Pesticide Usage in Rice Cultivation: Consequences for Soil and Water Health
Bibliographic record
Abstract
As global food demand continues to grow, the use of pesticides in rice cultivation has become a common practice to ensure high yields. However, the widespread application of these chemicals has significantly impacted soil and water health. This study provides an overview of the history and evolution of pesticide use in rice cultivation, explores the functions and application patterns of different types of pesticides, and further analyzes their effects on soil health. Through case studies, the study highlights the long-term impacts of pesticide use on soil in certain rice-producing regions. Pesticides entering water bodies through runoff and leaching can have significant negative effects on water quality. These chemicals, once in rivers, lakes, and groundwater, can lead to water pollution, degrade water quality, and consequently threaten the health of aquatic ecosystems. This study aims to systematically assess the environmental consequences of pesticide use in rice cultivation, particularly its impact on soil and water health, to fill the existing knowledge gaps and provide scientific evidence for the development of more sustainable agricultural practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".