Thinner topsoil improves vine growth and fruit composition in Mid-Atlantic United States vineyards

As environmental issues come more to the fore, vineyards residues are being looked at as solutions rather than problems. Aiming to develop a sustainable methodology for musts acidity correction in the process of winemaking, much needed in warm regions, the present study was performed according to Circular Economy values.

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.

The past three decades of terrestrial remote sensing research have delivered unprecedented insights into our fundamental ability to detect, quantify, and differentiate plant disease (Gold 2021). However, much of our fundamental knowledge in this domain has come from studies in non-agricultural systems and until recently, most agricultural studies, when extant, have focused on tree crops where canopy closure and large plot and plant size facilitate stress detection at low spatial resolution. Recent engineering innovations and advancements in constellation architecture design have refined the accuracy and scalability of airborne and spaceborne sensing platforms, enabling us to monitor diverse specialty crops, including grapevine, planted in smaller, spatially varied fields.

The spatial distribution of powdery mildew is often heterogeneous between neighboring plots, with higher disease pressure in certain places

The northern root-knot nematode (Meloidogyne hapla) is one of the most prevalent plant-parasitic nematodes affecting Washington State Vitis vinifera vineyards. This nematode induces small galls on roots, restricting water and nutrient uptake. In new vineyards this can impede establishment. In existing vineyards, it can exacerbate decline in chronically stressed vines. While preplant fumigation is a common strategy for M. hapla management, its efficacy is temporary and relies on broad-spectrum chemicals that undergo frequent regulatory scrutiny. The trap crop litchi tomato (Solanum sisymbriifolium) showed promise in reducing plant-parasitic nematode densities in potato. This prompted field greenhouse experiments to evaluate its potential to reduce M. hapla in V. vinifera.