Clemens P. Beyer, Excequel Ponce, Sebastian Veas, Italo F. Cuneo, Miguel Urrestarazu, Romina Pedreschi, Juan E. Alvaro
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引用次数: 0
Key message
Prolonged hydric deficit significantly reduces the transpiration and growth of adult avocado trees, while also accumulating soluble sugars in the leaves and reducing root volume and diameter.
Abstract
Climate change and exploitation is expected to drastically reduce water availability in central Chile by placing avocado (Persea americana Mill.) production at severe risk owing to the crop´s high water footprint. Current research on avocado hydric stress remains limited in scope and has typically focused exclusively on aerial plant parts of avocado, with little attention paid to belowground responses. Root phenotyping, defined as the systematic evaluation of root system architecture (RSA), offers a powerful tool for investigating stress adaptation, particularly when combined with aerial phenotyping and metabolomics. In the present study, 28-month-old avocado trees (cv. Hass) grafted onto Mexicola rootstock were grown in 55-L rhizotrons under drip fertigation and subjected to four months of water deficit (“drought stress”). Aerial phenotyping included tree height, trunk area, leaf water potential (LWP) and stomatal conductance (gs). Root phenotyping was conducted using an artificial intelligence-based deep learning convolutional neural network with RootPainter coupled with RhizoVision Explorer for quantitative trait extraction, including root volume, length, branching, diameter, and luminance (bright vs. dark roots). Polar metabolites from the roots and leaves were analyzed using gas chromatography–mass spectrometry. Hydric stress caused a significant reduction in gs and LWP, which, in turn, led to decreased aerial growth and an accumulation of soluble sugars in the leaves. Root system analysis revealed contrasting trends in root volume, diameter and other related traits. In conclusion, the integration of AI-based root phenotyping with aerial phenotyping and metabolomics has proved to be an effective approach to demonstrate the effects of water deficit in a scarcely investigated species.
长期水分亏缺显著降低了成年鳄梨的蒸腾作用和生长，同时也使叶片中的可溶性糖积累，减少了根的体积和直径。气候变化和开发预计将大大减少智利中部的水资源供应，使鳄梨（Persea americana Mill.）的生产面临严重的风险，因为该作物的高水足迹。目前对鳄梨水分胁迫的研究范围仍然有限，并且通常只关注鳄梨的地上植物部分，很少关注地下的响应。根系表型是根系结构（RSA）的系统评价，特别是当与空气表型和代谢组学相结合时，它为研究胁迫适应提供了有力的工具。在本研究中，28个月大的牛油果树(cv。嫁接到墨西拉砧木上的哈斯（Hass）在55-L的根茎上生长，在滴灌施肥下进行4个月的水分亏缺（“干旱胁迫”）。空中表型包括树高、树干面积、叶片水势和气孔导度。根系表型分析采用基于人工智能的深度学习卷积神经网络，结合RootPainter和RhizoVision Explorer进行定量性状提取，包括根体积、长度、分枝、直径和亮度（明亮与黑暗的根）。采用气相色谱-质谱联用分析了根和叶的极性代谢物。水分胁迫导致gs和LWP显著降低，进而导致地上生长减少和叶片中可溶性糖的积累。根系分析显示根系体积、根径及其他相关性状有明显的变化趋势。综上所述，基于人工智能的根系表型与空气表型和代谢组学的整合已被证明是一种有效的方法，可以证明水分亏缺对一个很少被研究的物种的影响。
期刊介绍：
Trees - Structure and Function publishes original articles on the physiology, biochemistry, functional anatomy, structure and ecology of trees and other woody plants. Also presented are articles concerned with pathology and technological problems, when they contribute to the basic understanding of structure and function of trees. In addition to original articles and short communications, the journal publishes reviews on selected topics concerning the structure and function of trees.
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