期刊
JOURNAL OF EXPERIMENTAL BOTANY
卷 68, 期 5, 页码 965-982出版社
OXFORD UNIV PRESS
DOI: 10.1093/jxb/erw494
关键词
Lateral root branching; pea root system; phenotyping; root architecture model; root elongation rate; temporal scaling
资金
- European Union's Seventh Framework Programme [613781]
- Transnational Access capacities of the European Plant Phenotyping Network (EPPN) - FP7 Research Infrastructures Programme of the European Union [284443]
Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an R-Spearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research.
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