The influence of rising carbon dioxide on maize development: genotypic differences in growth, lignification and folate pathway
Bibliographic record
Abstract
Abstract BACKGROUND Rising atmospheric carbon dioxide (CO 2 ) is a key driver of climate change, making it essential to understand its effects on crop growth and metabolism. This study examines maize C01 (inbred) and B73 (mutant), under elevated CO 2 (600, 1200 and 1800 ppm) at three growth stages [40, 70 and 90 days after sowing (DAS)]. RESULTS At 600 ppm CO 2 , plant height, leaf area and biomass increased, whereas higher concentrations led to significant declines. C01 accumulated more sugars and total non‐structural carbohydrates, whereas B73 showed higher starch levels. Chlorophyll and carotenoid contents decreased under elevated CO 2 , with the most pronounced reductions at 1800 ppm. Folate content peaked at 70 DAS, with B73 exhibiting consistently higher levels than C01. Lignin accumulation and composition varied across genotypes, tissues and CO 2 levels. At 600 ppm, lignin content increased in leaves and stems but declined at 1800 ppm. The syringyl‐to‐guaiacyl ratio and lignin monomer composition differed between genotypes, with C01 displaying stronger phloroglucinol staining and higher lignin content than B73. Gene expression analysis revealed that key lignin biosynthesis genes ( ZmPAL , ZmCAD , ZmCCR , Zm4CL , ZmCOMT and ZmCCoAOMT ) were upregulated at 600 ppm but significantly downregulated at 1800 ppm, particularly in B73. CONCLUSION These findings highlight genotype‐specific responses to elevated CO 2 , emphasizing its influence on maize growth, lignin biosynthesis and folate metabolism. The study provides valuable insights for future crop management and breeding strategies in the face of rising atmospheric CO 2 levels. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.000 |
| 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".