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Updated: Jul 21, 2026

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
Published on: October 5, 2016
Data on Swiss grape growers' production, pest and labour management decisions
Philipp Höper1, Lucca Zachmann1, Robert Finger1
1Agricultural Economics and Policy Group, ETH Zurich, Switzerland.
Abstract:
This dataset comprises survey responses from 489 grape growers in Switzerland, focusing on their decisions related to production, pest management, risk management, behavioural factors, and labour management. The online survey was conducted in early spring 2025 and includes details on grape variety selection, farm practices, and characteristics of both the farmers and their farms. Additional data covers all other relevant pest management strategies targeting weeds, insects, and fungal threats. Farmer-specific attributes such as education, gender, age, and sources of information were recorded, alongside general labour force characteristics and perceptions regarding recruitment, and mechanization. Behavioural factors including risk and time preferences, self-efficacy, and locus of control were assessed using self-report scales. Farm-level data includes marketing approaches, labels and production systems, agri-environmental programs, and pesticide application equipment. The survey responses were linked with environmental variables-such as temperature and rainfall-and spatial data on the infection risk of Oidium and Peronospora viticola. Innovatively, the data contains the adoption stage of growers (not just binary adoption) for four key pesticide-reducing practices (i.e. the plantation of fungus-resistant varieties, the use of plant resistance inducers, inorganic materials and mechanical weeding), along with their views on influencing factors and labour demands associated with these measures. This dataset provides an extensive resource for standalone analyses on production, pest management, risk management, behavioural factors, and labour management, as well as for use in meta-analyses or as part of a panel dataset combined with previous similar surveys.
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