Aspects concernant les relations entre quelques composantes de la biomasse viticole, en fonction de l’offre des ressources écologiques

Aims: The optimization of nitrogen use efficiency (NUE, i.e. uptake, assimilation and partitioning) is a solution towards the sustainable production of premium wines, while reducing fertilization and environmental impact. The influence of crop load on the accumulation of N compounds in fruits is still poorly understood. The present study assesses the impacts of bunch thinning on NUE and the consequences on the free amino N (FAN) profile in fruits.

Rotundone accumulation and biosynthesis is a complicated process. Previous research highlighted that these phenomenons were affected under ecophysiological conditions by viticultural practices (e.g. defoliation or irrigation). Individually, these practices often impact several abiotic factors that are difficult to separate such as temperature, water or nitrogen status, or radiation. Such dissociation can be achieved under controlled environmental conditions using potted vines.

[English version below]

Les terroirs viticoles, et plus particulièrement ceux des vignobles AOC, sont aujourd’hui menacés par de multiples agressions. Ces territoires sont non seulement l’outil de production

Context. Botrytis bunch rot occurrence is the most important limitation for the wine industry in humid climate viticulture.

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.