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. Hitherto, most virtual water ‘trade’ studies have focused on its potential contribution to saving water, especially in water short regions. Very little, however, has been said about the opportunity cost of the associated water. The present research critically evaluates the strategic importance of green water (soil water originating from rainfall) in relation to international commodity trade. Besides having a lower opportunity cost, the use of green water for the production of crops has generally less negative environmental externalities than the use of blue water (irrigation with water abstracted from ground or surface water systems). Although it is widely known that major grain exporters – the USA, Canada, France, Australia and Argentina – produce grain in highly productive rain-fed conditions, green water volumes in exports have rarely been estimated. The present study corroborates that green water is by far the largest share of virtual water in maize, soybean and wheat exports from its main exporting countries (USA, Canada, Australia and Argentina) during the period 2000–2004. Insofar virtual water is ‘traded’ towards water-scarce nations that heavily depend on their blue water resources, green virtual-water ‘trade’ related to these commodities plays a role in ensuring water and water-dependent food security and avoiding further potential damage to the water environments in both importing and exporting countries. This potential of international green virtual-water ‘trade’, however, is constrained by factors such as technology, the potential for further increases in the productivity of soil and irrigation water, the level of socio-economic development, national food policies and international trade agreements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".