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用于评估大麦耐盐性的生长曲线记录

Growth curve registration for evaluating salinity tolerance in barley.

作者信息

Meng Rui, Saade Stephanie, Kurtek Sebastian, Berger Bettina, Brien Chris, Pillen Klaus, Tester Mark, Sun Ying

机构信息

Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900 Saudi Arabia.

Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900 Saudi Arabia.

出版信息

Plant Methods. 2017 Mar 23;13:18. doi: 10.1186/s13007-017-0165-7. eCollection 2017.

Abstract

BACKGROUND

Smarthouses capable of non-destructive, high-throughput plant phenotyping collect large amounts of data that can be used to understand plant growth and productivity in extreme environments. The challenge is to apply the statistical tool that best analyzes the data to study plant traits, such as salinity tolerance, or plant-growth-related traits.

RESULTS

We derive family-wise salinity sensitivity (FSS) growth curves and use registration techniques to summarize growth patterns of HEB-25 barley families and the commercial variety, Navigator. We account for the spatial variation in smarthouse microclimates and in temporal variation across phenotyping runs using a functional ANOVA model to derive corrected FSS curves. From FSS, we derive corrected values for family-wise salinity tolerance, which are strongly negatively correlated with Na but not significantly with K, indicating that Na content is an important factor affecting salinity tolerance in these families, at least for plants of this age and grown in these conditions.

CONCLUSIONS

Our family-wise methodology is suitable for analyzing the growth curves of a large number of plants from multiple families. The corrected curves accurately account for the spatial and temporal variations among plants that are inherent to high-throughput experiments.

摘要

背景

能够进行无损、高通量植物表型分析的智能温室可收集大量数据,这些数据可用于了解极端环境下的植物生长和生产力。挑战在于应用最能分析数据的统计工具来研究植物性状,如耐盐性或与植物生长相关的性状。

结果

我们得出了全家族盐敏感性(FSS)生长曲线,并使用配准技术总结了HEB - 25大麦家族和商业品种Navigator的生长模式。我们使用功能方差分析模型来考虑智能温室小气候的空间变化以及不同表型测定运行中的时间变化,以得出校正后的FSS曲线。从FSS中,我们得出了全家族耐盐性的校正值,这些值与钠呈强烈负相关,但与钾无显著相关性,这表明钠含量是影响这些家族耐盐性的一个重要因素,至少对于这个年龄且在这些条件下生长的植物是如此。

结论

我们的全家族方法适用于分析多个家族中大量植物的生长曲线。校正后的曲线准确地考虑了高通量实验中植物固有的空间和时间变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dad6/5363050/35a887f05396/13007_2017_165_Fig1_HTML.jpg

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