, L. J.-M. (2022). The sugarless grape trait characterised by single berry phenotyping. OENO One, 56(3), 89–102. https://doi.org/10.20870/oeno-one.2022.56.3.5495
  • 6. Henderson, C.R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31(2):423-447. https://doi.org/10.2307/2529430
  • 7. Brault, C., Flutre, T., & Doligez, A. (2021). Scripts and data of the genetic analysis of Syrah x Grenache progeny, Recherche Data Gouv, V1, https://doi.org/10.15454/NOUQY2
  • 8. VAN LEEUWEN C., ROBY J.-P., ALONSO-VILLAVERDE V. and GINDRO K., 2013. Impact of clonal variability in Vitis vinifera Cabernet franc on grape composition, wine quality, leaf blade stilbene content and downy mildew resistance. J. Agric. Food Chem., 61, n°1, 19-24. http://dx.doi.org/10.1021/jf304687c
  • 9. Savoi, S., Torregrosa, L. & Romieu, C. (2021). Transcripts switched off at the stop of phloem unloading highlight the energy efficiency of sugar import in the ripening V. vinifera fruit., Horticulture Research. 8. 193-207. https://doi.org/10.1038/s41438-021-00628-6

Authors

Victoria Lesbats-Sichel

Affiliation : Petrus, Pomerol, France - UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France

Country : France

Patrice This

Affiliation : UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France - UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France

Country : France

Thierry Lacombe

Affiliation : UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France - UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France

Country : France

Loïc Le Cunff

Affiliation : Institut Français de la Vigne et du Vin, F-34398 Montpellier, France - UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France

Country : France

Charles Romieu

charles.romieu@inrae.fr

Affiliation : UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France - UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France

Country : France

References

  • Lang, A., & Thorpe, M. R. (1989). Xylem, phloem and transpiration flows in a grape: application of a technique for measuring the volume of attached fruits to high resolution using Archimedes’ principle. J. Exp. Bot. 40, 1069–1078. https://doi.org/10.1093/jxb/40.10.1069
  • Bigard, A., Romieu, C., Ojeda, H., & Torregrosa, L. J.-M. (2022). The sugarless grape trait characterised by single berry phenotyping. OENO One, 56(3), 89–102. https://doi.org/10.20870/oeno-one.2022.56.3.5495
  • Van Leeuwen C., Bois B., Brillante L., Destrac Irvine A., Gowdy M., Martin D., Plantevin M., De Rességuier L., Santesteban L. G., Zufferey V. (2023). Carbon isotope discrimination (so-called δ13C) measured on grape use is an accessible tool to monitor vine water status in production conditions. IVES Technical Reviews, 8 September 2023. https://doi.org/10.20870/IVES-TR.2023.7742
  • Savoi, S., Torregrosa, L. & Romieu, C. (2021). Transcripts switched off at the stop of phloem unloading highlight the energy efficiency of sugar import in the ripening V. vinifera fruit., Horticulture Research, 8. 193-207. https://doi.org/10.1038/s41438-021-00628-6
  • Henderson, C.R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31(2):423-447. https://doi.org/10.2307/2529430
  • Brault, C., Flutre, T., & Doligez, A. (2021). Scripts and data of the genetic analysis of Syrah x Grenache progeny, Recherche Data Gouv, V1, https://doi.org/10.15454/NOUQY2
  • van Leeuwen C., Roby J.-P., Alonso-Villaverde V. and Gindro K. (2013). Impact of clonal variability in Vitis vinifera Cabernet franc on grape composition, wine quality, leaf blade stilbene content and downy mildew resistance. J. Agric. Food Chem., 61, n°1, 19-24. http://dx.doi.org/10.1021/jf304687c

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