Soils, climate and vine management: their influence on Marlborough Sauvignon blanc wine style

A pedological study was carried out from 2009 to 2017 in Beaujolais vineyard, to improve physical and chemical knowledge of soils. It was completed in 2016 and 2017 by the current study, dealing with microbial aspects, in order to build a reference frame for improved advice in soil management. Microbial biomass was measured on representative plots of the six most common soil types identified in Beaujolais and, for each soil type, on plots with different levels of the main impacting parameters: total organic carbon, pH, cation exchange capacity, extractable copper. A total of 59 soil samples were collected. Confirming the results of various trials carried out in Beaujolais over the past 20 years, the results of the present study showed that the soils were still alive, but exhibited a large variability of biological parameters, which appeared dependant on both pedological and anthropic factors. Therefore, a good interpretation of biological parameters and advice for vine growers must rely on a pedologically-based referential with differentiated main driving factors. For example, the control of pH is of primary importance in granitic soils and in no way organic matter addition can improve soil quality if pH is too low. Conversely, in calcareous soils, biological parameters are more directly affected by direct or indirect (cover crops for example) inputs of organic matter. The use of biological parameters, such as microbial biomass, is of great potential value to improve advice on agro-viticultural practices (soil management, fertilization, liming, etc.), basis of a sustainable wine production on fragile soils.

Probably one of the most counterintuitive impacts of climate change on vine is the increased frequency of late frost. Champagne, due to its septentrional position is historically and regularly affected by this meteorological hazard. Champagne has therefore developed a strong experience in frost protection with first experiments dating from the end of 19th century. Frost protection can be divided in two parts: passive and active. Passive protection includes all the methods that do not seek to modify the vine’s environment or resistance at the time of frost. The most iconic passive protection in Champagne is the establishment of the individual reserve. This reserve allows to stock a certain quantity of clear wine during a surplus year to compensate a meteorological hazard like frost during the following years. Other common passive methods are the control of planting area (walls, bushes, topography), the choice of grape variety, late pruning, or the impact of grass cover and tillage. Active frost protection is also divided in two parts. Most of the existing techniques tend to modify vine’s environment. Most of the time they provide warmth (candles, heaters, windmills, heating cables…), or stabilise bud’s temperature above a lethal threshold (water sprinkling). The other way to actively fight is to enhance the resistance of buds to frost (elicitors). The Comité Champagne evaluates frost protection methods following three main axes: the efficiency, the profitability, and the environmental impact through a lifecycle assessment. This study will present the results on both passive and active protection following these three axes.

The parameters that determine the grape quality, and therefore the optimal harvest time, suffer variations during berry ripening, related to climate change, with the widely known problem of the gap between technological and phenolic maturities. However, there are few studies about its incidence on grape nitrogen composition. For this reason, the use of an elicitor, methyl jasmonate (MeJ), alone or with urea, is proposed as a tool to reduce climatic decoupling, allowing to establish the harvest time in order to achieve the optimum grape quality. The aim was to study the effect of MeJ and MeJ+Urea foliar applications on the evolution of Tempranillo amino acids content throughout the grape maturation. Three treatments were foliarly applied, at veraison and 7 days later: control (water), MeJ (10 mM) and MeJ+Urea (10 mM+6 kg N/ha). Grape samples were taken at five stages of maturation: day before the first and second applications, 15 days after the second application (pre-harvest), harvest day, and 15 days after harvest (post-harvest). The amino acids analysis of the samples was carried out by HPLC. Results showed that the evolution of amino acids was similar regardless of the treatment; however, foliar applications influenced the nitrogen compounds content, i.e., there was no qualitative effect but quantitative one. Most of the amino acids reached their maximum concentration in pre-harvest, being higher in grapes from the treatments than in the control. In general, no differences in grape amino acids content were observed between MeJ and MeJ+Urea treatments. Foliar applications with MeJ and MeJ+Urea enhanced the grape amino acids content, without affecting their profile, helping to optimize their quality and allowing to establish a more complete grape ripening standard. Therefore, MeJ and MeJ+Urea foliar applications can be a simple agronomic practice, which has shown promising results in order to enhance the grape quality.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

Climate influence on grapevine physiology is prevalent and this influence is only expected to increase with climate change. Although governed by a general determinism, climate influence on grapevine physiology may present variations according to the terroir. In addition, these site-specific differences are likely to be enhanced when climate influence is studied using farm data. Indeed, farm data integrate additional sources of variation such as a varying representativity of the conditions actually experienced in the field. Nevertheless, there is a real challenge in valuing farm data to enable grape growers to understand their own terroir and consequently adapt their practices to the local conditions. In such a context, this article proposes a framework to site-specifically study climate influence on grapevine physiology using farm data. It focuses on improving the analysis of time series of weather data. The analytical framework includes the synchronization of time series using site-specific thermal indices computed with an original method called Extended Growing Degree Days (eGDD). Synchronized time series are then analyzed using a Bayesian functional Linear regression with Sparse Steps functions (BLiSS) in order to detect site-specific periods of strong climate influence on yield development. The article focuses on temperature and rain influence on grape yield development as a case study. It uses data from three commercial vineyards respectively situated in the Bordeaux region (France), California (USA) and Israel. For all vineyards, common periods of climate influence on yield development were found. They corresponded to already known periods, for example around veraison of the year before harvest. However, the periods differed in their precise timing (e.g. before, around or after veraison), duration and correlation direction with yield. Other periods were found for only one or two vineyards and/or were not referred to in literature, for example during the winter before harvest.