Data on transgenerational memory effects of photosynthetic efficiency of twelve wheat varieties under elevated carbon dioxide concentration and reduced soil water availability

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Bernd J. Berauer , Suraj Chaudhary , Lorenz Kottmann , Andreas H. Schweiger
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引用次数: 0

Abstract

This data [1] represents ACi curves of twelve winter wheat varieties, which were grown under elevated and ambient CO2 concentrations within a FACE experiment and the subsequent F1 generation was exposed to ambient and elevated CO2 concentrations in a highly controlled environment using climate chambers. The 12 winter wheat genotypes (Triticum aestivum L.) were selected based on their susceptibilty to leaf rust (Puccinia triticina Eriks.) and Fusarium head blight (Fusarium graminearum Schwabe) according to the descriptive variety list of the German Federal Office of Plant Varietes (Beschreibende Sortenliste, Bundessortenamt 2024). The aim was to obtain a diverse set of varieties with the widest possible range of susceptibilities to leaf rust and fusarium head blight. Photosynthesis was measured using the novel Dynamic Assimilation Technique, thus not with the common steady-state approach. The individual wheat plants were measured twice, once under saturating soil water availability (θFC) and once under reduced soil water availability (θcsoil). θcsoil represents the gravimetric water content when the soil matric potential drops below the root matric potential, thus the onset of plant drought stress (sensu Cai et al. [2]). The photosynthesis data was used to fit ACi curves and extract the maximum Rubisco carboxylation rate [Vcmax], maximum rate of electron transport [Jmax] and dark respiration [Rd]. At both measurements we determined BBCH and plant height to quantify plant morphological development, as well as leaf water potential to quantify plant ecohydrologic status. At the end of the experiment, biomass was harvested and reported. Further, we provide environmental data of the climate chambers in use.
Within the data repository, we provide comprehensive experimental data on the investigation of transgenerational memory effects on photosynthetic efficiency. We provide photosynthetic raw data as well as processed (merged) and derived (extracted ACi fit) data. Additionally, we provide the R-code to reproduce the calculation of the derived parameters.
Data on transgenerational memory effects (that is, the influence of the parental environment on offspring phenotype and performance) are scarce, i.e. on the adaptive capacity of the photosynthetic apparatus. Thus, the data provided here can contribute to closing this gap. The highly controlled environment allows to closely investigate cause-effect relationships, thereby contributing to a mechanistic understanding of the transgenerational memory effects on photosynthetic efficiency and how this is altered by reduced soil water availability. By using a recently developed methodological approach, the data contributes to further investigate the quality of the method and establish it within the field of plant ecophysiology.
二氧化碳浓度升高和土壤水分有效性降低条件下12个小麦品种光合效率的跨代记忆效应
该数据[1]代表了12个冬小麦品种的ACi曲线,这些品种在FACE实验中生长在高浓度和高浓度的CO2环境中,随后的F1代在高度控制的环境中使用气候室暴露于高浓度和高浓度的CO2环境中。根据德国联邦植物品种办公室(Beschreibende Sortenliste, Bundessortenamt 2024)的描述品种清单,选取了12个冬小麦基因型(Triticum aestivum L.)对叶锈病(Puccinia triticina Eriks.)和赤霉病(Fusarium graminearum Schwabe)的敏感性。目的是获得一套多样化的品种,尽可能广泛地对叶锈病和枯萎病敏感。光合作用的测量采用了新的动态同化技术,而不是常用的稳态方法。对小麦单株进行两次测量,一次在饱和土壤水分有效性(θFC)下,一次在降低土壤水分有效性(θcsoil)下。θcsoil表示土壤基质势低于根基质势时的重量含水量,即植物发生干旱胁迫(sensu Cai et al.[2])。利用光合作用数据拟合ACi曲线,提取最大Rubisco羧化速率[Vcmax]、最大电子传递速率[Jmax]和暗呼吸速率[Rd]。在这两种测量方法中,我们通过测定树篱高和株高来量化植物形态发育,通过测定叶片水势来量化植物生态水文状况。实验结束时,收获生物量并进行报告。此外,我们还提供了使用中的气候室的环境数据。在数据存储库中,我们提供了关于光合效率跨代记忆效应研究的综合实验数据。我们提供光合作用原始数据以及处理(合并)和派生(提取ACi拟合)数据。此外,我们还提供了r代码来重现导出参数的计算。关于跨代记忆效应(即亲本环境对后代表型和表现的影响)的数据很少,即光合机构的适应能力。因此,这里提供的数据有助于缩小这一差距。高度控制的环境允许密切研究因果关系,从而有助于对光合效率的跨代记忆效应的机制理解,以及土壤水分可用性减少如何改变这种效应。通过使用最近开发的方法方法,数据有助于进一步调查方法的质量,并在植物生态生理学领域建立它。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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