Integrating crop redistribution and improved managements could meet China’s food demand with lower environmental costs
Bibliographic record
Abstract
Abstract Sustainably feeding the growing population in China attracts attention globally. Despite practices success, producing enough food to simultaneously address resource and pollution problems has been infeasible. To assess how to achieve this goal in 2035, we created a pathway that synergistic combining improved managements and cropland redistribution based on 11.1 million farmer surveys and 4,272 georeferenced field observations. Here, we firstly selected the practices of top 10% performers in crop yield and nitrogen (N) efficiency as crop-specific attainable improved managements at the county level. The optimized crop distribution within improved managements was then performed to minimize inputs (N and phosphorus fertilizer, irrigation water) or environmental impacts (reactive N [Nr] loss and greenhouse gas [GHG] emissions). We identified that combing improved managements and cropland redistribution could produce enough food demands in 2035, with 24% more production compared to 2012. It also reduced the inputs and environmental impacts in a range of 19%-35%, mainly sourced from the central and eastern coastal areas by improved productivity and diminished cropland of fruit and vegetables. These findings highlight the necessity for a synergistic combination of measures to sustainably feed the growing population and establish a more realistic and effective policy.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".