Concurrent Improvement in Maize Grain Yield and Nitrogen Use Efficiency by Enhancing Inherent Soil Productivity
Bibliographic record
Abstract
Quantifying the relationships of maize yield and nitrogen use efficiency (NUE) to inherent soil productivity (ISP) could provide a theoretical basis for implementing strategies that concurrently narrow the yield gap, increase NUE, and improve soil quality. A field study under irrigation conditions was conducted at five locations with large differences in ISP (estimated by maize grain yield without using fertilizers) across various ecological regions in Inner Mongolia, China. Our results showed that the changes in maize yield and nitrogen partial factor productivity (PFPN) to ISP followed a linear-plateau model, with a common inflection point. When ISP was below 8.0 t ha–1, maize yield and PFPN were stagnating at a low level, due to a trade-off between the contribution of soil and chemical fertilizers to yield. When ISP exceeded 8.0 t ha–1, the contribution rate of soil to yield stabilized at 80%, resulting in a simultaneous increase in maize yield by 1.2 t ha–1 and PFPN by 4.6 kg kg–1, for increasing ISP at each t ha–1. Our results indicated that while keeping other management strategies unchanged, to increase maize yield and PFPN by 15% or further 30%, it is necessary to increase ISP to 9.3 and 10.4 t ha–1, respectively. In this scenario, N input will be reduced by 33.5 and 47.5%, and apparent N losses will be reduced by 11.7 and 21.5%, respectively. Therefore, enhancing ISP could lead to a simultaneous improvement in yield and NUE as well as strongly support the green development of maize production.
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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 | 0.001 | 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 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".