Straw mulching combined with phosphorus fertilization increased photosynthesis rate and grain yield of wheat due to reduced stomatal and mesophyll limitations
Bibliographic record
Abstract
Abstract Phosphorus (P) has been proposed as an important factor determining photosynthesis through improvements in leaf anatomical traits and carboxylation efficiency. However, little is known about the combined effects of straw mulch and P fertilizer on the relationship between flag leaf P content and photosynthesis. This study was conducted to determine the combined effects of straw mulch combined with P fertilizer on wheat flag leaf photosynthetic capacity and grain yield. We performed field experiments during 2020–2022 to investigate the combined effects of straw mulch (0 and 8000 kg ha−1) with P fertilizer (0, 75, and 120 kg P2O5 ha−1) in Southwest China. Straw mulch with 75 kg P2O5 ha−1 gave an 18.3% yield advantage over no mulch with 120 kg P2O5 ha−1. Straw mulching with P fertilizer increased flag leaf P content and increased stomatal density. These changes increased stomatal conductance, mesophyll conductance, and net photosynthetic rate (Pn) by 17.4%, 16.3%, and 20.8%, respectively, compared with no‐mulch plots. The increased Pn was found associated with decreased stomatal and mesophyll limitation. Straw mulching with P fertilizer increased the activities of ribulose‐1,5‐bisphosphate carboxylase‐oxygenase and sucrose synthesis enzymes, thus promoting sucrose synthesis in flag leaves, which is beneficial for increasing grain number per meter square and grain yield. Straw mulching combined with 75 kg P2O5 ha−1 increased flag leaves P content and stomatal density, and increased stomatal and mesophyll conductance, resulting in improved leaf photosynthesis and grain yield.
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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".