Water Scarcity Crossing National Borders
Bibliographic record
Abstract
Historically, water scarcity was a local issue. It was up to national governments to balance water supply and demand. Now this is changing as scarcity crosses national boundaries via the international grain trade. Since it takes 1,000 tons of water to produce one ton of grain, importing grain is the most efficient way to import water. Countries are, in effect, using grain to balance their water books. Similarly, trading in grain futures is in a sense trading in water futures. Falling water tables are already adversely affecting harvests in some countries, including China, the world's largest grain producer. Overpumping has largely depleted the shallow aquifer under the North China Plain, forcing farmers to turn to the region's deep fossil aquifer, which is not replenishable. Wheat farmers in some areas of the Plain are now pumping from a depth of 300 meters, or nearly 1,000 feet. Overall, China's grain production has fallen from its historical peak of 392 million tons in 1998 to an estimated 358 million tons in 2005. This drop of 34 million tons exceeds the Canadian wheat harvest. China largely covered the drop-off in production by drawing down its once vast stocks until 2004, at which point it imported 7 million tons of grain. Water shortages are even more serious in India simply because the margin between actual food consumption and survival is so precarious. At this point, the harvests of wheat and rice, India's principal food grains, are still increasing. But within the next few years, the loss of irrigation water could override technological progress and start shrinking the harvest in some parts of the country, as it is already doing in China. After China and India, there is a second tier of countries with large water deficits -- Algeria, Egypt, Iran, Mexico, and Pakistan. Three of these -- Algeria, Egypt, and Mexico -- already import much of their grain. However, in a parallel move with China, water-short Pakistan abruptly turned to the world market in 2004 for imports of 1.5 million tons of wheat. Its need for imports is likely to climb in the years ahead. The Middle East and North Africa -- from Morocco in the west through Iran in the east -- has become the world's fastest-growing grain import market. The demand for grain is driven both by rapid population growth and by rising affluence, much of the latter derived from the export of oil. With virtually every country in the region pressing against its water limits, the growing urban demand for water can be satisfied only by taking irrigation water from agriculture. Egypt, with some 74 million people, has become a major importer of wheat in recent years, vying with Japan -- traditionally the leading wheat importer -- for the top spot. It now imports 40 percent of its total grain supply, a number that edges steadily upward as its population outgrows the grain harvest produced with the Nile's water.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".