Organic mulches improve vine vigour, yield and physiological response in a semi-arid region

Context and purpose of the study. Sparkling wine production is majorly impacted by climate change as sugar accumulation and aromatic development in grapes are often decoupled.

Ecological sustainability refers to developing viticulture in adequate environmental conditions.

Pulsed electric fields (pef) technology holds significant promise for the agrifood industry, considering the capacity of inducing cell electroporation, due to the disruption of cellular membranes. Pef-induced permeabilization is dependent of the chosen treatment protocol (i.e. Pulse shape, electrical field strength, specific energy) and of the matrix’s characteristics (i.e. Cell radii and size, ph, electrical conductivity).

Viticultural potential has a complex conditioning, determined by relief,
soil, climate and lithology. Delineation of viticultural potential from vineyard areas is essential for the purpose to collect the necessary data for viticultural zoning. Using this data, we can achieve greater yield quality, which is the most important criteria in viticulture. The main purpose of this research is
characterizing of viticultural potential and zoning of homogeneous viticultural zones in Goriška Brda region by assessing the suitability of defined ecological factors.

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.