Analyzing the Characteristics of Cropping Intensity’s Change of Cultivated Land in China During 2010-2019
Bibliographic record
Abstract
The sustained stability of the arable land replanting index is crucial to the national food security strategy. The exploration of the temporal and spatial changes in the arable land replanting index is of great significance for agricultural development and food security evaluation. This study investigates China’s arable land replanting index from 2010 to 2019 using MODIS NDVI image data, S-G filtering, and quadratic differentiation methods. The results show that there are significant spatial differences in China’s arable land replanting index, with the Huang-Huai-Hai region mainly producing double-cropping crops, the Northeast Plain and Loess Plateau mainly producing single-cropping crops, and the area south of the Yangtze River mainly producing multiple-cropping crops. Overall, China’s food production is mainly based on single-season crops. There is a gradual shift towards double-season crops from north to south, with lower replanting indices in the northwest and higher indices in the eastern provinces. The south has significantly higher indices than the north. During the study period, the arable land replanting index showed an overall upward trend. There were significant increases in the replanting index in the northeast, the Loess Plateau, and the northern Huang-Huai-Hai region. However, there was a downward trend in the southern Huang-Huai-Hai region and the middle and lower reaches of the Yangtze River. It is crucial to maintain the effective planting area of arable land in the Loess Plateau and the double-season planting area of arable land in the Huang-Huai-Hai region while also addressing the downward trend of the arable land replanting index in the middle and lower reaches of the Yangtze River to ensure food security by stabilizing the index.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".