ArticleScientific reports2021
Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 30 citations in OpenAlex.
- Temporal phenotypic variation of spinach root traits and its relation to shoot performance.Scientific reports · 2024Article
- Evaluation of growth adaptation of Cinnamomum camphora seedlings in ionic rare earth tailings environment.Scientific reports · 2023Article
- Cover Crop Amendments and Lettuce Plant Growth Stages Alter Rhizobacterial Properties and Roles in Plant Performance.Microbial ecology · 2023Article
- Biochemical repercussions of light spectra on nitrogen metabolism in spinach (Frontiers in plant science · 2023Article
- Genetic dissection of nitrogen induced changes in the shoot and root biomass of spinach.Scientific reports · 2022Article
- Root system architecture for abiotic stress tolerance in potato: Lessons from plants.Frontiers in plant science · 2022Review
- Enhancing Sorghum Yield Through Efficient Use of Nitrogen - Challenges and Opportunities.Frontiers in plant science · 2022Review
- Article
- Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review.Biomolecules · 2021Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (r
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