Changes in regional grain yield responses to chemical fertilizer use in China over the last 20 years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it