Driving Factors for the Change of Fertilizer Use Intensity in China and Its Six Major Regions
Bibliographic record
Abstract
China has invested excessive amounts of fertilizer on limited farmland, which poses a threat to human health and agro-ecological environment. While a plethora of studies have explored how to reduce the total amount of chemical fertilizer, and scant attention has been paid to how to reduce the chemical fertilizer use intensity (FUI). This paper aims to explore the driving factors for the change of FUI to answer this question. It uses the official statistics of China from 1997 to 2017, as well as index decomposition analysis (IDA) and Laspeyres index decomposition method to obtain the following results. The change of fertilizer use intensity can be affected by three factors: input-output ratio of fertilizer (IOR), unit labor output (ULO) and labor input per unit sown area (LIU). At the national level, IOR is the most important factor in reducing the use of chemical fertilizers, while ULO is the most important factor in increasing. The factor of LIU can not only reduce the intensity of fertilizer use, but also increase the intensity. On a regional level, the fertilizer use intensity in Central-South China is the strongest, while that in the Southwest China is the smallest. Compared with other regions, the factors of ULO in Northwest China and IOR in East China have the greatest impact on fertilizer use intensity. In addition, LIU mainly reduces the intensity of fertilizer use in Northeast China, while this factor in North China is to increase the intensity. Our findings suggest that farmers should not increase labor productivity by investing chemical fertilizer. Improving the efficiency of fertilizer use and transferring rural labor force can reduce the fertilizer use intensity in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".