Dormant buds of temperate woody perennial plants must attain cold hardiness to survive winters and timely lose it in spring to break bud while avoiding damage from low temperatures and late frosts. Therefore, we asked: Can a cold hardiness model be used to predict budbreak? Here, we used a previously published cold hardiness model to predict bud cold hardiness of three grapevine (Vitis spp.) varieties ('Cabernet-Sauvignon', 'Riesling', and 'Concord') from historical temperature records of eight locations in North America and Europe. Based on those predictions and thresholds of cold hardiness at budbreak from literature, budbreak date was extracted. Despite being untrained on budbreak data, the model resulted in good predictions (RMSE = 7.3d, n = 329), further improved based on expected delays from estimated cold damage (RMSE = 7.2d). Both increasing and decreasing freeze damage risk trends were predicted with increasing temperature, depending on the range of mean dormant season temperature (MDST; 1 Nov-30 Apr) in each location. Spring phenology predictions in relation to MDST also showed warming to advance (MDST < 10°C) or delay (MDST > 10°C) budbreak. Cold hardiness dynamics represent a key advancement in spring phenological modeling that provides information on low-temperature damage potential for the entire dormant season alongside improved predictions of budbreak timing.

Temperate woody perennial plants form buds during late summer that contain leaves and flowers that emerge in the following growth season. To survive winter, dormant buds must attain cold hardiness, and timely lose it in spring to break bud while avoiding damage from low temperatures and late frosts. Here, we use an untrained process-based model to predict bud cold hardiness of three grapevine varieties ( V. vinifera 'Cabernet-Sauvignon' and 'Riesling', and V. hybrid 'Concord') from historical temperature records of eight different locations in North America and Europe (n = 329). Based on those predictions, and thresholds of cold hardiness at budbreak from literature, timing of budbreak was extracted. Despite being untrained to the data, the RMSE of budbreak predictions was 7.3 days (Bias=−0.83). Based on cold hardiness estimations and air temperature records, low temperature damage was quantified and validated through newspapers and extension records. In years × location where damage was predicted, corrections to budbreak based on delays expected resulted in improvements of predictions (RMSE=7.2d, Bias=0.58). Predictions of instances of freeze damage risk demonstrate genotypic adaptation to different environments. At the species level, increasing or decreasing trends in freeze damage risk are predicted, depending on the range of mean dormant season temperature (MDST; 1 Nov - 30 Apr) present in each location. Sensitivity analysis of predicted time to budbreak based on MDST shows a general advancement of phenology at −5.8d/°C. However, in much warmer locations, delays can be expected as temperatures continue to increase (+1.9d/°C for MDST>10°C). Through cold hardiness dynamics, the estimation of chilling accumulation appears as an important source of error for predictions of spring phenology across environments. Cold dynamics represents an advancement in phenological modeling that provides information for the entirety of the dormant season, as well as budbreak. ### Competing Interest Statement The authors have declared no competing interest.

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. High-resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases such as GDM over large, heterogeneous fields. Here, we investigated the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, New York, from June to August 2020, 2021, and 2022. Satellite imagery acquired within 72 h of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July to early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps toward incorporating satellite remote sensing in grapevine disease surveillance systems. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Increased map density and transferability of markers are essential for the genetic analysis of fruit quality and stress tolerance in interspecific grapevine populations. We used 1449 GBS and 2000 rhAmpSeq markers to develop a dense map for an interspecific F2 population (VRS-F2) that was derived by selfing a single F1 from a Vitis riparia x ‘Seyval blanc’ cross. The resultant map contained 2519 markers spanning 1131.3 cM and was highly collinear with the Vitis vinifera ‘PN40024’ genome. Quantitative trait loci (QTL) for berry skin color and flower type were used to validate the map. Four rhAmpSeq transferable markers were identified that can be used in pairs (one pistillate and one hermaphroditic) to predict pistillate and hermaphrodite flower type with ≥99.7% accuracy. Total and individual anthocyanin diglucoside QTL mapped to chromosome 9 near a 5-O-GLUCOSYLTRANSFERASE candidate gene. Malic acid QTL were observed on chromosome 1 and 6 with two MALATE DEHYRDROGENASE CYTOPLASMIC 1 and ALUMINUM-ACTIVATED MALATE TRANSPORTER 2-LIKE (ALMT) candidate genes, respectively. Modeling malic acid identified a potential QTL on chromosome 8 with peak position in proximity of another ALMT. A first-ever reported QTL for the grassy smelling volatile (E)-2-hexenal was found on chromosome 2 with a PHOSPHOLIPID HYDROPEROXIDE GLUTATHIONE PEROXIDASE candidate gene near peak markers.

