Yield Gaps, Indigenous Nutrient Supply, and Nutrient Use Efficiency of Wheat in China
Bibliographic record
Abstract
Great advances in food production have been made in China, but the continuous increase of nutrient inputs has caused a series of environmental problems. Nutrient management for crops must be improved. Yield gaps, indigenous nutrient supplies, and nutrient use efficiencies (NUEs) must be assessed to design management strategies for further yield increase. In this study, data from 1022 field experiments with wheat ( Triticum aestivum L.) conducted between 2000 and 2008 in north central China, the middle and lower reaches of the Yangtze River, and northwest China were analyzed. Treatments in these experiments consisted of a check without fertilizer use, an optimum nutrient application, the farmers’ practice, and a series of nutrient omission treatments. The results showed that gaps between attainable yields and yields in experimental plots with farmers’ practices averaged 0.76 Mg ha −1 . Indigenous nutrient supplies of N, P, and K averaged 133.0, 30.2, and 131.7 kg ha −1 , respectively, in the regions studied. On a national scale and under optimum fertilization, agronomic efficiency of N, P, and K were 9.8, 19.2, and 7.2 kg kg −1 , while recovery efficiencies were 37.9, 19.0, and 27.0%, respectively. Compared with values obtained 10 yr previous, agronomic efficiencies and recovery efficiencies determined between 2000 and 2008 were lower but also lower than world averages. Successive inputs of large amounts of nutrients significantly increased the indigenous nutrient supply and therefore are contributing to lower NUE because recommendations for N, P, and K have not been adjusted downward in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".