• Login
    View Item 
    •   Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Biological Sciences
    • View Item
    •   Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Biological Sciences
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of BrockUCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjectsProfilesView

    My Account

    LoginRegister

    Statistics

    Display statistics

    Utilisation of Proximal Sensing Technology to Map Variability in Ontario Vineyards

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Brock_Kotsaki_Eleni_2016.pdf
    Size:
    10.99Mb
    Format:
    PDF
    Download
    Author
    Kotsaki, Eleni
    Keyword
    Precision viticulture, proximal sensing technology, NDVI, spatial variability, temporal stability
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10464/9406
    Abstract
    Precision agriculture is a term used to refer to a suite of technologies used for the optimisation of production in agronomic crops. The overall goal of this study was to determine whether high resolution proximally sensed observations acquired by the GreenSeeker™ technology could be correlated with soil moisture, vine water status, yield components and grape composition, and whether temporally consistent relationships could be established. The research was carried out on three experimental sites involving two Riesling, two Cabernet franc and two Pinot noir blocks throughout the Niagara Region of Ontario (Canada). A grid of geolocated sentinel vines was chosen for each vineyard block. Data were collected three times during the growing season between fruit set and veraison [soil moisture, leaf water potential (ψ)], at harvest (yield components, berry composition) and in winter [vine size, winter hardiness (LT50)]. GreenSeeker™ observations were likewise collected from lag phase to just prior to harvest, through the calculation of Normalized Difference Vegetation Index (NDVI). Thereafter, relationships between vine water status, yield components and berry composition variables as well as data from the GreenSeeker™ were validated. Overall, higher NDVI values were associated with yield components and vine size, while lower NDVI values were correlated with better berry composition, suggesting that GreenSeeker™ is a practical tool for vineyard vegetative growth surveys, and for grape composition inferences. Clustering associations were made through k-means statistical analysis in conjunction with Moran's I spatial autocorrelation index; soil moisture followed by the NDVI had the strongest clustering patterns. The outcomes from proximal sensing technology allow opportunities to stream and compliment present agricultural practices towards higher accuracy and efficacy by means of exploiting the observed variation.
    Collections
    M.Sc. Biological Sciences

    entitlement

     
    DSpace software (copyright © 2002 - 2022)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.