Dormant season grapevine pruning requires skilled seasonal workers, but they are becoming less available. As workers hasten to prune more vines in less time due to the short-term seasonal hiring culture and low wages, vines are often pruned inconsistently, leading to imbalanced grapevines. In addition, grapevines cannot be pruned selectively using currently existing mechanical methods, thus manual follow-up operations are often required, further increasing production cost. In this paper, we present the design and field evaluation of a rugged and fully autonomous robot for end-to-end pruning of dormant season grapevines. The proposed design incorporates novel camera systems, a kinematically redundant manipulator, a ground robot, and novel algorithms in the perception system. The presented research prototype robot system was able to spur-prune a row of vines from both sides completely in 213 s/vine with a total pruning accuracy of 87%. Initial field tests of the autonomous system in a commercial vineyard have shown significant variability reduction in dormant season pruning when compared to mechanical prepruning trials. The design approach, system components, lessons learned, future enhancements, as well as a brief economic analysis are described in the manuscript.

Economic pressures in the New York Concord grape industry over the past 30 years have driven crop management practices toward less severe pruning to achieve larger crops. The purpose of this study was to investigate the effect of crop load on juice soluble solids and the seasonal change in vine pruning weight in New York Concord grapevines. Over a four-year period, vines were balanced pruned at two levels or fixed node pruned at two levels to give four pruning severities. For balanced pruning, vines were pruned to leave 33 or 66 fruiting nodes for the first 500 g pruning weight and an additional 11 nodes for each additional 500 g pruning weight. For fixed node pruning, vines were pruned to 100 or 120 fruiting nodes per vine. The 120-node vines were also manually clusterthinned at 30 days after bloom to target 0, 25, or 50% crop removal. In a second study, the 120-node pruning with midseason fruit-thinning was repeated over 11 years to assess seasonal differences on the crop load response. Crop load was measured as the yield-to-pruning weight ratio (Y:PW) and ranged from 1 to 40 in this study. On average, the industry standard of 16 Brix was achieved at a Y:PW of 20, and no seasonal pruning weight change was observed at a Y:PW of 17.5. There was a positive linear relationship between seasonal growing degree days and the Y:PW needed to reach 16 Brix, as well as between seasonal precipitation and the Y:PW required to observe no seasonal pruning weight change. The results from this study were used to improve crop load management recommendations for New York Concord vineyards under current practices.

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra.

• This trial illustrates the potential of using ‘prescription maps’ and variable-rate mechanization of shoot and crop thinning to manage variability in commercial vineyards. Figure 1. The Cornell Lake Erie Research and Extension Laboratory in Portland, NY showing the main building, barn, and one of the Concord research vineyards, creatively named the “Barn Block.” Variable-rate mechanical shoot thinning on May 27, 2020 (inset) was used to alter the shoot density across the block in a Cornell “C” pattern, which could be detected with proximal NDVI sensors at bloom.

This paper presents an application of Fuzzy Logic, well known for its linguistic modeling ability, in a multi-criteria decision making framework applied to spatial data sets. The Fuzzy Logic is integrated in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual spatial information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented in an open source software called GeoFIS. The potential of the method is illustrated using a typical farming decision: the design of a nitrogen fertilization map within a vineyard. The vineyard is a Concord (Vitis labrusca) juice grape vineyard in the Lake Erie region of New York state. The vineyard manager and a local research/extension viticulturist both used the tool to generate a prescription nitrogen map based on their knowledge and spatial crop and soil information. The process captured different preferences between the two users (industry vs. research) and generated different prescription maps that reflected their differing objectives, knowledge and risk perception in vine management. Although applied to vineyard data, this decision tool has a wide potential application to agri-environmental (and other) spatial data sets.

