Fixed pre-season weather windows eliminate endogeneity bias in interval-censored survival models of grapevine phenology

Abstract European grapevine ( Vitis vinifera L.) is a climate–sensitive perennial whose flowering and ripening govern yield and quality. Phenological records from monitoring programs are typically collected at irregular intervals, so true transition dates are interval-censored, and many site-years are right-censored. We develop a reproducible workflow that treats phenology as a time–to–event outcome: Status & Intensity observations from the USA–NPN are converted to interval bounds, linked to NASA POWER daily weather, and analyzed with parametric accelerated failure time (AFT) models (Weibull and log–logistic). To avoid outcome–dependent bias from aggregating weather up to the event date, antecedent conditions are summarized in fixed pre–season windows—DOY 1–120 before flowering and DOY 1–180 before ripening—and standardized; quality–control filters require at least 70% within–window data coverage. We illustrate the framework with USA–NPN records for V. vinifera spanning 2012–2025: 41,274 status records on 7 plants at 5 sites, of which 8,274 concern the two endpoints and collapse to 50 plant–site–year survival units carrying 30 flowering and 37 ripening events. Applied to these, the framework yields interpretable time–ratio effects and ready-to-use tabular and graphical outputs. Warmer pre–season conditions are associated with earlier ripening, whereas flowering responses are modest and uncertain in these data; precipitation effects are too imprecise at this sample size to be separated from no effect. The approach demonstrates how interval-censored survival models with exogenous weather windows can extract robust climate signals from citizen-science phenology while preserving observation uncertainty. We state the data conditions—visit frequency, right-censoring rate, and sample size—under which it transfers to other species and networks.