Changes in regional grain yield responses to chemical fertilizer use in China over the last 20 years
Bibliographic record
Abstract
A major challenge facing China is to meet the increasing food demand of its growing population in the face of decreasing arable land area, while sustaining or improving soil productivity and avoiding adverse environmental impacts from intensive agriculture.This study uses data from China Statistical Yearbooks to analyze trends in regional soil productivity and grain yields in the major grain-producing regions in North China (NC), Northeast China (NE), East China (EC), Central China (CC), and Southwest China (SW), associated with regional fertilizer use and annual climate variation in rainfall and mean temperature over the 20 years.During 1992-2012, the average fertilizer increase rates (in kg ha -1 year -1 ) were in the order of regions CC (6.6) > NC (4.8) > EC (2.4) > SW (2.1) > NE (1.3), while yield responses to fertilizer use (with regression model coefficients, in kg kg -1 ) were in the order: SW (-0.9) < CC (1.1) < NC (1.7) < EC (5.7) < NE (9.3), showing higher yield responses to fertilizer use for NE and EC than for other regions.The changes in regional grain yields also showed higher yield responses to soil-based productivity for NC, CC, and SW, or to annual climate variability for CC than for other regions, indicating that other factors (such as inherent soil productivity or annual climate variability could be more important than fertilizer in affecting yields.The strategies for regulating nutrient management are needed considerably based on regional indigenous soil nutrient supply under varying regional climate conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".