Global consumptive water use for crop production: The importance of green water and virtual water
Bibliographic record
Abstract
Over the last 4 decades the use of blue water has received increasing attention in water resources research, but little attention has been paid to the quantification of green water in food production and food trade. In this paper, we estimate both the blue and green water components of consumptive water use (CWU) for a wide range of agricultural crops, including seven cereal crops, cassava, cotton, groundnuts, potatoes, pulses, rapeseed, soybeans, sugar beets, sugarcane, and sunflower, with a spatial resolution of 30 arc min on the land surface. The results show that the global CWU of these crops amounted to 3823 km3 a−1 for the period 1998–2002. More than 80% of this amount was from green water. Around 94% of the world crop‐related virtual water trade has its origin in green water, which generally constitutes a low‐opportunity cost of green water as opposed to blue water. High levels of net virtual water import (NVWI) generally occur in countries with low CWU on a per capita basis, where a virtual water strategy is an attractive water management option to compensate for domestic water shortage for food production. NVWI is constrained by income; low‐income countries generally have a low level of NVWI. Strengthening low‐income countries economically will allow them to develop a virtual water strategy to mitigate malnutrition of their people.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 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".