Root System Response of Lentil to Varied Nitrogen Availability: Insights from Positron Emission Tomography (PET)
Bibliographic record
Abstract
Studying plant root system growth responses and carbon allocation is difficult due to the opaque nature of the soil. Novel imaging techniques such as positron emission tomography (PET) where the plant roots can be visualized in-situ can overcome those shortcomings. However, PET is a challenging technique to implement due to the lack of a standard workflow. Thus, the objectives of this research were 1) to develop a repeatable workflow for PET using lentils as a test crop species and 2) to use PET imaging to examine the response of lentil seedling roots to a gradient of soil nitrogen levels. Multiple preliminary experiments were conducted to set parameters such as dosing rate, and dosing time to develop a PET workflow to obtain three-dimensional images of the root system. It was found that dosing a lentil plant with 2 GBq of 11C-CO2 for 30 minutes, followed by 60 minutes of acclimatization in the growth chamber, produced an optimal 3D root system image. When fertilized with urea, 20-day-old lentils grown under higher nitrogen levels had a lower number of active first-order lateral roots and a lower root-to-shoot biomass ratio. This indicates that the roots use less carbon, and more carbon is allocated to the shoots when the nitrogen level in the soil is high. Morphologically, a lower number of fine total root length was seen with high nitrogen which contradicts previous findings. One factor that has not been considered is the change in root birth or death rate response (i.e., demographic response), in high nitrogen conditions. At high nitrogen conditions, the death rate of the fine roots increased, and so did fine root branching, leading to less total root length. Under low nitrogen treatment, total root length was higher due to the higher longevity of roots in nutrient-scarce conditions. Thus, this research highlights the possibility of PET imaging in understanding carbon allocation in lentil root system to varied nitrogen availability through a standard PET imaging workflow established by this experiment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".