Internal and external green-blue agricultural water footprints of nations, and related water and land savings through trade
Bibliographic record
Abstract
Abstract. The need to increase food production for a growing world population makes an assessment of global agricultural water productivities and virtual water flows important. Using the hydrology and agro-biosphere model LPJmL, we quantify at 0.5° resolution the blue (irrigation water) and green (precipitation water) virtual water content, i.e. the inverse of water productivity, for 11 of the world's major crop types. Based on these, we also quantify the water footprints (WFP) of all countries, for the period 1998-2002, distinguishing internal and external WFP (virtual water imported from other countries) and their blue and green components, respectively. Moreover, we calculate water savings and losses, and for the first time also land savings and losses, through international trade with these products. The consistent separation of blue and green water flows and footprints, which is needed due to the different sources and opportunity costs of these two water pools, shows that green water globally dominates both the internal and external WFP (84% of the global WFP and 94% of the external WFP rely on green water). Accordingly, some of the major exporters of the crops considered here (e.g. Argentina, Canada) export mainly green virtual water, but traditional rice exporters such as India and Pakistan mainly export blue virtual water. The external WFPs are found to be relatively small (6% of the total global blue WFP, 16% of the total global green WFP). Nevertheless, current trade saves significant water volumes and land areas (~263 km3 and ~41 Mha, respectively, equivalent to 5% of the sowing area of the crops considered here and 3.5% of the annual precipitation on this area). Linking the proportions of external to internal blue/green WFP with the per capita WFPs allows recognizing that only a few countries consume more water from abroad than from their own territory and have at the same time above average WFPs. Thus, countries with high levels of per capita water consumption affect mainly the water situation in their own country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".