High resolution climatic zoning of the Portuguese viticultural regions

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.

Observational historical geology seeks to establish the evolutionary history of the surface of Earth. This approach is applicable not only to bedrock, but to the soft material that lies at the surface, the stuff called soil by most people. The geologic perspective provides a view of this material that is quite different from that of soil science, at least as practiced by many in America.

Extension specialists often recommend nutrient monitoring through leaf blade or petiole sampling twice a season for each vineyard block. However, due to the time and labor required to collect a large, random sample, many growers complete the task infrequently or incorrectly. Readily available remote sensing images capture the vineyard variability at both spatial and temporal scales, which can capture canopy and soil variability and be used to guide growers to representative sampling locations.

In this audio recording of the IVES science meeting 2022, Maria Paissoni (Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Alba, Italy) speaks about sensory evaluation of the effect of anthocyanins on in-mouth perceptions. This presentation is based on an original article accessible for free on OENO One.

New and developing technologies, that provide sensors and the software systems for using and interpreting them, are becoming pervasive through our lives and society. From smart phones to cars to farm machinery, all contain a range of sensors that are monitored automatically with intelligent software, providing us with the information we need, when we need it. This technological revolution has the potential to monitor all aspects of vineyard activity, assisting growers to make the management choices they need to achieve the outcomes they want. For example, a future vineyard may possess automated imaging that generates a three dimensional model of the vine canopy, highlighting differences from the desired structure and how to use canopy management to improve fruit composition, or generates maps with yield estimates and measurements of berry composition throughout the growing season.