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Rinke, Wolfram
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Official Name
Rinke, Wolfram
Alternative Name
Rinke, Wolfram C.
Main Affiliation
ÖGAI Österreichische Gesellschaft für Artificial Intelligence
AAWE American Association of Wine Economists
Akademische Titel
Dipl.-Ing.
Email
wolfram.rinke@fh-burgenland.at
ORCID
Scopus Author ID
57203619840
Status
staff
Research Outputs
Now showing 1 - 4 of 4
- PublicationDo corporately organized wine producers cope better with unfavorable weather conditions than private wine estates? Hedonic pricing models for South African winesIn our research we want to estimate the effect of changing weather conditions on south African wine prices for both, small wine estates and corporately organized producers and to compare these results. We assume that the latter cope better with unfavourable weather conditions than private wine estates, whereas beneficial weather will be favourable for both of them. We focus on the South African domestic wine market, as prices on export markets are additionally influenced by other factors, such as marketing strategies, certifications, mark ups of wholesalers or retailers etc.
429 714 - PublicationAre South African grape growers aware of their climatic comparative advantages for growing certain grape varieties? A hedonic approach applying machine learningWe assume that each region has its own competitive advantage in growing certain grape varieties, as all varieties have different capabilities to cope with specific weather or climate conditions. In this paper we first analyze the effects of annual weather changes on yields in order to find, which weather is advantageous for which grape variety. This analysis should lead to some kind of ideal weather scenario for each grape variety (like dry and sunny with small rain etc.). In as second step the changing weather conditions in each region are reconciled with these scenarios in order to give recommendations where different grape varieties should be grown. In the third step we compare our recommendations with the distribution of grape varieties in South African wine regions and the uprooting and planting behavior of grape growers.
567 872 - PublicationConsumer preferences for certified wines in France: A comparison of sustainable labels(Firenze University Press, 2021)
; 89 1Scopus© Citations 9 - PublicationPricing Models for German Wine: Hedonic Regression vs. Machine LearningThis article examines whether there are different hedonic price models for different German wines by grape variety, and identifies influential factors that focus on weather variables and direct and indirect quality measures for wine prices. A log linear regression model is first applied only for Riesling, and then machine learning is used to find hedonic price models for Riesling, Silvaner, Pinot Blanc, and Pinot Noir. Machine learning exhibits slightly greater explanatory power, suggests adding additional variables, and allows for a more detailed interpretation of results. Gault&Millau points are shown to have a significant positive impact on German wine prices. The log linear approach suggests a huge effect of different quality categories on the wine prices for Riesling with the highest price premiums for Auslese and “Beerenauslese/Trockenbeerenauslese/Eiswein (Batbaice),” while the machine learning model shows, that additionally the alcohol level has a positive effect on wines in the quality categories “QbA,” “Kabinett,” and “Spätlese,” and a mostly negative one in the categories “Auslese” and “Batbaice.” Weather variables exert different affects per grape variety, but all grape varieties have problems coping with rising maximum temperatures in the winter and with rising minimum and maximum temperatures in the harvest season.
738 2Scopus© Citations 10