Effects of phosphite as a plant biostimulant on metabolism and stress response for better plant performance in Solanum tuberosum
Bibliographic record
Abstract
Food availability represents a major worldwide concern due to population growth, increased demand, and climate change. Therefore, it is imperative to identify compounds that can improve crop performance. Plant biostimulants have gained prominence because of their potentials to increase germination, productivity and quality of a wide range of horticultural and agronomic crops. Phosphite (Phi), an analog of orthophosphate, is an emerging biostimulant used in horticulture and agronomy. The aim of this study was to uncover the molecular mechanisms through which Phi acts as a biostimulant with potential effects of overall plant growth. Field and greenhouse experiments, using 4 potato cultivars, showed that following Phi applications, plant performance, including several physio-biochemical traits, crop productivity, and quality traits, were significantly improved. RNA sequencing of control and Phi-treated plants of cultivar Xingjia No. 2, at 0 h, 6 h, 24 h, 48 h, 72 h and 96 h after the Phi application for 24 h revealed extensive changes in the gene expression profiles. A total of 2856 differentially expressed genes were identified, suggesting that multiple pathways of primary and secondary metabolism, such as flavonoids biosynthesis, starch and sucrose metabolism, and phenylpropanoid biosynthesis, were strongly influenced by foliar applications of Phi. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses associated with defense responses revealed significant effects of Phi on a plethora of defense mechanisms. These results suggest that Phi acted as a biostimulant by priming the plants, that was, by triggering dynamic changes in gene expression and modulating metabolic fluxes in a way that allowed plants to perform better. Therefore, Phi usage has the potential to improve crop yield and health, alleviating the challenges posed by the need of feeding a growing world population, while minimizing the agricultural impact on human health and environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".