Plant Nitrogen Assimilation: A Climate Change Perspective
Bibliographic record
Abstract
Of all the essential macronutrients necessary for plant growth and development, nitrogen is required in the greatest amounts. Nitrogen is a key component of important biomolecules like proteins and has high nutritive importance for humans and other animals. Climate change factors, such as increasing levels of carbon dioxide, increasing temperatures, and increasing watering regime, directly or indirectly influence plant nitrogen uptake and assimilation dynamics. The impacts of these stressors can directly threaten our primary source of nitrogen as obtained from the soil by plants. In this review, we discuss how climate change factors can influence nitrogen uptake and assimilation in cultivated plants. We examine the effects of these factors alone and in combination with species of both C3 and C4 plants. Elevated carbon dioxide, e[CO2], causes the dilution of nitrogen in tissues of non-leguminous C3 and C4 plants but can increase nitrogen in legumes. The impact of high-temperature (HT) stress varies depending on whether a species is leguminous or not. Water stress (WS) tends to result in a decrease in nitrogen assimilation. Under some, though not all, conditions, e[CO2] can have a buffering effect against the detrimental impacts of other climate change stressors, having an ameliorating effect on the adverse impacts of HT or WS. Together, HT and WS are seen to cause significant reductions in biomass production and nitrogen uptake in non-leguminous C3 and C4 crops. With a steadily rising population and rapidly changing climate, consideration must be given to the morphological and physiological effects that climate change will have on future crop health and nutritional quality of N.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".