Background and goals There is currently no consensus on the ideal tissue type or on the ideal phenological stage at which to sample grapevine mineral nutrients. The goal of this study was to assess the concentrations and variability of nutrients in leaf blades and petioles during differing phenological stages and in different vineyards. Methods and key findings The study was conducted in the Finger Lakes region of New York State. Multivariate analysis showed that phenology, tissue type, vineyard site, and year all influence vine nutrient status. Tissue type accounted for more than twice the proportion of variance within the model than site, even though these vineyards consisted of different soil types, ages, rootstocks, and training systems. Within tissue type, leaf blade samples had lower variability than petioles; petiole samples had 30% greater variability in nutrient concentrations than leaf blades. Tissue samples taken at bloom had 66% greater variability than those sampled at veraison. Conclusions and significance The differences in nutrient concentrations among different tissue types and phenological time points underscore the importance of basing recommended nutrient concentrations on specific tissue types and time points. We concluded that nutrient recommendations should be developed based on leaf blade samples to assess vine nutrient status more precisely. Sampling tissue at veraison provided samples with lower variability. However, logistical considerations such as obtaining grapevine nutrient reports with sufficient time to address deficiencies within the current growing season, may be of greater importance to growers. The results within this study may be region- and/or cultivar-specific.

Background and goals Lake Erie Concord growers have access to highresolution spatial soil and production data, but lack protocols and information on the optimum time to collect these data. This study examines the type and timing of sensor information to support in- season management. Methods and key findings A three-year study in a 2.6 ha vineyard collected yield, pruning mass, canopy vigor, and soil data, including yield and pruning mass from the previous year, at 321 sites. Stepwise linear regression and random forest regression approaches were used to model site-specific yield and pruning mass using historical spatial production data, multi-temporal in-season canopy vigor, and soil data. The more complex yield elaboration process was best modelled with non-linear random forest regression, while the simpler development of pruning mass was best modelled by linear regression. Conclusions and significance Canopy vigor in the weeks preceding bloom was the most important predictor of the current season's yield and should be used to generate stratified sampling designs for crop estimation at 30 days after bloom. In contrast, pruning mass was not well- predicted by canopy vigor; even late-season canopy vigor, which is widely advocated to estimate pruning mass in viticulture. The previous year's pruning mass was the dominant predictor of pruning mass in the current season. To model pruning mass going forward, the best approach is to start measuring it. Further work is still needed to develop robust, local site-specific yield and pruning mass models for operational decision-making in Concord vineyards.

Management zoning has been one of the main ways that spatial agricultural data sets have been used in precision agriculture, particular as a means of data-fusion between multiple information layers.As with most precision agriculture technologies and methodologies, management zones began with arable cropping systems but have been adopted into perennial cropping systems.This review brief explores the evolution of management zones in agriculture (with an emphasis on horticulture), the basic concept, the key research areas in management zone delineation to date and the diverse ways that management zones have been applied into arable and perennial systems to support crop management.The future role of management zones, including their relevance in cropping systems with higher resolution information sources is discussed, along with the future need to have more spatio-temporally dynamic zones to respond to decision-specific management and to accommodate increasing availabilities of inseason information.To support this, a new concept of decision zones is proposed that is decision driven, more flexible in its data-fusion processing and simplifies the final zoning process.Management zones are expected to continue to be an important first step in understanding spatial relationships when entering into precision horticulture.They will also be important for site-specific crop management in the early stages of adoption when data layers are likely to be limited.However, as data and information layers accrue for a cropping system, and spatial understanding of production drivers and interactions develops, then 'decision zoning' should replace the current idea of management zones.

Canopy sensing in viticulture is widely associated with the term NDVI (normalized difference vegetation index). However, there are many other vegetative indices (VIs) that can be calculated from information captured with visible/near-infrared (NIR) sensors. A proximal canopy sensor was used to survey 27 vineyards in the Lake Erie Concord belt and stratified to collect pruning weights (PW) at a density of ~25 samples per vineyard. Seven VIs were derived from the sensor data and the first principal component (PC1) extracted from a principal components analysis of the seven VIs. The VIs and PC1 were regressed against the local PW measurements and ranked in terms of their goodness-of-fit. Over the 27 vineyards, there was no single VI that outperformed the others, although VIs that used the red-edge band had a slight advantage over VIs using the red band. It is therefore recommended to use the normalized difference red edge index (NDRE) in place of the NDVI when predicting PW from terrestrial-based proximal canopy surveys. The PC1 derived from the decomposition of all seven VIs did appear to convey some benefit to PW prediction compared with a single VI approach, particularly with just NDVI. More research into the potential for multivariate approaches is recommended.

