Best Irrigation Practices Designed for Pesticides Use to Reduce Environmental Impact on Groundwater Resource in the Tunisian Context
Bibliographic record
Abstract
The irrigated areas in Tunisia were esteemed in 2010 to over 420 thousand hectares and represents 8% of farmland, this little area providing 35% of the total crop production. This situation makes it exert enormous pressure on the irrigated sector that his intensification is very associated to increased inputs including especially pesticides. However, the irrational use and abuse of pesticides associated with an inadequate irrigation system management are a great threat of contamination to groundwater resources and constitute one of the greatest challenges facing Tunisian government today. According to FAO, 2013, the adoption of the concept of best practices can meet this challenge. These best practices are not only a practice that are best, but a practices that have been proven to work well and produce good results, and are therefore recommended as a model. This paper aims to analyze in a framework of global environmental approach, the role of the best irrigation practices (BIPs) to reduce environmental impact on groundwater resource. Finally, it was proposed a set of best irrigation practices completed by the technical recommendations for limiting the environmental impact of pesticide in groundwater resource.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".