Watershed Monitoring for the Assessment of Irrigation Water Use and Irrigation Contamination
Bibliographic record
Abstract
One of the main current questions on the sustainability of life in our planet is if in the next years there will be sufficient water to satisfy the necessities of agriculture and of the other users of this important resource (urban, industrial, touristic and ecological uses). Irrigation activities allow for the increase of agrarian yields, also allowing for a greater stability in food supply, mainly in those regions where the development of crops is limited by rain. In this way, agriculture consumes 70% of all water extracted from natural courses, being considered the main responsible factor for global fresh water shortage (FAO, 2002). Nevertheless, although the volumes employed by the agrarian sector are high, at a global level it is estimated that only 50% of the water extracted is finally utilized by plants; the remaining share ends up in drainage and irrigation return flows in rivers and aquifers (FAO, 2003). These volumes returned to water systems could contribute to a reduction in the impact generated by the extraction of resources if the water quality was not very distant from that of the original water extracted, due to the transport of salts and agrochemicals from the soil profile. Regarding the presence of agrochemicals, nitrate is a very important issue for water quality, and above all, is associated with notable changes implemented in agriculture in the last decades (OMS, 2004). The problem of nitrate with respect to other agrochemicals is its effect on human health by the simple fact of being present in high concentrations in potable water. The consumption of water with high concentrations of nitrate causes the development of methemoglobinemia in the blood, making the blood stream incapable of transporting enough oxygen through the organism and leading to death of the individual (OMS, 2004). On the other hand, the occurrence of high concentrations of nitrate in rivers and oceans is causing serious environmental effects on aquatic plants and animals, leading to the occurrence of anoxic zones and eutrophication of water resources (Diaz, 2001), as is evidenced on the coast of the United States (Scavia and Bricker, 2006) and China (Wang, 2006). The impacts generated by irrigation can be aggravated by physical (geology and climate) and agronomic (management of irrigation and fertilization) factors. For example, the natural salinity of the area in which irrigation is implemented can contribute significantly to the
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".