Concurrent Improvement in Maize Yield and Nitrogen Use Efficiency with Integrated Agronomic Management Strategies
Bibliographic record
Abstract
Low nitrogen use efficiency (NUE) and productivity with excessive use of N fertilizer in maize ( Zea mays L.) is a common issue for smallholder farm systems. A 2‐yr field study was conducted to assess the impacts of integrated agronomic management strategies (MT) on pre‐ and post‐silking N uptake, dry matter (DM) production dynamics, and their relationships to yield and NUE components. Three MTs were compared with the regional conventional farming practices (FP). We found that biomass yields differed significantly with similar harvest index (HI) among the MT treatments. While OPT‐1 produced 21% greater DM but only 4% greater grain yield, OPT‐2 had 27% higher yield, and 200% greater NUE, compared to FP. Our results showed the possibility of simultaneously achieving high yield and high NUE with optimized MT. In OPT‐2, 13% greater DM production and 12% more N uptake after anthesis contributed 5% more to grain yield and 18% more to grain N. In HY, excessive N input did not improve the stay‐green trait nor did it enhance grain yield. These results imply a better balance between improving total biomass and achieving more DM accumulation in the post‐silking period with gradually optimized agronomic management practices than other MT. The enhanced NUE observed in OPT‐2 primarily originated from the improved N recovery efficiency, which was associated with larger root biomass at silking and greater post‐silking N uptake. Further NUE improvement in maize could be anticipated through enhanced N internal efficiency by balancing DM and N accumulation between vegetative and reproductive periods.
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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".