Virtual Water and Food Security in Tunisian Semi-Arid Region
Why this work is in the frame
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Bibliographic record
Abstract
To confront water scarcity and support food security, the concept of virtual water is used. As defined by Allan (1997) virtual water is “the water embedded in key water-intensive commodities such as wheat” or “the water required for the production of commodities”. The importance of this concept is related to its potential contribution for saving water, especially in water short regions like Tunisia. This research study tries to evaluate the strategic importance of polluted or gray water, which is a component of virtual water. Reduction of virtual water for strategic agricultural products can be obtained by the gray water reduction. The latter is defined as “water required diluting polluted water to reach the normalized quality, different with countries”. Water pollution is especially related to use of chemical products (fertilizers, pesticides, etc.) for some crops like vegetables. Besides having a lower opportunity cost, the use of green water for crop production has generally less negative environmental externalities than the use of blue water (irrigation with water abstracted from ground or surface water systems). Tunisia exports some crops and gray water volumes in exports have rarely been estimated. Thus, estimation of gray water 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. In this context, Tunisian semi-arid region is chosen because the presence of a long period of dry and shiny, occurring after a cold and rainy one, useful for vegetables crops and family food security. The aim of this study is to present: Methodologies which can be used to reduce virtual water for some strategic vegetables crops in Tunisian semi-arid region, based on irrigation techniques improvements and the control of runoff and leaching water; Resources management practices that can be used to improve family income, especially women and children and target food security.
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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) | 0.000 | 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