Strategic importance of green water in international crop trade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it