Fichtl Lukas, Leitner Daniel, Schnepf Andrea, Schmidt Dominik, Kahlen Katrin, Friedel Matthias
Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany.
Forschungszentrum Juelich GmbH, Agrosphere (IBG-3), Juelich, Germany.
Plant Phenomics. 2024 Dec 11;6:0280. doi: 10.34133/plantphenomics.0280. eCollection 2024.
Understanding root system architecture (RSA) is essential for improving crop resilience to climate change, yet assessing root systems of woody perennials under field conditions remains a challenge. This study introduces a pipeline that combines field excavation, in situ 3-dimensional digitization, and transformation of RSA data into an interoperable format to analyze and model the growth and water uptake of grapevine rootstock genotypes. Eight root systems of each of 3 grapevine rootstock genotypes ("101-14", "SO4", and "Richter 110") were excavated and digitized 3 and 6 months after planting. We validated the precision of the digitization method, compared in situ and ex situ digitization, and assessed root loss during excavation. The digitized RSA data were converted to root system markup language (RSML) format and imported into the CPlantBox modeling framework, which we adapted to include a static initial root system and a probabilistic tropism function. We then parameterized it to simulate genotype-specific growth patterns of grapevine rootstocks and integrated root hydraulic properties to derive a standard uptake fraction (SUF) for each genotype. Results demonstrated that excavation and in situ digitization accurately reflected the spatial structure of root systems, despite some underestimation of fine root length. Our experiment revealed significant genotypic variations in RSA over time and provided new insights into genotype-specific water acquisition capabilities. Simulated RSA closely resembled the specific features of the field-grown and digitized root systems. This study provides a foundational methodology for future research aimed at utilizing RSA models to improve the sustainability and productivity of woody perennials under changing climatic conditions.
了解根系结构(RSA)对于提高作物对气候变化的适应能力至关重要,然而在田间条件下评估木本多年生植物的根系仍然是一项挑战。本研究引入了一种流程,该流程结合了田间挖掘、原位三维数字化以及将RSA数据转换为可互操作的格式,以分析和模拟葡萄砧木基因型的生长和水分吸收情况。在种植后3个月和6个月,对3种葡萄砧木基因型(“101-14”、“SO4”和“Richter 110”)的每种基因型的8个根系进行了挖掘和数字化处理。我们验证了数字化方法的精度,比较了原位和异位数字化,并评估了挖掘过程中的根系损失。将数字化的RSA数据转换为根系标记语言(RSML)格式,并导入到CPlantBox建模框架中,我们对该框架进行了调整,以纳入静态初始根系和概率向性函数。然后我们对其进行参数化,以模拟葡萄砧木基因型特异性的生长模式,并整合根系水力特性,得出每种基因型的标准吸收分数(SUF)。结果表明,尽管对细根长度存在一些低估,但挖掘和原位数字化准确反映了根系的空间结构。我们的实验揭示了RSA随时间的显著基因型差异,并为基因型特异性水分获取能力提供了新的见解。模拟的RSA与田间生长和数字化的根系的特定特征非常相似。本研究为未来旨在利用RSA模型提高木本多年生植物在不断变化的气候条件下的可持续性和生产力的研究提供了基础方法。