Vineyard Management Statistics 2022 - Everything You Need to Know


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Best Vineyard Management Statistics

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Vineyard Management Latest Statistics

  • Axes 1 and 2 explained 69% of the variance in the data. [0]
  • The soil samples clustered together distinctly, and separately from grapes along the first PCoA axis, which explained 66% of the variance in the data. [0]
  • The threeyear average under vine soil vegetation coverage rate for NV was greater than 70%, while coverage rates for CULT and GLY were less than 20% at veraison. [0]
  • Unclassified fungal genera in soil samples ranged from around 10% to more than 25% relative abundance. [0]
  • Full fungi profile (>1%). [0]
  • Although the samples did not seem to cluster based on treatments on PCoA plots using UniFrac distance metrics , the treatment effect was significant in year 2014 and 2016 according to PERMANOVA. [0]
  • Over 71% of the variance in grape fungal community structure was explained by the first two PCoA axes, but the grape samples were not structured as a function of under vine soil treatments. [0]
  • Unclassified genera accounted for 5 to more than 30% of the relative abundance in grape samples. [0]
  • The fungal genus Penicillium was found only in the 2014 grape samples, which was 16.6% in relative abundance, and Sporobolomyces was highest in relative abundance in 2015 and lowest in 2016 in grape samples. [0]
  • Many yeast genera commonly found in abundance in grapes, such as Candida, Pichia, Debaryomyces, Lipomyces, Kluyveromyces, and Issatchenkia, were not found or were not abundant (<1% in relative abundance). [0]
  • In brief, 2% Roundup was sprayed with electronic pumped spraying nozzle in rate about 3 kg a.i./ha. [0]
  • Then, the VI based OBIA algorithm developed for each camera automatically mapped the grapevines, bermudagrass, and bare soil (accuracies greater than 97.7%). [1]
  • Weeds are known to be a major problem in agriculture, leading to a 32% worldwide reduction in crop yields [15]. [1]
  • During each flight, the UAV route was configured to fly at 30 meters altitude with a forward lap of at least 90%. [1]
  • In addition, a side lap of 60% was programmed. [1]
  • As explained above, 25% of the GT full dataset from both the A2016 and A 2017 fields was used in the spectral analysis to select the optimal vegetation index that best discriminated bermudagrass and bare soil for each camera. [1]
  • In this experiment, a customizable 1 x 0.5 m grid size was selected according to the specifications of the intra. [1]
  • Next, the weed coverage (% of bermudagrass). [1]
  • As commented before, 75% of the GT full datasets corresponding to field A for both 2016 and 2017 were used to assess the classification accuracy. [1]
  • According to [64], two classes exhibit moderate separability when M exceeds 1 and good discrimination when it exceeds 2. [1]
  • Similar results of the classified area were obtained by using any of the sensors, e.g., 24.4% and 24.5% for the vine class in field. [1]
  • A2017 when employing the RGBsensor and RGNIRsensor orthomosaic, respectively; and similarly, for bare soil in field A 2016, reporting 82.8% and 81.7% of the classified area, which demonstrated the algorithm robustness. [1]
  • An increase of approximately 21% in the vineyard was observed in the comparison of 2016 and 2017 orthomosaics for both sensors. [1]
  • According to [72], among the recommendations for bermudagrass management, mowing should be minimized as stolons can cause weed dispersion. [1]
  • Furthermore, a reduction in the area occupied by bare soil was found using any of the sensors, which was quantified as 28.5% for the RGBorthomosaic image and 25.9% for the RGNIR. [1]
  • As mentioned in the OBIA algorithm description, the vine class was first separated from the rest of classes using DSM height information as described in [3], where overall accuracy values higher than 93.6% were achieved in the vine classification. [1]
  • The matrix indicated an overall accuracy higher than 97.7% in all of the cases studied, well above the minimum accepted value standardized at 85% by [73]. [1]
  • Moreover, high degrees of producer’s accuracy with values close to or even 100% were achieved in all the studied cases, which corresponded to null or very low values of omission error. [1]
  • For example, 99.6% and 99.9% of PA were obtained for the bermudagrass class using the RGB and RGNIR cameras in field A 2017, respectively; and moreover, OA values of 98.7% and 97.7% were reached for those respective cameras and field in 2016. [1]
  • fields in the early season obtaining 86% of OA in the confusion matrix; however, the precision of the OBIA algorithm was evaluated by comparing weed coverage over grid units, not over objects. [1]
  • according treatment thresholds a) 0%, b) 2.5%, and c) 5%. [1]