Crop load, the ratio of vine size to mass of fruit harvested, is fundamental to viticulture. Measuring vine size and crop yield, the components of crop load, has historically been a labour intensive exercise that has limited the use of crop load information to improve management in vineyards. Recent advances in assessing vine vigour, size and yield using geo-referenced sensors are starting to make high resolution crop load mapping possible. In this paper, the concept of crop load is revisited with an emphasis on how vine size and yield can be mapped in vineyards. Existing literature is reviewed on how vine size and yield vary spatially and temporally within vineyard blocks and the inference this has on the spatio-temporal variability of crop load. An example of crop load mapping using sensor technology is presented to illustrate recent advances in sensor technology in viticulture. Finally, some emerging technology and knowledge gaps for implementing spatial crop load information into vineyard management are discussed.

1 Sensors Provide Information to Guide Variable-Rate Mechanical Fruit Thinning and Prevent Overcropping of Concord Grapes Terence Bates1, Jackie Dresser2, Rhiann Eckstrom3, Golnaz Badr4, Thom Betts5, and James Taylor6 1Cornell University, Horticulture Section, Cornell Lake Erie Research and Extension Laboratory, Portland, NY 2,3,4Cornell Lake Erie Research and Extension Laboratory, Portland, NY 5grower cooperator in Westfield, NY 6ITAP, Montpellier, France Research Focus

Concord and Niagara grapevines in Fredonia, NY, were studied from 2001 to 2005 to evaluate the impact of cane length on yield, vine size, crop load, and juice soluble solids (JSS). Concord grapevines were manually pruned to 100 buds using three configurations: two-node spurs or five- or 10-node canes. Niagara grapes were manually pruned to 80 buds using three configurations: two-node spurs or five- or 10-node canes. In addition to standard viticulture measurements, yield was measured separately at each node position along the spur or cane in 2004 to 2005. There was a pattern in bud fruitfulness along the length of the cane, with the greatest yield originating from node positions three to six in Concord and two to six in Niagara; this did not change with pruning treatment. Since all vines were pruned to a constant total node number, five- and 10-node cane treatments had a greater proportion of more-fruitful buds and higher final yield than the two-node spurs in two out of five years. The higher yield on longer canes also led to greater crop load values indicative of overcropped vines, which decreased vine size. In contrast, two-node spur-pruning maintained balanced crop load values and adequate vine size. In the final two years of the study, there was no difference in final yield or JSS among pruning treatments because the longer cane treatments were starting the season with lower vine capacity.

The purpose of this research was to illustrate the use of sensor-derived spatial data in generating management classifications and applying variable-rate vineyard mechanization to improve vine balance and fruit quality. In a commercial ‘Concord’ vineyard in Westfield, New York, data from proximal sensors were used to generate spatial maps of soil apparent electrical conductivity, canopy Normalized Difference Vegetation Index (NDVI), and crop weight. Local block kriging was used on all spatial data layers after removal of outliers to predict values at common grid points which approximated the vineyard row and vine spacing so that relationships between data layers could be interrogated to determine which layers were indicative of overall vineyard production. Cluster analysis (k-means) was used to generate three management classifications and stratified manual viticulture sampling was used to predict the crop size and vine size in each region. Crop load is the relationship between vine fruit yield (crop size) and vine vegetative growth (vine size). The Ravaz Index (RI) is a practical indicator of crop load through the measurement of the crop weight — pruning weight ratio. In this study, RI was predicted mid-season in each management classification and crop load was adjusted through mechanized variable-rate fruit thinning. To achieve variable-rate fruit thinning, a spatial prescription map was generated and interfaced with precision agriculture hardware/software which controlled the hydraulic flow to the shaker head on a mechanical harvester. On-the-go variable-rate fruit thinning shifted the population mean 34% toward target RI values and decreased the standard deviation by 30% indicating that the vineyard was more balanced and more uniform at harvest.

Summary Goals: This report aims to present a clear protocol for (a) deploying proximal canopy sensors into single high-wire trellis Concord (Vitis labruscana cv. Bailey) vineyards and (b) converting the canopy sensor response into an indication of vine size (pruning weight). The protocol is designed to be robust and practical for easy adoption in commercial systems. Evidence will be presented of the efficacy of vine size prediction using the protocol in multiple research and commercial vineyards. Key Findings: Using different vineyards and pruning crews, the protocol performed well in >80% of vineyards and permitted growers to generate maps of actual vine size within vineyards. These maps provide a valuable indication of current site-specific production potential and a baseline to assess changes in vine size over time. In a few vineyards, the proposed simplified calibration process did not generate a clear relationship between canopy response and vine size, which may be due to changes in vine shape in highly mechanized systems. Impact and Significance: Managing vine size is critical to the long-term sustainability of cool climate viticulture and to managing quality in all viticulture systems. However, convincing growers to routinely measure vine size for more effective management has been difficult historically because of the time involved and the difficulty of translating the data into a decision process. The proposed protocol uses technology and targeted sampling to minimize the effort required, and presents more coherent information that allows fast grower responses. Grower adoption of this protocol should promote continual vine size measurement, with the goal of decreasing vine size variation within vineyards.

