A Sustainability Assessment of Five Major Food Crops’ Water Footprints in China from 1978 to 2010
Bibliographic record
Abstract
Rice, wheat, corn, soybeans, and sorghum are the five major crops in China, which account for 92% of the country’s total grain production and 33% of its water consumption. Combining water footprint analysis tools with sustainability assessment tools, the water sustainability of the five major crops can be analyzed. Based on ecological economics theory, this paper constructs a sustainability evaluation system of China’s five major crops’ water footprints and analyzes the national and provincial diversity of the sustainability of the five major crops’ water footprints using three dimensions: scale, distribution, and equity. We find that the interprovincial distribution equity sustainability divergence is the key bottleneck factor that restricts sustainability (more than scale and configuration). One key strategy is to arrange grain production at the national level, on the basis of considering the differences of water-resource endowment between different provinces to break through the bottleneck of the water-resource distribution sustainability of these five major food crops. This paper determines a general management model that can improve the sustainability of water resource management at the interprovincial level by comparing and analyzing the most sustainable and least sustainable provinces for the water footprint production of these five major crops.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".