  • In that sense, about a 14% raise in potential savings was achieved using a 5% weed threshold when compared to the more conservative one for the three cases analyzed. [1]
  • High values of map classification accuracy (>97.7%). [1]
  • According to Kaiser and Dickman , factors with eigenvalues greater than 1 were selected for cluster analysis. [2]
  • The second most common homogenous vineyard zone is zone 1 with 27.98% of the total Burgenland vineyard area. [2]
  • Zone 2 has the third most common type of vineyard site and covers 19.06% of the total Burgenland vineyard area. [2]
  • It is mostly situated in Mittelburgenland DAC in central Burgenland and comprises 77% of vineyard area. [2]
  • Zone 5 comprises 41% of Leithaberg DAC vineyards and is at lower altitude, with the strong Pannonian climate influence noted in zone 4. [2]
  • In practice, this seems to mean that the goal was to cover only the states accounting for 90% of annual production. [3]
  • In grapes, the 90% threshold was met by including only California and Washington. [3]
  • Yet in apples, this process resulted in inclusion of Virginia, with 3% of the apple acreage, and 10,000 acres in production – less than one third of the acreage in grapes in New York. [3]
  • But then every once in a while, weather forecasts disturb winegrowers, because they are not always 100% accurate. [4]
  • The 2019 Career & Salary Survey for Beverage Alcohol shows only 2% African American employees working in the 3. [5]
  • Yet the 2019 Wine Market Council Consumer Segmentation survey reports there are around 100 million wine drinkers in the US, and 11% of wine drinkers are African American. [5]
  • “Once we implement the findings of GRAPEX across our entire vineyard acreage, we will reduce the amount of water we apply for irrigation by up to 25%, and that’s a very, very big number.”. [6]
  • Overall, extensive vegetation management increased above‐ and below‐ground biodiversity and ecosystem service provision by 20% in comparison to intensive management. [7]
  • and biodiversity datasets extracted from 74 included studies. [7]
  • Percentage of parasitism and predation Pest‐related parameters Pest abundance Damage per vine and plot Soil water balance Soil water balance . [7]
  • Plots for mean effect sizes and 95% CIs were produced with the r package plotrix. [7]
  • About 40% of all datasets originated from irrigated vineyards, 50% were rainfed vineyards and the other studies did not provide information on the use of irrigation. [7]
  • Overall, there was a 19·8% increase in biodiversity and ecosystem service provision due to extensive vegetation management in comparison to the control treatment. [7]
  • The largest mean effect size (M = 53.2%). [7]
  • Mean and 95% confidence intervals of the effects of extensive vegetation management in vineyards on biodiversity and ecosystem services. [7]
  • If soil erosion was split up into two subsets of parameters measuring soil loss and in general erosion‐related soil parameters, there was a strong positive effect of extensive vegetation management on soil loss mitigation (M = 161.9%). [7]
  • Across studies, extensive vegetation management resulted in a 20% increased biodiversity and ES provision. [7]
  • We detected the strongest increase of 50% in biodiversity due to extensive vegetation management. [7]
  • A subset analysis of the ES type erosion protection resulted in the largest increase (160%). [7]
  • Differences were not related to the use of irrigation, as approximately half of all datasets originated from irrigated Mediterranean vineyards, whereas 83% of all datasets in continental or steppe climates descended from irrigated vineyards. [7]
  • However, it should be remarked that also sample sizes differed considerably with 56% of all datasets from studies comparing organic vs. conventional management investigated biodiversity. [7]
  • In general, organic management has been shown to increase biodiversity by 30%. [7]
  • Very Small 3,74433% Limited Production 5,45448%. [8]

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Reference


  1. nature – https://www.nature.com/articles/s41598-018-29346-1.
  2. plos – https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218132.
  3. oeno-one – https://oeno-one.eu/article/view/1907.
  4. cornell – https://grapesandwine.cals.cornell.edu/newsletters/appellation-cornell/2021-newsletters/issue-44-march-2021/research-focus.
  5. evineyardapp – https://www.evineyardapp.com/blog/2019/01/17/climate-weather-and-vineyard-management/.
  6. wineindustryadvisor – https://wineindustryadvisor.com/2020/06/30/solutions-and-words-of-wisdom-from-black-female-vineyard-manager-brenae-royal.
  7. nasa – https://www.nasa.gov/feature/raising-a-glass-in-wine-country-to-better-water-management.
  8. nih – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099225/.
  9. winesvinesanalytics – https://winesvinesanalytics.com/statistics/winery/.

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