Improving yield potential in crops under elevated CO2: Integrating the photosynthetic and nitrogen utilization efficiencies
Bibliographic record
Abstract
Increasing crop productivity to meet burgeoning human food demand is challenging under changing environmental conditions. Since industrial revolution atmospheric CO(2) levels have linearly increased. Developing crop varieties with increased utilization of CO(2) for photosynthesis is an urgent requirement to cope with the irreversible rise of atmospheric CO(2) and achieve higher food production. The primary effects of elevated CO(2) levels in most crop plants, particularly C(3) plants, include increased biomass accumulation, although initial stimulation of net photosynthesis rate is only temporal and plants fail to sustain the maximal stimulation, a phenomenon known as photosynthesis acclimation. Despite this acclimation, grain yield is known to marginally increase under elevated CO(2). The yield potential of C(3) crops is limited by their capacity to exploit sufficient carbon. The "C fertilization" through elevated CO(2) levels could potentially be used for substantial yield increase. Rubisco is the rate-limiting enzyme in photosynthesis and its activity is largely affected by atmospheric CO(2) and nitrogen availability. In addition, maintenance of the C/N ratio is pivotal for various growth and development processes in plants governing yield and seed quality. For maximizing the benefits of elevated CO(2), raising plant nitrogen pools will be necessary as part of maintaining an optimal C/N balance. In this review, we discuss potential causes for the stagnation in yield increases under elevated CO(2) levels and explore possibilities to overcome this limitation by improved photosynthetic capacity and enhanced nitrogen use efficiency. Opportunities of engineering nitrogen uptake, assimilatory, and responsive genes are also discussed that could ensure optimal nitrogen allocation toward expanding source and sink tissues. This might avert photosynthetic acclimation partially or completely and drive for improved crop production under elevated CO(2) levels.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".