Effects of cold plasma seed treatment on pea (Pisum sativum L.) plant performance under drought and well-watered conditions
Bibliographic record
Abstract
Cold plasma (CP) technology is an emerging technology with the potential to enhance agricultural productivity and sustainability. Although its application in crop production is still in the early stages, CP seed treatment has demonstrated promise in improving various growth parameters, especially in legumes. We hypothesized that CP seed treatment can improve nodulation, symbiotic nitrogen fixation (SNF), root and shoot growth, overall productivity, and drought stress resistance in field pea. A controlled environmental study was conducted to investigate the effects of dielectric barrier discharge-generated CP seed treatment for 6 min on yellow field pea under different moisture regimes [30%, 45%, 60%, and 75% field capacity (FC)], focusing on nodulation, and root and shoot growth parameters at the flowering stage. Based on experiment-1 findings, 30% and 75% FC were selected as drought and well-watered conditions, respectively, to study the effect of CP seed treatments on SNF parameters at the flowering stage and nitrogen fixation, yield, and seed quality parameters at maturity. CP seed treatment improved root growth parameters at the flowering stage and an increasing trend was observed for shoot and nodulation parameters across different moisture levels. As an independent factor, moisture stress negatively affected nodulation and shoot growth parameters at the flowering stage. CP seed treatment improved nitrogen fixation and yield parameters under well-watered conditions compared to drought conditions at seed maturity. However, the seed protein content or the quality was not improved by the CP seed treatment. Grain yield, yield parameters, grain nitrogen, and nitrogen fixation were reduced under drought stress compared to the well-watered condition. Therefore, these findings underscore the potential of CP to enhance crop performance in well-watered conditions. The underperformance of the CP-treated seeds at drought conditions is not well understood and warrants further investigation.
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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".