Field experiments are complex to interpret due to interactions between genotypes, environment, plant development, and cultivation practices. This complexity challenges the accurate phenotyping of individual plant traits over the season. Here, we quantified the primary sources of seasonal variation in stomatal conductance (gs) across 15 grapevine cultivar-rootstock combinations within a large-scale phenotyping platform, comprising over 6000 observations. Environment-related traits and date of measurement accounted for up to 76% of the variance, potentially obscuring cultivar-rootstock effects. Therefore, we integrated machine learning, spatiotemporal normalization of the gs response, and the use of mixed models to disentangle the influences of environmental factors, plant material, and crop performance related traits. After spatio-temporal normalization, cultivar and cultivar-rootstock interactions explained over 25% of the variation in gs, and Grenache exhibited the most conservative water use behaviour resulting in high water use efficiency. Specific rootstock-scion combinations also exhibited smaller, but still significant, differences in gs and water use efficiency, highlighting the specificity arising from the interaction within each rootstock-scion combination. The high variability in gs indicates that accurate quantification of rootstock-scion contributions to key traits in field studies is complex and requires accounting for spatial heterogeneity driven by the environment.
Bernardo et al. (2026) studied this question.