Abstract
Spatial variability in winegrape yield was studied over several vintages in blocks planted to Cabernet Sauvignon, Merlot and Ruby Cabernet in the Coonawarra, Clare Valley and Sunraysia regions of Australia using new yield monitoring technology, a differentially corrected global positioning system (GPS), a geographical information system and some simple methods of spatial analysis. In any given year, yield was highly variable and typically of the order of 10 fold (i.e. 2 to 20 t/ha). However, through the use of k-means clustering and a method based on assessment of the probability of achieving yield targets relative to the mean annual block yield, temporal stability in the patterns of yield variation was demonstrated, even though there were substantial year to year differences in mean annual yield in these blocks. The methods used to demonstrate temporal stability in the patterns of yield variation also promote identification of zones of characteristic performance within variable vineyard blocks. Of significance in this work was the finding that, whilst k-means clustering is the more statistically robust of the two methods used, the ability to incorporate expert knowledge into the yield target method enhances the ability of the manager to accommodate the effects of abnormal events (e.g. an unusually cold flowering period) in the zone identification process. Targeted harvesting of different zones, followed by comparison between commercial lots of wine, provided indication that wine characteristics vary from zone to zone. However, the ranking of wine scores for the various zones changed between seasons. Our results have important implications for the adoption of Precision Viticulture. In particular, they support the introduction of a system of zonal vineyard management. Thus, rather than being managed uniformly, individual blocks can be split into zones in which the management of both inputs to, and outputs from the production system can be applied differentially.
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Claims 7
Claim · model-written summary
Winegrape yield was highly variable within a year, typically about tenfold (2 to 20 t/ha).
- Taxon
- winegrape
Vitis viniferae0 - Outcome
- yielde1
- Value
- 2 to 20 t/hae1
- Qualifier
- highly variablee1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e1“yield was highly variable and typically of the order of 10 fold (i.e. 2 to 20 t/ha)”
Claim · model-written summary
Patterns of winegrape yield variation were temporally stable across seasons.
- Taxon
- winegrapeVitis viniferae0
- Outcome
- patterns of yield variatione2
- Qualifier
- temporal stabilitye2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e2“temporal stability in the patterns of yield variation was demonstrated”
Claim · model-written summary
K-means clustering was more statistically robust than the yield target method for zoning.
- Taxon
- winegrapeVitis viniferae0
- Outcome
- statistically robuste3
- Comparator
- yield target methode4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e3“k-means clustering is the more statistically robust of the two methods used”
e4“the ability to incorporate expert knowledge into the yield target method enhances the ability of the manager to accommodate the effects of abnormal events”
Claim · model-written summary
Incorporating expert knowledge into the yield target method helped managers accommodate abnormal events.
- Taxon
- winegrapeVitis viniferae0
- Outcome
- accommodate the effects of abnormal eventse4
- Qualifier
- incorporate expert knowledge into the yield target methode4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e4“the ability to incorporate expert knowledge into the yield target method enhances the ability of the manager to accommodate the effects of abnormal events”
Claim · model-written summary
Wine characteristics varied from zone to zone within winegrape blocks.
- Taxon
- winegrapeVitis viniferae0
- Outcome
- wine characteristicse5
- Qualifier
- vary from zone to zonee5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e5“wine characteristics vary from zone to zone”
Claim · model-written summary
The ranking of wine scores for vineyard zones changed between seasons.
- Taxon
- winegrapeVitis viniferae0
- Outcome
- ranking of wine scores for the various zonese6
- Qualifier
- changed between seasonse6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e6“the ranking of wine scores for the various zones changed between seasons”
Claim · model-written summary
Zonal vineyard management is recommended for winegrape blocks rather than uniform management.
- Taxon
- winegrapeVitis viniferae0
- Outcome
- zonal vineyard managemente7
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability in winegrape yield was studied over several vintages”
e7“they support the introduction of a system of zonal vineyard management”