Future Arable Land Requirement of Pig Production in China
Bibliographic record
Abstract
China’s pig industry is experiencing a dramatic increase to meet increasing consumption demand. How these changes influence the limited arable land resources through consuming grain as feed has not been clearly understood. In this manuscript, we calculate the arable land requirement for pig industry (LRP) from 2001 to 2013 and forecast future demand towards 2050 from the point of production, in order to quantify the pressure in different scenarios. The results indicate that the LRP has increased from 22.0 Million Ha in 2001 to 31.6 Million Ha in 2013. LRP will be 23.7-29.4 Million Ha in 2030 and 11.6-18.7 Million Ha in 2050 according to different scenarios. Logarithmic Mean Divisia Index (LMDI) decomposition method is assessed to the effect of population, consumption and technology for three time periods e.g. 2010-2030; 2030-2050 and 2010-2050. And technology will become primary reason. These findings could help optimizing the relationships between limited arable land resources and development of pig industry, and promote sustainable development of the pig industry.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".