Strategic importance of green water in international crop trade
Bibliographic record
Abstract
Virtual water is the volume of water used to produce a commodity or service. By importing agricultural\ncommodities and the virtual water embedded in them, a country saves the water it would have required to\nproduce those commodities domestically. Virtual-water ‘trade’, thus, has the potential to relieve water stress and\nimprove water security.\nThe present research critically evaluates the strategic importance and implications of green water (soil water) in\nrelation to international crop trade. Even if, traditionally, emphasis has been given to irrigation systems, today\nmost global crop production is rain-fed. Besides having a lower opportunity cost, green water use for the\nproduction of crops is considered more sustainable than the use of blue water (irrigation). Although green water\nrepresents the largest share of virtual water in the international trade of agricultural commodities, with exports\ngoing from highly productive rain-fed rich countries towards generally blue water based ones, green water\nvolumes have rarely been estimated.\nThe present study corroborates that green water is by far the largest share of virtual water embodied in maize,\nsoybean and wheat exports from the USA, Canada, Australia and Argentina during the period 2000-2004.\nAccordingly, green virtual-water flows can play a major role in ensuring water security and saving water in\nwater-deficit economies. The potential of international green virtual-water ‘trade’ for saving water and\nimproving water security, however, is constrained by factors such as technology, the potential for further\nincreases in the productivity, the level of socio-economic development, national food policies and international\ntrade agreements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".