Virtual water flows between nations in relation to trade in livestock and livestock products
Bibliographic record
Abstract
The virtual water content of a commodity is the volume of water used to produce this commodity. International\ntrade in food implies international flows of virtual water. For water-scarce countries it can be attractive to\nimport virtual water (through import of water-intensive products), thus relieving the pressure on the domestic\nwater resources.\nThis study aims to develop a methodology to assess the virtual water content of various types of livestock and\nlivestock products and to quantify the virtual water flows related to the international trade in livestock and its\nproducts. The results are then combined with the estimates of virtual water trade flows associated with\ninternational crop trade as reported in Hoekstra and Hung (2002, 2003), to get a comprehensive picture of the\ninternational virtual water flows. The study covers the period from 1995 to 1999.\nFirst, the virtual water content (m3/ton) of live animals is calculated, based on the virtual water content of their\nfeed and the volumes of drinking and service water consumed during their lifetime. Second, the virtual water\ncontent is calculated for each livestock product, taking into account the product fraction (ton of product\nobtained per ton of live animal) and the value fraction (ratio of value of one product from an animal to the sum\nof the market values of all products from the animal). Finally, virtual water flows between nations are derived\nfrom statistics on international product trade and virtual water content per product.\nThe global volume of international virtual water flows is estimated to be 1031 Gm3 per year (695 Gm3/yr from\nthe trade in crops and 336 Gm3/yr from trade in livestock and livestock products). This means that about 20% of\nthe global water use in agriculture is aimed at producing products for export. The countries with the largest net\nvirtual water export are: the United States, Australia, Canada, Argentina and Thailand. The countries with the\nlargest net virtual water import are: Japan, Sri Lanka, Italy, South Korea and the Netherlands.\nThe total water use within a country itself is not the correct measure of a nation’s actual appropriation of global\nwater resources. In the case of net import of virtual water into a country this virtual water volume should be\nadded to the total water use within the country, in order to get a picture of a nation’s real call on the global water\nresources. Similarly, in the case of net export of virtual water from a country this virtual water volume should be\nsubtracted from the volume of internal water use. The total of internal water use and net virtual water import can\nbe seen as the ‘water footprint’ of a country, the total volume of water needed to produce the goods and services\nconsumed by the inhabitants of the country. This concept is analogous to the ‘ecological footprint’ of a nation, a\nconcept that refers to the amount of land needed to produce the goods and services consumed by the inhabitants\nof a country. This study calculates the water footprint for each nation of the world. This study does not yet\ninclude green water use within the country. Future research on water footprints should include this part of water\nuse as well, to get a more realistic figure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".