Aims: Yield monitors are becoming more common in North America. This research evaluates the precision and accuracy of a retro-fitted, commercially available grape yield monitor mid-season, for crop estimation and crop thinning applications, and at harvest for yield mapping. Methods and Results: Several grape yield monitors were mounted on the discharge conveyor belt of grape harvesters in both commercial and research vineyards in North America. Sensor response was compared to manual measurements at multiple masses, ranging from 20 kg to 28 Mg over the course of three seasons. Measurements were taken during crop thinning and estimation (mid-season) and at harvest. Results showed that the grape yield monitor performance was sufficient to generate good spatial maps of the relative variation in harvest yield and mid-season thinned yield. However, at harvest the sensor showed a shift in response between days of up to ±15%, such that the generation of absolute yield maps required a daily calibration against a known mass. Within a day (single harvest operation) the sensor response did not appear to drift. Mid-season applications required a different calibration to harvest applications. Conclusion: The yield sensor worked well for both mid-season and at harvest operations in North American vineyards but required a daily calibration to avoid drift issues. The mid-season yield calibrations were different between seasons; however, the harvest calibration factor was stable between seasons. Significance and Impact of study: The study showed that a commercial yield monitor with correct calibration was effective at even low fruit flow. This opens the possibility of using a harvest sensor mid-season to mechanically estimate fruit load from small point samples and to map the amount of fruit removed during fruit thinning operations. This will improve the quality of information available to viticulturist to understand fruit and crop load. The commercial yield monitor is suitable for use in North American vineyards.

Pearson’s correlation is a commonly used descriptive statistic in many published precision agriculture studies, not only in the Precision Agriculture Journal, but also in other journals that publish in this domain. Very few of these articles take into consideration auto-correlation in data when performing correlation analysis, despite a statistical solution being available. A brief discussion on the need to consider auto-correlation and the effective sample size when using Pearson’s correlation in precision agriculture research is presented. The discussion is supported by an example using spatial data on vine size and canopy vigour in a juice-grape vineyard. The example data demonstrated that the p-value of the correlation between vine size and canopy vigour increased when auto-correlation was accounted for, potentially to a non-significant level depending on the desired α-level. The example data also demonstrated that the method by which data are processed (interpolated) to achieve co-located data will also affect the amount of auto-correlation and the effective sample size. The results showed that for the same variables, with different approaches to data co-location, a lower r-value may have a lower p-value and potentially hold more statistical significance.

A weekly survey of canopy NDVI with a proximal-mounted canopy sensor was undertaken in a cool-climate juicegrape vineyard. Sensing was performed at different positions in the canopy. Sensing around the top-wire led to saturation problems, however sensing in the growing region of the canopy led to consistently non-saturated results throughout the season. With this directed sensing, a spatial pattern in NDVI 2–4 weeks after flowering could be generated that approximated the spatial pattern in NDVI at the end of the season. This is earlier than has been previously reported and may allow for proactive within-season canopy management.

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Background and AimsStudies on vine size variation have generally been limited to small plot studies, particularly for correlation with canopy imaging. Research and anecdotal reports indicate there is a temporal stability in the spatial patterns of imagery of the canopy. This study directly examines and quantifies the spatial and temporal variation in vine size, rather than a canopy sensor response, at a block (approximate to 1 ha) level.Methods and ResultsThe mass of pruned wood for each individual vine was measured for 3 consecutive years in a 0.93-ha vineyard. The spatial and temporal variability in pruning mass was interrogated with geostatistics and map comparison methods. Potential management units were derived from these data and used to verify the temporal response in vine size.ConclusionsThe majority of variance in pruning mass occurred at a vine-to-vine scale; however, the autocorrelated variance exhibited a strong, stable spatial structure over 3 consecutive years. Map comparison methods were shown to be an alternate and visually demonstrable method of comparing spatio-temporal patterns.Significance of the StudyThe temporal stability of spatial patterns in vine size would indicate that it is not necessary to measure vine size annually and that historical information can drive site- or zone-specific management decisions. The large vine-to-vine variation indicates that high spatial resolution, vine-specific sensing and decision support systems are needed if the objective is to manage as much of the variability in vine size as possible.

# 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

Pruning weights are a useful indication for growers of vine size within their production systems. However, pruning weights are rarely collected in commercial situations, as there is little information to help growers determine how many samples to measure. An intensive study of individual vine pruning weights was undertaken over three years in a Concord (Vitis lambruscana Bailey) block at the Lake Erie Research and Extension Laboratory, New York. The population variances associated with individual and aggregated neighboring vine measurements were used to determine random sampling schemes to assist growers. A sampling scheme based on 23 random interpost samples (3-vine sampling) is suggested as a good compromise to minimize the time associated with sampling and to maximize the accuracy of measurement and the value of the information. A spatial analysis of the variance indicates that the same sampling scheme could be extrapolated to block sizes larger than the survey area (0.93 ha), provided that the management and environmental conditions can be considered uniform over the larger block.