# Quantitative Analysis of Phytic Acid in Grape Seeds, Stems, and Berries of Cabernet franc and Petit Verdot {#article-title-2} Phytic acid is a strong chelator and antioxidant naturally present in plant seeds. It has been shown to help prevent metal cation catalyzed oxidation and improve protein

The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields can allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse - during the growing season sparse samples are taken and extrapolated to determine overall yield. We present an automated method that uses computer vision to detect and count grape berries. The method could potentially be deployed across large vineyards taking measurements at every vine in a non-destructive manner. Our berry detection uses both shape and visual texture and we can demonstrate detection of green berries against a green leaf background. Berry detections are counted and the eventual harvest yield is predicted. Results are presented for 224 vines (over 450 meters) of two different grape varieties and compared against the actual harvest yield as groundtruth. We calibrate our berry count to yield and find that we can predict yield of individual vineyard rows to within 9.8% of actual crop weight.

1 Crop load – defined as the ratio of exposed leaf area to fruit – is the most relevant measure of vine performance. Too much leaf area promotes shading and reduces fruit quality – and sometimes bud fruitfulness. Too little leaf area per unit of fruit delays ripening and reduces vine size. Measures of crop load are useful to researchers and growers alike in evaluating success of vineyard management practices. The Ravaz index – which uses the ratio of yield to pruning weight to estimate crop load – is one common metric.

Canopy performance, the balance of crop weight and canopy volume, is a key indicator of value in viticultural production. Timely and dense measurement offer the potential to inform management practices and deliver significant improvement in production efficiency. Traditional measurement practices are labor intensive and provide sparse data that may not reflect vineyard variability. We propose and demonstrate a combination of visual and laser sensing mounted on vineyard machinery that provides dense maps of canopy performance indicators. Current industry practice for measuring grape crop weight involves manually counting clusters on a vine with destructive sampling to find the average weight of a single cluster. This paper presents an alternative utilizing vision and laser sensing. We demonstrate use of machine vision to automatically estimate the weight of the crop growing on a vine. Validation of the algorithm was performed by comparing weight estimates generated by the system to ground truth measurements collected by hand. Machine mounted laser scanners provide direct measurement of canopy shape and volume. Validation of the canopy volume measurement is provided by correlation with manually collected dormant vine pruning weight. Attaching these laser and camera sensors to vineyard machinery will allow crop weight and canopy volume measurements to be collected on a large scale quickly and economically. Experiments performed at vineyards growing Traminette and Riesling wine grapes and Concord juice grapes show that we were able to determine both crop weight and canopy volume to within 10% of their actual values.

Reports on a recent survey by GHN Career Management Consultants who suggest companies are not making the best use of high fliers. Provides a method of identification of high fliers, both practical and attitudinal and supplies development plans, such as learning in groups and mentoring. Concludes that for high flier development, individual reaction to formal or informal learning should be taken into account to develop a full range of styles.

The development of career theory has given little attention to mid-career adults relative to that given to school leavers and young adults. Furthermore, in looking at adult careers the focus has primarily been on the objective patterns of career movements rather than on subjective aspects of career. As objective careers become less easily measured, with the disappearance of clear career ladders, the importance of the subjective career increases. In a questionnaire study of senior executives (n = 132) who undertook a formal counselling programme, there was no evidence of any change in expectations of future employing organisations, or the career signals they held important. Post-counselling clients rated themselves more highly than pre-counselling clients on their knowledge and confidence, their self awareness in regard to careers, their ability to negotiate career change and their attribution of career progress to their own personal qualities. The implications of these findings for careers counselling practice, and for the skills of career management, are discussed.