Factors Affecting Farmer’s Chemical Fertilizers Consumption and Water Pollution in Northeastern Iran
Bibliographic record
Abstract
Pollution by fertilizers containing nitrogen is one of the most significant sources of water pollution, and agriculture sector has a considerable share in this type of pollution. In this study, factors affecting the level of contamination of surface and underground water resources by agricultural activities were examined. Data of 254 wheat farmers in the plain of Mashhad in Khorasan Razavi province in Iran were used for investigating the effect of some explanatory variables on the level of water pollution utilizing Ordered Logit Regression Model. The results show that main activity of farmers, years of experience, the level of education, awareness of organic farming, level of income, price of fertilizers and irrigation method have significant effect on the amount of fertilizers utilized by farmers and hence the level of water resources pollution. Efforts for decreasing pollution of water resources require strategies such as changing the main activity of farmers, increase the cost of using chemical fertilizers, create economic incentives for organic farming, and increase general information and knowledge of farmers.
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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 | 0.001 |
| 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".