Disentangling within season sources of variation for field-level phenotyping of grapevine

Abstract

Abstract 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 behavior 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.

Plant phenotyping relevance

大規模な圃場フェノタイピングで測定した気孔コンダクタンスを対象に、機械学習、時空間正規化、混合モデルを統合して環境変動と遺伝的要因を分離する手法が中心である。

Code and data availability

The paper's ~6000-observation grapevine stomatal conductance/fluorescence dataset and analysis workflow are not publicly deposited; the authors state the datasets will be provided upon request. Other URLs (CLIMATIK weather platform, facility DOI, tidymodels, vivid, arXiv reference) are generic tools, external services,

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