Biology subjects

McElrone, A.

Publications and source records attributed to McElrone, A..

3 recordsLinked to original sources

bioRxiv2026

Supplemental irrigation during heat waves affects yield but not whole-vine carbohydrates in wine grapes

As extreme heat events increase in frequency and intensity worldwide, understanding how woody perennial crops respond to higher maximum temperatures is critical. Perennials face distinct challenges, persisting across many seasons under increasingly variable and extreme conditions, and heat waves threaten the viability of wine grape cultivars through impacts on yield, wine quality, and long-term vine health. To test whether irrigation practices before and during heat waves affect grapevine carbon (C) storage and health, we experimentally manipulated irrigation regimes surrounding heat waves from 2019-2021 in a commercial Cabernet Sauvignon vineyard in the Lodi AVA of Californias Central Valley. Vine physiological traits and yield were measured throughout, and whole-vine nonstructural carbohydrate (NSC) concentrations were quantified after three growing seasons. Although lower supplemental irrigation reduced photosynthesis, stomatal conductance, and fruit yield, whole-vine NSCs did not differ significantly in any perennial organ by the experiments end, indicating that reproductive output and final NSC status responded to irrigation on different timescales. These results suggest that moderate supplemental irrigation during heat events is sufficient to mitigate negative impacts on yield and quality while supporting recovery of NSC reserves, though longer-term monitoring is needed to confirm that this short-term resilience persists.

Plant Biology↗

bioRxiv2026

Optimizing irrigation during heat events sustains grapevine physiology and fruit production

O_LIIncreasing frequency, intensity, and duration of heat waves (HWs) threaten agricultural production globally by constraining physiological function and fruit production. Supplemental irrigation mitigates heat stress in grapevine and other woody perennial crops, yet water scarcity necessitates optimized irrigation strategies during extreme heat. C_LIO_LIWe conducted a three-year field trial in a commercial Cabernet Sauvignon vineyard, applying differential irrigation only before and during naturally occurring HWs: baseline (50% ET), moderate (90-120% ET), and high (120-180% ET). We monitored water potentials, leaf gas exchange, canopy temperature, yield, and berry composition. C_LIO_LIBaseline irrigation consistently reduced net photosynthesis, stomatal conductance, and leaf cooling capacity during HWs. Moderate supplemental irrigation maintained gas exchange, transpiration, and leaf temperature, mitigating yield losses. Excessive irrigation beyond moderate levels provided no additional physiological benefit and decreased crop water use efficiency and berry quality. C_LIO_LIOur results demonstrate that targeted, event-based irrigation sustains grapevine physiological performance and fruit production under extreme heat, whereas both insufficient and excessive water negatively affect carbon assimilation, stomatal regulation, and crop productivity. These findings emphasize the importance of aligning water management with heat event timing to preserve vine function, optimize water use, and maintain yield and fruit quality in water-limited regions. C_LI

plant biology↗

bioRxiv2019

Digitally Deconstructing Leaves in 3D Using X-ray Microcomputed Tomography and Machine Learning

Premise of the studyX-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organisation. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small datasets, restricting its utility for phenotyping experiments and limiting our confidence in the conclusion of these studies due to low replication numbers. Methods and ResultsWe present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical trait quantification which dramatically reduces the time required to process single leaf microCT scans into detailed segmentations. By training the model on each scan using 6 hand segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. ConclusionOverall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.

plant biology↗