Strategies to Improve Grapevine Performance and Fruit Quality
A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1617
Special Issue Editors
Interests: environmental governance; institutional and ecological economics; climate change; biodiversity protection; land management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The greatest challenge for winegrowers currently is combining improved yield and fruit quality with the minimization of management cost and environmental impact. In recent decades, climate change has enhanced temperatures and reduced water availability, resulting in an earlier onset of phenological stages, severe yield losses, and altered grape quality. Grapevine performance and fruit quality can be assessed according to destructive and non-destructive methods for measuring several parameters such as yield, grape composition, and health status. Due to a rising number of geomatics, variable rate technologies, and robotics and artificial intelligence solutions, novel technologies based on rapid, cost-effective and non-destructive tools have become available to support winegrowers in the movement towards digital agriculture. Moreover, new cultural practices concerning canopy and floor management can help growers improve grapevine performance and vineyard efficiency. The aim of the current Special Issue is to disclose case studies describing a wide range of methods and tools developed worldwide to improve grapevine performance and fruit quality under a climate change scenario, with special emphasis on the application of digital technologies.
Dr. Alessandro Matese
Dr. Matteo Gatti
Guest Editors
Manuscript Submission Information
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Keywords
- yield
- phenology
- fruit health
- fruit quality
- climate change
- soil quality
- water stress
- cultural practices
- digital agriculture
- data analysis
- artificial intelligence (AI) and machine learning (ML) methodologies
- high-throughput field phenotyping (HTFP)
- vineyard zoning
- variable-rate technologies
- irrigation
- fertilization
- disease detection
- automation
