Do commonly used workload indicators capture the social sustainability of farm workload? Evidence from Switzerland
Year: 2026
Type: article
Abstract: Farm workload is typically measured using quantitative indicators that overlook how farmers actually experience their work. This study empirically examines how three commonly used workload indicators, namely self-reported working hours, available workforce, and modelled working hours relate to farmers’ perceived workload burden as a proxy for the social sustainability of farm labor. Data comprises 571 Swiss farms and their characteristics merged from three datasets: the Swiss agricultural census (AGIS), the LabourScope labour-requirement model, and an online survey conducted in 2025. Reliability analysis reveals poor internal consistency among the three workload indicators, indicating they cannot be used interchangeably. Across several regression specifications, self-reported working hours emerge as the strongest and most robust predictor of perceived burden across farm types and levels of on-farm diversification. Modelled working hours show little to no association with perceived burden, while available workforce exhibits mixed and generally weaker relationships. Farm-type–specific analyses reveal substantial heterogeneity. In dairy systems, none of the workload indicators explain perceived burden despite the highest reported working hours, whereas in arable and suckler cow farms perceived burden is linked to self-reported working hours.The findings suggest that time-based, structural, model-based workload measures capture distinct dimensions of farm work and do not necessarily reflect farmers’ perceived workload burden. Social sustainability assessments should align workload indicators with specific assessment goals (e.g., reducing perceived workload). Integrating perception-based workload measurement into national monitoring systems would strengthen social sustainability assessments and policies aimed at supporting farmers’ wellbeing.
Institution: Agroscope
Language: English
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,350
Funder: Agroscope
OpenAlex ID: W7171847306